cnn_based_autoencoder_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>z1</th>\n",
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       "      <th>z3</th>\n",
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       "      <th>start_point</th>\n",
       "      <th>end_point</th>\n",
       "      <th>isIntrusion</th>\n",
       "      <th>intrusion_zone</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>3.479755e-11</td>\n",
       "      <td>1.729669e-06</td>\n",
       "      <td>-1.074505e-06</td>\n",
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       "      <td>-1.978193e-06</td>\n",
       "      <td>-4.215552e-08</td>\n",
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       "      <td>4.933022e-08</td>\n",
       "      <td>-1.209462e-06</td>\n",
       "      <td>8.495825e-08</td>\n",
       "      <td>-1.360888e-07</td>\n",
       "      <td>-8.673617e-19</td>\n",
       "      <td>0.009132</td>\n",
       "      <td>40.904136</td>\n",
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       "      <th>1</th>\n",
       "      <td>-5.774669e-10</td>\n",
       "      <td>-9.667019e-07</td>\n",
       "      <td>-1.172746e-06</td>\n",
       "      <td>-8.148441e-07</td>\n",
       "      <td>-1.085682e-06</td>\n",
       "      <td>-2.618001e-08</td>\n",
       "      <td>1.536875e-06</td>\n",
       "      <td>-4.809667e-07</td>\n",
       "      <td>7.355430e-08</td>\n",
       "      <td>1.620232e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.559091e-07</td>\n",
       "      <td>9.168085e-07</td>\n",
       "      <td>8.087109e-07</td>\n",
       "      <td>7.078943e-07</td>\n",
       "      <td>4.946341e-07</td>\n",
       "      <td>-6.505213e-19</td>\n",
       "      <td>41.005079</td>\n",
       "      <td>81.806418</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.974670e-10</td>\n",
       "      <td>5.440602e-07</td>\n",
       "      <td>5.968525e-07</td>\n",
       "      <td>-2.136242e-08</td>\n",
       "      <td>-2.463316e-07</td>\n",
       "      <td>4.842712e-07</td>\n",
       "      <td>-8.860241e-07</td>\n",
       "      <td>6.470653e-07</td>\n",
       "      <td>5.430934e-07</td>\n",
       "      <td>-1.458603e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.413626e-06</td>\n",
       "      <td>1.379344e-07</td>\n",
       "      <td>-3.167636e-07</td>\n",
       "      <td>-5.100911e-07</td>\n",
       "      <td>-4.025520e-07</td>\n",
       "      <td>1.301043e-18</td>\n",
       "      <td>81.912590</td>\n",
       "      <td>122.817894</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.959882e-10</td>\n",
       "      <td>3.390513e-07</td>\n",
       "      <td>2.861875e-07</td>\n",
       "      <td>1.237342e-06</td>\n",
       "      <td>1.548561e-06</td>\n",
       "      <td>-4.017564e-07</td>\n",
       "      <td>-7.252355e-07</td>\n",
       "      <td>-5.407646e-07</td>\n",
       "      <td>-1.974723e-08</td>\n",
       "      <td>-8.548541e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>2.269420e-06</td>\n",
       "      <td>3.904686e-07</td>\n",
       "      <td>-5.901801e-07</td>\n",
       "      <td>9.370105e-07</td>\n",
       "      <td>1.087443e-07</td>\n",
       "      <td>-3.373336e-09</td>\n",
       "      <td>122.893114</td>\n",
       "      <td>163.760757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.426321e-10</td>\n",
       "      <td>-8.383938e-07</td>\n",
       "      <td>-8.720060e-08</td>\n",
       "      <td>-1.354042e-06</td>\n",
       "      <td>-1.309397e-06</td>\n",
       "      <td>9.558595e-07</td>\n",
       "      <td>9.235621e-07</td>\n",
       "      <td>1.726365e-06</td>\n",
       "      <td>1.359531e-07</td>\n",
       "      <td>-4.867452e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.116315e-06</td>\n",
       "      <td>-1.872924e-06</td>\n",
       "      <td>3.991558e-07</td>\n",
       "      <td>-2.448489e-07</td>\n",
       "      <td>2.474903e-07</td>\n",
       "      <td>3.373336e-09</td>\n",
       "      <td>163.940041</td>\n",
       "      <td>204.690356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "             z1            z2            z3            z4            z5  \\\n",
       "0  3.479755e-11  1.729669e-06 -1.074505e-06  1.696733e-06  1.408154e-06   \n",
       "1 -5.774669e-10 -9.667019e-07 -1.172746e-06 -8.148441e-07 -1.085682e-06   \n",
       "2  7.974670e-10  5.440602e-07  5.968525e-07 -2.136242e-08 -2.463316e-07   \n",
       "3 -2.959882e-10  3.390513e-07  2.861875e-07  1.237342e-06  1.548561e-06   \n",
       "4  1.426321e-10 -8.383938e-07 -8.720060e-08 -1.354042e-06 -1.309397e-06   \n",
       "\n",
       "             z6            z7            z8            z9           z10  ...  \\\n",
       "0 -1.247660e-06  4.392523e-07 -1.822577e-07 -1.978193e-06 -4.215552e-08  ...   \n",
       "1 -2.618001e-08  1.536875e-06 -4.809667e-07  7.355430e-08  1.620232e-06  ...   \n",
       "2  4.842712e-07 -8.860241e-07  6.470653e-07  5.430934e-07 -1.458603e-06  ...   \n",
       "3 -4.017564e-07 -7.252355e-07 -5.407646e-07 -1.974723e-08 -8.548541e-07  ...   \n",
       "4  9.558595e-07  9.235621e-07  1.726365e-06  1.359531e-07 -4.867452e-07  ...   \n",
       "\n",
       "            z92           z93           z94           z95           z96  \\\n",
       "0  4.510038e-07  4.933022e-08 -1.209462e-06  8.495825e-08 -1.360888e-07   \n",
       "1 -2.559091e-07  9.168085e-07  8.087109e-07  7.078943e-07  4.946341e-07   \n",
       "2 -1.413626e-06  1.379344e-07 -3.167636e-07 -5.100911e-07 -4.025520e-07   \n",
       "3  2.269420e-06  3.904686e-07 -5.901801e-07  9.370105e-07  1.087443e-07   \n",
       "4 -2.116315e-06 -1.872924e-06  3.991558e-07 -2.448489e-07  2.474903e-07   \n",
       "\n",
       "            z97  start_point   end_point  isIntrusion  intrusion_zone  \n",
       "0 -8.673617e-19     0.009132   40.904136          0.0             0.0  \n",
       "1 -6.505213e-19    41.005079   81.806418          0.0             0.0  \n",
       "2  1.301043e-18    81.912590  122.817894          0.0             0.0  \n",
       "3 -3.373336e-09   122.893114  163.760757          0.0             0.0  \n",
       "4  3.373336e-09   163.940041  204.690356          0.0             0.0  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df= pd.read_csv(\"DifAmp_Data_set.csv\")\n",
    "df= df.iloc[:,1:]\n",
    "\n",
    "# df.iloc[:,0:97]=df.iloc[:,0:97].abs()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,LeakyReLU,Conv1D,Flatten,MaxPooling1D\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.iloc[:, 0:97]\n",
    "x.shape\n",
    "x=np.asarray(x)\n",
    "x = x.reshape(1000, 1,97)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d (Conv1D)              (None, 1, 90)             8820      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu (LeakyReLU)      (None, 1, 90)             0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d (MaxPooling1D) (None, 1, 90)             0         \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 1, 45)             4095      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 1, 45)             0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 1, 45)             0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 1, 25)             1150      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 1, 25)             0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 1, 25)             0         \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 1, 45)             1170      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 1, 45)             0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1 (None, 1, 45)             0         \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, 1, 90)             4140      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 1, 90)             0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 90)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 97)                8827      \n",
      "=================================================================\n",
      "Total params: 28,202\n",
      "Trainable params: 28,202\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "model = Sequential()\n",
    "# model.add(Conv1D(90, kernel_size=5, strides=10,input_shape=(None,97),kernel_initializer= 'uniform',))\n",
    "model.add(Conv1D(90, kernel_size=1,input_shape=(1,97)))\n",
    "model.add(LeakyReLU())\n",
    "model.add(MaxPooling1D(pool_size=1))\n",
    "model.add(Conv1D(45, kernel_size=1))\n",
    "model.add(LeakyReLU())\n",
    "model.add(MaxPooling1D(pool_size=1))\n",
    "model.add(Conv1D(25, kernel_size=1))\n",
    "model.add(LeakyReLU())\n",
    "model.add(MaxPooling1D(pool_size=1))\n",
    "model.add(Conv1D(45, kernel_size=1))\n",
    "model.add(LeakyReLU())\n",
    "model.add(MaxPooling1D(pool_size=1))\n",
    "\n",
    "model.add(Conv1D(90, kernel_size=1))\n",
    "model.add(LeakyReLU())\n",
    "model.add(Flatten())\n",
    "model.add(Dense(97,activation=\"sigmoid\"))\n",
    "\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss='mae')  # mae --> mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1, 97)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0163\n",
      "Epoch 2/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 5.3306e-06\n",
      "Epoch 3/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.9858e-06\n",
      "Epoch 4/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.8879e-06\n",
      "Epoch 5/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.8535e-06\n",
      "Epoch 6/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.8392e-06\n",
      "Epoch 7/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.8326e-06\n",
      "Epoch 8/20\n",
      "1000/1000 [==============================] - ETA: 0s - loss: 4.8080e-0 - 1s 1ms/step - loss: 4.8292e-06\n",
      "Epoch 9/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.8274e-06\n",
      "Epoch 10/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.8263e-06\n",
      "Epoch 11/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.8257e-06\n",
      "Epoch 12/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.8253e-06\n",
      "Epoch 13/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.8251e-06\n",
      "Epoch 14/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.8250e-06\n",
      "Epoch 15/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.8249e-06\n",
      "Epoch 16/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.8248e-06\n",
      "Epoch 17/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.8248e-06\n",
      "Epoch 18/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.8248e-06\n",
      "Epoch 19/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.8247e-06\n",
      "Epoch 20/20\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 4.8247e-06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x25d44db7b08>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model.fit(x,x,batch_size=1,epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "pred =model.predict(x)\n",
    "\n",
    "plt.plot(pred[5])\n",
    "plt.show()\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred= model.predict(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 97)\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"
    },
    {
     "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": [
    "pred= model.predict(x)\n",
    "print(pred.shape)\n",
    "\n",
    "pred[1]\n",
    "plt.plot(x[124][0],'b')\n",
    "plt.plot(pred[124],\"g\")\n",
    "plt.show()\n",
    "a=1\n",
    "for i in pred:\n",
    "#     plt.plot(i)\n",
    "    plt.title(f\"{a}\")\n",
    "    a+=1\n",
    "    plt.plot(x[a-1][0],'b')\n",
    "    plt.plot(i,\"r\")\n",
    "    plt.show()\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-b893053e296a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     19\u001b[0m    \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxhline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'r'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinestyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'-'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m    \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxhline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'r'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinestyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'-'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m    \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{index} zone {ans_point}   real ans {y[index]}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'y' is not defined"
     ]
    },
    {
     "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": [
    " for index in range(len(pred)):\n",
    "    list1= list(pred[index])\n",
    "    list2=list(x[index][0])\n",
    "    ans=[]\n",
    "\n",
    "    los= keras.losses.mae(list1,list2) \n",
    "    for i in range(97):\n",
    "        ans.append(abs(list1[i]-list2[i]))\n",
    "    mean=np.mean(ans)\n",
    "    std=np.std(ans)\n",
    "#     print(los,mean,std)\n",
    "    ans_point=-1\n",
    "    for d in range(97):\n",
    "        if list2[d]>mean+2*std:\n",
    "            ans=d+1;\n",
    "    plt.plot(list1,\"g\")\n",
    "    plt.plot(list2,\"b\")\n",
    "\n",
    "    plt.axhline(y=mean+5*std, color='r', linestyle='-')\n",
    "    plt.axhline(y=-(mean+5*std), color='r', linestyle='-')\n",
    "    plt.title(f\"{index} zone {ans_point}   real ans {y[index]}\")\n",
    "\n",
    "            \n",
    "    plt.show()\n",
    "    break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "predicted_value=[]\n",
    "for index in range(len(pred)):\n",
    "    list1= list(pred[index])\n",
    "    list2=list(x[index][0])\n",
    "    los= keras.losses.mae(list1,list2) \n",
    "    ans=[]\n",
    "    for i in range(97):\n",
    "        ans.append(abs(list1[i]-list2[i]))\n",
    "    mean=np.mean(ans)\n",
    "    std=np.std(ans)\n",
    "    temp=[]\n",
    "    for d in range(97):\n",
    "        temp.append(abs(list1[d]-list2[d]))\n",
    "    plt.plot(temp,\"g\")\n",
    "    predicted_value.append(temp)\n",
    "\n",
    "    plt.axhline(y=mean+5*std, color='r', linestyle='-')\n",
    "    plt.title(f\"{index}\")\n",
    "\n",
    "            \n",
    "    plt.show()\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f56c1bb808>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(predicted_value[102],color=\"g\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "ans=[0 for x in range(1000)]\n",
    "thresh= mean+5*std\n",
    "for i in range(1000):\n",
    "    for j in range(97):\n",
    "        if predicted_value[i][j]>thresh:\n",
    "            if(ans[i]==0):\n",
    "                ans[i]=j+1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "187 93.5\n"
     ]
    }
   ],
   "source": [
    "correct=0\n",
    "for i in range(750,950):\n",
    "    if ans[i]==df.iloc[i,100]:\n",
    "        correct+=1\n",
    "    \n",
    "print(correct,correct/2)   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## accuracy = 93.5 %\n",
    " 187 cases out of 200 cases are predicted acurately"
   ]
  }
 ],
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