CreditCard.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlier detection\n",
    "\n",
    "Identifying fradulent transactions in credit card transactions. \n",
    "the dataset is downloaded from kaggle https://www.kaggle.com/mlg-ulb/creditcardfraud/data\n",
    "\n",
    "Intention of the notebook is to use one of the classification algorithm on skewed dataset and later undersample the data to build the model. Apply the model that was built on undersample data to whole dataset.\n",
    "\n",
    "Use a different classifier (SGD) and see any improvements in ROC parameter\n",
    "\n",
    "Use the scikit in-built outlier detection http://scikit-learn.org/stable/modules/outlier_detection.html\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the necessary python libraries and load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284807, 31)\n"
     ]
    },
    {
     "data": {
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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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.425966</td>\n",
       "      <td>0.960523</td>\n",
       "      <td>1.141109</td>\n",
       "      <td>-0.168252</td>\n",
       "      <td>0.420987</td>\n",
       "      <td>-0.029728</td>\n",
       "      <td>0.476201</td>\n",
       "      <td>0.260314</td>\n",
       "      <td>-0.568671</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208254</td>\n",
       "      <td>-0.559825</td>\n",
       "      <td>-0.026398</td>\n",
       "      <td>-0.371427</td>\n",
       "      <td>-0.232794</td>\n",
       "      <td>0.105915</td>\n",
       "      <td>0.253844</td>\n",
       "      <td>0.081080</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.229658</td>\n",
       "      <td>0.141004</td>\n",
       "      <td>0.045371</td>\n",
       "      <td>1.202613</td>\n",
       "      <td>0.191881</td>\n",
       "      <td>0.272708</td>\n",
       "      <td>-0.005159</td>\n",
       "      <td>0.081213</td>\n",
       "      <td>0.464960</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.167716</td>\n",
       "      <td>-0.270710</td>\n",
       "      <td>-0.154104</td>\n",
       "      <td>-0.780055</td>\n",
       "      <td>0.750137</td>\n",
       "      <td>-0.257237</td>\n",
       "      <td>0.034507</td>\n",
       "      <td>0.005168</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.644269</td>\n",
       "      <td>1.417964</td>\n",
       "      <td>1.074380</td>\n",
       "      <td>-0.492199</td>\n",
       "      <td>0.948934</td>\n",
       "      <td>0.428118</td>\n",
       "      <td>1.120631</td>\n",
       "      <td>-3.807864</td>\n",
       "      <td>0.615375</td>\n",
       "      <td>...</td>\n",
       "      <td>1.943465</td>\n",
       "      <td>-1.015455</td>\n",
       "      <td>0.057504</td>\n",
       "      <td>-0.649709</td>\n",
       "      <td>-0.415267</td>\n",
       "      <td>-0.051634</td>\n",
       "      <td>-1.206921</td>\n",
       "      <td>-1.085339</td>\n",
       "      <td>40.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.894286</td>\n",
       "      <td>0.286157</td>\n",
       "      <td>-0.113192</td>\n",
       "      <td>-0.271526</td>\n",
       "      <td>2.669599</td>\n",
       "      <td>3.721818</td>\n",
       "      <td>0.370145</td>\n",
       "      <td>0.851084</td>\n",
       "      <td>-0.392048</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073425</td>\n",
       "      <td>-0.268092</td>\n",
       "      <td>-0.204233</td>\n",
       "      <td>1.011592</td>\n",
       "      <td>0.373205</td>\n",
       "      <td>-0.384157</td>\n",
       "      <td>0.011747</td>\n",
       "      <td>0.142404</td>\n",
       "      <td>93.20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
       "      <td>-0.338262</td>\n",
       "      <td>1.119593</td>\n",
       "      <td>1.044367</td>\n",
       "      <td>-0.222187</td>\n",
       "      <td>0.499361</td>\n",
       "      <td>-0.246761</td>\n",
       "      <td>0.651583</td>\n",
       "      <td>0.069539</td>\n",
       "      <td>-0.736727</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.246914</td>\n",
       "      <td>-0.633753</td>\n",
       "      <td>-0.120794</td>\n",
       "      <td>-0.385050</td>\n",
       "      <td>-0.069733</td>\n",
       "      <td>0.094199</td>\n",
       "      <td>0.246219</td>\n",
       "      <td>0.083076</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 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",
       "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
       "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
       "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
       "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
       "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \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",
       "5  0.260314 -0.568671  ...   -0.208254 -0.559825 -0.026398 -0.371427   \n",
       "6  0.081213  0.464960  ...   -0.167716 -0.270710 -0.154104 -0.780055   \n",
       "7 -3.807864  0.615375  ...    1.943465 -1.015455  0.057504 -0.649709   \n",
       "8  0.851084 -0.392048  ...   -0.073425 -0.268092 -0.204233  1.011592   \n",
       "9  0.069539 -0.736727  ...   -0.246914 -0.633753 -0.120794 -0.385050   \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",
       "5 -0.232794  0.105915  0.253844  0.081080    3.67      0  \n",
       "6  0.750137 -0.257237  0.034507  0.005168    4.99      0  \n",
       "7 -0.415267 -0.051634 -1.206921 -1.085339   40.80      0  \n",
       "8  0.373205 -0.384157  0.011747  0.142404   93.20      0  \n",
       "9 -0.069733  0.094199  0.246219  0.083076    3.68      0  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV, SGDClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, recall_score, roc_curve, roc_auc_score, auc, fbeta_score, precision_score \n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv('creditcard.csv');\n",
    "print(df.shape)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observing the data-frame implies there exists 31 features for the data, out of which 28 features are PCA dimension the rest Time, Amount and Class are the original value. Class with value of 0 indicates a normal transaction, whereas 1 indicates a fraud transaction. Time feature indicates the seconds elapsed from the first transaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# utility functions copied from \n",
    "# http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    # print(cm)\n",
    "\n",
    "    im = plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar(im, fraction=0.046, pad=0.04)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    \n",
    "# utility function to plot a roc curve\n",
    "\n",
    "def plot_roc_curve(y_test, y_pred):\n",
    "    fpr, tpr, thresholds = roc_curve(y_test,y_pred)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    \n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "    plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f'% roc_auc)\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.plot([0,1],[0,1],'r--')\n",
    "    plt.xlim([-0.1,1.1])\n",
    "    plt.ylim([-0.1,1.1])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot the no of records for each 'Class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G6ktbbXWSkwbq+yX5epLrk3wiyU6t/hNteXVbv2jEc5Qk9WTUsLm/qh6YWkiyA93v2Qzz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f53c9faa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_classes = pd.value_counts(df['Class'], sort = True).sort_index()\n",
    "count_classes.plot(kind = 'bar')\n",
    "plt.title(\"Fraud class histogram\")\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "492\n"
     ]
    }
   ],
   "source": [
    "number_records_fraud = len(df[df.Class == 1])\n",
    "fraud_indices = np.array(df[df.Class == 1].index)\n",
    "print(number_records_fraud)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize the amount column and add it as new column to data-frame, drop the old column 'Amount' and 'Time'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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>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>Class</th>\n",
       "      <th>normAmount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>0</td>\n",
       "      <td>0.244964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>0</td>\n",
       "      <td>-0.342475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>0</td>\n",
       "      <td>1.160686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>0</td>\n",
       "      <td>0.140534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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>0</td>\n",
       "      <td>-0.073403</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",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10     ...           V21       V22       V23  \\\n",
       "0  0.098698  0.363787  0.090794     ...     -0.018307  0.277838 -0.110474   \n",
       "1  0.085102 -0.255425 -0.166974     ...     -0.225775 -0.638672  0.101288   \n",
       "2  0.247676 -1.514654  0.207643     ...      0.247998  0.771679  0.909412   \n",
       "3  0.377436 -1.387024 -0.054952     ...     -0.108300  0.005274 -0.190321   \n",
       "4 -0.270533  0.817739  0.753074     ...     -0.009431  0.798278 -0.137458   \n",
       "\n",
       "        V24       V25       V26       V27       V28  Class  normAmount  \n",
       "0  0.066928  0.128539 -0.189115  0.133558 -0.021053      0    0.244964  \n",
       "1 -0.339846  0.167170  0.125895 -0.008983  0.014724      0   -0.342475  \n",
       "2 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0    1.160686  \n",
       "3 -1.175575  0.647376 -0.221929  0.062723  0.061458      0    0.140534  \n",
       "4  0.141267 -0.206010  0.502292  0.219422  0.215153      0   -0.073403  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['normAmount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1,1))\n",
    "df = df.drop(['Time','Amount'],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a classification model on the data, for demonstration purpose we are using Logistic Regression classifier. with out much parameter tuning lets apply the following steps\n",
    "        separate the features, target \n",
    "        split the data into train and test\n",
    "        fit the train data\n",
    "        predict on test data\n",
    "        observe the recall score\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.62\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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AtWpJR1eE3NwwX+urr8Lo0WHWEREREZHyrOxnVzsBmOPui8Lp7SngcKC2mVWK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\nUcMaqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f53cff89b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_unbalanced = df['Class']\n",
    "X_unbalanced = df.drop(['Class'], axis=1)\n",
    "\n",
    "\n",
    "X_ub_train, X_ub_test, y_ub_train, y_ub_test = train_test_split(X_unbalanced,y_unbalanced, test_size=0.3)\n",
    "\n",
    "# played with different C values and sticked with .1 for demonstration purpose\n",
    "\n",
    "lrclf_ub = LogisticRegression(penalty='l1', C=0.1)\n",
    "\n",
    "\n",
    "lrclf_ub.fit(X_ub_train, y_ub_train)\n",
    "\n",
    "y_ub_predict = lrclf_ub.predict(X_ub_test)\n",
    "\n",
    "print(recall_score(y_ub_test, y_ub_predict))\n",
    "\n",
    "cnf_ub_matrix = confusion_matrix(y_ub_test, y_ub_predict)\n",
    "\n",
    "plt.figure(1, figsize=(15,8))\n",
    "plt.subplot(121)\n",
    "plot_roc_curve(y_ub_test, y_ub_predict)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_confusion_matrix(cnf_ub_matrix, classes=['Non-Fraud','Fraud'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the ROC curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's undersample the data, as there are 492 records of fraud, lets randomly pick 492 records of non-fradulent records and fit the model with new data. there are other methods of doing balancing methodolgies like SMOTE( which does combination of under and over sampling) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(984, 30)\n"
     ]
    }
   ],
   "source": [
    "# Picking the indices of the normal classes\n",
    "normal_indices = df[df.Class == 0].index\n",
    "\n",
    "# Out of the indices we picked, randomly select \"x\" number (number_records_fraud)\n",
    "random_normal_indices = np.random.choice(normal_indices, number_records_fraud, replace = False)\n",
    "\n",
    "random_normal_indices = np.array(random_normal_indices)\n",
    "undersampleIndices = np.concatenate([random_normal_indices,fraud_indices])\n",
    "\n",
    "# print(undersampleIndices)\n",
    "\n",
    "under_sampled_data = df.iloc[undersampleIndices,:]\n",
    "\n",
    "print(under_sampled_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>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>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>normAmount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58544</th>\n",
       "      <td>1.210477</td>\n",
       "      <td>-0.221134</td>\n",
       "      <td>-0.261920</td>\n",
       "      <td>-0.230975</td>\n",
       "      <td>0.198673</td>\n",
       "      <td>0.198393</td>\n",
       "      <td>-0.036606</td>\n",
       "      <td>-0.024491</td>\n",
       "      <td>0.242190</td>\n",
       "      <td>-0.215023</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100576</td>\n",
       "      <td>-0.147072</td>\n",
       "      <td>-0.517652</td>\n",
       "      <td>-0.143802</td>\n",
       "      <td>-1.265440</td>\n",
       "      <td>0.307922</td>\n",
       "      <td>1.011320</td>\n",
       "      <td>-0.076274</td>\n",
       "      <td>0.000828</td>\n",
       "      <td>-0.063768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>-0.629947</td>\n",
       "      <td>1.241011</td>\n",
       "      <td>1.290353</td>\n",
       "      <td>0.111235</td>\n",
       "      <td>-0.441852</td>\n",
       "      <td>-1.128839</td>\n",
       "      <td>0.429558</td>\n",
       "      <td>0.242220</td>\n",
       "      <td>-0.760241</td>\n",
       "      <td>-0.712779</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.140103</td>\n",
       "      <td>-0.060370</td>\n",
       "      <td>-0.319637</td>\n",
       "      <td>0.147350</td>\n",
       "      <td>0.711308</td>\n",
       "      <td>-0.689641</td>\n",
       "      <td>-0.038185</td>\n",
       "      <td>-0.006292</td>\n",
       "      <td>0.103116</td>\n",
       "      <td>-0.328241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206353</th>\n",
       "      <td>1.765662</td>\n",
       "      <td>-0.794697</td>\n",
       "      <td>-0.548170</td>\n",
       "      <td>0.182711</td>\n",
       "      <td>-0.624822</td>\n",
       "      <td>-0.027562</td>\n",
       "      <td>-0.596968</td>\n",
       "      <td>0.103264</td>\n",
       "      <td>0.791356</td>\n",
       "      <td>0.172832</td>\n",
       "      <td>...</td>\n",
       "      <td>0.076357</td>\n",
       "      <td>0.291409</td>\n",
       "      <td>0.660530</td>\n",
       "      <td>0.124794</td>\n",
       "      <td>0.820970</td>\n",
       "      <td>-0.407881</td>\n",
       "      <td>0.584345</td>\n",
       "      <td>-0.065018</td>\n",
       "      <td>-0.032257</td>\n",
       "      <td>0.110549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42763</th>\n",
       "      <td>0.508566</td>\n",
       "      <td>-1.565965</td>\n",
       "      <td>0.491658</td>\n",
       "      <td>0.624973</td>\n",
       "      <td>-0.676783</td>\n",
       "      <td>1.761306</td>\n",
       "      <td>-0.610791</td>\n",
       "      <td>0.439902</td>\n",
       "      <td>-0.608499</td>\n",
       "      <td>0.338849</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000761</td>\n",
       "      <td>0.073832</td>\n",
       "      <td>0.336275</td>\n",
       "      <td>-0.207624</td>\n",
       "      <td>-0.934187</td>\n",
       "      <td>0.121180</td>\n",
       "      <td>-0.129830</td>\n",
       "      <td>0.095455</td>\n",
       "      <td>0.075304</td>\n",
       "      <td>0.926158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252261</th>\n",
       "      <td>1.581389</td>\n",
       "      <td>-1.970693</td>\n",
       "      <td>0.059951</td>\n",
       "      <td>-0.119292</td>\n",
       "      <td>-2.027480</td>\n",
       "      <td>-0.137369</td>\n",
       "      <td>-1.238874</td>\n",
       "      <td>0.046221</td>\n",
       "      <td>0.560226</td>\n",
       "      <td>0.676874</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069952</td>\n",
       "      <td>-0.011094</td>\n",
       "      <td>0.021432</td>\n",
       "      <td>0.091618</td>\n",
       "      <td>-0.101056</td>\n",
       "      <td>-0.769973</td>\n",
       "      <td>0.607576</td>\n",
       "      <td>-0.021870</td>\n",
       "      <td>0.012900</td>\n",
       "      <td>0.714260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              V1        V2        V3        V4        V5        V6        V7  \\\n",
       "58544   1.210477 -0.221134 -0.261920 -0.230975  0.198673  0.198393 -0.036606   \n",
       "1363   -0.629947  1.241011  1.290353  0.111235 -0.441852 -1.128839  0.429558   \n",
       "206353  1.765662 -0.794697 -0.548170  0.182711 -0.624822 -0.027562 -0.596968   \n",
       "42763   0.508566 -1.565965  0.491658  0.624973 -0.676783  1.761306 -0.610791   \n",
       "252261  1.581389 -1.970693  0.059951 -0.119292 -2.027480 -0.137369 -1.238874   \n",
       "\n",
       "              V8        V9       V10     ...           V20       V21  \\\n",
       "58544  -0.024491  0.242190 -0.215023     ...      0.100576 -0.147072   \n",
       "1363    0.242220 -0.760241 -0.712779     ...     -0.140103 -0.060370   \n",
       "206353  0.103264  0.791356  0.172832     ...      0.076357  0.291409   \n",
       "42763   0.439902 -0.608499  0.338849     ...      0.000761  0.073832   \n",
       "252261  0.046221  0.560226  0.676874     ...     -0.069952 -0.011094   \n",
       "\n",
       "             V22       V23       V24       V25       V26       V27       V28  \\\n",
       "58544  -0.517652 -0.143802 -1.265440  0.307922  1.011320 -0.076274  0.000828   \n",
       "1363   -0.319637  0.147350  0.711308 -0.689641 -0.038185 -0.006292  0.103116   \n",
       "206353  0.660530  0.124794  0.820970 -0.407881  0.584345 -0.065018 -0.032257   \n",
       "42763   0.336275 -0.207624 -0.934187  0.121180 -0.129830  0.095455  0.075304   \n",
       "252261  0.021432  0.091618 -0.101056 -0.769973  0.607576 -0.021870  0.012900   \n",
       "\n",
       "        normAmount  \n",
       "58544    -0.063768  \n",
       "1363     -0.328241  \n",
       "206353    0.110549  \n",
       "42763     0.926158  \n",
       "252261    0.714260  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_us = under_sampled_data['Class']\n",
    "X_us = under_sampled_data.drop(['Class'], axis=1)\n",
    "\n",
    "X_us.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hyper tuning for parameters, One of the parameter for Logistic Regression is 'C'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1\n"
     ]
    }
   ],
   "source": [
    "# Lets use LogisticRegressionCV to determine the parameters\n",
    "# using default solver lbfgs and penalty is l2\n",
    "# cross fold validation is StratifiedKFold\n",
    "\n",
    "c_range=[0.01,0.1,1,10,100]\n",
    "lrcv = LogisticRegressionCV(Cs=c_range, scoring ='roc_auc', solver ='liblinear', penalty='l1', cv= 15, random_state=89, max_iter= 10000)\n",
    "\n",
    "lrcv.fit(X_us, y_us)\n",
    "\n",
    "# print(\"scores :\"+lrcv.scores_)\n",
    "best_C = lrcv.C_[0]\n",
    "print(best_C)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a new classifier with the undersampled data, using the same solver and penalty used during tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.929577464789\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
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G3NvBfosBV5X7jgJOAK6KiFENu+0JfB5YHBgCfLOLUy9BcQ+Wpki4nAZ8CtgQ2Bw4IiJW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\nRERERERERLS9BDAiIiIiIiIiou39D2mh2TIOoTgjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f53c9faba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#split the undersample and pass it to new classifier\n",
    "X_us_train, X_us_test, y_us_train, y_us_test = train_test_split(X_us, y_us, test_size=0.3)\n",
    "\n",
    "#Create a new Logistic Regression classifier with penality and C\n",
    "\n",
    "us_lrclf = LogisticRegression(penalty='l1',solver='liblinear', C=best_C)\n",
    "\n",
    "us_lrclf.fit(X_us_train, y_us_train)\n",
    "\n",
    "y_us_predict = us_lrclf.predict(X_us_test)\n",
    "\n",
    "# print(accuracy_score(y_us_test, y_us_predict))\n",
    "\n",
    "\n",
    "print(recall_score(y_us_test, y_us_predict))\n",
    "\n",
    "cnf_us_matrix = confusion_matrix(y_us_test, y_us_predict)\n",
    "\n",
    "plt.figure(1, figsize=(15,8))\n",
    "plt.subplot(121)\n",
    "plot_roc_curve(y_us_test, y_us_predict)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_confusion_matrix(cnf_us_matrix, classes=['Non-Fraud','Fraud'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The recall score improved from near 60's to  near 90's when data set is un-skewed (undersampled)\n",
    "\n",
    "Plot the ROC curve on the new classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.884057971014\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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IOOWUWkcjSZL04QSWFGMCQ9KHMHcuTJrUcpJiypTG4wcOLJIRm24KBx7YeNnR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f53fef7080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets apply on the whole data set with previously trained classifier\n",
    "\n",
    "y = df['Class']\n",
    "X = df.drop(['Class'], axis=1)\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3)\n",
    "\n",
    "# us_lrclf.fit(X_train, y_train)\n",
    "\n",
    "y_predict = us_lrclf.predict(X_test)\n",
    "print(recall_score(y_test, y_predict))\n",
    "\n",
    "cnf_matrix = confusion_matrix(y_test, y_predict)\n",
    "\n",
    "plt.figure(1, figsize=(15,8))\n",
    "plt.subplot(121)\n",
    "plot_roc_curve(y_test, y_predict)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_confusion_matrix(cnf_matrix, classes=['Non-Fraud','Fraud'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By applying the classifier trained on un-skewed data set to whole data set the recall_score is dropped by 3 to 4 points. A decent classifier though"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Let's see whether the following transaction is fraud or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n",
      "[[ 0.85217682  0.14782318]]\n"
     ]
    }
   ],
   "source": [
    "test_record = [[0.20168,0.49748,1.37391,0.57140,-0.630657,-0.53965,-0.07627,-0.91710,0.27000,-0.48028,-0.51253,0.68090,0.08992,-0.157405,-0.58881,-0.47790,0.22538,-0.66868,-0.02359,-0.22230,0.71896,-0.17187,-0.16620,0.77647,0.81846,0.44254,0.14275,0.21923,0.1254368]]\n",
    "\n",
    "y_test_record_class = us_lrclf.predict(test_record)\n",
    "y_test_record_proba = us_lrclf.predict_proba(test_record)\n",
    "\n",
    "print(y_test_record_class)\n",
    "print(y_test_record_proba)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SGDClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Let's  apply a different classifier Stochastic Gradient Descent Classifier on the balanced data-set and observe the ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.929577464789\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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1g6lTYaed4Jln4Kyzqq5GkiRJi8AAow95663254/oaE6JiRM7nj9i2LB3woZV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f541404438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "us_sgdclf = SGDClassifier(loss='modified_huber', penalty='l1', alpha=0.0001, l1_ratio=1, max_iter=10000)\n",
    "\n",
    "us_sgdclf.fit(X_us_train,y_us_train)\n",
    "\n",
    "y_us_sgd = us_sgdclf.predict(X_us_test)\n",
    "\n",
    "cnf_us_sgd_matrix = confusion_matrix(y_us_test, y_us_sgd)\n",
    "\n",
    "print(recall_score(y_us_test,y_us_sgd))\n",
    "\n",
    "plt.figure(1, figsize=(15,8))\n",
    "plt.subplot(121)\n",
    "plot_roc_curve(y_us_test, y_us_sgd)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_confusion_matrix(cnf_us_sgd_matrix, classes=['Non-Fraud','Fraud'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the above built model on the entire data set and observe the ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.913043478261\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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AzwIXRcTAzDyDoqvjt5nZLzN/1VEhEbEicAGwd2auBGwHPNLGeqsAo8t1VwV+AoyOiFUr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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f5413ebe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test_sgd = us_sgdclf.predict(X_test)\n",
    "\n",
    "print(recall_score(y_test,y_test_sgd))\n",
    "\n",
    "#print data-frame to observer false positives\n",
    "\n",
    "cnf_sgd_matrix = confusion_matrix(y_test, y_test_sgd)\n",
    "\n",
    "plt.figure(1, figsize=(15,8))\n",
    "plt.subplot(121)\n",
    "plot_roc_curve(y_test, y_test_sgd)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_confusion_matrix(cnf_sgd_matrix, classes=['Non-Fraud','Fraud'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Isolation Forest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(170589, 30)\n",
      "(57109, 30)\n",
      "(57109, 30)\n"
     ]
    }
   ],
   "source": [
    "# separate anomaly from normal\n",
    "\n",
    "normal = df[df['Class']==0]\n",
    "anomaly = df[df['Class'] ==1]\n",
    "\n",
    "train, normal_test,_,_ = train_test_split(normal, normal, test_size=.4, random_state = 42)\n",
    "\n",
    "\n",
    "normal_cv, normal_test,_,_ = train_test_split(normal_test, normal_test, test_size =.5, random_state = 42)\n",
    "\n",
    "# break the anomaly into half\n",
    "anomaly_cv, anomaly_test, _,_ = train_test_split(anomaly, anomaly, test_size=.5, random_state = 42)\n",
    "\n",
    "# append the anomalies to cv and test\n",
    "cv = normal_cv.append(anomaly_cv).sample(frac=1).reset_index(drop=True)\n",
    "test = normal_test.append(anomaly_test).sample(frac=1).reset_index(drop=True)\n",
    "\n",
    "print(train.shape)\n",
    "print(cv.shape)\n",
    "print(test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.153019253963\n",
      "-0.0509512996118\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import IsolationForest\n",
    "\n",
    "us_ifclf = IsolationForest(n_estimators=40, max_samples= train.shape[0],bootstrap=True, random_state=42, max_features= 28, n_jobs=4)\n",
    "\n",
    "us_ifclf.fit(train.drop('Class', axis=1).values)\n",
    "\n",
    "#print the mean values of decision function values for cross validation scores\n",
    "non_anomolus = us_ifclf.decision_function(cv[cv['Class'] == 0].drop('Class', axis=1).values)\n",
    "anomolus = us_ifclf.decision_function(cv[cv['Class'] == 1].drop('Class', axis=1).values)\n",
    "\n",
    "#Mean values\n",
    "print(non_anomolus.mean())\n",
    "print(anomolus.mean())\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems like the mean of normal class is at 0.15 while anomaly class is around -0.04, need to figure out the epsilon (optimal distance of distribution to consider a record as anomaly)<br> Let's plot the distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8rqqeneTvkzx9rd4LAADAkWbNwmJ3nzWj+Y+W6fvGJG9cZtmuJA+Y0f7pJI86lBoBAACY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\ncffHkvx2d//xkr5HdfdXD1O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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f542540cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1,figsize=(15,8))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.xlabel('Score distribution for non anomolus')\n",
    "plt.ylabel('Frequency')\n",
    "plt.hist(non_anomolus, bins=50)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.xlabel('Score distribution for fraud')\n",
    "plt.ylabel('Frequency')\n",
    "plt.hist(anomolus, bins=50)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.58651026393 104\n",
      "0.00904522613065\n"
     ]
    }
   ],
   "source": [
    "#commented code that gives the best epsilon value\n",
    "\n",
    "thresholds = np.linspace(-.2, .2, 200)\n",
    "y_scores = us_ifclf.decision_function(cv.drop('Class', axis=1).values)\n",
    "scores = []\n",
    "for threshold in thresholds:\n",
    "    y_hat = (y_scores < threshold).astype(int)\n",
    "    scores.append([recall_score(y_pred=y_hat, y_true=cv['Class'].values),\n",
    "                  precision_score(y_pred=y_hat, y_true=cv['Class'].values),\n",
    "                  fbeta_score(y_pred=y_hat, y_true=cv['Class'].values, beta=2)])\n",
    "\n",
    "scores = np.array(scores)\n",
    "print(scores[:, 2].max(), scores[:, 2].argmax())\n",
    "#threshold value for which fbeta score is max\n",
    "print(thresholds[scores[:, 2].argmax()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.731707317073\n"
     ]
    },
    {
     "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56529</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>66</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1\n",
       "0  56529  334\n",
       "1     66  180"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epsilon = 0.03\n",
    "\n",
    "y_hat_test = (us_ifclf.decision_function(test.drop('Class', axis=1).values) < epsilon).astype(int)\n",
    "\n",
    "print(recall_score(test['Class'].values, y_hat_test))\n",
    "pd.DataFrame(confusion_matrix(test['Class'].values, y_hat_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.904471544715\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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LD+5pp9U6GkmSpHcm8D/XmMCQml5rK1QqXVdPvPBCMbZhteWXX5CI2HPPzgfH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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f54250b080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Apply train test split on normal and append the anomaly to test size and find the recall score\n",
    "\n",
    "X_normal,X_test,_,_ = train_test_split(normal,normal, test_size=.2, random_state= 42)\n",
    "\n",
    "directtest = X_test.append(anomaly).sample(frac=1).reset_index(drop=True)\n",
    "\n",
    "\n",
    "# directly fit an isolation forest\n",
    "\n",
    "newifclf = IsolationForest(n_estimators=50, max_samples= train.shape[0],bootstrap=True, random_state=42, max_features= 28)\n",
    "\n",
    "newifclf.fit(X_normal)\n",
    "\n",
    "newifclf.predict(X_normal)\n",
    "\n",
    "y_pred = newifclf.predict(directtest)\n",
    "\n",
    "new_y_pred = np.empty(y_pred.shape)\n",
    "\n",
    "for i in range(len(y_pred)):\n",
    "    if(y_pred[i] == -1):\n",
    "        new_y_pred[i]=1\n",
    "    else:\n",
    "        new_y_pred[i]=0\n",
    "        \n",
    "new_y_test = directtest['Class'].values\n",
    "\n",
    "print(recall_score(new_y_test,new_y_pred))\n",
    "\n",
    "cnf_isf_matrix = confusion_matrix(new_y_test,new_y_pred)\n",
    "\n",
    "#print dataframe\n",
    "\n",
    "plt.figure(1, figsize=(15,8))\n",
    "plt.subplot(121)\n",
    "plot_roc_curve(new_y_test, new_y_pred)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_confusion_matrix(cnf_isf_matrix, classes=['Non-Fraud','Fraud'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}