smote-with-imbalance-data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7be856b9-7903-5cf3-19b3-fce7ece95502"
   },
   "source": [
    "SMOTE with Imbalance Data using imblearn module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "21256b6b-21dd-5dbb-7e8a-43af8b852cdd"
   },
   "outputs": [],
   "source": [
    "import pandas  as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "6b44dc91-42aa-a99a-d4ff-e9e2f8002bdf"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 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",
       "\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",
       "\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",
       "\n",
       "[3 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('../input/creditcard.csv')\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "ece4502d-7ac4-adcc-8a44-0c33a92f5e44"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+YJIb27YzkqTVX5Tkola/JsmCoTbHJLm1vY4Z8RwlSZ2MGjaPVdXjUytJtmXwdzYzORc4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc773c7d080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.value_counts(data['Class']).plot.bar()\n",
    "plt.title('Fraud class histogram')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Frequency')\n",
    "data['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f66f8804-2605-d915-cb7a-d6f1b0afcf13"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "12741d1e-6168-ca3b-71ea-bffc3541dece"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:3: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "data['normAmount'] = StandardScaler().fit_transform(data['Amount'].reshape(-1, 1))\n",
    "data = data.drop(['Time', 'Amount'], axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "03be36b1-e891-c7e9-a8f4-5422ffcc3c79"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "ada96df8-6788-87df-6ffc-2fe29ad94521"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X: (284807, 29)\n",
      "Shape of y: (284807, 1)\n"
     ]
    }
   ],
   "source": [
    "X = np.array(data.ix[:, data.columns != 'Class'])\n",
    "y = np.array(data.ix[:, data.columns == 'Class'])\n",
    "print('Shape of X: {}'.format(X.shape))\n",
    "print('Shape of y: {}'.format(y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "81da29db-3d0f-60bf-3b67-0fd8dd336353"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "7676067e-cd33-37e7-fcd6-f7112eba414c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number transactions X_train dataset:  (199364, 29)\n",
      "Number transactions y_train dataset:  (199364, 1)\n",
      "Number transactions X_test dataset:  (85443, 29)\n",
      "Number transactions y_test dataset:  (85443, 1)\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "print(\"Number transactions X_train dataset: \", X_train.shape)\n",
    "print(\"Number transactions y_train dataset: \", y_train.shape)\n",
    "print(\"Number transactions X_test dataset: \", X_test.shape)\n",
    "print(\"Number transactions y_test dataset: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "fd8a5374-412b-0175-e18e-aac83de1ef08"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before OverSampling, counts of label '1': [345]\n",
      "Before OverSampling, counts of label '0': [199019] \n",
      "\n",
      "After OverSampling, the shape of train_X: (398038, 29)\n",
      "After OverSampling, the shape of train_y: (398038,) \n",
      "\n",
      "After OverSampling, counts of label '1': 199019\n",
      "After OverSampling, counts of label '0': 199019\n"
     ]
    }
   ],
   "source": [
    "print(\"Before OverSampling, counts of label '1': {}\".format(sum(y_train==1)))\n",
    "print(\"Before OverSampling, counts of label '0': {} \\n\".format(sum(y_train==0)))\n",
    "\n",
    "sm = SMOTE(random_state=2)\n",
    "X_train_res, y_train_res = sm.fit_sample(X_train, y_train.ravel())\n",
    "\n",
    "print('After OverSampling, the shape of train_X: {}'.format(X_train_res.shape))\n",
    "print('After OverSampling, the shape of train_y: {} \\n'.format(y_train_res.shape))\n",
    "\n",
    "print(\"After OverSampling, counts of label '1': {}\".format(sum(y_train_res==1)))\n",
    "print(\"After OverSampling, counts of label '0': {}\".format(sum(y_train_res==0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "57ba161f-88a6-6c42-0eb9-dbb91984b7df"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "593d69fa-b8fe-3451-96aa-ebc920e97ede"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] C=1.0 ...........................................................\n",
      "[CV] C=2.0 ...........................................................\n",
      "[CV] C=3.0 ...........................................................\n",
      "[CV] ............................ C=1.0, score=0.944239, total=   8.2s\n",
      "[CV] C=4.0 ...........................................................\n",
      "[CV] ............................ C=2.0, score=0.944239, total=   8.5s\n",
      "[CV] C=5.0 ...........................................................\n",
      "[CV] ............................ C=3.0, score=0.944277, total=   8.8s\n",
      "[CV] C=6.0 ...........................................................\n",
      "[CV] ............................ C=4.0, score=0.944277, total=   8.4s\n",
      "[CV] C=7.0 ...........................................................\n",
      "[CV] ............................ C=5.0, score=0.944277, total=   8.8s\n",
      "[CV] C=8.0 ...........................................................\n",
      "[CV] ............................ C=6.0, score=0.944277, total=   8.7s\n",
      "[CV] C=9.0 ...........................................................\n",
      "[CV] ............................ C=7.0, score=0.944277, total=   8.4s\n",
      "[CV] C=10.0 ..........................................................\n",
      "[CV] ............................ C=8.0, score=0.944277, total=   8.5s\n",
      "[CV] C=1.0 ...........................................................\n",
      "[CV] ............................ C=9.0, score=0.944277, total=   8.5s\n",
      "[CV] C=2.0 ...........................................................\n",
      "[CV] ........................... C=10.0, score=0.944277, total=   8.4s\n",
      "[CV] C=3.0 ...........................................................\n",
      "[CV] ............................ C=1.0, score=0.944390, total=   8.3s\n",
      "[CV] C=4.0 ...........................................................\n",
      "[CV] ............................ C=2.0, score=0.944377, total=   7.8s\n",
      "[CV] C=5.0 ...........................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=3)]: Done  12 tasks      | elapsed:   36.2s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ............................ C=3.0, score=0.944377, total=   7.2s\n",
      "[CV] C=6.0 ...........................................................\n",
      "[CV] ............................ C=4.0, score=0.944377, total=   7.5s\n",
      "[CV] C=7.0 ...........................................................\n",
      "[CV] ............................ C=5.0, score=0.944377, total=   7.9s\n",
      "[CV] C=8.0 ...........................................................\n",
      "[CV] ............................ C=6.0, score=0.944377, total=   8.0s\n",
      "[CV] C=9.0 ...........................................................\n",
      "[CV] ............................ C=7.0, score=0.944377, total=   8.3s\n",
      "[CV] C=10.0 ..........................................................\n",
      "[CV] ............................ C=8.0, score=0.944365, total=   7.7s\n",
      "[CV] C=1.0 ...........................................................\n",
      "[CV] ............................ C=9.0, score=0.944365, total=   7.6s\n",
      "[CV] C=2.0 ...........................................................\n",
      "[CV] ........................... C=10.0, score=0.944365, total=   8.4s\n",
      "[CV] C=3.0 ...........................................................\n",
      "[CV] ............................ C=1.0, score=0.944352, total=   8.6s\n",
      "[CV] C=4.0 ...........................................................\n",
      "[CV] ............................ C=2.0, score=0.944403, total=   9.2s\n",
      "[CV] C=5.0 ...........................................................\n",
      "[CV] ............................ C=3.0, score=0.944403, total=   8.7s\n",
      "[CV] C=6.0 ...........................................................\n",
      "[CV] ............................ C=4.0, score=0.944403, total=   8.5s\n",
      "[CV] C=7.0 ...........................................................\n",
      "[CV] ............................ C=5.0, score=0.944403, total=   8.3s\n",
      "[CV] C=8.0 ...........................................................\n",
      "[CV] ............................ C=6.0, score=0.944403, total=   8.9s\n",
      "[CV] C=9.0 ...........................................................\n",
      "[CV] ............................ C=7.0, score=0.944403, total=   8.6s\n",
      "[CV] C=10.0 ..........................................................\n",
      "[CV] ............................ C=8.0, score=0.944403, total=   8.4s\n",
      "[CV] C=1.0 ...........................................................\n",
      "[CV] ............................ C=9.0, score=0.944403, total=   8.6s\n",
      "[CV] C=2.0 ...........................................................\n",
      "[CV] ........................... C=10.0, score=0.944403, total=   9.1s\n",
      "[CV] C=3.0 ...........................................................\n",
      "[CV] ............................ C=1.0, score=0.945332, total=   9.5s\n",
      "[CV] C=4.0 ...........................................................\n",
      "[CV] ............................ C=3.0, score=0.945332, total=   9.4s\n",
      "[CV] C=5.0 ...........................................................\n",
      "[CV] ............................ C=2.0, score=0.945332, total=  10.7s\n",
      "[CV] C=6.0 ...........................................................\n",
      "[CV] ............................ C=4.0, score=0.945332, total=   9.1s\n",
      "[CV] C=7.0 ...........................................................\n",
      "[CV] ............................ C=5.0, score=0.945320, total=   8.2s\n",
      "[CV] C=8.0 ...........................................................\n",
      "[CV] ............................ C=6.0, score=0.945320, total=   9.3s\n",
      "[CV] C=9.0 ...........................................................\n",
      "[CV] ............................ C=7.0, score=0.945320, total=  10.6s\n",
      "[CV] C=10.0 ..........................................................\n",
      "[CV] ............................ C=8.0, score=0.945320, total=   8.6s\n",
      "[CV] C=1.0 ...........................................................\n",
      "[CV] ............................ C=9.0, score=0.945320, total=   8.9s\n",
      "[CV] C=2.0 ...........................................................\n",
      "[CV] ............................ C=1.0, score=0.946951, total=   8.3s\n",
      "[CV] C=3.0 ...........................................................\n",
      "[CV] ........................... C=10.0, score=0.945320, total=  10.8s\n",
      "[CV] C=4.0 ...........................................................\n",
      "[CV] ............................ C=2.0, score=0.946976, total=   9.5s\n",
      "[CV] C=5.0 ...........................................................\n",
      "[CV] ............................ C=3.0, score=0.947001, total=   8.0s\n",
      "[CV] C=6.0 ...........................................................\n",
      "[CV] ............................ C=4.0, score=0.947027, total=   9.2s\n",
      "[CV] C=7.0 ...........................................................\n",
      "[CV] ............................ C=5.0, score=0.947027, total=   9.7s\n",
      "[CV] C=8.0 ...........................................................\n",
      "[CV] ............................ C=6.0, score=0.947027, total=   8.4s\n",
      "[CV] C=9.0 ...........................................................\n",
      "[CV] ............................ C=7.0, score=0.947027, total=   9.8s\n",
      "[CV] C=10.0 ..........................................................\n",
      "[CV] ............................ C=8.0, score=0.947027, total=   9.2s\n",
      "[CV] ............................ C=9.0, score=0.947014, total=   7.4s\n",
      "[CV] ........................... C=10.0, score=0.947001, total=   6.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=3)]: Done  50 out of  50 | elapsed:  2.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=3,\n",
       "       param_grid={'C': array([  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.])},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, precision_recall_curve, auc, roc_auc_score, roc_curve, recall_score, classification_report\n",
    "\n",
    "parameters = {\n",
    "    'C': np.linspace(1, 10, 10)\n",
    "             }\n",
    "lr = LogisticRegression()\n",
    "clf = GridSearchCV(lr, parameters, cv=5, verbose=5, n_jobs=3)\n",
    "clf.fit(X_train_res, y_train_res.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "de1dc6a4-78ab-7699-d9f1-cd0c2bf0920c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 4.0}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "371add68-bdac-8d2e-996e-ab73d2ca0c0c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibLinear]"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=4, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l1', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=5, warm_start=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr1 = LogisticRegression(C=4,penalty='l1', verbose=5)\n",
    "lr1.fit(X_train_res, y_train_res.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "3676564a-8c1f-6be6-c261-4b432da10efc"
   },
   "outputs": [],
   "source": [
    "import itertools\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",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=0)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        #print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        1#print('Confusion matrix, without normalization')\n",
    "\n",
    "    #print(cm)\n",
    "\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, cm[i, j],\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')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e447c6f2-f2d9-34b5-38c8-3edcb3951560"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "8a268d17-9066-e6e7-b353-888a38d92919"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the train dataset: 92.17391304347827%\n"
     ]
    },
    {
     "data": {
      "image/png": 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80Sn8+MKLOOLIo7nt1gl874c/5rZbJ3DkUcfUWM/223fnpZdeYPGiRXTedlsef+xR+vTZ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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc75fc27e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train_pre = lr1.predict(X_train)\n",
    "\n",
    "cnf_matrix_tra = confusion_matrix(y_train, y_train_pre)\n",
    "\n",
    "print(\"Recall metric in the train dataset: {}%\".format(100*cnf_matrix_tra[1,1]/(cnf_matrix_tra[1,0]+cnf_matrix_tra[1,1])))\n",
    "\n",
    "\n",
    "class_names = [0,1]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix_tra , classes=class_names, title='Confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3a7d2ff1-65b9-6892-68d9-3edf9dc013fb"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "07a325b5-f22a-7097-6744-7c34f9172351"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the testing dataset: 91.83673469387755%\n"
     ]
    },
    {
     "data": {
      "image/png": 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qBYxNIweqgIkRcaekh4GJkkYB84DhABExS9JE4GlgDXBe6noAOBcYA3QCJqcN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc75d376f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pre = lr1.predict(X_test)\n",
    "\n",
    "cnf_matrix = confusion_matrix(y_test, y_pre)\n",
    "\n",
    "print(\"Recall metric in the testing dataset: {}%\".format(100*cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1])))\n",
    "#print(\"Precision metric in the testing dataset: {}%\".format(100*cnf_matrix[0,0]/(cnf_matrix[0,0]+cnf_matrix[1,0])))\n",
    "# Plot non-normalized confusion matrix\n",
    "class_names = [0,1]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix , classes=class_names, title='Confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "319d3106-7ad9-ae8e-ad9e-ba9ac8d290eb"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "189cc505-86a2-d981-ed10-7aec87382303"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibLinear]"
     ]
    }
   ],
   "source": [
    "tmp = lr1.fit(X_train_res, y_train_res.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "10ff8704-9c38-8b00-8193-7843e413947e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XUzXx0kuh1PDll/DMMzB6NOy6a9xRicQulTaKAcCx7j4dwMzaAv8AupTyvGbA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\nEBGRpJQoREQkKSUKERFJ6v8BLXO3+HIr0cgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc75d234908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_sample_score = tmp.decision_function(X_test)\n",
    "\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_sample_score)\n",
    "\n",
    "roc_auc = auc(fpr,tpr)\n",
    "\n",
    "# Plot ROC\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b',label='AUC = %0.3f'% roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([-0.1,1.0])\n",
    "plt.ylim([-0.1,1.01])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "b2cde884-cb11-68ca-f2d5-a45cb0c830ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9838909912117163"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc"
   ]
  }
 ],
 "metadata": {
  "_change_revision": 440,
  "_is_fork": false,
  "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}