Deep-Learning-+-Sampling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/Users/rodrigobeceiro/anaconda/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import keras\n",
    "\n",
    "np.random.seed(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
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       "      <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",
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       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
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       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
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       "      <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",
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       "      <td>0.101288</td>\n",
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       "      <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",
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       "<p>5 rows × 31 columns</p>\n",
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       "   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": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "data['normalizedAmount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1,1))\n",
    "data = data.drop(['Amount'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
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       "      <td>1.800499</td>\n",
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       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "      <td>1.160686</td>\n",
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       "    <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",
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       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
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       "      <td>-0.108300</td>\n",
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       "      <td>-1.175575</td>\n",
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       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "      <td>0.140534</td>\n",
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       "      <th>4</th>\n",
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       "      <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",
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       "      <td>0.141267</td>\n",
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       "   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  \\\n",
       "0  0.098698  0.363787        ...        -0.018307  0.277838 -0.110474   \n",
       "1  0.085102 -0.255425        ...        -0.225775 -0.638672  0.101288   \n",
       "2  0.247676 -1.514654        ...         0.247998  0.771679  0.909412   \n",
       "3  0.377436 -1.387024        ...        -0.108300  0.005274 -0.190321   \n",
       "4 -0.270533  0.817739        ...        -0.009431  0.798278 -0.137458   \n",
       "\n",
       "        V24       V25       V26       V27       V28  Class  normalizedAmount  \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 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
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       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
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       "      <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  \\\n",
       "0  0.098698  0.363787  0.090794        ...        -0.018307  0.277838   \n",
       "1  0.085102 -0.255425 -0.166974        ...        -0.225775 -0.638672   \n",
       "2  0.247676 -1.514654  0.207643        ...         0.247998  0.771679   \n",
       "3  0.377436 -1.387024 -0.054952        ...        -0.108300  0.005274   \n",
       "4 -0.270533  0.817739  0.753074        ...        -0.009431  0.798278   \n",
       "\n",
       "        V23       V24       V25       V26       V27       V28  Class  \\\n",
       "0 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053      0   \n",
       "1  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724      0   \n",
       "2  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0   \n",
       "3 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458      0   \n",
       "4 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153      0   \n",
       "\n",
       "   normalizedAmount  \n",
       "0          0.244964  \n",
       "1         -0.342475  \n",
       "2          1.160686  \n",
       "3          0.140534  \n",
       "4         -0.073403  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['Time'],axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = data.iloc[:, data.columns != 'Class']\n",
    "y = data.iloc[:, data.columns == 'Class']"
   ]
  },
  {
   "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>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class\n",
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199364, 29)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85443, 29)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = np.array(X_train)\n",
    "X_test = np.array(X_test)\n",
    "y_train = np.array(y_train)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Dense(units=16, input_dim = 29,activation='relu'),\n",
    "    Dense(units=24,activation='relu'),\n",
    "    Dropout(0.5),\n",
    "    Dense(20,activation='relu'),\n",
    "    Dense(24,activation='relu'),\n",
    "    Dense(1,activation='sigmoid'),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 16)                480       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 24)                408       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 24)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 20)                500       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 24)                504       \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 25        \n",
      "=================================================================\n",
      "Total params: 1,917\n",
      "Trainable params: 1,917\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "199364/199364 [==============================] - 27s 136us/step - loss: 0.0100 - acc: 0.9979\n",
      "Epoch 2/5\n",
      "199364/199364 [==============================] - 33s 167us/step - loss: 0.0040 - acc: 0.9993\n",
      "Epoch 3/5\n",
      "199364/199364 [==============================] - 34s 173us/step - loss: 0.0038 - acc: 0.9994\n",
      "Epoch 4/5\n",
      "199364/199364 [==============================] - 32s 163us/step - loss: 0.0035 - acc: 0.9994\n",
      "Epoch 5/5\n",
      "199364/199364 [==============================] - 33s 165us/step - loss: 0.0034 - acc: 0.9994\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x11d417358>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
    "model.fit(X_train,y_train,batch_size=15,epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85443/85443 [==============================] - 4s 42us/step\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.004728495871635026, 0.9993914071369217]\n"
     ]
    }
   ],
   "source": [
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.metrics import confusion_matrix\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",
    "    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=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.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_test = pd.DataFrame(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cnf_matrix = confusion_matrix(y_test, y_pred.round())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[85281    15]\n",
      " [   36   111]]\n"
     ]
    }
   ],
   "source": [
    "print(cnf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[85281    15]\n",
      " [   36   111]]\n"
     ]
    }
   ],
   "source": [
    "plot_confusion_matrix(cnf_matrix, classes=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uv/QStuzRg6t++atqV7XTG/uF4/jH3+5m+fLlvG9Af7537nn02nZbvvG1M1m+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\nWREOkmZmRfw/+4zKQVWcHzsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[284248     67]\n",
      " [   105    387]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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H0ZcWlPeliw+eFhG7N2X9ldKcPc95QM+czz2A15qxPjOrAAFVeNXerLPtTwC9JG0jqS0w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\nRlpQSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X)\n",
    "y_expected = pd.DataFrame(y)\n",
    "cnf_matrix = confusion_matrix(y_expected, y_pred.round())\n",
    "plot_confusion_matrix(cnf_matrix,classes=[0,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "492\n"
     ]
    }
   ],
   "source": [
    "fraud_indices = np.array(data[data.Class == 1].index)\n",
    "number_records_fraud = len(fraud_indices)\n",
    "print(number_records_fraud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "normal_indices = data[data.Class == 0].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "492\n"
     ]
    }
   ],
   "source": [
    "random_normal_indices = np.random.choice(normal_indices, number_records_fraud, replace=False)\n",
    "random_normal_indices = np.array(random_normal_indices)\n",
    "print(len(random_normal_indices))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "984\n"
     ]
    }
   ],
   "source": [
    "under_sample_indices = np.concatenate([fraud_indices,random_normal_indices])\n",
    "print(len(under_sample_indices))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "under_sample_data = data.iloc[under_sample_indices,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_undersample = under_sample_data.iloc[:,under_sample_data.columns != 'Class']\n",
    "y_undersample = under_sample_data.iloc[:,under_sample_data.columns == 'Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_undersample,y_undersample, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = np.array(X_train)\n",
    "X_test = np.array(X_test)\n",
    "y_train = np.array(y_train)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 16)                480       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 24)                408       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 24)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 20)                500       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 24)                504       \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 25        \n",
      "=================================================================\n",
      "Total params: 1,917\n",
      "Trainable params: 1,917\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "688/688 [==============================] - 1s 933us/step - loss: 0.6047 - acc: 0.9026\n",
      "Epoch 2/5\n",
      "688/688 [==============================] - 0s 163us/step - loss: 0.2955 - acc: 0.9070\n",
      "Epoch 3/5\n",
      "688/688 [==============================] - 0s 173us/step - loss: 0.2184 - acc: 0.9113\n",
      "Epoch 4/5\n",
      "688/688 [==============================] - 0s 178us/step - loss: 0.1792 - acc: 0.9302\n",
      "Epoch 5/5\n",
      "688/688 [==============================] - 0s 170us/step - loss: 0.1637 - acc: 0.9346\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x11f5e3470>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
    "model.fit(X_train,y_train,batch_size=15,epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[151   0]\n",
      " [ 14 131]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Z0SSzt42OIruOFhw4h+Qe3iLgUUkT0+97k8zgmpmteDi5KDrq6bXO0D4K3FRRPrl+4ZhZ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\ncnF1aoEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_expected = pd.DataFrame(y_test)\n",
    "cnf_matrix = confusion_matrix(y_expected, y_pred.round())\n",
    "plot_confusion_matrix(cnf_matrix, classes=[0,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[283816    499]\n",
      " [    55    437]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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najV7pVXJVw+3J1WfbZc0hOwl9Gy40UZVbk15Zr/1FocdfCAACxct5LDDj2DA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\nB1fLALtaAxqaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X)\n",
    "y_expected = pd.DataFrame(y)\n",
    "cnf_matrix = confusion_matrix(y_expected, y_pred.round())\n",
    "plot_confusion_matrix(cnf_matrix, classes=[0,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting imbalanced-learn\n",
      "  Downloading https://files.pythonhosted.org/packages/ff/da/6a37e9fc0deccc07c8b2d42344722dbce85553e9e64aa6e3ac7a1e8c4a2f/imbalanced_learn-0.4.1-py3-none-any.whl (166kB)\n",
      "Collecting scikit-learn>=0.20 (from imbalanced-learn)\n",
      "  Downloading https://files.pythonhosted.org/packages/72/c8/7a449014b506d4fed3d7fef5b86c0210ff882a13da82626a12ce9f7db616/scikit_learn-0.20.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (7.8MB)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.8.2 in /Users/rodrigobeceiro/anaconda/lib/python3.6/site-packages (from imbalanced-learn) (1.14.2)\n",
      "Requirement already satisfied, skipping upgrade: scipy>=0.13.3 in /Users/rodrigobeceiro/anaconda/lib/python3.6/site-packages (from imbalanced-learn) (1.0.1)\n",
      "Installing collected packages: scikit-learn, imbalanced-learn\n",
      "  Found existing installation: scikit-learn 0.19.1\n",
      "    Uninstalling scikit-learn-0.19.1:\n",
      "      Successfully uninstalled scikit-learn-0.19.1\n",
      "  Found existing installation: imbalanced-learn 0.3.3\n",
      "    Uninstalling imbalanced-learn-0.3.3:\n",
      "      Successfully uninstalled imbalanced-learn-0.3.3\n",
      "Successfully installed imbalanced-learn-0.4.1 scikit-learn-0.20.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using pip version 18.0, however version 18.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "pip install -U imbalanced-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_resample, y_resample = SMOTE().fit_sample(X,y.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_resample = pd.DataFrame(y_resample)\n",
    "X_resample = pd.DataFrame(X_resample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_resample,y_resample,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = np.array(X_train)\n",
    "X_test = np.array(X_test)\n",
    "y_train = np.array(y_train)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "398041/398041 [==============================] - 69s 173us/step - loss: 0.0336 - acc: 0.9886\n",
      "Epoch 2/5\n",
      "398041/398041 [==============================] - 63s 158us/step - loss: 0.0123 - acc: 0.9968\n",
      "Epoch 3/5\n",
      "398041/398041 [==============================] - 63s 159us/step - loss: 0.0101 - acc: 0.9976\n",
      "Epoch 4/5\n",
      "398041/398041 [==============================] - 63s 158us/step - loss: 0.0090 - acc: 0.9979\n",
      "Epoch 5/5\n",
      "398041/398041 [==============================] - 64s 162us/step - loss: 0.0087 - acc: 0.9981\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x11f1fdc50>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
    "model.fit(X_train,y_train,batch_size=15,epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[85343   335]\n",
      " [    6 84905]]\n"
     ]
    },
    {
     "data": {
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B4MbfjeWeMU8CMPa2r7Njp/YsW7EKgM9efCvzFr5fuxoVrWbS3UrlINnIvvut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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_expected = pd.DataFrame(y_test)\n",
    "cnf_matrix = confusion_matrix(y_expected, y_pred.round())\n",
    "plot_confusion_matrix(cnf_matrix, classes=[0,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[283249   1066]\n",
      " [     0    492]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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beeut7LU1Cxcu5Le/+RXHjzgRgNdee41Dv3Ug11x3w5Jrova5Wp1tb/H7PCWN\nIHsJPT169mzh1lRWx44dufT3l/P1ffdm8eLFDP/OMWyz7bYt3SxrwqW//Q33TBjPZ599xvEjTmLw\nV/YA4MLzz+Wd+fM57dTvAtnv9dEnJucrqua0r7BYGDV2Xa5ihUubAuMjYrll7xvTv/+A8F9Ks+rZ\ndecBTJkyuaKxrs8Xdozr/vJgQXm/uOU6Uyr46uEW1eI9TzNr+9rbZFAhHDzNrGzt7HJmQap5q9It\nwGPA1pLq0ys9zawdUoFbe1K1nmeeV4iaWTsiavP+ZQ/bzaw8NXqfp4OnmZWtBmOng6eZVUANRk8H\nTzMrU/t7br0QDp5mVpaGVZVqjYOnmZXPwdPMrHi1OGz3YshmVrZKvYZD0iuSnpE0TdLklLaupPsk\nzUo/10npknSZpNmSpkvql1PO8JR/lqThOen9U/mz07ElR30HTzMrW4WfMPpKRPTNWUDkDOD+iOgF\n3J++A+wD9ErbCOBKyIItMBLYGRgIjGwIuCnPiJzjhhZ9somDp5mVp9DIWfrIfhgwKn0eBRyQkz46\nMo8Da0vaCNgbuC8i3omIBcB9wNC0b82IeCyy5eRG55RVNAdPMytLNtuugjagi6TJOduyryIN4G+S\npuTs2yAi5gKkn11Tejcg90VT9SktX3p9I+kl8YSRmZWtiE7lvGbW89w1It6Q1BW4T9LzRVYbJaSX\nxD1PMytfhYbtEfFG+vkWcCfZNcs305Cb9POtlL0e6JFzeHfgjWbSuzeSXhIHTzMrWyXenilpNUlr\nNHwGhgDPAuOAhhnz4cBd6fM44Kg06z4IeC8N6ycCQyStkyaKhgAT074PJA1Ks+xH5ZRVNA/bzaxs\nFVpVaQPgznT3UEfg5oi4V9Ik4Na0JvBrwMEp/wTga8Bs4CPgaICIeEfSecCklO/ciGh4GeVJZG/2\n7Qzck7aSOHiaWdkqETsj4iVgh0bS5wN7NpIewMlNlHUtcG0j6ZOBgt6p1hwHTzMrixdDNjMrhRdD\nNjMrTQ3GTgdPM6uAGoyeDp5mViYvhmxmVjQvhmxmVioHTzOz4nnYbmZWAt+qZGZWghqMnQ6eZlYm\n3yRvZlY8P55pZlai2gudDp5mVgE12PF08DSz8vlWJTOzUtRe7HTwNLPy1WDsdPA0s/JINLxWuKY4\neJpZ+Wovdjp4mln5ajB2OniaWflqcNTu4Glm5fJiyGZmRcsez2zpVqx4Dp5mVjYHTzOzEnjYbmZW\nLC9JZ2ZWPOFblczMSlOD0dPB08zKVouPZ9a1dAPMrO1TgVuz5UhDJb0gabakM6rV3kpw8DSz8lUg\nekrqAFwB7ANsAxwmaZuqtbkuE+y8AAAFeklEQVRMDp5mVjYV+F8zBgKzI+KliPgEGAMMq3rjS9Sq\nrnlOnTplXueV9GpLt6MKugDzWroRVpT2+jvbpNIFPjV1ysRVO6lLgdlXkTQ55/tVEXFV+twNeD1n\nXz2wcyXaWA2tKnhGxPot3YZqkDQ5Iga0dDuscP6dFS4ihlaoqMa6plGhsivOw3Yzay3qgR4537sD\nb7RQW5rl4GlmrcUkoJekzSR1Ag4FxrVwm5rUqobt7dhVzWexVsa/sxUsIhZJOgWYCHQAro2IGS3c\nrCYpotVeUjAza7U8bDczK4GDp5lZCRw8zcxK4OBZJZK2lvRFSSulx86sDfDvygrlCaMqkHQg8Etg\nTtomA9dHxPst2jBrkqStImJm+twhIha3dJusdXPPs8IkrQQcAhwbEXsCd5Hd+PtjSWu2aOOsUZL2\nA6ZJuhkgIha7B2rNcfCsjjWBXunzncB4oBPwbakGFz5sxSStBpwCnAZ8IulGcAC15jl4VlhEfApc\nAhwoabeI+Ax4BJgGfKlFG2fLiYgPgWOAm4HTyRauWBJAW7Jt1ro5eFbHP4G/AUdK2j0iFkfEzcDG\nwA4t2zRbVkS8ERH/iYh5wAlA54YAKqmfpN4t20Jrjfx4ZhVExMeSbiJbEebM9I9vIbABMLdFG2d5\nRcR8SScAv5H0PNljgl9p4WZZK+TgWSURsUDS1cC/yHozHwNHRMSbLdsya05EzJM0nWxF870ior6l\n22Stj29VWgHSxEOk65/WyklaB7gV+GFETG/p9ljr5OBp1ghJq0TExy3dDmu9HDzNzErg2XYzsxI4\neJqZlcDB08ysBA6eZmYlcPBsQyQtljRN0rOS/ixp1TLKGixpfPq8v6Qz8uRdW9J3S6jjbEmnF5q+\nTJ7rJR1URF2bSnq22DaalcrBs235b0T0jYjtgE+AE3N3KlP07zQixkXERXmyrA0UHTzN2jMHz7br\nn8CWqcf1nKQ/AlOBHpKGSHpM0tTUQ10dQNJQSc9LegQ4sKEgSd+RdHn6vIGkOyU9nbZdgIuALVKv\n9zcp348kTZI0XdI5OWX9TNILkv4ObN3cSUg6PpXztKTbl+lNf1XSPyXNTMvGIamDpN/k1H1CuX+Q\nZqVw8GyDJHUke3TwmZS0NTA6InYEPgTOAr4aEf3IFmL+gaRVgKuBrwO7ARs2UfxlwMMRsQPQD5gB\nnAG8mHq9P5I0hGzJvYFAX6C/pN0l9Sd71/aOZMF5pwJO546I2CnV9xxwbM6+TYEvA/sCf0rncCzw\nXkTslMo/XtJmBdRjVlF+tr1t6SxpWvr8T+AaspWaXo2Ix1P6IGAb4NG0dGgn4DGgN/ByRMwCSKsG\njWikjj2Ao2DJkmzvpccVcw1J21Pp++pkwXQN4M6I+CjVMa6Ac9pO0vlklwZWJ3tnd4Nb0yOtsyS9\nlM5hCLB9zvXQtVLdMwuoy6xiHDzblv9GRN/chBQgP8xNAu6LiMOWydeXbJWnShBwYUT87zJ1nFZC\nHdcDB0TE05K+AwzO2bdsWZHqPjUicoMskjYtsl6zsnjY3v48DuwqaUsASatK2gp4HthM0hYp32FN\nHH8/cFI6tkN6dcgHZL3KBhOBY3KupXaT1BX4B/ANSZ0lrUF2iaA5awBz0+tLDl9m38GS6lKbNwde\nSHWflPIjaau0GrzZCuWeZzsTEW+nHtwtklZOyWdFxExJI4C7Jc0jW91+u0aK+B5wlaRjgcXASRHx\nmKRH061A96Trnn2Ax1LP9z9ky+1NlTSWbNX8V8kuLTTn58ATKf8zLB2kXwAeJlsH9cS0Tur/kV0L\nnaqs8reBAwr70zGrHC8MYmZWAg/bzcxK4OBpZlYCB08zsxI4eJqZlcDB08ysBA6eZmYlcPA0MyvB\n/wcqz/LsDS2QNAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X)\n",
    "y_expected = pd.DataFrame(y)\n",
    "cnf_matrix = confusion_matrix(y_expected, y_pred.round())\n",
    "plot_confusion_matrix(cnf_matrix, classes=[0,1])\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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}