diff --git a/Adaptive Workflow Design/Adaptive_Workflow_Design.ipynb b/Adaptive Workflow Design/Adaptive_Workflow_Design.ipynb new file mode 100644 index 0000000..9eb3926 --- /dev/null +++ b/Adaptive Workflow Design/Adaptive_Workflow_Design.ipynb @@ -0,0 +1,220 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "os-fssuNPbN2" + }, + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "1. Data Preparation &Predictive Modeling" + ], + "metadata": { + "id": "NhV6qqJuP3Ed" + } + }, + { + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "# Load historical data\n", + "data = pd.read_csv('/content/drive/MyDrive/GirlsScriptOpenSource/automarket/Adaptive Workflow Design/synthetic_workflow_data.csv')\n", + "\n", + "# Preprocess data (e.g., handling missing values, feature engineering)\n", + "data.fillna(method='ffill', inplace=True)\n", + "\n", + "# Separate numerical and categorical features\n", + "numerical_features = data.select_dtypes(include=['number']).columns\n", + "categorical_features = data.select_dtypes(include=['object']).columns\n", + "\n", + "# Apply one-hot encoding to categorical features\n", + "encoder = OneHotEncoder(handle_unknown='ignore')\n", + "encoded_data = encoder.fit_transform(data[categorical_features])\n", + "encoded_df = pd.DataFrame(encoded_data.toarray(), columns=encoder.get_feature_names_out(categorical_features))\n", + "\n", + "# Combine numerical and encoded categorical features\n", + "X = pd.concat([data[numerical_features], encoded_df], axis=1)\n", + "y = data['target']\n", + "\n", + "# Split data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Train a predictive model\n", + "model = RandomForestRegressor(n_estimators=100, random_state=42)\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Validate the model\n", + "y_pred = model.predict(X_test)\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "print(f'Mean Squared Error: {mse}')" + ], + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wMOJp1_hQWWW", + "outputId": "0f87c75c-3c40-4388-9f81-6a346836cd87" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mean Squared Error: 4.6624224771870305e-06\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Adaptive Algorithm Implementation" + ], + "metadata": { + "id": "sdBOYA5mQf1K" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "# Function to dynamically adjust workflow parameters\n", + "def adjust_workflow(parameters, predictions):\n", + " adjusted_parameters = parameters.copy()\n", + " for param, value in parameters.items():\n", + " if param in predictions:\n", + " adjusted_parameters[param] = value * predictions[param]\n", + " return adjusted_parameters\n", + "\n", + "# Example usage\n", + "current_parameters = {'task1': 1.0, 'task2': 0.8, 'task3': 1.2}\n", + "predictions = {'task1': 1.1, 'task2': 0.9, 'task3': 1.05}\n", + "new_parameters = adjust_workflow(current_parameters, predictions)\n", + "print(new_parameters)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Kw6Zzx_tQFed", + "outputId": "336c46d0-584b-400b-a6c8-72faa744c6f4" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'task1': 1.1, 'task2': 0.7200000000000001, 'task3': 1.26}\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "I0TSAD6uQk99" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Task Prioritization and Workflow Rerouting" + ], + "metadata": { + "id": "BGsXaL7oQm2z" + } + }, + { + "cell_type": "code", + "source": [ + "from collections import deque\n", + "\n", + "# Sample workflow tasks and their priorities\n", + "tasks = deque([\n", + " {'name': 'task1', 'priority': 1},\n", + " {'name': 'task2', 'priority': 2},\n", + " {'name': 'task3', 'priority': 3},\n", + "])\n", + "\n", + "# Function to prioritize tasks based on system load\n", + "def prioritize_tasks(tasks, system_load):\n", + " for task in tasks:\n", + " task['priority'] *= system_load\n", + " tasks = deque(sorted(tasks, key=lambda x: x['priority'], reverse=True))\n", + " return tasks\n", + "\n", + "# Example usage\n", + "system_load = 1.2\n", + "prioritized_tasks = prioritize_tasks(tasks, system_load)\n", + "print(prioritized_tasks)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sj91ecj5Qp8e", + "outputId": "7eb451b5-5b67-4f08-cfcf-98d5a903f4a5" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "deque([{'name': 'task3', 'priority': 3.5999999999999996}, {'name': 'task2', 'priority': 2.4}, {'name': 'task1', 'priority': 1.2}])\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# We can do Integration and Real-Time Monitoring with kafka if we needed for that we need to save this model and we can use there this model" + ], + "metadata": { + "id": "iC15QxomQrQ4" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "j9IK9crfQxMJ" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Adaptive Workflow Design/requirement.txt b/Adaptive Workflow Design/requirement.txt new file mode 100644 index 0000000..5f084e1 --- /dev/null +++ b/Adaptive Workflow Design/requirement.txt @@ -0,0 +1,6 @@ +here the important libraries which have imported during the project- +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.ensemble import RandomForestRegressor +from sklearn.metrics import mean_squared_error +from sklearn.preprocessing import OneHotEncoder \ No newline at end of file diff --git a/Adaptive Workflow Design/workflow_data.csv b/Adaptive Workflow Design/workflow_data.csv new file mode 100644 index 0000000..10097df --- /dev/null +++ b/Adaptive Workflow Design/workflow_data.csv @@ -0,0 +1,1001 @@ +task_id,task_type,execution_time,system_load,external_factor,priority,target +1,type3,0.6203506184924633,0.8812510210967053,1.2132068832826177,1,1.8882071138132372 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