Assignment_4_ECG_offline_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use Autoencoder to implement anomaly detection. Build the model by using:\n",
    "a. Import required libraries<br>\n",
    "b. Upload / access the dataset<br>\n",
    "c. Encoder converts it into latent representation<br>\n",
    "d. Decoder networks convert it back to the original input<br>\n",
    "e. Compile the models with Optimizer, Loss, and Evaluation Metrics<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "64fe3cf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "791c2d97",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'dataset/ecg-csv/ecg-csv/ecg.csv'\n",
    "df = pd.read_csv(path,header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1539bb2b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.578621</td>\n",
       "      <td>0.257740</td>\n",
       "      <td>0.228077</td>\n",
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       "      <td>-0.754680</td>\n",
       "      <td>0.042321</td>\n",
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       "      <td>0.555784</td>\n",
       "      <td>0.476333</td>\n",
       "      <td>0.773820</td>\n",
       "      <td>1.119621</td>\n",
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       "      <th>2</th>\n",
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       "      <td>-4.584095</td>\n",
       "      <td>-4.187449</td>\n",
       "      <td>-3.151462</td>\n",
       "      <td>-1.742940</td>\n",
       "      <td>-1.490659</td>\n",
       "      <td>-1.183580</td>\n",
       "      <td>-0.394229</td>\n",
       "      <td>...</td>\n",
       "      <td>1.284825</td>\n",
       "      <td>0.886073</td>\n",
       "      <td>0.531452</td>\n",
       "      <td>0.311377</td>\n",
       "      <td>-0.021919</td>\n",
       "      <td>-0.713683</td>\n",
       "      <td>-0.532197</td>\n",
       "      <td>0.321097</td>\n",
       "      <td>0.904227</td>\n",
       "      <td>-0.421797</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.490473</td>\n",
       "      <td>-1.914407</td>\n",
       "      <td>-3.616364</td>\n",
       "      <td>-4.318823</td>\n",
       "      <td>-4.268016</td>\n",
       "      <td>-3.881110</td>\n",
       "      <td>-2.993280</td>\n",
       "      <td>-1.671131</td>\n",
       "      <td>-1.333884</td>\n",
       "      <td>-0.965629</td>\n",
       "      <td>...</td>\n",
       "      <td>0.491173</td>\n",
       "      <td>0.350816</td>\n",
       "      <td>0.499111</td>\n",
       "      <td>0.600345</td>\n",
       "      <td>0.842069</td>\n",
       "      <td>0.952074</td>\n",
       "      <td>0.990133</td>\n",
       "      <td>1.086798</td>\n",
       "      <td>1.403011</td>\n",
       "      <td>-0.383564</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.800232</td>\n",
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       "      <td>-2.384761</td>\n",
       "      <td>-3.973292</td>\n",
       "      <td>-4.338224</td>\n",
       "      <td>-3.802422</td>\n",
       "      <td>-2.534510</td>\n",
       "      <td>-1.783423</td>\n",
       "      <td>-1.594450</td>\n",
       "      <td>-0.753199</td>\n",
       "      <td>...</td>\n",
       "      <td>0.966606</td>\n",
       "      <td>1.148884</td>\n",
       "      <td>0.958434</td>\n",
       "      <td>1.059025</td>\n",
       "      <td>1.371682</td>\n",
       "      <td>1.277392</td>\n",
       "      <td>0.960304</td>\n",
       "      <td>0.971020</td>\n",
       "      <td>1.614392</td>\n",
       "      <td>1.421456</td>\n",
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       "      <th>4993</th>\n",
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       "      <td>-2.957065</td>\n",
       "      <td>-2.931897</td>\n",
       "      <td>-2.664816</td>\n",
       "      <td>-2.090137</td>\n",
       "      <td>-1.461841</td>\n",
       "      <td>...</td>\n",
       "      <td>1.513738</td>\n",
       "      <td>1.757705</td>\n",
       "      <td>2.291923</td>\n",
       "      <td>2.704595</td>\n",
       "      <td>2.451519</td>\n",
       "      <td>2.017396</td>\n",
       "      <td>1.704358</td>\n",
       "      <td>1.688542</td>\n",
       "      <td>1.629593</td>\n",
       "      <td>1.342651</td>\n",
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       "      <th>4994</th>\n",
       "      <td>-2.060402</td>\n",
       "      <td>-2.860116</td>\n",
       "      <td>-3.405074</td>\n",
       "      <td>-3.748719</td>\n",
       "      <td>-3.513561</td>\n",
       "      <td>-3.006545</td>\n",
       "      <td>-2.234850</td>\n",
       "      <td>-1.593270</td>\n",
       "      <td>-1.075279</td>\n",
       "      <td>-0.976047</td>\n",
       "      <td>...</td>\n",
       "      <td>1.123739</td>\n",
       "      <td>1.388947</td>\n",
       "      <td>2.079675</td>\n",
       "      <td>2.433375</td>\n",
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       "      <td>1.696818</td>\n",
       "      <td>1.483832</td>\n",
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       "      <td>-3.167849</td>\n",
       "      <td>-2.638360</td>\n",
       "      <td>-1.664162</td>\n",
       "      <td>-0.935655</td>\n",
       "      <td>-0.866953</td>\n",
       "      <td>-0.645363</td>\n",
       "      <td>...</td>\n",
       "      <td>0.205543</td>\n",
       "      <td>-0.472419</td>\n",
       "      <td>-1.310147</td>\n",
       "      <td>-2.029521</td>\n",
       "      <td>-3.221294</td>\n",
       "      <td>-4.176790</td>\n",
       "      <td>-4.009720</td>\n",
       "      <td>-2.874136</td>\n",
       "      <td>-2.008369</td>\n",
       "      <td>-1.808334</td>\n",
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       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>-0.547705</td>\n",
       "      <td>-1.889545</td>\n",
       "      <td>-2.839779</td>\n",
       "      <td>-3.457912</td>\n",
       "      <td>-3.929149</td>\n",
       "      <td>-3.966026</td>\n",
       "      <td>-3.492560</td>\n",
       "      <td>-2.695270</td>\n",
       "      <td>-1.849691</td>\n",
       "      <td>-1.374321</td>\n",
       "      <td>...</td>\n",
       "      <td>1.218185</td>\n",
       "      <td>1.258419</td>\n",
       "      <td>1.907530</td>\n",
       "      <td>2.280888</td>\n",
       "      <td>1.895242</td>\n",
       "      <td>1.437702</td>\n",
       "      <td>1.193433</td>\n",
       "      <td>1.261335</td>\n",
       "      <td>1.150449</td>\n",
       "      <td>0.804932</td>\n",
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       "      <th>4997</th>\n",
       "      <td>-1.351779</td>\n",
       "      <td>-2.209006</td>\n",
       "      <td>-2.520225</td>\n",
       "      <td>-3.061475</td>\n",
       "      <td>-3.065141</td>\n",
       "      <td>-3.030739</td>\n",
       "      <td>-2.622720</td>\n",
       "      <td>-2.044092</td>\n",
       "      <td>-1.295874</td>\n",
       "      <td>-0.733839</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.896575</td>\n",
       "      <td>-1.512234</td>\n",
       "      <td>-2.076075</td>\n",
       "      <td>-2.586042</td>\n",
       "      <td>-3.322799</td>\n",
       "      <td>-3.627311</td>\n",
       "      <td>-3.437038</td>\n",
       "      <td>-2.260023</td>\n",
       "      <td>-1.577823</td>\n",
       "      <td>-0.684531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4998 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6    \\\n",
       "0    -0.112522 -2.827204 -3.773897 -4.349751 -4.376041 -3.474986 -2.181408   \n",
       "1    -1.100878 -3.996840 -4.285843 -4.506579 -4.022377 -3.234368 -1.566126   \n",
       "2    -0.567088 -2.593450 -3.874230 -4.584095 -4.187449 -3.151462 -1.742940   \n",
       "3     0.490473 -1.914407 -3.616364 -4.318823 -4.268016 -3.881110 -2.993280   \n",
       "4     0.800232 -0.874252 -2.384761 -3.973292 -4.338224 -3.802422 -2.534510   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "4993  0.608558 -0.335651 -0.990948 -1.784153 -2.626145 -2.957065 -2.931897   \n",
       "4994 -2.060402 -2.860116 -3.405074 -3.748719 -3.513561 -3.006545 -2.234850   \n",
       "4995 -1.122969 -2.252925 -2.867628 -3.358605 -3.167849 -2.638360 -1.664162   \n",
       "4996 -0.547705 -1.889545 -2.839779 -3.457912 -3.929149 -3.966026 -3.492560   \n",
       "4997 -1.351779 -2.209006 -2.520225 -3.061475 -3.065141 -3.030739 -2.622720   \n",
       "\n",
       "           7         8         9    ...       130       131       132  \\\n",
       "0    -1.818286 -1.250522 -0.477492  ...  0.160348  0.792168  0.933541   \n",
       "1    -0.992258 -0.754680  0.042321  ...  0.560327  0.538356  0.656881   \n",
       "2    -1.490659 -1.183580 -0.394229  ...  1.284825  0.886073  0.531452   \n",
       "3    -1.671131 -1.333884 -0.965629  ...  0.491173  0.350816  0.499111   \n",
       "4    -1.783423 -1.594450 -0.753199  ...  0.966606  1.148884  0.958434   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "4993 -2.664816 -2.090137 -1.461841  ...  1.513738  1.757705  2.291923   \n",
       "4994 -1.593270 -1.075279 -0.976047  ...  1.123739  1.388947  2.079675   \n",
       "4995 -0.935655 -0.866953 -0.645363  ...  0.205543 -0.472419 -1.310147   \n",
       "4996 -2.695270 -1.849691 -1.374321  ...  1.218185  1.258419  1.907530   \n",
       "4997 -2.044092 -1.295874 -0.733839  ... -0.896575 -1.512234 -2.076075   \n",
       "\n",
       "           133       134       135       136       137       138       139  \n",
       "0     0.796958  0.578621  0.257740  0.228077  0.123431  0.925286  0.193137  \n",
       "1     0.787490  0.724046  0.555784  0.476333  0.773820  1.119621 -1.436250  \n",
       "2     0.311377 -0.021919 -0.713683 -0.532197  0.321097  0.904227 -0.421797  \n",
       "3     0.600345  0.842069  0.952074  0.990133  1.086798  1.403011 -0.383564  \n",
       "4     1.059025  1.371682  1.277392  0.960304  0.971020  1.614392  1.421456  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "4993  2.704595  2.451519  2.017396  1.704358  1.688542  1.629593  1.342651  \n",
       "4994  2.433375  2.159484  1.819747  1.534767  1.696818  1.483832  1.047612  \n",
       "4995 -2.029521 -3.221294 -4.176790 -4.009720 -2.874136 -2.008369 -1.808334  \n",
       "4996  2.280888  1.895242  1.437702  1.193433  1.261335  1.150449  0.804932  \n",
       "4997 -2.586042 -3.322799 -3.627311 -3.437038 -2.260023 -1.577823 -0.684531  \n",
       "\n",
       "[4998 rows x 140 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(140, axis=1) \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "feb13464",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "StandardScaler = StandardScaler()\n",
    "df = StandardScaler.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bc7ef96d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocess the data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test = train_test_split(df, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f51f5a08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "140\n",
      "140\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(x_train.shape[1])\n",
    "print(x_test.shape[1])\n",
    "type(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "35c7e167",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Model, Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras import layers, models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3d494ae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = tf.keras.models.Sequential([\n",
    "    layers.Input(shape=(x_train.shape[1],)),\n",
    "    layers.Dense(32, activation='relu'),\n",
    "    layers.Dense(16, activation='relu'),\n",
    "    layers.Dense(8, activation='relu')\n",
    "])\n",
    "        \n",
    "decoder = tf.keras.models.Sequential([\n",
    "    layers.Input(shape=(8,)),\n",
    "    layers.Dense(16, activation='relu'),\n",
    "    layers.Dense(32, activation='relu'),\n",
    "    layers.Dense(x_train.shape[1], activation='linear')  # Use linear activation for reconstruction\n",
    "])\n",
    "        \n",
    "model = tf.keras.models.Sequential([\n",
    "    encoder,\n",
    "    decoder\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5a35d9e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss ='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6898f208",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "134/134 [==============================] - 4s 9ms/step - loss: 0.6704 - val_loss: 0.3898\n",
      "Epoch 2/10\n",
      "134/134 [==============================] - 1s 6ms/step - loss: 0.3183 - val_loss: 0.2976\n",
      "Epoch 3/10\n",
      "134/134 [==============================] - 1s 6ms/step - loss: 0.2725 - val_loss: 0.2760\n",
      "Epoch 4/10\n",
      "134/134 [==============================] - 1s 6ms/step - loss: 0.2534 - val_loss: 0.2611\n",
      "Epoch 5/10\n",
      "134/134 [==============================] - 1s 6ms/step - loss: 0.2399 - val_loss: 0.2495\n",
      "Epoch 6/10\n",
      "134/134 [==============================] - 1s 6ms/step - loss: 0.2255 - val_loss: 0.2337\n",
      "Epoch 7/10\n",
      "134/134 [==============================] - 1s 6ms/step - loss: 0.2075 - val_loss: 0.2184\n",
      "Epoch 8/10\n",
      "134/134 [==============================] - 1s 6ms/step - loss: 0.1922 - val_loss: 0.2064\n",
      "Epoch 9/10\n",
      "134/134 [==============================] - 1s 6ms/step - loss: 0.1812 - val_loss: 0.1983\n",
      "Epoch 10/10\n",
      "134/134 [==============================] - 1s 6ms/step - loss: 0.1745 - val_loss: 0.1961\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    x_train, \n",
    "    x_train, \n",
    "    validation_data=(x_test,x_test),\n",
    "    epochs=10,\n",
    "    batch_size = 30,\n",
    "    shuffle=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cc65fa34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 43,
     "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": [
    "import seaborn as sns\n",
    "sns.lineplot(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9b234474",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "mse = np.mean(np.power(x_test - predictions, 2), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f5885109",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4894422503744985"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = np.percentile(mse, 95)  # Adjust the percentile as needed\n",
    "threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "660c4900",
   "metadata": {},
   "outputs": [],
   "source": [
    "anomalies = mse > threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3ce17f3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Anomalies: 50\n"
     ]
    }
   ],
   "source": [
    "# Calculate the number of anomalies\n",
    "num_anomalies = np.sum(anomalies)\n",
    "print(f\"Number of Anomalies: {num_anomalies}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3ec2f159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the anomalies\n",
    "import matplotlib.pyplot as plot\n",
    "\n",
    "plt.plot(mse, marker='o', linestyle='', markersize=3, label='MSE')\n",
    "plt.axhline(threshold, color='r', linestyle='--', label='Anomaly Threshold')\n",
    "plt.xlabel('Sample Index')\n",
    "plt.ylabel('MSE')\n",
    "plt.title('Anomaly Detection Results')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "817a5cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(x_test[0], label='Original ECG')\n",
    "plt.plot(predictions[0], label='Reconstructed ECG')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.legend()\n",
    "plt.title('Normal ECG')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0c61e421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "sns.heatmap(confusion_matrix(anomalies, anomalies), annot = True, fmt = 'd')\n",
    "plt.xlabel(\"Predicted label\", fontsize = 14)\n",
    "plt.ylabel(\"True label\", fontsize = 14)\n",
    "plt.title(\"Confusion Matrix\", fontsize = 14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d57de4c8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce316b03",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "lab_env",
   "language": "python",
   "name": "lab_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}