02_end_to_end_machine_learning_project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ankur_\\Anaconda3\\envs\\unsupervisedLearning\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "'''Main'''\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "'''Data Viz'''\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "import matplotlib as mpl\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "'''Data Prep'''\n",
    "from sklearn import preprocessing as pp \n",
    "from scipy.stats import pearsonr \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import StratifiedKFold \n",
    "from sklearn.metrics import log_loss \n",
    "from sklearn.metrics import precision_recall_curve, average_precision_score \n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix, classification_report \n",
    "\n",
    "'''Algos'''\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Acquire Data\n",
    "current_path = os.getcwd()\n",
    "file = os.path.sep.join(['', 'datasets', 'credit_card_data', 'credit_card.csv'])\n",
    "data = pd.read_csv(current_path + file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore Data\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
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       "      <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",
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       "      <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",
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       "    <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",
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       "    <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",
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       "    <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>V21</th>\n",
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       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.919560e-15</td>\n",
       "      <td>5.688174e-16</td>\n",
       "      <td>-8.769071e-15</td>\n",
       "      <td>2.782312e-15</td>\n",
       "      <td>-1.552563e-15</td>\n",
       "      <td>2.010663e-15</td>\n",
       "      <td>-1.694249e-15</td>\n",
       "      <td>-1.927028e-16</td>\n",
       "      <td>-3.137024e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.537294e-16</td>\n",
       "      <td>7.959909e-16</td>\n",
       "      <td>5.367590e-16</td>\n",
       "      <td>4.458112e-15</td>\n",
       "      <td>1.453003e-15</td>\n",
       "      <td>1.699104e-15</td>\n",
       "      <td>-3.660161e-16</td>\n",
       "      <td>-1.206049e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.552563e-15  2.010663e-15 -1.694249e-15 -1.927028e-16 -3.137024e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "           ...                 V21           V22           V23           V24  \\\n",
       "count      ...        2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean       ...        1.537294e-16  7.959909e-16  5.367590e-16  4.458112e-15   \n",
       "std        ...        7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min        ...       -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%        ...       -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%        ...       -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%        ...        1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max        ...        2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   1.453003e-15  1.699104e-15 -3.660161e-16 -1.206049e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
       "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
       "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
       "       'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of fraudulent transactions: 492\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of fraudulent transactions:\", data['Class'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nanCounter = np.isnan(data).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "Class     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nanCounter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "distinctCounter = data.apply(lambda x: len(x.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      124592\n",
       "V1        275663\n",
       "V2        275663\n",
       "V3        275663\n",
       "V4        275663\n",
       "V5        275663\n",
       "V6        275663\n",
       "V7        275663\n",
       "V8        275663\n",
       "V9        275663\n",
       "V10       275663\n",
       "V11       275663\n",
       "V12       275663\n",
       "V13       275663\n",
       "V14       275663\n",
       "V15       275663\n",
       "V16       275663\n",
       "V17       275663\n",
       "V18       275663\n",
       "V19       275663\n",
       "V20       275663\n",
       "V21       275663\n",
       "V22       275663\n",
       "V23       275663\n",
       "V24       275663\n",
       "V25       275663\n",
       "V26       275663\n",
       "V27       275663\n",
       "V28       275663\n",
       "Amount     32767\n",
       "Class          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distinctCounter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Generate feature matrix and labels array\n",
    "dataX = data.copy().drop(['Class'],axis=1)\n",
    "dataY = data['Class'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Feature scaling\n",
    "featuresToScale = dataX.drop(['Time'],axis=1).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11',\n",
       "       'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21',\n",
       "       'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featuresToScale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sX = pp.StandardScaler(copy=True)\n",
    "dataX.loc[:,featuresToScale] = sX.fit_transform(dataX[featuresToScale])\n",
    "scalingFactors = pd.DataFrame(data=[sX.mean_,sX.scale_],index=['Mean','StDev'],columns=featuresToScale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>1.165980e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.373150e-15</td>\n",
       "      <td>2.086869e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.490107e-15</td>\n",
       "      <td>-5.556467e-16</td>\n",
       "      <td>1.177556e-16</td>\n",
       "      <td>-2.406455e-15</td>\n",
       "      <td>2.239751e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>6.406703e-16</td>\n",
       "      <td>1.656562e-16</td>\n",
       "      <td>-3.444850e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.471968e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.687098e-15</td>\n",
       "      <td>-3.666453e-16</td>\n",
       "      <td>-1.220404e-16</td>\n",
       "      <td>88.349619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StDev</th>\n",
       "      <td>1.958692e+00</td>\n",
       "      <td>1.651306e+00</td>\n",
       "      <td>1.516252e+00</td>\n",
       "      <td>1.415866e+00</td>\n",
       "      <td>1.380244e+00</td>\n",
       "      <td>1.332269e+00</td>\n",
       "      <td>1.237091e+00</td>\n",
       "      <td>1.194351e+00</td>\n",
       "      <td>1.098630e+00</td>\n",
       "      <td>1.088848e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.709237e-01</td>\n",
       "      <td>7.345227e-01</td>\n",
       "      <td>7.257003e-01</td>\n",
       "      <td>6.244592e-01</td>\n",
       "      <td>6.056460e-01</td>\n",
       "      <td>5.212772e-01</td>\n",
       "      <td>4.822262e-01</td>\n",
       "      <td>4.036318e-01</td>\n",
       "      <td>3.300827e-01</td>\n",
       "      <td>250.119670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 V1            V2            V3            V4            V5  \\\n",
       "Mean   1.165980e-15  3.416908e-16 -1.373150e-15  2.086869e-15  9.604066e-16   \n",
       "StDev  1.958692e+00  1.651306e+00  1.516252e+00  1.415866e+00  1.380244e+00   \n",
       "\n",
       "                 V6            V7            V8            V9           V10  \\\n",
       "Mean   1.490107e-15 -5.556467e-16  1.177556e-16 -2.406455e-15  2.239751e-15   \n",
       "StDev  1.332269e+00  1.237091e+00  1.194351e+00  1.098630e+00  1.088848e+00   \n",
       "\n",
       "          ...               V20           V21           V22           V23  \\\n",
       "Mean      ...      6.406703e-16  1.656562e-16 -3.444850e-16  2.578648e-16   \n",
       "StDev     ...      7.709237e-01  7.345227e-01  7.257003e-01  6.244592e-01   \n",
       "\n",
       "                V24           V25           V26           V27           V28  \\\n",
       "Mean   4.471968e-15  5.340915e-16  1.687098e-15 -3.666453e-16 -1.220404e-16   \n",
       "StDev  6.056460e-01  5.212772e-01  4.822262e-01  4.036318e-01  3.300827e-01   \n",
       "\n",
       "           Amount  \n",
       "Mean    88.349619  \n",
       "StDev  250.119670  \n",
       "\n",
       "[2 rows x 29 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scalingFactors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>-8.157366e-16</td>\n",
       "      <td>3.154853e-17</td>\n",
       "      <td>-4.409878e-15</td>\n",
       "      <td>-6.734811e-16</td>\n",
       "      <td>-2.874435e-16</td>\n",
       "      <td>4.168992e-16</td>\n",
       "      <td>-8.767997e-16</td>\n",
       "      <td>-2.423604e-16</td>\n",
       "      <td>3.078727e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>2.754870e-16</td>\n",
       "      <td>1.685077e-17</td>\n",
       "      <td>1.478472e-15</td>\n",
       "      <td>-6.797197e-16</td>\n",
       "      <td>1.234659e-16</td>\n",
       "      <td>-7.659279e-16</td>\n",
       "      <td>3.247603e-16</td>\n",
       "      <td>-2.953495e-18</td>\n",
       "      <td>5.401572e-17</td>\n",
       "      <td>3.202236e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>1.000002e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.879855e+01</td>\n",
       "      <td>-4.403529e+01</td>\n",
       "      <td>-3.187173e+01</td>\n",
       "      <td>-4.013919e+00</td>\n",
       "      <td>-8.240810e+01</td>\n",
       "      <td>-1.963606e+01</td>\n",
       "      <td>-3.520940e+01</td>\n",
       "      <td>-6.130252e+01</td>\n",
       "      <td>-1.222802e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.069146e+01</td>\n",
       "      <td>-4.741907e+01</td>\n",
       "      <td>-1.506565e+01</td>\n",
       "      <td>-7.175446e+01</td>\n",
       "      <td>-4.683638e+00</td>\n",
       "      <td>-1.975033e+01</td>\n",
       "      <td>-5.401098e+00</td>\n",
       "      <td>-5.590660e+01</td>\n",
       "      <td>-4.674612e+01</td>\n",
       "      <td>-3.532294e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-4.698918e-01</td>\n",
       "      <td>-3.624707e-01</td>\n",
       "      <td>-5.872142e-01</td>\n",
       "      <td>-5.993788e-01</td>\n",
       "      <td>-5.010686e-01</td>\n",
       "      <td>-5.766822e-01</td>\n",
       "      <td>-4.478860e-01</td>\n",
       "      <td>-1.746805e-01</td>\n",
       "      <td>-5.853631e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.746334e-01</td>\n",
       "      <td>-3.109433e-01</td>\n",
       "      <td>-7.473476e-01</td>\n",
       "      <td>-2.591784e-01</td>\n",
       "      <td>-5.854676e-01</td>\n",
       "      <td>-6.084001e-01</td>\n",
       "      <td>-6.780717e-01</td>\n",
       "      <td>-1.755053e-01</td>\n",
       "      <td>-1.604440e-01</td>\n",
       "      <td>-3.308401e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>9.245351e-03</td>\n",
       "      <td>3.965683e-02</td>\n",
       "      <td>1.186124e-01</td>\n",
       "      <td>-1.401724e-02</td>\n",
       "      <td>-3.936682e-02</td>\n",
       "      <td>-2.058046e-01</td>\n",
       "      <td>3.241723e-02</td>\n",
       "      <td>1.871982e-02</td>\n",
       "      <td>-4.681169e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.104705e-02</td>\n",
       "      <td>-4.009429e-02</td>\n",
       "      <td>9.345377e-03</td>\n",
       "      <td>-1.792420e-02</td>\n",
       "      <td>6.765678e-02</td>\n",
       "      <td>3.183240e-02</td>\n",
       "      <td>-1.081217e-01</td>\n",
       "      <td>3.325174e-03</td>\n",
       "      <td>3.406368e-02</td>\n",
       "      <td>-2.652715e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>6.716939e-01</td>\n",
       "      <td>4.867202e-01</td>\n",
       "      <td>6.774569e-01</td>\n",
       "      <td>5.250082e-01</td>\n",
       "      <td>4.433465e-01</td>\n",
       "      <td>2.991625e-01</td>\n",
       "      <td>4.611107e-01</td>\n",
       "      <td>2.740785e-01</td>\n",
       "      <td>5.435305e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.725733e-01</td>\n",
       "      <td>2.537392e-01</td>\n",
       "      <td>7.283360e-01</td>\n",
       "      <td>2.364319e-01</td>\n",
       "      <td>7.257153e-01</td>\n",
       "      <td>6.728006e-01</td>\n",
       "      <td>4.996663e-01</td>\n",
       "      <td>2.255648e-01</td>\n",
       "      <td>2.371526e-01</td>\n",
       "      <td>-4.471707e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>1.253351e+00</td>\n",
       "      <td>1.335775e+01</td>\n",
       "      <td>6.187993e+00</td>\n",
       "      <td>1.191874e+01</td>\n",
       "      <td>2.521413e+01</td>\n",
       "      <td>5.502015e+01</td>\n",
       "      <td>9.747824e+01</td>\n",
       "      <td>1.675153e+01</td>\n",
       "      <td>1.419494e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>5.113464e+01</td>\n",
       "      <td>3.703471e+01</td>\n",
       "      <td>1.447304e+01</td>\n",
       "      <td>3.607668e+01</td>\n",
       "      <td>7.569684e+00</td>\n",
       "      <td>1.442532e+01</td>\n",
       "      <td>7.293975e+00</td>\n",
       "      <td>7.831940e+01</td>\n",
       "      <td>1.025434e+02</td>\n",
       "      <td>1.023622e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575 -8.157366e-16  3.154853e-17 -4.409878e-15 -6.734811e-16   \n",
       "std     47488.145955  1.000002e+00  1.000002e+00  1.000002e+00  1.000002e+00   \n",
       "min         0.000000 -2.879855e+01 -4.403529e+01 -3.187173e+01 -4.013919e+00   \n",
       "25%     54201.500000 -4.698918e-01 -3.624707e-01 -5.872142e-01 -5.993788e-01   \n",
       "50%     84692.000000  9.245351e-03  3.965683e-02  1.186124e-01 -1.401724e-02   \n",
       "75%    139320.500000  6.716939e-01  4.867202e-01  6.774569e-01  5.250082e-01   \n",
       "max    172792.000000  1.253351e+00  1.335775e+01  6.187993e+00  1.191874e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -2.874435e-16  4.168992e-16 -8.767997e-16 -2.423604e-16  3.078727e-16   \n",
       "std    1.000002e+00  1.000002e+00  1.000002e+00  1.000002e+00  1.000002e+00   \n",
       "min   -8.240810e+01 -1.963606e+01 -3.520940e+01 -6.130252e+01 -1.222802e+01   \n",
       "25%   -5.010686e-01 -5.766822e-01 -4.478860e-01 -1.746805e-01 -5.853631e-01   \n",
       "50%   -3.936682e-02 -2.058046e-01  3.241723e-02  1.871982e-02 -4.681169e-02   \n",
       "75%    4.433465e-01  2.991625e-01  4.611107e-01  2.740785e-01  5.435305e-01   \n",
       "max    2.521413e+01  5.502015e+01  9.747824e+01  1.675153e+01  1.419494e+01   \n",
       "\n",
       "           ...                V20           V21           V22           V23  \\\n",
       "count      ...       2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean       ...       2.754870e-16  1.685077e-17  1.478472e-15 -6.797197e-16   \n",
       "std        ...       1.000002e+00  1.000002e+00  1.000002e+00  1.000002e+00   \n",
       "min        ...      -7.069146e+01 -4.741907e+01 -1.506565e+01 -7.175446e+01   \n",
       "25%        ...      -2.746334e-01 -3.109433e-01 -7.473476e-01 -2.591784e-01   \n",
       "50%        ...      -8.104705e-02 -4.009429e-02  9.345377e-03 -1.792420e-02   \n",
       "75%        ...       1.725733e-01  2.537392e-01  7.283360e-01  2.364319e-01   \n",
       "max        ...       5.113464e+01  3.703471e+01  1.447304e+01  3.607668e+01   \n",
       "\n",
       "                V24           V25           V26           V27           V28  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   1.234659e-16 -7.659279e-16  3.247603e-16 -2.953495e-18  5.401572e-17   \n",
       "std    1.000002e+00  1.000002e+00  1.000002e+00  1.000002e+00  1.000002e+00   \n",
       "min   -4.683638e+00 -1.975033e+01 -5.401098e+00 -5.590660e+01 -4.674612e+01   \n",
       "25%   -5.854676e-01 -6.084001e-01 -6.780717e-01 -1.755053e-01 -1.604440e-01   \n",
       "50%    6.765678e-02  3.183240e-02 -1.081217e-01  3.325174e-03  3.406368e-02   \n",
       "75%    7.257153e-01  6.728006e-01  4.996663e-01  2.255648e-01  2.371526e-01   \n",
       "max    7.569684e+00  1.442532e+01  7.293975e+00  7.831940e+01  1.025434e+02   \n",
       "\n",
       "             Amount  \n",
       "count  2.848070e+05  \n",
       "mean   3.202236e-16  \n",
       "std    1.000002e+00  \n",
       "min   -3.532294e-01  \n",
       "25%   -3.308401e-01  \n",
       "50%   -2.652715e-01  \n",
       "75%   -4.471707e-02  \n",
       "max    1.023622e+02  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataX.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "correlationMatrix = pd.DataFrame(data=[],index=dataX.columns,columns=dataX.columns)\n",
    "for i in dataX.columns:\n",
    "    for j in dataX.columns:\n",
    "        correlationMatrix.loc[i,j] = np.round(pearsonr(dataX.loc[:,i],dataX.loc[:,j])[0],2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "correlation_file = os.path.sep.join(['', 'datasets', 'credit_card_data', 'correlationMatrix.csv'])\n",
    "correlationMatrix.to_csv(current_path+correlation_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency Percentage')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_classes = pd.value_counts(data['Class'],sort=True).sort_index()\n",
    "ax = sns.barplot(x=count_classes.index, y=tuple(count_classes/len(data)))\n",
    "ax.set_title('Frequency Percentage by Class')\n",
    "ax.set_xlabel('Class')\n",
    "ax.set_ylabel('Frequency Percentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Model Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(dataX, \n",
    "                                    dataY, test_size=0.33, \n",
    "                                    random_state=2018, stratify=dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "190820"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93987"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0017293784718582959"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.sum()/len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0017236426314277506"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.sum()/len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "k_fold = StratifiedKFold(n_splits=5,shuffle=True,random_state=2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "penalty = 'l2'\n",
    "C = 1.0\n",
    "class_weight = 'balanced'\n",
    "random_state = 2018\n",
    "solver = 'liblinear'\n",
    "n_jobs = 1\n",
    "\n",
    "logReg = LogisticRegression(penalty=penalty, C=C, \n",
    "            class_weight=class_weight, random_state=random_state, \n",
    "                            solver=solver, n_jobs=n_jobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Log Loss:  0.10080139188958696\n",
      "CV Log Loss:  0.10490645274118293\n",
      "Training Log Loss:  0.12098957040484648\n",
      "CV Log Loss:  0.11634801169793386\n",
      "Training Log Loss:  0.1074616029843435\n",
      "CV Log Loss:  0.10845630232487576\n",
      "Training Log Loss:  0.10228137039781758\n",
      "CV Log Loss:  0.10321736161148198\n",
      "Training Log Loss:  0.11476012373315266\n",
      "CV Log Loss:  0.1160124452312548\n",
      "Logistic Regression Log Loss:  0.10978811472134588\n"
     ]
    }
   ],
   "source": [
    "trainingScores = []\n",
    "cvScores = []\n",
    "predictionsBasedOnKFolds = pd.DataFrame(data=[],\n",
    "                                        index=y_train.index,columns=[0,1])\n",
    "\n",
    "model = logReg\n",
    "\n",
    "for train_index, cv_index in k_fold.split(np.zeros(len(X_train))\n",
    "                                          ,y_train.ravel()):\n",
    "    X_train_fold, X_cv_fold = X_train.iloc[train_index,:], \\\n",
    "        X_train.iloc[cv_index,:]\n",
    "    y_train_fold, y_cv_fold = y_train.iloc[train_index], \\\n",
    "        y_train.iloc[cv_index]\n",
    "    \n",
    "    model.fit(X_train_fold, y_train_fold)\n",
    "    loglossTraining = log_loss(y_train_fold, \n",
    "                               model.predict_proba(X_train_fold)[:,1])\n",
    "    trainingScores.append(loglossTraining)\n",
    "    \n",
    "    predictionsBasedOnKFolds.loc[X_cv_fold.index,:] = \\\n",
    "        model.predict_proba(X_cv_fold)  \n",
    "    loglossCV = log_loss(y_cv_fold, \n",
    "                         predictionsBasedOnKFolds.loc[X_cv_fold.index,1])\n",
    "    cvScores.append(loglossCV)\n",
    "    \n",
    "    print('Training Log Loss: ', loglossTraining)\n",
    "    print('CV Log Loss: ', loglossCV)\n",
    "    \n",
    "loglossLogisticRegression = log_loss(y_train, \n",
    "                                     predictionsBasedOnKFolds.loc[:,1])\n",
    "print('Logistic Regression Log Loss: ', loglossLogisticRegression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preds = pd.concat([y_train,predictionsBasedOnKFolds.loc[:,1]], axis=1)\n",
    "preds.columns = ['trueLabel','prediction']\n",
    "predictionsBasedOnKFoldsLogisticRegression = preds.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "precision, recall, thresholds = precision_recall_curve(preds['trueLabel'],\n",
    "                                                       preds['prediction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "average_precision = average_precision_score(preds['trueLabel'],\n",
    "                                            preds['prediction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Precision-Recall curve: Average Precision = 0.73')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.step(recall, precision, color='k', alpha=0.7, where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "\n",
    "plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n",
    "          average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(preds['trueLabel'],preds['prediction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "areaUnderROC = auc(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n",
    "plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic: \\\n",
    "          Area under the curve = {0:0.2f}'.format(areaUnderROC))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_estimators = 10\n",
    "max_features = 'auto'\n",
    "max_depth = None\n",
    "min_samples_split = 2\n",
    "min_samples_leaf = 1\n",
    "min_weight_fraction_leaf = 0.0\n",
    "max_leaf_nodes = None\n",
    "bootstrap = True\n",
    "oob_score = False\n",
    "n_jobs = -1\n",
    "random_state = 2018\n",
    "class_weight = 'balanced'\n",
    "\n",
    "RFC = RandomForestClassifier(n_estimators=n_estimators, \n",
    "        max_features=max_features, max_depth=max_depth,\n",
    "        min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf,\n",
    "        min_weight_fraction_leaf=min_weight_fraction_leaf, \n",
    "        max_leaf_nodes=max_leaf_nodes, bootstrap=bootstrap, \n",
    "        oob_score=oob_score, n_jobs=n_jobs, random_state=random_state, \n",
    "        class_weight=class_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Log Loss:  0.0003951763883952557\n",
      "CV Log Loss:  0.014479198936303003\n",
      "Training Log Loss:  0.0004501221178398935\n",
      "CV Log Loss:  0.005712702421375242\n",
      "Training Log Loss:  0.00043128813023860164\n",
      "CV Log Loss:  0.00908372752510077\n",
      "Training Log Loss:  0.0004341676022058672\n",
      "CV Log Loss:  0.013491161736979267\n",
      "Training Log Loss:  0.0004275530435950083\n",
      "CV Log Loss:  0.009963232439211515\n",
      "Random Forests Log Loss:  0.010546004611793962\n"
     ]
    }
   ],
   "source": [
    "trainingScores = []\n",
    "cvScores = []\n",
    "predictionsBasedOnKFolds = pd.DataFrame(data=[],\n",
    "                                        index=y_train.index,columns=[0,1])\n",
    "\n",
    "model = RFC\n",
    "\n",
    "for train_index, cv_index in k_fold.split(np.zeros(len(X_train)),\n",
    "                                          y_train.ravel()):\n",
    "    X_train_fold, X_cv_fold = X_train.iloc[train_index,:], \\\n",
    "        X_train.iloc[cv_index,:]\n",
    "    y_train_fold, y_cv_fold = y_train.iloc[train_index], \\\n",
    "        y_train.iloc[cv_index]\n",
    "    \n",
    "    model.fit(X_train_fold, y_train_fold)\n",
    "    loglossTraining = log_loss(y_train_fold, \\\n",
    "                                model.predict_proba(X_train_fold)[:,1])\n",
    "    trainingScores.append(loglossTraining)\n",
    "    \n",
    "    predictionsBasedOnKFolds.loc[X_cv_fold.index,:] = \\\n",
    "        model.predict_proba(X_cv_fold)  \n",
    "    loglossCV = log_loss(y_cv_fold, \\\n",
    "        predictionsBasedOnKFolds.loc[X_cv_fold.index,1])\n",
    "    cvScores.append(loglossCV)\n",
    "    \n",
    "    print('Training Log Loss: ', loglossTraining)\n",
    "    print('CV Log Loss: ', loglossCV)\n",
    "    \n",
    "loglossRandomForestsClassifier = log_loss(y_train, \n",
    "                                          predictionsBasedOnKFolds.loc[:,1])\n",
    "print('Random Forests Log Loss: ', loglossRandomForestsClassifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = pd.concat([y_train,predictionsBasedOnKFolds.loc[:,1]], axis=1)\n",
    "preds.columns = ['trueLabel','prediction']\n",
    "predictionsBasedOnKFoldsRandomForests = preds.copy()\n",
    "\n",
    "precision, recall, thresholds = precision_recall_curve(preds['trueLabel'],\n",
    "                                                       preds['prediction'])\n",
    "average_precision = average_precision_score(preds['trueLabel'],\n",
    "                                            preds['prediction'])\n",
    "\n",
    "plt.step(recall, precision, color='k', alpha=0.7, where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "\n",
    "plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n",
    "          average_precision))\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(preds['trueLabel'],preds['prediction'])\n",
    "areaUnderROC = auc(fpr, tpr)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n",
    "plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic: \\\n",
    "          Area under the curve = {0:0.2f}'.format(\n",
    "          areaUnderROC))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "params_xGB = {\n",
    "    'nthread':16, #number of cores\n",
    "    'learning rate': 0.3, #range 0 to 1, default 0.3\n",
    "    'gamma': 0, #range 0 to infinity, default 0 \n",
    "        # increase to reduce complexity (increase bias, reduce variance)\n",
    "    'max_depth': 6, #range 1 to infinity, default 6\n",
    "    'min_child_weight': 1, #range 0 to infinity, default 1\n",
    "    'max_delta_step': 0, #range 0 to infinity, default 0\n",
    "    'subsample': 1.0, #range 0 to 1, default 1\n",
    "        # subsample ratio of the training examples\n",
    "    'colsample_bytree': 1.0, #range 0 to 1, default 1 \n",
    "        # subsample ratio of features\n",
    "    'objective':'binary:logistic',\n",
    "    'num_class':1,\n",
    "    'eval_metric':'logloss',\n",
    "    'seed':2018,\n",
    "    'silent':1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-logloss:0.437923+2.07981e-05\ttest-logloss:0.438039+2.79786e-05\n",
      "[50]\ttrain-logloss:0.0001304+9.26499e-06\ttest-logloss:0.0030798+0.000632359\n",
      "[100]\ttrain-logloss:5.36e-05+1.85472e-06\ttest-logloss:0.0033054+0.000701632\n",
      "[150]\ttrain-logloss:3.98e-05+1.16619e-06\ttest-logloss:0.0033992+0.000714492\n",
      "[200]\ttrain-logloss:3.36e-05+1.0198e-06\ttest-logloss:0.0034614+0.000731778\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ankur_\\Anaconda3\\envs\\unsupervisedLearning\\lib\\site-packages\\numpy\\core\\fromnumeric.py:51: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n",
      "will be corrected to return the positional minimum in the future.\n",
      "Use 'series.values.argmin' to get the position of the minimum now.\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Log Loss:  0.0008836359029379572\n",
      "CV Log Loss:  0.0039039689005126712\n",
      "[0]\ttrain-logloss:0.437948+2.04216e-05\ttest-logloss:0.438052+0.000128319\n",
      "[50]\ttrain-logloss:0.0001512+7.80769e-06\ttest-logloss:0.0034364+0.000861248\n",
      "[100]\ttrain-logloss:5.98e-05+2.63818e-06\ttest-logloss:0.0037256+0.000870639\n",
      "[150]\ttrain-logloss:4.4e-05+1.54919e-06\ttest-logloss:0.0038536+0.000859705\n",
      "[200]\ttrain-logloss:3.7e-05+1.54919e-06\ttest-logloss:0.0039128+0.000865355\n",
      "Training Log Loss:  0.000991403023630954\n",
      "CV Log Loss:  0.0021829197781246894\n",
      "[0]\ttrain-logloss:0.437958+1.92832e-05\ttest-logloss:0.438062+6.62311e-05\n",
      "[50]\ttrain-logloss:0.0001388+7.70454e-06\ttest-logloss:0.0033034+0.000974077\n",
      "[100]\ttrain-logloss:5.74e-05+2.72764e-06\ttest-logloss:0.0035838+0.00112909\n",
      "[150]\ttrain-logloss:4.24e-05+1.85472e-06\ttest-logloss:0.0036884+0.00118733\n",
      "[200]\ttrain-logloss:3.6e-05+1.41421e-06\ttest-logloss:0.0037368+0.00122368\n",
      "Training Log Loss:  0.0007905643530946553\n",
      "CV Log Loss:  0.002716246122009492\n",
      "[0]\ttrain-logloss:0.437929+2.39115e-05\ttest-logloss:0.438063+9.60096e-05\n",
      "[50]\ttrain-logloss:0.000129+4.3359e-06\ttest-logloss:0.0030022+0.00070503\n",
      "[100]\ttrain-logloss:5.46e-05+1.74356e-06\ttest-logloss:0.0032642+0.000798811\n",
      "[150]\ttrain-logloss:4.08e-05+1.16619e-06\ttest-logloss:0.003353+0.000826868\n",
      "[200]\ttrain-logloss:3.48e-05+7.48331e-07\ttest-logloss:0.0034076+0.000835515\n",
      "Training Log Loss:  0.0005697291437183928\n",
      "CV Log Loss:  0.003359532657256653\n",
      "[0]\ttrain-logloss:0.437943+2.001e-05\ttest-logloss:0.438082+5.33067e-05\n",
      "[50]\ttrain-logloss:0.000151+1.04499e-05\ttest-logloss:0.0032488+0.000701945\n",
      "[100]\ttrain-logloss:6.02e-05+2.48193e-06\ttest-logloss:0.0035832+0.000785033\n",
      "[150]\ttrain-logloss:4.38e-05+1.72047e-06\ttest-logloss:0.003688+0.000817591\n",
      "[200]\ttrain-logloss:3.68e-05+1.16619e-06\ttest-logloss:0.0037638+0.000841582\n",
      "Training Log Loss:  0.0009231702977125061\n",
      "CV Log Loss:  0.002620785686174852\n",
      "XGBoost Gradient Boosting Log Loss:  0.0029566906288156715\n"
     ]
    }
   ],
   "source": [
    "trainingScores = []\n",
    "cvScores = []\n",
    "predictionsBasedOnKFolds = pd.DataFrame(data=[],\n",
    "                                    index=y_train.index,columns=['prediction'])\n",
    "\n",
    "for train_index, cv_index in k_fold.split(np.zeros(len(X_train)),\n",
    "                                          y_train.ravel()):\n",
    "    X_train_fold, X_cv_fold = X_train.iloc[train_index,:], \\\n",
    "        X_train.iloc[cv_index,:]\n",
    "    y_train_fold, y_cv_fold = y_train.iloc[train_index], \\\n",
    "        y_train.iloc[cv_index]\n",
    "    \n",
    "    dtrain = xgb.DMatrix(data=X_train_fold, label=y_train_fold)\n",
    "    dCV = xgb.DMatrix(data=X_cv_fold)\n",
    "    \n",
    "    bst = xgb.cv(params_xGB, dtrain, num_boost_round=2000, \n",
    "                 nfold=5, early_stopping_rounds=200, verbose_eval=50)\n",
    "    \n",
    "    best_rounds = np.argmin(bst['test-logloss-mean'])\n",
    "    bst = xgb.train(params_xGB, dtrain, best_rounds)\n",
    "    \n",
    "    loglossTraining = log_loss(y_train_fold, bst.predict(dtrain))\n",
    "    trainingScores.append(loglossTraining)\n",
    "    \n",
    "    predictionsBasedOnKFolds.loc[X_cv_fold.index,'prediction'] = \\\n",
    "        bst.predict(dCV)\n",
    "    loglossCV = log_loss(y_cv_fold, \\\n",
    "        predictionsBasedOnKFolds.loc[X_cv_fold.index,'prediction'])\n",
    "    cvScores.append(loglossCV)\n",
    "    \n",
    "    print('Training Log Loss: ', loglossTraining)\n",
    "    print('CV Log Loss: ', loglossCV)\n",
    "    \n",
    "loglossXGBoostGradientBoosting = \\\n",
    "    log_loss(y_train, predictionsBasedOnKFolds.loc[:,'prediction'])\n",
    "print('XGBoost Gradient Boosting Log Loss: ', loglossXGBoostGradientBoosting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NTpzwNoZTp+Dpp2HbNm/j8MoH2T2iLfcdBZrjXGwjUTj0TFGOMx++tsUdd1aiEpEuQBeAihUrZp5sTN62ciXU9vs8ROOFmjUhTLp5iweqLF3KWwcOwJEjXocTtHBIVP4el+63fKqq43AebUyDBg0iswxrQictDTZs8K7KZexY+OkniPGojdL8+adfX3CBNzGkO3kSkpKgXz9v4/BK5cpwhbd3MKZPn06ZMmVo1KgRAMP37CEuLo6iRYN5AHp4CIdEtQWo4DNcHud5SibabN8O//43HD0amuW/9FJolhtppkyB22/3Ogrjkd9++42ePXsyY8YM6tSpw6JFi4iLi6NEiRJeh3bewiFRzQB6iMhknEYUB+z+VITavBnWrMl6+vXX504chQtDmTK5s67MYmPhrbe8K1VVqADly3uzbuOp1NRUhg0bxnPPPceRI0coUqQIXbp0IcarYzEHhTxRicgkoBlQUkS2AM8CcQCqmgx8DtwMrAOOAJ1CHZMJgmpgVWnTpkHbtoEt8/rr4Zpr/lxcWUlMhDZtQrNsY8LU3Llz6dq1K8uXLwegXbt2DB06lLJly3ocWc7IjVZ/HbKZrsDDoY4j6n37LWzcmLPLPHoUevQI/n0tWmQ9rVkzGDDgvEMyxpzp8OHD3HLLLezdu5fKlSszevRoWrZs6XVYOSocqv5MsFRh0ybYsQOeeAJWrYKdO0O7zkCqD9LSYO1aqFYttLEYk8epKmlpacTGxlKoUCEGDx7MunXr6N+/PwULFvQ6vBxniSrcfPYZLFx47nmefz7raZ1yuOZUBDp0gOuuy9nlGmPOy5o1a+jWrRstW7akn9uqslNOn/dhxhKVl06dgpQUOHYM5s2DgQPh8OHgllG6NNx7L7RqBY0aed8s2RgTEkePHmXQoEG88sornDhxgvXr19OrVy/y58/vdWghZ4nKC2vWwLBhkJyc9TzPPnvuZVSq5CQo8fczNGNMNPnqq6/o3r0769evB+CBBx7glVdeyRNJCixR5Y6TJ+HKK50qvZgY/63orr7auff03HPO63z20RiT1/3xxx907tyZyZMnA5CYmEhycjJNmzb1OLLcZVfDnHTyJPz+u/P6hx/gzTed39XMmnV6Ht8k1auX0wrub3+zxGSMOUvBggXZvHkz8fHxPPvss/Tu3TvPlKJ82dUxJ3z/vdN1TnbNrosUgb17neo6Ee9+FGqMCVuLFy/moosuolKlSsTExPD2228TFxdHpTDpN9ALlqiys3MnrFvnvH7ySacbIN/7Qqqnp6crW9aZ59AhGDUK/vIXiI+Hxo2dEpYxxmRy6NAhnnnmGYYPH86NN97IZ599hohQzX7uYYnKL1V49VXn90nvZvUYLT+eesppxn3ttaGLzRgTVVSVadOm0bNnT7Zu3UpMTAw1atTg5MmTxMXFeR1eWLBEldn69VC16tnjmzRx/lepAs88c/b0Sy6xpuHGmKBs3LiRHj168NlnnwHQsGFDxo4dy2WXXeZxZOHFElW6WbOcUtRXX50ed9FFMGiQU0KqXt272IwxUefgwYPUq1ePAwcOcOGFFzJo0CAeeughYu32wFnybqJatOh0a7zU1LMbQgwbBo8+mvtxGWPyhAsvvJCHH36YDRs2MGTIEMp41eN/BMibiWrzZmjQwP+0CROc+0zlyuVqSMaY6LZnzx6eeOIJbrjhBtq1awfAiy++GBWP4Qi1vJGoNm6EkSOdrorAaYmXrlcvSL9h2aqV82NbY4zJIarKO++8Q9++fdmzZw/ffPMNt912G/ny5bMkFaDoS1SHDzuPvEhNdYZ374aHHvI/77hx0Llz7sVmjMlTVq9eTbdu3fj+++8BuPbaaxk9ejT57Af+QYmuvZWaCt26wXvv+Z/esSM0bOi8TkiA5s1zLzZjTJ5x7NgxBg4cyKuvvkpqaioXX3wxQ4YM4a677kKsf86gRU+imjYNbrvt9HB8PPg+PKx7d3tUhTEmV6gqkydPJjU1lS5dujBo0CCKFy/udVgRK7IT1V13wcqVTu8R27efHl+njtOt0UUXeRebMSZP2bZtGwULFqRYsWLEx8fz9ttvExsby5VXXul1aBEvcu/kHT0KEyfC0qVnJqk5c2DZMktSxphccerUKUaMGEHNmjXp379/xvi//vWvlqRySOSWqNauPf168WKnb71atSAP9ixsjPHGokWLeOihh1i0aBHglKpOnTplP9rNYZFbojp50vl/441Qrx7UrWtJyhiTKw4cOEDPnj1p1KgRixYtokKFCnzyySdMmzbNklQIRG6JKp3bR5YxxuSGvXv3UqdOHbZt20ZsbCyPPfYYzz33HIULF/Y6tKgV2YnqiivssRnGmFxVvHhxmjVrxoYNG0hOTqZu3bpehxT1IjtRVazodQTGmCh34sQJBg8eTPPmzWncuDEAY8eOpWDBgtazRC6J7ERVooTXERhjotj3339Pt27dWL16NUlJSSxevJiYmBir5stlkf11IDHR6wiMMVFo9+7ddOrUiWbNmrF69WqqVavGkCFDrATlkcje6/b0S2NMDkpLS2P8+PHUqFGDCRMmkD9/fp577jmWLVtGixYtvA4vz4rsqr+yZb2OwBgTRfbu3Uvfvn3Zt28fLVq0YPTo0VS3h6Z6LrITVbVqXkdgjIlwR44cIS4ujri4OEqWLMnIkSMB6NChg3UgGyYiu+rvwgu9jsAYE8E+++wzEhMTGTp0aMa4O++8kzvvvNOSVBiJ7ERVsqTXERhjItCWLVv4xz/+QatWrdi4cSPTp08nLS3N67BMFiI7UVmXScaYIJw8eZJhw4ZRq1YtPv74YwoVKsSQIUP4/vvvrUVfGMuVe1Qi0hJ4A4gF3lLVlzNNrwi8AxRz53lSVT8/50LtoDLGBGHnzp20bNmSxYsXA9CmTRuGDx9OhQoVPI7MZCfkV3sRiQVGATcBCUAHEUnINNsA4ENVvQy4Axid7YKtmG6MCULJkiUpWLAgFStWZPr06UybNs2SVITIjRJVI2Cdqm4AEJHJQGtglc88CqS3jCgKbMt2qVaiMsacg6rywQcf0KhRI6pUqUJMTAyTJk2iePHiFCpUyOvwTBBy42pfDtjsM7zFHefrOeBuEdkCfA484m9BItJFRFJEJCXNWuQYY7Kwbt06WrZsSYcOHejRoweqCkCFChUsSUWg3EhU/jKKZhruAExQ1fLAzcB7InJWbKo6TlUbqGoDu/FpjMns+PHjDBw4kNq1a/PVV19x0UUXceutt3odlvmTcqPqbwvgWxFcnrOr9h4AWgKo6jwRKQCUBHbmQnzGmCgwe/ZsunXrxpo1awDo2LEjr732GqVKlfI4MvNn5UaiWghUE5HKwFacxhJ3ZppnE9ACmCAitYACwK5ciM0YEwW2b99Oy5YtOXHiBDVq1GDMmDFce+21XodlckjIE5WqnhSRHsBMnKbn41V1pYi8AKSo6gzgMeBNEemNUy14n6ZXKhtjjB9paWmICCJCmTJlGDBgACLC448/zgUXXOB1eCYHSaTmgwb582vKiRNeh2GM8cDy5cvp2rUrjz76KO3atfM6nIgiIotUtYHXcQQjsjulNcbkKYcPH+b5559nyJAhnDp1imPHjtG2bVvrly/KWdM5Y0xE+L//+z8SEhJ47bXXSEtLo0ePHnz77beWpPIAK1EZY8La7t276dy5M5988gkAl112GWPHjqVhw4YeR2Zyi5WojDFhrVChQixfvpzChQszbNgwFixYYEkqj7ESlTEm7Pz888/UqFGDYsWKER8fzwcffEDp0qUpX76816EZD1iJyhgTNvbt20e3bt1o0qQJ/fv3zxhfv359S1J5mJWojDGeU1UmTZpE79692blzJ/ny5aNYsWKoqjWWMMElKhHJD1RU1XUhiscYk8esXbuW7t27M2vWLACaNm1KcnIyiYmJHkdmwkXAVX8i8jdgOfC1O1xPRKaFKjBjTPT77bffSEpKYtasWZQoUYLx48fz/fffW5IyZwimRPUCcAUwG0BVl4hI1ZBEZYzJEy655BJuv/128ufPz6uvvkrJkiW9DsmEoWAaU6Sq6v5M4yKz/yVjjCd27NjB3Xffzbx58zLGvfPOO4wfP96SlMlSMCWq1SLSDohxe0J/FJgfmrCMMdEkLS2NcePG8eSTT3LgwAHWrVvHvHnzEBFiY2O9Ds+EuWBKVD2A+kAa8DFwDCdZGWNMlpYuXcqVV15Jt27dOHDgAC1btmTixInWms8ELJhEdaOqPqGql7l/TwI3hSowY0xk++OPP3jssceoX78+P//8M2XLlmXKlCl8/vnnVKlSxevwTAQJJlEN8DOuv59xxhjDkSNHGD9+PKpKz549Wb16NbfffruVpEzQsr1HJSI34jwmvpyIDPGZdCFONaAxxgCwadMmypQpQ1xcHKVKleLtt9+mQoUK1K9f3+vQTAQLpES1E1iBc09qpc/fV1jVnzEGSE1N5bXXXqNWrVoMHTo0Y3ybNm0sSZk/LdsSlaouBhaLyH9U9VguxGSMiSBz586la9euLF++HIBffvnF44hMtAnmHlU5EZksIstEZG36X8giM8aEtb1799KlSxeuuuoqli9fTpUqVfjiiy8YP36816GZKBPM76gmAAOBwThVfp2we1TG5Enr1q3jyiuvZNeuXcTFxdGvXz/69+9PfHy816GZKBRMoiqoqjNFZLCqrgcGiMiPoQrMGBO+qlSpQvXq1UlISGDMmDHUqlXL65BMFAsmUR0Xp13pehHpCmwFSoUmLGNMODl69Cgvv/wy9957L1WqVCEmJoYZM2Zw0UUXWXNzE3LBJKreQGGgJ/AvoChwfyiCMsaEj6+++oru3buzfv16Fi1axKeffgpA8eLFPY7M5BUBJypV/dl9eQi4B0BE7JGbxkSp7du306dPHyZPngxAYmIiTz75pMdRmbwooFZ/ItJQRNqISEl3OFFE3sU6pTUm6pw6dYrRo0dTs2ZNJk+eTHx8PK+88gqLFy+madOmXodn8qBsE5WIDAL+A9wFfCki/XGeSbUUqB7a8M4ZmGerNiaabdiwgV69enHw4EH+9re/sWrVKvr160dcXJzXoZk8KpCqv9ZAXVU9KiLFgW3u8JrQhmaMyS2HDx+mYMGCiAjVqlXjlVdeoVKlSrRp08YaSxjPBVL1d0xVjwKo6l7gF0tSxkQHVeWjjz6iRo0aTJkyJWN87969ufXWWy1JmbAQSImqioh87L4WoJLPMKp6W0giM8aE1K+//kqPHj34/PPPAZg4cSLt2rXzOCpjzhZIovpHpuGRoQjEGJM7Tpw4wZAhQ3jhhRc4evQoRYsWZdCgQXTp0sXr0IzxK5BOaWflRiDGmNBbv349rVu3ZuXKlQB06NCBIUOG8Je//MXjyIzJWjA/+DXGRLiyZcty7NgxqlatyujRo7n++uu9DsmYbAXTe/p5E5GWIrJGRNaJiN9fDIpIOxFZJSIrRWRibsRlTLRTVd577z32798PQHx8PJ999hnLly+3JGUiRtCJSkQuCHL+WGAUTo/rCUAHEUnINE814J/AVaqaCPQKNi5jzJlWrVpFs2bN6NixI/37988YX6NGDQoUKOBhZMYEJ+BEJSKNRGQ58D93uK6IjAjgrY2Adaq6QVVPAJNxfpvlqzMwSlX3AajqzkDjMsac6ciRI/Tv35969erxww8/cPHFF3PllVd6HZYx5y2YEtVwoBWwB0BVlwLXBvC+csBmn+Et7jhf1YHqIjJHROaLSMsg4jLGuL788ktq167NSy+9RGpqKg899BBr1qzhrrvu8jo0Y85bMI0pYlT1t0w/ADwVwPv8/WJQ/cRRDWgGlAd+FJHaqrr/jAWJdAG6AFwWGxtg2MbkDcuWLeOmm24CoE6dOowdO5YmTZp4HJUxf14wiWqziDQC1L3v9AgQyKPotwAVfIbL43TDlHme+aqaCvwqImtwEtdC35lUdRwwDqDBBRdkTnbG5DmqmtF7RFJSEl26dKF69er07NnT+uYzUSOYqr9uQB+gIrADaOyOy85CoJqIVBaR/MAdwIxM83yCW43o9tBeHdgQRGzG5DkpKSk0atSIefPmZYwbO3Ysjz32mCUpE1WCSVQnVfUOVS3p/t2hqruze5OqngR6ADOB1cCHqrrKDan2AAAf10lEQVRSRF4Qkb+7s80E9ojIKpye2R9X1T1BbosxecKBAwd45JFHaNSoESkpKQwcONDrkIwJKVENrAZNRNYDa4APgI9V9VAoA8tOgwsu0JTjx70MwZhcpapMmTKFXr16sX37dmJjY+nduzfPPvsshQsX9jo8EyFEZJGqNvA6jmAE84TfS0XkSpyqu+dFZAkwWVUnhyw6YwwAW7ZsoXPnznz55ZcANG7cmOTkZOrWretxZMaEXlA/+FXVuaraE7gcOIjzQEVjTIjlz5+f+fPnU6xYMcaOHcucOXMsSZk8I+ASlYgUxvmh7h1ALWA6YL8iNCZE5s6dS8OGDYmLi6NUqVJ89NFHJCYmUrp0aa9DMyZXBVOiWoHT0u9VVa2qqo+p6s8hisuYPGvXrl3cd999XHXVVQwdOjRjfPPmzS1JmTwpmN9RVVHVtJBFYkwel5aWxttvv02/fv3Yu3cv+fPnJ9DGTsZEs2wTlYi8rqqPAR+JyFlnjT3h15g/b8WKFXTr1o2ffvoJgOuuu47Ro0dTrVo1jyMzxnuBlKg+cP/bk32NCYGUlBSaNGnCyZMnKV26NEOHDuWOO+4gU3dlxuRZgTzhd4H7spaqnpGsRKQHYE8ANuZPuPzyy7nyyitJTEzkpZdeolixYl6HZExYCaYxxf1+xj2QU4EYk1ds2bKFO++8kw0bnF7CYmJi+Oabbxg9erQlKWP8COQeVXucJumVReRjn0lFgP3+32WMyezkyZOMHDmSp59+mj/++IMTJ04wdepUAOubz5hzCOQe1QKcZ1CVx3lSb7pDwOJQBGVMtFmwYAEPPfQQS5YsAeC22247o+m5MSZrgdyj+hX4Ffgm9OEYE13279/PU089RXJyMqpKxYoVGTVqFK1atfI6NGMiRrb3qETke/f/PhHZ6/O3T0T2hj5EYyLXtm3bePPNN4mNjaVfv36sWrXKkpQxQQqk6i/9cfMlQxmIMdFiy5YtlCtXDhEhISGB5ORkGjVqRJ06dbwOzZiIlG2Jyqc3igpArKqeApoADwGFQhibMRHl+PHjvPjii1StWpUpU6ZkjH/ggQcsSRnzJwTTPP0TnMfQXwq8i9Mx7cSQRGVMhJk9ezZ169blmWee4fjx4yxcuNDrkIyJGsEkqjRVTQVuA4ap6iNAudCEFQD71b4JAzt37qRjx440b96cNWvWUKNGDWbPns1rr73mdWjGRI1gOqU9KSJtgXuANu44+/GHybNSUlK44YYb2LdvHwUKFGDAgAH07duXCy64wOvQjIkqwSSq+4HuOI/52CAilYFJoQnLmPCXmJhI8eLFadiwIaNHj+bSSy/1OiRjopIE8xgBEckHVHUH16nqyZBEFYAGBQpoyrFjXq3e5EGHDx/m1VdfpXfv3hldHe3YsYNSpUpZB7ImYojIIlVt4HUcwQjmCb9/Bd4DtgIC/EVE7lHVOaEKzphwMWPGDB555BE2bdrE7t27GTXK6aTFHmRoTOgFU/U3FLhZVVcBiEgtnMQVUZnZmGBs2rSJnj17Mn36dMDp6bxTp04eR2VM3hJMq7/86UkKQFVXA/lzPiRjvJeamsrrr79OQkIC06dPp0iRIrzxxhssWLCABg3su5kxuSmYEtV/RWQsTikK4C6sU1oTpebPn0/fvn0BuP322xk2bBjlynn3awxj8rKAG1OISAGgJ9AU5x7VD8AIVfWkRYM1pjA57dixYxQoUCBjuF+/fjRr1oybb77Zw6iMyVmR2JgioEQlInWAS4GVqvq/kEcVAEtUJqeoKhMnTqRv3758/PHHNGnSxOuQjAmZSExUgfSe/hRO90l3AV+LiL8n/RoTkdauXcv111/P3Xffze+//86ECRO8DskYk0kgjSnuApJUtS3QEOgW2pCMCb1jx47x3HPPUadOHWbNmkWJEiUYP348Y8aM8To0Y0wmgTSmOK6qhwFUdZeIBNNS0Jiws3TpUtq2bcv//ufUYt9///288sorlCxpT7IxJhwFkqiqiMjH7msBLvUZRlVvC0lkxoRImTJl2LVrFwkJCYwZM4arr77a65CMMecQSKL6R6bhkaEIxJhQSUtLY9KkSbRr1464uDhKlSrFt99+S2JiIvnz208BjQl32SYqVZ2VG4EYEwpLliyha9eu/Pzzz2zdupV+/foBcNlll3kcmTEmUHa/yUSlQ4cO0adPH+rXr8/PP/9M2bJlqVatmtdhGWPOQ64kKhFpKSJrRGSdiDx5jvluFxEVkYhq42/Ch6oybdo0EhISGDp0KAA9e/Zk9erV3HrrrR5HZ4w5H8F0oQSAiFygqseDmD8WGAVcD2wBForIDN9+A935iuD0fPFzsDEZk27mzJncdpvTvqdBgwYkJydTv359j6MyxvwZAZeoRKSRiCwH/ucO1xWREQG8tRHOs6s2qOoJYDLQ2s98LwKvAtbdhAmKb+8qN9xwAzfffDMjRoxg/vz5lqSMiQLBVP0NB1oBewBUdSlwbQDvKwds9hne4o7LICKXARVU9dNzLUhEuohIioiknDp1KojQTbSaM2cOjRs3ZsOGDQDExMTw6aef0qNHD2JjYz2OzhiTE4JJVDGq+lumcYFkC3+PPs34Cuz+gHgo8Fh2C1LVcaraQFUb2EUob9u7dy+dO3emadOmLFiwgEGDBmVMs6ftGhNdgklUm0WkEaAiEisivYC1AbxvC1DBZ7g8sM1nuAhQG/hORDYCjYEZ1qDC+KOqvPvuu9SoUYO33nqLuLg4BgwYwPDhw70OzRgTIsE0puiGU/1XEdgBfENg/f4tBKqJSGWcx9jfAdyZPlFVDwAZfdeIyHdAX1VNCSI2kwesW7eOzp0789133wFwzTXXMGbMGGrVquVtYMaYkAo4UanqTpwkExRVPSkiPYCZQCwwXlVXisgLQIqqzgh2mSZvOnHiBD/99BMlS5bk9ddf55577rFqPmPygGAenPgmPveW0qlql5wOKhD2PKq8YeHChTRo0CAjIU2bNo2rr76aEiVKeByZMZEpKp9H5eMbYJb7NwcoBQT8eypjgrF9+3Y6dOhAo0aNmDJlSsb4W2+91ZKUMXlMMFV/H/gOi8h7wNc5HpHJ006dOkVycjJPPfUUBw8eJD4+nn379nkdljHGQ0H3TOGjMnBJTgVizH//+1+6du3KwoULAWjVqhUjRoygUqVK3gZmjPFUwIlKRPZx+h5VDLAXyLLfPmOC8cUXX9CqVSvS0tIoV64cI0aMoE2bNtZYwhgTWKIS52pRF6d5OUCaBtoKw5gANGvWjGrVqnHzzTfz/PPPU6RIEa9DMsaEiYAaU7hJaZqqnnL/LEmZP+XXX3+lY8eO7N+/H4D4+HiWLFnCkCFDLEkZY84QTKu/BSJyecgiMXnCiRMnePnll0lMTOS9997j+eefz5hWoEABDyMzxoSrbKv+RCSfqp4EmgKdRWQ9cBinDz9VVW+Sl927iDg//vgj3bp1Y+XKlQDceeedPPHEEx5HZYwJd4Hco1oAXA60CXEsJkrt2bOHfv36MX78eACqVq3KmDFjuO666zyOzBgTCQJJVAKgqutDHIuJUkuWLGH8+PHkz5+ff/7znzz55JNWzWeMCVggiepiEemT1URVHZKD8ZgosWPHDkqXLg1AixYteOWVV2jdujU1atTwODJjTKQJpDFFLFAY53Ec/v6MyXDkyBGeeuopKlasyLx58zLG9+vXz5KUMea8BFKi2q6qL4Q8EhPxvvjiCx5++GF+/fVXAH744QeaNGnicVTGmEgX8D0qY7KydetWevXqxdSpUwFISkoiOTnZkpQxJkcEkqhahDwKE7G++uorbr/9dg4dOkShQoV4/vnnefTRR8mX7890I2mMMadlezVR1b25EYiJTPXq1SM2NpY2bdrwxhtvULFiRa9DMsZEmWB6pjCGAwcOMHDgQFJTUwEoVaoUy5cvZ9q0aZakjDEhYfUzJiCqyocffkivXr34/fffyZ8/P/369QOgfPnyHkdnjIlmlqhMttavX8/DDz/MzJkzAWjSpAktW7b0OCpjTF5hVX8mS8ePH2fgwIHUrl2bmTNnUqxYMcaOHctPP/1EUlKS1+EZY/IIK1GZLH388cc8/fTTANx9990MHjw4o7cJY4zJLZaozBlSU1OJi4sDoH379sycOZOOHTvSvHlzjyMzxuRVVvVnAEhLS+Ott96icuXKbNiwAYCYmBgmTJhgScoY4ylLVIYVK1Zw9dVX07lzZ7Zu3cqECRO8DskYYzJYosrDDh8+zBNPPMFll13GnDlzKF26NBMnTjzjqbvGGOM1u0eVR/3000/cfffd/Pbbb4gI3bt351//+hfFihXzOjRjjDmDJao8qnjx4mzbto169eqRnJzMFVdc4XVIxhjjl1X95REnT55k6tSpqCoACQkJzJ49m4ULF1qSMsaENUtUecCCBQto2LAhbdu2ZcqUKRnjr7rqKuvl3BgT9ixRRbH9+/fTvXt3GjduzJIlS7jkkkvsHpQxJuJYoopCqsqkSZOoWbMmY8aMITY2lieeeIKVK1dyww03eB2eMcYExep9otCECRO4//77Aad6b8yYMdSpU8fjqIwx5vzkSolKRFqKyBoRWSciT/qZ3kdEVonIMhGZJSKX5EZc0eqOO+6gUaNGvPXWW/zwww+WpIwxES3kiUpEYoFRwE1AAtBBRBIyzbYYaKCqScBU4NVQxxVNvv32W6655hr2798PQHx8PPPnz+eBBx4gJsZqd40xkS03rmKNgHWqukFVTwCTgda+M6jqbFU94g7OB+xJfAHYuXMn99xzDy1atOCHH35g2LBhGdNExMPIjDEm5+RGoioHbPYZ3uKOy8oDwBf+JohIFxFJEZGUk6dO5WCIkSUtLY1x48ZRo0YN3n//fQoUKMDAgQN56qmnvA7NGGNyXG40pvD31V79zihyN9AAuMbfdFUdB4wDaBAf73cZ0W7FihV06dKFefPmAXDjjTcyatQoLr30Uo8jM8aY0MiNRLUFqOAzXB7YlnkmEbkO6A9co6rHcyGuiPT7778zb948ypQpw7Bhw2jbtq1V8xljolpuVP0tBKqJSGURyQ/cAczwnUFELgPGAn9X1Z25EFNEWbZsWcbr6667jgkTJrB69WratWtnScoYE/VCnqhU9STQA5gJrAY+VNWVIvKCiPzdne01oDAwRUSWiMiMLBZ3Wh64QG/atIk2bdpQt27djKo+gHvvvZeiRYt6GJkxxuSeXPnBr6p+DnyeadwzPq+vy404IkVqaipvvPEGzz77LEeOHKFIkSJs2rSJJk2aeB2aMcbkOuuZIszMmzePrl27ZlT3tW3blqFDh1Ku3LkaShpjTPSyRBVG3nnnHe677z4AKleuzMiRI7n55pu9DcoYYzxmiSqM3HTTTZQqVYoHH3yQ/v37U7BgQa9DMsYYz1mi8tCaNWsYOnQoI0aMIC4ujlKlSrFhwwYKFSrkdWjGGBM2rCM4Dxw7doxnn32WpKQkxo4dy4gRIzKmWZIyxpgzWYkql3399dd0796ddevWAXD//ffTsWNHj6MyxpjwZYkql/z+++/06dOHSZMmAZCQkMCYMWO4+uqrPY7MGGPCm1X95ZLPP/+cSZMmER8fz6BBg1i8eLElKWOMCYCVqEJo7969FC9eHID77ruPtWvX8tBDD1G5cmWPIzPGmMhhJaoQOHToEH369KFSpUr8+uuvAMTExPDyyy9bkjLGmCBZospBqsq0adNISEhg6NChHD58mG+//dbrsIwxJqJZ1V8O2bhxI4888giffvopAA0aNGDs2LFcfvnlHkdmjDGRzUpUOeCDDz4gISGBTz/9lAsvvJCRI0cyf/58S1LGGJMDrESVA+rUqcPJkydp3749Q4YMoWzZsl6HZEzYS01NZcuWLRw7dszrUKJSgQIFKF++PHFxcV6H8qdZojoPe/fu5d133+XRRx9FREhISGD16tX2OHhjgrBlyxaKFClCpUqV7AGgOUxV2bNnD1u2bImKBlxW9RcEVeXdd9+lRo0a9O7dmylTpmRMsyRlTHCOHTtGiRIlLEmFgIhQokSJqCmtWokqQL/88gvdunXju+++A6BZs2YkJSV5G5QxEc6SVOhE0761ElU2jh49ytNPP01SUhLfffcdJUuW5J133uHbb7+lZs2aXodnjDFRzxJVNkaNGsXAgQNJTU2lc+fOrFmzho4dO0bVtxVj8qrY2Fjq1atH7dq1ueWWW9i/f3/GtJUrV9K8eXOqV69OtWrVePHFF1HVjOlffPEFDRo0oFatWtSsWZO+fft6sQl5giUqP06dOpXx+uGHH6Z169b89NNPjBs3LqNLJGNM5IuPj2fJkiWsWLGC4sWLM2rUKMCpSfn73//Ok08+ydq1a1m6dClz585l9OjRAKxYsYIePXrw/vvvs3r1alasWEGVKlVyNLaTJ0/m6PIimSUqH6dOnWLUqFEkJiZmfLOKj4/nk08+4aqrrvI4OmOimEho/oLQpEkTtm7dCsDEiRO56qqruOGGGwAoWLAgI0eO5OWXXwbg1VdfpX///hnV//ny5aN79+5nLfOPP/6gU6dO1KlTh6SkJD766CMAChcunDHP1KlTue+++wCnT9A+ffpw7bXX8vjjj1OpUqUzSnlVq1Zlx44d7Nq1i3/84x80bNiQhg0bMmfOnKC2NdJYYwrXf//7X7p27crChQsBmDRpEt26dfM4KmNMbjh16hSzZs3igQceAJxqv/r1658xz6WXXsoff/zBwYMHWbFiBY899li2y33xxRcpWrQoy5cvB2Dfvn3Zvmft2rV88803xMbGkpaWxrRp0+jUqRM///wzlSpVonTp0tx555307t2bpk2bsmnTJm688UZWr159HlseGfJ8ojp48CDPPPMMI0aMIC0tjfLlyzNixAhat27tdWjG5B0+935y09GjR6lXrx4bN26kfv36XH/99W44muV96GDuT3/zzTdMnjw5Y/iiiy7K9j1t27YlNjYWgPbt2/PCCy/QqVMnJk+eTPv27TOWu2rVqoz3HDx4kEOHDlGkSJGAY4skebrq78svv6RWrVq88cYbiAh9+vRh1apVtGnTxhpLGJMHpN+j+u233zhx4kTGParExERSUlLOmHfDhg0ULlyYIkWKkJiYyKJFi7JdflYJz3dc5t86FSpUKON1kyZNWLduHbt27eKTTz7htttuAyAtLY158+axZMkSlixZwtatW6M2SUEeT1T58uVj27ZtNGrUiJSUFF5//fWo/rCNMf4VLVqU4cOHM3jwYFJTU7nrrrv46aef+OabbwCn5NWzZ0/69esHwOOPP85LL73E2rVrASdxDBky5Kzl3nDDDYwcOTJjOL3qr3Tp0qxevTqjai8rIsKtt95Knz59qFWrFiVKlPC73CVLlvzJPRDe8lSiOnHiBF9++WXG8HXXXcfMmTOZO3cu9erV8zAyY4zXLrvsMurWrcvkyZOJj49n+vTpDBw4kBo1alCnTh0aNmxIjx49AEhKSmLYsGF06NCBWrVqUbt2bbZv337WMgcMGMC+ffuoXbs2devWZfbs2QC8/PLLtGrViubNm1OmTJlzxtW+fXvef//9jGo/gOHDh5OSkkJSUhIJCQkkJyfn4J4IP6Ie1Q3/WQ0KFtSUI0cCnv/HH3+ka9eurFq1irlz59KkSZMQRmeMyc7q1aupVauW12FENX/7WEQWqWoDj0I6L1Ffotq9ezf3338/V199NatWraJatWqkpaV5HZYxxpgARW2iUlXefvttatasydtvv03+/Pl59tlnWbZsmf0myhhjIkjUNk9/6aWXGDBgAAAtWrRg9OjRVK9e3eOojDG+ztUM3Pw5kXpbx5/ILVFlc3A/+OCD1KxZk/fff5+vv/7akpQxYaZAgQLs2bMnqi6o4SL9eVQFChTwOpQcEbmNKQoV0pTDhzOGv/jiC5KTk5k6dWrGEy3T0tKIiYncXGxMNLMn/IZWVk/4jcTGFBFf9bd161Z69erF1KlTAZgwYQKdO3cGsCRlTBiLi4uLiqfPmtDLlSu5iLQUkTUisk5EnvQz/QIR+cCd/rOIVMp2oaoMHz6cWrVqMXXqVAoVKsTgwYPp1KlTCLbAGGOMV0Je9SciscBa4HpgC7AQ6KCqq3zm6Q4kqWpXEbkDuFVV2/tdoKtQTIwecWNv06YNb7zxBhUrVgzVZhhjTFSIxKq/3ChRNQLWqeoGVT0BTAYy9/jaGnjHfT0VaCHZNAU6okqFChWYPn0606ZNsyRljDFRKjfuUZUDNvsMbwGuyGoeVT0pIgeAEsBu35lEpAvQxR08vnnz5hXWyzkAJcm0r/Iw2xen2b44zfbFaTW8DiBYuZGo/JWMMtc3BjIPqjoOGAcgIimRVnwNFdsXp9m+OM32xWm2L04TkZTs5wovuVH1twWo4DNcHtiW1Twikg8oCuzNhdiMMcaEudxIVAuBaiJSWUTyA3cAMzLNMwO41319O/CtRuoPvIwxxuSokFf9ufecegAzgVhgvKquFJEXgBRVnQH8G3hPRNbhlKTuCGDR40IWdOSxfXGa7YvTbF+cZvvitIjbFxHbM4Uxxpi8wbpuMMYYE9YsURljjAlrYZ+oQtL9UgQKYD/0EZFVIrJMRGaJyCVexJkbstsXPvPdLiIqIlHbLDmQfSEi7dxjY6WITMztGHNLAOdIRRGZLSKL3fPkZi/izA0iMl5EdorIiiymi4gMd/fVMhG5PLdjDIqqhu0fTuOL9UAVID+wFEjINE93INl9fQfwgddxe7QfrgUKuq+7ReN+CHRfuPMVAX4A5gMNvI7bw+OiGrAYuMgdLuV13B7ui3FAN/d1ArDR67hDuD+uBi4HVmQx/WbgC5zfsDYGfvY65nP9hXuJKiTdL0WgbPeDqs5W1SPu4Hyc36tFo0COCYAXgVeBaH6GRCD7ojMwSlX3AajqzlyOMbcEsi8UuNB9XZSzf88ZNVT1B879W9TWwLvqmA8UE5EyuRNd8MI9UfnrfqlcVvOo6kkgvfulaBLIfvD1AM63pWiU7b4QkcuACqr6aW4G5oFAjovqQHURmSMi80WkZa5Fl7sC2RfPAXeLyBbgc+CR3AktLAV7TfFUuD+PKse6X4pwAW+jiNwNNACuCWlE3jnnvhCRGGAocF9uBeShQI6LfDjVf81wStk/ikhtVd0f4thyWyD7ogMwQVVfF5EmOL/drK2qaaEPL+xE1HUz3EtU1v2SI5D9gIhcB/QH/q6qx3MpttyW3b4oAtQGvhORjTj17zOitEFFoOfHdFVNVdVfgTU4iSvaBLIvHgA+BFDVeUABnM5q86KArinhItwTlXW/5Mh2P7jVXWNxklS03oeAbPaFqh5Q1ZKqWklVK+Hcr/u7qkZcR5wBCOT8+ASnoQ0iUhKnKnBDrkaZOwLZF5uAFgAiUgsnUe3K1SjDxwygo9v6rzFwQFW3ex1UVsK66k9D1/1SRAlwP7wGFAamuG1JNqnq3z0LOkQC3Bd5QoD7YiZwg4isAk4Bj6vqHu+iDo0A98VjwJsi0hunmuu+KPxSC4CITMKp7i3p3pN7FogDUNVknHt0NwPrgCNAWD8a3bpQMsYYE9bCverPGGNMHmeJyhhjTFizRGWMMSasWaIyxhgT1ixRGWOMCWuWqEzYEpFTIrLE56/SOeatlFVP0UGu8zu3B+6lbrdDNc5jGV1FpKP7+j4RKesz7S0RScjhOBeKSL0A3tNLRAr+2XUbk9ssUZlwdlRV6/n8bcyl9d6lqnVxOjt+Ldg3q2qyqr7rDt4HlPWZ9qCqrsqRKE/HOZrA4uwFWKIyEccSlYkobsnpRxH5r/t3pZ95EkVkgVsKWyYi1dzxd/uMHysisdms7gegqvveFu5zjJa7z/q5wB3/spx+Dthgd9xzItJXRG7H6XfxP+46492SUAMR6SYir/rEfJ+IjDjPOOfh06GoiIwRkRRxnj/1vDuuJ07CnC0is91xN4jIPHc/ThGRwtmsxxhPWKIy4Szep9pvmjtuJ3C9ql4OtAeG+3lfV+ANVa2Hkyi2uF3mtAeucsefAu7KZv23AMtFpAAwAWivqnVwenTpJiLFgVuBRFVNAgb6vllVpwIpOCWfeqp61GfyVOA2n+H2wAfnGWdLnK6S0vVX1QZAEnCNiCSp6nCcvtyuVdVr3e6UBgDXufsyBeiTzXqM8URYd6Fk8ryj7sXaVxww0r0ncwqn77rM5gH9RaQ88LGq/k9EWgD1gYVuF1PxOEnPn/+IyFFgI86jIGoAv6rqWnf6O8DDwEic5129JSKfAQE/VkRVd4nIBreftf+565jjLjeYOAvhdBnk+4TWdiLSBef8LoPzkMBlmd7b2B0/x11Pfpz9ZkzYsURlIk1vYAdQF6dG4KwHI6rqRBH5GfgbMFNEHsR5rME7qvrPANZxl28ntiLi9/lmbv9yjXA6Or0D6AE0D2JbPgDaAb8A01RVxckaAceJ8yTbl4FRwG0iUhnoCzRU1X0iMgGn89XMBPhaVTsEEa8xnrCqPxNpigLb3WcI3YNTmjiDiFQBNrjVXTNwqsBmAbeLSCl3nuIickmA6/wFqCQiVd3he4Dv3Xs6RVX1c5yGCv5a3h3CefSIPx8DbXCek/SBOy6oOFU1FacKr7FbbXghcBg4ICKlgZuyiGU+cFX6NolIQRHxVzo1xnOWqEykGQ3cKyLzcar9DvuZpz2wQkSWADVxHrm9CueC/pWILAO+xqkWy5aqHsPpXXqKiCwH0oBknIv+p+7yvscp7WU2AUhOb0yRabn7gFXAJaq6wB0XdJzuva/Xgb6quhRYDKwExuNUJ6YbB3whIrNVdRdOi8RJ7nrm4+wrY8KO9Z5ujDEmrFmJyhhjTFizRGWMMSasWaIyxhgT1ixRGWOMCWuWqIwxxoQ1S1TGGGPCmiUqY4wxYe3/AVRn8me9QhynAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = pd.concat([y_train,predictionsBasedOnKFolds.loc[:,'prediction']], axis=1)\n",
    "preds.columns = ['trueLabel','prediction']\n",
    "predictionsBasedOnKFoldsXGBoostGradientBoosting = preds.copy()\n",
    "\n",
    "precision, recall, thresholds = \\\n",
    "    precision_recall_curve(preds['trueLabel'],preds['prediction'])\n",
    "average_precision = \\\n",
    "    average_precision_score(preds['trueLabel'],preds['prediction'])\n",
    "\n",
    "plt.step(recall, precision, color='k', alpha=0.7, where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "\n",
    "plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n",
    "          average_precision))\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(preds['trueLabel'],preds['prediction'])\n",
    "areaUnderROC = auc(fpr, tpr)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n",
    "plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic: \\\n",
    "        Area under the curve = {0:0.2f}'.format(areaUnderROC))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "params_lightGB = {\n",
    "    'task': 'train',\n",
    "    'application':'binary',\n",
    "    'num_class':1,\n",
    "    'boosting': 'gbdt',\n",
    "    'objective': 'binary',\n",
    "    'metric': 'binary_logloss',\n",
    "    'metric_freq':50,\n",
    "    'is_training_metric':False,\n",
    "    'max_depth':4,\n",
    "    'num_leaves': 31,\n",
    "    'learning_rate': 0.01,\n",
    "    'feature_fraction': 1.0,\n",
    "    'bagging_fraction': 1.0,\n",
    "    'bagging_freq': 0,\n",
    "    'bagging_seed': 2018,\n",
    "    'verbose': 0,\n",
    "    'num_threads':16\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's binary_logloss: 0.00671734\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[2]\tvalid_0's binary_logloss: 0.00665205\n",
      "[3]\tvalid_0's binary_logloss: 0.00658131\n",
      "[4]\tvalid_0's binary_logloss: 0.00651963\n",
      "[5]\tvalid_0's binary_logloss: 0.00645601\n",
      "[6]\tvalid_0's binary_logloss: 0.0064027\n",
      "[7]\tvalid_0's binary_logloss: 0.0063482\n",
      "[8]\tvalid_0's binary_logloss: 0.00629691\n",
      "[9]\tvalid_0's binary_logloss: 0.00624143\n",
      "[10]\tvalid_0's binary_logloss: 0.00618662\n",
      "[11]\tvalid_0's binary_logloss: 0.00613514\n",
      "[12]\tvalid_0's binary_logloss: 0.006083\n",
      "[13]\tvalid_0's binary_logloss: 0.00603881\n",
      "[14]\tvalid_0's binary_logloss: 0.0059963\n",
      "[15]\tvalid_0's binary_logloss: 0.00595386\n",
      "[16]\tvalid_0's binary_logloss: 0.00591356\n",
      "[17]\tvalid_0's binary_logloss: 0.0058727\n",
      "[18]\tvalid_0's binary_logloss: 0.00583538\n",
      "[19]\tvalid_0's binary_logloss: 0.00579581\n",
      "[20]\tvalid_0's binary_logloss: 0.00575713\n",
      "[21]\tvalid_0's binary_logloss: 0.00572147\n",
      "[22]\tvalid_0's binary_logloss: 0.00568495\n",
      "[23]\tvalid_0's binary_logloss: 0.00564997\n",
      "[24]\tvalid_0's binary_logloss: 0.00561454\n",
      "[25]\tvalid_0's binary_logloss: 0.00558266\n",
      "[26]\tvalid_0's binary_logloss: 0.0055497\n",
      "[27]\tvalid_0's binary_logloss: 0.00551967\n",
      "[28]\tvalid_0's binary_logloss: 0.00548498\n",
      "[29]\tvalid_0's binary_logloss: 0.0054516\n",
      "[30]\tvalid_0's binary_logloss: 0.00542237\n",
      "[31]\tvalid_0's binary_logloss: 0.00539378\n",
      "[32]\tvalid_0's binary_logloss: 0.00536332\n",
      "[33]\tvalid_0's binary_logloss: 0.00533526\n",
      "[34]\tvalid_0's binary_logloss: 0.00531058\n",
      "[35]\tvalid_0's binary_logloss: 0.00528473\n",
      "[36]\tvalid_0's binary_logloss: 0.00525823\n",
      "[37]\tvalid_0's binary_logloss: 0.00523215\n",
      "[38]\tvalid_0's binary_logloss: 0.00520789\n",
      "[39]\tvalid_0's binary_logloss: 0.00518604\n",
      "[40]\tvalid_0's binary_logloss: 0.00516296\n",
      "[41]\tvalid_0's binary_logloss: 0.00513912\n",
      "[42]\tvalid_0's binary_logloss: 0.0051161\n",
      "[43]\tvalid_0's binary_logloss: 0.00509511\n",
      "[44]\tvalid_0's binary_logloss: 0.00507423\n",
      "[45]\tvalid_0's binary_logloss: 0.00505366\n",
      "[46]\tvalid_0's binary_logloss: 0.00503295\n",
      "[47]\tvalid_0's binary_logloss: 0.00501366\n",
      "[48]\tvalid_0's binary_logloss: 0.0049946\n",
      "[49]\tvalid_0's binary_logloss: 0.00497638\n",
      "[50]\tvalid_0's binary_logloss: 0.0049584\n",
      "[51]\tvalid_0's binary_logloss: 0.00494204\n",
      "[52]\tvalid_0's binary_logloss: 0.00492332\n",
      "[53]\tvalid_0's binary_logloss: 0.00490403\n",
      "[54]\tvalid_0's binary_logloss: 0.00488791\n",
      "[55]\tvalid_0's binary_logloss: 0.00487208\n",
      "[56]\tvalid_0's binary_logloss: 0.00485383\n",
      "[57]\tvalid_0's binary_logloss: 0.00483714\n",
      "[58]\tvalid_0's binary_logloss: 0.00482116\n",
      "[59]\tvalid_0's binary_logloss: 0.00480551\n",
      "[60]\tvalid_0's binary_logloss: 0.00478831\n",
      "[61]\tvalid_0's binary_logloss: 0.00477221\n",
      "[62]\tvalid_0's binary_logloss: 0.00475772\n",
      "[63]\tvalid_0's binary_logloss: 0.00474175\n",
      "[64]\tvalid_0's binary_logloss: 0.00472775\n",
      "[65]\tvalid_0's binary_logloss: 0.00471048\n",
      "[66]\tvalid_0's binary_logloss: 0.0046906\n",
      "[67]\tvalid_0's binary_logloss: 0.00467659\n",
      "[68]\tvalid_0's binary_logloss: 0.00465998\n",
      "[69]\tvalid_0's binary_logloss: 0.00464055\n",
      "[70]\tvalid_0's binary_logloss: 0.00462487\n",
      "[71]\tvalid_0's binary_logloss: 0.00461122\n",
      "[72]\tvalid_0's binary_logloss: 0.00459528\n",
      "[73]\tvalid_0's binary_logloss: 0.00457917\n",
      "[74]\tvalid_0's binary_logloss: 0.00456347\n",
      "[75]\tvalid_0's binary_logloss: 0.00454675\n",
      "[76]\tvalid_0's binary_logloss: 0.00453424\n",
      "[77]\tvalid_0's binary_logloss: 0.004519\n",
      "[78]\tvalid_0's binary_logloss: 0.00450675\n",
      "[79]\tvalid_0's binary_logloss: 0.00449048\n",
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      "Training Log Loss:  0.0011256757099073179\n",
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      "Training until validation scores don't improve for 200 rounds.\n",
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      "Early stopping, best iteration is:\n",
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      "Training Log Loss:  0.00033988683406131287\n",
      "CV Log Loss:  0.002497638465146632\n",
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      "Training until validation scores don't improve for 200 rounds.\n",
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      "Early stopping, best iteration is:\n",
      "[740]\tvalid_0's binary_logloss: 0.00260879\n",
      "Training Log Loss:  0.0004968701501582628\n",
      "CV Log Loss:  0.002608792241842391\n",
      "[1]\tvalid_0's binary_logloss: 0.00666681\n",
      "Training until validation scores don't improve for 200 rounds.\n",
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      "Early stopping, best iteration is:\n",
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      "Training Log Loss:  0.0010110750620652951\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1112]\tvalid_0's binary_logloss: 0.00258431\n",
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      "Early stopping, best iteration is:\n",
      "[918]\tvalid_0's binary_logloss: 0.00256208\n",
      "Training Log Loss:  0.0004579022781293811\n",
      "CV Log Loss:  0.002562075874454823\n",
      "LightGBM Gradient Boosting Log Loss:  0.0029732268054261826\n"
     ]
    }
   ],
   "source": [
    "trainingScores = []\n",
    "cvScores = []\n",
    "predictionsBasedOnKFolds = pd.DataFrame(data=[],\n",
    "                                index=y_train.index,columns=['prediction'])\n",
    "\n",
    "for train_index, cv_index in k_fold.split(np.zeros(len(X_train)),\n",
    "                                          y_train.ravel()):\n",
    "    X_train_fold, X_cv_fold = X_train.iloc[train_index,:], \\\n",
    "        X_train.iloc[cv_index,:]\n",
    "    y_train_fold, y_cv_fold = y_train.iloc[train_index], \\\n",
    "        y_train.iloc[cv_index]\n",
    "    \n",
    "    lgb_train = lgb.Dataset(X_train_fold, y_train_fold)\n",
    "    lgb_eval = lgb.Dataset(X_cv_fold, y_cv_fold, reference=lgb_train)\n",
    "    gbm = lgb.train(params_lightGB, lgb_train, num_boost_round=2000,\n",
    "                   valid_sets=lgb_eval, early_stopping_rounds=200)\n",
    "    \n",
    "    loglossTraining = log_loss(y_train_fold, \\\n",
    "                gbm.predict(X_train_fold, num_iteration=gbm.best_iteration))\n",
    "    trainingScores.append(loglossTraining)\n",
    "    \n",
    "    predictionsBasedOnKFolds.loc[X_cv_fold.index,'prediction'] = \\\n",
    "        gbm.predict(X_cv_fold, num_iteration=gbm.best_iteration) \n",
    "    loglossCV = log_loss(y_cv_fold, \\\n",
    "        predictionsBasedOnKFolds.loc[X_cv_fold.index,'prediction'])\n",
    "    cvScores.append(loglossCV)\n",
    "    \n",
    "    print('Training Log Loss: ', loglossTraining)\n",
    "    print('CV Log Loss: ', loglossCV)\n",
    "    \n",
    "loglossLightGBMGradientBoosting = \\\n",
    "    log_loss(y_train, predictionsBasedOnKFolds.loc[:,'prediction'])\n",
    "print('LightGBM Gradient Boosting Log Loss: ', loglossLightGBMGradientBoosting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = pd.concat([y_train,predictionsBasedOnKFolds.loc[:,'prediction']], axis=1)\n",
    "preds.columns = ['trueLabel','prediction']\n",
    "predictionsBasedOnKFoldsLightGBMGradientBoosting = preds.copy()\n",
    "\n",
    "precision, recall, thresholds = \\\n",
    "    precision_recall_curve(preds['trueLabel'],preds['prediction'])\n",
    "average_precision = \\\n",
    "    average_precision_score(preds['trueLabel'],preds['prediction'])\n",
    "\n",
    "plt.step(recall, precision, color='k', alpha=0.7, where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "\n",
    "plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n",
    "          average_precision))\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(preds['trueLabel'],preds['prediction'])\n",
    "areaUnderROC = auc(fpr, tpr)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n",
    "plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic: \\\n",
    "Area under the curve = {0:0.2f}'.format(areaUnderROC))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Test Set Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictionsTestSetLogisticRegression = \\\n",
    "    pd.DataFrame(data=[],index=y_test.index,columns=['prediction'])\n",
    "predictionsTestSetLogisticRegression.loc[:,'prediction'] = \\\n",
    "    logReg.predict_proba(X_test)[:,1]\n",
    "logLossTestSetLogisticRegression = \\\n",
    "    log_loss(y_test, predictionsTestSetLogisticRegression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictionsTestSetRandomForests = \\\n",
    "    pd.DataFrame(data=[],index=y_test.index,columns=['prediction'])\n",
    "predictionsTestSetRandomForests.loc[:,'prediction'] = \\\n",
    "    RFC.predict_proba(X_test)[:,1]\n",
    "logLossTestSetRandomForests = \\\n",
    "    log_loss(y_test, predictionsTestSetRandomForests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictionsTestSetXGBoostGradientBoosting = \\\n",
    "    pd.DataFrame(data=[],index=y_test.index,columns=['prediction'])\n",
    "dtest = xgb.DMatrix(data=X_test)\n",
    "predictionsTestSetXGBoostGradientBoosting.loc[:,'prediction'] = \\\n",
    "    bst.predict(dtest)\n",
    "logLossTestSetXGBoostGradientBoosting = \\\n",
    "    log_loss(y_test, predictionsTestSetXGBoostGradientBoosting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictionsTestSetLightGBMGradientBoosting = \\\n",
    "    pd.DataFrame(data=[],index=y_test.index,columns=['prediction'])\n",
    "predictionsTestSetLightGBMGradientBoosting.loc[:,'prediction'] = \\\n",
    "    gbm.predict(X_test, num_iteration=gbm.best_iteration)\n",
    "logLossTestSetLightGBMGradientBoosting = \\\n",
    "    log_loss(y_test, predictionsTestSetLightGBMGradientBoosting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log Loss of Logistic Regression on Test Set:  0.11427630795342297\n",
      "Log Loss of Random Forests on Test Set:  0.008977637185458636\n",
      "Log Loss of XGBoost Gradient Boosting on Test Set:  0.0024394184927239784\n",
      "Log Loss of LightGBM Gradient Boosting on Test Set:  0.0024124563836657015\n"
     ]
    }
   ],
   "source": [
    "print(\"Log Loss of Logistic Regression on Test Set: \", \\\n",
    "          logLossTestSetLogisticRegression)\n",
    "print(\"Log Loss of Random Forests on Test Set: \", \\\n",
    "          logLossTestSetRandomForests)\n",
    "print(\"Log Loss of XGBoost Gradient Boosting on Test Set: \", \\\n",
    "          logLossTestSetXGBoostGradientBoosting)\n",
    "print(\"Log Loss of LightGBM Gradient Boosting on Test Set: \", \\\n",
    "          logLossTestSetLightGBMGradientBoosting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0BpZ5uH4v7/gvz5kPTYp3x/0ryYjIo8CjAJUqVUo92RiTEzZvhqZNYdcuryPx3403eh2B5+ps386o1as9OzjwMsmk9YjfNEtBVd/CeRQoMTExQX4YZYLOyZPQtauzkzRn77vvTr+WNJ/QHVweeADef9/rKHLc2rVrWbp0Kffddx8A96ty1Z9/UrVqVU/i8TLJxAMVfYYr4DzPxOQ1334Lq1YFbvlLlsBnnwVu+XnNk0/CG294HYVJ5ejRowwaNIjXXnuNsLAwmjZtSvXq1RERvOxU2MskMx3oJiITcRr8D1h7TB70zz9www05t75vv825deVG4eFw+eVeR2FSmTFjBl27duWPP5wnpnfq1IlSpUp5HJUjYElGRD4DrgZKi0g8MAAIB1DVWOAb4GZgE3AU6BioWEwQu/XW06979QrcevLlg3bt4BK7Ut7kHtu3b6dHjx5MnjwZgHr16hEbG8tll13mcWSnBfLqsnaZTFega6DWn+fMmQOPPeZcWRNK/nJrSB97DIYP9zYWY0JM165dmTZtGoUKFWLgwIE8+eST5M8fXE9wCa5oTPq2b4dXX4XDh9OeHsoNnGXLWoIxxk8nT548lUheffVVwsPDGT58eNBeeWtJJqtU4eefnZ1+TnrpJVidZucJZ3rtNadaKJSUKgWRkV5HYUxQO3DgAP369WPjxo3MnDkTEaFmzZpMmjTJ69AyZEnGH4cPQ1KS83rpUrj2Wu9iqVkTnnkm7WklSjhtHOHhORuTMSZgVJVJkybRo0cPduzYQVhYGMuXL6dhw4Zeh+YXSzKZeeMN6Nkz7Wn33JOzsRQtCi+8ABUq5Ox6jTGe+P333+nWrRszZ84E4LLLLiM2NpZ69ep5HJn/LMlkpn//06+LFnX+588Pr7wCjz7qTUzGmFxv2LBh9O/fn+PHj1O8eHFeffVVHn74YfLlC63O8y3JpOfLL2HlytMN7VOmwF13eRuTMSbPOHr0KMePH6dDhw4MGzaMMmXKeB3SWREN9s7uUomJidG4uLjArmT3bjj//DPHbdtm1VTGmIDZtWsXGzZs4IorrgAgISGBRYsWceWVV2bL8kVkiarGZMvCsiC0zrtyyocfnn79wgvw+eeWYIwxAZGcnMw777xDzZo1adWqFXv37gUgIiIi2xKMl6y6zNfYsdC3L+zf7wxXrgwDBngbkzEm11q9ejWdO3dmwYIFAFx//fUcPXqUkiVLehxZ9rEzmRQHDzo99aYkmAIFYMwYb2MyxuRKR44coU+fPjRs2JAFCxZQtmxZPvvsM2bNmkWFXFZrknvOZJKS4Ouvz/5ZF743NM2fDzExULBg9sRmjDE+7r777lM3VHbp0oXBgwdTvHhxr8MKiNBPMsnJTv9XM2ZkzyXFUVHQvPm5L8cYY9LRp08fdu7cybhx47j00tz91PnQTzI33wyzZp05rlOns1tWgQLQvfu5x2SMMa6TJ0/y5ptvsmXLFkaOHAnA1VdfTVxcXMjd83I2QjfJPPUUzJwJa9c6w+HhULEijBgBd9zhbWzGGAP8+uuvPPbYYyxfvhyARx99lNq1awPkiQQDoZpkkpKcZJKibFnYutU5EzHGGI/t37+f559/ntjYWFSVypUrM3r06FMJJi8JzVSamOj8Dw93eib+7TdLMMaYoDBx4kSioqIYN24cYWFh9OnThzVr1nCr7wP68pDQPJNxO4tDFfLgkYExJnh9++237Ny5k2bNmjFu3Djq1q3rdUieCs0k88svzv8gewKcMSbvSUhIYPv27Vx00UUADB06lObNm/PAAw/kmXaXjIRmCaTcDWuXGhtjPDR37lzq1avHLbfcwokTJwAoXbo0HTt2tATjCs1S+Okn538uv77cGBOcdu7cSYcOHWjRogUbN24EID4+3uOoglNoJpmvv3b+FynibRzGmDwlOTmZ8ePHExUVxYQJE4iMjGTQoEGsWLHiVHWZOVPoNWokJ59+ff/93sVhjMlz7rrrLqZPnw7ADTfcwJgxY6hWrZrHUQW30DuT+e23068vuMC7OIwxeU6rVq244IIL+Pzzz5kxY4YlGD+E3kPLRDQO4M47YepUr8MxxuRi06dPJz4+ni5dugCgqhw+fJgiIVhV79VDy0I3ySQlgV29YYwJgK1bt/LEE08wbdo0IiIiWLt2bci3udiTMbPiuusswRhjsl1iYiLDhw+nVq1aTJs2jSJFijB06FAqV67sdWghK/Qa/gGio72OwBiTyyxcuJDHHnuMlStXAnDPPffw+uuvU758eY8jC22hmWRq1PA6AmNMLtO/f39WrlxJ1apVGT16NDfffLPXIeUKoVnnZN3JGGPOkapy8ODBU8OjR4/m+eefZ/Xq1ZZgslFoJpnjx72OwBgTwjZs2MB1111Hq1atSLn4qWbNmgwePJhChQp5HF3uEppJxqrLjDFn4fjx4wwYMIB69eoxd+5cli9fzpYtW7wOK1cLzSRTsKDXERhjQszs2bOpW7cuAwcO5MSJEzz00ENs2LCBqlWreh1arhbQJCMiN4rIBhHZJCLPpjG9kojME5FlIrJSRKwi1BiTrVSVhx56iJYtW7Jp0yZq1arF/PnzeffddylVqpTX4eV6AUsyIhIGjAFuAmoB7USkVqrZ+gFfqGpDoC0wNlDxGGPyJhGhSpUqFCxYkFdeeYVly5bR3B4TkmMCeZlWE2CTqm4GEJGJwB3AWp95FCjqvi4G/OXXkkWyL0pjTK6zfPlyduzYwU033QRAnz596NChg1WNeSCQ1WXlgW0+w/HuOF8vAPeJSDzwDdA9rQWJyKMiEicicYEI1BiTOxw6dIhevXrRqFEjHnjgAfbu3QtARESEJRiPBDLJpHW6kbqjtHbAB6paAbgZ+FhE/hWTqr6lqjFe9LtjjAl+qsrUqVOpVasWr7/+OgD33nsv4eHhHkdmAlldFg9U9BmuwL+rwzoBNwKo6i8iEgmUBv7JcMlWXWaMcf35559069aNr92HGcbExDB+/HguueQSjyMzENgzmcVADRGpKiIFcBr2p6eaZyvQAkBEooFIYFcAYzLG5CKqSuvWrfn6668pWrQoo0ePZuHChZZggkjAkoyqngS6AbOAdThXka0RkYEicrs721PAIyKyAvgMeFBD7dkDxpgcl+w+IVdEGDZsGG3atGH9+vV07dqVsLAwj6MzvkLzeTJz58I113gdijEmh+3Zs4dnn3VuuXv77bc9jia02PNkssLaZIzJU1SVDz/8kKioKN555x0++ugj4uPjvQ7L+CE0k4wxJs9Yt24d11xzDQ8++CC7d+/m6quvZsWKFVSoUMHr0IwfLMkYY4KSqtK/f3/q16/PDz/8QOnSpfnwww+ZO3cuUVFRXodn/BSaScaqy4zJ9USE7du3k5iYyCOPPMKGDRu4//77Efv9h5TQbPj//nu46iqvQzHGZLO//vqL3bt3U69ePQB2797Nhg0baNasmceRhT5r+DfG5FlJSUmMHj2a6Oho2rZty4kTJwAoXbq0JZgQZ0nGGOOppUuX0rRpU7p3787BgwepVq3aGY9FNqHNryQjIgVEpHqgg/Gb1ckaE/IOHjzIk08+SePGjYmLi6NChQpMmTKF6dOnU7p0aa/DM9kk0yQjIrcAq4DZ7nADEZka6MCMMbmXqnLllVcyatQoRIRevXqxdu1a7rrrLmvYz2X8OZMZCFwK7AdQ1eVA8JzVGGNCjojQs2dPmjRpQlxcHMOHD6dIkSJeh2UCwJ9emBNVdX+qowtvL0mzIx1jQsqJEycYMWIEYWFh9O7dG4D777+f++67z/oay+X8STLrROQ/QD4RqQo8CSwMbFjGmNzixx9/pHPnzqxdu5aIiAjuv/9+ypYti4hYgskD/Kku6wY0ApKBKcBxnERjjDHp2r17Nw899BBXXnkla9eupUaNGnz99deULVvW69BMDvInydygqn1UtaH79yxwU6ADM8aEJlXl/fffJyoqivfff58CBQowYMAAVq5cyXXXXed1eCaH+ZNk+qUxrm92B5Il1iZjTFCbMGECe/bs4dprr2XlypW88MILREZGeh2W8UC6bTIicgPOo5HLi8gIn0lFcarOjDEGgKNHj3LgwAHKlSuHiDB27FgWL15M+/bt7ZLkPC6jhv9/gNU4bTBrfMYfAp4NZFDGmNAxY8YMunbtykUXXcTs2bMREWrWrEnNmjW9Ds0EgXSTjKouA5aJyCeqejwHY8qcHRkZ47nt27fTo0cPJk+eDECRIkXYs2eP3a1vzuBPm0x5EZkoIitFZGPKX8AjM8YEpaSkJEaNGkV0dDSTJ0/mvPPOY/jw4SxZssQSjPkXf+6T+QAYBAzDuaqsI9YmY0yelJyczFVXXcWCBQsAuPPOOxk5ciSVKlXyODITrPw5kymkqrMAVPV3Ve0HXBPYsIwxwShfvny0bNmSihUrMm3aNKZOnWoJxmTInzOZBHEuD/ldRDoD24EygQ0rE9YmY0yOUFW++OIL8ufPT+vWrQHo06cPvXr1onDhwh5HZ0KBP0mmJ1AYeAIYDBQDHgpkUMYY7/3+++906dKFb7/9lvPPP59rr72WEiVKEBERQUREhNfhmRCRaZJR1UXuy0NABwARqRDIoIwx3klISOC1115j8ODBHD9+nBIlSjB48GCKFSvmdWgmBGWYZESkMVAe+ElVd4tIbaAPcC3gXaKx6jJjAuL777/n8ccfZ/369QB06NCBYcOGUaaMtzXkJnSl2/AvIq8AnwDtgZki0heYB6wALs6Z8IwxOSUpKYkuXbqwfv16atasydy5c/noo48swZhzktGZzB1AfVU9JiIlgb/c4Q05E5oxJtCSk5M5fvw4hQoVIiwsjHHjxjF//nyeeeYZa3cx2SKjS5iPq+oxAFXdC6y3BGNM7rFq1SqaN29O9+7dT4276qqr6N+/vyUYk20yOpO5SESmuK8FqOIzjKq2CmhkGbE2GWPO2pEjRxg4cCAjRozg5MmT/PHHH+zbt48SJUp4HZrJhTJKMq1TDY8OZCDGmMD73//+R7du3di6dSsiQpcuXRg8eDDFixf3OjSTS2XUQeacnAzEGBM4J0+epE2bNkyZ4lRGNGjQgPHjx9OkSROPIzO5nT/dygQfqy4zJkvy589PsWLFKFy4MK+//jqLFy+2BGNyRECTjIjcKCIbRGSTiKT5DBoR+Y+IrBWRNSLyaSDjMSYvWbRoEYsWLTo1/Nprr7Fu3Tp69OhB/vz+dPZhzLnze0sTkQhVTcjC/GHAGOB6IB5YLCLTVXWtzzw1gOeAZqq6T0TsgnxjztH+/ft57rnnGD9+PFFRUSxfvpwCBQpQqlQpr0MzeVCmZzIi0kREVgG/ucP1ReRNP5bdBNikqptV9QQwEefeG1+PAGNUdR+Aqv6TpeiNMaeoKp9++ilRUVHExsYSFhbG7bffTlJSktehmTzMn+qyUcCtwB4AVV2Bf139lwe2+QzHu+N8XQxcLCILRGShiNzox3KtTcaYVH777TdatmxJ+/bt2blzJ82aNWPZsmUMGTKEggULeh2eycP8qS7Lp6p/ypk7dn8OjdLKBJrG+msAV+P0hfajiNRR1f1nLEjkUeBRgEZ+rNiYvCQxMZFrr72W+Ph4SpYsydChQ+nYsSP58oXmdT0md/EnyWwTkSaAuu0s3QF/Hr8cD1T0Ga6A0zVN6nkWqmoi8IeIbMBJOot9Z1LVt4C3AGJEUicqY/IkVUVECA8PZ/DgwcybN4+hQ4dy/vnnex2aMaf4c6jzONALqATsBJq64zKzGKghIlVFpADQFpieap6vcKveRKQ0TvXZ5kyXbNVlJg/buXMnHTp0YNCgQafG3X///bz//vuWYEzQ8edM5qSqts3qglX1pIh0A2YBYcB7qrpGRAYCcao63Z3WUkTW4lTB9VbVPVldlzF5QXJyMm+//TbPPvss+/fvp3jx4vTo0YMiRYp4HZox6RLVjGufROR3YAPwOTBFVQ/lRGDpiRHRuF9/hcaNvQzDmBy1YsUKOnfuzMKFCwG48cYbGTNmDBdddJHHkZlQISJLVDUmp9ebaXWZqlYDBuG0ua8Ska9EJMtnNsaYrEtMTOTpp5+mUaNGLFy4kHLlyvHFF1/wzTffWIIxIcGvy09U9WdVfQK4BDiI8zAK5bZlAAAfyUlEQVQz71ibjMkj8ufPz7Jly0hOTqZ79+6sW7eOe+65B7HfgAkRmbbJiEhhnJso2wLRwDTg8gDHZUyetXXrVpKSkqhatSoiQmxsLAcOHCAmJsdrOow5Z/6cyazGuaJsqKpWV9WnVHVRZm8yxmRNYmIiw4YNIzo6mkceeYSU9tIaNWpYgjEhy5+ryy5S1eSAR5IVVlVgcplffvmFzp07s3LlSgBKlizJ0aNHOe+88zyOzJhzk26SEZHhqvoU8KWkcQOkp0/GNCaX2LdvH88++yxvvfUWAFWrVmXMmDHcdNNNHkdmTPbI6Ezmc/e/PRHTmABISEigQYMGbN26lfDwcHr37k3fvn0pVKiQ16EZk20yejLmr+7LaFU9I9G4N1nakzONOQcRERF06tSJOXPmMG7cOGrVquV1SMZkO39uxlyqqpekGrdMVRsGNLJ0xIho3JIlcMklmc9sTBA5fvw4r7zyCjVr1uTee+8FnMcih4WF2SXJJuC8uhkzozaZNjiXLVcVkSk+k4oA+9N+lzEmLbNnz6ZLly5s2rSJMmXKcNddd1GwYEF7QqXJ9TLawn/FeYZMBZwnXKY4BCwLZFDG5BZ///03vXr14rPPPgOgdu3axMbG2jNeTJ6RUZvMH8AfwHc5F46frGrBBLmkpCTGjx/P888/z4EDByhYsCADBgygZ8+eFChQwOvwjMkxGVWX/aCqV4nIPs582JgAqqolAx6dMSEqKSmJN998kwMHDnDzzTczevRoqlat6nVYxuS4jKrLUh6xXDonAjEm1B06dIikpCSKFy9OgQIFePvtt9m5cyetWrWyhn2TZ6XbrYzPXf4VgTBVTQIuAx4D7DZkY1yqypQpU4iOjuapp546Nf6KK66gdevWlmBMnuZP32Vf4Tx6uRrwEU4nmZ8GNKrM2I/WBIktW7Zw++2307p1a7Zv387q1as5fvy412EZEzT8STLJqpoItALeUNXuQPnAhmVMcEtMTOTVV1+lVq1afP311xQtWpTRo0fz888/ExkZ6XV4xgQNvx6/LCL3AB2AO91x4YELyZjgdvToUZo2bcqqVasAaNu2LSNGjKBcuXIeR2ZM8PEnyTwEdMHp6n+ziFQFPgtsWJmw6jLjoUKFChETE8PRo0cZO3YsLVu29DokY4JWpt3KAIhIfqC6O7hJVU8GNKoMxIho3PLlUL++VyGYPEZV+eijj6hWrRpXXHEFAAcOHKBAgQJ2U6UJGUHXrUwKEWkOfAxsx7lH5gIR6aCqCwIdnDFeW7duHY8//jg//PAD0dHRLF++nAIFClCsWDGvQzMmJPhTXfY6cLOqrgUQkWicpGOP6jO51rFjxxg8eDBDhw4lMTGR888/n+eee47wcGuONCYr/EkyBVISDICqrhMRb/vFsDYZE0AzZ86ka9eubN68GYBHHnmEIUOGULKkdXJhTFb5k2SWish4nLMXgPZYB5kmlzp8+DAdOnRg9+7d1KlTh9jYWJo1a+Z1WMaELH+STGfgCeAZnDaZ+cCbgQzKmJyUlJREcnIy4eHhFC5cmJEjRxIfH0/Pnj2tesyYc5RhkhGRukA1YKqqDs2ZkPxg1WUmmyxZsoTHHnuMO+64g/79+wOceqCYMebcpXvHv4g8j9OlTHtgtog8lGNRGRNgBw8e5Mknn6RJkyYsWbKEjz/+mMTERK/DMibXyahbmfZAPVW9B2gMPJ4zIRkTOKrKpEmTiIqKYtSoUYgIvXr1YunSpVY1ZkwAZFRdlqCqRwBUdZeI+NPPmTFB69ChQ7Rp04YZM2YAcOmllxIbG0uDBg08jsyY3CujJHORiExxXwtQzWcYVW0V0MgyYm0y5iwULlyYhIQEihUrxpAhQ3j00UfJl8+OnYwJpIySTOtUw6MDGYgxgTB//nzKlStHjRo1EBHee+89IiMjKVu2rNehGZMnpJtkVHVOTgZiTHbavXs3zzzzDO+//z4tWrRg9uzZiAiVK1f2OjRj8pTQrCuw6jKTjuTkZN577z1q1qzJ+++/T4ECBWjevDlJSUleh2ZMnhTQJCMiN4rIBhHZJCLPZjDf3SKiImL9oZmztmbNGq6++mo6derE3r17adGiBatWrWLAgAHkz+/PfcfGmOzm9y9PRCJUNSEL84cBY4DrgXhgsYhM9+0HzZ2vCE6PAov8XbYxqR04cICmTZty+PBhypQpw4gRI7j33nsRO+s1xlOZnsmISBMRWQX85g7XFxF/upVpgvPsmc2qegKYCNyRxnwvAUMBezC6ybKU5yEVK1aMPn360LlzZ9avX0/79u0twRgTBPypLhsF3ArsAVDVFcA1fryvPLDNZzjeHXeKiDQEKqrq1xktSEQeFZE4EYlzR/ixepObbd++nbvvvpsJEyacGte3b1/GjRtHiRIlPIzMGOPLnySTT1X/TDXOn1bUtDLBqcdwujd3vg48ldmCVPUtVY3x4qluJricPHmSkSNHEhUVxZdffsmAAQNONerbmYsxwcefJLNNRJoAKiJhItID2OjH++KBij7DFYC/fIaLAHWA70VkC9AUmG6N/yY9ixcv5tJLL6VHjx4cPnyYO++8kx9++IGwsDCvQzPGpMOfJPM40AuoBOzESQb+9GO2GKghIlXdh5y1BaanTFTVA6paWlWrqGoVYCFwu6rGZbpkO2LNU44cOUK3bt249NJLWbp0KZUqVWLatGlMnTqVihUrZr4AY4xnMr26TFX/wUkQWaKqJ0WkGzALCAPeU9U1IjIQiFPV6RkvwRhH/vz5+e6778iXLx+9evViwIABnHfeeV6HZYzxg6RcnZPuDCJv49OWkkJVHw1UUBmJEdG4tWshOtqL1Zsc8vvvv1O8eHFKlSoFOFVlkZGR1K1b1+PIjAlNIrLEi3Ztf6rLvgPmuH8LgDKA3/fLGJMVCQkJDBo0iDp16tCnT59T4xs3bmwJxpgQ5E912ee+wyLyMTA7YBH5w9pkcqXvv/+exx9/nPXr1wPOlWRJSUnWsG9MCDubbmWqAtbLoMk2//zzDw888ADXXHMN69evp2bNmsydO5cPPvjAEowxIS7TMxkR2cfpNpl8wF4g3X7IjMmK3bt3Ex0dzd69e4mIiKBv374888wzREREeB2aMSYbZJhkxLm7rT6w3R2VrJldKZATrLos1yhdujR33HEH8fHxjB07lurVq3sdkjEmG2WYZFRVRWSqqjbKqYBM7nbkyBEGDhzILbfcwpVXXgnA2LFjiYiIsDv2jcmF/GmT+VVELgl4JCbX+9///ketWrUYOnQoXbp0ITk5GYDIyEhLMMbkUumeyYhIflU9CVwBPCIivwNHcPokU1W1xGP8sm3bNp588kmmTp0KQMOGDRk/fjz58oXmM/OMMf7LqLrsV+AS4M4cisV/dtQbEk6ePMmoUaP473//y5EjRyhcuDCDBg2ia9eu9hAxY/KIjH7pAqCqv+dQLCaXOXjwIK+88gpHjhyhdevWvPHGG1SoUMHrsIwxOSijJHO+iPRKb6KqjghAPCbE7d+/n4IFCxIREUHJkiUZP348ERER3HLLLV6HZozxQEaV4mFAYZwu+dP6845VlwUdVeXTTz+lZs2aDB069NT4Vq1aWYIxJg/L6Exmh6oOzLFITMjauHEjXbp0Yc6cOQDMnz8fVbUrxowxGZ7J2B7CZOj48eO8+OKL1K1blzlz5lCyZEneffddZs2aZQnGGANkfCbTIseiMCHn77//5sorr+S3334D4MEHH+S1116jdOnSHkdmjAkm6SYZVd2bk4FkiR0le65s2bJUrFiR/PnzM27cOK666iqvQzLGBCG7WcH4JTk5mbfffptrrrmGiy++GBHh008/pUSJEhQoUMDr8IwxQcpuuTaZWrFiBc2aNaNz58506dKFlD5Sy5YtawnGGJOh0EwyVl2WIw4fPszTTz9No0aNWLhwIRdeeCGdO3f2OixjTAix6jKTpq+++oru3bsTHx9Pvnz56N69O4MGDaJo0aJeh2aMCSGWZMy/bN++nbZt25KQkECjRo2IjY0lJibG67CMMSHIkowBIDExkfz58yMilC9fnsGDB1OgQAG6dOlij0A2xpw1a5Mx/PzzzzRq1IgJEyacGvfUU0/RvXt3SzDGmHMSmknGZIu9e/fy2GOP0axZM1atWsXYsWMJhqdrG2NyD0syeZCq8vHHHxMVFcVbb71FeHg4ffv2Ze7cudYdjDEmW4Vmm4ztCM/azp07adeuHfPmzQPgqquuYty4cURHR3scmTEmN7IzmTymePHi7Nixg9KlS/PBBx8wb948SzDGmIAJzTMZkyWzZ8/mkksuoVSpUkRERDBp0iTKlStHqVKlvA7NGJPL2ZlMLrZjxw7atWtHy5Yt6dOnz6nxderUsQRjjMkRoZlkrE0mQ0lJSYwdO5aoqCgmTpxIwYIFqVmzpl05ZozJcVZdlsssXbqUzp07s3jxYgBuueUWRo8eTZUqVbwNzBiTJ1mSyUW2bNlCkyZNSEpKonz58owaNYq77rrLLks2xngmoElGRG4ERgJhwDuqOiTV9F7Aw8BJYBfwkKr+6ceCsz/YXKBKlSp07NiRIkWK8OKLL1KkSBGvQzLG5HEBa5MRkTBgDHATUAtoJyK1Us22DIhR1XrAZGBooOLJjbZs2cJtt93GDz/8cGrcW2+9xYgRIyzBGGOCQiDPZJoAm1R1M4CITATuANamzKCq83zmXwjcF8B4co3ExERGjBjBiy++yLFjx9i9eze//PILgFWNGWOCSiCvLisPbPMZjnfHpacTMCOtCSLyqIjEiUhcNsYXkn766ScaNmzIs88+y7Fjx2jbti1TpkzxOixjjElTIJNMWofUaV5DKyL3ATHAa2lNV9W3VDVGVWPcN2RXjCFj3759PPzwwzRv3pw1a9ZQrVo1Zs2axWeffUa5cuW8Ds8YY9IUyCQTD1T0Ga4A/JV6JhG5DugL3K6qCQGMJ6QlJyczbdo0wsPD6d+/P6tWraJly5Zeh2WMMRkKZJvMYqCGiFQFtgNtgXt9ZxCRhsB44EZV/SeAsYSk9evXU7VqVSIiIihVqhSffPIJlSpVIioqyuvQjDHGLwE7k1HVk0A3YBawDvhCVdeIyEARud2d7TWgMDBJRJaLyHS/Fp7Lq8uOHj1K3759qVevHkOHnr7grmXLlpZgjDEhJaD3yajqN8A3qcb91+f1dYFcfyiaOXMmXbp04Y8//gBg9+7dHkdkjDFnz+74DxJ//fUXPXr0YNKkSQDUrVuX2NhYLr/8co8jM8aYs2dJJghs3LiRmJgYDh06RKFChXjhhRfo0aMH4eHhXodmjDHnJDSTTC5rk6lRowaNGzfmvPPO480336Ry5cpeh2SMMdkiNLv6D3EHDx6kR48ebNy4EXDu0p8+fTrTp0+3BGOMyVVC80wmRKkqkydP5sknn2THjh2sX7+emTNnAnDeeed5HJ0xxmS/0EwyIVhdtnnzZrp168aMGU7POU2bNuXVV1/1OCpjjAksqy4LsBMnTvDyyy9Tu3ZtZsyYQfHixYmNjWXBggXUr1/f6/CMMSagQvNMJoRs27aNgQMHkpCQQPv27Rk+fDhly5b1OixjjMkRlmQCYN++fRQvXhwRoVq1aowcOZLq1avTokULr0MzxpgcFZrVZUHaJpOcnMx7771H9erVmTBhwqnxjz32mCUYY0yeFJpJJgitWbOGq6++mk6dOrF3795TDfzGGJOXWZI5R0ePHuW5556jQYMG/Pjjj5QpU4ZPPvmETz75xOvQjDHGc6HZJhMk1WUbN27khhtuYMuWLYgInTt35uWXX6ZEiRJeh2aMMUEhNJNMkKhcuTKRkZHUr1+f2NhYmjZt6nVIxgS9xMRE4uPjOX78uNeh5EqRkZFUqFAhaPo+tCSTBSdPniQ2NpZ27dpRqlQpIiIimDlzJuXLlyd/fitKY/wRHx9PkSJFqFKlChIktRK5haqyZ88e4uPjqVq1qtfhANYm47dff/2VJk2a0L17d/r06XNqfOXKlS3BGJMFx48fp1SpUpZgAkBEKFWqVFCdJYZmksnBjfPAgQN069aNpk2bsmzZMipVqsQdd9yRY+s3JjeyBBM4wVa2oZlkcoCqMnHiRKKiohgzZgxhYWE888wzrF27lttuu83r8IwxJiRYkknHihUraNeuHX///TeXX345S5cu5dVXX7Xeko3JBcLCwmjQoAF16tThtttuY//+/aemrVmzhmuvvZaLL76YGjVq8NJLL6Gqp6bPmDGDmJgYoqOjiYqK4umnn/biI4SM0EwyATodTEpKOvW6QYMG9OzZk7fffpsff/yRunXrBmSdxpicV7BgQZYvX87q1aspWbIkY8aMAeDYsWPcfvvtPPvss2zcuJEVK1bw888/M3bsWABWr15Nt27dmDBhAuvWrWP16tVcdNFF2RrbyZMns3V5XgvNJBMA8+bNo06dOsyfP//UuBEjRvDwww+TL58VkzEBIRKYvyy47LLL2L59OwCffvopzZo1o2XLlgAUKlSI0aNHM2TIEACGDh1K3759iYqKAiB//vx06dLlX8s8fPgwHTt2pG7dutSrV48vv/wSgMKFC5+aZ/LkyTz44IMAPPjgg/Tq1YtrrrmG3r17U6VKlTPOrqpXr87OnTvZtWsXrVu3pnHjxjRu3JgFCxZk6bN6Ic9fFvXPP//Qu3dvPvroI8BJLFdeeaXHURljckJSUhJz5syhU6dOgFNV1qhRozPmqVatGocPH+bgwYOsXr2ap556KtPlvvTSSxQrVoxVq1YBTqe5mdm4cSPfffcdYWFhJCcnM3XqVDp27MiiRYuoUqUKZcuW5d5776Vnz55cccUVbN26lRtuuIF169adxSfPOXk2ySQnJ/Puu+/Sp08f9u3bR0REBP369aN3795eh2ZM3uHT1pGTjh07RoMGDdiyZQuNGjXi+uuvd8PRdK/OyspVW9999x0TJ048NexPLyD33HMPYWFhALRp04aBAwfSsWNHJk6cSJs2bU4td+3atafec/DgQQ4dOkSRIkX8ji2nhWY90Dm2yfzxxx80b96cRx99lH379tGyZUtWr15Nv379iIiIyKYgjTHBKqVN5s8//+TEiROn2mRq165NXFzcGfNu3ryZwoULU6RIEWrXrs2SJUsyXX56ycp3XOp7WXwvKrrsssvYtGkTu3bt4quvvqJVq1aAc3D8yy+/sHz5cpYvX8727duDOsFAqCaZc1S0aFE2btzIBRdcwMSJE5k5cybVq1f3OixjTA4rVqwYo0aNYtiwYSQmJtK+fXt++uknvvvuO8A543niiSd45plnAOjduzcvv/wyGzduBJyd/ogRI/613JYtWzJ69OhTwynVZWXLlmXdunWnqsPSIyLcdddd9OrVi+joaEqVKpXmcpcvX36OJRB4eSbJzJo1i4SEBABKlSrF9OnTWb9+PW3atAm6m5eMMTmnYcOG1K9fn4kTJ1KwYEGmTZvGoEGDqFmzJnXr1qVx48Z069YNgHr16vHGG2/Qrl07oqOjqVOnDjt27PjXMvv168e+ffuoU6cO9evXZ968eQAMGTKEW2+9lWuvvZZy5cplGFebNm2YMGHCqaoygFGjRhEXF0e9evWoVasWsbGx2VgSgSHqUZ3o2YoR0bh//oHzz/dr/m3btvHEE0/w1Vdf8dJLL9GvX78AR2iMyci6deuIjo72OoxcLa0yFpElqhqT07Hk2jOZkydPMmLECKKjo/nqq68oXLgwJUuW9DosY4zJU3Ll1WULFy6kc+fOrFixAoDWrVszcuRIypcv73FkxhiTt+S6JLNo0SIuv/xyVJUqVaowevRobrnlFq/DMsb4yOhSYXNugq0JJDSTTAYbZ5MmTbjhhhto2LAh/fr1o1ChQjkYmDEmM5GRkezZs8e6+w+AlOfJREZGeh3KKaHZ8L9rF5QuDcBvv/1Gz549GTFiBBdffDHgXFZoXcEYE5zsyZiBld6TMb1q+A/NMxkgISGBIUOG8Morr5CQkEBkZCSTJ08GsARjTBALDw8Pmqc2msAL6N5YRG4UkQ0isklEnk1jeoSIfO5OXyQiVfxZ7pwff6RevXq88MILJCQk0LFjx5C4XtwYY/KagFWXiUgYsBG4HogHFgPtVHWtzzxdgHqq2llE2gJ3qWqbNBfoKiWie93X0dHRxMbGWoeWxhiTidx4n0wTYJOqblbVE8BEIPVzi+8APnRfTwZaSCYtgftw6hxffvllli9fbgnGGGOCWCDPZO4GblTVh93hDsClqtrNZ57V7jzx7vDv7jy7Uy3rUeBRd7AOsDogQYee0sDuTOfKG6wsTrOyOM3K4rSaqprjvWkGsuE/rTOS1BnNn3lQ1beAtwBEJM6LU75gZGVxmpXFaVYWp1lZnCYicZnPlf0CWV0WD1T0Ga4A/JXePCKSHygG7MUYY0yuEMgksxioISJVRaQA0BaYnmqe6cAD7uu7gbkaajfuGGOMSVfAqstU9aSIdANmAWHAe6q6RkQGAnGqOh14F/hYRDbhnMG09WPRbwUq5hBkZXGalcVpVhanWVmc5klZhNwd/8YYY0KH3RpvjDEmYCzJGGOMCZigTTKB6pImFPlRFr1EZK2IrBSROSJS2Ys4c0JmZeEz390ioiKSay9f9acsROQ/7raxRkQ+zekYc4ofv5FKIjJPRJa5v5ObvYgz0ETkPRH5x70HMa3pIiKj3HJaKSKXBDwoVQ26P5wLBX4HLgIKACuAWqnm6QLEuq/bAp97HbeHZXENUMh9/XheLgt3viLAfGAhEON13B5uFzWAZUAJd7iM13F7WBZvAY+7r2sBW7yOO0BlcSVwCbA6nek3AzNw7lFsCiwKdEzBeiYTkC5pQlSmZaGq81T1qDu4EOeepNzIn+0C4CVgKJCb+5L3pyweAcao6j4AVf0nh2PMKf6UhQJF3dfF+Pc9e7mCqs4n43sN7wA+UsdCoLiIlAtkTMGaZMoD23yG491xac6jqieBA0CpHIkuZ/lTFr464Ryp5EaZloWINAQqqurXORmYB/zZLi4GLhaRBSKyUERuzLHocpY/ZfECcJ+IxAPfAN1zJrSgk9X9yTkL1ufJZFuXNLmA359TRO4DYoCrAhqRdzIsCxHJB7wOPJhTAXnIn+0iP06V2dU4Z7c/ikgdVd0f4Nhymj9l0Q74QFWHi8hlOPfn1VHV5MCHF1RyfL8ZrGcy1iXNaf6UBSJyHdAXuF1VE3IotpyWWVkUwelA9XsR2YJT5zw9lzb++/sbmaaqiar6B7ABJ+nkNv6URSfgCwBV/QWIxOk8M6/xa3+SnYI1yViXNKdlWhZuFdF4nASTW+vdIZOyUNUDqlpaVauoahWc9qnbVdWTjgEDzJ/fyFc4F4UgIqVxqs8252iUOcOfstgKtAAQkWicJLMrR6MMDtOB+92rzJoCB1R1RyBXGJTVZRq4LmlCjp9l8RpQGJjkXvuwVVVv9yzoAPGzLPIEP8tiFtBSRNYCSUBvVd3jXdSB4WdZPAW8LSI9caqHHsyNB6Ui8hlO9Whpt/1pABAOoKqxOO1RNwObgKNAx4DHlAvL2RhjTJAI1uoyY4wxuYAlGWOMMQFjScYYY0zAWJIxxhgTMJZkjDHGBIwlGRN0RCRJRJb7/FXJYN4q6fU4m8V1fu/24rvC7Yal5lkso7OI3O++flBELvSZ9o6I1MrmOBeLSAM/3tNDRAqd67qNORuWZEwwOqaqDXz+tuTQeturan2cjldfy+qbVTVWVT9yBx8ELvSZ9rCqrs2WKE/HORb/4uwBWJIxnrAkY0KCe8byo4gsdf8uT2Oe2iLyq3v2s1JEarjj7/MZP15EwjJZ3XyguvveFu4zSFa5z+qIcMcPkdPP8BnmjntBRJ4Wkbtx+pD7xF1nQfcMJEZEHheRoT4xPygib55lnL/g07mhiIwTkThxnh3zojvuCZxkN09E5rnjWorIL245ThKRwpmsx5izZknGBKOCPlVlU91x/wDXq+olQBtgVBrv6wyMVNUGODv5eLcLkTZAM3d8EtA+k/XfBqwSkUjgA6CNqtbF6SHjcREpCdwF1FbVesAg3zer6mQgDueMo4GqHvOZPBlo5TPcBvj8LOO8EafrmBR9VTUGqAdcJSL1VHUUTt9U16jqNW73Mv2A69yyjAN6ZbIeY85aUHYrY/K8Y+6O1lc4MNptg0jC6YcrtV+AviJSAZiiqr+JSAugEbDY7XKnIE7CSssnInIM2ILTFXxN4A9V3ehO/xDoCozGeVbNOyLyf4DfjxVQ1V0istntN+o3dx0L3OVmJc7zcLpQ8X2y4X9E5FGc33U5nIdzrUz13qbu+AXuegrglJsxAWFJxoSKnsBOoD7OGfi/Hkimqp+KyCLgFmCWiDyM07X5h6r6nB/raO/bmaaIpPl8IrevrCY4HS62BboB12bhs3wO/AdYD0xVVRVnj+93nDhPfxwCjAFaiUhV4GmgsaruE5EPcDqBTE2A2araLgvxGnPWrLrMhIpiwA73+R8dcI7izyAiFwGb3Sqi6TjVRnOAu0WkjDtPSRGp7Oc61wNVRKS6O9wB+MFtwyimqt/gNKqndYXXIZxHD6RlCnAnzjNOPnfHZSlOVU3EqfZq6la1FQWOAAdEpCxwUzqxLASapXwmESkkImmdFRqTLSzJmFAxFnhARBbiVJUdSWOeNsBqEVkOROE8ZnYtzs74WxFZCczGqUrKlKoex+mldpKIrAKSgVicHfbX7vJ+wDnLSu0DIDal4T/VcvcBa4HKqvqrOy7LcbptPcOBp1V1BbAMWAO8h1MFl+ItYIaIzFPVXThXvn3mrmchTlkZExDWC7MxxpiAsTMZY4wxAWNJxhhjTMBYkjHGGBMwlmSMMcYEjCUZY4wxAWNJxhhjTMBYkjHGGBMw/w9bWz9jdWT69AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision, recall, thresholds = \\\n",
    "    precision_recall_curve(y_test,predictionsTestSetLogisticRegression)\n",
    "average_precision = \\\n",
    "    average_precision_score(y_test,predictionsTestSetLogisticRegression)\n",
    "\n",
    "plt.step(recall, precision, color='k', alpha=0.7, where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "\n",
    "plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n",
    "          average_precision))\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test,predictionsTestSetLogisticRegression)\n",
    "areaUnderROC = auc(fpr, tpr)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n",
    "plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic: \\\n",
    "Area under the curve = {0:0.2f}'.format(areaUnderROC))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision, recall, thresholds = \\\n",
    "    precision_recall_curve(y_test,predictionsTestSetRandomForests)\n",
    "average_precision = \\\n",
    "    average_precision_score(y_test,predictionsTestSetRandomForests)\n",
    "\n",
    "plt.step(recall, precision, color='k', alpha=0.7, where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "\n",
    "plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n",
    "          average_precision))\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test,predictionsTestSetRandomForests)\n",
    "areaUnderROC = auc(fpr, tpr)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n",
    "plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic: \\\n",
    "Area under the curve = {0:0.2f}'.format(areaUnderROC))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision, recall, thresholds = \\\n",
    "    precision_recall_curve(y_test,predictionsTestSetXGBoostGradientBoosting)\n",
    "average_precision = \\\n",
    "    average_precision_score(y_test,predictionsTestSetXGBoostGradientBoosting)\n",
    "\n",
    "plt.step(recall, precision, color='k', alpha=0.7, where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "\n",
    "plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n",
    "          average_precision))\n",
    "\n",
    "fpr, tpr, thresholds = \\\n",
    "    roc_curve(y_test,predictionsTestSetXGBoostGradientBoosting)\n",
    "areaUnderROC = auc(fpr, tpr)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n",
    "plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic: \\\n",
    "Area under the curve = {0:0.2f}'.format(areaUnderROC))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VVFZWsnjxYpYuXcqgQYPo0aPH1qaqxsbGra9BgwZxxBFHdMDWWEu63E+hjho1Ki6++GJ3Zpt1E+effz6bNm2ib9++W/syiv9WVFRw++2307dv37LF2dVJeigiJu7M/3a5Hy4ys+7l7LPPZuHChQwcOBBJ1NbWIonq6moqKip46KGHmDt3Lm+88YYTRZk4UZhZWdXW1nLooYdmzvd9U+XlRGFmXUJE0NDQQGNj43ad5pY/Jwoz69QWL15MU1MTJ5988tYrqHr06MEvf/lLBgwYUObodg1OFGbWqR1++OE88cQTDBo0iNraWpYvX86yZcs444wzGD16NOvXr2fVqlVbr5iqr69HEl/96lc56KCDyh1+t+CrnsysS3nqqae48sorGThwIL169QKgoaGBHj160KtXLxobG3nmmWfo06cPU6dO5WMf+xjDhw9vY6ndn696MrNdxv7778+FF15IU1MTFRUVVFVVbdfZXV9fz8yZM9mwYQNXX301Gzdu5Nxzzy1jxF2fE4WZdTlZLQo1NTWcf/75bNy4ke9///usX7+e+vp6NmzYwOuvv87GjRvZsmXL1tc+++zDnnvu2YHRdz1OFGbW7VRWVtK7d28kcccdd7Bw4UIaGxsBaGxsRBJbtmxBEoMHD+Z73/ser7/++tZxd5Jvz4nCzLqliooKdt99d5qamhgyZAhNTU1bb9jr168fAHPmzGHt2rWcfvrpWxNIr169mDVrFmvXrt26jF09cThRmFm39eUvfzlzfo8ePbjrrrsYN24cW7Zs4Z577mH16tVMmzZtuzI33XQTPXr0yDvcTsuJwsx2WePHj2f8+PFbx0eOHMn111/PfvvtR2NjI6+88gqLFy/m1VdfZciQIWWMtLycKMzMUvvvvz/f/OY3t47fd999LFmypIwRdQ4V5Q7AzKyzevnllwH48Y9/zJVXXklXu++svThRmJm1oqamhvr6eu68804uu+wyli1bVu6QysJNT2ZmrfjIRz7CpEmTePTRR7n55pvZvHlzuUMqC59RmJll6NWrF9XV1bv0o86dKMzMLJMThZmZZXKiMDOzTE4UZmaWyYnCzMwyOVGYmVkmJwozM8vkRGFmZpmcKMzMLJMThZlZGzZu3AjADTfcwE033bTLPRzQz3oyM2vDa6+9xubNm/n973/PLbfcwujRoxk3bly5w+owThRmZm047rjjeNe73sXq1au55ZZbWL9+fblD6lBOFGZmbaioqGD06NFs3ryZiopdr8V+19tiMzPbIbkmCklHSXpa0hJJX2th/t6S7pK0UNKjko7JMx4zM9txuSUKSZXApcDRwFjgJElji4p9A7guIg4EpgGX5RWPmZntnDzPKA4GlkTE0oioB2YDxxeVCaBPOtwXWJljPGZmthPy7MweBiwvGF8BvK+ozEzgDkn/BvQCJre0IEkzgBkA/fv3b/dAzcysdXmeUbT0u4HFd6mcBFwVEXsBxwDXSHpLTBFxRURMjIiJdXV1OYRqZmatyTNRrACGF4zvxVublk4DrgOIiAeAnsCAHGMyM7MdlGeimA+MkTRKUg1JZ/WcojLPA0cASHonSaJ4OceYzMxsB+WWKCKiAfgicDvwJMnVTYsknSdpSlrsbOB0SY8A1wKnxq72EBUz6zIaGhoAuO+++7j//vt3mWc+5XpndkTcCtxaNO1bBcNPAIfmGYOZWXtZtWoVW7Zs4brrrmPOnDlccsklTJw4sdxh5c6P8DAzK9HkyZNZv349dXV13H333bz88q7RUu5HeJiZlaiqqoqpU6ey7777UllZWe5wOowThZmZZXKiMDOzTE4UZmaWyYnCzMwyOVGYmVkmJwozM8vkRGFmZpmcKMzMLJMThZmZZXKiMDOzTE4UZmaWyYnCzMwyOVGYmVkmJwozM8vkRGFmZpmcKMzMLJMThZmZZXKiMDOzTE4UZmaWyYnCzMwyOVGYmVkmJwozM8vkRGFmZpmcKMzMLJMThZmZZXKiMDOzTE4UZmaWyYnCzMwyOVGYmVmmkhOFpGGSPiDpg82vPAMzM+usNmzYAMBFF13EOeecw8svv1zmiPJVUqKQ9D3gfuAbwDnp6ysl/N9Rkp6WtETS11opM1XSE5IWSfrtDsRuZlYWvXr14s0332Tt2rXcdtttzJ07t9wh5aqqxHIfBfaLiM2lLlhSJXAp8I/ACmC+pDkR8URBmTHA14FDI2KtpD1LD93MrDzGjBnDBRdcwOrVq/n5z39OQ0NDuUPKValNT0uB6h1c9sHAkohYGhH1wGzg+KIypwOXRsRagIh4aQfXYWZWFrvtths1NTXlDqNDlHpG8QbwsKQ7ga1nFRFxRsb/DAOWF4yvAN5XVGZfAEn3A5XAzIi4rcSYzMysA5SaKOakrx2hFqZFC+sfA0wC9gLulTQuIl7bbkHSDGAGQP/+/XcwDDMzeztKShQRcbWkGtIzAODpiNjSxr+tAIYXjO8FrGyhzIPpspZJepokccwvWv8VwBUAo0aNKk42ZmaWo1KvepoEPEPSOX0ZsLiEy2PnA2MkjUqTzDTeelZyE3B4uo4BJIloacnRm5lZ7kpteroYODIingaQtC9wLXBQa/8QEQ2SvgjcTtL/8IuIWCTpPGBBRMxJ5x0p6QmgETgnItbs/OaYmVl7KzVRVDcnCYCIWCypzaugIuJW4Naiad8qGA7grPRlZmadUKmJYoGkWcA16fgngYfyCcnMzDqTUhPF54AvAGeQXM10D0lfhZmZdXOlXvW0Gfiv9GVmZruQzEQh6bqImCrpMd56DwQR8e7cIjMzs06hrTOKM9O/x+YdiJmZdU6Z91FExKp08BVgeUQ8B/QAxvPWm+fMzKwbKvWhgPcAPSUNA+4EPg1clVdQZmbWeZSaKBQRbwAfA34cEf8EjM0vLDMz6yxKThSS3k9y/8T/pNNKvbTWzMy6sFITxZdIfmDoxvQxHO8A7sovLDMz6yxKvY/ibuDugvGlJDffmZlZN9fWfRSXRMSXJN1My/dRTMktMjMz6xTaOqNofrbTD/IOxMzMOqfMRBERzQ/+WwBsiogmAEmVJPdTmJlZN1dqZ/adQG3B+G7An9s/HDMz62xKTRQ9I+L15pF0uDajvJmZdROlJoqNkt7TPCLpIGBTPiGZmVlnUupNc18CrpfU/HynIcCJ+YRkZmadSan3UcyXtD+wH8kPFz0VEVtyjczMzDqFkpqeJNUCXwXOjIjHgJGS/OhxM7NdQKl9FL8E6oH3p+MrgAtyicjMzDqVUhPFPhFxEbAFICI2kTRBmZlZN1dqoqiXtBvpYzwk7QNszi0qMzPrNEq96unbwG3AcEm/AQ4FTs0rKDMz6zzaTBSSBDxF8qNFh5A0OZ0ZEa/kHJuZmXUCbSaKiAhJN0XEQWz70SIzM9tFlNpH8aCk9+YaiZmZdUql9lEcDnxW0rPARpLmp4iId+cVmJmZdQ6lJoqjc43CzMw6rbZ+4a4n8FlgNPAYMCsiGjoiMDMz6xza6qO4GphIkiSOBi7OPSIzM+tU2mp6GhsRBwBImgX8Nf+QzMysM2nrjGLrE2Ld5GRm1rLNmzdTX19f7jBy01aiGC9pffraALy7eVjS+rYWLukoSU9LWiLpaxnlTpAUkibu6AaYmZXLunXriAh+9rOfMWXKFBYsWFDukHKRmSgiojIi+qSvuoioKhjuk/W/kiqBS0n6NsYCJ0ka20K5OuAMYN7Ob4aZWcfba6+9aGhooLa2lueff5758+eXO6RclHrD3c44GFgSEUsjoh6YDRzfQrnzgYuAN3OMxcys3dXV1XHRRRcxffp0ampqiIhyh5SLPBPFMGB5wfiKdNpWkg4EhkfELVkLkjRD0gJJCzZs2ND+kZqZWavyTBQt/V7F1nQrqQL4IXB2WwuKiCsiYmJETKyrq2vHEM3MrC15JooVwPCC8b2AlQXjdcA4YG76aJBDgDnu0DYz61zyTBTzgTGSRkmqAaYBc5pnRsS6iBgQESMjYiTwIDAlIrrnZQNmZl1Ubokive/ii8DtwJPAdRGxSNJ5kqbktV4zM2tfpT4UcKdExK3ArUXTvtVK2Ul5xmJmZjsnz6YnMzPrBpwozMwskxOFmZllcqIwM7NMThRmZpbJicLMzDI5UZiZWSYnCjMzy+REYWZmmZwozMwskxOFmZllcqIwM7NMThRmZpbJicLMzDI5UZiZWSYnCjMzy+REYWZmmZwozMwskxOFmZllcqIwM7NMThRmZpbJicLMzDI5UZiZWSYnCjMzy+REYWZmmZwozMwskxOFmZllcqIwM7NMThRmZpbJicLMzDI5UZiZWSYnCjMzy5RropB0lKSnJS2R9LUW5p8l6QlJj0q6U9KIPOMxM7Mdl1uikFQJXAocDYwFTpI0tqjYQmBiRLwbuAG4KK94zMxs5+R5RnEwsCQilkZEPTAbOL6wQETcFRFvpKMPAnvlGI+Zme2EPBPFMGB5wfiKdFprTgP+2NIMSTMkLZC0YMOGDe0YopmZtaUqx2WrhWnRYkHpZGAicFhL8yPiCuAKgFGjRrW4DDMzy0eeiWIFMLxgfC9gZXEhSZOB/wAOi4jNOcZjZmY7Ic+mp/nAGEmjJNUA04A5hQUkHQj8DJgSES/lGIuZme2k3BJFRDQAXwRuB54ErouIRZLOkzQlLfZ9oDdwvaSHJc1pZXFmZlYmeTY9ERG3ArcWTftWwfDkPNdvZmZvn+/MNjOzTLmeUZiZ7Qoikosxb7rpJiKCI488kj333JO+ffuWObL24TMKM7O3SRJbtmzhpZdeYtasWUyfPp0TTjiBG2+8sdyhtQsnCjOzt2mPPfbg3HPP5Qtf+ALV1dX079+flStXMm/evHKH1i6cKMzM2kGfPn0YPHgw3/jGN5gxYwY9evQod0jtxonCzMwyOVGYmVkmJwozM8vkRGFmZpmcKMzMLJMThZmZZXKiMDOzTE4UZmY5WbNmDStXrtz6iI+uyonCzCwHEcFDDz3E9OnTmTVrVrnDeVv8UEAzs3YmiQMOOIANGzawdOlS/va3v5U7pLfFZxRmZjn4+Mc/zmc+8xlqa2vLHcrb5kRhZmaZnCjMzCyTE4WZmWVyojAzs0xOFGZmlsmJwszMMjlRmJnlKCJoamrq0ndn+4Y7M7OczZs3j6OOOop+/fpx7LHHcsghhzBmzJhyh1UyJwozsxx95CMfYdGiRTz++OOsWrWKZcuWce+993LFFVeUO7SSOVGYmeVowoQJTJgwAYAXX3yR66+/nnnz5nHBBRdw6KGHcvjhh5c5wra5j8LMrIMMHjyYAw44gLq6Ov7whz9wySWXlDukkjhRmJl1oEmTJvHlL3+ZESNGsHTpUmbNmsX8+fPLHVYmNz2ZmXWwmpoahg4dyuLFi7n88supqKjg6KOPZty4cZxwwgnlDu8t1NUu2Ro1alRcfPHFVFdXlzsUM7O3Ze3atTz99NPcc889bN68mZqaGv70pz/Rs2dPJLXruiQ9FBETd+Z/fUZhZlYmu+++O4cccgjvfe97ufXWW7n//vs57rjjqKysZNKkSWzZsoXJkyez++678853vrNscTpRmJmVWWVlJR/4wAdYu3Yt69atY/369cyePRtJ3HbbbVRVVTF79mwGDRpEZWVlh8fnRGFm1gn079+f6dOnA9DQ0EBTUxNPPvkkf//733nggQc46aST6NmzJ0cccQRDhw7lxBNP7LAm+FwThaSjgB8BlcCVEfGfRfN7AL8CDgLWACdGxLN5xmRm1tlVVSW75vHjxzNmzBgaGxtZs2YNzz77LNdeey0VFRUsWbKEgw8+mAMPPJDa2lr69u2bXzx5LVhSJXAp8I/ACmC+pDkR8URBsdOAtRExWtI04HvAiXnFZGbW1dTW1vLxj38cgPr6epYvX85Pf/pTbr75Zm677TZqamqoqamhsrKSgQMHMmDAAPbdd1+qqqqQRGVlJYMHD35bMeR5RnEwsCQilgJImg0cDxQmiuOBmenwDcBPJCnauBRr06ZN1NfXt3/EZmad3ODBgznzzDN59dVXeeaZZwBYvnw5tbW1PPzww9TV1TFv3jwigs2bN1NRUdHcr7Hbzq4zz0QxDFheML4CeF9rZSKiQdI6oD/wSmEhSTOAGenolpNPPvn5XCLuevpZjHZzAAAFhElEQVQA68sdRCfhutjGdbHNrlgXrd1IPWpnF5hnomjpIuDiM4VSyhARVwBXAEhasLPXAnc3rottXBfbuC62cV1sI2nBzv5vno/wWAEMLxjfC1jZWhlJVUBf4NUcYzIzsx2UZ6KYD4yRNEpSDTANmFNUZg5wSjp8AvCXtvonzMysY+XW9JT2OXwRuJ3k8thfRMQiSecBCyJiDjALuEbSEpIziWklLLrrPMQ9f66LbVwX27gutnFdbLPTddHlnvVkZmYdy48ZNzOzTE4UZmaWqdMmCklHSXpa0hJJX2thfg9Jv0vnz5M0suOj7Bgl1MVZkp6Q9KikOyWNKEecHaGtuigod4KkkNRtL40spS4kTU0/G4sk/bajY+woJXxH9pZ0l6SF6ffkmHLEmTdJv5D0kqTHW5kvSf+d1tOjkt5T0oIjotO9SDq//w68A6gBHgHGFpX5PHB5OjwN+F254y5jXRwO1KbDn9uV6yItVwfcAzwITCx33GX8XIwBFgK7p+N7ljvuMtbFFcDn0uGxwLPljjunuvgg8B7g8VbmHwP8keQetkOAeaUst7OeUWx9/EdE1APNj/8odDxwdTp8A3CE2vuXPjqHNusiIu6KiDfS0QdJ7lnpjkr5XACcD1wEvNmRwXWwUuridODSiFgLEBEvdXCMHaWUugiSu7QhuV+r+J6ubiEi7iH7XrTjgV9F4kGgn6QhbS23syaKlh7/May1MhHRADQ//qO7KaUuCp1GcsTQHbVZF5IOBIZHxC0dGVgZlPK52BfYV9L9kh5Mn+bcHZVSFzOBkyWtAG4F/q1jQut0dnR/AnTe36Not8d/dAMlb6ekk4GJwGG5RlQ+mXUhqQL4IXBqRwVURqV8LqpImp8mkZxl3itpXES8lnNsHa2UujgJuCoiLpb0fpL7t8ZFRFP+4XUqO7Xf7KxnFH78xzal1AWSJgP/AUyJiM0dFFtHa6su6oBxwFxJz5K0wc7pph3apX5H/hARWyJiGfA0SeLobkqpi9OA6wAi4gGgJzCgQ6LrXEranxTrrInCj//Yps26SJtbfkaSJLprOzS0URcRsS4iBkTEyIgYSdJfMyUidvphaJ1YKd+Rm0gudEDSAJKmqKUdGmXHKKUungeOAJD0TpJE8XKHRtk5zAGmp1c/HQKsi4hVbf1Tp2x6ivwe/9HllFgX3wd6A9en/fnPR8SUsgWdkxLrYpdQYl3cDhwp6QmgETgnItaUL+p8lFgXZwM/l/RlkqaWU7vjgaWka0maGgek/THfBqoBIuJykv6ZY4AlwBvAp0tabjesKzMza0edtenJzMw6CScKMzPL5ERhZmaZnCjMzCyTE4WZmWVyojArIqlR0sOSHpd0s6R+7bz8UyX9JB2eKekr7bl8s/bmRGH2VpsiYkJEjCO5R+cL5Q7IrJycKMyyPUDBQ9MknSNpfvos/+8UTJ+eTntE0jXptOPS30pZKOnPkgaVIX6zt61T3plt1hlIqiR57MOsdPxIkmclHUzycLU5kj4IrCF5ztahEfGKpD3SRdwHHBIRIelfgH8nuUPYrEtxojB7q90kPQyMBB4C/pROPzJ9LUzHe5MkjvHADRHxCkBEND+cci/gd+nz/muAZR0SvVk7c9OT2VttiogJwAiSHXxzH4WAC9P+iwkRMToiZqXTW3oWzo+Bn0TEAcC/kjyIzqzLcaIwa0VErAPOAL4iqZrkoXOfkdQbQNIwSXsCdwJTJfVPpzc3PfUFXkiHT8Gsi3LTk1mGiFgo6RFgWkRckz6i+oH0Kb2vAyenTyr9LnC3pEaSpqlTSX5V7XpJL5A88nxUObbB7O3y02PNzCyTm57MzCyTE4WZmWVyojAzs0xOFGZmlsmJwszMMjlRmJlZJicKMzPL9P8BUeHWk6PulOUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision, recall, thresholds = \\\n",
    "    precision_recall_curve(y_test,predictionsTestSetLightGBMGradientBoosting)\n",
    "average_precision = \\\n",
    "    average_precision_score(y_test,predictionsTestSetLightGBMGradientBoosting)\n",
    "\n",
    "plt.step(recall, precision, color='k', alpha=0.7, where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "\n",
    "plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n",
    "          average_precision))\n",
    "\n",
    "fpr, tpr, thresholds = \\\n",
    "    roc_curve(y_test,predictionsTestSetLightGBMGradientBoosting)\n",
    "areaUnderROC = auc(fpr, tpr)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n",
    "plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic: \\\n",
    "Area under the curve = {0:0.2f}'.format(areaUnderROC))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictionsBasedOnKFoldsFourModels = pd.DataFrame(data=[],index=y_train.index)\n",
    "predictionsBasedOnKFoldsFourModels = predictionsBasedOnKFoldsFourModels.join(\n",
    "    predictionsBasedOnKFoldsLogisticRegression['prediction'].astype(float), \\\n",
    "    how='left').join(predictionsBasedOnKFoldsRandomForests['prediction'] \\\n",
    "    .astype(float),how='left',rsuffix=\"2\").join( \\\n",
    "    predictionsBasedOnKFoldsXGBoostGradientBoosting['prediction'].astype(float), \\\n",
    "    how='left',rsuffix=\"3\").join( \\\n",
    "    predictionsBasedOnKFoldsLightGBMGradientBoosting['prediction'].astype(float), \\\n",
    "    how='left',rsuffix=\"4\")\n",
    "predictionsBasedOnKFoldsFourModels.columns = \\\n",
    "    ['predsLR','predsRF','predsXGB','predsLightGBM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_trainWithPredictions = \\\n",
    "    X_train.merge(predictionsBasedOnKFoldsFourModels,\n",
    "                  left_index=True,right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "params_lightGB = {\n",
    "    'task': 'train',\n",
    "    'application':'binary',\n",
    "    'num_class':1,\n",
    "    'boosting': 'gbdt',\n",
    "    'objective': 'binary',\n",
    "    'metric': 'binary_logloss',\n",
    "    'metric_freq':50,\n",
    "    'is_training_metric':False,\n",
    "    'max_depth':4,\n",
    "    'num_leaves': 31,\n",
    "    'learning_rate': 0.01,\n",
    "    'feature_fraction': 1.0,\n",
    "    'bagging_fraction': 1.0,\n",
    "    'bagging_freq': 0,\n",
    "    'bagging_seed': 2018,\n",
    "    'verbose': 0,\n",
    "    'num_threads':16\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's binary_logloss: 0.00599212\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[2]\tvalid_0's binary_logloss: 0.00593191\n",
      "[3]\tvalid_0's binary_logloss: 0.00587654\n",
      "[4]\tvalid_0's binary_logloss: 0.00581906\n",
      "[5]\tvalid_0's binary_logloss: 0.00576445\n",
      "[6]\tvalid_0's binary_logloss: 0.00572145\n",
      "[7]\tvalid_0's binary_logloss: 0.00566985\n",
      "[8]\tvalid_0's binary_logloss: 0.00562048\n",
      "[9]\tvalid_0's binary_logloss: 0.00557534\n",
      "[10]\tvalid_0's binary_logloss: 0.00553477\n",
      "[11]\tvalid_0's binary_logloss: 0.00549438\n",
      "[12]\tvalid_0's binary_logloss: 0.00545357\n",
      "[13]\tvalid_0's binary_logloss: 0.00541382\n",
      "[14]\tvalid_0's binary_logloss: 0.00537935\n",
      "[15]\tvalid_0's binary_logloss: 0.00534618\n",
      "[16]\tvalid_0's binary_logloss: 0.00531406\n",
      "[17]\tvalid_0's binary_logloss: 0.00527889\n",
      "[18]\tvalid_0's binary_logloss: 0.00524801\n",
      "[19]\tvalid_0's binary_logloss: 0.00521777\n",
      "[20]\tvalid_0's binary_logloss: 0.00518255\n",
      "[21]\tvalid_0's binary_logloss: 0.005152\n",
      "[22]\tvalid_0's binary_logloss: 0.00512167\n",
      "[23]\tvalid_0's binary_logloss: 0.00509255\n",
      "[24]\tvalid_0's binary_logloss: 0.00506433\n",
      "[25]\tvalid_0's binary_logloss: 0.00503595\n",
      "[26]\tvalid_0's binary_logloss: 0.00501025\n",
      "[27]\tvalid_0's binary_logloss: 0.00498261\n",
      "[28]\tvalid_0's binary_logloss: 0.00495784\n",
      "[29]\tvalid_0's binary_logloss: 0.00493368\n",
      "[30]\tvalid_0's binary_logloss: 0.00491171\n",
      "[31]\tvalid_0's binary_logloss: 0.00488877\n",
      "[32]\tvalid_0's binary_logloss: 0.00486311\n",
      "[33]\tvalid_0's binary_logloss: 0.00484135\n",
      "[34]\tvalid_0's binary_logloss: 0.00481891\n",
      "[35]\tvalid_0's binary_logloss: 0.00479714\n",
      "[36]\tvalid_0's binary_logloss: 0.0047767\n",
      "[37]\tvalid_0's binary_logloss: 0.00475594\n",
      "[38]\tvalid_0's binary_logloss: 0.00473425\n",
      "[39]\tvalid_0's binary_logloss: 0.00471497\n",
      "[40]\tvalid_0's binary_logloss: 0.00469566\n",
      "[41]\tvalid_0's binary_logloss: 0.00467713\n",
      "[42]\tvalid_0's binary_logloss: 0.00465861\n",
      "[43]\tvalid_0's binary_logloss: 0.0046397\n",
      "[44]\tvalid_0's binary_logloss: 0.00462186\n",
      "[45]\tvalid_0's binary_logloss: 0.00460436\n",
      "[46]\tvalid_0's binary_logloss: 0.00458651\n",
      "[47]\tvalid_0's binary_logloss: 0.00456925\n",
      "[48]\tvalid_0's binary_logloss: 0.00455174\n",
      "[49]\tvalid_0's binary_logloss: 0.0045351\n",
      "[50]\tvalid_0's binary_logloss: 0.00451786\n",
      "[51]\tvalid_0's binary_logloss: 0.00450196\n",
      "[52]\tvalid_0's binary_logloss: 0.00448624\n",
      "[53]\tvalid_0's binary_logloss: 0.004465\n",
      "[54]\tvalid_0's binary_logloss: 0.00445007\n",
      "[55]\tvalid_0's binary_logloss: 0.0044307\n",
      "[56]\tvalid_0's binary_logloss: 0.00441606\n",
      "[57]\tvalid_0's binary_logloss: 0.00440146\n",
      "[58]\tvalid_0's binary_logloss: 0.00438756\n",
      "[59]\tvalid_0's binary_logloss: 0.00437369\n",
      "[60]\tvalid_0's binary_logloss: 0.0043602\n",
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      "Early stopping, best iteration is:\n",
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      "Training Log Loss:  0.001197110097277555\n",
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     "output_type": "stream",
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      "Early stopping, best iteration is:\n",
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      "Training Log Loss:  0.0002246969559824029\n",
      "CV Log Loss:  0.001978994143520206\n",
      "[1]\tvalid_0's binary_logloss: 0.00573339\n",
      "Training until validation scores don't improve for 200 rounds.\n",
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     "output_type": "stream",
     "text": [
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      "Early stopping, best iteration is:\n",
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      "Training Log Loss:  0.0006714911207077039\n",
      "CV Log Loss:  0.0028332411337841043\n",
      "[1]\tvalid_0's binary_logloss: 0.00631655\n",
      "Training until validation scores don't improve for 200 rounds.\n",
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      "Early stopping, best iteration is:\n",
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      "Training Log Loss:  0.0007670985194299071\n",
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      "Training until validation scores don't improve for 200 rounds.\n",
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      "[465]\tvalid_0's binary_logloss: 0.00257243\n",
      "[466]\tvalid_0's binary_logloss: 0.002572\n",
      "[467]\tvalid_0's binary_logloss: 0.00257181\n",
      "[468]\tvalid_0's binary_logloss: 0.0025717\n",
      "[469]\tvalid_0's binary_logloss: 0.00257128\n",
      "[470]\tvalid_0's binary_logloss: 0.00257215\n",
      "[471]\tvalid_0's binary_logloss: 0.00257302\n",
      "[472]\tvalid_0's binary_logloss: 0.0025739\n",
      "[473]\tvalid_0's binary_logloss: 0.00257432\n",
      "[474]\tvalid_0's binary_logloss: 0.00257639\n",
      "[475]\tvalid_0's binary_logloss: 0.00257671\n",
      "[476]\tvalid_0's binary_logloss: 0.00257732\n",
      "[477]\tvalid_0's binary_logloss: 0.00257771\n",
      "[478]\tvalid_0's binary_logloss: 0.00257812\n",
      "[479]\tvalid_0's binary_logloss: 0.00257901\n",
      "[480]\tvalid_0's binary_logloss: 0.00257884\n",
      "[481]\tvalid_0's binary_logloss: 0.00258006\n",
      "[482]\tvalid_0's binary_logloss: 0.00258079\n",
      "[483]\tvalid_0's binary_logloss: 0.002582\n",
      "[484]\tvalid_0's binary_logloss: 0.00258187\n",
      "[485]\tvalid_0's binary_logloss: 0.002582\n",
      "[486]\tvalid_0's binary_logloss: 0.00258274\n",
      "[487]\tvalid_0's binary_logloss: 0.00258241\n",
      "[488]\tvalid_0's binary_logloss: 0.00258337\n",
      "[489]\tvalid_0's binary_logloss: 0.00258338\n",
      "[490]\tvalid_0's binary_logloss: 0.0025836\n",
      "[491]\tvalid_0's binary_logloss: 0.00258482\n",
      "[492]\tvalid_0's binary_logloss: 0.00258588\n",
      "[493]\tvalid_0's binary_logloss: 0.00258655\n",
      "[494]\tvalid_0's binary_logloss: 0.002587\n",
      "[495]\tvalid_0's binary_logloss: 0.00258688\n",
      "[496]\tvalid_0's binary_logloss: 0.00258824\n",
      "[497]\tvalid_0's binary_logloss: 0.00258805\n",
      "[498]\tvalid_0's binary_logloss: 0.00258813\n",
      "[499]\tvalid_0's binary_logloss: 0.00258936\n",
      "[500]\tvalid_0's binary_logloss: 0.00258996\n",
      "[501]\tvalid_0's binary_logloss: 0.00259105\n",
      "[502]\tvalid_0's binary_logloss: 0.00259172\n",
      "[503]\tvalid_0's binary_logloss: 0.00259204\n",
      "[504]\tvalid_0's binary_logloss: 0.00259174\n",
      "[505]\tvalid_0's binary_logloss: 0.00259211\n",
      "[506]\tvalid_0's binary_logloss: 0.00259345\n",
      "[507]\tvalid_0's binary_logloss: 0.0025938\n",
      "[508]\tvalid_0's binary_logloss: 0.00259418\n",
      "[509]\tvalid_0's binary_logloss: 0.00259414\n",
      "[510]\tvalid_0's binary_logloss: 0.00259401\n",
      "[511]\tvalid_0's binary_logloss: 0.00259294\n",
      "[512]\tvalid_0's binary_logloss: 0.00259388\n",
      "[513]\tvalid_0's binary_logloss: 0.00259439\n",
      "[514]\tvalid_0's binary_logloss: 0.00259472\n",
      "[515]\tvalid_0's binary_logloss: 0.00259441\n",
      "[516]\tvalid_0's binary_logloss: 0.00259449\n",
      "[517]\tvalid_0's binary_logloss: 0.00259584\n",
      "[518]\tvalid_0's binary_logloss: 0.00259581\n",
      "[519]\tvalid_0's binary_logloss: 0.00259575\n",
      "[520]\tvalid_0's binary_logloss: 0.00259787\n",
      "[521]\tvalid_0's binary_logloss: 0.00259822\n",
      "[522]\tvalid_0's binary_logloss: 0.00259849\n",
      "[523]\tvalid_0's binary_logloss: 0.00259849\n",
      "[524]\tvalid_0's binary_logloss: 0.00259858\n",
      "[525]\tvalid_0's binary_logloss: 0.00260072\n",
      "[526]\tvalid_0's binary_logloss: 0.00260035\n",
      "[527]\tvalid_0's binary_logloss: 0.00260071\n",
      "[528]\tvalid_0's binary_logloss: 0.00260014\n",
      "[529]\tvalid_0's binary_logloss: 0.00260065\n",
      "[530]\tvalid_0's binary_logloss: 0.00260221\n",
      "[531]\tvalid_0's binary_logloss: 0.00260194\n",
      "[532]\tvalid_0's binary_logloss: 0.00260237\n",
      "[533]\tvalid_0's binary_logloss: 0.00260273\n",
      "[534]\tvalid_0's binary_logloss: 0.00260234\n",
      "[535]\tvalid_0's binary_logloss: 0.00260272\n",
      "[536]\tvalid_0's binary_logloss: 0.00260337\n",
      "[537]\tvalid_0's binary_logloss: 0.00260304\n",
      "[538]\tvalid_0's binary_logloss: 0.00260296\n",
      "[539]\tvalid_0's binary_logloss: 0.00260238\n",
      "[540]\tvalid_0's binary_logloss: 0.00260239\n",
      "[541]\tvalid_0's binary_logloss: 0.00260333\n",
      "[542]\tvalid_0's binary_logloss: 0.00260384\n",
      "[543]\tvalid_0's binary_logloss: 0.00260355\n",
      "[544]\tvalid_0's binary_logloss: 0.00260357\n",
      "[545]\tvalid_0's binary_logloss: 0.002603\n",
      "[546]\tvalid_0's binary_logloss: 0.00260516\n",
      "[547]\tvalid_0's binary_logloss: 0.00260479\n",
      "[548]\tvalid_0's binary_logloss: 0.00260562\n",
      "[549]\tvalid_0's binary_logloss: 0.00260489\n",
      "[550]\tvalid_0's binary_logloss: 0.0026048\n",
      "[551]\tvalid_0's binary_logloss: 0.0026051\n",
      "[552]\tvalid_0's binary_logloss: 0.0026053\n",
      "[553]\tvalid_0's binary_logloss: 0.00260667\n",
      "[554]\tvalid_0's binary_logloss: 0.00260597\n",
      "[555]\tvalid_0's binary_logloss: 0.00260566\n",
      "[556]\tvalid_0's binary_logloss: 0.00260649\n",
      "[557]\tvalid_0's binary_logloss: 0.00260526\n",
      "[558]\tvalid_0's binary_logloss: 0.00260664\n",
      "[559]\tvalid_0's binary_logloss: 0.00260649\n",
      "[560]\tvalid_0's binary_logloss: 0.00260671\n",
      "[561]\tvalid_0's binary_logloss: 0.00260604\n",
      "[562]\tvalid_0's binary_logloss: 0.00260646\n",
      "[563]\tvalid_0's binary_logloss: 0.00260684\n",
      "Early stopping, best iteration is:\n",
      "[363]\tvalid_0's binary_logloss: 0.00255577\n",
      "Training Log Loss:  0.0010162518068455493\n",
      "CV Log Loss:  0.002555772222526404\n",
      "Ensemble Log Loss:  0.002885415974220497\n"
     ]
    }
   ],
   "source": [
    "trainingScores = []\n",
    "cvScores = []\n",
    "predictionsBasedOnKFoldsEnsemble = \\\n",
    "    pd.DataFrame(data=[],index=y_train.index,columns=['prediction'])\n",
    "\n",
    "for train_index, cv_index in k_fold.split(np.zeros(len(X_train)), \\\n",
    "                                          y_train.ravel()):\n",
    "    X_train_fold, X_cv_fold = \\\n",
    "        X_trainWithPredictions.iloc[train_index,:], \\\n",
    "        X_trainWithPredictions.iloc[cv_index,:]\n",
    "    y_train_fold, y_cv_fold = y_train.iloc[train_index], y_train.iloc[cv_index]\n",
    "    \n",
    "    lgb_train = lgb.Dataset(X_train_fold, y_train_fold)\n",
    "    lgb_eval = lgb.Dataset(X_cv_fold, y_cv_fold, reference=lgb_train)\n",
    "    gbm = lgb.train(params_lightGB, lgb_train, num_boost_round=2000,\n",
    "                   valid_sets=lgb_eval, early_stopping_rounds=200)\n",
    "    \n",
    "    loglossTraining = log_loss(y_train_fold, \\\n",
    "        gbm.predict(X_train_fold, num_iteration=gbm.best_iteration))\n",
    "    trainingScores.append(loglossTraining)\n",
    "    \n",
    "    predictionsBasedOnKFoldsEnsemble.loc[X_cv_fold.index,'prediction'] = \\\n",
    "        gbm.predict(X_cv_fold, num_iteration=gbm.best_iteration) \n",
    "    loglossCV = log_loss(y_cv_fold, \\\n",
    "        predictionsBasedOnKFoldsEnsemble.loc[X_cv_fold.index,'prediction'])\n",
    "    cvScores.append(loglossCV)\n",
    "    \n",
    "    print('Training Log Loss: ', loglossTraining)\n",
    "    print('CV Log Loss: ', loglossCV)\n",
    "    \n",
    "loglossEnsemble = log_loss(y_train, \\\n",
    "        predictionsBasedOnKFoldsEnsemble.loc[:,'prediction'])\n",
    "print('Ensemble Log Loss: ', loglossEnsemble)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances: [165, 216, 120, 106, 281, 50, 77, 74, 100, 38, 96, 220, 130, 91, 593, 144, 146, 80, 63, 258, 111, 109, 140, 110, 54, 88, 178, 72, 150, 318, 438, 164, 320, 0]\n"
     ]
    }
   ],
   "source": [
    "print('Feature importances:', list(gbm.feature_importance()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u3AsvvMCLL75IfX09RxxxBFOnTu3zOM2Gu6I9uEjSvcBxwFjgVeBLQCVARHxXSSfDLcAJQCNwfkT0elvYKVOmxE033eSmpyGivb2djRs3UlVV1XkEkvvqGNdTn9Ts2bNpbm6mtraWKVOmcOedd1JbW9uZWMwsIekvETF9p/53sD3hzonCckUES5Ys4Qc/+AFtbW3sueeeHHPMMdxwww3dlm9qaiKTydDY2MiGDRs6m7oaGho6O+Kz2Sxjx47lmGOO6ee1MSueXUkUPjHeBjVJ7Lfffpx66qn85je/4ZVXXuGhhx6ipaWF1tZWlixZQlVVFa2trWzdurWz0729vZ329nYkdQ5HBG1tbZ2d7DfccAPl5eW0t7ezdu1a1q9f3/m+oaGBlpYWKisr2bhxI1u3bqWiooKWlhYqKiqYNWsWY8eO3S7WjiS0detWWlpayGQybNq0iYggm80SERx++OFUV1eXqDbNuucjChtSbr/9dpYuXcro0aPZY489KCsro7m5md13352Kigqy2Sy77bYb5eXlRETnsCSqqqqoqanhkUceYdWqVYwZM4bKysrORNLW1tZZrr29na1btzJq1CgqKytpaWlh5MiRnUcq48ePZ/z48VRWVtLY2NiZpDoSUtdXx6nChx12GO985zupqqoim82yfPlyRo4cSTabZenSpYwaNaqzI3/GjBkcfXSv16iaAW56MttONpvdro9jRzU2NvLoo4+y1157UVlZSX19PZWVlYwcOZK6urrOJNNxdlfu340bN/K1r32N8ePHU1VV1Zl8IoIRI0ZQW1tLe3s7I0aM6DxyGDFiBBUVFfz7v/87NTU11NTUbHeU097eTnV1deddCXbbbTdeeeUVqqurOeyww2hqauK0007jAx/4AFu2bCGTybB58+bOo6pMJsOUKVN26GLJ9vZ2li9f3nm9S8eym5qaiAhee+01mpubO+u7vr6ed73rXT3Wd8eZah2fzciRI3f4c7Fd40RhNgS89NJLrF69mjFjxlBXV0dlZWXnKce5Cam8vJybb76ZNWvWcOCBB/Liiy9SVVXFuHHjXnfU0jFcXl7ORRddRCaTYfny5dTU1JDNZlm2bBk1NTWd4ysrK2lra6OpqYnW1tbt4us639xkJonJkyczduxYysrK2LBhA1u3bqWsrIwtW7Zsd9QEMGPGDPbff39WrVq1XSz19fWdyz3zzDPZe++9+/UzGMqcKMyGsccee4wFCxZw4IEH0tbWxqhRozrvy1VXV8ecOXOIiO3OBmtubu5smmtsbGT33XenvLycpqamzma6trY2JkyYQEVFRecpy9lslpEjR1JZWUlFRQU1NTUsW7aMhx9+mAkTJpDNZjsvymxpaaGurq6zj6iuro7169ezaNGizkSYm0A6+ow6mgtHjhzJqaeeyrHHHksmk2H16tWMGDGCbDZLW1sbxx9/PBMmTChZvQ82ThRm1qOOM8M6mtA6jkpyX7t6S5yOJqqOI598Fi1a1JkIOprgKioqOmMpKyvjpptuoqGhgdra2s7mutymuIigurqaiRMnkslkuOCCCzj55JN3aR2GOp/1ZGY96jgzrJg6NvKFOPDAA3stc91117FgwQJeeeUV9tprr84muJEjR1JVVcVtt93G1q1byWazLF68mEceecSJooh8RGFmg9qsWbOICCZPnkwmk+HMM8/kjDPOKHVYA86uHFH48lUzG9Q6zuaqqqpiyZIlfPOb3+SZZ54pcVRDi48ozGzImDVrFs3NzdTX1/PQQw/5+Sk5fERhZkaSKKqqqmhsbOSmm24qdThDhhOFmQ0pV199Na2trbz00kulDmXIcKIwsyGlrq6OiRMn+g7Cfcg1aWZmeTlRmJlZXk4UZmaWlxOFmZnl5URhZmZ5OVGYmVleThRmNuRs2LCBhQsX8uCDD/Lyyy+XOpxBz4nCzIaktrY2brzxRmbOnMndd99d6nAGNScKMxtyrrzySmbMmMHee+/Nxo0beeSRR0od0qDmRGFmQ87o0aM5+uijueCCC5g0aRJr164lk8mUOqxBy4nCzIa0DRs20NDQwFe/+tVShzJoOVGY2ZD2wQ9+kKamJv73f/+31KEMWk4UZjakHXbYYRx00EG7/Fzw4cyJwszM8nKiMLMhr6GhgaVLl7Jhw4ZShzIoOVGY2ZDX2NhIRPDlL3+51KEMSk4UZjbkXXzxxbS0tLBo0aJShzIoOVGY2ZA3YcIEDj74YHdo7yQnCjMbFlauXMmyZcuYN29eqUMZdIqaKCSdIGmhpMWSrulm+hsk/U7S05L+KumkYsZjZsNXdXU1ra2tXHvttaxevbrU4QwqRUsUksqBW4ETgUOAMyQd0qXY9cD9EfFGYCZwW7HiMbPh7eqrr2bkyJFks1lmzZpV6nAGlWIeURwFLI6IlyIiA9wHnNKlTACj0uHRwKoixmNmw9zHP/5xWltbWbp0aalDGVSKmSj2AZbnvF+Rjss1CzhL0grgUeAT3c1I0iWS5kuav3nz5mLEambDwB577MG0adNKHcagU8xE0d3pBdHl/RnAPRExETgJ+LGk18UUEXdExPSImF5fX1+EUM3MrCfFTBQrgH1z3k/k9U1LFwL3A0TEn4FqYGwRYzIzsx1UzETxFDBV0hRJVSSd1XO7lFkGvBtA0sEkiWJtEWMyM7MdVLREERGtwBXAPOB5krObnpU0W9KMtNhVwMWSFgD3AudFRNfmKTMzK6GKYs48Ih4l6aTOHffFnOHngLcWMwYzM9s1vjLbzMzycqIwM7O8nCjMzCwvJwozM8vLicLMhpWXX36ZVatW8cwzz5Q6lEHDicLMhp329nYuu+wyFi9eXOpQBgUnCjMbVr7whS9QUVHBli1bOO+881i1yvci7Y0ThZkNO9dccw0RQUNDA9dddx2+zjc/JwozG3bq6ur49Kc/TSaTYenSpbS1tZU6pAHNicLMhqU999yT6dOnU1bmzWBvXENmZpaXE4WZmeXlRGFmw1Y2m2Xt2rVs3bq11KEMaE4UZjZsLV26lIjg2muvLXUoA5oThZkNW6effjotLS08//zzZLPZUoczYDlRmNmwdfDBB3PUUUchqdShDGhOFGZmlpcThZmZ5eVEYWZmeTlRmJlZXk4UZmaWlxOFmZnl5URhZmZ5OVGYmVleThRmNqw1Nzezbt06NmzYUOpQBiwnCjMb1pYtW0ZEdD71zl7PicLMhrWzzjqLlpYW/vGPf/h+Tz1wojCzYW3KlCkceuihbNq0ieXLl5c6nAHJicLMhr2VK1fS3t7O5z//edrb20sdzoDjRGFmw96ll15KNptlxYoVbn7qhhOFmQ1748eP59hjj6W8vLzUoQxIFYUWlLQPMCn3fyLi8WIEZWZmA0dBiULSvwAfAZ4D2tLRAeRNFJJOAG4GyoE7I+LGbsp8GJiVzm9BRHy00ODNzKz4Cj2iOBX4PxHRUuiMJZUDtwLvAVYAT0maGxHP5ZSZClwLvDUiNkoaX3joZmZ9Z+3atWzatIm//e1vTJ8+vdThDCiF9lG8BFTu4LyPAhZHxEsRkQHuA07pUuZi4NaI2AgQEWt2cBlmZn1i7dq1tLW18aUvfYm2trbe/2EYKTRRNALPSPqepH/rePXyP/sAuSclr0jH5ToQOFDSf0l6Im2qMjPrd1dddRXjx49n48aNPvOpi0Kbnuamrx3R3dPKu14fXwFMBY4DJgJ/lDQtIjZtNyPpEuASgD322GMHwzAz611tbS1TpkzhxRdfLHUoA05BiSIifiipiuQIAGBhRPSWclcA++a8nwis6qbME+m8lkhaSJI4nuqy/DuAOwCmTJnim7GYmfWjgpqeJB0HvEjSOX0bsEjS23v5t6eAqZKmpElmJq8/KvkV8M50GWNJEtFLBUdvZmZFV2jT003AeyNiIYCkA4F7gTf39A8R0SrpCmAeyemxd0fEs5JmA/MjYm467b2SOk67/WxErN/51TEzs75WaKKo7EgSABGxSFKvZ0FFxKPAo13GfTFnOIDPpC8zs5Jqb2+noaGBxsZGqqurSx3OgFHoWU/zJd0l6bj09X3gL8UMzMysvz333HO0t7fzmc98xs+myFFoorgceBb4JPApkiu0LytWUGZmpXDaaaeRyWRYtGgRzc3NpQ5nwCj0rKcW4F/Tl5nZkHTIIYfwtre9jRdeeKHUoQwoeROFpPsj4sOS/sbrr4EgIg4vWmRmZjYg9HZE8an07/uLHYiZmQ1MefsoImJ1OrgOWB4RLwMjgCN4/cVzZmY2BBXamf04UJ0+k+I3wPnAPcUKyszMBo5CE4UiohH4EPCdiPggcEjxwjIzs4Gi4EQh6VjgTOCRdFzBT8czM7PBq9BEcSXJA4YeTG/DsR/wu+KFZWZmA0Wh11H8AfhDzvuXSC6+MzOzIS7vEYWkb6d/H5I0t+urf0I0M+s/CxcuZPPmzdx///2lDmXA6O2I4sfp328WOxAzs4FgzJgxrF+/nu9///vMnDmTESNGlDqkksubKCKi48Z/84GmiGgHkFROcj2FmdmQcumll/LLX/6ShQsX+tnZqUI7s38D1Oa8rwH+s+/DMTMrvYoKn9SZq9BEUR0RWzrepMO1ecqbmQ1aq1evZsuWLfzhD3/ovfAwUGii2CrpTR1vJL0ZaCpOSGZmpbVmzRpaW1v56le/SiaTKXU4Jbcj11H8QtIfJf0R+DlwRfHCMjMrneuvv56DDjqIjRs3snz58lKHU3IFJYqIeAo4iOQBRh8DDs7p6DYzG1IksWLFCiKCT3/608O+U7ugRCGpFvgc8KmI+BswWZJvPW5mQ9ZFF11Ea2srK1asoLGxsdThlFShTU8/ADLAsen7FcANRYnIzGwAmDBhAu9617uorKwsdSglV2ii2D8ivg5kASKiCVDRojIzGwBWrlxJU1MTjz32WKlDKalCE0VGUg3p41Al7Q+0FC0qM7MBoKWlhWw2y4033sjs2bNLHU7JFJoovgT8P2BfST8luQDv6qJFZWY2AFxxxRUcddRRbN26lTlz5rBly5be/2kI6vXyQ0kCXiB5aNExJE1On4qIdUWOzcys5E4//XRGjRrFn//852F79lOviSIiQtKvIuLNbHtokZmZDROFNj09IektRY3EzMwGpELvfPVO4DJJS4GtJM1PERGHFyswMzMbGApNFCcWNQozMxuw8iYKSdXAZcABwN+AuyKitT8CMzOzgaG3PoofAtNJksSJwE1Fj8jMzAaU3pqeDomIwwAk3QX8T/FDMjOzgaS3I4psx4CbnMzMhqfeEsURkl5LX5uBwzuGJb3W28wlnSBpoaTFkq7JU+50SSFp+o6ugJmZFVfepqeIKN/ZGUsqB24F3kNyt9mnJM2NiOe6lKsHPgk8ubPLMjOz4in0grudcRSwOCJeiogMcB9wSjflvgx8HWguYixmZraTipko9gFynyG4Ih3XSdIbgX0j4uF8M5J0iaT5kuZv3ry57yM1M7MeFTNRdPe8iuicKJUB3wKu6m1GEXFHREyPiOn19fV9GKKZmfWmmIliBbBvzvuJwKqc9/XANOD36a1BjgHmukPbzGxgKWaieAqYKmmKpCpgJjC3Y2JENETE2IiYHBGTgSeAGRExv4gxmZnZDipaokivu7gCmAc8D9wfEc9Kmi1pRrGWa2ZmfavQmwLulIh4FHi0y7gv9lD2uGLGYmZmO6eYTU9mZkPCunXryGQyPP7446UOpSScKMzMerF27Vqy2SyzZ8+muXn4XfLlRGFm1otPfOITTJ8+nUwm40RhZmavV1ZWxm677UZ5+U7f1WhQc6IwM7O8nCjMzCwvJwozM8vLicLMzPJyojAzs7ycKMzMCvDKK6/Q2trKQw89VOpQ+p0ThZlZAdrb28lms3zrW99i69atpQ6nXzlRmJkV4Nxzz+Xtb387bW1tZDKZUofTr5wozMwKVFNTQ1nZ8NtsDr81NjOzHeJEYWZWoJaWFtra2pgzZw7Lli0rdTj9xonCzKxAzz33HK2trdx6661ccMEFw6avwonCzKxAl156KdOmTaOhoYFVq1axefPmUofUL5wozMwKVF9fz9lnn82MGTOoqCjqA0IHFCcKMzPLy4nCzMzycqIwM7O8nCjMzCwvJwozsx20Zs0aAH71q1+VOJJ4HuAvAAAK/ElEQVT+4URhZraDysrKyGQy3HLLLWzcuLHU4RSdE4WZ2Q467bTTeM973kM2m6WlpaXU4RSdE4WZ2U4YTjcIHB5raWZmO82JwszM8nKiMDOzvJwozMx2QmtrKwArV64scSTF50RhZrYTXnjhBbLZLFdeeSWNjY2lDqeonCjMzHbC2WefTXl5OevWrWPTpk2lDqeoipooJJ0gaaGkxZKu6Wb6ZyQ9J+mvkn4jaVIx4zEz6yt1dXWcfPLJVFZWljqUoitaopBUDtwKnAgcApwh6ZAuxZ4GpkfE4cADwNeLFY+Zme2cYh5RHAUsjoiXIiID3AecklsgIn4XER2Ne08AE4sYj5mZ7YRiJop9gOU571ek43pyIfBYdxMkXSJpvqT5w+XRg2ZmA0Uxn+WnbsZFtwWls4DpwDu6mx4RdwB3AEyZMqXbeZiZWXEUM1GsAPbNeT8RWNW1kKTjgeuAd0TE0L+7lpnZIFPMpqengKmSpkiqAmYCc3MLSHoj8D1gRkSsKWIsZma2k4qWKCKiFbgCmAc8D9wfEc9Kmi1pRlrsG0Ad8AtJz0ia28PszMysRIrZ9EREPAo82mXcF3OGjy/m8s3MbNf5ymwzs100b968UodQVE4UZmY7ac899ySTyfCd73yHtWvXljqconGiMDPbSVOnTuWEE06gpaVlSN8Y0InCzGwX1NTUUF5eXuowisqJwszM8nKiMDOzvJwozMwsLycKMzPLy4nCzMzycqIwM7O8nCjMzCwvJwozM8vLicLMzPJyojAzs7ycKMzMLC8nCjMzy8uJwszM8nKiMDOzvJwozMwsLycKMzPLy4nCzKwPzJkzZ8g+5c6JwsxsF4wbN45MJsM999zDySefzNNPP13qkPqcE4WZ2S6YOnUq11xzDfX19bz66qtcfvnlrFu3rtRh9SknCjOzXbTHHntw1VVXceSRR7Jp0yYnCjMzez1JTJ06laqqKu68807a29tLHVKfcaIwM+sjEydOpKWlhV//+tcsXry41OH0GScKM7M+stdee3HGGWfQ3t5OJpMpdTh9xonCzKyPSSp1CH3KicLMrAgWLFhAc3NzqcPoE04UZmZ9qKamhmw2y7e//W1OOukk7r33XiKi1GHtEicKM7M+dPDBB3POOecwatQoVq9ezTe+8Q2uueaaQd1nUVHqAMzMhhJJTJs2jWnTprFkyRJuvfVW5s2bx5NPPslb3vIW9t13Xy677DKqqqpKHWrBNNgOiaZMmRI33XQTlZWVpQ7FzKxXTU1N3HfffSxfvpxMJoMkxo4dy/ve9z4gOVOqtraWiooKamtrOe6444qyfZP0l4iYvlP/W8xEIekE4GagHLgzIm7sMn0E8CPgzcB64CMRsTTfPJ0ozGwwiggaGhr43ve+x+bNmzvPjCorS3oAOt6PGjWKmpoaqqurGTduHBFBVVUVe+65J21tbRxwwAF86EMfora2doeWvyuJomhNT5LKgVuB9wArgKckzY2I53KKXQhsjIgDJM0E/gX4SLFiMjMrFUmMGTOGz33uc7S2ttLY2Egmk6G5uZm2tjaam5t5/PHHqaysZPPmzbS1tbFy5Uo2bNhAbW0tzz77LBs2bKCiooI777yT+vp6WltbiQjGjRtHRUUF5eXllJWVUVFRwaRJk5gwYQLl5eWUl5fvUuzF7KM4ClgcES8BSLoPOAXITRSnALPS4QeAWyQpejnMaWpqGtQdQ2Zm5eXl1NTUUFNT0znuox/9aLdlI4LW1la2bt3Kb3/7287xDQ0N1NbWsmXLFtrb24kIlixZQllZGU8++SSScm8lsmOHIDmKmSj2AZbnvF8BHN1TmYholdQA7AFsd0ctSZcAl6Rvs2edddayokQ8+IwCXit1EAOE62Ib18U2w7UuujujdfLOzqyYiaK7SxO7HikUUoaIuAO4A0DS/J1tZxtqXBfbuC62cV1s47rYRtL8nf3fYl5HsQLYN+f9RGBVT2UkVQCjgQ1FjMnMzHZQMRPFU8BUSVMkVQEzgbldyswFzk2HTwd+21v/hJmZ9a+iNT2lfQ5XAPNITo+9OyKelTQbmB8Rc4G7gB9LWkxyJDGzgFnfUayYByHXxTaui21cF9u4LrbZ6boYdBfcmZlZ//K9nszMLC8nCjMzy2vAJgpJJ0haKGmxpGu6mT5C0s/T6U9Kmtz/UfaPAuriM5Kek/RXSb+RNKkUcfaH3uoip9zpkkLSkD01spC6kPTh9LvxrKSf9XeM/aWA38gbJP1O0tPp7+SkUsRZbJLulrRG0t97mC5J/5bW018lvamgGUfEgHuRdH7/A9gPqAIWAId0KfMx4Lvp8Ezg56WOu4R18U6gNh2+fDjXRVquHngceAKYXuq4S/i9mAo8DeyWvh9f6rhLWBd3AJenw4cAS0sdd5Hq4u3Am4C/9zD9JOAxkmvYjgGeLGS+A/WIovP2HxGRATpu/5HrFOCH6fADwLs11J4/mOi1LiLidxHRmL59guSalaGokO8FwJeBrwND4/Fi3SukLi4Gbo2IjQARsaafY+wvhdRFkFylDcn1Wl2v6RoSIuJx8l+Ldgrwo0g8AYyRNKG3+Q7URNHd7T/26alMRLQCHbf/GGoKqYtcF5LsMQxFvdaFpDcC+0bEw/0ZWAkU8r04EDhQ0n9JeiK9m/NQVEhdzALOkrQCeBT4RP+ENuDs6PYEGLgPLuqz238MAQWvp6SzgOnAO4oaUenkrQtJZcC3gPP6K6ASKuR7UUHS/HQcyVHmHyVNi4hNRY6tvxVSF2cA90TETZKOJbl+a1pEtHfzv0PZTm03B+oRhW//sU0hdYGk44HrgBkR0dJPsfW33uqiHpgG/F7SUpI22LlDtEO70N/InIjIRsQSYCFJ4hhqCqmLC4H7ASLiz0A1MLZfohtYCtqedDVQE4Vv/7FNr3WRNrd8jyRJDNV2aOilLiKiISLGRsTkiJhM0l8zIyJ2+mZoA1ghv5FfkZzogKSxJE1RL/VrlP2jkLpYBrwbQNLBJIlibb9GOTDMBc5Jz346BmiIiNW9/dOAbHqK4t3+Y9ApsC6+AdQBv0j785dFxIySBV0kBdbFsFBgXcwD3ivpOaAN+GxErC9d1MVRYF1cBXxf0qdJmlrOG4o7lpLuJWlqHJv2x3wJqASIiO+S9M+cBCwGGoHzC5rvEKwrMzPrQwO16cnMzAYIJwozM8vLicLMzPJyojAzs7ycKMzMLC8nCrMuJLVJekbS3yU9JGlMH8//PEm3pMOzJP1TX87frK85UZi9XlNEHBkR00iu0fl4qQMyKyUnCrP8/kzOTdMkfVbSU+m9/P85Z/w56bgFkn6cjvtA+qyUpyX9p6Q9SxC/2S4bkFdmmw0EkspJbvtwV/r+vST3SjqK5OZqcyW9HVhPcp+tt0bEOkm7p7P4E3BMRISki4CrSa4QNhtUnCjMXq9G0jPAZOAvwK/T8e9NX0+n7+tIEscRwAMRsQ4gIjpuTjkR+Hl6v/8qYEm/RG/Wx9z0ZPZ6TRFxJDCJZAPf0Uch4Gtp/8WREXFARNyVju/uXjjfAW6JiMOAS0luRGc26DhRmPUgIhqATwL/JKmS5KZzF0iqA5C0j6TxwG+AD0vaIx3f0fQ0GliZDp+L2SDlpiezPCLiaUkLgJkR8eP0FtV/Tu/SuwU4K71T6VeAP0hqI2maOo/kqWq/kLSS5JbnU0qxDma7ynePNTOzvNz0ZGZmeTlRmJlZXk4UZmaWlxOFmZnl5URhZmZ5OVGYmVleThRmZpbX/w8AZlDjLDyuMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = pd.concat([y_train,predictionsBasedOnKFoldsEnsemble.loc[:,'prediction']], axis=1)\n",
    "preds.columns = ['trueLabel','prediction']\n",
    "\n",
    "precision, recall, thresholds = \\\n",
    "    precision_recall_curve(preds['trueLabel'],preds['prediction'])\n",
    "average_precision = \\\n",
    "    average_precision_score(preds['trueLabel'],preds['prediction'])\n",
    "\n",
    "plt.step(recall, precision, color='k', alpha=0.7, where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "\n",
    "plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(\n",
    "          average_precision))\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(preds['trueLabel'],preds['prediction'])\n",
    "areaUnderROC = auc(fpr, tpr)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n",
    "plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic: \\\n",
    "Area under the curve = {0:0.2f}'.format(areaUnderROC))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatterData = predictionsTestSetLightGBMGradientBoosting.join(y_test,how='left')\n",
    "scatterData.columns = ['Predicted Probability','True Label']\n",
    "ax = sns.regplot(x=\"True Label\", y=\"Predicted Probability\", color='k', \n",
    "                 fit_reg=False, scatter_kws={'alpha':0.1},\n",
    "                 data=scatterData).set_title( \\\n",
    "                'Plot of Prediction Probabilities and the True Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatterDataMelted = pd.melt(scatterData, \"True Label\", \\\n",
    "                            var_name=\"Predicted Probability\")\n",
    "ax = sns.stripplot(x=\"value\", y=\"Predicted Probability\", \\\n",
    "                   hue='True Label', jitter=0.4, \\\n",
    "                   data=scatterDataMelted).set_title( \\\n",
    "                   'Plot of Prediction Probabilities and the True Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Pipeline for New Data'"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Pipeline for New Data'''\n",
    "# first, import new data into a dataframe called 'newData'\n",
    "# second, scale data\n",
    "# newData.loc[:,featuresToScale] = sX.transform(newData[featuresToScale])\n",
    "# third, predict using LightGBM\n",
    "# gbm.predict(newData, num_iteration=gbm.best_iteration)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}