03_0922_HAR(BiLSTM).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7047c526",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import mean\n",
    "from numpy import std\n",
    "from numpy import dstack\n",
    "from pandas import read_csv\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "import keras\n",
    "import zipfile\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import seaborn as sns\n",
    "\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Activation\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Dense, Input, Dropout, Flatten, BatchNormalization, LSTM, Bidirectional\n",
    "from tensorflow.keras.layers import concatenate, Dropout\n",
    "from keras.utils import to_categorical\n",
    "from tensorflow.keras import optimizers\n",
    "from keras.utils import to_categorical\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "869ed90d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_file(filepath):\n",
    "    dataframe = read_csv(filepath, header=None, delim_whitespace=True)\n",
    "    return dataframe.values\n",
    " \n",
    "\n",
    "def load_group(filenames, prefix=''):\n",
    "    loaded = list()\n",
    "    for name in filenames:\n",
    "        data = load_file(prefix + name)\n",
    "        loaded.append(data)\n",
    "    loaded = dstack(loaded)\n",
    "    return loaded\n",
    " \n",
    "def load_dataset_group(group, prefix=''):\n",
    "    filepath = prefix + group + '/Inertial Signals/'\n",
    "    filenames = list()\n",
    "    filenames += ['total_acc_x_'+group+'.txt', 'total_acc_y_'+group+'.txt', 'total_acc_z_'+group+'.txt']\n",
    "    filenames += ['body_acc_x_'+group+'.txt', 'body_acc_y_'+group+'.txt', 'body_acc_z_'+group+'.txt']\n",
    "    filenames += ['body_gyro_x_'+group+'.txt', 'body_gyro_y_'+group+'.txt', 'body_gyro_z_'+group+'.txt']\n",
    "    X = load_group(filenames, filepath)\n",
    "    y = load_file(prefix + group + '/y_'+group+'.txt')\n",
    "    return X, y\n",
    "\n",
    "def load_dataset(prefix=''):\n",
    "    trainX, trainy = load_dataset_group('train', prefix + 'HAR/')\n",
    "    testX, testy = load_dataset_group('test', prefix + 'HAR/')\n",
    "    trainy = trainy - 1\n",
    "    testy = testy - 1\n",
    "    trainy = to_categorical(trainy)\n",
    "    testy = to_categorical(testy)\n",
    "    print(\"trainX shape: \",trainX.shape)\n",
    "    print(\"trainy shape: \", trainy.shape)\n",
    "    print(\"testX shape: \", testX.shape)\n",
    "    print(\"testy shape: \", testy.shape)\n",
    "    return trainX, trainy, testX, testy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d844e416",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainX shape:  (7352, 128, 9)\n",
      "trainy shape:  (7352, 6)\n",
      "testX shape:  (2947, 128, 9)\n",
      "testy shape:  (2947, 6)\n"
     ]
    }
   ],
   "source": [
    "trainX, trainy, testX, testy = load_dataset('data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "19715f39",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "validation_split = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0fe6f1cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]\n",
    "model = Sequential()\n",
    "model.add(Bidirectional(LSTM(64, input_shape=(n_timesteps,n_features), return_sequences=True)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Bidirectional(LSTM(32)))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(16))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(n_outputs, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7aee6339",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "11453a8c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "184/184 [==============================] - 55s 211ms/step - loss: 0.9248 - accuracy: 0.7052 - val_loss: 1.1617 - val_accuracy: 0.6506\n",
      "Epoch 2/30\n",
      "184/184 [==============================] - 36s 197ms/step - loss: 0.3763 - accuracy: 0.9022 - val_loss: 0.3915 - val_accuracy: 0.8960\n",
      "Epoch 3/30\n",
      "184/184 [==============================] - 37s 201ms/step - loss: 0.2212 - accuracy: 0.9361 - val_loss: 0.2452 - val_accuracy: 0.9232\n",
      "Epoch 4/30\n",
      "184/184 [==============================] - 35s 192ms/step - loss: 0.1783 - accuracy: 0.9393 - val_loss: 0.2329 - val_accuracy: 0.9273\n",
      "Epoch 5/30\n",
      "184/184 [==============================] - 33s 181ms/step - loss: 0.1595 - accuracy: 0.9369 - val_loss: 0.2106 - val_accuracy: 0.9327\n",
      "Epoch 6/30\n",
      "184/184 [==============================] - 34s 185ms/step - loss: 0.1428 - accuracy: 0.9498 - val_loss: 0.2634 - val_accuracy: 0.8872\n",
      "Epoch 7/30\n",
      "184/184 [==============================] - 34s 184ms/step - loss: 0.1380 - accuracy: 0.9490 - val_loss: 0.2759 - val_accuracy: 0.8967\n",
      "Epoch 8/30\n",
      "184/184 [==============================] - 34s 184ms/step - loss: 0.1204 - accuracy: 0.9520 - val_loss: 0.2288 - val_accuracy: 0.9252\n",
      "Epoch 9/30\n",
      "184/184 [==============================] - 34s 183ms/step - loss: 0.1162 - accuracy: 0.9560 - val_loss: 0.3889 - val_accuracy: 0.9096\n",
      "Epoch 10/30\n",
      "184/184 [==============================] - 34s 184ms/step - loss: 0.1102 - accuracy: 0.9531 - val_loss: 0.3136 - val_accuracy: 0.9007\n",
      "Epoch 11/30\n",
      "184/184 [==============================] - 33s 179ms/step - loss: 0.1104 - accuracy: 0.9551 - val_loss: 0.2539 - val_accuracy: 0.9252\n",
      "Epoch 12/30\n",
      "184/184 [==============================] - 33s 181ms/step - loss: 0.1122 - accuracy: 0.9539 - val_loss: 0.2933 - val_accuracy: 0.8939\n",
      "Epoch 13/30\n",
      "184/184 [==============================] - 33s 182ms/step - loss: 0.1105 - accuracy: 0.9531 - val_loss: 0.3071 - val_accuracy: 0.9021\n",
      "Epoch 14/30\n",
      "184/184 [==============================] - 34s 183ms/step - loss: 0.1099 - accuracy: 0.9522 - val_loss: 0.2871 - val_accuracy: 0.8953\n",
      "Epoch 15/30\n",
      "184/184 [==============================] - 34s 183ms/step - loss: 0.1095 - accuracy: 0.9488 - val_loss: 0.2734 - val_accuracy: 0.9062\n",
      "Epoch 16/30\n",
      "184/184 [==============================] - 34s 183ms/step - loss: 0.0986 - accuracy: 0.9589 - val_loss: 0.3231 - val_accuracy: 0.9028\n",
      "Epoch 17/30\n",
      "184/184 [==============================] - 34s 183ms/step - loss: 0.1071 - accuracy: 0.9553 - val_loss: 0.2850 - val_accuracy: 0.9273\n",
      "Epoch 18/30\n",
      "184/184 [==============================] - 35s 190ms/step - loss: 0.1145 - accuracy: 0.9536 - val_loss: 0.2469 - val_accuracy: 0.9191\n",
      "Epoch 19/30\n",
      "184/184 [==============================] - 35s 188ms/step - loss: 0.1090 - accuracy: 0.9561 - val_loss: 0.4270 - val_accuracy: 0.8899\n",
      "Epoch 20/30\n",
      "184/184 [==============================] - 33s 180ms/step - loss: 0.0995 - accuracy: 0.9577 - val_loss: 0.3427 - val_accuracy: 0.9075\n",
      "Epoch 21/30\n",
      "184/184 [==============================] - 34s 182ms/step - loss: 0.1027 - accuracy: 0.9578 - val_loss: 0.3152 - val_accuracy: 0.8980\n",
      "Epoch 22/30\n",
      "184/184 [==============================] - 34s 185ms/step - loss: 0.1031 - accuracy: 0.9572 - val_loss: 0.2942 - val_accuracy: 0.8967\n",
      "Epoch 23/30\n",
      "184/184 [==============================] - 34s 183ms/step - loss: 0.1074 - accuracy: 0.9541 - val_loss: 0.4794 - val_accuracy: 0.8273\n",
      "Epoch 24/30\n",
      "184/184 [==============================] - 34s 183ms/step - loss: 0.1043 - accuracy: 0.9560 - val_loss: 0.2861 - val_accuracy: 0.8899\n",
      "Epoch 25/30\n",
      "184/184 [==============================] - 34s 183ms/step - loss: 0.1033 - accuracy: 0.9536 - val_loss: 0.2372 - val_accuracy: 0.9286\n",
      "Epoch 26/30\n",
      "184/184 [==============================] - 34s 184ms/step - loss: 0.1025 - accuracy: 0.9543 - val_loss: 0.3091 - val_accuracy: 0.9007\n",
      "Epoch 27/30\n",
      "184/184 [==============================] - 34s 187ms/step - loss: 0.0951 - accuracy: 0.9568 - val_loss: 0.2690 - val_accuracy: 0.9109\n",
      "Epoch 28/30\n",
      "184/184 [==============================] - 34s 183ms/step - loss: 0.0950 - accuracy: 0.9577 - val_loss: 0.2880 - val_accuracy: 0.9014\n",
      "Epoch 29/30\n",
      "184/184 [==============================] - 34s 186ms/step - loss: 0.0937 - accuracy: 0.9582 - val_loss: 0.3433 - val_accuracy: 0.8960\n",
      "Epoch 30/30\n",
      "184/184 [==============================] - 36s 194ms/step - loss: 0.0891 - accuracy: 0.9607 - val_loss: 0.2686 - val_accuracy: 0.9116\n"
     ]
    }
   ],
   "source": [
    "train_epochs = 30\n",
    "history = model.fit(trainX, trainy, epochs=train_epochs, batch_size=batch_size, verbose=True,  validation_split=validation_split, shuffle=True)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0a8f280d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c47073f1d0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'Validation'], loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5b94c894",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the BiLSTM model 0.9239904880523682\n",
      "93/93 [==============================] - 8s 55ms/step\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_accuracy = model.evaluate(testX, testy, batch_size=batch_size, verbose=0)\n",
    "print(\"Accuracy of the BiLSTM model\", test_accuracy)\n",
    "predictions=model.predict(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6f27e443",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.1791199e-06, 1.8380745e-04, 1.4174317e-05, 5.1609944e-03,\n",
       "        9.9462318e-01, 1.4692253e-05],\n",
       "       [2.6207160e-06, 8.3300838e-05, 7.7788882e-06, 4.5896522e-03,\n",
       "        9.9530953e-01, 7.1000986e-06],\n",
       "       [2.6620123e-06, 7.3674521e-05, 5.4953298e-06, 2.4018176e-02,\n",
       "        9.7589773e-01, 2.2294059e-06],\n",
       "       ...,\n",
       "       [1.5554141e-06, 9.9998093e-01, 1.4688655e-05, 8.1368751e-07,\n",
       "        1.2951467e-06, 6.7951072e-07],\n",
       "       [3.2323185e-06, 9.9998498e-01, 1.0417796e-05, 5.3101348e-07,\n",
       "        6.2297050e-07, 2.0854206e-07],\n",
       "       [1.8994550e-06, 9.9997330e-01, 2.3039755e-05, 6.1663042e-07,\n",
       "        8.4423681e-07, 3.4164816e-07]], dtype=float32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "065691b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_results(testy,predictions,classNames):\n",
    "    y_test=np.argmax(testy, axis=1)\n",
    "    y_scores=np.argmax(predictions, axis=1)\n",
    "    classes = len(classNames)\n",
    "    cm = confusion_matrix(y_test, y_scores)\n",
    "    print(\"**** Confusion Matrix ****\")\n",
    "    print(cm)\n",
    "    print(\"**** Classification Report ****\")\n",
    "    print(classification_report(y_test, y_scores, target_names=classNames))\n",
    "    con = np.zeros((classes,classes))\n",
    "    for x in range(classes):\n",
    "        for y in range(classes):\n",
    "            con[x,y] = cm[x,y]\n",
    "\n",
    "    plt.figure(figsize=(10,8))\n",
    "    df = sns.heatmap(con, annot=True, fmt='g', cmap='Blues',xticklabels= classNames , yticklabels= classNames)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Actual')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d42af8f1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Confusion Matrix ****\n",
      "[[466   1  29   0   0   0]\n",
      " [  7 436  27   1   0   0]\n",
      " [  0   0 420   0   0   0]\n",
      " [  0   1   0 378 107   5]\n",
      " [  0   0   0  46 486   0]\n",
      " [  0   0   0   0   0 537]]\n",
      "**** Classification Report ****\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Walking       0.99      0.94      0.96       496\n",
      "      Walkup       1.00      0.93      0.96       471\n",
      "    Walkdown       0.88      1.00      0.94       420\n",
      "     Sitting       0.89      0.77      0.83       491\n",
      "    Standing       0.82      0.91      0.86       532\n",
      "      Laying       0.99      1.00      1.00       537\n",
      "\n",
      "    accuracy                           0.92      2947\n",
      "   macro avg       0.93      0.92      0.92      2947\n",
      "weighted avg       0.93      0.92      0.92      2947\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classNames=[\"Walking\",\"Walkup\",\"Walkdown\",\"Sitting\",\"Standing\",\"Laying\"]\n",
    "display_results(testy,predictions,classNames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab4210c8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}