one_class_SVM_credit_card.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </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  \\\n",
       "0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024  ...   -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  ...   -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.drop('Time',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc7fdcb6940>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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GfaqmY1vHQowllUz/jOs6FqD7/BCOnRnMfF+NXhPvnhnE0fcHpt2DgmUYbFjRgMuRMXzU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7fdcb6908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "sns.FacetGrid(data, hue=\"Class\", size=7).map(plt.scatter, \"V11\", \"V12\").add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_good=data[data['Class']!=1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_good=data_good[['Class']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/5.0.0/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "data_good.drop(['Class'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_fraud=data[data['Class']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_fraud=data_fraud[['Class']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/5.0.0/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "data_fraud.drop(['Class'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test=train_test_split(data_good,Y_good,random_state=0,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199020, 29)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85295, 29)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import OneClassSVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneClassSVM(cache_size=200, coef0=0.0, degree=3, gamma=0.1, kernel='rbf',\n",
       "      max_iter=-1, nu=0.1, random_state=None, shrinking=True, tol=0.001,\n",
       "      verbose=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=0.1)\n",
    "clf.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_train = clf.predict(X_train)\n",
    "y_pred_test = clf.predict(X_test)\n",
    "y_pred_outliers = clf.predict(data_fraud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_train=1+Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_train=Y_train.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_test=Y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_fraud=-2+Y_fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_fraud=Y_fraud.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_error_train = y_pred_train[y_pred_train == -1].size\n",
    "FP = y_pred_test[y_pred_test == -1].size\n",
    "FN = y_pred_outliers[y_pred_outliers == 1].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_correct_train = y_pred_train[y_pred_train == 1].size\n",
    "TN = y_pred_test[y_pred_test == 1].size\n",
    "TP = y_pred_outliers[y_pred_outliers == -1].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Recall=TP/(TP+FN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Precision=TP/(TP+FP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
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