04_1006_HAR(feature,CNN_LSTM).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3aa9f5eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import mean,std, dstack\n",
    "import pandas as pd\n",
    "from pandas import read_csv\n",
    "import seaborn as sns\n",
    "\n",
    "import keras\n",
    "import zipfile\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "\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, LSTM\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "92428b5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_new_feature_name_df(old_feature_name_df):\n",
    "    feature_dup_df = pd.DataFrame(data=old_feature_name_df.groupby('column_name').cumcount(),\n",
    "                                  columns=['dup_cnt'])\n",
    "    feature_dup_df = feature_dup_df.reset_index()\n",
    "    new_feature_name_df = pd.merge(old_feature_name_df.reset_index(), feature_dup_df, how='outer')\n",
    "    new_feature_name_df['column_name'] = new_feature_name_df[['column_name', 'dup_cnt']].apply(lambda x : x[0]+'_'+str(x[1]) \n",
    "                                                                                         if x[1] >0 else x[0] ,  axis=1)\n",
    "    new_feature_name_df = new_feature_name_df.drop(['index'], axis=1)\n",
    "    return new_feature_name_df\n",
    "\n",
    "def get_human_dataset( ):\n",
    "    \n",
    "    feature_name_df = pd.read_csv('./data/HAR/features.txt',sep='\\s+',\n",
    "                        header=None,names=['column_index','column_name'])\n",
    "    \n",
    "    new_feature_name_df = get_new_feature_name_df(feature_name_df)\n",
    "    \n",
    "    feature_name = new_feature_name_df.iloc[:, 1].values.tolist()\n",
    "    \n",
    "\n",
    "    X_train = pd.read_csv('./data/HAR/train/X_train.txt',sep='\\s+', names=feature_name )\n",
    "    X_test = pd.read_csv('./data/HAR/test/X_test.txt',sep='\\s+', names=feature_name)\n",
    "    \n",
    "\n",
    "    y_train = pd.read_csv('./data/HAR/train/y_train.txt',sep='\\s+',header=None,names=['action'])\n",
    "    y_test = pd.read_csv('./data/HAR/test/y_test.txt',sep='\\s+',header=None,names=['action'])\n",
    "    \n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8caabaab",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = get_human_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b482521c",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "validation_split = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1be745e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_sequences(sequences, n_steps):\n",
    "    X, y = list(), list()\n",
    "    for i in range(len(sequences)):\n",
    "\n",
    "        end_ix = i + n_steps\n",
    "\n",
    "        if end_ix > len(sequences):\n",
    "            break\n",
    "\n",
    "        seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return np.array(X), np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "30d84592",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7323, 30, 561) (7323,)\n",
      "(2918, 30, 561) (2918,)\n"
     ]
    }
   ],
   "source": [
    "y_train_array = np.array(y_train)\n",
    "train_set = np.c_[X_train, y_train_array]\n",
    "\n",
    "y_test_array = np.array(y_test)\n",
    "test_set = np.c_[X_test, y_test_array]\n",
    "\n",
    "X_train, y_train = split_sequences(train_set, 30)\n",
    "print(X_train.shape, y_train.shape)\n",
    "\n",
    "X_test, y_test = split_sequences(test_set, 30)\n",
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0e41dceb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7323, 7)\n",
      "(2918, 7)\n"
     ]
    }
   ],
   "source": [
    "y_train = to_categorical(y_train)\n",
    "print(y_train.shape)\n",
    "\n",
    "y_test = to_categorical(y_test)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "60e2c444",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_timesteps, n_features, n_outputs = X_train.shape[1], X_train.shape[2], y_train.shape[1]\n",
    "model = Sequential()\n",
    "model.add(Conv1D(filters=64, kernel_size=4, activation='relu', input_shape=(n_timesteps, n_features)))\n",
    "model.add(Conv1D(filters=64, kernel_size=4, activation='relu'))\n",
    "model.add(LSTM(64, return_sequences=True))\n",
    "model.add(LSTM(32))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dense(n_outputs, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bdb9ef2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3c248319",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "92/92 [==============================] - 16s 86ms/step - loss: 1.1025 - accuracy: 0.5615 - val_loss: 0.5823 - val_accuracy: 0.7795\n",
      "Epoch 2/20\n",
      "92/92 [==============================] - 6s 68ms/step - loss: 0.5236 - accuracy: 0.8068 - val_loss: 0.3738 - val_accuracy: 0.8621\n",
      "Epoch 3/20\n",
      "92/92 [==============================] - 7s 73ms/step - loss: 0.3538 - accuracy: 0.8749 - val_loss: 0.3136 - val_accuracy: 0.8819\n",
      "Epoch 4/20\n",
      "92/92 [==============================] - 7s 73ms/step - loss: 0.2607 - accuracy: 0.9090 - val_loss: 0.2201 - val_accuracy: 0.9126\n",
      "Epoch 5/20\n",
      "92/92 [==============================] - 6s 67ms/step - loss: 0.2003 - accuracy: 0.9312 - val_loss: 0.2340 - val_accuracy: 0.9304\n",
      "Epoch 6/20\n",
      "92/92 [==============================] - 6s 66ms/step - loss: 0.1723 - accuracy: 0.9379 - val_loss: 0.1952 - val_accuracy: 0.9304\n",
      "Epoch 7/20\n",
      "92/92 [==============================] - 7s 71ms/step - loss: 0.1516 - accuracy: 0.9469 - val_loss: 0.1595 - val_accuracy: 0.9461\n",
      "Epoch 8/20\n",
      "92/92 [==============================] - 6s 65ms/step - loss: 0.0977 - accuracy: 0.9679 - val_loss: 0.1547 - val_accuracy: 0.9427\n",
      "Epoch 9/20\n",
      "92/92 [==============================] - 6s 65ms/step - loss: 0.0816 - accuracy: 0.9742 - val_loss: 0.1090 - val_accuracy: 0.9597\n",
      "Epoch 10/20\n",
      "92/92 [==============================] - 6s 65ms/step - loss: 0.0864 - accuracy: 0.9727 - val_loss: 0.3566 - val_accuracy: 0.8969\n",
      "Epoch 11/20\n",
      "92/92 [==============================] - 6s 66ms/step - loss: 0.0620 - accuracy: 0.9819 - val_loss: 0.1285 - val_accuracy: 0.9543\n",
      "Epoch 12/20\n",
      "92/92 [==============================] - 6s 65ms/step - loss: 0.0414 - accuracy: 0.9860 - val_loss: 0.1877 - val_accuracy: 0.9454\n",
      "Epoch 13/20\n",
      "92/92 [==============================] - 6s 69ms/step - loss: 0.1299 - accuracy: 0.9595 - val_loss: 0.1025 - val_accuracy: 0.9611\n",
      "Epoch 14/20\n",
      "92/92 [==============================] - 6s 67ms/step - loss: 0.0530 - accuracy: 0.9841 - val_loss: 0.1120 - val_accuracy: 0.9597\n",
      "Epoch 15/20\n",
      "92/92 [==============================] - 7s 72ms/step - loss: 0.0275 - accuracy: 0.9930 - val_loss: 0.0940 - val_accuracy: 0.9679\n",
      "Epoch 16/20\n",
      "92/92 [==============================] - 6s 68ms/step - loss: 0.0262 - accuracy: 0.9928 - val_loss: 0.3361 - val_accuracy: 0.9017\n",
      "Epoch 17/20\n",
      "92/92 [==============================] - 6s 65ms/step - loss: 0.0260 - accuracy: 0.9927 - val_loss: 0.0926 - val_accuracy: 0.9679\n",
      "Epoch 18/20\n",
      "92/92 [==============================] - 6s 65ms/step - loss: 0.0223 - accuracy: 0.9952 - val_loss: 0.1424 - val_accuracy: 0.9563\n",
      "Epoch 19/20\n",
      "92/92 [==============================] - 6s 66ms/step - loss: 0.0372 - accuracy: 0.9879 - val_loss: 0.1498 - val_accuracy: 0.9522\n",
      "Epoch 20/20\n",
      "92/92 [==============================] - 6s 66ms/step - loss: 0.0216 - accuracy: 0.9939 - val_loss: 0.1275 - val_accuracy: 0.9631\n"
     ]
    }
   ],
   "source": [
    "train_epochs = 20\n",
    "history = model.fit(X_train, y_train, epochs=train_epochs, batch_size=batch_size, verbose=True,  validation_split=validation_split, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4238af74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17196c5ad50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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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": 15,
   "id": "ade4f537",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the CNN+LSTM model 0.9599040150642395\n",
      "92/92 [==============================] - 3s 15ms/step\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_accuracy = model.evaluate(X_test, y_test, batch_size=batch_size, verbose=0)\n",
    "print(\"Accuracy of the CNN+LSTM model\", test_accuracy)\n",
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8fd26749",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.8442653e-07, 1.9304882e-06, 1.5354228e-05, ..., 4.7830720e-03,\n",
       "        9.9519557e-01, 1.0461785e-06],\n",
       "       [1.6091661e-07, 2.0272269e-06, 1.2679380e-05, ..., 3.6601529e-03,\n",
       "        9.9632096e-01, 8.1191098e-07],\n",
       "       [8.6811915e-06, 8.6773016e-06, 5.7548485e-05, ..., 6.4329481e-01,\n",
       "        3.5640642e-01, 2.1539530e-04],\n",
       "       ...,\n",
       "       [2.9655275e-06, 1.9694083e-04, 9.3344116e-01, ..., 7.4705906e-07,\n",
       "        4.3589913e-05, 7.7843415e-06],\n",
       "       [2.1079545e-06, 8.8706533e-05, 9.9136972e-01, ..., 7.1606911e-07,\n",
       "        5.6364712e-05, 7.1436557e-06],\n",
       "       [3.4080942e-06, 9.3054725e-04, 6.3470584e-01, ..., 9.8747432e-07,\n",
       "        9.3553732e-05, 6.8793279e-06]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "89d4c297",
   "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": 18,
   "id": "55147698",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Confusion Matrix ****\n",
      "[[490   0   6   0   0   0]\n",
      " [  0 405  66   0   0   0]\n",
      " [  1   1 418   0   0   0]\n",
      " [  0   0   0 464  22   5]\n",
      " [  0   1   0  12 490   0]\n",
      " [  0   0   0   3   0 534]]\n",
      "**** Classification Report ****\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Walking       1.00      0.99      0.99       496\n",
      "      Walkup       1.00      0.86      0.92       471\n",
      "    Walkdown       0.85      1.00      0.92       420\n",
      "     Sitting       0.97      0.95      0.96       491\n",
      "    Standing       0.96      0.97      0.97       503\n",
      "      Laying       0.99      0.99      0.99       537\n",
      "\n",
      "    accuracy                           0.96      2918\n",
      "   macro avg       0.96      0.96      0.96      2918\n",
      "weighted avg       0.96      0.96      0.96      2918\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(y_test,predictions,classNames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9ef4e84",
   "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
}