CreditCard.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn as sk\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import classification_report\n",
    "# Pandas options\n",
    "pd.set_option('display.max_colwidth', 1000, 'display.max_rows', None, 'display.max_columns', None)\n",
    "\n",
    "# Plotting options\n",
    "%matplotlib inline\n",
    "mpl.style.use('ggplot')\n",
    "sns.set(style='whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions = pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "transactions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V13</th>\n",
       "      <th>V14</th>\n",
       "      <th>V15</th>\n",
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       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</th>\n",
       "      <th>V20</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",
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       "      <th>173438</th>\n",
       "      <td>121482.0</td>\n",
       "      <td>0.243283</td>\n",
       "      <td>1.135203</td>\n",
       "      <td>-2.331887</td>\n",
       "      <td>-0.652055</td>\n",
       "      <td>1.533160</td>\n",
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       "      <td>-0.894073</td>\n",
       "      <td>-0.950879</td>\n",
       "      <td>0.206441</td>\n",
       "      <td>0.798093</td>\n",
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       "      <td>-0.480278</td>\n",
       "      <td>-1.487661</td>\n",
       "      <td>0.361336</td>\n",
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       "      <td>0.238467</td>\n",
       "      <td>-0.120756</td>\n",
       "      <td>0.204054</td>\n",
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       "      <td>0.544186</td>\n",
       "      <td>-0.164264</td>\n",
       "      <td>-0.059531</td>\n",
       "      <td>42.81</td>\n",
       "      <td>0</td>\n",
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       "      <th>158953</th>\n",
       "      <td>112017.0</td>\n",
       "      <td>-1.279762</td>\n",
       "      <td>0.489341</td>\n",
       "      <td>1.191725</td>\n",
       "      <td>-2.492003</td>\n",
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       "      <td>-0.270046</td>\n",
       "      <td>-0.163012</td>\n",
       "      <td>0.480909</td>\n",
       "      <td>-1.408978</td>\n",
       "      <td>-0.187827</td>\n",
       "      <td>0.313922</td>\n",
       "      <td>0.295039</td>\n",
       "      <td>1.024890</td>\n",
       "      <td>-0.322894</td>\n",
       "      <td>-1.304094</td>\n",
       "      <td>1.623305</td>\n",
       "      <td>-0.347883</td>\n",
       "      <td>-0.691074</td>\n",
       "      <td>0.440871</td>\n",
       "      <td>0.108248</td>\n",
       "      <td>0.242232</td>\n",
       "      <td>0.616035</td>\n",
       "      <td>-0.379450</td>\n",
       "      <td>-0.451476</td>\n",
       "      <td>0.872741</td>\n",
       "      <td>-0.118898</td>\n",
       "      <td>0.014242</td>\n",
       "      <td>0.014217</td>\n",
       "      <td>3.90</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>247832</th>\n",
       "      <td>153705.0</td>\n",
       "      <td>-0.459015</td>\n",
       "      <td>-0.207853</td>\n",
       "      <td>2.189452</td>\n",
       "      <td>-2.001899</td>\n",
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       "      <td>0.218596</td>\n",
       "      <td>-0.624701</td>\n",
       "      <td>0.353164</td>\n",
       "      <td>0.207161</td>\n",
       "      <td>-0.315515</td>\n",
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       "      <td>-0.314411</td>\n",
       "      <td>0.094696</td>\n",
       "      <td>-1.403423</td>\n",
       "      <td>-1.731459</td>\n",
       "      <td>0.915786</td>\n",
       "      <td>0.368293</td>\n",
       "      <td>-1.413631</td>\n",
       "      <td>-0.064663</td>\n",
       "      <td>0.114322</td>\n",
       "      <td>0.189288</td>\n",
       "      <td>0.846375</td>\n",
       "      <td>-0.106292</td>\n",
       "      <td>1.118757</td>\n",
       "      <td>-0.308195</td>\n",
       "      <td>-0.342372</td>\n",
       "      <td>0.429987</td>\n",
       "      <td>0.229823</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
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       "      <td>39880.0</td>\n",
       "      <td>1.035117</td>\n",
       "      <td>0.999347</td>\n",
       "      <td>-1.600972</td>\n",
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       "      <td>-0.014340</td>\n",
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       "      <td>0.269243</td>\n",
       "      <td>-0.279794</td>\n",
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       "      <td>-0.261424</td>\n",
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       "      <td>0.082753</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0</td>\n",
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       "      <td>-1.504848</td>\n",
       "      <td>0.338793</td>\n",
       "      <td>1.072892</td>\n",
       "      <td>-0.183773</td>\n",
       "      <td>1.221131</td>\n",
       "      <td>0.105930</td>\n",
       "      <td>0.596836</td>\n",
       "      <td>-0.061391</td>\n",
       "      <td>1.960715</td>\n",
       "      <td>-0.103012</td>\n",
       "      <td>1.370989</td>\n",
       "      <td>-1.347672</td>\n",
       "      <td>1.633386</td>\n",
       "      <td>0.735098</td>\n",
       "      <td>-3.314060</td>\n",
       "      <td>-0.829446</td>\n",
       "      <td>0.583925</td>\n",
       "      <td>-0.360004</td>\n",
       "      <td>0.574669</td>\n",
       "      <td>0.045812</td>\n",
       "      <td>-0.664692</td>\n",
       "      <td>-0.609464</td>\n",
       "      <td>0.353599</td>\n",
       "      <td>0.667511</td>\n",
       "      <td>0.117457</td>\n",
       "      <td>0.034938</td>\n",
       "      <td>0.513421</td>\n",
       "      <td>0.013636</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "            Time        V1        V2        V3        V4        V5        V6  \\\n",
       "173438  121482.0  0.243283  1.135203 -2.331887 -0.652055  1.533160 -0.499443   \n",
       "158953  112017.0 -1.279762  0.489341  1.191725 -2.492003 -0.245443 -0.270046   \n",
       "247832  153705.0 -0.459015 -0.207853  2.189452 -2.001899 -0.922265  0.218596   \n",
       "39549    39880.0  1.035117  0.999347 -1.600972  1.522032  1.364316 -0.014340   \n",
       "151375   95462.0 -1.504848  0.338793  1.072892 -0.183773  1.221131  0.105930   \n",
       "\n",
       "              V7        V8        V9       V10       V11       V12       V13  \\\n",
       "173438  1.154317  0.154697 -0.894073 -0.950879  0.206441  0.798093  0.845151   \n",
       "158953 -0.163012  0.480909 -1.408978 -0.187827  0.313922  0.295039  1.024890   \n",
       "247832 -0.624701  0.353164  0.207161 -0.315515 -1.196272 -0.314411  0.094696   \n",
       "39549   0.355807  0.269243 -0.279794 -1.702323  1.838606 -0.726576 -1.638358   \n",
       "151375  0.596836 -0.061391  1.960715 -0.103012  1.370989 -1.347672  1.633386   \n",
       "\n",
       "             V14       V15       V16       V17       V18       V19       V20  \\\n",
       "173438 -0.480278 -1.487661  0.361336  0.170312  0.606358  0.238467 -0.120756   \n",
       "158953 -0.322894 -1.304094  1.623305 -0.347883 -0.691074  0.440871  0.108248   \n",
       "247832 -1.403423 -1.731459  0.915786  0.368293 -1.413631 -0.064663  0.114322   \n",
       "39549  -2.965882  2.286755 -0.261424  4.108374 -0.549548 -2.502939 -0.269538   \n",
       "151375  0.735098 -3.314060 -0.829446  0.583925 -0.360004  0.574669  0.045812   \n",
       "\n",
       "             V21       V22       V23       V24       V25       V26       V27  \\\n",
       "173438  0.204054  0.538671 -0.089063 -0.157341 -0.160672  0.544186 -0.164264   \n",
       "158953  0.242232  0.616035 -0.379450 -0.451476  0.872741 -0.118898  0.014242   \n",
       "247832  0.189288  0.846375 -0.106292  1.118757 -0.308195 -0.342372  0.429987   \n",
       "39549  -0.084477 -0.061830 -0.025012 -0.998561  0.452603 -0.181386  0.102682   \n",
       "151375 -0.664692 -0.609464  0.353599  0.667511  0.117457  0.034938  0.513421   \n",
       "\n",
       "             V28  Amount  Class  \n",
       "173438 -0.059531   42.81      0  \n",
       "158953  0.014217    3.90      0  \n",
       "247832  0.229823    1.00      0  \n",
       "39549   0.082753    1.99      0  \n",
       "151375  0.013636    0.01      0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.998273\n",
       "1    0.001727\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions['Class'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = transactions.drop(labels='Class', axis=1) # Features\n",
    "y = transactions.loc[:,'Class']               # Response\n",
    "del transactions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1, stratify=y)\n",
    "del X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(227845, 30)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56962, 30)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    227845.000000\n",
       "mean      94707.617670\n",
       "std       47523.204111\n",
       "min           0.000000\n",
       "25%       54086.000000\n",
       "50%       84609.000000\n",
       "75%      139261.000000\n",
       "max      172792.000000\n",
       "Name: Time, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['Time'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.loc[:,'Time'] = X_train.Time / 3600\n",
    "X_test.loc[:,'Time'] = X_test.Time / 3600\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9999074074074075"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['Time'].max() / 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mrityunjay/.conda/envs/project/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Transaction Times')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4), dpi=80)\n",
    "sns.distplot(X_train['Time'], bins=48, kde=False)\n",
    "plt.xlim([0,48])\n",
    "plt.xticks(np.arange(0,54,6))\n",
    "plt.xlabel('Time After First Transaction (hr)')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Transaction Times')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    227845.000000\n",
       "mean         88.709296\n",
       "std         250.026305\n",
       "min           0.000000\n",
       "25%           5.550000\n",
       "50%          22.000000\n",
       "75%          77.890000\n",
       "max       25691.160000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['Amount'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mrityunjay/.conda/envs/project/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Transaction Amounts')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4), dpi=80)\n",
    "sns.distplot(X_train['Amount'], bins=300, kde=False)\n",
    "plt.ylabel('Count')\n",
    "plt.title('Transaction Amounts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mrityunjay/.conda/envs/project/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Transaction Amounts')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4), dpi=80)\n",
    "sns.boxplot(X_train['Amount'])\n",
    "plt.title('Transaction Amounts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.910303546516744"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['Amount'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.loc[:,'Amount'] = X_train['Amount'] + 1e-9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.loc[:,'Amount'], maxlog, (min_ci, max_ci) = sp.stats.boxcox(X_train['Amount'], alpha=0.01)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13436569191158662"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxlog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.13291390124729957, 0.1358266545085391)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(min_ci, max_ci)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mrityunjay/.conda/envs/project/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Transaction Amounts (Box-Cox Transformed)')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4), dpi=80)\n",
    "sns.distplot(X_train['Amount'], kde=False)\n",
    "plt.xlabel('Transformed Amount')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Transaction Amounts (Box-Cox Transformed)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    227845.000000\n",
       "mean          3.985515\n",
       "std           2.972505\n",
       "min          -6.982733\n",
       "25%           1.927181\n",
       "50%           3.831861\n",
       "75%           5.919328\n",
       "max          21.680566\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['Amount'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11421481857917302"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['Amount'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.loc[:,'Amount'] = X_test['Amount'] + 1e-9 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.loc[:,'Amount'] = sp.stats.boxcox(X_test['Amount'], lmbda=maxlog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mrityunjay/.conda/envs/project/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f46251b3fd0>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(X_train['Time'].apply(lambda x: x % 24), X_train['Amount'], kind='hex',height=12, xlim=(0,24), ylim=(-7.5,14)).set_axis_labels('Time of Day (hr)','Transformed Amount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_vars = ['V%i' % k for k in range(1,29)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train[pca_vars].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'V1-V28 Means')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4), dpi=80)\n",
    "sns.barplot(x=pca_vars, y=X_train[pca_vars].mean(), color='darkblue')\n",
    "plt.xlabel('Column')\n",
    "plt.ylabel('Mean')\n",
    "plt.title('V1-V28 Means')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'V1-V28 Standard Deviations')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4), dpi=80)\n",
    "sns.barplot(x=pca_vars, y=X_train[pca_vars].std(), color='darkred')\n",
    "plt.xlabel('Column')\n",
    "plt.ylabel('Standard Deviation')\n",
    "plt.title('V1-V28 Standard Deviations')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'V1-V28 Skewnesses')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4), dpi=80)\n",
    "sns.barplot(x=pca_vars, y=X_train[pca_vars].skew(), color='darkgreen')\n",
    "plt.xlabel('Column')\n",
    "plt.ylabel('Skewness')\n",
    "plt.title('V1-V28 Skewnesses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'V8')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4), dpi=80)\n",
    "sns.distplot(X_train['V8'], bins=300, kde=False)\n",
    "plt.ylabel('Count')\n",
    "plt.title('V8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'V8')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 960x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(12,4), dpi=80)\n",
    "sns.boxplot(X_train['V8'])\n",
    "plt.title('V8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'V1-V28 Kurtoses')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4), dpi=80)\n",
    "plt.yscale('log')\n",
    "sns.barplot(x=pca_vars, y=X_train[pca_vars].kurtosis(), color='darkorange')\n",
    "plt.xlabel('Column')\n",
    "plt.ylabel('Kurtosis')\n",
    "plt.title('V1-V28 Kurtoses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'V1-V28 Medians')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(12,4), dpi=80)\n",
    "sns.barplot(x=pca_vars, y=X_train[pca_vars].median(), color='darkblue')\n",
    "plt.xlabel('Column')\n",
    "plt.ylabel('Median')\n",
    "plt.title('V1-V28 Medians')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'V1-V28 IQRs')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4), dpi=80)\n",
    "sns.barplot(x=pca_vars, y=X_train[pca_vars].quantile(0.75) - X_train[pca_vars].quantile(0.25), color='darkred')\n",
    "plt.xlabel('Column')\n",
    "plt.ylabel('IQR')\n",
    "plt.title('V1-V28 IQRs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def confusion(classifier, X_test, y_test):\n",
    "    y_pred  = classifier.predict(X_test)\n",
    "    return confusion_matrix(y_test, y_pred).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show(tn,fp,fn,tp):\n",
    "    print(\"TN:\" + str(tn) + \" FP:\" + str(fp) + \" FN:\" + str(fn) + \" TP:\" + str(tp) + \n",
    "          \" FNR=\" + str(fn/(fn+tp)) + \" FPR=\" + str(fp/(fp+tn)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN:56859 FP:5 FN:14 TP:84 FNR=0.14285714285714285 FPR=8.792909397861564e-05\n"
     ]
    }
   ],
   "source": [
    "show(*confusion(RandomForestClassifier(random_state=0, n_jobs=-1, n_estimators=10).fit(X_train,y_train),X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN:56858 FP:6 FN:21 TP:77 FNR=0.21428571428571427 FPR=0.00010551491277433877\n"
     ]
    }
   ],
   "source": [
    "show(*confusion(RandomForestClassifier(random_state=0, n_jobs=-1, n_estimators=10, class_weight=\"balanced\").fit(X_train,y_train),X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN:56856 FP:8 FN:17 TP:81 FNR=0.17346938775510204 FPR=0.00014068655036578504\n"
     ]
    }
   ],
   "source": [
    "show(*confusion(RandomForestClassifier(random_state=0, n_jobs=-1, n_estimators=10, class_weight=\"balanced_subsample\").fit(X_train,y_train),X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_neg = 10**-5\n",
    "w_pos_range = np.exp(np.arange(np.log(1), np.log(10**9)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w_pos: 1.0\n",
      "TN:56857 FP:7 FN:18 TP:80 FNR=0.1836734693877551 FPR=0.0001231007315700619\n",
      "w_pos: 2.718281828459045\n",
      "TN:56858 FP:6 FN:20 TP:78 FNR=0.20408163265306123 FPR=0.00010551491277433877\n",
      "w_pos: 7.38905609893065\n",
      "TN:56859 FP:5 FN:21 TP:77 FNR=0.21428571428571427 FPR=8.792909397861564e-05\n",
      "w_pos: 20.085536923187668\n",
      "TN:56857 FP:7 FN:20 TP:78 FNR=0.20408163265306123 FPR=0.0001231007315700619\n",
      "w_pos: 54.598150033144236\n",
      "TN:56858 FP:6 FN:19 TP:79 FNR=0.19387755102040816 FPR=0.00010551491277433877\n",
      "w_pos: 148.4131591025766\n",
      "TN:56858 FP:6 FN:18 TP:80 FNR=0.1836734693877551 FPR=0.00010551491277433877\n",
      "w_pos: 403.4287934927351\n",
      "TN:56853 FP:11 FN:16 TP:82 FNR=0.16326530612244897 FPR=0.00019344400675295442\n",
      "w_pos: 1096.6331584284585\n",
      "TN:56854 FP:10 FN:19 TP:79 FNR=0.19387755102040816 FPR=0.00017585818795723128\n",
      "w_pos: 2980.9579870417283\n",
      "TN:56841 FP:23 FN:14 TP:84 FNR=0.14285714285714285 FPR=0.00040447383230163194\n",
      "w_pos: 8103.083927575384\n",
      "TN:56771 FP:93 FN:14 TP:84 FNR=0.14285714285714285 FPR=0.001635481148002251\n",
      "w_pos: 22026.465794806718\n",
      "TN:54891 FP:1973 FN:9 TP:89 FNR=0.09183673469387756 FPR=0.034696820483961735\n",
      "w_pos: 59874.14171519782\n",
      "TN:39955 FP:16909 FN:4 TP:94 FNR=0.04081632653061224 FPR=0.2973586100168824\n",
      "w_pos: 162754.79141900392\n",
      "TN:37007 FP:19857 FN:5 TP:93 FNR=0.05102040816326531 FPR=0.34920160382667415\n",
      "w_pos: 442413.3920089205\n",
      "TN:17565 FP:39299 FN:3 TP:95 FNR=0.030612244897959183 FPR=0.6911050928531233\n",
      "w_pos: 1202604.2841647768\n",
      "TN:16725 FP:40139 FN:2 TP:96 FNR=0.02040816326530612 FPR=0.7058771806415307\n",
      "w_pos: 3269017.3724721107\n",
      "TN:16401 FP:40463 FN:2 TP:96 FNR=0.02040816326530612 FPR=0.7115749859313449\n",
      "w_pos: 8886110.520507872\n",
      "TN:17421 FP:39443 FN:3 TP:95 FNR=0.030612244897959183 FPR=0.6936374507597074\n",
      "w_pos: 24154952.7535753\n",
      "TN:9821 FP:47043 FN:0 TP:98 FNR=0.0 FPR=0.8272896736072032\n",
      "w_pos: 65659969.13733051\n",
      "TN:1456 FP:55408 FN:0 TP:98 FNR=0.0 FPR=0.9743950478334271\n",
      "w_pos: 178482300.96318725\n",
      "TN:1570 FP:55294 FN:0 TP:98 FNR=0.0 FPR=0.9723902644907146\n",
      "w_pos: 485165195.4097903\n",
      "TN:1109 FP:55755 FN:0 TP:98 FNR=0.0 FPR=0.980497326955543\n"
     ]
    }
   ],
   "source": [
    "for w_pos in w_pos_range:\n",
    "    print(\"w_pos: \" + str(w_pos))\n",
    "    show(*confusion(RandomForestClassifier(random_state=0, n_jobs=-1, n_estimators=10, class_weight={0: w_neg, 1: w_pos}).fit(X_train,y_train),X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = RandomForestClassifier(random_state=0, n_jobs=-1, n_estimators=10, class_weight={0: 10**-5, 1: 403.42 }).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7394181666607467\n"
     ]
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "average_precision = average_precision_score(y_test,y_pred)\n",
    "print(average_precision_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "import matplotlib.patches as mpatches\n",
    "import time\n",
    "\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import collections\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import NearMiss\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "df = pd.read_csv('creditcard.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
    "\n",
    "\n",
    "std_scaler = StandardScaler()\n",
    "rob_scaler = RobustScaler()\n",
    "\n",
    "df['scaled_amount'] = rob_scaler.fit_transform(df['Amount'].values.reshape(-1,1))\n",
    "df['scaled_time'] = rob_scaler.fit_transform(df['Time'].values.reshape(-1,1))\n",
    "\n",
    "df.drop(['Time','Amount'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>scaled_amount</th>\n",
       "      <th>scaled_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>...</th>\n",
       "      <th>V20</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>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.783274</td>\n",
       "      <td>-0.994983</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>...</td>\n",
       "      <td>0.251412</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.269825</td>\n",
       "      <td>-0.994983</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>...</td>\n",
       "      <td>-0.069083</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.983721</td>\n",
       "      <td>-0.994972</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>...</td>\n",
       "      <td>0.524980</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.418291</td>\n",
       "      <td>-0.994972</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>...</td>\n",
       "      <td>-0.208038</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.670579</td>\n",
       "      <td>-0.994960</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>...</td>\n",
       "      <td>0.408542</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   scaled_amount  scaled_time        V1        V2        V3        V4  \\\n",
       "0       1.783274    -0.994983 -1.359807 -0.072781  2.536347  1.378155   \n",
       "1      -0.269825    -0.994983  1.191857  0.266151  0.166480  0.448154   \n",
       "2       4.983721    -0.994972 -1.358354 -1.340163  1.773209  0.379780   \n",
       "3       1.418291    -0.994972 -0.966272 -0.185226  1.792993 -0.863291   \n",
       "4       0.670579    -0.994960 -1.158233  0.877737  1.548718  0.403034   \n",
       "\n",
       "         V5        V6        V7        V8  ...       V20       V21       V22  \\\n",
       "0 -0.338321  0.462388  0.239599  0.098698  ...  0.251412 -0.018307  0.277838   \n",
       "1  0.060018 -0.082361 -0.078803  0.085102  ... -0.069083 -0.225775 -0.638672   \n",
       "2 -0.503198  1.800499  0.791461  0.247676  ...  0.524980  0.247998  0.771679   \n",
       "3 -0.010309  1.247203  0.237609  0.377436  ... -0.208038 -0.108300  0.005274   \n",
       "4 -0.407193  0.095921  0.592941 -0.270533  ...  0.408542 -0.009431  0.798278   \n",
       "\n",
       "        V23       V24       V25       V26       V27       V28  Class  \n",
       "0 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053      0  \n",
       "1  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724      0  \n",
       "2  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0  \n",
       "3 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458      0  \n",
       "4 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_amount = df['scaled_amount']\n",
    "scaled_time = df['scaled_time']\n",
    "\n",
    "df.drop(['scaled_amount', 'scaled_time'], axis=1, inplace=True)\n",
    "df.insert(0, 'scaled_amount', scaled_amount)\n",
    "df.insert(1, 'scaled_time', scaled_time)\n",
    "\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Frauds 99.83 % of the dataset\n",
      "Frauds 0.17 % of the dataset\n",
      "Train: [ 30473  30496  31002 ... 284804 284805 284806] Test: [    0     1     2 ... 57017 57018 57019]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 30473  30496  31002 ... 113964 113965 113966]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 81609  82400  83053 ... 170946 170947 170948]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [150654 150660 150661 ... 227866 227867 227868]\n",
      "Train: [     0      1      2 ... 227866 227867 227868] Test: [212516 212644 213092 ... 284804 284805 284806]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Label Distributions: \n",
      "\n",
      "[0.99827076 0.00172924]\n",
      "[0.99827952 0.00172048]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "print('No Frauds', round(df['Class'].value_counts()[0]/len(df) * 100,2), '% of the dataset')\n",
    "print('Frauds', round(df['Class'].value_counts()[1]/len(df) * 100,2), '% of the dataset')\n",
    "\n",
    "X = df.drop('Class', axis=1)\n",
    "y = df['Class']\n",
    "\n",
    "sss = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)\n",
    "\n",
    "for train_index, test_index in sss.split(X, y):\n",
    "    print(\"Train:\", train_index, \"Test:\", test_index)\n",
    "    original_Xtrain, original_Xtest = X.iloc[train_index], X.iloc[test_index]\n",
    "    original_ytrain, original_ytest = y.iloc[train_index], y.iloc[test_index]\n",
    "\n",
    "\n",
    "original_Xtrain = original_Xtrain.values\n",
    "original_Xtest = original_Xtest.values\n",
    "original_ytrain = original_ytrain.values\n",
    "original_ytest = original_ytest.values\n",
    "\n",
    "\n",
    "train_unique_label, train_counts_label = np.unique(original_ytrain, return_counts=True)\n",
    "test_unique_label, test_counts_label = np.unique(original_ytest, return_counts=True)\n",
    "print('-' * 100)\n",
    "\n",
    "print('Label Distributions: \\n')\n",
    "print(train_counts_label/ len(original_ytrain))\n",
    "print(test_counts_label/ len(original_ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of X (train): 227846 | Length of y (train): 227846\n",
      "Length of X (test): 56961 | Length of y (test): 56961\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "accuracy: 0.9429357853619781\n",
      "precision: 0.06055442977345941\n",
      "recall: 0.9111976630963973\n",
      "f1: 0.11175825890195594\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "\n",
    "\n",
    "print('Length of X (train): {} | Length of y (train): {}'.format(len(original_Xtrain), len(original_ytrain)))\n",
    "print('Length of X (test): {} | Length of y (test): {}'.format(len(original_Xtest), len(original_ytest)))\n",
    "\n",
    "\n",
    "accuracy_lst = []\n",
    "precision_lst = []\n",
    "recall_lst = []\n",
    "f1_lst = []\n",
    "auc_lst = []\n",
    "\n",
    "\n",
    "log_reg_sm = LogisticRegression()\n",
    "\n",
    "\n",
    "\n",
    "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "rand_log_reg = RandomizedSearchCV(LogisticRegression(), log_reg_params, n_iter=4)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for train, test in sss.split(original_Xtrain, original_ytrain):\n",
    "    pipeline = imbalanced_make_pipeline(SMOTE(sampling_strategy='minority'), rand_log_reg) # SMOTE happens during Cross Validation not before..\n",
    "    model = pipeline.fit(original_Xtrain[train], original_ytrain[train])\n",
    "    best_est = rand_log_reg.best_estimator_\n",
    "    prediction = best_est.predict(original_Xtrain[test])\n",
    "    \n",
    "    accuracy_lst.append(pipeline.score(original_Xtrain[test], original_ytrain[test]))\n",
    "    precision_lst.append(precision_score(original_ytrain[test], prediction))\n",
    "    recall_lst.append(recall_score(original_ytrain[test], prediction))\n",
    "    f1_lst.append(f1_score(original_ytrain[test], prediction))\n",
    "    auc_lst.append(roc_auc_score(original_ytrain[test], prediction))\n",
    "    \n",
    "print('---' * 45)\n",
    "print('')\n",
    "print(\"accuracy: {}\".format(np.mean(accuracy_lst)))\n",
    "print(\"precision: {}\".format(np.mean(precision_lst)))\n",
    "print(\"recall: {}\".format(np.mean(recall_lst)))\n",
    "print(\"f1: {}\".format(np.mean(f1_lst)))\n",
    "print('---' * 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9880620073383544\n"
     ]
    }
   ],
   "source": [
    "labels = ['No Fraud', 'Fraud']\n",
    "smote_prediction = best_est.predict(original_Xtest)\n",
    "print(accuracy_score(original_ytest, smote_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_score = best_est.decision_function(original_Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.75\n"
     ]
    }
   ],
   "source": [
    "average_precision = average_precision_score(original_ytest, y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'OverSampling Precision-Recall curve: \\n Average Precision-Recall Score =0.75')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(original_ytest, y_score)\n",
    "\n",
    "plt.step(recall, precision, color='r', alpha=0.2,\n",
    "         where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2,\n",
    "                 color='#F59B00')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('OverSampling Precision-Recall curve: \\n Average Precision-Recall Score ={0:0.2f}'.format(\n",
    "          average_precision), fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}