07_1103_FraudDetection(VAE).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "076b4dda",
   "metadata": {},
   "source": [
    "### Load required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e8452b24",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from tensorflow.keras import regularizers\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from keras.models import Sequential, Model, load_model\n",
    "from keras.layers import Dense, Input, Lambda\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras import backend as K\n",
    "from keras import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "tf.keras.backend.set_floatx('float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f992f32f",
   "metadata": {},
   "source": [
    "### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb25d899",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284807, 30)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>V10</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",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\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>0.090794</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>0.0</th>\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>-0.166974</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>1.0</th>\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>0.207643</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>1.0</th>\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>-0.054952</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>2.0</th>\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>0.753074</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            V1        V2        V3        V4        V5        V6        V7  \\\n",
       "Time                                                                         \n",
       "0.0  -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "0.0   1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "1.0  -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "1.0  -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "2.0  -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "            V8        V9       V10  ...       V21       V22       V23  \\\n",
       "Time                                ...                                 \n",
       "0.0   0.098698  0.363787  0.090794  ... -0.018307  0.277838 -0.110474   \n",
       "0.0   0.085102 -0.255425 -0.166974  ... -0.225775 -0.638672  0.101288   \n",
       "1.0   0.247676 -1.514654  0.207643  ...  0.247998  0.771679  0.909412   \n",
       "1.0   0.377436 -1.387024 -0.054952  ... -0.108300  0.005274 -0.190321   \n",
       "2.0  -0.270533  0.817739  0.753074  ... -0.009431  0.798278 -0.137458   \n",
       "\n",
       "           V24       V25       V26       V27       V28  Amount  Class  \n",
       "Time                                                                   \n",
       "0.0   0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "0.0  -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "1.0  -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "1.0  -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "2.0   0.141267 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/creditcard.csv', index_col=0)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "745866b7",
   "metadata": {},
   "source": [
    "#### Data check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46528b9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.998273\n",
       "1    0.001727\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85a6f8c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11',\n",
       "       'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21',\n",
       "       'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount', 'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cebc4bcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "85851fed",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Amount'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf0d4248",
   "metadata": {},
   "source": [
    "### Train/test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1fc791be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frauds shape: (492, 29)\n",
      "Normal shape: (284315, 29)\n"
     ]
    }
   ],
   "source": [
    "frauds = df[df.Class == 1]\n",
    "#normal = df[df.Class == 0].sample(1000)\n",
    "normal = df[df.Class == 0]\n",
    "print(\"Frauds shape: {}\".format(frauds.shape))\n",
    "print(\"Normal shape: {}\".format(normal.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "54c7a86c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 29)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat([frauds, normal])\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "93a3c174",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (199013, 28)\n",
      "X_test shape: (85443, 28)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = train_test_split(data, test_size=0.3, random_state=42)\n",
    "X_train, X_train_f = X_train[X_train.Class == 0], X_train[X_train.Class == 1]\n",
    "X_train, X_train_f = X_train.drop(['Class'], axis=1), X_train_f.drop(['Class'], axis=1)\n",
    "X_train = X_train.values\n",
    "\n",
    "X_test, y_test = X_test.drop(['Class'], axis=1).values, X_test['Class'].values\n",
    "\n",
    "print(\"X_train shape: {}\".format(X_train.shape))\n",
    "print(\"X_test shape: {}\".format(X_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "746b84db",
   "metadata": {},
   "source": [
    "### Visualize the data with TSNE and PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fdf2812c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = df[df.Class == 1]\n",
    "x2 = df[df.Class == 0].sample(1000)\n",
    "\n",
    "dfd = x1.append(x2).sample(frac=1).reset_index(drop=True)\n",
    "x = dfd.drop(['Class'], axis=1).values\n",
    "y = dfd['Class'].values\n",
    "\n",
    "SEED = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "71abd557",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne = TSNE(n_components=2, random_state=SEED)\n",
    "xtsne = tsne.fit_transform(x)\n",
    "plt.figure(figsize=(12,8));\n",
    "plt.scatter(xtsne[np.where(y==0), 0], y=xtsne[np.where(y==0), 1], color=\"blue\", label='Normal')\n",
    "plt.scatter(x=xtsne[np.where(y==1), 0], y=xtsne[np.where(y==1), 1], color=\"red\", label='Fraud')\n",
    "plt.title('t-SNE Plot')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6c0d4dc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = PCA(n_components=2, random_state=SEED)\n",
    "xpca = pca.fit_transform(x)\n",
    "plt.figure(figsize=(12,8));\n",
    "plt.scatter(x=xpca[np.where(y==0), 0], y=xpca[np.where(y==0), 1], color=\"blue\", label='Normal')\n",
    "plt.scatter(x=xpca[np.where(y==1), 0], y=xpca[np.where(y==1), 1], color=\"red\", label='Fraud')\n",
    "plt.title('PCA Plot')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31a69585",
   "metadata": {},
   "source": [
    "#### Normalize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8e0631dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "scaler.fit_transform(X_train);\n",
    "scaler.transform(X_test);\n",
    "scaler.transform(X_train_f);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e19b52",
   "metadata": {},
   "source": [
    "### Variation AutoEncoder Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "277184b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sampling(inputs):\n",
    "    z_mean, z_log_var = inputs\n",
    "    epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim), mean=0.,\n",
    "                              stddev=1.)\n",
    "    return z_mean + K.exp(z_log_var / 2) * epsilon\n",
    "\n",
    "latent_dim = 7;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "10aafb4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)        [(None, 28)]                 0         []                            \n",
      "                                                                                                  \n",
      " dense (Dense)               (None, 14)                   406       ['input_1[0][0]']             \n",
      "                                                                                                  \n",
      " dense_1 (Dense)             (None, 7)                    105       ['dense[0][0]']               \n",
      "                                                                                                  \n",
      " dense_2 (Dense)             (None, 7)                    105       ['dense[0][0]']               \n",
      "                                                                                                  \n",
      " lambda (Lambda)             (None, 7)                    0         ['dense_1[0][0]',             \n",
      "                                                                     'dense_2[0][0]']             \n",
      "                                                                                                  \n",
      " dense_3 (Dense)             (None, 14)                   112       ['lambda[0][0]']              \n",
      "                                                                                                  \n",
      " dense_4 (Dense)             (None, 28)                   420       ['dense_3[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 1148 (4.48 KB)\n",
      "Trainable params: 1148 (4.48 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "input_dim = X_train.shape[1]\n",
    "\n",
    "input_layer = Input(shape=(input_dim, ))\n",
    "encoder = Dense(14, activation=\"relu\")(input_layer)\n",
    "\n",
    "z_mean = Dense(latent_dim)(encoder)\n",
    "z_log_var = Dense(latent_dim)(encoder)\n",
    "\n",
    "z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])\n",
    "\n",
    "hid_decoder = Dense(14, activation='relu')(z)\n",
    "decoded_mean = Dense(input_dim)(hid_decoder)\n",
    "\n",
    "vae = Model(inputs=input_layer, outputs=decoded_mean)\n",
    "\n",
    "vae.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0846de30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def vae_loss(x, decoded_mean):\n",
    "#     rec_loss = K.sum(K.square(x - decoded_mean), axis=-1)\n",
    "#     kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)\n",
    "#     return K.mean((rec_loss + kl_loss) / 2)\n",
    "\n",
    "rec_loss = 28 * metrics.mean_squared_error(input_layer, decoded_mean)\n",
    "kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)\n",
    "vae_loss = K.mean(rec_loss + kl_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "40a93f7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 100\n",
    "batch_size = 32\n",
    "\n",
    "vae.add_loss(vae_loss)\n",
    "vae.compile(optimizer='adam')\n",
    "checkpointer = ModelCheckpoint(filepath=\"fraudVAE.pth\", verbose=0, save_best_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99bdb89e",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c24d5c33",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "5580/5598 [============================>.] - ETA: 0s - loss: 24.7412INFO:tensorflow:Assets written to: fraudVAE.pth\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: fraudVAE.pth\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5598/5598 [==============================] - 17s 3ms/step - loss: 24.7297 - val_loss: 20.7688\n",
      "Epoch 2/100\n",
      "5588/5598 [============================>.] - ETA: 0s - loss: 19.9884INFO:tensorflow:Assets written to: fraudVAE.pth\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: fraudVAE.pth\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5598/5598 [==============================] - 14s 3ms/step - loss: 19.9829 - val_loss: 18.8457\n",
      "Epoch 3/100\n",
      "5586/5598 [============================>.] - ETA: 0s - loss: 18.4667INFO:tensorflow:Assets written to: fraudVAE.pth\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: fraudVAE.pth\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5598/5598 [==============================] - 15s 3ms/step - loss: 18.4645 - val_loss: 17.9729\n",
      "Epoch 4/100\n",
      "5593/5598 [============================>.] - ETA: 0s - loss: 17.8185INFO:tensorflow:Assets written to: fraudVAE.pth\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: fraudVAE.pth\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5598/5598 [==============================] - 18s 3ms/step - loss: 17.8182 - val_loss: 17.6058\n",
      "Epoch 5/100\n",
      "5591/5598 [============================>.] - ETA: 0s - loss: 17.4716INFO:tensorflow:Assets written to: fraudVAE.pth\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: fraudVAE.pth\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5598/5598 [==============================] - 16s 3ms/step - loss: 17.4681 - val_loss: 17.2786\n",
      "Epoch 6/100\n",
      "5589/5598 [============================>.] - ETA: 0s - loss: 17.2767INFO:tensorflow:Assets written to: fraudVAE.pth\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: fraudVAE.pth\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5598/5598 [==============================] - 14s 3ms/step - loss: 17.2758 - val_loss: 17.2105\n",
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    },
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      "5598/5598 [==============================] - 16s 3ms/step - loss: 17.0156 - val_loss: 16.9929\n",
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      "5598/5598 [==============================] - 15s 3ms/step - loss: 16.9787 - val_loss: 16.9440\n",
      "Epoch 14/100\n",
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    },
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      "5598/5598 [==============================] - 15s 3ms/step - loss: 16.9439 - val_loss: 16.9283\n",
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      "5598/5598 [==============================] - 15s 3ms/step - loss: 16.9053 - val_loss: 16.8691\n",
      "Epoch 16/100\n",
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    },
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      "5598/5598 [==============================] - 15s 3ms/step - loss: 16.8777 - val_loss: 16.8495\n",
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    },
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      "5598/5598 [==============================] - 18s 3ms/step - loss: 16.8435 - val_loss: 16.8245\n",
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     "text": [
      "5598/5598 [==============================] - 15s 3ms/step - loss: 16.8498 - val_loss: 16.8014\n",
      "Epoch 21/100\n",
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    },
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      "5598/5598 [==============================] - 17s 3ms/step - loss: 16.7563 - val_loss: 16.7244\n",
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    },
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      "5598/5598 [==============================] - 16s 3ms/step - loss: 16.7461 - val_loss: 16.7171\n",
      "Epoch 26/100\n",
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    },
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    },
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    },
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      "5598/5598 [==============================] - 14s 2ms/step - loss: 16.6763 - val_loss: 16.6946\n",
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    },
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    },
    {
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     "text": [
      "5598/5598 [==============================] - 14s 2ms/step - loss: 16.6871 - val_loss: 16.6659\n",
      "Epoch 31/100\n",
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    },
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    },
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    },
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    },
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    },
    {
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     "text": [
      "5598/5598 [==============================] - 14s 2ms/step - loss: 16.6414 - val_loss: 16.6007\n",
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    },
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      "INFO:tensorflow:Assets written to: fraudVAE.pth\\assets\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "5598/5598 [==============================] - 13s 2ms/step - loss: 16.6348 - val_loss: 16.5995\n",
      "Epoch 83/100\n",
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      "Epoch 88/100\n",
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      "Epoch 90/100\n",
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      "Epoch 91/100\n",
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      "Epoch 92/100\n",
      "5598/5598 [==============================] - 11s 2ms/step - loss: 16.6338 - val_loss: 16.6074\n",
      "Epoch 93/100\n",
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      "Epoch 94/100\n",
      "5598/5598 [==============================] - 11s 2ms/step - loss: 16.6271 - val_loss: 16.6693\n",
      "Epoch 95/100\n",
      "5598/5598 [==============================] - 11s 2ms/step - loss: 16.6458 - val_loss: 16.6626\n",
      "Epoch 96/100\n",
      "5598/5598 [==============================] - 11s 2ms/step - loss: 16.6391 - val_loss: 16.6488\n",
      "Epoch 97/100\n",
      "5598/5598 [==============================] - 11s 2ms/step - loss: 16.6375 - val_loss: 16.6170\n",
      "Epoch 98/100\n",
      "5598/5598 [==============================] - 11s 2ms/step - loss: 16.6342 - val_loss: 16.6243\n",
      "Epoch 99/100\n",
      "5598/5598 [==============================] - 11s 2ms/step - loss: 16.6341 - val_loss: 16.6098\n",
      "Epoch 100/100\n",
      "5598/5598 [==============================] - 11s 2ms/step - loss: 16.6237 - val_loss: 16.6587\n"
     ]
    }
   ],
   "source": [
    "history = vae.fit(X_train, X_train,\n",
    "                    epochs=epochs,\n",
    "                    batch_size=batch_size,\n",
    "                    shuffle=True,\n",
    "                    validation_split=0.1,\n",
    "                    callbacks=[checkpointer]).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e5436792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history['loss'])\n",
    "plt.plot(history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9812b662",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d8ef9f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "#vae = load_model('fraudVAE.pth') \n",
    "# ISSUE: fail to load model using lambda layer  // ref (NameError: Exception encountered when calling layer \"lambda\"0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac2e5cde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2671/2671 [==============================] - 4s 1ms/step\n"
     ]
    },
    {
     "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>reconstruction_error</th>\n",
       "      <th>true_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>85443.000000</td>\n",
       "      <td>85443.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.452781</td>\n",
       "      <td>0.00165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.451049</td>\n",
       "      <td>0.04059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.030951</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.231820</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.327128</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.499623</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.997036</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       reconstruction_error   true_class\n",
       "count          85443.000000  85443.00000\n",
       "mean               0.452781      0.00165\n",
       "std                1.451049      0.04059\n",
       "min                0.030951      0.00000\n",
       "25%                0.231820      0.00000\n",
       "50%                0.327128      0.00000\n",
       "75%                0.499623      0.00000\n",
       "max               73.997036      1.00000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = vae.predict(X_test)\n",
    "mse = np.mean(np.power(X_test - predictions, 2), axis=1)\n",
    "error_df = pd.DataFrame({'reconstruction_error': mse,\n",
    "                        'true_class': y_test})\n",
    "error_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4da1351d",
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 2.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "79018bfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups = error_df.groupby('true_class')\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for name, group in groups:\n",
    "    ax.plot(group.index, group.reconstruction_error, marker='o', ms=3.5, linestyle='',\n",
    "            label= \"Fraud\" if name == 1 else \"Normal\")\n",
    "ax.hlines(threshold, ax.get_xlim()[0], ax.get_xlim()[1], colors=\"r\", zorder=100, label='Threshold')\n",
    "ax.legend()\n",
    "plt.title(\"Reconstruction error for different classes\")\n",
    "plt.ylabel(\"Reconstruction error\")\n",
    "plt.xlabel(\"Data point index\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a97a4779",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "LABELS = ['Normal','Fraud']\n",
    "\n",
    "y_pred = [1 if e > threshold else 0 for e in error_df.reconstruction_error.values]\n",
    "conf_matrix = confusion_matrix(error_df.true_class, y_pred)\n",
    "plt.figure(figsize=(12, 12))\n",
    "sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78da0e18",
   "metadata": {},
   "source": [
    "### Visualize the latent space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e8f06329",
   "metadata": {},
   "outputs": [],
   "source": [
    "inp_layer = vae.input\n",
    "out_layer = vae.layers[4].output\n",
    "\n",
    "encoder = Model(inputs=inp_layer, outputs=out_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3ef81e12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)        [(None, 28)]                 0         []                            \n",
      "                                                                                                  \n",
      " dense (Dense)               (None, 14)                   406       ['input_1[0][0]']             \n",
      "                                                                                                  \n",
      " dense_1 (Dense)             (None, 7)                    105       ['dense[0][0]']               \n",
      "                                                                                                  \n",
      " dense_2 (Dense)             (None, 7)                    105       ['dense[0][0]']               \n",
      "                                                                                                  \n",
      " lambda (Lambda)             (None, 7)                    0         ['dense_1[0][0]',             \n",
      "                                                                     'dense_2[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 616 (2.41 KB)\n",
      "Trainable params: 616 (2.41 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c880a548",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6220/6220 [==============================] - 8s 1ms/step\n",
      "11/11 [==============================] - 0s 2ms/step\n",
      "2671/2671 [==============================] - 4s 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train_encoded = encoder.predict(X_train)\n",
    "X_train_f_encoded = encoder.predict(X_train_f)\n",
    "X_test_encoded = encoder.predict(X_test)\n",
    "\n",
    "X_encoded = np.vstack((X_train_encoded, X_test_encoded))\n",
    "X_encoded = np.vstack((X_encoded, X_train_f_encoded))\n",
    "y_main = np.hstack((np.zeros(len(X_train)), y_test))\n",
    "y_main = np.hstack((y_main, np.ones(len(X_train_f_encoded))))\n",
    "\n",
    "encoded_df = pd.DataFrame(np.hstack((X_encoded, y_main[np.newaxis].transpose())))\n",
    "\n",
    "plt.subplots(figsize=(8,8))\n",
    "plt.scatter(encoded_df[encoded_df[7]==0][0],encoded_df[encoded_df[7]==0][1], color='blue', label='Normal')\n",
    "plt.scatter(encoded_df[encoded_df[7]==1][1],encoded_df[encoded_df[7]==1][1], color='red', label='Fraud');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76315e2f",
   "metadata": {},
   "source": [
    "### Generate Fraud data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "be8e4e14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "fraud_encodings = encoder.predict(X_train_f,batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c57d398e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(351, 7)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud_encodings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "12e7ce7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_3 (InputLayer)        [(None, 7)]               0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 14)                112       \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 28)                420       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 532 (2.08 KB)\n",
      "Trainable params: 532 (2.08 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "inp_layer = Input(shape=(latent_dim,))\n",
    "h_decoded = vae.layers[5](inp_layer)\n",
    "x_decoded_mean = vae.layers[6](h_decoded)\n",
    "\n",
    "generator = Model(inputs=inp_layer, outputs=x_decoded_mean )\n",
    "\n",
    "generator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f854f78e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "more_frauds = generator.predict(fraud_encodings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d88eae8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x3 = more_frauds\n",
    "\n",
    "genClass = np.full((x3.shape[0], 1), 2)\n",
    "x3 = np.hstack((x3, genClass))\n",
    "\n",
    "x3 = pd.DataFrame(x3)\n",
    "\n",
    "x3.columns = ['V1','V2','V3','V4','V5','V6','V7','V8','V9','V10',\n",
    "              'V11','V12','V13','V14','V15','V16','V17','V18','V19','V20',\n",
    "              'V21','V22','V23','V24','V25','V26','V27','V28','Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "21472ca5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
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       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
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       "      <th>0</th>\n",
       "      <td>-2.581928</td>\n",
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       "      <td>-0.178790</td>\n",
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       "      <td>-0.084779</td>\n",
       "      <td>-0.122519</td>\n",
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       "      <td>0.038014</td>\n",
       "      <td>-0.111928</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>-3.731385</td>\n",
       "      <td>1.248842</td>\n",
       "      <td>-2.943464</td>\n",
       "      <td>1.450462</td>\n",
       "      <td>0.976391</td>\n",
       "      <td>-1.205422</td>\n",
       "      <td>-1.132689</td>\n",
       "      <td>0.247417</td>\n",
       "      <td>-0.431154</td>\n",
       "      <td>-0.597348</td>\n",
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       "      <td>-1.886240</td>\n",
       "      <td>-0.899697</td>\n",
       "      <td>0.376252</td>\n",
       "      <td>-0.089358</td>\n",
       "      <td>-0.091137</td>\n",
       "      <td>0.133788</td>\n",
       "      <td>-0.020744</td>\n",
       "      <td>0.028054</td>\n",
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       "      <td>-11.594732</td>\n",
       "      <td>1.226524</td>\n",
       "      <td>-1.979849</td>\n",
       "      <td>0.991889</td>\n",
       "      <td>-6.693286</td>\n",
       "      <td>8.321036</td>\n",
       "      <td>-2.774864</td>\n",
       "      <td>-0.815930</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.398868</td>\n",
       "      <td>0.791185</td>\n",
       "      <td>-0.819754</td>\n",
       "      <td>-0.026452</td>\n",
       "      <td>0.295966</td>\n",
       "      <td>0.293447</td>\n",
       "      <td>-0.094465</td>\n",
       "      <td>-0.467262</td>\n",
       "      <td>-0.328767</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.726672</td>\n",
       "      <td>1.290514</td>\n",
       "      <td>1.225336</td>\n",
       "      <td>2.757220</td>\n",
       "      <td>0.645075</td>\n",
       "      <td>0.371755</td>\n",
       "      <td>0.665076</td>\n",
       "      <td>0.066785</td>\n",
       "      <td>-1.370964</td>\n",
       "      <td>0.805101</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018431</td>\n",
       "      <td>0.034664</td>\n",
       "      <td>0.128342</td>\n",
       "      <td>-0.038838</td>\n",
       "      <td>-0.037353</td>\n",
       "      <td>-0.117493</td>\n",
       "      <td>-0.172137</td>\n",
       "      <td>0.009423</td>\n",
       "      <td>0.005840</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.094130</td>\n",
       "      <td>0.913824</td>\n",
       "      <td>-1.427350</td>\n",
       "      <td>2.581344</td>\n",
       "      <td>0.945777</td>\n",
       "      <td>-0.376949</td>\n",
       "      <td>0.532624</td>\n",
       "      <td>-0.128809</td>\n",
       "      <td>-0.648908</td>\n",
       "      <td>0.113497</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004006</td>\n",
       "      <td>0.048201</td>\n",
       "      <td>0.108831</td>\n",
       "      <td>-0.057503</td>\n",
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       "      <td>-0.128316</td>\n",
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       "      <td>-0.009966</td>\n",
       "      <td>-0.045179</td>\n",
       "      <td>-0.013882</td>\n",
       "      <td>-0.047404</td>\n",
       "      <td>-0.061351</td>\n",
       "      <td>0.032390</td>\n",
       "      <td>-0.003306</td>\n",
       "      <td>0.017164</td>\n",
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       "      <th>347</th>\n",
       "      <td>-16.379808</td>\n",
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       "      <td>-8.616181</td>\n",
       "      <td>2.121989</td>\n",
       "      <td>-14.124516</td>\n",
       "      <td>-4.659420</td>\n",
       "      <td>-2.999813</td>\n",
       "      <td>-3.427755</td>\n",
       "      <td>...</td>\n",
       "      <td>2.853714</td>\n",
       "      <td>-6.251483</td>\n",
       "      <td>1.302073</td>\n",
       "      <td>0.291136</td>\n",
       "      <td>-0.003233</td>\n",
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       "      <td>-0.016590</td>\n",
       "      <td>-0.669065</td>\n",
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       "      <td>-0.104759</td>\n",
       "      <td>-0.510761</td>\n",
       "      <td>-2.280032</td>\n",
       "      <td>...</td>\n",
       "      <td>0.109681</td>\n",
       "      <td>0.170345</td>\n",
       "      <td>0.452584</td>\n",
       "      <td>-0.186057</td>\n",
       "      <td>-0.294366</td>\n",
       "      <td>0.037769</td>\n",
       "      <td>-0.062999</td>\n",
       "      <td>0.064035</td>\n",
       "      <td>0.012876</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>349</th>\n",
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       "      <td>4.332595</td>\n",
       "      <td>-8.778335</td>\n",
       "      <td>1.863233</td>\n",
       "      <td>-0.552695</td>\n",
       "      <td>2.917660</td>\n",
       "      <td>-6.144680</td>\n",
       "      <td>7.445211</td>\n",
       "      <td>-2.925618</td>\n",
       "      <td>0.394219</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.750163</td>\n",
       "      <td>0.534243</td>\n",
       "      <td>-0.630465</td>\n",
       "      <td>-0.010535</td>\n",
       "      <td>0.479870</td>\n",
       "      <td>0.225663</td>\n",
       "      <td>-0.182818</td>\n",
       "      <td>-0.415844</td>\n",
       "      <td>-0.235804</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>0.025778</td>\n",
       "      <td>0.797512</td>\n",
       "      <td>-0.107607</td>\n",
       "      <td>0.440006</td>\n",
       "      <td>0.683307</td>\n",
       "      <td>-0.547540</td>\n",
       "      <td>0.745587</td>\n",
       "      <td>-0.119334</td>\n",
       "      <td>-0.416165</td>\n",
       "      <td>-0.171950</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.056257</td>\n",
       "      <td>-0.010264</td>\n",
       "      <td>-0.037833</td>\n",
       "      <td>-0.009830</td>\n",
       "      <td>-0.036556</td>\n",
       "      <td>-0.077251</td>\n",
       "      <td>-0.009348</td>\n",
       "      <td>-0.002468</td>\n",
       "      <td>0.014894</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            V1        V2         V3        V4        V5        V6         V7  \\\n",
       "0    -2.581928  2.704674  -3.330681 -0.367649 -0.251604 -1.336160  -0.372700   \n",
       "1    -3.731385  1.248842  -2.943464  1.450462  0.976391 -1.205422  -1.132689   \n",
       "2   -11.368033  4.041985 -11.594732  1.226524 -1.979849  0.991889  -6.693286   \n",
       "3    -0.726672  1.290514   1.225336  2.757220  0.645075  0.371755   0.665076   \n",
       "4     1.094130  0.913824  -1.427350  2.581344  0.945777 -0.376949   0.532624   \n",
       "..         ...       ...        ...       ...       ...       ...        ...   \n",
       "346   0.141897  0.705428  -0.487458 -0.110273  0.718068 -0.763892   0.757603   \n",
       "347 -16.379808  5.959908 -10.464544  0.236681 -8.616181  2.121989 -14.124516   \n",
       "348   0.030221  1.963742  -3.789804  1.958025  2.409731 -1.202701   1.291569   \n",
       "349  -9.232622  4.332595  -8.778335  1.863233 -0.552695  2.917660  -6.144680   \n",
       "350   0.025778  0.797512  -0.107607  0.440006  0.683307 -0.547540   0.745587   \n",
       "\n",
       "           V8        V9       V10  ...       V20       V21       V22  \\\n",
       "0    1.532759 -0.072532 -1.342818  ... -0.178790  0.149397 -0.003829   \n",
       "1    0.247417 -0.431154 -0.597348  ... -1.886240 -0.899697  0.376252   \n",
       "2    8.321036 -2.774864 -0.815930  ... -0.398868  0.791185 -0.819754   \n",
       "3    0.066785 -1.370964  0.805101  ... -0.018431  0.034664  0.128342   \n",
       "4   -0.128809 -0.648908  0.113497  ...  0.004006  0.048201  0.108831   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "346 -0.146103 -0.128316 -0.547947  ... -0.056643 -0.009966 -0.045179   \n",
       "347 -4.659420 -2.999813 -3.427755  ...  2.853714 -6.251483  1.302073   \n",
       "348 -0.104759 -0.510761 -2.280032  ...  0.109681  0.170345  0.452584   \n",
       "349  7.445211 -2.925618  0.394219  ... -0.750163  0.534243 -0.630465   \n",
       "350 -0.119334 -0.416165 -0.171950  ... -0.056257 -0.010264 -0.037833   \n",
       "\n",
       "          V23       V24       V25       V26       V27       V28  Class  \n",
       "0   -0.084779 -0.122519  0.064003  0.038014 -0.111928 -0.031978    2.0  \n",
       "1   -0.089358 -0.091137  0.133788 -0.020744  0.028054  0.022995    2.0  \n",
       "2   -0.026452  0.295966  0.293447 -0.094465 -0.467262 -0.328767    2.0  \n",
       "3   -0.038838 -0.037353 -0.117493 -0.172137  0.009423  0.005840    2.0  \n",
       "4   -0.057503 -0.083553  0.007871 -0.112939  0.018052  0.011396    2.0  \n",
       "..        ...       ...       ...       ...       ...       ...    ...  \n",
       "346 -0.013882 -0.047404 -0.061351  0.032390 -0.003306  0.017164    2.0  \n",
       "347  0.291136 -0.003233  0.311270 -0.016590 -0.669065 -0.234157    2.0  \n",
       "348 -0.186057 -0.294366  0.037769 -0.062999  0.064035  0.012876    2.0  \n",
       "349 -0.010535  0.479870  0.225663 -0.182818 -0.415844 -0.235804    2.0  \n",
       "350 -0.009830 -0.036556 -0.077251 -0.009348 -0.002468  0.014894    2.0  \n",
       "\n",
       "[351 rows x 29 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "62540edf",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = df[df.Class == 1]\n",
    "x2 = df[df.Class == 0].sample(1000)\n",
    "x3 = x3\n",
    "\n",
    "dfd = x1.append(x2).sample(frac=1).reset_index(drop=True)\n",
    "dfd = dfd.append(x3).sample(frac=1).reset_index(drop=True)\n",
    "x, y = dfd.drop(['Class'], axis=1).values, dfd['Class'].values\n",
    "\n",
    "SEED = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2164fee0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.981073</td>\n",
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       "      <td>-1.056164</td>\n",
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       "      <td>0.735623</td>\n",
       "      <td>-2.607306</td>\n",
       "      <td>1.190766</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.079825</td>\n",
       "      <td>0.232926</td>\n",
       "      <td>0.660218</td>\n",
       "      <td>-0.043841</td>\n",
       "      <td>0.006211</td>\n",
       "      <td>-0.529942</td>\n",
       "      <td>-0.012445</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.742875</td>\n",
       "      <td>0.129421</td>\n",
       "      <td>0.437505</td>\n",
       "      <td>1.594440</td>\n",
       "      <td>-0.186034</td>\n",
       "      <td>-0.441448</td>\n",
       "      <td>-0.493048</td>\n",
       "      <td>0.952185</td>\n",
       "      <td>0.082712</td>\n",
       "      <td>-0.795485</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073099</td>\n",
       "      <td>-0.231754</td>\n",
       "      <td>-1.079812</td>\n",
       "      <td>0.112445</td>\n",
       "      <td>-0.085211</td>\n",
       "      <td>-0.862918</td>\n",
       "      <td>-1.033244</td>\n",
       "      <td>-0.136979</td>\n",
       "      <td>-0.045538</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.269019</td>\n",
       "      <td>-1.049485</td>\n",
       "      <td>0.690348</td>\n",
       "      <td>-0.368530</td>\n",
       "      <td>-1.446805</td>\n",
       "      <td>0.081722</td>\n",
       "      <td>-1.061758</td>\n",
       "      <td>0.248508</td>\n",
       "      <td>0.079047</td>\n",
       "      <td>0.634561</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.565449</td>\n",
       "      <td>-0.507644</td>\n",
       "      <td>-0.851706</td>\n",
       "      <td>-0.004601</td>\n",
       "      <td>0.017472</td>\n",
       "      <td>0.221532</td>\n",
       "      <td>1.072242</td>\n",
       "      <td>-0.042064</td>\n",
       "      <td>-0.004055</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.548788</td>\n",
       "      <td>1.808698</td>\n",
       "      <td>-0.953509</td>\n",
       "      <td>2.213085</td>\n",
       "      <td>-2.015728</td>\n",
       "      <td>-0.913457</td>\n",
       "      <td>-2.356013</td>\n",
       "      <td>1.197169</td>\n",
       "      <td>-1.678374</td>\n",
       "      <td>-3.538650</td>\n",
       "      <td>...</td>\n",
       "      <td>0.390786</td>\n",
       "      <td>0.855138</td>\n",
       "      <td>0.774745</td>\n",
       "      <td>0.059037</td>\n",
       "      <td>0.343200</td>\n",
       "      <td>-0.468938</td>\n",
       "      <td>-0.278338</td>\n",
       "      <td>0.625922</td>\n",
       "      <td>0.395573</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.059124</td>\n",
       "      <td>1.614806</td>\n",
       "      <td>-0.649401</td>\n",
       "      <td>2.066073</td>\n",
       "      <td>2.652684</td>\n",
       "      <td>-0.446829</td>\n",
       "      <td>2.456776</td>\n",
       "      <td>-0.731886</td>\n",
       "      <td>-0.910232</td>\n",
       "      <td>0.418503</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.221121</td>\n",
       "      <td>0.261565</td>\n",
       "      <td>1.074848</td>\n",
       "      <td>-0.226621</td>\n",
       "      <td>0.449134</td>\n",
       "      <td>-0.244955</td>\n",
       "      <td>-0.136384</td>\n",
       "      <td>-0.061163</td>\n",
       "      <td>0.016408</td>\n",
       "      <td>0.0</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>1838</th>\n",
       "      <td>-4.806649</td>\n",
       "      <td>4.493135</td>\n",
       "      <td>-2.941746</td>\n",
       "      <td>-1.533288</td>\n",
       "      <td>-0.748879</td>\n",
       "      <td>-0.412758</td>\n",
       "      <td>-0.626433</td>\n",
       "      <td>1.414857</td>\n",
       "      <td>2.479406</td>\n",
       "      <td>3.543429</td>\n",
       "      <td>...</td>\n",
       "      <td>1.436092</td>\n",
       "      <td>-0.655500</td>\n",
       "      <td>-1.661810</td>\n",
       "      <td>0.597773</td>\n",
       "      <td>-0.002850</td>\n",
       "      <td>-0.357817</td>\n",
       "      <td>-0.201918</td>\n",
       "      <td>0.577284</td>\n",
       "      <td>0.865760</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1839</th>\n",
       "      <td>1.397040</td>\n",
       "      <td>-1.127020</td>\n",
       "      <td>-1.319603</td>\n",
       "      <td>1.276149</td>\n",
       "      <td>-0.111331</td>\n",
       "      <td>0.388876</td>\n",
       "      <td>0.046203</td>\n",
       "      <td>0.086879</td>\n",
       "      <td>0.605192</td>\n",
       "      <td>0.262953</td>\n",
       "      <td>...</td>\n",
       "      <td>0.340824</td>\n",
       "      <td>0.225587</td>\n",
       "      <td>0.014675</td>\n",
       "      <td>-0.133403</td>\n",
       "      <td>0.152982</td>\n",
       "      <td>-0.089531</td>\n",
       "      <td>-0.705534</td>\n",
       "      <td>-0.038416</td>\n",
       "      <td>0.004198</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1840</th>\n",
       "      <td>-0.639191</td>\n",
       "      <td>-0.085595</td>\n",
       "      <td>1.265452</td>\n",
       "      <td>1.401166</td>\n",
       "      <td>-0.260542</td>\n",
       "      <td>1.009795</td>\n",
       "      <td>1.301999</td>\n",
       "      <td>-0.135258</td>\n",
       "      <td>-0.431521</td>\n",
       "      <td>-0.091353</td>\n",
       "      <td>...</td>\n",
       "      <td>0.861307</td>\n",
       "      <td>0.340331</td>\n",
       "      <td>0.760170</td>\n",
       "      <td>0.353377</td>\n",
       "      <td>-0.778893</td>\n",
       "      <td>-0.070681</td>\n",
       "      <td>-0.033355</td>\n",
       "      <td>-0.061962</td>\n",
       "      <td>-0.062687</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1841</th>\n",
       "      <td>-0.278910</td>\n",
       "      <td>0.971748</td>\n",
       "      <td>0.426315</td>\n",
       "      <td>-0.765525</td>\n",
       "      <td>1.617922</td>\n",
       "      <td>-0.574353</td>\n",
       "      <td>1.546111</td>\n",
       "      <td>-0.653585</td>\n",
       "      <td>-0.196572</td>\n",
       "      <td>-0.696537</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132742</td>\n",
       "      <td>-0.372875</td>\n",
       "      <td>-0.719818</td>\n",
       "      <td>-0.148126</td>\n",
       "      <td>0.404917</td>\n",
       "      <td>-0.297241</td>\n",
       "      <td>0.089815</td>\n",
       "      <td>-0.386614</td>\n",
       "      <td>-0.269385</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1842</th>\n",
       "      <td>2.072492</td>\n",
       "      <td>-0.039817</td>\n",
       "      <td>-1.122372</td>\n",
       "      <td>0.379965</td>\n",
       "      <td>-0.053946</td>\n",
       "      <td>-1.124611</td>\n",
       "      <td>0.195711</td>\n",
       "      <td>-0.337264</td>\n",
       "      <td>0.520589</td>\n",
       "      <td>0.067410</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.206121</td>\n",
       "      <td>-0.296498</td>\n",
       "      <td>-0.709571</td>\n",
       "      <td>0.319356</td>\n",
       "      <td>-0.104751</td>\n",
       "      <td>-0.271829</td>\n",
       "      <td>0.204377</td>\n",
       "      <td>-0.069622</td>\n",
       "      <td>-0.060961</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1843 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0    -0.981073 -1.054425  1.535837 -1.286927 -1.056164 -0.221630 -1.688985   \n",
       "1    -1.742875  0.129421  0.437505  1.594440 -0.186034 -0.441448 -0.493048   \n",
       "2     1.269019 -1.049485  0.690348 -0.368530 -1.446805  0.081722 -1.061758   \n",
       "3    -1.548788  1.808698 -0.953509  2.213085 -2.015728 -0.913457 -2.356013   \n",
       "4    -0.059124  1.614806 -0.649401  2.066073  2.652684 -0.446829  2.456776   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "1838 -4.806649  4.493135 -2.941746 -1.533288 -0.748879 -0.412758 -0.626433   \n",
       "1839  1.397040 -1.127020 -1.319603  1.276149 -0.111331  0.388876  0.046203   \n",
       "1840 -0.639191 -0.085595  1.265452  1.401166 -0.260542  1.009795  1.301999   \n",
       "1841 -0.278910  0.971748  0.426315 -0.765525  1.617922 -0.574353  1.546111   \n",
       "1842  2.072492 -0.039817 -1.122372  0.379965 -0.053946 -1.124611  0.195711   \n",
       "\n",
       "            V8        V9       V10  ...       V20       V21       V22  \\\n",
       "0     0.735623 -2.607306  1.190766  ... -0.079825  0.232926  0.660218   \n",
       "1     0.952185  0.082712 -0.795485  ... -0.073099 -0.231754 -1.079812   \n",
       "2     0.248508  0.079047  0.634561  ... -0.565449 -0.507644 -0.851706   \n",
       "3     1.197169 -1.678374 -3.538650  ...  0.390786  0.855138  0.774745   \n",
       "4    -0.731886 -0.910232  0.418503  ... -0.221121  0.261565  1.074848   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "1838  1.414857  2.479406  3.543429  ...  1.436092 -0.655500 -1.661810   \n",
       "1839  0.086879  0.605192  0.262953  ...  0.340824  0.225587  0.014675   \n",
       "1840 -0.135258 -0.431521 -0.091353  ...  0.861307  0.340331  0.760170   \n",
       "1841 -0.653585 -0.196572 -0.696537  ...  0.132742 -0.372875 -0.719818   \n",
       "1842 -0.337264  0.520589  0.067410  ... -0.206121 -0.296498 -0.709571   \n",
       "\n",
       "           V23       V24       V25       V26       V27       V28  Class  \n",
       "0    -0.043841  0.006211 -0.529942 -0.012445  0.090579 -0.058876    0.0  \n",
       "1     0.112445 -0.085211 -0.862918 -1.033244 -0.136979 -0.045538    0.0  \n",
       "2    -0.004601  0.017472  0.221532  1.072242 -0.042064 -0.004055    0.0  \n",
       "3     0.059037  0.343200 -0.468938 -0.278338  0.625922  0.395573    1.0  \n",
       "4    -0.226621  0.449134 -0.244955 -0.136384 -0.061163  0.016408    0.0  \n",
       "...        ...       ...       ...       ...       ...       ...    ...  \n",
       "1838  0.597773 -0.002850 -0.357817 -0.201918  0.577284  0.865760    0.0  \n",
       "1839 -0.133403  0.152982 -0.089531 -0.705534 -0.038416  0.004198    0.0  \n",
       "1840  0.353377 -0.778893 -0.070681 -0.033355 -0.061962 -0.062687    1.0  \n",
       "1841 -0.148126  0.404917 -0.297241  0.089815 -0.386614 -0.269385    0.0  \n",
       "1842  0.319356 -0.104751 -0.271829  0.204377 -0.069622 -0.060961    0.0  \n",
       "\n",
       "[1843 rows x 29 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5c1dec5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne = TSNE(n_components=2, random_state=SEED)\n",
    "xtsne = tsne.fit_transform(x)\n",
    "plt.figure(figsize=(12,8));\n",
    "plt.scatter(x=xtsne[np.where(y==0), 0], y=xtsne[np.where(y==0), 1], color=\"blue\", label='Normal')\n",
    "plt.scatter(x=xtsne[np.where(y==1), 0], y=xtsne[np.where(y==1), 1], color=\"red\", label='Fraud')\n",
    "plt.scatter(x=xtsne[np.where(y==2), 0], y=xtsne[np.where(y==2), 1], color=\"yellow\", label='Generated Fraud')\n",
    "plt.title('t-SNE Plot')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a276674a",
   "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
}