WS_8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Workshop - 8 Deep Learning Based Time Series Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -umpy (c:\\programdata\\anaconda3\\lib\\site-packages)\n",
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     ]
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      "Requirement a"
     ]
    },
    {
     "data": {
      "text/html": [
       "<b>limit_output extension: Maximum message size of 10000 exceeded with 12083 characters</b>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "!pip install -U sktime\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import GlobalAvgPool1D\n",
    "from keras.layers import Dropout\n",
    "from keras.layers.convolutional import Conv1D\n",
    "from keras.layers.convolutional import MaxPooling1D\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.datasets import load_italy_power_demand\n",
    "from sktime.utils.data_processing import from_nested_to_2d_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=4.5pt  face = 'georgia' style='Line-height :3'><div style='text-align: justify; padding:35px; margin-top:-35px; margin-bottom:-35px'>\n",
    "    Here we will use the dataset of italy power demand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1096, 1) (1096,)\n"
     ]
    }
   ],
   "source": [
    "xdf, ydf = load_italy_power_demand( return_X_y=True)\n",
    "print(xdf.shape,ydf.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=4.5pt  face = 'georgia' style='Line-height :3'><div style='text-align: justify; padding:35px; margin-top:-35px; margin-bottom:-35px'>\n",
    "    Let's transform it to the conventional from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1096, 24)\n",
      "(1096,)\n"
     ]
    }
   ],
   "source": [
    "x = from_nested_to_2d_array(xdf)\n",
    "\n",
    "x = x.values\n",
    "print(x.shape)\n",
    "\n",
    "y = ydf.values\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels ['1' '2'] count [547 549]\n"
     ]
    }
   ],
   "source": [
    "labels, count = np.unique(y, return_counts=True)\n",
    "print('labels',lables, 'count', count)\n",
    "\n",
    "y = [0 if yi=='2' else 1 for yi in y]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels [0 1] count [549 547]\n"
     ]
    }
   ],
   "source": [
    "y =np.array(y)\n",
    "labels, count = np.unique(y, return_counts=True)\n",
    "print('labels',labels, 'count', count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=4.5pt  face = 'georgia' style='Line-height :3'><div style='text-align: justify; padding:35px; margin-top:-35px; margin-bottom:-35px'>\n",
    "    And we can visualize each instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "instance = 9\n",
    "plt.plot(x[instance,:])\n",
    "print(y[instance]);\n",
    "plt.show()\n",
    "instance = 99\n",
    "plt.plot(x[instance,:])\n",
    "print(y[instance]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=4.5pt  face = 'georgia' style='Line-height :3'><div style='text-align: justify; padding:35px; margin-top:-35px; margin-bottom:-35px'>\n",
    "    Now we can make train-test split, and transform datasets to use conventional form for keras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((548,), (548,), (548, 24, 1), (548, 24, 1))"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "TEST_SIZE = int(x.shape[0]*0.5)\n",
    "\n",
    "x_train,x_test = x[:TEST_SIZE,:,np.newaxis],x[-TEST_SIZE:,:,np.newaxis]\n",
    "y_train,y_test = y[:TEST_SIZE],y[-TEST_SIZE:]\n",
    "\n",
    "y_train.shape,y_test.shape,x_train.shape,x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=4.5pt  face = 'georgia' style='Line-height :3'><div style='text-align: justify; padding:35px; margin-top:-35px; margin-bottom:-35px'>\n",
    "    Here we will build a base-line model for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_23\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_46 (Conv1D)           (None, 22, 64)            256       \n",
      "_________________________________________________________________\n",
      "conv1d_47 (Conv1D)           (None, 20, 64)            12352     \n",
      "_________________________________________________________________\n",
      "dropout_23 (Dropout)         (None, 20, 64)            0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_23 (MaxPooling (None, 10, 64)            0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling1d_21  (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 12,673\n",
      "Trainable params: 12,673\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "epochs = 100\n",
    "batch_size = 32\n",
    "\n",
    "\n",
    "n_timesteps = x_train.shape[1]\n",
    "n_features  = 1\n",
    "n_outputs   = labels.shape[0]\n",
    "\n",
    "# # scale data\n",
    "# x_train, x_test = scale_data(x_train, x_test, param)\n",
    "\n",
    "\n",
    "model = Sequential()\n",
    "#24\n",
    "model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))\n",
    "#22x64\n",
    "model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))\n",
    "#20x64*64\n",
    "model.add(Dropout(0.5))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "\n",
    "#10x64*64\n",
    "model.add(GlobalAvgPool1D())\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "model.summary()\n",
    "\n",
    "# # fit network\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=4.5pt  face = 'georgia' style='Line-height :3'><div style='text-align: justify; padding:35px; margin-top:-35px; margin-bottom:-35px'>\n",
    "    Let's train it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "18/18 [==============================] - 1s 3ms/step - loss: 0.6926 - accuracy: 0.4918\n",
      "Epoch 2/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.6820 - accuracy: 0.5846\n",
      "Epoch 3/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.6740 - accuracy: 0.6051\n",
      "Epoch 4/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.6649 - accuracy: 0.6090\n",
      "Epoch 5/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.6508 - accuracy: 0.6103\n",
      "Epoch 6/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.6450 - accuracy: 0.6724\n",
      "Epoch 7/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.6388 - accuracy: 0.6818\n",
      "Epoch 8/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.6463 - accuracy: 0.6110\n",
      "Epoch 9/100\n",
      "18/18 [==============================] - 0s 4ms/step - loss: 0.6381 - accuracy: 0.6617\n",
      "Epoch 10/100\n",
      "18/18 [==============================] - 0s 4ms/step - loss: 0.6141 - accuracy: 0.6584\n",
      "Epoch 11/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.6156 - accuracy: 0.7019\n",
      "Epoch 12/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.5980 - accuracy: 0.6853\n",
      "Epoch 13/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.6011 - accuracy: 0.6863\n",
      "Epoch 14/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.5990 - accuracy: 0.6903\n",
      "Epoch 15/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.5871 - accuracy: 0.7666\n",
      "Epoch 16/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.5953 - accuracy: 0.6638\n",
      "Epoch 17/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.5914 - accuracy: 0.6534\n",
      "Epoch 18/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.5814 - accuracy: 0.7288\n",
      "Epoch 19/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.5473 - accuracy: 0.7729\n",
      "Epoch 20/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.5461 - accuracy: 0.7916\n",
      "Epoch 21/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.5391 - accuracy: 0.7890\n",
      "Epoch 22/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.5405 - accuracy: 0.8111\n",
      "Epoch 23/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.5158 - accuracy: 0.7950\n",
      "Epoch 24/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.5097 - accuracy: 0.8267\n",
      "Epoch 25/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.4851 - accuracy: 0.8076\n",
      "Epoch 26/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.4842 - accuracy: 0.8096\n",
      "Epoch 27/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.4991 - accuracy: 0.8021\n",
      "Epoch 28/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.5004 - accuracy: 0.7650\n",
      "Epoch 29/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.4682 - accuracy: 0.8376\n",
      "Epoch 30/100\n",
      "18/18 [==============================] - 0s 3ms/step - loss: 0.4422 - accuracy: 0.8443\n",
      "Epoch 31/100\n",
      "18/18 [============================..] - ETA: 0s - loss: 0.4392 - accuracy: 0.87"
     ]
    },
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   "source": [
    "model.fit(x_train, \n",
    "          y_train, \n",
    "          epochs=epochs, \n",
    "          batch_size=batch_size, \n",
    "          verbose=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=4.5pt  face = 'georgia' style='Line-height :3'><div style='text-align: justify; padding:35px; margin-top:-35px; margin-bottom:-35px'>\n",
    "    Now we can evaluate it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18/18 [==============================] - 0s 940us/step - loss: 0.1940 - accuracy: 0.9416\n"
     ]
    }
   ],
   "source": [
    "# evaluate model\n",
    "_, accuracy = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=4.5pt  face = 'georgia' style='Line-height :3'><div style='text-align: justify; padding:35px; margin-top:-35px; margin-bottom:-35px'>\n",
    "1. Try to improve the accuracy of the CNN model using your experience in neural-network architectures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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