A4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "85e99f60",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd;\n",
    "from sklearn.preprocessing import MinMaxScaler \n",
    "from keras.models import Model, Sequential\n",
    "from keras.layers import Dense, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "a4c194a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>172786.0</td>\n",
       "      <td>-11.881118</td>\n",
       "      <td>10.071785</td>\n",
       "      <td>-9.834783</td>\n",
       "      <td>-2.066656</td>\n",
       "      <td>-5.364473</td>\n",
       "      <td>-2.606837</td>\n",
       "      <td>-4.918215</td>\n",
       "      <td>7.305334</td>\n",
       "      <td>1.914428</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>1.014480</td>\n",
       "      <td>-0.509348</td>\n",
       "      <td>1.436807</td>\n",
       "      <td>0.250034</td>\n",
       "      <td>0.943651</td>\n",
       "      <td>0.823731</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>172787.0</td>\n",
       "      <td>-0.732789</td>\n",
       "      <td>-0.055080</td>\n",
       "      <td>2.035030</td>\n",
       "      <td>-0.738589</td>\n",
       "      <td>0.868229</td>\n",
       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>-1.016226</td>\n",
       "      <td>-0.606624</td>\n",
       "      <td>-0.395255</td>\n",
       "      <td>0.068472</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>24.79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>1.919565</td>\n",
       "      <td>-0.301254</td>\n",
       "      <td>-3.249640</td>\n",
       "      <td>-0.557828</td>\n",
       "      <td>2.630515</td>\n",
       "      <td>3.031260</td>\n",
       "      <td>-0.296827</td>\n",
       "      <td>0.708417</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.232045</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>67.88</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.392087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.265245</td>\n",
       "      <td>0.800049</td>\n",
       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>172792.0</td>\n",
       "      <td>-0.533413</td>\n",
       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
       "      <td>-0.506271</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.649617</td>\n",
       "      <td>1.577006</td>\n",
       "      <td>-0.414650</td>\n",
       "      <td>0.486180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261057</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.376777</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>-0.473649</td>\n",
       "      <td>-0.818267</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>217.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284807 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time         V1         V2        V3        V4        V5  \\\n",
       "0            0.0  -1.359807  -0.072781  2.536347  1.378155 -0.338321   \n",
       "1            0.0   1.191857   0.266151  0.166480  0.448154  0.060018   \n",
       "2            1.0  -1.358354  -1.340163  1.773209  0.379780 -0.503198   \n",
       "3            1.0  -0.966272  -0.185226  1.792993 -0.863291 -0.010309   \n",
       "4            2.0  -1.158233   0.877737  1.548718  0.403034 -0.407193   \n",
       "...          ...        ...        ...       ...       ...       ...   \n",
       "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
       "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
       "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
       "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
       "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
       "0       0.462388  0.239599  0.098698  0.363787  ... -0.018307  0.277838   \n",
       "1      -0.082361 -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672   \n",
       "2       1.800499  0.791461  0.247676 -1.514654  ...  0.247998  0.771679   \n",
       "3       1.247203  0.237609  0.377436 -1.387024  ... -0.108300  0.005274   \n",
       "4       0.095921  0.592941 -0.270533  0.817739  ... -0.009431  0.798278   \n",
       "...          ...       ...       ...       ...  ...       ...       ...   \n",
       "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
       "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n",
       "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n",
       "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n",
       "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n",
       "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n",
       "...          ...       ...       ...       ...       ...       ...     ...   \n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
       "\n",
       "        Class  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "...       ...  \n",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[284807 rows x 31 columns]"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv('/home/aditya/Downloads/creditcard.csv')\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "ee893743",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = raw_data.drop(\"Time\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "38764023",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data.Class.unique()\n",
    "features = raw_data.drop(\"Class\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "4dd5c8e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(features,raw_data['Class'],random_state=4,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "587ed986",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = x_train.loc[y_train[y_train==1].index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "85cc9a18",
   "metadata": {},
   "outputs": [],
   "source": [
    "minmax = MinMaxScaler(feature_range=(0, 1))\n",
    "x_train_scaled = minmax.fit_transform(train_data)\n",
    "x_test_scaled = minmax.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "ce1825e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AutoEncoder(Model):\n",
    "    def __init__(self,output_unit,ldim=8):\n",
    "        super().__init__()\n",
    "        self.encoder = Sequential([\n",
    "#             Dense(64,activation='relu'),\n",
    "#             Dropout(0.1),\n",
    "#             Dense(32,activation='relu'),\n",
    "#             Dropout(0.1),\n",
    "            Dense(16,activation='relu'),\n",
    "            Dropout(0.1),\n",
    "            Dense(ldim,activation='relu')\n",
    "        ])\n",
    "        self.decoder = Sequential([\n",
    "            Dense(16,activation='relu'),\n",
    "#             Dropout(0.1),\n",
    "#             Dense(32,activation='relu'),\n",
    "#             Dropout(0.1),\n",
    "#             Dense(64,activation='relu'),\n",
    "            Dropout(0.1),\n",
    "            Dense(output_unit,activation='sigmoid')\n",
    "        ])\n",
    "    def call(self,inputs):\n",
    "        encoded = self.encoder(inputs)\n",
    "        decoded = self.decoder(encoded)\n",
    "        return decoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "3e057df5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1/1 [==============================] - 1s 571ms/step - loss: 0.0313 - mse: 0.0694 - val_loss: 0.0434 - val_mse: 0.1008\n",
      "Epoch 2/20\n",
      "1/1 [==============================] - 0s 151ms/step - loss: 0.0306 - mse: 0.0680 - val_loss: 0.0427 - val_mse: 0.0992\n",
      "Epoch 3/20\n",
      "1/1 [==============================] - 0s 143ms/step - loss: 0.0302 - mse: 0.0670 - val_loss: 0.0421 - val_mse: 0.0978\n",
      "Epoch 4/20\n",
      "1/1 [==============================] - 0s 136ms/step - loss: 0.0297 - mse: 0.0659 - val_loss: 0.0414 - val_mse: 0.0964\n",
      "Epoch 5/20\n",
      "1/1 [==============================] - 0s 140ms/step - loss: 0.0292 - mse: 0.0648 - val_loss: 0.0409 - val_mse: 0.0951\n",
      "Epoch 6/20\n",
      "1/1 [==============================] - 0s 142ms/step - loss: 0.0288 - mse: 0.0640 - val_loss: 0.0403 - val_mse: 0.0939\n",
      "Epoch 7/20\n",
      "1/1 [==============================] - 0s 139ms/step - loss: 0.0283 - mse: 0.0629 - val_loss: 0.0398 - val_mse: 0.0927\n",
      "Epoch 8/20\n",
      "1/1 [==============================] - 0s 149ms/step - loss: 0.0279 - mse: 0.0619 - val_loss: 0.0393 - val_mse: 0.0917\n",
      "Epoch 9/20\n",
      "1/1 [==============================] - 0s 144ms/step - loss: 0.0277 - mse: 0.0613 - val_loss: 0.0389 - val_mse: 0.0907\n",
      "Epoch 10/20\n",
      "1/1 [==============================] - 0s 158ms/step - loss: 0.0272 - mse: 0.0604 - val_loss: 0.0385 - val_mse: 0.0898\n",
      "Epoch 11/20\n",
      "1/1 [==============================] - 0s 147ms/step - loss: 0.0270 - mse: 0.0600 - val_loss: 0.0381 - val_mse: 0.0889\n",
      "Epoch 12/20\n",
      "1/1 [==============================] - 0s 148ms/step - loss: 0.0267 - mse: 0.0592 - val_loss: 0.0378 - val_mse: 0.0881\n",
      "Epoch 13/20\n",
      "1/1 [==============================] - 0s 132ms/step - loss: 0.0265 - mse: 0.0588 - val_loss: 0.0375 - val_mse: 0.0874\n",
      "Epoch 14/20\n",
      "1/1 [==============================] - 0s 139ms/step - loss: 0.0262 - mse: 0.0581 - val_loss: 0.0371 - val_mse: 0.0867\n",
      "Epoch 15/20\n",
      "1/1 [==============================] - 0s 139ms/step - loss: 0.0260 - mse: 0.0576 - val_loss: 0.0369 - val_mse: 0.0860\n",
      "Epoch 16/20\n",
      "1/1 [==============================] - 0s 142ms/step - loss: 0.0258 - mse: 0.0571 - val_loss: 0.0366 - val_mse: 0.0854\n",
      "Epoch 17/20\n",
      "1/1 [==============================] - 0s 143ms/step - loss: 0.0256 - mse: 0.0566 - val_loss: 0.0363 - val_mse: 0.0848\n",
      "Epoch 18/20\n",
      "1/1 [==============================] - 0s 143ms/step - loss: 0.0254 - mse: 0.0562 - val_loss: 0.0361 - val_mse: 0.0843\n",
      "Epoch 19/20\n",
      "1/1 [==============================] - 0s 146ms/step - loss: 0.0253 - mse: 0.0559 - val_loss: 0.0359 - val_mse: 0.0838\n",
      "Epoch 20/20\n",
      "1/1 [==============================] - 0s 147ms/step - loss: 0.0251 - mse: 0.0555 - val_loss: 0.0357 - val_mse: 0.0833\n",
      "29\n"
     ]
    }
   ],
   "source": [
    "model = AutoEncoder(output_unit =  x_train_scaled.shape[1])\n",
    "model.compile(optimizer='adam',loss='msle',metrics=['mse'])\n",
    "h = model.fit(\n",
    "    x_train_scaled,\n",
    "    x_train_scaled,\n",
    "    validation_data=(x_test_scaled,x_test_scaled),epochs=20,batch_size=512\n",
    ")\n",
    "print(x_train_scaled.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "c9e455e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "53001fa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(h.history['loss'])\n",
    "plt.plot(h.history['mse'])\n",
    "plt.plot(h.history['val_loss'])\n",
    "plt.plot(h.history['val_mse'])\n",
    "plt.legend(['loss','val_loss'])\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('MSLE Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "a73b29a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "2ccdde2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_threshold(model,x_train_scaled):\n",
    "    recons = model.predict(x_train_scaled)\n",
    "    recons_error = tf.keras.metrics.msle(recons,x_train_scaled)\n",
    "    threshold = np.mean(recons_error.numpy()) + np.std(recons_error.numpy())\n",
    "    return threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "25a643d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pred(model,x_test_scaled,threshold):\n",
    "    pred = model.predict(x_test_scaled)\n",
    "    error = tf.keras.metrics.msle(pred,x_test_scaled)\n",
    "    AnomalyMask = pd.Series(error)>threshold\n",
    "    return AnomalyMask.map(lambda x:0.0 if x==True else 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "e2947b62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 843us/step\n",
      "0.032778860288831674\n"
     ]
    }
   ],
   "source": [
    "threshold = find_threshold(model,x_train_scaled)\n",
    "print(threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "af8cce99",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "1d6b9994",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2671/2671 [==============================] - 2s 594us/step\n"
     ]
    }
   ],
   "source": [
    "pred = get_pred(model,x_test_scaled,threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "c91a9272",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8605151972660136"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8c4075f",
   "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}