{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\"> Credit Fraud Detector </h1>\n",
    "\n",
    "데이터 : https://www.kaggle.com/mlg-ulb/creditcardfraud\n",
    "\n",
    "Card fraud detection 데이터입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "\n",
    "import matplotlib.patches as mpatches\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_columns=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kakao/.pyenv/versions/3.6.3/lib/python3.6/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": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "import collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import NearMiss\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import KFold, StratifiedKFold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 데이터 로딩"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "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",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V13</th>\n",
       "      <th>V14</th>\n",
       "      <th>V15</th>\n",
       "      <th>V16</th>\n",
       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</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",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>-0.551600</td>\n",
       "      <td>-0.617801</td>\n",
       "      <td>-0.991390</td>\n",
       "      <td>-0.311169</td>\n",
       "      <td>1.468177</td>\n",
       "      <td>-0.470401</td>\n",
       "      <td>0.207971</td>\n",
       "      <td>0.025791</td>\n",
       "      <td>0.403993</td>\n",
       "      <td>0.251412</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>1.612727</td>\n",
       "      <td>1.065235</td>\n",
       "      <td>0.489095</td>\n",
       "      <td>-0.143772</td>\n",
       "      <td>0.635558</td>\n",
       "      <td>0.463917</td>\n",
       "      <td>-0.114805</td>\n",
       "      <td>-0.183361</td>\n",
       "      <td>-0.145783</td>\n",
       "      <td>-0.069083</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>0.624501</td>\n",
       "      <td>0.066084</td>\n",
       "      <td>0.717293</td>\n",
       "      <td>-0.165946</td>\n",
       "      <td>2.345865</td>\n",
       "      <td>-2.890083</td>\n",
       "      <td>1.109969</td>\n",
       "      <td>-0.121359</td>\n",
       "      <td>-2.261857</td>\n",
       "      <td>0.524980</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>-0.054952</td>\n",
       "      <td>-0.226487</td>\n",
       "      <td>0.178228</td>\n",
       "      <td>0.507757</td>\n",
       "      <td>-0.287924</td>\n",
       "      <td>-0.631418</td>\n",
       "      <td>-1.059647</td>\n",
       "      <td>-0.684093</td>\n",
       "      <td>1.965775</td>\n",
       "      <td>-1.232622</td>\n",
       "      <td>-0.208038</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>-0.822843</td>\n",
       "      <td>0.538196</td>\n",
       "      <td>1.345852</td>\n",
       "      <td>-1.119670</td>\n",
       "      <td>0.175121</td>\n",
       "      <td>-0.451449</td>\n",
       "      <td>-0.237033</td>\n",
       "      <td>-0.038195</td>\n",
       "      <td>0.803487</td>\n",
       "      <td>0.408542</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10       V11       V12       V13       V14  \\\n",
       "0  0.098698  0.363787  0.090794 -0.551600 -0.617801 -0.991390 -0.311169   \n",
       "1  0.085102 -0.255425 -0.166974  1.612727  1.065235  0.489095 -0.143772   \n",
       "2  0.247676 -1.514654  0.207643  0.624501  0.066084  0.717293 -0.165946   \n",
       "3  0.377436 -1.387024 -0.054952 -0.226487  0.178228  0.507757 -0.287924   \n",
       "4 -0.270533  0.817739  0.753074 -0.822843  0.538196  1.345852 -1.119670   \n",
       "\n",
       "        V15       V16       V17       V18       V19       V20       V21  \\\n",
       "0  1.468177 -0.470401  0.207971  0.025791  0.403993  0.251412 -0.018307   \n",
       "1  0.635558  0.463917 -0.114805 -0.183361 -0.145783 -0.069083 -0.225775   \n",
       "2  2.345865 -2.890083  1.109969 -0.121359 -2.261857  0.524980  0.247998   \n",
       "3 -0.631418 -1.059647 -0.684093  1.965775 -1.232622 -0.208038 -0.108300   \n",
       "4  0.175121 -0.451449 -0.237033 -0.038195  0.803487  0.408542 -0.009431   \n",
       "\n",
       "        V22       V23       V24       V25       V26       V27       V28  \\\n",
       "0  0.277838 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053   \n",
       "1 -0.638672  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724   \n",
       "2  0.771679  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752   \n",
       "3  0.005274 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458   \n",
       "4  0.798278 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   \n",
       "\n",
       "   Amount  Class  \n",
       "0  149.62      0  \n",
       "1    2.69      0  \n",
       "2  378.66      0  \n",
       "3  123.50      0  \n",
       "4   69.99      0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./inputs/creditcard.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Amount : Transaction amount\n",
    "- Class : 1 for fraudulent transactions, 0 otherwise\n",
    "- Time : Number of seconds elapsed between this transaction and the first transaction in the dataset\n",
    "- V1 ~ V28 : may be result of a PCA Dimensionality reduction to protect user identities and sensitive features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "376ce881-463a-4a09-9ac0-c63f85577eec",
    "_kg_hide-input": true,
    "_uuid": "93031e732e5aca3a2b4984799d6bf58d76e4b52d"
   },
   "outputs": [
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       "      <th>Time</th>\n",
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       "      <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>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V13</th>\n",
       "      <th>V14</th>\n",
       "      <th>V15</th>\n",
       "      <th>V16</th>\n",
       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</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>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",
       "      <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>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\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>1.673327e-15</td>\n",
       "      <td>-1.254995e-15</td>\n",
       "      <td>8.176030e-16</td>\n",
       "      <td>1.206296e-15</td>\n",
       "      <td>4.913003e-15</td>\n",
       "      <td>1.437666e-15</td>\n",
       "      <td>-3.800113e-16</td>\n",
       "      <td>9.572133e-16</td>\n",
       "      <td>1.039817e-15</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",
       "      <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>1.088850e+00</td>\n",
       "      <td>1.020713e+00</td>\n",
       "      <td>9.992014e-01</td>\n",
       "      <td>9.952742e-01</td>\n",
       "      <td>9.585956e-01</td>\n",
       "      <td>9.153160e-01</td>\n",
       "      <td>8.762529e-01</td>\n",
       "      <td>8.493371e-01</td>\n",
       "      <td>8.381762e-01</td>\n",
       "      <td>8.140405e-01</td>\n",
       "      <td>7.709250e-01</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>-2.458826e+01</td>\n",
       "      <td>-4.797473e+00</td>\n",
       "      <td>-1.868371e+01</td>\n",
       "      <td>-5.791881e+00</td>\n",
       "      <td>-1.921433e+01</td>\n",
       "      <td>-4.498945e+00</td>\n",
       "      <td>-1.412985e+01</td>\n",
       "      <td>-2.516280e+01</td>\n",
       "      <td>-9.498746e+00</td>\n",
       "      <td>-7.213527e+00</td>\n",
       "      <td>-5.449772e+01</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",
       "    </tr>\n",
       "    <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>-5.354257e-01</td>\n",
       "      <td>-7.624942e-01</td>\n",
       "      <td>-4.055715e-01</td>\n",
       "      <td>-6.485393e-01</td>\n",
       "      <td>-4.255740e-01</td>\n",
       "      <td>-5.828843e-01</td>\n",
       "      <td>-4.680368e-01</td>\n",
       "      <td>-4.837483e-01</td>\n",
       "      <td>-4.988498e-01</td>\n",
       "      <td>-4.562989e-01</td>\n",
       "      <td>-2.117214e-01</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>-9.291738e-02</td>\n",
       "      <td>-3.275735e-02</td>\n",
       "      <td>1.400326e-01</td>\n",
       "      <td>-1.356806e-02</td>\n",
       "      <td>5.060132e-02</td>\n",
       "      <td>4.807155e-02</td>\n",
       "      <td>6.641332e-02</td>\n",
       "      <td>-6.567575e-02</td>\n",
       "      <td>-3.636312e-03</td>\n",
       "      <td>3.734823e-03</td>\n",
       "      <td>-6.248109e-02</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>4.539234e-01</td>\n",
       "      <td>7.395934e-01</td>\n",
       "      <td>6.182380e-01</td>\n",
       "      <td>6.625050e-01</td>\n",
       "      <td>4.931498e-01</td>\n",
       "      <td>6.488208e-01</td>\n",
       "      <td>5.232963e-01</td>\n",
       "      <td>3.996750e-01</td>\n",
       "      <td>5.008067e-01</td>\n",
       "      <td>4.589494e-01</td>\n",
       "      <td>1.330408e-01</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>2.374514e+01</td>\n",
       "      <td>1.201891e+01</td>\n",
       "      <td>7.848392e+00</td>\n",
       "      <td>7.126883e+00</td>\n",
       "      <td>1.052677e+01</td>\n",
       "      <td>8.877742e+00</td>\n",
       "      <td>1.731511e+01</td>\n",
       "      <td>9.253526e+00</td>\n",
       "      <td>5.041069e+00</td>\n",
       "      <td>5.591971e+00</td>\n",
       "      <td>3.942090e+01</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",
       "</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  1.165980e-15  3.416908e-16 -1.373150e-15  2.086869e-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   9.604066e-16  1.490107e-15 -5.556467e-16  1.177556e-16 -2.406455e-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",
       "                V10           V11           V12           V13           V14  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   2.239751e-15  1.673327e-15 -1.254995e-15  8.176030e-16  1.206296e-15   \n",
       "std    1.088850e+00  1.020713e+00  9.992014e-01  9.952742e-01  9.585956e-01   \n",
       "min   -2.458826e+01 -4.797473e+00 -1.868371e+01 -5.791881e+00 -1.921433e+01   \n",
       "25%   -5.354257e-01 -7.624942e-01 -4.055715e-01 -6.485393e-01 -4.255740e-01   \n",
       "50%   -9.291738e-02 -3.275735e-02  1.400326e-01 -1.356806e-02  5.060132e-02   \n",
       "75%    4.539234e-01  7.395934e-01  6.182380e-01  6.625050e-01  4.931498e-01   \n",
       "max    2.374514e+01  1.201891e+01  7.848392e+00  7.126883e+00  1.052677e+01   \n",
       "\n",
       "                V15           V16           V17           V18           V19  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   4.913003e-15  1.437666e-15 -3.800113e-16  9.572133e-16  1.039817e-15   \n",
       "std    9.153160e-01  8.762529e-01  8.493371e-01  8.381762e-01  8.140405e-01   \n",
       "min   -4.498945e+00 -1.412985e+01 -2.516280e+01 -9.498746e+00 -7.213527e+00   \n",
       "25%   -5.828843e-01 -4.680368e-01 -4.837483e-01 -4.988498e-01 -4.562989e-01   \n",
       "50%    4.807155e-02  6.641332e-02 -6.567575e-02 -3.636312e-03  3.734823e-03   \n",
       "75%    6.488208e-01  5.232963e-01  3.996750e-01  5.008067e-01  4.589494e-01   \n",
       "max    8.877742e+00  1.731511e+01  9.253526e+00  5.041069e+00  5.591971e+00   \n",
       "\n",
       "                V20           V21           V22           V23           V24  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   6.406703e-16  1.656562e-16 -3.444850e-16  2.578648e-16  4.471968e-15   \n",
       "std    7.709250e-01  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min   -5.449772e+01 -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%   -2.117214e-01 -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%   -6.248109e-02 -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    1.330408e-01  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    3.942090e+01  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   5.340915e-16  1.687098e-15 -3.666453e-16 -1.220404e-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  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "결측값을 확인합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "03ddb929-5bc8-4af4-90cd-21dcbb57560d",
    "_kg_hide-input": true,
    "_uuid": "38bec67888aa534e9739e95ef9fac62d27a87021"
   },
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "결측값은 존재하지 않습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "6a526b6c-8463-4f6f-92b0-e8a3a21cbb2e",
    "_kg_hide-input": true,
    "_uuid": "479a5f12d3dd68262316a17b4b7b3499e0a2cbe0"
   },
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fraud vs Normal 데이터의 비율을 살펴보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.82725143693798\n",
      "0.1727485630620034\n"
     ]
    }
   ],
   "source": [
    "# Normal data\n",
    "print(df['Class'].value_counts()[0]/len(df) * 100)\n",
    "# Fraud data\n",
    "print(df['Class'].value_counts()[1]/len(df) * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Imbalanced data**입니다. \n",
    "\n",
    "데이터에 대하여 normal이라고만 예측하는 모델을 만들면, 학습데이터의 경우 99.8%의 정확도를 가지게 됩니다. \n",
    "\n",
    "Fraud에 대한 패턴을 찾아내어야 하는데 쉽지 않은 문제입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "657bc987-4b15-4cfa-b290-c39a2632e2ac",
    "_kg_hide-input": true,
    "_uuid": "337caaf6ed3f65beedb24a74eebb22d97ff52ba4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Class Distributions')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Class', data=df)\n",
    "plt.title('Class Distributions', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Transaction amount\n",
    "- Transaction time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "cee315f2-325f-42b6-a640-736f10c272cc",
    "_kg_hide-input": true,
    "_uuid": "cfa51792bf6f8a6b318ae1bffcff4e922b1d1917"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kakao/.pyenv/versions/3.6.3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6448: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/kakao/.pyenv/versions/3.6.3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6448: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 172792.0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize=(18,12))\n",
    "\n",
    "amount_val = df['Amount'].values\n",
    "time_val = df['Time'].values\n",
    "\n",
    "sns.distplot(amount_val, ax=ax[0], color='r')\n",
    "ax[0].set_title('Distribution of Transaction Amount', fontsize=14)\n",
    "ax[0].set_xlim([min(amount_val), max(amount_val)])\n",
    "\n",
    "sns.distplot(time_val, ax=ax[1], color='b')\n",
    "ax[1].set_title('Distribution of Transaction Time', fontsize=14)\n",
    "ax[1].set_xlim([min(time_val), max(time_val)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 데이터 전처리"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Scailing\n",
    "- StandardScaler : 평균과 표준편차 사용\n",
    "- MinMaxScaler : 최대/최소값이 각각 1, 0이 되도록 스케일링\n",
    "- MaxAbsScaler : 최대절대값과 0이 각각 1, 0이 되도록 스케일링\n",
    "- [**RobustScaler**](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html) : 중앙값(median)과 IQR(interquartile range) 사용. 아웃라이어의 영향을 최소화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "d5d64bf0-2fbb-4096-a265-f68887bf2fde",
    "_kg_hide-input": true,
    "_uuid": "1501ec379c9b5c39c3857ba0febd0aedee9c30d5"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "rob_scaler = RobustScaler()\n",
    "\n",
    "# Imbalanced data : train/test set을 구분하고 train에서 학습 후, test에 적용해야 하지만, imbalanced인 관계로 우선 전체 대상으로 학습\n",
    "df['scaled_amount'] = rob_scaler.fit_transform(df['Amount'].values.reshape(-1,1))\n",
    "df['scaled_time'] = rob_scaler.fit_transform(df['Time'].values.reshape(-1,1))\n",
    "\n",
    "df.drop(['Time','Amount'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>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>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V13</th>\n",
       "      <th>V14</th>\n",
       "      <th>V15</th>\n",
       "      <th>V16</th>\n",
       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</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>Class</th>\n",
       "      <th>scaled_amount</th>\n",
       "      <th>scaled_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>-0.551600</td>\n",
       "      <td>-0.617801</td>\n",
       "      <td>-0.991390</td>\n",
       "      <td>-0.311169</td>\n",
       "      <td>1.468177</td>\n",
       "      <td>-0.470401</td>\n",
       "      <td>0.207971</td>\n",
       "      <td>0.025791</td>\n",
       "      <td>0.403993</td>\n",
       "      <td>0.251412</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "      <td>1.783274</td>\n",
       "      <td>-0.994983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>1.612727</td>\n",
       "      <td>1.065235</td>\n",
       "      <td>0.489095</td>\n",
       "      <td>-0.143772</td>\n",
       "      <td>0.635558</td>\n",
       "      <td>0.463917</td>\n",
       "      <td>-0.114805</td>\n",
       "      <td>-0.183361</td>\n",
       "      <td>-0.145783</td>\n",
       "      <td>-0.069083</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.269825</td>\n",
       "      <td>-0.994983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>0.624501</td>\n",
       "      <td>0.066084</td>\n",
       "      <td>0.717293</td>\n",
       "      <td>-0.165946</td>\n",
       "      <td>2.345865</td>\n",
       "      <td>-2.890083</td>\n",
       "      <td>1.109969</td>\n",
       "      <td>-0.121359</td>\n",
       "      <td>-2.261857</td>\n",
       "      <td>0.524980</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "      <td>4.983721</td>\n",
       "      <td>-0.994972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>-0.226487</td>\n",
       "      <td>0.178228</td>\n",
       "      <td>0.507757</td>\n",
       "      <td>-0.287924</td>\n",
       "      <td>-0.631418</td>\n",
       "      <td>-1.059647</td>\n",
       "      <td>-0.684093</td>\n",
       "      <td>1.965775</td>\n",
       "      <td>-1.232622</td>\n",
       "      <td>-0.208038</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "      <td>1.418291</td>\n",
       "      <td>-0.994972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>-0.822843</td>\n",
       "      <td>0.538196</td>\n",
       "      <td>1.345852</td>\n",
       "      <td>-1.119670</td>\n",
       "      <td>0.175121</td>\n",
       "      <td>-0.451449</td>\n",
       "      <td>-0.237033</td>\n",
       "      <td>-0.038195</td>\n",
       "      <td>0.803487</td>\n",
       "      <td>0.408542</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "      <td>0.670579</td>\n",
       "      <td>-0.994960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10       V11       V12       V13       V14  \\\n",
       "0  0.098698  0.363787  0.090794 -0.551600 -0.617801 -0.991390 -0.311169   \n",
       "1  0.085102 -0.255425 -0.166974  1.612727  1.065235  0.489095 -0.143772   \n",
       "2  0.247676 -1.514654  0.207643  0.624501  0.066084  0.717293 -0.165946   \n",
       "3  0.377436 -1.387024 -0.054952 -0.226487  0.178228  0.507757 -0.287924   \n",
       "4 -0.270533  0.817739  0.753074 -0.822843  0.538196  1.345852 -1.119670   \n",
       "\n",
       "        V15       V16       V17       V18       V19       V20       V21  \\\n",
       "0  1.468177 -0.470401  0.207971  0.025791  0.403993  0.251412 -0.018307   \n",
       "1  0.635558  0.463917 -0.114805 -0.183361 -0.145783 -0.069083 -0.225775   \n",
       "2  2.345865 -2.890083  1.109969 -0.121359 -2.261857  0.524980  0.247998   \n",
       "3 -0.631418 -1.059647 -0.684093  1.965775 -1.232622 -0.208038 -0.108300   \n",
       "4  0.175121 -0.451449 -0.237033 -0.038195  0.803487  0.408542 -0.009431   \n",
       "\n",
       "        V22       V23       V24       V25       V26       V27       V28  \\\n",
       "0  0.277838 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053   \n",
       "1 -0.638672  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724   \n",
       "2  0.771679  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752   \n",
       "3  0.005274 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458   \n",
       "4  0.798278 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   \n",
       "\n",
       "   Class  scaled_amount  scaled_time  \n",
       "0      0       1.783274    -0.994983  \n",
       "1      0      -0.269825    -0.994983  \n",
       "2      0       4.983721    -0.994972  \n",
       "3      0       1.418291    -0.994972  \n",
       "4      0       0.670579    -0.994960  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "cdb9bb1e-9fab-4fd1-a409-468ba8bc36ee",
    "_kg_hide-input": true,
    "_uuid": "a33d701247ab45d849c5e94735346a738a6c6970"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scaled_amount</th>\n",
       "      <th>scaled_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>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V13</th>\n",
       "      <th>V14</th>\n",
       "      <th>V15</th>\n",
       "      <th>V16</th>\n",
       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</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>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.783274</td>\n",
       "      <td>-0.994983</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>-0.551600</td>\n",
       "      <td>-0.617801</td>\n",
       "      <td>-0.991390</td>\n",
       "      <td>-0.311169</td>\n",
       "      <td>1.468177</td>\n",
       "      <td>-0.470401</td>\n",
       "      <td>0.207971</td>\n",
       "      <td>0.025791</td>\n",
       "      <td>0.403993</td>\n",
       "      <td>0.251412</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.269825</td>\n",
       "      <td>-0.994983</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>-0.166974</td>\n",
       "      <td>1.612727</td>\n",
       "      <td>1.065235</td>\n",
       "      <td>0.489095</td>\n",
       "      <td>-0.143772</td>\n",
       "      <td>0.635558</td>\n",
       "      <td>0.463917</td>\n",
       "      <td>-0.114805</td>\n",
       "      <td>-0.183361</td>\n",
       "      <td>-0.145783</td>\n",
       "      <td>-0.069083</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.983721</td>\n",
       "      <td>-0.994972</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>0.207643</td>\n",
       "      <td>0.624501</td>\n",
       "      <td>0.066084</td>\n",
       "      <td>0.717293</td>\n",
       "      <td>-0.165946</td>\n",
       "      <td>2.345865</td>\n",
       "      <td>-2.890083</td>\n",
       "      <td>1.109969</td>\n",
       "      <td>-0.121359</td>\n",
       "      <td>-2.261857</td>\n",
       "      <td>0.524980</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.418291</td>\n",
       "      <td>-0.994972</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>-0.054952</td>\n",
       "      <td>-0.226487</td>\n",
       "      <td>0.178228</td>\n",
       "      <td>0.507757</td>\n",
       "      <td>-0.287924</td>\n",
       "      <td>-0.631418</td>\n",
       "      <td>-1.059647</td>\n",
       "      <td>-0.684093</td>\n",
       "      <td>1.965775</td>\n",
       "      <td>-1.232622</td>\n",
       "      <td>-0.208038</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.670579</td>\n",
       "      <td>-0.994960</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>0.753074</td>\n",
       "      <td>-0.822843</td>\n",
       "      <td>0.538196</td>\n",
       "      <td>1.345852</td>\n",
       "      <td>-1.119670</td>\n",
       "      <td>0.175121</td>\n",
       "      <td>-0.451449</td>\n",
       "      <td>-0.237033</td>\n",
       "      <td>-0.038195</td>\n",
       "      <td>0.803487</td>\n",
       "      <td>0.408542</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   scaled_amount  scaled_time        V1        V2        V3        V4  \\\n",
       "0       1.783274    -0.994983 -1.359807 -0.072781  2.536347  1.378155   \n",
       "1      -0.269825    -0.994983  1.191857  0.266151  0.166480  0.448154   \n",
       "2       4.983721    -0.994972 -1.358354 -1.340163  1.773209  0.379780   \n",
       "3       1.418291    -0.994972 -0.966272 -0.185226  1.792993 -0.863291   \n",
       "4       0.670579    -0.994960 -1.158233  0.877737  1.548718  0.403034   \n",
       "\n",
       "         V5        V6        V7        V8        V9       V10       V11  \\\n",
       "0 -0.338321  0.462388  0.239599  0.098698  0.363787  0.090794 -0.551600   \n",
       "1  0.060018 -0.082361 -0.078803  0.085102 -0.255425 -0.166974  1.612727   \n",
       "2 -0.503198  1.800499  0.791461  0.247676 -1.514654  0.207643  0.624501   \n",
       "3 -0.010309  1.247203  0.237609  0.377436 -1.387024 -0.054952 -0.226487   \n",
       "4 -0.407193  0.095921  0.592941 -0.270533  0.817739  0.753074 -0.822843   \n",
       "\n",
       "        V12       V13       V14       V15       V16       V17       V18  \\\n",
       "0 -0.617801 -0.991390 -0.311169  1.468177 -0.470401  0.207971  0.025791   \n",
       "1  1.065235  0.489095 -0.143772  0.635558  0.463917 -0.114805 -0.183361   \n",
       "2  0.066084  0.717293 -0.165946  2.345865 -2.890083  1.109969 -0.121359   \n",
       "3  0.178228  0.507757 -0.287924 -0.631418 -1.059647 -0.684093  1.965775   \n",
       "4  0.538196  1.345852 -1.119670  0.175121 -0.451449 -0.237033 -0.038195   \n",
       "\n",
       "        V19       V20       V21       V22       V23       V24       V25  \\\n",
       "0  0.403993  0.251412 -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1 -0.145783 -0.069083 -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2 -2.261857  0.524980  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3 -1.232622 -0.208038 -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4  0.803487  0.408542 -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Class  \n",
       "0 -0.189115  0.133558 -0.021053      0  \n",
       "1  0.125895 -0.008983  0.014724      0  \n",
       "2 -0.139097 -0.055353 -0.059752      0  \n",
       "3 -0.221929  0.062723  0.061458      0  \n",
       "4  0.502292  0.219422  0.215153      0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_amount = df['scaled_amount']\n",
    "scaled_time = df['scaled_time']\n",
    "\n",
    "df.drop(['scaled_amount', 'scaled_time'], axis=1, inplace=True)\n",
    "df.insert(0, 'scaled_amount', scaled_amount)\n",
    "df.insert(1, 'scaled_time', scaled_time)\n",
    "\n",
    "# Amount and Time are Scaled!\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a59c8c8d-a4bc-4671-aa2f-9f959c142cde",
    "_uuid": "5119c4ea9e0b9031dbc5937b56323da224985024"
   },
   "source": [
    "## 2.2 Splitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal 99.83 % of the dataset\n",
      "Frauds 0.17 % of the dataset\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "print('Normal', round(df['Class'].value_counts()[0]/len(df) * 100,2), '% of the dataset')\n",
    "print('Frauds', round(df['Class'].value_counts()[1]/len(df) * 100,2), '% of the dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Stratified** train, test을 생성합니다.\n",
    "\n",
    "참고 : https://en.wikipedia.org/wiki/Stratified_sampling#/media/File:Stratified_sampling.PNG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Class', axis=1)\n",
    "y = df['Class']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stratified data를 완벽하게 보장받기 위하여, class 비율에 따라서 train set과 validation set을 나누어주는 `StratifiedKFold`를 사용하겠습니다. (Sklearn에서 제공해주는 train_test_split을 사용하지 않겠습니다.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "c6c962cc-6f38-4a00-bcd7-ce9d91db954c",
    "_kg_hide-input": true,
    "_uuid": "9f7b5d920703b3a3c8c0f62bc6042e4615bc8324"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========\n",
      "Train: [ 30473  30496  31002 ... 284804 284805 284806] Test: [    0     1     2 ... 57017 57018 57019]\n",
      "train Counter({0: 227452, 1: 393})\n",
      "test Counter({0: 56863, 1: 99})\n",
      "===========\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 30473  30496  31002 ... 113964 113965 113966]\n",
      "train Counter({0: 227452, 1: 393})\n",
      "test Counter({0: 56863, 1: 99})\n",
      "===========\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 81609  82400  83053 ... 170946 170947 170948]\n",
      "train Counter({0: 227452, 1: 394})\n",
      "test Counter({0: 56863, 1: 98})\n",
      "===========\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [150654 150660 150661 ... 227866 227867 227868]\n",
      "train Counter({0: 227452, 1: 394})\n",
      "test Counter({0: 56863, 1: 98})\n",
      "===========\n",
      "Train: [     0      1      2 ... 227866 227867 227868] Test: [212516 212644 213092 ... 284804 284805 284806]\n",
      "train Counter({0: 227452, 1: 394})\n",
      "test Counter({0: 56863, 1: 98})\n"
     ]
    }
   ],
   "source": [
    "sss = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)\n",
    "\n",
    "for train_index, test_index in sss.split(X, y):\n",
    "    print('===========')\n",
    "    print(\"Train:\", train_index, \"Test:\", test_index)\n",
    "    original_X_train, original_X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    original_y_train, original_y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "    print('train', Counter(original_y_train))\n",
    "    print('test', Counter(original_y_test))\n",
    "\n",
    "# CV 중 마지막 fold를 선택합니다.\n",
    "original_X_train = original_X_train.values\n",
    "original_X_test = original_X_test.values\n",
    "original_y_train = original_y_train.values\n",
    "original_y_test = original_y_test.values\n",
    "\n",
    "# Unique label에 대한 count\n",
    "train_unique_label, train_counts_label = np.unique(original_y_train, return_counts=True)\n",
    "test_unique_label, test_counts_label = np.unique(original_y_test, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.99827076 0.00172924]\n",
      "[0.99827952 0.00172048]\n"
     ]
    }
   ],
   "source": [
    "print(train_counts_label / len(original_y_train))\n",
    "print(test_counts_label / len(original_y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "956d34b9-e562-4b70-a2f8-fbe060273a83",
    "_uuid": "cc554c4ffec656cb38d01c034f2cd338e1cb4565"
   },
   "source": [
    "## 2.3 <font color='red'>Random Under-Sampling</font>\n",
    "<img src=\"http://glemaitre.github.io/imbalanced-learn/_images/sphx_glr_plot_random_under_sampler_001.png\">\n",
    "\n",
    "**Random Under Sampling** : Imbalanced data에서, data의 균형을 맞추기 위하여, 데이터를 **랜덤하게 제거**하는 과정입니다. 이 과정을 통해서, overfitting을 피할 수 있습니다.\n",
    "\n",
    "- Imbalanced label의 비율 및 카운트를 구합니다.\n",
    "- 훨씬 적은 라벨의 수에 맞추어(Fraud), major label에서 label를 추출하여(Non fraud, normal) 1:1의 비율을 맞춥니다.\n",
    "- 추출된 데이터를 merge하고, 데이터를 shuffle 합니다.\n",
    "\n",
    "이 과정에서 *당연히* 정보의 손실(information loss)가 발생합니다. 우리의 문제에서는 492개의 non-fraud transaction을 284,315개의 non-fraud transaction에서 뽑는 것이기 때문에 많은 양의 정보 손실이 발생할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "f0acfc44-eb2a-4356-ad03-d0c12807acd7",
    "_kg_hide-input": true,
    "_uuid": "e3a2b89752681164f14c8273452fc66734d7f41b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>scaled_amount</th>\n",
       "      <th>scaled_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>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V13</th>\n",
       "      <th>V14</th>\n",
       "      <th>V15</th>\n",
       "      <th>V16</th>\n",
       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</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>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>127888</th>\n",
       "      <td>-0.171872</td>\n",
       "      <td>-0.072639</td>\n",
       "      <td>1.460847</td>\n",
       "      <td>-1.074161</td>\n",
       "      <td>0.806203</td>\n",
       "      <td>-1.373100</td>\n",
       "      <td>-1.602463</td>\n",
       "      <td>-0.321650</td>\n",
       "      <td>-1.296488</td>\n",
       "      <td>-0.033152</td>\n",
       "      <td>-1.684100</td>\n",
       "      <td>1.367716</td>\n",
       "      <td>-0.198279</td>\n",
       "      <td>-0.523943</td>\n",
       "      <td>1.127650</td>\n",
       "      <td>-0.581928</td>\n",
       "      <td>1.030338</td>\n",
       "      <td>-0.468772</td>\n",
       "      <td>0.619806</td>\n",
       "      <td>-0.211164</td>\n",
       "      <td>-0.802999</td>\n",
       "      <td>-0.320319</td>\n",
       "      <td>-0.028066</td>\n",
       "      <td>0.402251</td>\n",
       "      <td>0.024936</td>\n",
       "      <td>0.093987</td>\n",
       "      <td>0.287697</td>\n",
       "      <td>-0.075641</td>\n",
       "      <td>0.072501</td>\n",
       "      <td>0.024716</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64411</th>\n",
       "      <td>1.089779</td>\n",
       "      <td>-0.394236</td>\n",
       "      <td>-10.527304</td>\n",
       "      <td>7.639745</td>\n",
       "      <td>-13.443115</td>\n",
       "      <td>4.303403</td>\n",
       "      <td>-8.048210</td>\n",
       "      <td>-3.466997</td>\n",
       "      <td>-8.643193</td>\n",
       "      <td>7.284105</td>\n",
       "      <td>-2.362097</td>\n",
       "      <td>-5.996596</td>\n",
       "      <td>2.589104</td>\n",
       "      <td>-5.962261</td>\n",
       "      <td>-0.468955</td>\n",
       "      <td>-6.778184</td>\n",
       "      <td>0.907947</td>\n",
       "      <td>-3.970130</td>\n",
       "      <td>-6.857613</td>\n",
       "      <td>-2.511736</td>\n",
       "      <td>0.511762</td>\n",
       "      <td>0.847085</td>\n",
       "      <td>0.937416</td>\n",
       "      <td>-0.931178</td>\n",
       "      <td>-0.235697</td>\n",
       "      <td>-0.031393</td>\n",
       "      <td>0.591558</td>\n",
       "      <td>-0.263516</td>\n",
       "      <td>1.108897</td>\n",
       "      <td>0.219021</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78342</th>\n",
       "      <td>0.243834</td>\n",
       "      <td>-0.319670</td>\n",
       "      <td>0.133631</td>\n",
       "      <td>0.536547</td>\n",
       "      <td>1.116747</td>\n",
       "      <td>1.464957</td>\n",
       "      <td>0.026074</td>\n",
       "      <td>-0.330254</td>\n",
       "      <td>0.464883</td>\n",
       "      <td>-0.198005</td>\n",
       "      <td>0.507389</td>\n",
       "      <td>-0.542993</td>\n",
       "      <td>-0.635510</td>\n",
       "      <td>0.322065</td>\n",
       "      <td>0.420942</td>\n",
       "      <td>-1.831082</td>\n",
       "      <td>-0.508533</td>\n",
       "      <td>-0.484960</td>\n",
       "      <td>0.920510</td>\n",
       "      <td>0.367846</td>\n",
       "      <td>0.517373</td>\n",
       "      <td>0.147626</td>\n",
       "      <td>0.058681</td>\n",
       "      <td>0.651525</td>\n",
       "      <td>0.088175</td>\n",
       "      <td>0.335017</td>\n",
       "      <td>-0.933050</td>\n",
       "      <td>-0.443964</td>\n",
       "      <td>0.101112</td>\n",
       "      <td>-0.008271</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92777</th>\n",
       "      <td>3.813317</td>\n",
       "      <td>-0.242002</td>\n",
       "      <td>-6.133987</td>\n",
       "      <td>2.941499</td>\n",
       "      <td>-5.593986</td>\n",
       "      <td>3.258845</td>\n",
       "      <td>-5.315512</td>\n",
       "      <td>-0.637328</td>\n",
       "      <td>-4.476488</td>\n",
       "      <td>1.695994</td>\n",
       "      <td>-1.606743</td>\n",
       "      <td>-5.117259</td>\n",
       "      <td>3.977222</td>\n",
       "      <td>-8.066096</td>\n",
       "      <td>-0.760888</td>\n",
       "      <td>-8.660771</td>\n",
       "      <td>2.220238</td>\n",
       "      <td>-6.237597</td>\n",
       "      <td>-11.046382</td>\n",
       "      <td>-4.155879</td>\n",
       "      <td>1.763611</td>\n",
       "      <td>-0.815086</td>\n",
       "      <td>0.868340</td>\n",
       "      <td>0.793736</td>\n",
       "      <td>0.217347</td>\n",
       "      <td>-0.021985</td>\n",
       "      <td>0.145882</td>\n",
       "      <td>0.665088</td>\n",
       "      <td>-1.684186</td>\n",
       "      <td>0.310195</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156988</th>\n",
       "      <td>-0.307413</td>\n",
       "      <td>0.289066</td>\n",
       "      <td>0.745153</td>\n",
       "      <td>2.809299</td>\n",
       "      <td>-5.825406</td>\n",
       "      <td>5.835566</td>\n",
       "      <td>0.512320</td>\n",
       "      <td>-0.615622</td>\n",
       "      <td>-2.916576</td>\n",
       "      <td>0.776710</td>\n",
       "      <td>-1.878832</td>\n",
       "      <td>-4.546936</td>\n",
       "      <td>4.698795</td>\n",
       "      <td>-8.507786</td>\n",
       "      <td>0.718249</td>\n",
       "      <td>-7.628710</td>\n",
       "      <td>-2.249578</td>\n",
       "      <td>-2.558669</td>\n",
       "      <td>-5.304624</td>\n",
       "      <td>-0.956938</td>\n",
       "      <td>-0.585322</td>\n",
       "      <td>0.402837</td>\n",
       "      <td>0.284841</td>\n",
       "      <td>-0.874383</td>\n",
       "      <td>-0.083995</td>\n",
       "      <td>-0.651442</td>\n",
       "      <td>0.454594</td>\n",
       "      <td>0.050376</td>\n",
       "      <td>0.756953</td>\n",
       "      <td>0.383869</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        scaled_amount  scaled_time         V1        V2         V3        V4  \\\n",
       "127888      -0.171872    -0.072639   1.460847 -1.074161   0.806203 -1.373100   \n",
       "64411        1.089779    -0.394236 -10.527304  7.639745 -13.443115  4.303403   \n",
       "78342        0.243834    -0.319670   0.133631  0.536547   1.116747  1.464957   \n",
       "92777        3.813317    -0.242002  -6.133987  2.941499  -5.593986  3.258845   \n",
       "156988      -0.307413     0.289066   0.745153  2.809299  -5.825406  5.835566   \n",
       "\n",
       "              V5        V6        V7        V8        V9       V10       V11  \\\n",
       "127888 -1.602463 -0.321650 -1.296488 -0.033152 -1.684100  1.367716 -0.198279   \n",
       "64411  -8.048210 -3.466997 -8.643193  7.284105 -2.362097 -5.996596  2.589104   \n",
       "78342   0.026074 -0.330254  0.464883 -0.198005  0.507389 -0.542993 -0.635510   \n",
       "92777  -5.315512 -0.637328 -4.476488  1.695994 -1.606743 -5.117259  3.977222   \n",
       "156988  0.512320 -0.615622 -2.916576  0.776710 -1.878832 -4.546936  4.698795   \n",
       "\n",
       "             V12       V13       V14       V15       V16        V17       V18  \\\n",
       "127888 -0.523943  1.127650 -0.581928  1.030338 -0.468772   0.619806 -0.211164   \n",
       "64411  -5.962261 -0.468955 -6.778184  0.907947 -3.970130  -6.857613 -2.511736   \n",
       "78342   0.322065  0.420942 -1.831082 -0.508533 -0.484960   0.920510  0.367846   \n",
       "92777  -8.066096 -0.760888 -8.660771  2.220238 -6.237597 -11.046382 -4.155879   \n",
       "156988 -8.507786  0.718249 -7.628710 -2.249578 -2.558669  -5.304624 -0.956938   \n",
       "\n",
       "             V19       V20       V21       V22       V23       V24       V25  \\\n",
       "127888 -0.802999 -0.320319 -0.028066  0.402251  0.024936  0.093987  0.287697   \n",
       "64411   0.511762  0.847085  0.937416 -0.931178 -0.235697 -0.031393  0.591558   \n",
       "78342   0.517373  0.147626  0.058681  0.651525  0.088175  0.335017 -0.933050   \n",
       "92777   1.763611 -0.815086  0.868340  0.793736  0.217347 -0.021985  0.145882   \n",
       "156988 -0.585322  0.402837  0.284841 -0.874383 -0.083995 -0.651442  0.454594   \n",
       "\n",
       "             V26       V27       V28  Class  \n",
       "127888 -0.075641  0.072501  0.024716      0  \n",
       "64411  -0.263516  1.108897  0.219021      1  \n",
       "78342  -0.443964  0.101112 -0.008271      0  \n",
       "92777   0.665088 -1.684186  0.310195      1  \n",
       "156988  0.050376  0.756953  0.383869      1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터를 shuffle 한다.\n",
    "df = df.sample(frac=1)\n",
    "\n",
    "# 데이터 비율에 맞게 추출한다.\n",
    "fraud_df = df.loc[df['Class'] == 1]\n",
    "non_fraud_df = df.loc[df['Class'] == 0][:492]\n",
    "\n",
    "# 데이터를 merge 한다.\n",
    "normal_distributed_df = pd.concat([fraud_df, non_fraud_df])\n",
    "\n",
    "# 데이터를 shuffle 한다.\n",
    "new_df = normal_distributed_df.sample(frac=1, random_state=42)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subsample에서의 데이터 비율\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    0.5\n",
       "0    0.5\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Subsample에서의 데이터 비율')\n",
    "new_df['Class'].value_counts() / len(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "73454100-dc69-49fd-b1b2-f72e326bca5d",
    "_kg_hide-input": true,
    "_uuid": "68b42e92df59f10fbd3ba700389796c4506af604"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Class', data=new_df)\n",
    "plt.title('Equally Distributed Classes', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0abc31ee-a78e-43af-822f-f06772d00c1c",
    "_uuid": "88477bac6687f110e9d64ec22837c250d85d2a2b"
   },
   "source": [
    "# 3. EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Correlation 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Imbalanced data (원래 데이터에서의 상관관계분석)\n",
    "\n",
    "대부분의 라벨이 0이기 때문에 대부분의 상관관계가 0에 가깝게 나옵니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_cell_guid": "9f353623-9435-4bb2-b854-b4a201ec7dd9",
    "_kg_hide-input": true,
    "_uuid": "e2f417c5d7c633a1e3cdfaa78acd6bd77a38400e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,\"Imbalanced Correlation Matrix \\n (don't use for reference)\")"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax1 = plt.subplots(1, 1, figsize=(24,20))\n",
    "\n",
    "corr = df.corr()\n",
    "sns.heatmap(corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax1)\n",
    "ax1.set_title(\"Imbalanced Correlation Matrix \\n (don't use for reference)\", fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Subsample data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'SubSample Correlation Matrix \\n (use for reference)')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax2 = plt.subplots(1, 1, figsize=(24,20))\n",
    "\n",
    "sub_sample_corr = new_df.corr()\n",
    "sns.heatmap(sub_sample_corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax2)\n",
    "ax2.set_title('SubSample Correlation Matrix \\n (use for reference)', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 음의 상관관계가 강한 특성들 : `V17`, `V14`, `V12`, `V10`\n",
    "\n",
    "Feature의 값이 작을수록 더욱 fraud 같다는 관계를 보여주는 특성들입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "2f02c21f-daa3-4251-a8e9-acad09a5ce0f",
    "_kg_hide-input": true,
    "_uuid": "318d0e7e0443f99139be21c00a7abc663be26385"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'V10 vs Class Negative Correlation')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V17\", data=new_df, ax=axes[0])\n",
    "axes[0].set_title('V17 vs Class Negative Correlation')\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df, ax=axes[1])\n",
    "axes[1].set_title('V14 vs Class Negative Correlation')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V12\", data=new_df, ax=axes[2])\n",
    "axes[2].set_title('V12 vs Class Negative Correlation')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V10\", data=new_df, ax=axes[3])\n",
    "axes[3].set_title('V10 vs Class Negative Correlation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 양의 상관관계가 강한 특성들 : `V11`, `V4`, `V2`, `V19`\n",
    "\n",
    "Feature의 값이 클수록 더욱 fraud 같다는 관계를 보여주는 특성들입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "b457b10e-c17c-4cb2-9719-6d4128377c9f",
    "_kg_hide-input": true,
    "_uuid": "7bfc46c028f8602ee949de83629082633aa47b2c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'V19 vs Class Positive Correlation')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V11\", data=new_df,ax=axes[0])\n",
    "axes[0].set_title('V11 vs Class Positive Correlation')\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V4\", data=new_df, ax=axes[1])\n",
    "axes[1].set_title('V4 vs Class Positive Correlation')\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V2\", data=new_df, ax=axes[2])\n",
    "axes[2].set_title('V2 vs Class Positive Correlation')\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V19\", data=new_df, ax=axes[3])\n",
    "axes[3].set_title('V19 vs Class Positive Correlation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Anomaly Detection:\n",
    "<img src=\"https://discourse-cdn-sjc1.com/business6/uploads/analyticsvidhya/original/2X/d/d11281b44c2e440b36aaf29156b5032105d2d06b.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "각 feature 별로, target variable(calss)에 강한 상관관계를 가지는 extreme outliers를 제거 할 것입니다.\n",
    "\n",
    "V17은 fraud class에 outlier가 없습니다. V17은 제외하고 음의 상관관계가 강했던 데이터를 살펴보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "9c690dfa-8fed-44e5-99f5-ff4eb6f87f16",
    "_kg_hide-input": true,
    "_uuid": "b6963900379db5b0d4adf92f8c7f959164e9119f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kakao/.pyenv/versions/3.6.3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6448: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/kakao/.pyenv/versions/3.6.3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6448: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/kakao/.pyenv/versions/3.6.3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6448: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'V10 Distribution \\n (Fraud Transactions)')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B3g2IaQzwdty2+PeNVeLu78duMLOBZva7yNDQIoILfGdgcNyxa6M/uHs5wVzm6PDWPwN9ga1m9pCZXR+p0kfPcUZkuOa2yN/L9siu6DkmAsvd/XgTPtsYYKsHQ2mjca4juKiPjWm3xd3LYt7vjX4Od68huIvzmJnNNbP7zGxEgnOV89G/HRFpv5QjlCOUI0SkPsoTyhPKE+2MCkYSK7rwXEGCfWXuviXmtSduf2nsGzM7HXiR4GL4CYIhk38X2Z0d+TNMMDQyVlZjg2+E0gTbHiO4QH6JYCjpBIIKfXZcu6q4907k/yd33wKMIBhCWwH8GlhqZjlmFgJeIxje+XngPD6asxt/jnSJXVCuzs8B4O5fB8YBrwIzgPfM7Oa4Y3oAhWmIU0RaF+UI5QjlCBGpj/KE8oTyRDujgpHE+oCgCj72VA2TcC7Bv6+vuvtSd98EDIxrUwh0MbP8mG3xj9fcAJwfty3+fUpE5v1eAPzM3V919/UEF8FeDe3L3cvc/Tl3/zLBXN5JkddpBNX/77n7G+6+AegZd/gqYHKCuc5RlQRDPOuzATjdzPpFN5jZWUA34L0GfpaN7v4zd78CeJyP5pVjZkbw72VlQ/oUkTZJOUI5ItFnUY4QkSjlCeWJRJ9FeaINU8FIPuTBYyDnEyxs1lSbCSr8XzazYWb2aYJKe6ylBEMQf2hmI8zsRuCuuDa/AGab2TfMbKQFK/fPSUF8J4kMm/wAuM3MRkdW8P9TJMakmdldZna7mZ0ZuTtyG8Ec4K3AAYJE+kULnjxwGfCzuC5+HznnMxY8zeB0C57AMDWyfzsw0szGmVkvM0t0N+EFYBvwJzObaGbnA38AFrn7siQ/Rw8z+7mZTbfgSQtTCRJsbJI4E+gKvJ5MnyLSdilHKEfEfQ7lCBGpRXlCeSLucyhPtAMqGEm8h4BPRirkjebuK4GvAF8nuCjcDnwjrk0hwVMArgTeJag23x/XZgnwBYIEsZZgsbvvNSW2U/gMwdMC1gCPEMy7PdjAPoqAfwDeIoh5FnCNRx7DCdxM8JSH9QQX+K/GHuzuR4HpkX5eJfi7+RbBYngQzGteRLBAYCEJkp4Hjyn9GMEw1iUEi8ltAW5qwOeoJPi7eBTYRDBv/DXg2zFtbgGec/eG/h2JSNukHKEcEaUcISKJKE8oT0QpT7QD0RXmRT5kZm8D/+Xuf2zpWKT1MrPOBIljtruvPlV7EWkflCMkGcoRIh2X8oQkQ3mibdAII0nkLvRvQ05tKPAtXeBFOhzlCEnGUJQjRDoq5QlJxlCUJ1o9jTASEREREREREZFaVPkVEREREREREZFaVDASEREREREREZFaMls6gHi9evXyoUOHtnQYIiKt0ooVKw65e++WjqMlKU+IiCSmHBFQnhARSayheaLVFYyGDh3K8uXLWzoMEZFWycx2tHQMLU15QkQkMeWIgPKEiEhiDc0TmpImIiIiIiIiIiK1qGAkIiIiIiIiIiK1qGAkIiIiIiIiIiK1qGAkIiIiIiIiIiK1qGAkIiIiIiIiIiK1qGAkIiIiIiIiIiK1qGAkIiIiIiIiIiK1qGAkIiIiIiIiIiK1qGAkIiIiIiIiIiK1qGAkIiIiIiIiIiK1qGAkIiIiIiIiIiK1qGAkIiIiIiIiIiK1qGAkIiIiIiIiIiK1ZLZ0ACIdSXj1wgYfE5pwScrjEBGRjm1+ze5GHzszY1AKIxERkdZg/46apNr1G5KR5kikNdEIIxERERERERERqUUFIxERERERERERqUUFIxERERERERERqUUFIxERERERERERqUUFIxERERERERERqUUFIxERERERERERqUUFIxERERERERERqUUFIxERERERERERqSWzpQMQERERkYabX7M7qXbuzlEqOeIVdLccupNNpumeoYiIiNRPBSMRERGRdsjdWe9Hec+PUURlZGMwvHyc9WCy9SZk1qIxioiISOulgpGIiIhIO3PCa3gzvI8dlNCPXM6yvvS1XIqoZIeXsMaPsN/LuTQ0gM6W1dLhioiISCuU1HhkM5ttZu+b2RYzuy/B/ulmttLMqs3shrh9t5nZ5sjrtlQFLiIiIiInK/Nqng1vZyclnG99uDo0mLGhAnpaJ063rswIDWCGDeAIJ3gpvJNKr2npkEVERKQVOmXByMwygF8DVwJjgVvMbGxcs53A7cCf447tAXwHmAKcB3zHzAqaHraIiIiIxKv2MK+Fd1NGNR8LDWZcqAeWYNrZiFBXrggNopgqlvgB3L0FohUREZHWLJkpaecBW9x9K4CZPQZcC7wXbeDu2yP7wnHHXgHMc/cjkf3zgNnAX5ocuYiIiEgbl+zC1clwdxaE93KICmaFBtLf8upt39/yOMd6scIPMZA8Rlv3lMUiIiIibV8yU9IGArti3u+ObEtGU44VERERkSSt8SNsj0xDG2JdkjpmgvVkAHm85Qco9ao0RygiIiJtSat4pqqZfcHMlpvZ8sLCwpYOR0RERKRNOeonWOGHGEYXzmrA7P+QGdNC/ajBWRMMCBcREREBkisY7QFOi3k/KLItGUkd6+4Puftkd5/cu3fvJLsWERERkbA7i8L7yCLEhaG+Cdcsqk9Xy2aUdWODH6NEo4xEREQkIpmC0TvASDMbZmbZwM3A80n2/xowy8wKIotdz4psExEREZEUWOdHOEgFF1of8iyZ5SlPNtF64jhr/HCKoxMREZG26pQFI3evBu4mKPRsAJ5w9/Vm9oCZzQEws3PNbDdwI/Cgma2PHHsE+FeCotM7wAPRBbBFREREpGnKvZqVfpjTyGe4dW10P10sm9HWnY1epFFGIiIiAiT3lDTc/WXg5bht98f8/A7BdLNEx/4O+F0TYhQRERGRBFb5YaoJMyXUp8FT0eJNtJ6878d4z49ynvVJUYQiIiLSVrWKRa9FREREpGGOeyUb/CijrTsFltPk/jpbFqfRmU1eRNg9BRGKiIhIW6aCkYiIiEgb9I4XEsKYZL1S1ufoUDfKqWEXJSnrU0RERNqmxq2MKCKEVy9s6RBERKSDOuIVbPViJlrPRi90nchgOpNLBu+HixiS0SVl/YqIiEjboxFGIiIiIm3Maj9CFiHGWY+U9hsyY6R1YycllHl1SvuWjsvMZpvZ+2a2xczuS7B/upmtNLNqM7shZvsEM3vbzNab2Voz+2TzRi4i0rGpYCQiIiLShhz3Srb6cc6w7uRYRsr7H23dcGCzF6W8b+l4zCwD+DVwJTAWuMXMxsY12wncDvw5bnsZ8Fl3PxOYDfzczLqnN2IREYlSwUhERESkDVnrRzCMcVaQlv67Ww59yWWLH09L/9LhnAdscfet7l4JPAZcG9vA3be7+1ogHLd9k7tvjvy8FzgI9G6esEVERAUjERERkTaizKvZ5EWMsq7kW1bazjPMunCEExz3yrSdQzqMgcCumPe7I9saxMzOA7KBD+rY/wUzW25mywsLCxsVqIiI1KaCkYiIiEgbscGPUoNztvVM63mGWmcAtntxWs8jkgwz6w/8Eficu4cTtXH3h9x9srtP7t1bg5BERFJBT0kTERERaYWOHzrC3s3bKT1aREZWJr2GDOS9IZWclplPN8tO67m7WDY9yWG7l3A26S1OSbu3Bzgt5v2gyLakmFlX4CXgn919aYpjExGReqhgJCIiItJKVFVWsuyZ13j76VfZuX7TSfszuuUxbvZ0Cm+7md6DGzyrp0GGWhdW+CHKvJo806+M0mjvACPNbBhBoehm4FPJHGhm2cAzwB/c/an0hSgiIoko+4uIiIi0AusX/43HH/j/OLrvIKeNHcmcez/PkHFn0LVnAVUnKnl58woOLFnL+mffYO1Tc5n2yTnMuffzZOd2Sks80YLRdi9mbJoW2Jb2z92rzexu4DUgA/idu683sweA5e7+vJmdS1AYKgCuMbPvRZ6MdhMwHehpZrdHurzd3Vc3/ycREel4VDASSRMPh+HwXnzvVigrhlAIsnKwwWdAz/6YWUuHKCIirUBNVTX/9+//zeK/PE//EUP5x4d+xOgLzqmVJw57BaHRNVx3zeUMPWK88l9/5M0/P8uGt5Zz58+/Q/8RQ1MeVwHZdCWL7V7CWFQwksZz95eBl+O23R/z8zsEU9Xij3sUeDTtAYqISEIqGImkgRfuwde/DSfKICsHuvUCD8PxI/iK+dC9N5x5Ada5e0uHKiIiLaiitIz/ved7vL90JTNu+wTX3HMHWdknr0/0nh8jA2OUdadTrww+ef89TLhiOn/45o/42a33cOfPv8Po889JaWxmxlDrwrt+hEqvIdsyUtq/iIi0Pft31CTdtt8Q5Y22TgUjkUZwd3YeKGTNpq2UVlSQ36kT40YMZXDvnrB5Bex8Hzp3x8acB70HYqHgYunhGtizBd+yBn9nHkyZjeV1aeFPIyIiLaG0tJT//uK32b52A5/+169y/vWzE7ar8jAf+HFOty50iinajJ4yka/+5Zf85h/+md/8w79w16//jTMuSG3RaLB1Zq0fYQ9lDEP5SkREpCNRwUikAdyd+fPn8+CDD7J+/fqT9o/t2507x/Rh5vSphEZNwjJqV9UtlAGnjYaCvviyV/Hl84OiUU5uc30EERFpBaqqqrj77rvZvnYDt//7t5l4xcV1tt3mxVQRZrSdPCq1R/8+3PPwT/nF577GQ1+6ny/9778zbMLYlMXZl1yyCLHbSxlmKhiJiIh0JKGWDkCkrTh69Cj/9E//xJe//GVKSkr41m038syP/4WF//0jnvnht7hvxnjKyiv4ysJN3PvySo6VltfZl3Xujk26DCrL8dULcfdm/CQiItKS3J0HHniApUuXcsv3vlJvsQhgkx+jK1n0I/HNhfzuXbn7f39Mt949+Z8vf4cjew+kLNaQGQPIY7eXKleJiIh0MCoYiSRhz549fOpTn2LBggV89atf5aWXXuLW2ZcyavBAenXryogjW/j0kDye+5e/5+uf/gSL16zn09/5D3YfPFRnn9a9dzBl7Vgh7N/efB9GRERa1OOPP85TTz3FXXfdxfnXXVFv2+NeyT7KGW3d631YQpeeBdz1qweorqrif+75LlUnKlMW7yDLp4QqikhdnyIiItL6qWAkcgp79+7l1ltv5fDhwzz88MPceeedZMRONdu2Dgp3Y2ecS+Zpo7j96pn87l/+iaPFJXzmuz9hb+HhujsfMBy6FOCbVuI1yS8gJyIibdN7773HD3/4Q6ZNm8aXv/zlU7bf5EUYMNK6nrJtv+FD+OwP72P3hi08+9OHUhBtYJDlA7DbS1PWp4iIiLR+KhiJ1OP48ePcddddlJSU8MgjjzBp0qRa+/3wPnzzaug3FAaf8eH2iaOG88j9X6GispK7fvQrjpUk/iXbzLDRk6GiFHZuSOdHERGRFlZRUcHXv/51unfvzo9+9CNCofp/DQu7s8mLGEQ++ZaV1DnOuuR8Znz2Eyz683O8u/DtVIRNV8umK1kqGImIiHQwKhiJ1MHd+eY3v8mOHTv45S9/yZgxY2rvr6zA1y6G/K7YmRecNFVg1OCB/PKrf8+ug4e479cPEw6HE57HevaH3gPxre/ilSfS9nlERKRl/fKXv2Tr1q384Ac/oEePHqdsv58ySqlmpHVr0Hnm3Pt5Bow6nce+93PKioobG24tgyyffZRR7YlzmYiItF8nyp09H9RwYGcYD2s9u45EBSOROjzyyCMsXLiQb3zjG5x//vkn7ff3V0DVCWz8dCwz8Z3fyWNG8s3P3MDi1et5+MX5dZ7LRkyE6irYtzVl8YuISOuxbt06Hn74YW666SamTp2a1DFb/DhZhBhinRt0rsysLG79t69RcuQYT//HbxoT7klOs85U4xyg7gc6iIhI++LuHNkfZs+WMFUnoLTIVTTqYFQwEklg8+bN/OxnP+Oyyy7j05/+9En7wzs3wt4PYNhZWJeCevu6+fLpzJpyDr944jk27dyTsI117QFde+K7t+gpNCIi7Uw4HOb73/8+PXv25Gtf+1pSx9R4mG1ezBDrTKY1/Ne108aO5LLbb2LZs3PZsuLdBh8frz95GLDXy5rcl4iItA0VpXCs0OnczThtdIheA4yyYjiwS6NNOwoVjETihMNh7r//fvLz83nggQdOmmrm1VWEX3sEcjs8G7Z6AAAgAElEQVRjp487ZX9mxv133EKXvDz+30N/pKauqWkDR0DJUThezyLZIiLS5rzwwgusXr2ae++9ly5duiR1zC5KqSTMiCQWu67LFXd9ioJ+vXny335JTXXTHqyQZSF6k6uCkYhIB3LsUJiMTOg1yMjIMLr2DFHQxyg7HkxTk/ZPBSOROI8//jirV6/mvvvuS7jGhC+fB0cOYGOnYBmZSfVZ0LUz37rtJtZ9sIPH5r6ZuFH/YRDKwPdsaUr4IiLSipSWlvLTn/6UcePGcd111yV93BY/TicyGEh+o8+dk5fLx7/59+zdvI2/PvVSo/uJGmB5FFJOhVc3uS8REWndKiuc8mLo2sMIhT66gd61p4FB8VEVjDoCFYxEYhQVFfGLX/yCKVOmMGfOnJP2e0UZ4WWvYMPPxnoNbFDfV104mQvOOoNfPfViwqemWVY29BsC+7bhNfplXESkPXjwwQcpLCzk29/+9imfihZV6TXs9BJOty6E4ka5NtT4mRdx+jln8dpv/sSJsqatP9Tf8nBgoxc1qR8REWn9ig45ZpECUYyMTCO/q1FyzLWWUQeggpFIjAcffJCioiK++c1vnjQVDSD8zmtQUUpo2vUN7tvM+MZnbqCkrJz//r/Ed3pt4Ihg8esDOxrcv0hLMrPZZva+mW0xs/sS7M8xs8cj+5eZ2dDI9iwze8TM3jWzDWb2reaOXSRddu/eze9//3uuvfZaJkyYkPRx272EGrxJ09GizIw5//R5jh86wpt/erZJffUllxDwXvhIk+MSEZHWq7rKKT7mdCkwMjJP/k7UpcAI10Bpah7EKa2YCkYiEXv27OHRRx/l+uuvZ8yYMSft97JifPlcbPRkrO+QRp1j1OCB3HDpRTw27012Hzx0coOCvtApDz+ws1H9i7QEM8sAfg1cCYwFbjGzsXHNPg8cdfcRwH8CP45svxHIcfdxwCTgrmgxSaSte/DBBzEz7r333gYdt82Pk08mfchNSRzDzzmLsy4+n/m/e5zSouON7ie6jtF6P5qSuEREpHUqLXLwk0cXReV2hoxMKD6qxa/bOxWMRCJ+85vg0cNf+tKXEu4PL3sFqioJXZT8GhSJfPHjVxEKhfjN0y+ftM/MoPcgOLwPDzdtgVKRZnQesMXdt7p7JfAYcG1cm2uBRyI/PwVcZsEwPgfyzSwTyAUqgcZ/oxVpJXbv3s2zzz7LTTfdRN++fZM+rtJr2E0Zw6xLwpGujXXNPXdQUVLG/N8+3qR+BlgeW72Ycq1jJCLSbpWXOJnZkN0pcR4yM7oUGOXFwWgkab9UMBKh9i/2/fr1O2m/V5ThqxZgY6ZgvQY06Vx9e3TnkzOn8/ziZezYd/Ck/dZrENRUw5EDTTqPSDMaCOyKeb87si1hG3evBoqAngTFo1JgH7AT+Im7nzTfxcy+YGbLzWx5YWFh6j+BSIpFRxfdeeedDTpuh5cQxjk9BdPRYg0YNYzJV1/Gm396lmMHEoxwTVJ/yyOMs9GPpTA6ERFpLTzslJdAXuf6b1rkdwv2l5eoYNSeqWAkAjz4w+8RMvj8hWcRXr3w5NcrD0PVCejW88NtTXHnnCvIyszgf59/7eSdPfsFT0s7tKdJ5xBpI84DaoABwDDgq2Z2enwjd3/I3Se7++TevXs3d4wiDbJnz55GjS4C2ObFkelonVIe11X/+Flqqqt545GnGt1HX3LJwFgf1rQ0EZH2qKIM3CG3S/0Fo+xOEMqAipOf5SPtiApG0uEVFhby3KKlXH/xBfTt0f2k/R4O4zs3QEFfrGvPlJyzV/euXH/JhTy/eBkHjtS+S2sZmdCjHxTuTsm5RJrBHuC0mPeDItsStolMP+sGHAY+Bbzq7lXufhD4KzA57RGLpFF0dNHf/d3fNei4YDpaacqno0X1GtSfc2bP4K9PvkRZUeNWKs20ECOtG+9pHSMRkXapLDJiKDe//nZmRqc8qCjVCKP2TAUj6fAeffRRqmtquO1jMxM3OLADKsqwofFr+DbN7R+bibvzx1deP2mf9RoIZcV4qZZykTbhHWCkmQ0zs2zgZuD5uDbPA7dFfr4BeMPdnWAa2qUAZpYPnA9sbJaoRdJg7969PPPMM40aXbQz8nS0YdYlTdHBzDtuorK8gsWPxf8vmrwzrYCtfpwyrWMkItLulBc7nfIhlHHqGxed8o2qSq1j1J6pYCQdWmlpKX/5y1+4/LyJDOnX56T97o7v2AB5XYLFqFNoUJ9eXHH+OTzx+hJKyytq7+wdWf5Fo4ykDYisSXQ38BqwAXjC3deb2QNmNifS7LdATzPbAnwFuC+y/ddAZzNbT1B4etjd1zbvJxBJnUcffRR354477mjwsdu8mDwy6Zuip6MlMnD06Yyddi4L//QslRUnGtXH2FABDlrHSESknamuciorIPcU6xdF5eYH7TTKqP1SwUg6tJdeeoni4mI+e9WliRscPwxFh7DBY9IyPeDW2TMoLa/gxSV/q7Xd8rpAfjetYyRthru/7O6j3H24u38/su1+d38+8nOFu9/o7iPc/Tx33xrZXhLZfqa7j3X3/2jJzyHSFKWlpTz55JPMmjWLAQMa9oCEag+zi1KGWue05JtYMz/3SUqOHGPZs3Mbdfwo60YmxvrwSevTi4hIGxZdwDrvFOsXRWXngoW0jlF7poKRdFjuzmOPPcbo0aOZMPKkNXaDNnu2BKu5DUi8v6nOHjGMMUNP47H5iwhm58ToNQCOHsRrNORfRKQtePrppykpKeG22247deM4eyilBmdoGqejRY0492yGjDuDN37/JDXVNQ0+PtsyGKV1jERE2p3y0uCrT3aSz12IrmNUrhFG7ZYKRtJhrVmzhg0bNnDzzTcnvJvrNdWwbxv0G4JlZaclBjPjkzOns2nnHlZt2lp7X0EfCNfA/h1pObeIiKROTU0Nf/zjH5kwYQLjx49v8PHbvYRsQvQnLw3R1WZmzLzjJg7t3sfaN/7aqD7GWgHbvJhSr0pxdCIi0lJOlDk5uTRopGtuvlF1AmqqVTRqj1Qwkg7rscceIy8vj2uuuSZxgwM7oboKGzgirXF8bOq5dM7txOPzFtXe0T1YU8l3b07r+UVEpOkWLFjArl27GjW6KOzODi9hsHUmlObpaFFnX3ohPQb0ZfFjLzTq+DNDPbSOkYhIOxKucapOQE5ew/JQpw/XMUpHVNLSVDCSDuno0aO88sorzJkzh/z8xM+M9N2bIbcLFDTsKTcNldcph+suvoDXlq3kcNFHT0WznFzI64Lv3pTW84uISNM98sgjDBgwgJkz63jiZj0OUM4JahhqndMQWWKhjAym3nQ1m/+2mv0fNHwk6wjrShYh1oc1LU1EpD04EXkGT6fchhWMcnIBg4oyjTBqj1Qwkg7p2WefpbKykltuuSXhfi8rhqMHsIHD0774KMBNM6dRVV3N0wvfrr2joA++Zwvu4bTHICIijbNx40aWL1/OrbfeSmZmZoOP3+7FZGAMovkKRgAXfHw2mVlZLH684aOMousYrdc6RiIi7cKJSMEnp4Ezoy1kZOdAZYUKRu2RCkbS4YTDYR5//HHOOeccRo0albCN7/0g+GHg8GaJafjA/pw3dhRPvr6YcPij4pAV9IXyEji8v1niEBGRhnviiSfIycnh+uuvb/Cx7s52L2Yg+WRZ8/5a1qVHdyZeMZ2/PTePE2XlDT5+bKiAHV5MidYxEhFp806UQ2YWZGQ2/GZ5Tq5xopyTH+IjbZ4KRtLhrFq1ih07dnDjjTcm3O/usG879OiHdUo8XS0dPnHpVPYUHmbl+x98tPHDdYw0LU1EpDUqKyvjhRde4IorrqB79+4NPv4oJyihmiHNOB0t1rSb51BRWsY7L77e4GPHWoHWMRIRaSeiC143Rnan4Fk9erhz+6OCkXQ4L7zwArm5uVx++eWJGxQfgbLjWL+hzRrXpZPGk5uTw/OLl320Ma8L5HXVwtciIq3UK6+8QklJCTfddFOjjt/pwSqhg1uoYDR0/BgGnTGcxX95vsF3hqPrGL2ndYxERNq0qhNOdVXDF7yOyomse1TZ8MGq0sqpYCQdSmVlJa+++iozZ86se7Hr/dvBDPoObtbY8jrlcPl5E5i7bCUnKoPh/WaGDRqpgpGISCv1xBNPMHz4cM4555xGHb/TS+hFJ/Ks4WsfpYKZMe3mOezdvI2tq9Y36FitYyQi0j6UFEXWL2rggtdR2Z2CP09oHaN2RwUj6VDefPNNioqKmDNnTsL97g77d0DPAVj0yteM5kybQnFZOQtXrv1wmw0aCUWH8GL9Qi4i0pps3LiRtWvXcuONNzbqAQnHvZKDlDPYmm/6cyKTrppBTl4uS59+tcHHah0jEZG2r+RYtGDUuONDGUZmNlSWq2DU3rTM7SyRFvLCCy/Qq1cvzj///MQNig5BeQk2fHzzBhZx3pmj6VPQjReW/I0rzp8EgEUW3vZ9W7Euk1okLhEROdmTTz5JdnZ2nTchTmVN+DBOy01Hi8rJy2XiFRez8tWF3PDtfyQnL/lvDLHrGE223qdsP79md6PjnJkxqNHHiohI3UqOhcnKCQo/jZXTCU5UpDAoaRU0wkg6jKKiIhYuXMhVV11V52OPg+loIehzWvMGF5ERCvGxqeeyePU6jh4vCTb2Pg1CGUFsIiLSKpSXl3+42HVBQUGj+ljhh8glg140/4jWeBdcfwWV5RWsmruoQcdpHSMRkbavpMgbPR0tKjvXqK6EcI1GGbUnKhhJh/Haa69RVVVV/3S0Azug1wAsK7uZo/vINRdNobomzCtLlwMEsfQaEEyVExGRVmH+/PkUFxdzww03NOr4ag+zNnyY06xzo6azpdqwiWfSZ+gglj7zWoOO0zpGIiJtW1WlU3Xio3WIGiunU5DLNMqofVHBSDqM559/nuHDhzN27NjEDY4fgYoyrO+Q5g0szughgxg1eCAvxDwtzfoNxfdvb/ATbEREJD2ee+45Bg4cyOTJkxt1/CYvopTqFp+OFmVmTLl2Fh+seJfCnXsadKzWMRIRabvKioPvF9mdmjrCKPhT6xi1LyoYSYewb98+VqxYwdVXX13nnVw/uAsw6D2weYNL4GNTz2Xtlu3sLTwMBAUjykvg+OGWDUxERDhw4ABvv/02c+bMIRRq3K9SK8OHyMAYSF6Ko2u88+ZcjoVCLHt2boOOi65jtEGjjERE2pyy49GCUdP6ycgMXhph1L6oYCQdwrx58wCYPXt23Y0O7oSCPi3ydLR4s6YEj2ee+7dVQKRgBFrHSESkFXjxxRcJh8ONXuwagoLRmVZAtmWkMLKm6d63F2OmTmLZc/MI19QkfdxI60Y2IdZpHSMRkTanrNjJzAqKPU1hZmR3gqoKjTBqT5IqGJnZbDN738y2mNl9CfbnmNnjkf3LzGxoZHuWmT1iZu+a2QYz+1ZqwxdJzty5cxk1ahRDhw5NuN/LiqHkGNZCi13HG9y3N2cMGcS8SMGI3oO08LWISCvg7jz33HNMmDChzpxyKvu9jD2Uck6oV2qDS4Hzr7uCYwcKeX/pqqSPybIQZ1h31vmRNEYmbV0S3yemm9lKM6s2sxvi9t1mZpsjr9uaL2qR9q+s2MnrYilZTy8rx6g8gZbRaEdOWTAyswzg18CVwFjgFjOLXwTm88BRdx8B/Cfw48j2G4Ecdx8HTALuihaTRJpLYWEhK1euZNasWXU3Orgr+LOVFIwgGGW0etNWDhw4gGVmBVPltPC1iEiL2rhxI5s3b+baa69tdB+rwocAmNgKC0ZnXXIBuV3yWf7yGw06blyoB7u9lKN+Ik2RSVuW5PeJncDtwJ/jju0BfAeYApwHfMfMGvdoQhGpxd0/LBilQnYOeBhqtKRdu5HMCKPzgC3uvtXdK4HHgPjfkq4FHon8/BRwmQUlSgfyzSwTyAUqgeMpiVwkSfPnz8fd6y0Y+cFd0Lk7ltelGSOr3+XnTQQ+mk6nha9FRFrec889R1ZWVv1TnE9hZfgQA8ijn7We9YuisnKyGT9zGmvn/5XKiuSLP2dZDwDWhTXKSBI65fcJd9/u7muBcNyxVwDz3P2Iux8F5gGN/x9QRD50ohxqqiGva2oKRlmRhbMrde+g3UimYDQQ2BXzfndkW8I27l4NFAE9CYpHpcA+grsGP3HXeGVpXnPnzmXYsGGMGDEi4X4vL4FjB1vV6CKA0wf2Y8Sg/sydGyw+av2GQkUpFB1q2cBERDqo6upqXnzxRWbMmEH37t0b1Ue5V/OeH2VSqHeKo0udyR+bQUVpGevfXJr0MUOtC53J4l39mieJJfN9oknHmtkXzGy5mS0vLCxsdKAiHUn0CWmpHGEEUKl1jNqNdC96fR5QAwwAhgFfNbPT4xvpAi/pcuTIEd555x1mzZpV99PRtq0Dd6z3oGaO7tRmTTmH5cuXc+jQIS18LSLSwt5++20OHz7cpMWu3/UjVOOtcv2iqJHnjqdr7x4sf2lB0seEzDjTClgXPqKRsNIi3P0hd5/s7pN79269BVmR1iTVBaOMTCMjE6o0wqjdSKZgtAeIHXoxKLItYZvI9LNuwGHgU8Cr7l7l7geBvwKT40+gC7yky+uvv05NTU3909G2vgtZOdCtZzNGlpxZUybi7rz++uvBwtcZmSoYiYi0kJdffpkuXbowbdq0RvexMnyIPDIZZd1SGFlqhTIymHTlDN5b/DfKioqTPm5cqAeHOcE+ytIYnbRRyXyfSMexIlKPsuNOdi5kZqWmYATB16rKE7px0F4kUzB6BxhpZsPMLBu4GXg+rs3zQPSJBTcAb3hwe2kncCmAmeUD5wMbUxG4SDLmzZvHoEGDGDNmTML97uFghFGvAZile8Bdw40YNIChQ4fy6quvYhmZ0HPARwt0i4hIs6msrGT+/PlcfvnlZGdnN6oPd2dN+DDjrAeZrTDnxJp81Qyqq6pYPW9x0seMC0XXMTqarrCk7Urm+0RdXgNmmVlBZLHrWZFtItJEqVzwOio7x6iq0JPS2otT/rYSWZPoboIL8wbgCXdfb2YPmFl0TPZvgZ5mtgX4ChB9VOavgc5mtp4gUTwcWcxOJO2Ki4t5++23652Oxv4dUFaM9Up2Gn3zMjNmzZrFO++8Q1FREdZnEF64u6XDEhHpcBYvXkxJSQlXXXVVo/vY5aUc4QQTQq1vRGu8084cRe8hA1n+cvLT0vqSS286sTZ8OI2RSVuUzPcJMzvXzHYTPGX5wcj3ByLrn/4rwXeJd4AHtCaqSNOFw055iZOf4oJRVg6Ew8Fi2tL2ZSbTyN1fBl6O23Z/zM8VBBf3+ONKEm0XaQ6LFy+murqayy67rM42vvVdwKDXgOYLrIFmzJjBQw89xJIlS7iy9yB83Vt4WXGreqKbiEh79/LLL1NQUMCUKVMa3ccaDwop49tAwcjMmHzVpbz6m0c5duAQ3fuees0lM2N8qCd/De+n2sOtfhSVNK8kvk+8QzDdLNGxvwN+l9YARTqYijJwh9xUjzDqFDwsXesYtQ/K5NJuLVy4kIKCAsaPH19nm/DWd6H/UCy7UzNG1jBnn302PXv2ZMGCBR8+yU2jjEREmk9ZWRkLFizgiiuuIDMzqXttCa0JH2aQ5dPTWm/OiTX5Y5fi7qyeuyjpYyaGelFODRv9WBojExGRpiovCaaM5XZO/Qgj0JPS2gsVjKRdqq6uZtGiRUyfPp2MjIyEbby8BPZuxYaNa+boGiYUCnHxxRezaNEiqgv6Bxu1jpGISLNZuHAh5eXlXHnllY3uo8Jr2OBHGW+tf3RRVJ+hgxgw6nRWNWAdo7OsgEyM1ZqWJiLSqn1YMMpPbcEoIxNCGVCpEUbtggpG0i6tWrWKoqIiZsyYUWcb37YOcELDW3fBCOCSSy6huLiYVRs3Q343XAUjEZFm8/LLL9OnTx8mTZrU6D42+FGq8TYxHS3WxFnT2LZqPUUHDyXVvpNlMsYKWOXJtRcRkZZRUepk5aT2CWkQTE/OyoEqPSmtXVDBSNqlhQsXkpWVxdSpU+ts49vWQ6d86DesGSNrnAsvvJDs7GwWLFiA9dbC1yIizaW4uJhFixYxe/bsOkesJmNN+DDZhBhj3VMYXfpNuHxaMC1t/pKkj5kY6sluL+Wgl6cxMhERaYryEk/5dLSo7ByjsiItXUszU8FI2qUFCxZw7rnn0rlz54T73R3fvh4bMgYLtf7/DfLz85kyZQoLFizAew+CQ3vwcE1LhyUi0u4tXLiQqqoqZs+e3aR+1oQPM9YKyLbGF51aQr/hQ+g3fAir5yY/LW1iKFggW9PSRERar/IST/l0tKisHAjXQFWlRhm1da3/m7JIA23fvp1t27bVOx2Nw/ug5Bg29Mxmi6upZsyYwc6dO9lWlRk8p/LI/pYOSUSk3Zs3bx59+vSp9wEKp3LQy9lLGWe3seloURMun8YHK97l+KGjSbXvTx59yWVVWNPSRERao6pKp7oq9QteR2XnBP1G10mStksFI2l3Fi5cCFD/+kXb1wNgw9pWwQjgzQ3bAbSOkYhImpWVlbF48WJmzpxJqAmjUddERtpMaKMFo4mzpuPurH09uWlpZsaEUE/W+RFOuEbDioi0Nul6QlpU9ElpKhi1fSoYSbuzcOFCRo4cycCBA+ts49vfg4I+WLdezRhZ0/Tr148xY8aw4J2VEMrQOkYiImm2ZMkSKioqmDVrVpP6WRM+TC86MYC8FEXWvPqPHEqfoYMa9LS0c0N9qCTMGte0NBGR1iZdT0iLyswGDMpLVTBq61QwknaluLiYFStW1D+6qKYa37WRtjQdLeqSSy5h9erVFOX1hIMqGImIpNO8efPo3r17k56OVu1h1vkRxod6YpaeX8zTzcyYcPk0tryzhuIjx5I6Zqx1pwtZLA0fTHN0IiLSUOUljoUgJ033McyMrGyNMGoPVDCSduXtt9+murqaiy++uO5G+7ZC5QlsyNjmCyxFpk2bRjgcZumxak1JExFJo8rKShYuXMill15KZmZmo/vZ7EWUU8P4NjodLWrCrOmEa8K8+8ZbSbXPsBCTQ71ZGS6kysNpjk5ERBqivNTplG9pvZGRnQMVKhi1eSoYSbuyaNEiunTpwtlnn11nm/C298AMGzKmGSNLjbPPPptu3bqxZPtBKDmKV5S2dEgiIu3S0qVLKSkpScl0tBDGOOuRoshaxqAzhtPrtAGsbsC0tCmhPpRTw7t+JI2RiYhIQwVPSEvvObJyjIoyCIdVNGrLVDCSdsPdWbJkCRdeeGG9d4N9+3roNwzr1PbWksjIyODCCy9kyfrNhN3h0J6WDklEpF2aO3cu+fn5XHDBBU3qZ40fZpR1I88aP0qpNTAzJsyaxvvLVnHsWHLT0s6yHuSRybLwgTRHJyIiyQqHnRNl6VvwOiorB9zhRFlaTyNppoKRtBubN2/mwIEDTJs2rc42fqIc9m3Dhra90UVR06dP59DRY2w6UoYXqmAkIpJq1dXVvPHGG1xyySVkZ2c3up8ir2SrF3N2qG2PLoqaePk0wtU1LFiwIKn2WRZiUqgX74QLqda0NBGRVqGiLCjkpL9gFPSvdYzaNhWMpN1YvDgYJn/RRRfV2cZ3bwYPY4PPaK6wUm7q1KkALNlfgmuEkYhIyq1cuZKjR482eTrau+FgKtZ4a9vrF0WdduYoegzoy6uvvpr0MVNCfSilmnV+NI2RiYhIsirS/IS0qOyc4E8VjNo2FYyk3Vi8eDGjR4+mb9++dbbxXe9DKAMbOKIZI0ut3r17M2bMGBbvL8EL9aQ0EZFUmzt3Lp06dar3BkQy1vkR8snkdOuaoshalpkxfuZFvP3225SWJreG3njrST6ZLA7vS3N0IiKSjGgBJ90jjEIZRlZOsMC2tF0qGEm7UFpaysqVK0/5y73v3AgDhmNZOc0UWXpMnz6dNXsKKd69A3ddhEVEUiUcDjN//nwuuugi8vIav9adu7MufISxVkAojU+haW7jZlxAVVUVS5YsSap9tmVwYagvfwsfpMyr0xydiIicSnmpk5UDmVnpz025+aYRRm2cCkbSLixbtoyqqqpTrF9UBvu3Y4NHN2Nk6XHRRRdRE3aWbtsLpUUtHY6ISLuxdu1aDhw4wOWXX96kfg5QTiEVjGsn6xdFnT7xLLp168brr7+e9DHTQwOoJMxSLX4tItLigiekNc+NjNzOKhi1dSoYSbuwaNEi8vLymDhxYp1tfNcmcG/T6xdFTZgwgS75efx1zzEtfC0ikkLz5s0jKyuLSy65pEn9rIusX3RWOysYZWRmcMkll7Bo0SKqqqqSOmakdaU/ebypaWkiIi2uvMTTPh0tKrezUV0FVZUqGrVVKhhJm+fuLFmyhAsuuKDep9n4zvchI7NNr18UlZmZyQXnT2HxnmP4wV0tHY6ISLuxYMECzj33XLp2bdq6Q+v8KAXkMIDGT2trrS699FKKiopYuXJlUu3NjIsz+rPRj3HcK9McnYiI1KWq0qmuSv/6RVHR82iUUdulgpG0edu3b2fPnj1JrV9kA0dgmVnNFFl6TZ1+CQfLKvngvXdbOhQRkXZhx44dbNu2rcmji8KR9YvOChVg7Wj9oqipU6eSnZ3doGlp00L9MWCTaxq1iEhLKW+mJ6RFRc+jglHbpYKRtHlvvfX/s3ffwXFfV4Lvv6e70ciJOZNiEgkwBwQGMClbtqRZaUeWR2s/e8cj19P4eTzeWXvL65HTbHmc9r1n17q8z+O11zW2VLKtkW1SoigSAEkATTADIEGKOQcQIFIjNLrP+6MbNEUCIEKjA3A+VSwA/bv34oCFQvfv9D3nlgGwZs2aXsdoeytcv4BMj//+Rd26f97yQ0eiHIkxxowMJSUlAGzYsGFI61zQFprxsVhGVjlat9TUVAoLC9m5c2e/D14YJ0kskbGc0EYCdliDMcZEzLXz/g/9A2hpDPT4eLglpoA4LGEUzyxhZOJeeXk5U6dOZfr06b2O0YsnAUVmxn//om7Tpk1j+rhsKhkubJoAACAASURBVE6cQzUQ7XCMMSbulZSUMHv2bGbMmDGkdap1ZPYvutvmzZu5fPkyJ0+e7Pecx53T8NLFOZqHMTJjjDG98XUAAq7eu3iElYgET0prtYRRvHJFOwBjhqKrqwuPx8OTTz7Z57Z/PX8cXAkweXYEoxt+a5Yt5o8le/HVXcM9fkq0wzHGmLjV2tpKZWUlL7/88oDm7fBfuu+xYv8VMnFzKFAXrvBizqZNm3jttdfYuXMnDz/cv927y2Ucabg4FmhgtnNoPaKMMcYMnK9DSXAT0XLp5DRobbKEUbyyHUYmrlVXV9PS0kJhYWGf4/TiiRHVv6hb4dq1tPr8HC0rjXYoxhgT18rKyvD5fEMuRwuoco02psjIa3Z9t/Hjx7NkyRJ27tzZ7zkOEXIkm6u0Ua8dwxidMcaYnvg6ISExst8zOVVo90IgYEmjeGQ7jExcKysrQ0QoKCi481jgcPGHxmhnO9y4CHOX3XctHvQV8+rJmTgEyt79EytmTvrQNceyjcMbmDHGjCDFxcWkp6ezfPnyIa1zgzZ8BJg6whNGAFu2bOEHP/gB169fZ+LEif2a87BkckDrOKYNrJNJD55gjDEmLFQVXwekZkT2MIbkNAGFdi+kpEX0W5swsB1GJq6Vl5ezcOFCsrOzex/UcD34cczIe2GalZlJ7vgMyk+ei3YoxhgTtwKBAKWlpaxdu5aEhKHtRL2iXgAmkxqO0GLa5s2bAQa0yyhJXMyRDD7QRjp0eJqsGmOMuZ+vM/gx0juMktLspLR4ZgkjE7daW1s5cuRIn6ejAWj9NXC6IHNshCKLrILZU6i6cosWb1u0QzHGmLh07Ngx6urq2Lhx45DXuqytjCORJHEOPbAYN3v2bGbOnDmghBHAIsmmC6VWbw9TZMYYY+7lC1UCJyRGeIdRqiWM4pkljEzc2r9/Pz6f74EJI+qvQ9Z4xDEyX7wXLpiDX5V9NbXRDsUYY+JSSUkJIsL69euHtI5PA9ygjSky8ncXQbBp6pYtW/B4PLS0tPR73lhJYgop1GgDAbUbCGOMiQRfR/DvbaR3GLkShIRESxjFK0sYmbhVVlZGYmIiK1as6HWMdrZDy21kBJajdVu2cD7JLgflh45GOxRjjIlLxcXFLF26lDFjxgxpnWt4CcCIb3h9t82bN+Pz+di9e/eA5i12jKGVLs5o0zBFZowx5m6+jmDRhdMZ2R1GEOxj1N5qCaN4ZAkjE7fKy8tZuXIliYl9pMnrrwU/juCEkTt7PCsmZlBeczLaoRhjTNy5efMm1dXVQz4dDYL9ixzAJEZPwmjZsmVkZmZSUlIyoHnTSSUTN1Vaj9ouI2OMGXa+DiXBHZ3vnZwmtLWo/b2PQ5YwMnHpxo0bfPDBBxQWFvY57k7/ooyR2b8IgNQMCqdkcfZGPdduNUQ7GmOMiSulpaUAYelfdEVbmUAyCTJ6Xl45nU6KioooLS3F7+9/E2sRYbGMoY4OrmE9+IwxZrh1dkS+f1G35FShywddnVH59mYIRs8rGjOiVFRUAPSvf1H2RMQxcn/VxeGkcM5UAMqrjkc5GjNaicgTInJCRE6JyJd7uJ4oIq+HrntEZNZd15aISLmI1IhIlYgkRTJ2M7qVlJQwadIkHn744SGt065+6uhg6ijpX3S3DRs20NDQQFVV1YDmzZMMEnFSFagfpsiMMcYA+P1KwB/5/kXdku2ktLg1cu+izYhWVlZGdnY2CxYs6HWMdrRBayMyZmIEI4uOeTOnMTbZTXm1Nb42kSciTuDHwJNADvBxEcm5Z9hngAZVnQv8EPhOaK4L+BXwiqrmAhsBX4RCN6NcZ2cne/fuZcOGDYgM7V3Xq7QCo6t/Ubd169bhdDopLi4e0DyXOMiRLM7Twm21t52NMWa4ROuEtG6WMIpfljAycUdVKS8vp6CgAEdfO4dGQf+ibo70MRRMzqC8qpZAIBDtcMzokwecUtUzqtoJ/AZ45p4xzwC/CH3+JrBFgnfojwFHVfUIgKreUtX+17UYMwT79+/H6/WGpX/RZfWSgIMJJIchsviSmZnJ8uXLB9zHCCBHsnEgVKvtMjLGmOESrRPSuiUmg8MBXmt8HXcsYWTizunTp7lx40b/+he5EiB9aKfexIW0LAqnZFLf1MzJi1eiHY0ZfaYCF+/6+lLosR7HqGoX0AiMBeYDKiLvishBEfmHCMRrDBA8HS0xMZGCgoIhr3VFvUwiGccQdyrFq40bN1JbW8u1a9cGNC9FXMyVDE5qI+2WKzbGmGFxZ4dRQnS+v4gEG183W8Io3ljCyMSd8vJyoD/9i66N+P5Fd6RlUTA5E4CKKitLM3HFBawDPhH6+JyIbLl3kIh8VkT2i8j+mzdvRjpGMwKpKiUlJeTl5ZGcPLRdQS3qo5FOpozC/kXdupuGD2aX0WIZgx/luNrBDcYYMxy6T0gTR/Te1EhJF7yWMIo7o+BO2ow0FRUVzJgxg6lT793A8Gfa7gVvMzIKytEASEljYnoKs8Zmsu/YiWhHY0afy8D0u76eFnqsxzGhvkWZwC2Cu5FKVbVOVb3AVmDFvd9AVX+qqqtUddX48eOH4Ucwo825c+e4cOFCmE5H8wIwdRT2L+o2e/Zspk2bNuA+RgBjJJEppFCrtwnYkcvGGBN2vs7olaN1S04XOtuhy2d/5+OJK9oBGDMQfr+fyspKnnjiib4H3ulfNPIbXgOIONC0LPKmjeFPtafo8vtxRzsoM5pUAvNE5CGCiaEXgZfuGfM28EmgHHge2KmqKiLvAv8gIilAJ7CBYFNsY4ZV906YsPQvopUknIwhyq/Go0hE2LhxI2+++Sbt7e0kJQ3ssMOFjizeD1zhEq3MIG2YojTGmNFHVTl/4QyHT7xD7al9XL12Cb/fT2ZmFjkLlrK2cDMFeUU4nc5hjSPlrsbX6dmjs3w7HtkOIxNXjh8/TnNzM/n5+X2OC/YvckN6doQiiwFpWeSNT6W1rZ1jZy9EOxozioR6Er0KvAscB95Q1RoR+YaIfCw07GfAWBE5BXwR+HJobgPwA4JJp8PAQVX9U6R/BjP6FBcXM2/evD53q/aHqnJFvUyRlCGftBbvNm7cSHt7Ox6PZ8BzZ5JOMk5qA7eHITITC0TkCRE5ISKnROTLPVxPFJHXQ9c9IjIr9HiCiPxCRKpE5LiIfCXSsRsTry5fvsw/fvOLfPW7z/G7P/xPVAOsXrWWovWPMnXyDPaUvc9Xv/63/If/+DR7y3cNayzJ6cHnSCtLiy+2w8jEle4XoXl5eX0PrL8GYyYiMnpyopKWxepxwXd0PdUnWPZ8lAMyo4qqbiVYTnb3Y1+76/N24IVe5v4K+NWwBmjMXZqbmzlw4ACf+tSnhrxWI5146WIKo7d/UbfVq1eTkpJCcXHxgHduOUWYL5kc1Xpa1UeqRKkzqxkWIuIEfgw8SrAUuVJE3lbVY3cN+wzQoKpzReRF4DvAXxJ87khU1cWh3ajHROTXqnousj+FMfHlrbfe4lvf+hYBv/L0lr/mpZc+weSp4z40xu/vYvfe9/nlv/6Er379b9m84Um+9IWvk5wc/hLrpJTgSWltLZYwiiej527ajAgej4c5c+bQVw8TbWuBtpbR07+oW3oWY5ISmDdlAvuOnYx2NMYYE7P27t1LV1dXWPoXXbb+RXe43W4KCwspKSlBB9GLaIFkocAJbQx/cCba8oBTqnpGVTuB3wDP3DPmGeAXoc/fBLZIcNueAqmh/nfJBMuXmyITtjHxJxAI8P3vf5+vfOUr5Obm8t//22957olXGT9h7H1jnU4XG4se56c/eoNP/4e/pXj3u7z6d5/gZt31sMfVfVKa7TCKL7bDyMQNn8/HgQMHePbZZ/seWB/6A5c9OvoX3ZEWLL/Lf2gKb1bW0NnZidttnYyMMeZeJSUlZGZmsnTp0iGvdUVbScNFOqNnR8wO/6Ver00sWsr777/P/67dw5T5Dw1o3QxxMzXU/Hq5jh31JX4jzFTg4l1fXwLu7S9wZ4yqdolIIzCWYPLoGeAqkAL8narW3/sNROSzwGcBZsyYEe74jYkLqso3vvENXn/9df7yL/+Sr371q9RWKs0NirOPO3+XK4GXX/obFjy8iNe+/UW+8J8+xQ++8y9MnDA5rPElpwtN9YGwrmmGl+0wMnGjuroar9fbv/5FCYmjq38RQGIyuNysnppNe6ePI0eORDsiY4yJOYFAgNLSUtavX4/LNbT3zQKqXMHLVEm15EZITlGwZLy6dOB9jADmSyatdHGdtnCGZeJbHuAHpgAPAX8vIrPvHWSnaRoD3/3ud3n99df567/+a/7xH/8Rl8uFr0NJSKRfz1OrV67lu//0P2lsus1/+spf09gU3r5yKWlCZxv4u2yXUbywhJGJG939i1avXt33wPprkD1x1L14FxFIy2LVmGQcIuzbty/aIRljTMypqqqivr4+LKejndVmOglY/6K7ZI4fy/ScedSUVAxq/kxJx4lwWq3iaIS5DEy/6+tpocd6HBMqP8sEbhE8dfMdVfWp6g1gL7Bq2CM2Js688cYb/PznP+ell17i7/7u7+7cC/k6wO3u/31RzoIl/Ldv/JhrN67wX7/+eTo7O8MWozW+jj+WMDJxw+PxsGDBArKze985pLdvQnvr6Otf1C09i4yuFhbMmk5FxeBerBtjzEhWXFyMw+Fg3bp1Q16rOlQVM8X6F33Iog0FnD1ynNbbA0/6JIiDGZLGWW0mMIg+SCZmVQLzROQhEXEDLwJv3zPmbeCToc+fB3ZqsBnWBWAzgIikAgVAbUSiNiZOHDp0iG9+85usX7+er3zlK3eSRX6/0uULFl8MxOLcFXz5S9+mquYgP/n/vhe2OFPSgnFZ4+v4YQkjExc6Ojo4dOjQg8vRLoReP4wZZf2LQiQtC7p85C+YzZEjR2hrsy39xhhzt5KSEpYvX05WVtaQ16oO1JNNIiliLSHvlluUjwYCHNtTOaj5cySDNvxcxRvmyEy0qGoX8CrwLnAceENVa0TkGyLysdCwnwFjReQU8EXgy6HHfwykiUgNwcTTz1X1aGR/AmNiV2NjI3//93/P5MmT+f73v/+hcuv21mBiZqAJI4DNG57khb/4JL9/+1/ZVfpOWGJNSgVx2A6jeGIJIxMXjhw5QkdHR/8SRu4kSBv6jUBc6m58PXMyPp+PQ4cORTkgY4yJHdevX+f48eNhKUfrVD/H9badjtaD6bnzSB+bTfUgy9Kmk0oCDitLG2FUdauqzlfVOar67dBjX1PVt0Oft6vqC6o6V1XzVPVM6PGW0OO5qpqjqt+N5s9hTKx57bXXuHnzJt/73vdIT0//0LW2luDHhMTBter47Ke/wMIFS/jB//MN6m7dGGqodlJaHLKEkYkLHo8Hh8PBqlW9l6yrajBhNAr7F92RlgnAionpuFyuO32fjDHGBHcXAWFJGJ3URnwErBytBw6Hg9yifI7v3Y+/yz/g+S5xMDNUlua3sjRjjOnV9u3beeedd3j11VdZsmTJfdfv7DAa5MHJLlcCX/nSP+HzdfL9//s1NAx/k1PTLWEUTyxhZOKCx+MhNzf3vqz5h9y+Ac0No7d/ESDuJEhMJtnXyuLFiy1hZIwxdykpKWHKlCnMmzdvyGtVB+pxIEzGEkY9yS3Ko62phTOHqgc1f45k0EmAK7SGOTJjjBkZGhsb+eY3v8nChQv59Kc/3eOYtlbF6QKHc/Bvpk+fNov/+H/8X1TsK+X94q2DXqdbSobQ2Q6+DksaxQNLGJmY5/V6OXr06IPL0c4fD34yihNGQLAcr+U2eXl5VFdX09pqL7aNMaajo4Py8nI2bNgQll2o1drAHMnALc4wRDfyLFizEqfLRU3p4E7snEIKToSLas9hxhjTkx//+MfU19fzrW99i4SEhB7HtLXooPoX3eu5j77EgvmL+B8//S4trc1DWis1M/gc3NpkCaN40K+EkYg8ISInROSUiHy5h+uJIvJ66LpHRGbddW2JiJSLSI2IVIlIUvjCN6PBoUOH8Pl8D04YnTsW7OGTmhGhyGJUeja03CY/Lw+/38/+/fujHZExxkTdvn37aGtrY+PGjUNey6tdnNYmFknvp3aOdkmpKcxZtZia0sHtdHWJgymkcFFbwhyZMcbEvzNnzvDrX/+aF154gZycnB7HqCrtrTro/kV3czqdfOFv/ysNt2/xv/73j4e0VmqGJYziyQMTRiLiJHg6wZNADvBxEbn3t/IzQIOqzgV+CHwnNNcF/Ap4RVVzgY2AL2zRm1HB4/HgcrlYvnx5r2NUA+iFWmTWwtHbvyhE0sdAIMCyGRNJSEiwsjRjjCFYjpaUlEReXt6Q1zquDQRQFjnGhCGykSu3KJ9rp89z6/K1Qc2fIWk04aNRO8McmTHGxLd//ud/Jikpic9//vO9junqhC4fuMOwwwjg4Xm5fOSJ5/m3P/6Gy1cuDHqdBLfgTgKvJYziQn92GOUBp1T1jKp2Ar8BnrlnzDPAL0KfvwlskeBd+2PAUVU9AqCqt1R14N0Pzajm8XhYsmQJqampvQ+6cRHaWpCZPWfYR5WM4A1M4u1rLF++nIqKwZ1SY4wxI4WqUlxcTGFhIUlJQ9/oXBWoJwEH8yUzDNGNXIuKgjuDB7vLaJoEn/dtl5ExxvzZ3r17KSkp4ZVXXmHMmN7fuGi70/A6fG+mf/KvPofT6eJffvmjIa2TmiG2wyhO9CdhNBW4eNfXl0KP9ThGVbuARmAsMB9QEXlXRA6KyD8MPWQzmrS0tFBTU/PAd4T1XLB/kcxcGImwYltqBjic6PUL5OfnU1tby+3bt6MdlTHGRM3p06e5fPlyWE5HA6jWehZIlvUveoAJs6YxfsaUQSeMMsRNFm7rY2SMMSFdXV185zvfYfr06bz88st9jm1rCSWMwrTDCGDc2Ak8/9zL7Czeygenjg96nZQMoa1FCfgtaRTrhrvptQtYB3wi9PE5Edly7yAR+ayI7BeR/Tdv3hzmkEw82b9/P36/n4KCgj7H6fljMHYykm79JEQckJ6NXj9Pfn4+qkplZWW0wzLGmKgpLi4GCEvC6LZ2cFFbrRytn3KL8vlg3xE629oHNX+apHIVLz4NhDkyY4yJP3/4wx/44IMP+NKXvoTb7e5zbHurIgKuvocN2IsvfJqM9Ex++i8/HPQaqRkOVMHbYgmjWNefhNFlYPpdX08LPdbjmFDfokzgFsHdSKWqWqeqXmArsOLeb6CqP1XVVaq6avz48QP/KcyI5fF4cLvdLFu2rNcx2uVDL520crS7ZYyF6xdZtCiX5ORk9u0b3Ck1xhgzEpSUlLBgwQImTRr6KZo1gQYAa3jdT7lF+fg6OjnpOTyo+TMkDT/KFbxhjswYY+JLV1cXP/nJT8jJyeHRRx994Pi2FiUplbD3d01LTecTL36W/QfLOHBocK0v7jS+brSEUazrT8KoEpgnIg+JiBt4EXj7njFvA58Mff48sFNVFXgXWCwiKaFE0gbgWHhCN6OBx+Nh+fLlJCb2sZfyyhnwdSKzLGHUTTLGQGcbbm8jK1assD5GxphRq7GxkUOHDoWtHK1K60nFxWwZ5Sdy9tOcVYtxJycNuixtEsm4EC5ZHyNjzCi3bds2Lly4wCuvvNKvJFBbKySnDs9hQM9+9EUmTpjMz3/5/xK87R+YpFRwOK3xdTx4YMIo1JPoVYLJn+PAG6paIyLfEJGPhYb9DBgrIqeALwJfDs1tAH5AMOl0GDioqn8K/49hRqLbt29TW1v7wP5FgfPHQASZ/nCEIosDocbX3X2MTp06RV1dXZSDMsaYyNuzZw9+vz98/YsC9eRINo5RfiJnfyW43SwoXElNqWdQNxVOcTCRFK5p2zBEZ4wx8cHv9/OTn/yEefPmsWXLfR1e7qOqtHuV5LThea5yuxN58YXPUHP8CEerDwx4vohY4+s40a8eRqq6VVXnq+ocVf126LGvqerboc/bVfUFVZ2rqnmqeuauub9S1VxVXaSq1vTa9FtlZSWq+uD+ReeOwaSHkKSUCEUWB9KyQo2vg32MACtLM8aMSiUlJWRnZ7NkyZIhr3Vdvdyk3foXDVBuUR4N125y9YNzg5o/WZKpp4MW9YU3MGOMiRPbt2/nzJkzvPLKKzgcD76Fb/eCBiBpmHYYATz52LNkZY7h12/8bFDzU0IJo8G8mWAiZ7ibXhszaB6Ph+TkZBYtWtTrGO1og6tnkVl2OtrdxOGEcVPg+gVycnJIS0vD4xlcOYAxxsQrv99PaWkp69evx+kc+olm1d39iyxhNCC5RcGdwtWDLUuT4BtCtWonfhpjRp9AIMBPfvITZs+ezeOPP96vOd0npKWkD1/CKDExiX/33F/hqdzNqTO1A56flin4u6DdDsKMaZYwMjHL4/GwcuXKPk8A0IsnQAPW8LoHMnEmev08TqeT1atXW8LIGDPqHDlyhMbGRjZu3BiW9aq0nmzcTMV2tA5E5oRxTFs4d9B9jMaThBPheChhZ4wxo0lJSQknT57kb/7mb/r95kdbczBhNFwlad2effpFUlJS+fUb/zLguWlZwdiab9spmLHMEkYmJtXV1XHq1KkH9i/Sc8fAlYBMnRuhyOKHTJwB3mZouU1eXh7nz5/n6tWr0Q7LGGMipqSkBKfTydq1a4e8VkCVmkA9ixxjwn7izGiQW5TP2cPHaG1sGvBclzgYT5LtMDLGjEq/+MUvmDx5Mk899VS/53hbFHciuBKG9/kqLS2Dj33kLykufYcrVy8OaG5KuuBwQkuDlaTFMksYmZjU3W+nu/9Ob/T8cWTafMSVEImw4opMnAmAXjt3pw+U7TIyxowmxcXFrFy5koyMoZ9odlFbaMLHIrFytMHILcpDAwFq9w68OSoEy9LOaDPt2hXmyIwxJnadOHECj8fDSy+9hMvl6ve8thYleRjL0e72/LMv43Q6ef23/2tA80SEtCyh+bYljGKZJYxMTPJ4PKSlpZGT03upmbY0Qt1lZKb1L+rRxJnBxtdXTjN//nyysrKs8bUxZtS4cuUKJ0+eDN/paFoPWP+iwZq56GHSsjMHXZY2WVIIoJzUxjBHZowxseuXv/wlycnJPP/88/2eo6rBhNEwl6N1Gzt2PI9seprt771NS8vAdpGmZwneJiXgt6RRrLKEkYlJHo+HVatW9ZlJ1/PHAJBZ1r+oJ5Lghokz0MuncTgc5OXlUVFRYScRGGNGhZKSEoCw9S+qDjQwmRTGSVJY1httHE4nC9et5tieSgJ+/4DnTyAJB8LxgJWlGWNGh1u3bvHHP/6RZ555hqysrH7P62wHfxekRChhBPDcx16ivaONd977twHNS8tyoAqtTXZ/EqssYWRizrVr1zh//ny/ytFISoUJMyIUWfyRKXPg2lk04CcvL4+rV69y6dKlaIdljDHDrqSkhOnTp/PQQw8Nea0uDXBMG1jkyA5DZKNXblE+rbebOF91YsBz3eJklqRzXK3xtTFmdHj99dfp7Ozk5ZdfHtC87hPSIlWSBjBv7kIW5SznrT/8mkCg/02s07NDja+tj1HMsoSRiTndfXb6ShipKnruGDJjAeKwX+PeyJQ54OuEG5fu9DGqqKiIclTGGDO82traqKioYOPGjWFpUH1am2jHb+VoQ7RwzUocTsegy9IWSCantIkutRN1jDEjW2dnJ7/+9a9Zv349s2fPHtBcb4ROSLvXcx/7OJevXKDywN5+z3EnCe4kaLE+RjHL7rRNzPF4PGRmZvLwww/3PujWFWiuRx7KjVxgcUimzgFAr5xi9uzZjBs3zhpfG2NGPI/HQ0dHR1j7FwmQK7bDaChSMtN5aFnuoBNGcyUTHwEuakuYIzPGmNiyfft26urqBry7CII7jFwJkOAehsD6sH7to4zJHsfv3/7XAc0LNr62NwJilSWMTMzxeDzk5eXh6GPnkJ6pBkBmL45UWPEpYyykZqJXziAi5Ofn4/F4rI+RMWZEKy4uJiUlhdWrV4dlvepAPTMlnXSJ8KvvESi3KI9Ltae5fb1uwHPnOIKn3Z3WgTVVNcaYePPGG28wffp01q5dO+C53SekhWOH7UAkJCTw0adeYN/+PVy+cqHf89KzHHR4wddh9yexyBJGJqZcunSJK1euPLh/0ZkqGDsFyRgbocjik4ggU+egl08DwTK/uro6zpw5E+XIjDFmeKgqpaWlFBYW4nYPPcHToX5OaiOLxcrRwiG3KPj8XrN74Kd2TiSZNBIsYWSMGdHOnj1LZWUlL7zwQp9voPfG26wRbXh9t48+9e9xOJy89Yff9HtOWncfIytLi0mWMDIxpbu/Tp/9izo70EsnkdmLIhVWXJMpc+D2DbS1yfoYGWNGvJMnT3L16tWwnY5Wq7fpQq3hdZhMnjuL7MkTBlWWJiLMkQxOWcLIGDOCvfHGG7hcLp577rkBz/V1KF2+yPcv6jZ27HjWrdnC9h3/RmdnZ7/mpGcJ4oCmW1aWFossYWRiyr59+xg3bhxz5szpdYxerAV/l5Wj9dOf+xidZtq0aUyePJl9+wb+zq4xxsSDkpISAIqKisKyXnWgHifCAutfFBYiQm5RPifKD+Lr583E3eZIBpe0lQ71D0N0xhgTXR0dHbz11ls88sgjjBs3bsDzvaET0lIieELavZ5+8t/R1NzInvL3+zXe4RTSs4SmW7bDKBZZwsjEDFWloqKC/Pz8Pmtu9UwVJLiRafMjGF0cmzgLHE70ymlEhIKCAjwez4COvDTGmHhRXFxMbm4uEyZMCMt61VrPPMkkSZxhWc8Ey9I629o5VXl0wHPnODIIoJzV5mGIzBhjouu9997j9u3bvPDCC4OaH60T0u62YlkBEydMYes7v+33nIyxQkuT4u+ypFGscUU7AGO6nT17lps3b94pRwscLu5xnB6vhKwJaPVe7E/Kg0mCGybNQi+cAILlfr///e85ceIECxcujHJ0xhgTPg0NDRw5coTPfe5zYVmvRX2c1Waedw7shYA8PQAAIABJREFUSGPTt/l5S0lIdFNT6mHh2lUDmjtHuhtfN7KArOEIzxhjoqa72XV3G4mB8jYrThe4k8Ic2AA4HA6eevwv+Pn//hGXLl1i2rRpD5yTMcYBGqCpXsmeEL1kl7mf7TAyMaNf/Ytam6CtGRk3JVJhjQgyKweunkHbvXf+fz2ewR1rbIwxsWr37t0EAgE2bNgQlvVqtAEFFlnD67ByJycxL28ZNaX7BnxqZ7YkMpZEa3xtjBlxhtrsGkINrzMif0LavZ547BlEhN/+tn+7jNKzBRFoqrcKiFhjO4xMzPB4PEyePJnp06f3PujmpeDHcVMjE9QI4ZiVg7/sD+iFWibNX8HMmTPxeDx86lOfinZoxhgTNiUlJYwbN47c3NywrFcdqCcRJ3NDu1pM+ORuyOfY7n3cOHeJiQ/18bzfg9mSwemAJYyMMSPL7373O5xOJ88+++yg5qsq3iZl3NTo7wmZMH4yeavW8eabv+f5j34Op/PBZd3uJKi7EiAxue8edZNmWol4JEX/t8kYIBAI4PF4KCgo6Lt/0c1LkJaFpKRHMLoRYMocSEhEz9UAUFBQQGVlJV1dXVEOzBhjwqOrq4s9e/ZQVFQ06Hdm71UVqCdXsnGJvVwKt0VFwd2ugzktbY4jg2u00aK+cIdljDFR4ff7efvttykqKmL8+PGDWqOzHfxdkBrFhtd3e+rxv6Cu7jqVB/b2a3xSqtDRBoGANR2JJfYKyMSEEydO0NjY2Hc5mq8DGq7D+AfXwZoPE6cLmf4wev4YAHl5ebS2tnLs2LEoR2aMMeFx6NAhmpqawlaOdlPbuIqXxQ4rRxsOY6ZMZPLcWYNLGIV2fJ2xsjRjzAhRXl7OjRs3Br27CMDbFP0T0u5WmL+R7Kwx/W5+nZwqoNDhHebAzIBYSZqJCd39dPpKGFF3BVSRCQPbum6CZFYOuvMo2nSLvLw8IPj/vmTJkjtjems03hfHso3hCdAYY4aguLiYhIQE1q5dG5b1jgbqAVhs/YuGTW5RHjt/+VvaWlpJTkvt97xZEtxlfF5bWMLY4QrPGGMi5ve//z2ZmZls3Lhx0Gt0n5AWKwmjhIQEtmz6CG/94dc0NTeSkZ7Z5/ik0NNAW6tG9ZQ382G2w8jEBI/Hw6xZs5g0aVKvY/TGxWBxa6a9OBwMmZUDgJ47xrhx45g3b96dRuPGGBPvSkpKWLVqFamp/U889KVK68kmkWkSnvXM/XKL8gl0+TlRfnBA8zLEzRgSOa/NwxSZMcZETlNTEzt27ODpp5/G7XYPeh1vs+JOApc7dpItj27+KF1dXRSXvvPAsQ6nkJgM7S1WkhZLLGFkoq6rq4vKysq+y9ECAai7DOOnItZLYnDGTYXUzDt9jPLz8zl48CCdnZ1RDswYY4bm0qVLnD59OmzlaAFVqgP1LHaMifpJMyPZQ8tySc5Io6Zk4GVpMyWNc5YwMsaMANu2baOzs5PnnntuSOu0NmvM7C7qNm/uQmbOmMP29//Qr/HJaUK7FwJ+SxrFCrvzNlFXU1NDa2tr3+VoDdehy4eMt3K0wRIRZFYOeu4YGgiQn59Pe3s7R48ejXZoxhgzJCUlJQBD2sp/t3PaTDM+llg52rByupwsXLOKmt37CAQGdpTyLEnninrxqR3BbIyJb2+99RZz584lJydn0GsEAkpbS+wljESEx7Z8lJpjh7l85cIDx3eXorW3Dndkpr8sYWSirrt/UXdfnZ7ojUvgcMDYyZEKa0SSucugrQW9eILVq1cjInf+/40xJl4VFxcza9YsZs6cOaj5O/yXPvTvd/6zADRox33X7v1nhia3KI/mWw1cOvbBgObNlHT8KJe0ZZgiM8aY4Xf27FkOHz7Mc889N6Qdre2toAFIyYithBHAI5s/goiwY+cfHzg2MQVEwGtlaTHDEkYm6ioqKpg/fz5jx/bcm0hV4cZ5GDsZcSVEOLqRRWYvgQQ3WltJZmYmCxcutISRMSaueb1e9u3bF7bdRQBXtJVsEkkROxtkuC1cF3zzorp034DmzXSkAXDOEkbGmDj2pz/9CRHh6aefHtI63ubgbsvUGNthBDBh/GSWLVnN9vf/ELyv64PDISSlQpsljGKGJYxMVHV2dnLw4MG+y9Eab0G7F5k4K2JxjVTiTkTmLENPHkADfgoKCjh8+DBtbW3RDs0YYwalrKyMzs7OsPUv6tIA12hjqqSEZT3Tt/QxWcxcsoCa0oG9eTGJFBJxWONrY0zcUlW2bt1KXl4eEyZMGNJa3mYFIWZPF3t0y0e5cvUix44feeDY5DTB1wFdPksaxQJLGJmoOnz4MB0dHRQUFPQ6Rq+fB3HAhGkRjGzkkgWrwduMXqglLy8Pn8/HoUOHoh2WMcYMyq5du0hPT2flypVhWe8abfhROx0tgnKL8rlQfYKmuoZ+z3GIMEPSLWFkjIlbJ06c4OzZszz55JNDXqu1SUlODZ40FouK1j6K253I9p0Pbn795z5GljCKBZYwMlHl8XhwOBysWrWqx+uqCtdD5WgJiRGObmSS2YvBnYger2TVqlU4nU727RtYKYAxxsSCQCBAaWkp69atIyEhPCXLl7UVB8EdLCYycouCu4yP7RlgWZqkcV5bHljiYGKDiDwhIidE5JSIfLmH64ki8nroukdEZt11bYmIlItIjYhUiUhSJGM3Zjhs3boVp9PJY489NuS1WhuV1IzYvbVPTU1jXeFmikvewefz9TnWnQQOJ3it4jgmxO5vlRkVPB4Pubm5ZGRk9DyguR7aWpCJMyIb2AgmCW5k7nL05AFSkhJZvHgxFRUV0Q7LGGMGrKqqirq6OjZt2hS2NS9rKxNJJkHsJVKkTFswh4zxYwZcljZL0mmlizrahykyEy4i4gR+DDwJ5AAfF5F7j4T6DNCgqnOBHwLfCc11Ab8CXlHVXGAj0PcdpzExrrscbc2aNWRnZw9pLV+n0tkOqZmxubuo26NbPkpTcyOeytI+x4kIyaE+RvaGQPTZqyETNV6vl6NHj/Z9Otq188FW+ROmRzCykU8WrIb2VvRsNfn5+VRXV9PaaudXGmPiy65du3A6naxfvz4s67VpF7foYKqVo0WUiJC7Po/asgP4fV39njdTgo2vz1vj63iQB5xS1TOq2gn8BnjmnjHPAL8Iff4msEWCx0Y9BhxV1SMAqnpLVf0RituYYVFVVcXly5fDVo4GkBqDJ6TdbfXKNWRnjWH7+/0rS/P7wNcZgcBMnyxhZKLm0KFD+Hy+XvsX3SlHGzMJcdvO43CS2YshLRvd/x75+fn4/X72798f7bCMMWZAdu3axYoVK8jKygrLelfUC2AJoyjILcqnvcXL6UPV/Z4zQ9IQ4Jz1MYoHU4GLd319KfRYj2NUtQtoBMYC8wEVkXdF5KCI/ENP30BEPisi+0Vk/82bN8P+AxgTTlu3biUhIYEtW7YMea3WxlDCKMZ3GDmdLjZvfIqKfSU0Nzf2OfZOHyM7LS3qLGFkoqaiogKXy8WKFSt6HtB4C7zNyKRZEY1rNBCnC8eqR9Dzx1k2bRwJCQl4PAMrBTDGmGi6fPkyJ0+eDGs52iVaceNgHPYmRaQ9XLgCp8tFTUn/n4uSxMVEkrloO4xGOhewDvhE6ONzInLfXbaq/lRVV6nqqvHjx0c6RmP6LRAI8M4771BUVNR7W44BaG1S3EmQ4I7thBHAo5ufxufzsbvs/T7HudzgSgCvJYyizhJGJmo8Hg9Lly4lJaXnxqJ69XSw49nEmRGObHSQpUWQkIj7aCnLli2zPkbGmLhSXFwMwMaNG8OynqpyWVuZQgoOif0X3SNNUmoKc1ctGXAfo2mSxsWAlVTHgcvA3f0FpoUe63FMqG9RJnCL4G6kUlWtU1UvsBXo5d1GY2LfwYMHuX79eljK0aC74XV8PG/Nn5fLlMnT2VWyrc9xIkJymtDegvUxijJLGJmoaGpqoqamhvz8/B6vq78Lrp6DCdOQBHdEYxstJCkVWbIOPVZB/vKl1NbWcrvFXnQbY+LDrl27mDVrFg899FBY1mvERytdVo4WRbkb8rl+9iJ1F6/2e850SeUqXnwaGMbITBhUAvNE5CERcQMvAm/fM+Zt4JOhz58HdmrwTvFdYLGIpIQSSRuAYxGK25iw27p1K0lJSWF5w8PvV9paNObL0bqJCJs3PMnBwx4abt/qc2xyGgQC0NEWoeBMjyxhZKJi//79BAKB3hNGZ6rA14FMnhPhyEYXx8pHIRAgLz14CkHlsZPRDskYYx6otbWVffv2hbccLVTWNM0SRlGTWxQ8BGMgu4ymSxoBlKuh/lMmNoV6Er1KMPlzHHhDVWtE5Bsi8rHQsJ8BY0XkFPBF4MuhuQ3ADwgmnQ4DB1X1T5H+GYwJh66uLt599102btxIaurQn2+8dxpex89t/aYNTxAIBCjd816f45JSrY9RLIif3ywzolRUVJCYmMiyZct6vK415eBOgnFTIhzZ6CLZE5CcfBbdqiU5OZmK6tpoh2SMMQ+0d+9efD5f2MrRAC5qK5m4yRDb1RotE2ZOY/zMqQNOGAHWxygOqOpWVZ2vqnNU9duhx76mqm+HPm9X1RdUda6q5qnqmbvm/kpVc1V1kar22PTamHiwb98+6uvrw1eO1hQfDa/v9tCsecycMYedJe/0Oc6VILiToM0SRlFlCSMTFWVlZaxcuRK3+/4X5truRU8dhkmzEIf9ig43x8YXSHAnsGr6eEsYGWPiwq5du8jMzOz90IQB8mmAq3iZbruLom7RxgI+2HeE9tb+7RiaIik4EUsYGWPiwtatW0lNTaWoqCgs67U2KU4XJCaHZbmI6C5Lq6o+wM26632OTU4T2r0QCFjSKFrsbtxE3I0bNzh9+jRr1qzp8boe94C/C5kyO8KRjU6Sno1jzTMUZArnrt7gSl19tEMyxphe+f1+SkpKKCoqwuVyhWXNK3jxo8wI7VYx0bN4YyFdPh/H9+6/79oO/6X7/hUHrpBOAgcDdT1ev/ufMcZEU2dnJ++99x5btmwhKSk8p3F2N7yWODusYdOGJ1BVSnZv73NcUqqgCh1WdRw1ljAyEVdWVgZAYWHhfddUlcDBncGT0TLGRjq0UUtWPUJhzjwAyg9XRzkaE49E5AkROSEip0Tkyz1cTxSR10PXPSIy657rM0SkRUS+FKmYTXw6cuQIDQ0NYS1Hu6QtuBAmEUdv0Y5Qs5cvIiUznaqdZf2eky2JNNAxjFEZY8zQlZWV0dTUFLZyNA0o3qb4aXh9t+nTZjFvzkJ2PuC0tKTQxt/2VtthFC2WMDIRV1ZWRnZ2NgsWLLj/4uUPoO4yjuWb4i5THs/E6eLhT3yesckJlO/dY8dXmgERESfwY+BJIAf4uIjk3DPsM0CDqs4Ffgh8557rPwD6ftVgDMFyNJfLxfr168OynqpyQVuZSipOsZdF0eZ0OVm0oYCa0n34u/z9mjOGRJrw0WUnpRljYtjWrVvJzMzstcpioLwtSiAAaVnxec+0aeOTHK89ytVrve8AdTqDfYwsYRQ99srIRJSqUl5eTkFBAY4e+hMFDu6CxGRkYc+np5nh45g+j4KHZ+M5d5XAqaPRDsfElzzglKqeUdVO4DfAM/eMeQb4RejzN4EtEsoKi8izwFmgJkLxmji2a9cuVq1aRXp6eljWu4yXFnzWvyiGLN5UiLepmTMH+7fjNTvUqLyBzuEMyxhjBq29vZ3333+fRx99tMceroPRcjuYREnLjM9b+k3rHwdg1wOaXyelBvsYqfUxior4/O0yceuDDz6grq6ux8y6tjahJ/Yji9Yi7sQoRGfWFORT397Ficoy9MbFaIdj4sdU4O5fmEuhx3ocEzpeuZHg8clpwH8Gvt7XNxCRz4rIfhHZf/PmzbAFbuLLxYsXOX36dFjL0Q4H6oA/n7Zlom/h2lW43AlUFZf3a3w2wdcMDWplacaY2FRaWorX6w1bORpAS2Ow4XVSnL7fMWnSVHIWLGVXad8Jo+TuPkZtEQrMfIgljExElZcHX/z1mDA6uhsCfhzLN0U6LBNSuHghABUNPvRIKdrQ98kFxoTBa8APVfs+4khVf6qqq1R11fjx4yMTmYk5u3btAmDTpvA9TxwK3CIbN2mSELY1zdAkpiQzP385VTvL+lUinYEbJ2J9jIwxMWvbtm2MHTuWvLy8sK3ZcltJy4y/htd327TxCU6druXCxTO9jrE+RtFlCSMTUWVlZcycOZMpU6Z86HHt8hE4sAOZmYOMnRyl6MzEMVnMnjqJikaF5DT04E60yU5NMw90GZh+19fTQo/1OEZEXEAmcAvIB/5ZRM4BXwD+i4i8OtwBm/i0a9cu5syZw4wZM8KyXpt2cVwbbHdRDFq8qZC6S1e5eurcA8c6RMjEbTuMjDExqbW1leLiYh5//PGwne4Z8IcaXsdp/6JuG9c/joj0WZbmdAkJidBmCaOosISRiZjOzk7279/f8+6iqj3Q2ogUfiQKkZm7FS5awIETp+lcUgSuBPTADtTbHO2wTGyrBOaJyEMi4gZeBN6+Z8zbwCdDnz8P7NSg9ao6S1VnAf8d+CdV/VGkAjfxo7m5mf3794e1HK1a6/Gj1r8oBi3eWABA1a7+laWNkUTqbYeRMSYGFRcX097eHtZyNG+zohq//Yu6jRs7gSWLVrKzZFufO0qTu/sY2cE8ERffv2Emrhw5cgSv10thYeGHHld/F4GKrTBlDjKjh5PTTEQVLl5Ie6ePIxdvICsfAVV0/3tohzfaoZkYFepJ9CrwLnAceENVa0TkGyLysdCwnxHsWXQK+CLw5ehEa+JVaWkpXV1dbN68OWxrHgrcIhknE0kJ25omPDInjGPGoof7nTDKwk0rXXRq/05WM8aYSNm6dSsTJ05kxYoVYVuzpTHU8DrOdxgBbN74JBcunuXM2ZO9jklKBQ1Ap/UxijhLGJmIKSsrw+FwkJ//4RPQ9LgHmm7hKPxIXNfgjhSrF87D6XBQXnUcSctCVmyBznZ0//toly/a4ZkYpapbVXW+qs5R1W+HHvuaqr4d+rxdVV9Q1bmqmqeq9xWrq+prqvq9SMdu4sOOHTsYN24cy5YtC8t6qsrhQB2LZQxOe+6JSUs2FXK+qpbGm7ceODZbgo2vb9tJacaYGNLU1MTu3bt58sknezwherBabiuuBEhMDtuSUVO09lEcDic7S7b1OiYpNfg83e61HUaRZgkjEzHl5eUsWrSIjIyMO49pwE+gfCuMn4bMWRrF6Ey3tJRklsx7iPLqWgAkaxyybCO03EZr+teA1Bhjwqmjo4PS0lI2b94cthfcF7WVW3SwzDEuLOuZ8Fu8OVjCXl1c8cCxWaGT0m5bHyNjTAx5//338fl8YS1HA2htVNKy4rvhdbesrDGsXF5Acek7vd5nuBIEpwus4CHyLGFkIqKpqYmqqqr7+hfp0d1QfxXH2mdGxB+8kaJw0QJqzlzgdksrADJuCjJvOVw7D+ePRzk6Y8xoU1ZWhtfr5ZFHHgnbmoe0DoDljrFhW9OE1+S5sxg7bRJHd5Y9cGwGCTgQGmyHkTEmhmzdupVp06axePHisK3p9yveZiU1c+TcO23a8ARXrl7ixMnqXsckpUB7m71xHWmWMDIRsW/fPgKBwIf6F2m7l0Dp72D6fGR++Gp6zdAVLl6IqrKv5sSfH3woFybMQE8eQBtuRC84Y8yos2PHDtLS0u4raR6Kw4E6ZkoaYyQpbGua8BIRFm9aw0nPITq8fTeucIiQZSelGWNiSH19PeXl5Tz11FNhfWPc2zQyGl7fbV3hZlwuF7tKez8tLTFF6OqEzg5LGkVSv37LROQJETkhIqdE5L5GpSKSKCKvh657RGTWPddniEiLiHwpPGGbeFNWVkZKSsqHek8Eyv4Aba04t3zcdhfFmMVzZpGSlEh5Ve2dx0QEWbwGklLRmnI0YI1FjTHDz+/3s2vXLjZs2IDb7Q7Lmi3qo1YbWSFWjhbrlmxaQ1enj+N79z9wbJa4rYeRMSZmvPfee/j9/gGVo10773/gv8tngq/B07NHzv1Tenomq1eupbj0XQKBQI9jklKCP2/LbUsYRdIDE0Yi4gR+DDwJ5AAfF5Gce4Z9BmhQ1bnAD4Hv3HP9B0DvXazMiFdeXs6qVavuvNjX+mvogR3IknXIxJlRjs7cK8HlZHXOfMqrPlx+Ji43sjAfWhvhTO9bRo0xJlwOHjxIQ0NDWMvRDgbqCKCscowP25pmeMxesYjUrAyO7NjzwLHZJNKMD5/2fLNhjDGRtHXrVmbPns3DDz8c1nU7vOBOAnfSyEkYAWwqeoIbN69xrPZIj9fdoQbfzQ32Nz6S+rPDKA84papnVLUT+A3wzD1jngF+Efr8TWCLhLaMiMizwFmgJjwhm3hz+fJlzp07d6ccTQN+/H/6GSQk4lj/F1GOzvRm7ZIcLt6o4/y1D5efyfipMGkWeqYKbWmMUnTGmNFix44duN1u1q9fH7Y1KwM3GEMisyXjwYNNVDldTpZsXkN1SQW+zr53D2VJ8E2pRttlZIyJshs3blBZWRn2cjSADq+OqN1F3dYUbCIhwc2ukp7L0hwOwZ0MLQ22wyiS+pMwmgpcvOvrS6HHehyjql1AIzBWRNKA/wx8feihmni1e/dugDsv9tWzDa6cxvHYXyFpmdEMzfRh3dLgRsI9R47dd00WrAanC63dF+mwjDGjiKqyY8cO1qxZQ2pqaljW7FQ/R/QWKx3jcVg5dFxY+sg62lu8nKw41Oe47NBJadbHyBgTbe+8EzzxK9yno/m7lC4fqPavfO3a+fhpIZGamkbB6vWU7N6O399z3EnJQvNttVObI2i4O2W9BvxQVVv6GiQinxWR/SKy/+bNm8Mckom0PXv2MGXKFGbPno1eP09gz78hC/Jw5BREOzTTh5mTJjB94nj2HL5/c6AkJiNzlsCtq+jFk1GIzhgzGhw/fpwrV66EtRytSuvpIMBqK0eLG/MLlpOUlvLAsrQM3AhYHyNjTNRt27aNBQsWMHv27LCu2x46Vj4xZWS+4bFxwxPcqr9JVc3BHq8npkDAD95mSxhFSn8SRpeB6Xd9PS30WI9jRMQFZAK3gHzgn0XkHPAF4L+IyKv3fgNV/amqrlLVVePH2wu4kaSzs5Py8vLg7qL2Vvxv/Q9IScfx2F9FOzTTD+uX5rLv2Ak6On33X5w+H9zJBPa8FfnAjDGjwo4dO3A4HGzatClsa1YGbpKMk1zJDtuaZngluN0s2lDA0Z1l+Lt6f7fcKUKmnZRmjImyy5cvc/jwYZ566qmwr93hDSZKEpPDvnRMKMzfQFJicq+npXU3vm62srSIcfVjTCUwT0QeIpgYehF46Z4xbwOfBMqB54GdGtwndqfhgIi8BrSo6o/CELeJE4cOHcLr9bJu7Rr8v/8RNNfj/Pg/IMlp0Q7N9MO6pTn86/ZiDtSeYs2ShR+6Jk4XzF6E1lYSuFCLY8aCKEVpjIklO/yXBj33Eee0D6+1YwcrVqxgzJgxQw0LgIAqBwI3WeEYh0tGznHEo8HSR9ax/087OX2wivl5y3odl00it2iPYGTGGPNh27YFz3p64oknwr52R5viTgr28xmJkpNSKMgvonT3e3z+c1/B6fxwusLlBlcCtDZawihSHvhqKdST6FXgXeA48Iaq1ojIN0TkY6FhPyPYs+gU8EXgy8MVsIkvpaWluFwu8prPwMWTOJ76NDJ1brTDMv20Omc+CS4Xe4700rN+2jxIyyKw5y2rJTbGhNX58+f54IMPwlqOVqu3acLHapkQtjVNZCxcu4qEpEQOv7e7z3FZ4qYZH112UpoxJkq2bdvGkiVLmD59+oMHD4Cq0u4dueVo3TYVPcHtxnoOH6m875qIkJoptFjCKGL69faaqm5V1fmqOkdVvx167Guq+nbo83ZVfUFV56pqnqqe6WGN11T1e+EN38S6PXv2sOKhqaR8sA9Z8zHrWxRnUpISWb1wHrt7SRiJ04Uj/0m4eBKuno1wdMaYkWzHjh0AYU0YVQSuk4CD5Y6xYVvTREZiSjI561Zz9P29BAK9J4OySUSxk9KMMdFx9uxZjh07FvZm1wC+DtAAJKWEfemYkr96PcnJKews2dbj9dRMwdusBAKWNIoE249ths21a9c4efIkazMUWf04jnXPRDskMwhrl+Zw5vI1rty81eN1WbwO3IkEDu2McGTGmJFs+/btLFy4kKlT7z2YdXACqngCN1gh40iS/lTkm1iz9JF1NN64xfmjtb2OyRI3ALfVEkbGmMgb1nK0O/2LRvYOo8TEJNYWbGL33h34fPf3UU3LFDRgja8jxRJGZlioKqU/DW4oW//E0zg2/XvEji+OS+uW5gKw5+ixHq9LYjKSuwY9vg/1NkcyNGPMCHXlyhWOHj0a1ndoa/U2t+mkwGHlaPFq0YYCnC5Xn6elZYZOSmvAGl8bYyJv27ZtrFy5kkmTJoV97fY2cDggITHsS8ecTRuepLmliQOHyu+7lpoZTGFYH6PIsISRCTtVJfDer9hTVs7ErAwefun/tGRRHJszdRKTx41hz+Fe+hgBjuWbwN+FVvV95LExxvTH9u3bAXj88cfDtmZ54DpuHKxwjAvbmiayktNTebhwOYd37O61b55LHKSTYDuMjDERd/LkSU6dOjUsp6NBcIdRYgqj4r5q1Yo1pKam93haWlIKOF2WMIoUSxiZsFINEHjvV/gO7KTihpd1Wx7F4bBfs3gmIqxbmkNFzQk6u7p6HjN+GkyfT+DQLrSP3hLGGNMf27ZtIycnhxkzZoRlve5ytOVWjhb3lj2ynluXrnH5xOlex2STaDuMjDERt3XrVhwOB4899ljY1w4ElM72kV+O1s3tdrN+zRb2lu2ks/PDf8/l/2fvvuOrqtLF/3/WPicnJ72RBAgldCSEFnrvRRHEPurVcRxnRofRuV69zh29/kZnnNEZvX6d5gyOY1dQUARp0kF678XQAoH03nPOXr/lli9qAAAgAElEQVQ/TkAgJxDglJTn/XrllWSvnbUfyHkl2c9ez7OUIiRcGl/7itzJC4/R2sT85iP0rtXsi02iuLyCESNG+Dss4QHDeydRWl7BnqO1+tlfYPQdC4U56BP7fRiZEKKpSU9PZ+/evR7t/3BI51NIFUOMeI/NKfyj55ghKMNg9/K6V7RGqUAKqcIpu3cKIXxEa82SJUsYNGgQLVp4fiVrZbnrfVPfIe1iY0ZNprSshK07NtQaC4lQlBVptDS+9jp5zCaumbl7Ta1jWmv0oS2u3bI69GT9zjQshsGgUKfb80XjMjipG1aLwfo9BxjQo+slY+e/v9p0QkAg5rfzoTjvivMZfUZ7KVIhRGPnjXK0DWYmgRj0lXK0Ri8sOpLO/Xux+5t13DLzIbelGZHY0EARVUTRDJp9CCH87uDBg6SlpfHoo496Zf7zDa+b+g5pF+vXZxDh4ZGsXruU4UPGXjIWGqE4Z0JZiSYkvPkk0fxBEkbCM47vq0kWJaG69GXDe0vp3aUD4SHN6KdaExYaHETfrp34ds9BnvrBDLfnKMOCbtke0o+hHdUoa4CPoxRCNAVLly69rnK0Fc4zbo87tMl68xztVCjfmuc8EaLws36TRjLnt3/m7NETJHTrWGs8SgWCdjW+loSREMIXFi9ejNVqZfz48V6Zv7JMYw0Ai7X5JEes1gBGDBvPytWLqKgox24PujDmanztpLRIExLuvxibAylJEzdMZ5xCp+6GVh1RXfqRU1jEwROnGdEnyd+hCQ8a1juJI6fOkJVXUOc5qlUHMJ2QddqHkQkhmgpvlKOlUUIVJl1UhMfmFP7Ve8IIDIvBjiWr3Y5HYgOQxtdCCJ8wTZMlS5YwbNgwIiMjvXKNivLmVY523piRk6moKGfLtvWXHA8KBcMija99QRJG4oboolzXzliRsaikISil2Lj3EPD9duyiaTifANyw92DdJ0XGgT0Efa7uXkdCCFEXb5SjfWcWEoyV1siK16YiLDqSroP6smvpWre7pZ3fKU0aXwshfGHPnj2cO3eOKVOmeGV+R7XGWd28ytHO69OrP1GR0axee+luaecbX0vCyPukJK2Zu5H+QtrpRO/9FgJsqD6jURYLAOt27ScmIpzu7dt4KErREHRrl0B8dCRrdu1jxuihbs9RSqFbdYCTB9CV5ajAILfnCSGEO9dbjlaXcu3gNKUkq2iMZrANcXOSMmU0H//v66TtP0r75G61xiOxyQojIYRPLF68GJvNxrhx47wyf2WZ631zXGFksVgZOXwiS5fPp7y8jKCgYDJOOQFQCkoKNOdOOtz2s2vZ3uLrcJskWWEkrptO3QWlhaieQy8kBqodTr7dc4BRfXtiGPLyakqUUozqm8zGvYeoqq6u+7xWHUBryDjlw+iEEI1dbnqGx8vRjukiNEg5WhPUa9xwLFYrO5a6L0uLUoEUUIUpO6UJIbzI6XSydOlSRo0aRWhoqFeuUVGuQYHN7pXpG7wxoyZTWVnBxs1rLjlus4NpgrPu2xLhAXJHL66Lzs+EkwehTVdUi4QLx3cc/o6S8gpG90v2Y3TCW0b1S6asopJth76r8xwVFgWhUeiMEz6MTAjR2O3+xtWfwFMJI601R3UhLQgkWknj46YmODyUm4b3Z9fSdZimWWs8ChsmmiLkTkII4T3bt28nJyfHa+Vo4Gp4HWgHw2h+K4wAkpP60SImjtXrLi1Ls9ld/x9VFf6IqvmQhJG4Zto00Qc2Q1AoqlvKJWNrdu7DFmBlSPJNfopOeNPgnt2w2wJYs2PfFc9T8e2gIBtdWe6jyIQQjd2ub9aRlJRE27ZtPTJfNhXkUkk35Z0GpML/UqaMoSAzmxO7a/fWi6xJEhZIHyMhhBctXryY4OBgRo0a5ZX5tdZUNtOG1+cZhsGoERPZun09JaXFF46fX3FVVSErSb1JEkbi2qV/5ypF697/kq3Ttdas2bmPQUndCLbL09ymyG6zMbhnd9bu2ue20egF8TX9R7Ldb3MthBAXy03P4NS+wx5tdn1Q5xOAQWcl++02VT1HDyYg0OZ2t7TzO6Xla0kYCSEg45SzXm/XoqqqimXLljFmzBiCg73TkbqqArQJzb0t6JiRk6murmbDplUXjhkWhTUAKmWFkVdJwkhcE+2oRqfugag4iL30KfCJs5mczsxmVF8pR2vKRvVLJj07l9QzZ+s+KTQSgkLRWWm+C0wI0WhtX+T6A/Dmm2/2yHwV2slxXUxnFY5NSdPLpsoeEkzSqEHsXrYOp+PSGz2bshCClQKk8bUQwjs2btxIYWEht9xyi9euUVnuekBrb8YrjAB63NSb+LhWtXZLs9llhZG3ScJIXBN94gBUVaC69q/VjX7Nzr2AK6Egmq7zCcE1O/fXeY5SCuLaQu45tEP6Rwgh6qa1ZvvXK+nYrycJCQlX/4J6OKoLcKLpIeVoTV6/yaMpzisgdfueWmNRBMoKowZEKTVZKXVEKZWqlPqVm/FApdScmvEtSqnEy8bbKaVKlFJP+ypmIa5k8eLFREREMGzYMK9do7IMDAtYbV67RKOglGL0iEls37mJouLCC8dtdkV1JWhTkkbeIgkjUW+6shxOHYSWiajIFrXG1+zcR7f2bWjdItoP0QlfiY+OpEeHthcShHVRce1cWxfkXGElkhCi2Ttz+BgZx9MYMNUz2xFrrTmkC4gniGjVTLeUaUaSRgwkMDiInUvX1hqLVDbZKa2BUEpZgL8BU4AewA+UUj0uO+0RIF9r3Rl4A3j1svH/A5Z4O1Yh6qO8vJyVK1cyadIkbDbvZXMqyjSBQbjdNr65GTNqMk6ng/UbVlw4dqGPkTwb8BpJGIl602lHwOlAde5da6ygpJRdR47J7mjNxOh+vdjz3Qnyi0rqPikyFgICpSxNCHFF275egcVqpe+kkR6ZL40SiqiW1UXNhC3ITvKYoexevh5H9aUrWqMIxImmRHZKawgGAqla6+Na6ypgNjD9snOmA+/XfDwXGKdq7pKVUrcBJ4ADPopXiCtavXo1ZWVlXi1HM52a6kopRzuva5ckWrdqw5qLdkv7fqc0eTDgLVZ/ByAaB+10wOnDENcWFRJRa/zb3QcwtWZ0v15+iE64Y+5e47W5R/dL5u/zFrF21z5uGzXE7TnKMNCxbSArDW2aKEPy00KIS5lOJzsWr6bHyIGERNx4c2qtNbvNXEIJoKM0u242+k0ZxfZFKzm8cSc9Rw26cDxK2UBDPlWE08zrOfwvATh90edngEF1naO1diilCoEYpVQF8CwwAaizHE0p9RPgJwDt2rXzXORCuLFo0SLi4+NJSUm5+snX6fxmw815h7SLKaUYPXIysz9/l4KCPCIjowkIBJSrObjwDrmDE/WTngrVVajEJLfDq7bvISYinJ4d5Rd0c3BTYlvioyNZ5aZnxMVUXFtwVENBlo8iE0I0Jke37qEoO89j5WhnKSOLCnqraAxZvt9s3DSsPyGR4WxdsPyS45G4dmwtkD5Gjd1vgDe01ldY1gxa61la6/5a6/6xsbG+iUw0SwUFBaxfv54pU6ZgsXhvY4WKMteqmea+Q9rFxoyajGk6WbfB9fNeKYUtUFYYeZMkjMRVaW2iTx6EiBauMqPLVFZVs273Acb1740hq0iaBcMwGNu/Nxv2HqS88go70MS0AmWgs9N9F5wQotHYvmgl9tBgeo4a7JH5dpm5BGOlq6q9ElY0XdaAAFJuHsO+1Rspu6hUOlBZCMZKPpIwagDSgYu3121Tc8ztOUopKxAB5OJaifRHpdRJ4JfAr5VSM70dsBB1Wb58OdXV1UydOtWr16ks1wTYwGKVByDnderQjXZtO7ByzfftzGx2JSuMvEju7sXVZZ6G8hJUYpLbhmsb9x2ivLKScQP6+D424TfjB/ShoqqaDXsP1nmOsgZAVBzkSMJICHGpqopK9iz/lj4TRhIQeOPlQpm6nHOUkayisCr586a5GTRtAo6qanZ9s+6S45HYKNBXeLAhfGUb0EUp1UEpZQPuBRZcds4C4KGaj+8EVmmXEVrrRK11IvD/gN9rrf/qq8CFuNyiRYtITEykR4/L+7Z7jtaayjIpR7ucUorxY6ayd992MrPOAa7G104HOB2yysgb5C8qcVX6zFGwh0B8W7fjK7ftJiw4iIFJXX0cmfCnlO5dCA8JZuW23Vc8T7VIgJICdHmpjyITQjQG+9dspqK0jP5Tx97wXFprtphZ2LFwk4ryQHSisWmb1JWWHdvVKkuLUoHkU4mWndL8SmvtAGYCy4BDwGda6wNKqZeUUtNqTnsHV8+iVOAp4Ff+iVaIumVlZbF161ZuueUWr+5c5qh2JUECg712iUZr3JibAVi5ehFwceNrv4XUpEnCSFyRLi+B3HOohM4oN09sHU4nq3fsZVTfZGxW6aHenARYLYzul8yanXupdjjrPjE2wfVeVhkJIS6y7esVRMa3oEv/G98sYaOZSSblDFCxBMjqomZJKcWAaRM4vnM/2WlnLxyPxIYDTSkOP0YnALTWi7XWXbXWnbTWL9cce0FrvaDm4wqt9V1a685a64Fa6+Nu5viN1vo1X8cuxHmLFy9Ga+3V3dEAKmv6F8kOabW1btWWnj368s3KhWitsdldx6WPkXfIX1XiinR6quuDhE5ux3ccTqWgpJRxA3r7MCrRUIwf0Iei0nK2Hzpa90khEWAPRuecrfscIUSzUpJfyMFvt5EyZQzGDTYMrdBOPnJ+RwyB0ruomRswdRxKKbYtXHHhWJRyNb6WPkZCCE9YvHgxPXr0oEOHDl69TkUZKMWFZIi41IRxUzmVdoxjx49gsYJhkRVG3iIJI1EnrU3X7mgxrVFBoW7PWbltN4EBAQzv7X73NNG0De3VA7stgBVXKEtTSkGLBMg9hzavsBJJCNFs7Fq2DtPhpL8Hdkf7ynmSPCoZasTLzmjNXFTLWLoO6svWhcsvlKBF4eqPJX2MhBA36uTJk+zbt8/rza7BtcIoMBivlr01ZqNGTMJqtbJ81ULXTml2WWHkLZIwEnXLOQcVZag2XdwOa61ZuX0Pw3r3INge6OPgREMQFGhjeJ8kVm3fg2madZ6nYhPAWQ352T6MTgjRUG1duJxWXRJJ6NbxhuY5YhYw3zzJcKMlLZU0ehAwcNp4cs9kcHzXAQDsyoodi6wwEkLcsIULXcmJKVOmePU6pqmprIDAIEkW1SUiPJJBA0aycvVinE6na6e0SqRfnRdIwkjUSaenQkAgxLVxO37geBoZufmM6y/laM3Z+P59yMovZP/xU3WfFN0KlELnSh8jIZq7c8dOcXLPIQbfNumGnpwW6yredOwjFjuPWLp7MELRmPUePxxbkJ0tX31z4ViU7JQmhLhBpmny1VdfMWTIEFq2bOnVa1WVA1r6F13NhLFTyc3LZteeLdjsoE1Xs3DhWZIwEm7p6irIPg2tOqIM9/0lVmzbhcUwGN0v2cfRiYZkVL9krBYL32zZWec5yhoAkbGQm+HDyIQQDdGWL5dhWC0MuIFyNFNr/u44SCFV/NKaTLCSTReES2BwEH0mjmDXsrVUVbhWFUXKTmlCiBu0Y8cO0tPTue2227x+rYpy188q2SHtyoYMGkVISBjLV339/U5p5X4OqgmShJFwLysNTBPVKtHtsNaapZt3MqhnNyLD3Pc3Es1DeEgwQ5NvYtnmnVf8Y1zFtIaiXLR0pBOiyavQTtJ1KQfNfPabeewz8zhmFpFVVcKWBcvpOXIwYTFR1zW3Q5v8xbmfnTqHBy1d6WiEezh60dgNnDaBipIy9q3eBEAUgVRhUoCsMhJCXJ/58+cTHBzMuHE33nvvairLwBoA1gBZYXQlNlsgo0dMZP2GFTh1GSB9jLxBEkbCLX3uBASFQkQLt+MHT6RxOjObyYNTfByZaIgmD0nhbE4e+46drPukmFau93mZPolJCOFbhbqKRc5TzHOe4EPzOxabp9mgM9mks9iss1ilz/LBusWU5BUQMD2FfWYexddYJlSpnbzu2MtGM5P7LZ2ZZGnrpX+NaMy6DOhNVMtYti5wlaVFKlfj6zO61J9hCSEaqfLycpYtW8bkyZMJDvb+sp+KMk2glKPVy/ixUykvL2Pz1rVYbVAl7eo8TtZwi1p0ZTnkZUCHnnX2l1iyaQdWi8G4AX18HJ1oiMak9CLAamXpph306lzHNqPhMWANQOee9W1wQgivKtHVzHeeZKl5mmpMYrEzQMUSq+xEYsNS82yqlGo+mP8B9hYRBA/vzuaaRFIL7HRQYXRUYXVeQ2vNJjOTj52p5FLBjy3dmWBx319PCMMwGDh9It+8/Sn557KIahkNwBldQjLRfo5OCNHYrFy5ktLSUqZPn+71azmqNc5qsEs5Wr306plCXGxLlq9aSNKPJ8kKIy+QhJGoLfMUaI1qmeh2WGvNss07GJJ8E5GhIb6NTTRI4SHBDOvlKkt7+v7bMYzaixeVYaCjW0LuOT9EKITwNK01K8x0PnGmUo6DkUYrbjXac0QXuD2/KqeQk+t3MPahO5ke2IUiXcUJXcwJXcw2nc02nc26qgy6GhG0V6EEYkGhOK6LOGjmc5Yy2qtQHrf0I8mQm35xZUNun8w3sz5h05fLmPLYAwRikC4rjIQQ12H+/Pm0bt2a/v37e/1aFa7KKllhVE+GYTB+zC3MnvseZf+RB5XRmKbGMOT/z1OkJE3Uos+dgNBIVJj7/hL7jp3kbE6elKOJS0wenEJGXj57Uk/UeY6KbgXlJeiCLB9GJoTwtFxdwe8du/iX8zAdVRh/tA7mcWsSbY26e9ptXbAC02ky6LZJAIQrG72NGG6zJHKv0YlBKo4EFcwuM4cPnN/xtvMws5yH2GBmEKeC+JmlB69YB0mySNRLTEJLug3px+YvlqBNk0gCpSRNCHHNMjMz2bRpE9OnT3f7QNTTKss0SkGg3euXajImjp+GaTrZsHURANVSluZRssJIXEKXl0JBNqpz3aVmSzftIMBqZWz/3j6MTDR0Y1J6YQtwlaX17drJ/Uk1fYz0yYOoPnE+jE6I5mWF88x1f+34q5R67TVz+bNjP1U4+ZGlGxOMNhh1lC+fp7Vm47zFdOjTg5Yd29UaD1MB9FLRjLe0QWtNCdVUY+JEE4P9qvML4c7QO2/m30/9lkMbthM1vJ0kjIQQ12zhwoWYpsm0adN8cr3KMo0tCJSskKm39u060aN7b1as/ZKhvR6gqkITGCT/f54iK4zEpbLSXO/rKEczTZNlm3cyrNdNhIdIca34XmhwECN6J/HNlp2Ypun+pJBwCAxGnzzo2+CEEDdMa81XzpP83rGLCGXjlYBBTLK0rVcy5+jW3WSfSmfYXVOveq5SijBlI1rZiVVBkiwS1y15zBBCoyPZOHcJkQRSTDVF19hoXQjRfFSWaypKv++Bo7Vm/vz59O3bl8TERK9f3zQ1leVgl2THNbt50gzSTh/jxOl9yIbMniUJI3EJnZkGIRGoEPfbFO9JPUFGXr6Uowm3Jg1OISu/kJ1HjrkdV0pBTCv0qUNoXUdSSQjR4Di0ySznIT5xpjLIiONl6wBaq/r3sNvw2SKCw8PoO2mkF6MU4lLWgAAGTZ/I/rWbCMhxrS6SVUZCiMtVlmvOnXCSnmqyc3U1u9ZWk5nm5MCBAxw7dswnza4Byoo0WkNgiCSMrtXokZOxBwaxYcd8aXztYZIwEhfoqgrIz4L42uUC5y3ZuB1bgJUxKb18GJloLEanJGO3BbB447Y6z1ExraCiFDJP+zAyIcT1qtAO/uTYwyrzLDOMRH5pScau6l/RXpSTz56V3zLotonY7IFejFSI2obeMQXTaZL61VpAEkZCiEuVFWnSU00qyyG6pSKxhwWLBY7tdfLpR19is9mYMmWKT2IpznclOuxBPrlckxISEsqoERPZsnMpxYVl/g6nSZGEkfhe1mlAo+LcJ4yqHU6WbNrO6H69CA2Wn2SithC7nbH9e7Nk0w6qHA73J13oY3TAh5EJIa5Hia7md45d7NV5/MRyE/daO7tWCl6DzV8uxXQ4GXbXLV6KUoi6xSW2ocuA3mybuxS7U3FGl/g7JCFEA+Go0mSdMbHZoW03g8hYg9YdLfQcaiUoooplyxcxYthYwsPdV154WnG+xmIFS4BPLtfk3Dz5dsorStm6ayVOh6wy8hRJGIkLdNZpsIdAuPsdaDbsPUheUQm3Dh/o48hEYzJtxCCKSstYv2u/23EVGAQtEqSPkRANXJGu4reOHZzQRfynNZlxloRrnsM0TTbMXUyXgX2I79DWC1EKcXUj7r2VvPRMLBuOkS4rjIQQuPoTZZ420Rri2xlYLN8/DDEMxZn8tZSWFdK3221UVfom+VBcYBIYzDU/mBEuyUn9aN2yHeu3fSl9jDxIEkYCAO2ohtyzENeuzh9SC9dvITI0hOF9knwcnWhMhiTfREx4GAu/3VrnOSoxCX3mKLpamo8K0RAV6SpedOzgrC7jGWsfBhrXt6vh4Y07yEvPkNVFwq96jR1GRFwM6bNXS0maEAKAgmxNZRnEJigCAmvf+3zx5Vxat06gW8dBnDnq9Ho8VZWueOzBkiy6XkopJk+cwdHjOzh58qS/w2kyJGEkXHLSwTRR8e6fABeXlbN6x16mDO2PzVr/3hWi+bFaLEwZ2p81O/dRWOL+D3OV2AOcDnT6d74NTghxVcW6it86dpKly/mVtQ99jJjrnmv97AWERkfSe/wwD0YoxLWxBFgZdtctpG3YTeapM5Toan+HJITwI6dTU5itCQ6D0Mjat8NpaWls3ryZO++8g1aJVjLTTMpLvLvKqOR8/yJJGN2QyROnoZTB8lVf+TuUJkMSRgKo2R0tIBCi3D9FXr5lF5XV1VKOJurl1uGDqHY4+GbLLrfjqm1XMCxSliZEA1OlnfzOsYtzuoxnrL1JMtyXKNdHdtpZDqzdwrC7bsEaIA0ZhH8NvfNmLFYLuZ+tJ036GAnRrBVma0wTouLd3wrPmzcPwzC4/fbbadvVgjIg7UgdvTk9pCjfRCmwSZvYGxLbIp7eScNYs+ErHHX1UxXXRBJGAm06XSuM4tqilPuXxIJvt9C+ZRy9OnfwcXSiMUrq2I6OrVuy8NstbseVzQ6tO6FPScJIiIbCoU2WmWc4rUv4L2svet3AyiKAdZ9+hbIYjLhnqociFOL6RcTGMGbiBPK+2sLR0mx/hyOE8BOnQ1OYqwmJUAQG1V7N43A4+OKLLxg5ciTx8fHYAhWtOxrkntOUFJhei6soVxMaqTAMWWF0o8aPmkF+YTbffvutv0NpEiRhJCA3AxzVqHj3u6Odzclj28GjTB0+UJqwiXpRSjF1+EB2HE4lPTvX7TlGYg/ISEOXy5PepkApNVkpdUQplaqU+pWb8UCl1Jya8S1KqcSa4xOUUjuUUvtq3o/1dewCTK1ZZZ4lg3J+bkmir9HihuarKC1j85dL6TtxJBFxNzaXEJ7y0A/uxywuZ/nXi/0dihDCTwqyNdqEqDj39zRr164lJyeHu+6668KxhE4WLFY4d8I7CSOnQ1NaqAmPkfssTxg8aBRhIVF8/tk8f4fSJEjCSKCz0sBihehWbscX1TQvlnI0cS2m1rxevlq32e24SuwBaPSpQ74LSniFUsoC/A2YAvQAfqCU6nHZaY8A+VrrzsAbwKs1x3OAW7XWycBDwIe+iVqcp7Vmo87kFCUMVfEMs7S84Tm3fPUNFSVljH5ghgciFMIzUlJSiOrWju2fLEJr2XJZiObGdGqK8lyri2x298mZzz//nNjYWEaOHHnhmMWqiGtjkHPWpKrC8z87igs0WkN4tNyae0JIqI1hA6azdu1qMjIy/B1OoyevymZOaxOyTkNsAspicTOumbdmIyndO9M2PtYPEYrGKiE2hsE9uzF/7SZM080TmVYdwBaEPnnA98EJTxsIpGqtj2utq4DZwPTLzpkOvF/z8VxgnFJKaa13aa3P1hw/AAQppQJ9ErUAYL/O55AuoJeKJsmIuuH5TNNk7cfzaZ/cncReN3kgQiE8QynF0PunU5R6hvUbpFRBiOamuMC1uiiijpU8GRnprFu3jknjZpCTrsg45bzwZrGB1nBsr+PCMU8pynX9nRwWJSuMPMEWCKMH34WpTT7//HN/h9PoScKouSvIhqoKVJz7crRth77jdGY2d44d7uPARFNwx5hhpGfnsuXAkVpjyrCg2neXxtdNQwJw+qLPz9Qcc3uO1toBFAKXN8m5A9ipta68/AJKqZ8opbYrpbZnZ0v/EU85pYvZrLPoQBgDlWceChzasJ3sU+myukg0SDdPvQVri3Bmvftvf4cihPAhrTVFuZrAIAgMdn/OgsWfoZTi1lvuqjVmC1QEhUJRnkabnl1ldH7VkzVAEkaeYFgUrVq1oW/yMD7//HOqq2VnzBsh+6M3czrzNCgDYi+/t3OZu+pbwoKDmDCwr48jE03BuP59iAgNYd7qDQxJrr3SQCX2QH+3C52fhapjhz7RPCilknCVqU10N661ngXMAujfv7/UknhAvq5ktXmOFtgZbbS60KNuhfPMDc275oN5hLeIps/EEZ4IUwiP6hwYQ8y9I9nx1685evQoXbt29XdIQggfKC+F6kqIbaPc9mStqqpk8bIvGDZ4DHGx7tt0RLQwyDhpUlrkalDtCaZTU5KviW8v6zg8KdAOY4bezev/eIJVq1YxadIkf4fUaMkrsxnTWkNWGsS0QllttcYLSkpZvnUXU4cPJCiw9rgQVxNoC+DW4QNZsW0P+UW1m1ur9q42N1KW1uilA20v+rxNzTG35yilrEAEkFvzeRvgS+BBrfUxr0crqNJOlpvpWFFMMBKw1rFD5rU6fSiVw5t2MuqBGVgDAjwypxCe1FoFE3/XcKx2G++///7Vv0AI0SQU5ZgYFgiNcJ/oWbNuGYWF+Uyfem+dcwSFgtXm2tHMU0oKNaYJ4TFyW+5JtiBF9w7Dad26NbNnz/Z3OI2avDKbs1QebEkAACAASURBVKw0KC+pc3e0Rd9uparawR1jhvk4MNGU3DFmGNUOBwu/3VJ7MLolhEVLWVrjtw3oopTqoJSyAfcCCy47ZwGuptYAdwKrtNZaKRUJLAJ+pbXe4LOImzGtNWvMcxRRxTgjgVDlucTOin/PwR4SzPC7p3psTiE8yaoMEqNa0nH6SBYuXIiUuArR9FWWa8qKITxaoerYtv6rr+fQtk0H+vUdXOc8SinCoxQVZVBV6ZmkUVGea57waClH86RAu8IwLEy/9W42b97M8ePH/R1So1WvhJFsl9w0mUd2AAri2tYa01ozd9UGkjq046bE2uNC1FfXdgkkd0pk3uoNtXalUUqhEnug0w6j3TXGFo1CTU+imcAy4BDwmdb6gFLqJaXUtJrT3gFilFKpwFPA+d8lM4HOwAtKqd01b1Kf6EX7dT6nKGGwiqOVqqORw3XIOXOOXcvWMeyuWwgOD/XYvEJ4WnsVSvgDo3A4HHz88cf+DkcI4WWZaa4G1WF1JGWOfneQg4f3MG3q3W7L1S4WWtOYujjPMwmjwhyToDBFgE0SRp5kC3K9Hz9qBgEBAbLK6AZcNWEk2yU3XfroToiKQ9nstcb2HTvF0dPpsrpIeMQdY4aReuYce747UWtMJfaAilLIPOX7wITHaK0Xa627aq07aa1frjn2gtZ6Qc3HFVrru7TWnbXWA7XWx2uO/05rHaK17nPRW5Y//y1NWbYuZ6vOoj2hJKkb3xHtYqs/mIdhGIz+j9s9Oq8QnpaowqhqF8nIcWOYPXs2JSW1S6aFEE2DNjVZp02CQqkzKTN/4afYA4OYPP7yDV5rswYogsOhOF9j3mDza6dDU5SniYyVZJGnWayu8kGbEc3kyZP54osv5Gf9darPCiPZLrkJ0rnnIPdsneVon69cT1CgjZuHDfBxZKIpunlof4LtgcxZsa7WmPQxEsI3qrSTleZZgrEy8qIm155QnFfApi+WMmDqOCLjW3hsXiG8ob1yrYCb8Oh9FBYW8umnn/o5IiGEt+Rna6oqIDza/W1vQUEeK1YvYvzYqYSGhtdrzvAoA9MJeRk3ljAqzNVoE6JipUuMpymlCAlXlBZpHnzwQUpLS5k3b56/w2qU6vPqlO2SmyB9dKfrAzcJo4LiEhZt2MbU4QMJCw7ycWSiKQoJsjN95GCWbNpBbmHRJWMqJBzi2kofIyG8bJPOooRqxhitsSuLR+de98lXVFdUMu7h2lsRC9HQtFdhAFiSEhg2bBjvv/8+5eXlfo5KCOENmWlOAgIhuI5c0FeL5lBdXcWdMx6o95xBYWAJgKzTzhuKrSDL1Yhb+hd5R0i4oqxYk9QjiZSUFD766COczhv7njVHPklnXrRd8k/djWutZ2mt+2ut+8fGxvoipGbPPLodWnVE2UNqjc1bvZHK6mrumzja94GJJuu+iaOpdjiYt7p2X2OVmIROT0VX1conCyE84KQu5qgupI+KoaUH+xYBlBeXsvaT+fQaO4yWndp7dG4hvCFUBRBHEMfNIn72s5+Rm5vL3Llz/R2WEMLDKss1+ZmauLaG21W1VVWVfLXwUwYNGEH7dp3qPa9SirAoRUG2pqLs+lcZFWSbRMQoDIskjLwhJFyhTSgvda0yOnPmDCtXrvR3WI1OfRJGsl1yE6MLcyDjFEa3lFpjTtNk9vK1DOjRla7tLl9IJsT165jQkiE9uzNn+XocDsclYyqxBzgd6DNH/ROcEE1YuXaw3swghkD6Ks+Xi639+EvKi0qY/Fj9n84K4W+djHCO6yL69+9PSkoK77zzDlVVVf4OSwjhQVmnXRuqxLd1v6p2xepF5BfkcdftD7kdv5KwmubX17vKqLxEU1EGkXFSjuYtweGu71FpoWbcuHEkJCTwwQcf+Dmqxqc+r1DZLrmJOV+Oprr0qzW2Zsdezubkcf+k0T6OSjQH900aTUZePqtWrbrkuGrTBSxW6WMkhBds0JlUYzLaaI3Fg32LwLW6aNUH80geO5S2N3X26NxCeFNHFUY2FRTpKh577DEyMzOZP3++v8MSQniI1pqs004iWijsIbV/92mt+fyLD+jYoSv9+gy65vkDbIrIWEXWaRN9Hc2vC7JdySzpX+Q9QaEKZUBpkcZisfDAAw+wY8cODhyQ+41rcdVXqGyX3PSYR3dCbBtUdHytsY+XraFlTBRjUnr5ITLR1I3ql0xCbAwfffTRJcdVQCCqTRfpYySEh53SJZzQxfRVMUR7Yc+J86uLpvxMVheJxqWTcjU0OaaLGDp0KMnJycyaNUtWGQnRRBRkayrLIb6d+9vd7Ts3cvJUKnfd/tB1bwIR385CVYWrsfa1ys8ysYfgNpklPMMwFMFhrsbXAHfccQfBwcG8++67fo6scalXSlO2S246dEkhnPkOo2vtcrTU02fZcuAI904YhdXi2YaoQgBYDIN7J4xk27ZtHD16afmZSuwB2WfQpYX+CU6IJqZam2w0M4jERi91+T4UN668uJTVH3xB8pghtO3RxePzC+FNHVU4ClfCSCnFzJkzSU9Pl110hGgiMtOcWG0Q3dL97e5n894nOqoFY0dNue5rRMUrAmyQlXZtZWmOKk1hriZKytG8LjRCUVqo0VoTFhbGPffcw5IlSzh9+vTVv1gAPmp6LRoOnboL0Cg3/Ys+XLoaW4CVO8cM831gotm4ffRQgoKCeO+99y45rtr3AECfOuSHqIRoenboHEpwMMJo6fFSNIC1n8ynrKiYybK6SDRCQcpKa0I4Zrp27hwxYgQpKSm89dZbsmOaEI1cVYUmL1MT18bAMGr//jtydD/bd27kjtsewGazXfd1DEMR29YgL0tTWV7/VUY550y0CbFt5Fbc20IjFI5qqChzff7DH/4Qi8XCO++849/AGhF5lTYz+sgOiIqDFpc2tM7OL+SrdZu5beQQosJD/RSdaA4iw0K54447+Prrr8nIyPh+IL492EPQJ6SuWIgblaMr2K/z6K4iPb4rGkBJfiEr3/2M5DFDaJfU1ePzC+ELnYxwjukitNYopXjyySfJzs7m008/9XdoQogbkHXaBO0qGXPn49lvExoazvSp997wtVolWlDA2eP1X2WUddokOEwREi7laN4WGun6Py4pcPWMiouL47bbbuPLL78kOzvbn6E1GpIwakZ0RSk67TCqa0qtWt0Pl67C6XTyw6nj/RSdaE5++MMfYprmJTsVKMNAtb8JffIAWl//FqVCNHem1qw3M7BjYaCK9co1vnn7UyrLKrj1yR95dN4VzjPX/SbEteqkwimkilwqARgwYADDhg3j7bffpqSkxM/RNT1KqclKqSNKqVSl1K/cjAcqpebUjG9RSiXWHJ+glNqhlNpX836sr2MXjYc2NRmnXM2ug0JrJ2ROnExl/caVzJh2HyEhN/6QPDBI0SLBIDPNpLrq6n+/lhVrSgo0cW2N6+6dJOovKExhGFBS8P335pFHHsHhcPD+++/7MbLGQxJGzYhO3Q2mE6PrpbujFZeVM2f5OiYO6kf7ltKTXHhfQkICU6ZMYc6cORQWft+zSHVMhpICyJa6YiGu1zLzNDlUMETFE6g8348uNz2D9Z8uYND0ibTqnOjx+YXwlY41ja+P66ILx375y19SUFAgNxIeppSyAH8DpgA9gB8opXpcdtojQL7WujPwBvBqzfEc4FatdTKuXZk/9E3UojHKy9JUVUDL9u5//30y523s9iDuuM1z5dQJnQxMJ2ScNK96btYZJyhokSC34b5gGIqQCHVJwqh9+/ZMnjyZ2bNnX3IfItyTV2ozog9thbBoaNXhkuNzVqyjpLyCR6ZN9FNkojl65JFHKCsrY/bs2ReOqY7JAOhj+/wVlhCNWp6uYLbzGG0IoaMK88o1Fv31fZShuPnx//DK/EL4SqIKxYK60McIoGfPnkyYMIF///vfUq7gWQOBVK31ca11FTAbmH7ZOdOB85m6ucA4pZTSWu/SWp+tOX4ACFLKC9s+iiYh46QTmx2i42uv3jl77jSr1i5h2i13ExEe6bFrBocZRMUrzp1w4nTUvcpIm5rsMyZRcQpboKwu8pXQyJrG1+b335tHH32U0tJSPvnkEz9G1jhIwqiZ0KVF6BMHUD0GodT33/bKqmo+XLKKIT2706NDOz9GKJqb7t27M2LECD788EMqK13lACo0EuLbYx7f6+fohGicPnGm4kQzzIj3ylL3M4ePsf3rlYy6/zaiWsmKVNG42ZSFtiqUYxetMAJ46qmnqK6u5s033/RTZE1SAnDx8uEzNcfcnqO1dgCFwOVbPN4B7NRaV15+AaXUT5RS25VS2yXZ1zyVlWgKczQt21tQbppdf/rZv7FYrNx9+w89fu02nS04quFMat29jDLSTKorIb6t7EbtS6ERCtN0vT7O6969O2PGjOH999+nuLjYj9E1fJIwaib04a2gTYykIZcc/2r9ZnIKivjx9El+ikw0Z4888gi5ubmXbGOsOiZDeiq6otSPkQnR+Bw1C1lvZjDVaEe4uv5dX+qitear12dhDwthwiM33ihUiIagiwonVRdiXtQ7LzExkfvuu48vvviCw4cP+zE6cTGlVBKuMrWfuhvXWs/SWvfXWvePjfVO/zbRsGWcdKIMiGtX+xb37LnTLPnmS26edDsxMZ5/fYRFGcS1NUhPNSnOr12aVlWhSTvs6q0U5Wb1k/Ce0EjX6+HisjSAmTNnUlhYKCXIVyEJo2bCPLAZ4tqiYttcOFZVVcWs+UtJ7pTIoKRufoxONFcDBw4kJSWFWbNmXVhlZHTqBVrLbmlCXANTa953HiESG7dZEr1yjX2rN3F4005ufvxBgiO8U+4mhK91U5GU4yRNX9rk+rHHHiM8PJxXX31VNmLwjHSg7UWft6k55vYcpZQViAByaz5vA3wJPKi1Pub1aEWj46jSZJ02adHKcFvu9f7Hb2GxWHjgBz/xWgyJPSzY7JC6x4HTeenPjZMHnZgmdOxplWbXPmYPAUtA7YRRjx49mDBhAu+99x75+fl+iq7hk4RRM6DzMuHccYwegy85PnfuXM7l5DHzrlvlB5fwKXP3Gszda9B71vLzKSPIzMxkzhsvu45npUGADXP78gvnnX8TQri3wcwgVRfxA0tn7Mrq8fmrK6v44o9v0apzIiPuudXj8wvhLzcZUQAc0pfeLERERDBz5kw2b97M6tWr/RFaU7MN6KKU6qCUsgH3AgsuO2cBrqbWAHcCq7TWWikVCSwCfqW13uCziEWDl3HKeeEtdY8D0wm2oEuPZ5xysnndUZavXMiMW39AixjvlVNbAxSde1spL4HD2xyUlWgc1ZrTR53knDVp09niduc24V1KKUIva3x93syZMykrK+Pdd9/1Q2SNgySMmgF9cDOgUDcNunCsoqKCf/7zn/Tr1olhvW7yX3Ci2RuU1I0BPboya/4yyiurXD22WiRATro81RWiHiq0k0+cqXRU4Yw0WnnlGqve+5zcMxnc8T+PYwnwfEJKCH9poezEEMhhXVBr7J577qFjx468+uqrF1bBiutT05NoJrAMOAR8prU+oJR6SSk1rea0d4AYpVQq8BTwq5rjM4HOwAtKqd01b9JETVygTU1hriYo1LXN/eXe/fCvBAUF84O7H/F6LJGxBh2TLZQUaPasrWbHympOH3USHa9I6CS33v4SGqkoLda1Vn517dqVm2++mY8++oicnBw/Rdewyau2idPaxDywCdWuGyo8+sLxOXPmkJWVxS/uniari4Tf/eKuqeQWFjFn+ToAVIsEqKqAQvnBLcTVLHCeJI9KHrJ0xfDCz/P8c1l886/Z9Jk4gm6D+np8fiH8rbsRxRGzoNZDioCAAJ577jnS0tJ4++23/RRd06G1Xqy17qq17qS1frnm2Ata6wU1H1dore/SWnfWWg/UWh+vOf47rXWI1rrPRW9Z/vy3iIalpFDjdEBEi9q3tke+O8C6b5dz14wHiYiI8kk8Ldtb6DsmgPj2BlFxBr2GW+k+IADDIvdc/hIWZYCGkvzaD6N//vOfU1lZyaxZs/wQWcMnCaMmTp86DAVZqF4jLhwrKyvj7bffZvDgwQzs0dWP0QnhktK9C0OTb+JfC5ZRWlEBsQmgFDrr9NW/WIhmLEdXsMA8xRAjnu6G57YIvtgXf/oH2jSZ8bTbPrNCNHrdVST5VJFJea2xoUOHcssttzBr1ixOnDjhh+iEEFeitaYgW2OzQ1Bo7bF/vfsm4WER3HX7Q+4n8BJboKJjTytd+1kvNF0W/hMe7UrWFeXVThh16NCBGTNmMHv2bE6ePOnjyBo+efU2cXr3GggKRXXrf+HYRx99RG5uLk888YT/AhPiMr+4+1byi0t4f9FKVEAgRMWDJIyEuKKPnd8B8ICli1fm37tqI7u/Wc+kn95PdOt4r1xDCH/rrlzJ1iNm7bI0gGeffRa73c5vf/tbKZUWooEpK4LqSohooWpVTWzZtp7tOzdy/70/ISQktI4ZRHNgDVCEhCuKcmvvYAfwxBNPEBAQwOuvv+7jyBo+SRg1YbqkEP3dLlTPYShrAAA5OTnMmjWLMWPG0LevlBaIhqNX5w5MGNiXfy/8hqy8AlR8OygtRJcU+js0IRqkw2YBG81MphntaaHsHp+/vLiUz373Z1p37cj4h+/2+PxCNBRtVAghWDnkpo8RQGxsLE8++SSbNm1i8eLFPo5OCFEXrTX5WSZWm6tHzcUcjmr+PutPtEloz4xp9/kpQtGQhEcrivM1plk78R8XF8ePf/xjVqxYwdatW/0QXcMlCaMmTO9bD6YTo8+oC8f+/Oc/U1lZyTPPPOPHyIRw77/um4HDafLmZwsgrmb33aw0/wYlRANkas37zqNEEcg0S6JXrrHg/71DUU4+P3jxP6XRtWjSDKXopiLrXGEEcO+995KcnMzvf/978vLyfBidEKIuZcWulpdRcbVXF3319RxOnznBY48+Q0BAgJ8iFA1JeIyBaUJpofuVog8//DCtWrXij3/8I6bpfiVScyQJoyZKmybm7rWo9jeholsCcPjwYebOncv9999Phw4d/ByhELW1jY/lgclj+GrdZg6dy4XwGOljJIQb681zHNdF3GfpjF1ZPD7/sZ37+XbOQkY/MIPE5O4en1+Ihqa7EclZyijUVW7HLRYLv/vd7yguLuall16S0jQh/ExrTX6m+9VFhUUFvP/R30npO5ghg0bVMYNobsJq+hgV5rr/+W232/nP//xPDhw4wIIFC3wZWoMmCaMmSqfuhqJcVJ/Rrs+15pVXXiEiIoLHHnvMv8EJcQU/nTGFyNAQ/vjhXNcqo8IcdEWZv8MSosGo0A4+dabSWYUz3Gjp8fkry8r5+Pk/Ed06nltm+rZJqBD+kqRcuyftN+tePdS1a1dmzpzJsmXLWLJkia9CE0K4kZepXauLYmuvLnr/o79TWlbCz3/6rFd3g8445azXm2gYbIGKoFAoyqt79dAtt9xCr169eP311ykqKvJhdA2XJIyaIK015uZFEBmL6toPgFWrVrFlyxZmzpxJRESEnyMUom5hwUHMvGsq2w59x/IzNf2LpCxNiAvmO0+STxUPWbpheOEP4S/++A9yTp/jP37/3wQGB3l8fiEaoo4qnFAC2K1zr3jej370I3r16sVLL71Edna2j6ITQlxMm5q0ww4CAiE06tLfg0eO7uerr2dz68130yHROxtCiMYrPNqgOE/XuUrUMAxeeOEF8vLyeOONN3wcXcMkCaMmSJ86BOdOYAyagjIslJaW8vLLL9O5c2fuuecef4cnxFXdOXY43dq34ZU5iyi2BqMzTvo7JCEahCxdztdmGsOMlnQ1PJ/837d6IxvnLmbcj+6mc/9eHp9fiIbKUIpeRjR7zdwrlptZrVb+8Ic/UF5ezgsvvCClaUL4QeZpk/ISiI43LllB5HQ6eO3N3xAVGcOPH37SjxGKhio8WuF0QGlR3T+7k5KSuP/++5kzZw579uzxYXQNkySMmiC9eRGERqB6DgPgzTffJCMjg5deegmrVRqXiobParHw4qP3k11QxF/2ZUJ+Frq8xN9hCeF3Hzu/QwH3WTp7fO6inDw+eeH/aNO9k5SiiWapt4qhgCpO6Sv/vunYsSNPP/00a9as4cMPP/RRdEIIAKdDc/qok7AoRXD4pWPz5n9E6rHD/OLxXxMaEuafAEWDFtHClf4oyL5ysv+JJ54gNjaWF198EYfD4YvQGixJGDUxOv0Y+tQhjAGTUdYA9u7dy0cffcS9995L3759/R2eEPWW3CmR+yeNZvbWA+zOKoZzJ/0dkhB+dcjMZ7OZxTRLIi2U3aNzm04nH/76j1SWlfPgq/+DVXaUEc1QLyMGgD1XKUsDeOCBBxg7diyvvfYa+/fv93ZoQogaZ4+bVFdC+5ssl6wuyshI590P/sbQQaMZOWy8HyMUDZnNrggJVxRkXXkXtNDQUJ577jkOHTrU7B8MSMKoCdFaY66bB/YQVJ9RVFdX88ILLxAXF8dTTz3l7/CEuGZP3D2N+KhIXtxyiqozx/wdjhB+49Qm7zqPEEMg04z2Hp9/6T8+5vDGHdzxq8dp1cnz8wvRGESrQNqpUPaYV08YKaV4+eWXadGiBU899RTFxcU+iFCI5q2yXJOe6iS6pSI8+vvbWK01//eXl1CG4smZz3m10bVo/KLiFEX5GkfVlVcZTZgwgTFjxvDmm29y/PhxH0XX8EjCqAnRR3eg0w5jjJiBstl55513OHLkCM8//zyhoaH+Dk+IaxYSZOe5h+8hNbeYf23aj84+4++QhPCLb8wznNIlPGjtSqCyeHTuA+u3svQfHzHotokMvfNmj84tRGPTW8VwWBdQoa9eghAZGclrr73G2bNn+d///V/pZySEl5085NpxLLHHpS025i+czbYdG/jJj54iLraVP0ITjUhUvAEaCrKvvMpIKcWLL75IUFAQzz77LNXV1T6KsGGRhFEToaurMFfNgdg2qD6j2LdvH3/729+YPHky48fLskzReI3t35upQ1L4554z7Pp6rr/DEcLnCnQlnzmPk6yiGaTiPDp3bnoGHzz7Cq27duDu534hT2VFs9fbiMGJ5oDOr9f5/fr146mnnmLZsmX885//9HJ0QjRfhbkmuWdNEjpbsAd//7vqVNox/vGv1xg0YATTp8rmPuLqQiMVVhvkZ109yR8bG8tvfvMb9u/fz6xZs3wQXcMjCaMmQm9dCkW5GOPvo6y8gmeeeebCC1yIxu75H99PfFgwz/7jQ0pk2b9oZj5xplKJk4et3Tya0KkoLePtJ/4/tDZ55I0XsAV5ti+SEI1RdxWJHQs7zJx6f83DDz/MrbfeyptvvsnKlSu9GJ0QzZNpak7sdxIYBK07fX/7Wl1dzct//B/s9iCe+eVL8tBD1ItSiqhYg/wss14rQydNmsStt97KW2+91Sx71knCqAnQmWmYm75GdeuP0a47r7zyCmlpabzyyitERHh+22UhfC0sOIhXHprB2eIyfv/r//Z3OEL4xArnGT52fMda8xw9VRSHzHxWOM/U6+1qnA4n7z79MudST/Lwa88R2y7BB/8iIRq+AGWQYrRgq5mFU1+5XOE8pRQvvfQSycnJ/Pd//zdHjx71cpRCNC9nj5uUFWs69LRisXyfFHrvo7/xXepBnn7yN8TExPoxQtHYRMUpHNVQUlC/UuLnn3+e2NhY/uu//qvZ9ayThFEjpyvLcS54C4JCMSY+wLJly5g7dy6PPvooAwcO9Hd4QnhMv8FDeaRfR75csYZFixb5OxwhvM7Umg1mBiFY6ataeGxerTXzXv07B9dv5a7nfsFNwwZ4bG4hmoIhRjzFVNe7LA3Abrfzl7/8hZCQEH72s5+RmZnpxQiFaD7KSzSnj7oaXUfHf3/runr1aj6Z8y9unnQ7I2RXNHGNImMNUJCXWb8HA+Hh4bz22mukp6fz3HPPNauedZIwasS01pjffAj5WVhu/Smp6Zn8+te/plevXvz85z/3d3hCeJQyDB5/5If0jQvjf59/niNHjvg7JCG86rAuIJdKBqk4ApTnfl2v/mAe6z9dwNiH7mT43VM9Nq8QTUVvFYMdC5vMa0v6xMfH89Zbb1FUVMSjjz5KYWGhlyIUomnLOOUk45STcycdHNrmajQcGqkuHN+x6QTPPPMsXTr34InHf+3naEVjZLUpIlsoctLrV5YGkJKSwtNPP83y5ct57733vBtgAyIJo0ZMb16MPrgZY9h0iiJaMnPmTIKDg/nzn/+MzWbzd3hCeFxgvzH83+huhNkD+MUvfkFBQYG/QxLCKwp1Fdt0Nq0JpqMK89i8G+cu5ss//ZM+E0Yw/akfe2xeIZoSm7LQ34hlq5mNo55laeclJSXx17/+lZMnT/L4449TXl7upSiFaPqK8zQVpRDTUmENcJWilVeU8cJLT2IxDF56/g0CA6X/nrg+sQkGleVQlFf/1UIPPfQQEydO5PXXX2f79u1ejK7hkIRRI2VuXYa5bh6qx2DMgVN45plnOHfuHG+++Sbx8fH+Dk8Ir1ARLYhNTuH/xiWRkZHB008/jdPp9HdYQnjcu84jONAMNeI91sRz++JVzH7x/3HT8AE8+OqvMCwWj8wrRFM0xIinhGr2X0NZ2nmDBw/mT3/6E7t27eLJJ5+ksrLSCxEK0bRVV2pyMzRBoRAW7fo9aJomf3rjBU6cSuW5Z/9Iy5bSf09cv+iWBoYFss/U/8GAUoqXX36Ztm3b8sQTT5CWlubFCBsGSRg1MlprzM2LMVfPQXXrj7r5R7z2+uusX7+eXz94F32MIszda+r9JkRjo/qOoXeY4vlH7mfDhg384Q9/aFZ1xKLp22ZmscnMpK+KIUoFemTOPSs38OH/vEqnlGR+/MYLBMgqVCGuqJeKJggLm8yM6/r6SZMm8Zvf/Ib1/397dx4fVX0ufvzznJnsGyEsYQ0BZAmoFAIRAUUF2yuChSKCrZWqFa/rrfoTl1v1ikpdz446/wAAIABJREFU6A+rFq9U3IpaxCIUUZQqAgrIFkX2NRAISSBA9mXmfO8fM2iIAbIMmYXn/XrNKyczZ8483zkz55l55vv9nuXLueuuu7RopFQ9GGPIz7YRPL1ATvxw8rc3XuCLLz/hlon3MCB9kH+DVEHP4RSS2lgcybFxu+v+XSI2NpYZM2ZgjGHSpEkcPVr/HxaCiRaMgoipKMP+8K/YX85FevTHGnkrs15/gzfffJPf/OY3jBs2xN8hKnXWSefzoWV7xsQUMnHiRGbPns2rr77q77CU8okSU8Vrrq2kSCx9JMkn21yz8N/MuvcJOvbqxqSXpxAepd33lTqTcHEwwGrFajuPMuNq0DbGjRvHE088wYoVK7j99tspLy/3cZRKhaZj+YbyUkhqKzjDPcWieQve5d05rzFqxDgmjLvZzxHWz4m5l+pyUU2rVXsLtwsKDtVv+HGnTp146aWXOHDgAHfddReVlZVnKUL/04JRkLB3fYf7jf/B7NiAddk4rFG3MW/+AqZNm8aIESN46KGH/B2iUk1CxMK6aAQcyeG+UZcxcuRIpk+fzvvvv+/v0JRqtDfc2zlOFbc50rB8MBRt+Xv/8vYsuoA7Zj5DZEy0D6JU6tww3GpPGW6WN7CXEcC1117Lk08+ycqVK5k0aRKFhYU+jFCp0FN4xOZoriEmQYht5smDy79awosznubiiy7j7tsf8dlQbaXik4TwyPoNSzuhX79+TJ06lXXr1vHAAw/gcjXsx4VApwWjAGdy9+H+4C/Yc6eDJTgm/D+sAb9gyZIlPProowwaNIinn34ay9Jdqc4d0qM/JLaG1Yt48sknGTJkCI8//jiffPKJv0NTqsFWunNZZucw2upEZyu+UdsyxvDJ/85mzpN/odelGfznjKe0WKRUPXWVeDpLHIvt/Y0a+jxmzBieffZZNmzYwG9+8xtycnJ8GKVSoaOq0rB9gwtnOLRsJ4gIK1cv5Ymp99Oj+/n88cFncej8e8qHRITWKQ6O5RtKCutfNBoxYgSTJ09m8eLFTJ48OSSLRk5/B6B+yhiD2bMJ880nmKzNEB6BNfRaJH044nDy0UcfMXnyZHr37s0LL7ygZ0RT5xyxLKyBI7AXzcK5fwvTp0/n1ltv5b777qOyspJRo0b5O0Sl6uWwKWemewtdJZ5fOVIbta2qikreefTPrP3o3/QfOYxfP3EfjjBN90rVl4hwpdWeV9xb2GKOkSaJDd7W1VdfTVJSEnfffTcTJkzglVdeoUePHj6MVp3L6jqUKTklcIsttm3Yvt5FVSW07WxhOYSvVy3lsSf/iy6p3Xn2yVeIjIzyd5gqiNT1fWE5DJYDDuy06da3/p0wJk6ciMvlYtq0aTgcDqZOnRpShU39BHmW1WdiaWO7IWcvZu8mKD4GEVFIt77QvhuEhWM2ruCDL77m0Zl/J71HV16+ZyJRO9ZQ/1qoUsFP0i6CrxZgL51L1O8e59VXX+WOO+7gwQcfpLy8nHHjxvk7RKXqxG1sXnZtwoXhLmdvHNLwHqOFhwuYec/j7P12C1ff/Tuu/P0E7bqvVCNcbCXztnsHn9rZpFkNLxgBDBw4kNmzZ3Prrbdy/fXXM2XKFEaMGOGjSJUKbns3uTl+2ND1Qge2DStWfs7/PHUvXTv34LmnXyU2tnE9b5U6FYdDSE6xOLjbpkN3Q1RM/T833XLLLbjdbqZPn44xhqeeeipkOnXoOKYAYCrLMbs3Ypb9E/P9VwBI70HIJWOQ1N5IWDjGGN5YuIQ/vvo2A8/vwYzJdxKjE5eqc5g4nFiXj4fDBzAbviA6OpoZM2YwePBgHnvsMWbOnKlnT1NB4R/uXWw2R7nZ0Z1kafiwse3fZPLM2P/k4PY93Pz/H+Xnt16vxSKlGilCHAy12vKNncdh0/hJq7t168acOXNIS0vj/vvvZ+rUqVRVVfkgUqWC16G9bg5l2bTtbNGqg4P5C9/jsSn/xXld03hu6kwtFqmzrm1nB2LBwV0Nn3h80qRJ3HvvvSxcuJDbbruN4uJiH0boP1ow8iNTWoS9eRXmyw8wOzZAbCLSbxhy8UikXRfE8nRlq3S5ePTVv/Pc7A+4MqMvL9//n0RFhEbFUqnGkPN+hnTqhb38Q0xpIZGRkbz00ktcddVV/PnPf+bhhx8O6bMWqOD3jZ3HfDuLYVY7LnW0bdA2bLebRX99m5dumUxkXAz3/v0F+gzXs2Yq5StXOToiwFz3bp9sr1WrVrz++uvccMMNvPXWW/z2t79l3759Ptm2UsHm8EE3u793k9hK6NBdeO6555j+0pNk9B/CtKl/IzYmzt8hqnNAeKTQqr1FXrZNeUnDf3D+/e9/z9NPP82aNWu44YYbyMvL82GU/qEFIz8w5aWeQtGKDyF7J7RJRS4eiZU+DGnR9qRfhAsKi/j9U3/hn0u/5rYxVzHt7psJDwvzY/RKBQ4RwbpiAlRVYH/5AQDh4eE8//zz3HnnnXz44YfcdNNNHDlyxM+RKvVT++1i/uraRBeJZ6Kje4O2kbc3mxd+dz8f//Ut0kdczgP/eJl23Tv7OFKlzm0tJJIrrfYstQ9ywJT4ZJthYWE8/PDDPP/88+zatYvRo0czZ84c7RmrzikFuTY7NriJby606VbGH/7wB2bNmsU1V49nyqMvEBWlJ2tQTaf9eQ4sC3Z/72rUsXj06NHMmDGDffv2MXbsWNatW+fDKJueFoyakKmqwN6+HrN8HmTvgPbdkEvHYPW+GIn76bj4Vd9v5VcPPsV3u/bwzJ2/465rR+rZ0JSqQVq0RdKHY75bjr0z03OdCHfccQfTpk3j+++/55e//CVfffWVnyNV6kcFpoKprg1E4OBe5wWE1XPeIrfLzZJZc/jTryaRs3MvNzz9AL+dOpmIaJ0QVKmzYbQjlQgc/MO1y6fbHTFiBPPnz+eCCy7gscceY9KkSRw4cMCnj6FUIDp80M22dS5i4gU7ZjvXXXctn3/+OZMnT+aeOx7B4dCpdlXTiogSOnb3nDHtyMHGzRI8ePBg3nnnHaKiorjxxht54403gvYHAa0+NAFj25isLZhl82DP99C6IzL4l1hpGUjETyvnVS43f353Hrc8/Rdio6J454kHuHrQAD9ErlRwsIaMhlYdsRfNwhQW/HD9VVddxZw5c0hISOCWW27hueee0yFqyu9KjYs/uTZQgosHnX1oIfWbj27n2u94fvwdzP/zTHoMSueR+X9jwKjhZylapRRAvIRztdWR1SaPnfZxn267TZs2vPbaazz88MOsW7eOkSNHsmLFCp8+hlKB5OBuN9vXu4mON2Tu/gc3/PZ6KioqeOutt5g4caLOv6f8JrmTRUyCsGezG1dl4wo83bt3Z+7cuQwdOpRnnnmGO++8k/z8fB9F2nS0YHSWmaO5mJUfYbaugYQkZODVWBcMQaJrH4+7Yfsuxj08ldcWfMrYywfxj6cepGenDk0ctVLBRZxhOK65DVxVuBe+6jnjoFe3bt14//33ue6665g1axZjxowJ+q6hKniVGRfPuDLZb0r4g/MCUq26T+R5ODuHWfdN4YWJ91FyrJCbpv03v3/hcRJaJp3FiJVSJ4xwpNCMcP7XvYUq49tz1FqWxQ033MC//vUvhg8fTu/evX26faUCgdtl2JHpYu9mN2Umi+deuZmpf3qKjIwM5s2bR9++ff0dojrHiQhdLnDgqoTtG1wYu3FFo7i4OF588UUmT57MihUrGDlyJAsXLgyq3kYSaMGmp6ebtWvX+juMRjMlx7GXzvWc9SwyGune39Oz6BQV82NFxUz/x3ze//cKkpMSeWTidVyefmETR61UYLP6DD3t7fb3X2N/9Dfk/MFY/zERqTHMZ+nSpUyZMoWDBw8yduxY7r33XhITG3ea5KYmIuuMMen+jsOfgjVPnOhZtMMUcrejNwMdrU+7/hJ3NgAFB3NZ/Oo7rPpwMQ6nk+E3X8cVE68lXM+UqVS9DHO0b/Q21tuHecaVyTVWCtc7z/NBVL6lOcIjWPNEQx3KOvnMTrZtcFWC2w0nvuo5HNCmk4OwCPzSg6ewwGZnpovC4+Ws3jSbd+f+LxEREUyePJnRo0efFFPN9ih1tiWnOE76PzfLza6NbtqkWqT28s3wyN27d/Pwww/z7bffcskll/Dggw+Smprqk23XR33zhA4O9TFjuzEbvsBePg+qKiG1N9L5fMRZ+0TVJeXl/P3jL5j1r08pq6hk4ohh3D52BDGR+kVAqfqyel+MOZqH+XoBdkQU1uXjT/oAMnToUAYMGMDLL7/Mm2++yccff8xNN93EjTfeSExMjB8jV6HumKngOde37DFF3OPszUXW6YtFAAd37GHp2//kmwVLEBEGXTuCK2+ZQLPWLZogYqVUbfpaLbjCascCO4u+dkt6WM38HZI6xxnbUF5qKC82lJcZKsrAXVX7ugd22jjCICZOiEsUElpYxDUXHI6zV0AqLzFkbXWTf8DFus0f8+EnL3IoN4dhw4bxxz/+kVatWp21x1aqoVqnOCgtMuTssYmMdtMm1XHmO51B586dmT17Nm+//TYvvfQSo0aNYsKECdx+++00axa4uUR7GPmQ2bcN97/fgbz9SKdeWMOux+zfVuu6RaVlzP18Ba8vXMKR44Vcnn4h94wbRdcODTutslLngjP1MAIwxmD/+13MuiVI/yuxho5DapksfteuXUyfPp0lS5aQlJTExIkTGTduHPHxdR8i5A/663Hw5Yl9djHPuDIppJK7nb3pb536w7HL5WLZsmXMnj2br7/+mrDICDKuuZIrbxlPYhv9UK1UY/iihxFAuXHxQNVqKrF5IiydVhI4k81rjvAItjxRX5UVhmN5NkfzbI7lG9wuz/Vh4Z6Je8MiPcuWUzjxu5ntgug4obTYUHLcczEGxIK4Zp7iUUILIbaZYFmNKyAZ23D8iOHQXjeHc1ys37SET5fPYteeraSlpfHAAw+QkZFxyvtrDyPV1Gr2MALP63jrOhdHcw0dujlof57ls955hw8f5sUXX+T9998nOjqaX//619x44400b97cJ9s/nfrmCS0Y+YA5koO99H3MzkyIS8S6fALSvR8igp259KR19+Xm896nXzL3i68oKSsno1d37h43ij7d9DTISp1JXQpGAMbY2Evexaz/N9L5AqxRt9Y6wTzAt99+ywsvvMDKlSuJiYnh2muvZfz48aSkpPgwct/RLwPBkyeMMSy3D/GaeytROHnAeSGda5mzyBjDjh07WLBgAfPnz+fw4cO0bNmSjAlXM3jc1cQ0C+wiplLBwlcFI/AUgh93rSWecP4nLJ0ECffZthtDc4RHsOSJ06leNDHGUFkOpUWG0kJPLyIAh9NTBIqKhahYweE8/ZfZ6l+K3S5DYYHh+GGb40c8BSQAywHxSUJCkkVcohAdJzjDTr3dQ1lujG2orICKMkN5iSfOkpISvvl2EZ8tf5uc3Cw6derEbbfdxsiRZz7rsxaMVKAwxpCfbSg+ZohrLqRlOH3aI2/79u3MmDGDxYsXExkZyZgxYxg/fjxdu3b12WPUpAWjJmRKCrG/mo/J/BLCwrEuGoGkD0fCfvzQYGcupaSsnCVrMpm39GvWbNmBw7L4xcB+TBwxjLTUjn5sgVLBpa4FoxPsDV9gL3kHEpKwfn4jVkrPU667ZcsWXn/9dRYtWoTb7SY9PZ1f/epXDBs2jNjY2EZG7jv6ZSA48kShqWSWexsr7Vx6SDPucfamebWzodm2zXfffcdnn33GkiVL2LdvHw6Hg6FDhzJmzBiGDBnCl1auH1ugVOjxZcEIYKt9jKdc62krMTzk7EMzifDp9htCc4RHMOSJM8nZ46KsBEoLDSVF5odhZhFRniJRdLwQHlm/+Yhq60VxQlWlofCI4dhhm8LDNmUlP97mDIfwCPHOf+TplWS7we2CspIfYzPGsDd7Iys3fMhX3yyivKKM87qm8evrbmHshCtxOOo2rEcLRiqQGGMoyDUczzdExQrdfuYgJsG35w7btWsXM2fOZNGiRVRVVdG/f3/Gjh3LFVdc4fNpM7Rg1ARM0VHsNZ9iMpeCqwr52VCsi0chMT/+Cpyfn8/y5cv57IP3+HrjFiqrXHRMbsnoSwcyashFJCcF10S7SgWC+haMAMz+7bgXvQbH8pFeA7GGjEYSTj0HTF5eHvPnz+eDDz4gKyuLsLAwBg0axPDhwxk8eLDfx9rrl4HAzhMuY/Opnc1c927KcTPO0YVRVgqWCNnZ2axevZrVq1ezatUq8vPzCQsLIyMjg+HDh3P55ZfTosWPr80Tk14rpXzD1wUjgEz7MNNc3xFDGPc6z6ebn+c00hzhEch54nSqKg1H82yO5toU5BqM7SnQRMV55h2KOkNvnzM5XcGopooyQ0mhoazIM0dSZYVnIm3bBoynJ5LDCRXlLrIObOKbDUv4avWn5OXnEBkRxWWX/oKrr7qWnt3PR0Tq9dhaMFKBqLTIcCTHpqoSkjtatO/mIDzCt/N/FRQU8MEHHzBnzhyys7OJiIhg6NChP3wPSUhIaPRjaMHoLDHGwIGd2JlLMVvXgG0jPQdgXTwSSWrD0aNHWbNmzQ9fBnbt2gVAmxbNGda/D8MH/Iy+3bv45awESp3LrD5DMVWV2CsXYlZ/DMYgaRlY/YZBcqdTvieNMWRmZrJ48WI+++wzDh48CHgmrMvIyOCiiy4iPT29ScYaV6dfBgIzT1QaN0vtHBa6s8iljJ6lUfTf7iZ/8242bdrE+vXrf3gNJSUlMWDAAC677DIuvfTSU86bpQUjpXzrbBSMALLsIqa5vuMw5Yy2OjHS0YlIafwEqQ2hOcIjEPPEqZSXGApybQoO2RQeNWAgLAIiY4SYOCEylkbPKXRCfYo2p+J2u9m5cyeZmZmsWrWKFSu+pri4EKfTSXrfixl6yc8ZNPByYmPiGvzYWjBSgSqpjcW+bW5y99lYFrROsWjTyUFktG+/49u2TWZmJosWLeKTTz7hyJEjWJZFnz59uPjiixkwYAAXXngh4eH1Hw6tBSMfMsZAfjb2trWeIlHBIVzOCA617cWeuPZszjrAli1b2Lx5Mzk5OQBER0fTt29fMjIyGDhwID0q87RIpJQfVe+VZAoLsNd+6hlGWlUBia09hd/U86FtKmLV/mHGGMOWLVtYtWoVq1atYt26dZSWlgLQpk0b0tLS6NmzJ2lpaXTu3Jl27drhdJ6dk1Dql4HAyRO2bbP+WDaLszJZvXsLx/YcIGxvAWZ3Pof27udEfk1OTub8888nIyODjIwMunSp248HWjBSyrfOVsEIoNhU8Zp7K1/buSQSwWhHJ4ZYbYiWpj0hseYIj0DJE7Wx3Yaio56hXwWHDGXFnlwRHSc0by0kJlvEJgi5+2w/RwqVlRXsz95L1r5d7NqznS3bNrJ120bKyjyfgVoktaJ/v0H0Tx9E+s8GEhfX+N4PSgWyE4XPsmLD/u1ujuTYGAPNWgot2lk0b201qhdgbdxuNxs3bmTZsmUsW7aMzZs3Y4zhww8/pHv37vXe3lkpGInIL4AXAAfwN2PMn2rcHgG8BfQDjgDXGWP2em97CLgZcAN3G2MWn+6x/HmAt6sqKc7aQe7Wb8nbsYW83dvIO3qcvNJKsl1O9hVXkZ1/BJfLcyoCESElJeWHL4v9+vWjd+/ehIWF/bjNGpNeK6WaVm3D2Ex5KWbbWszmVZh92wAD4ZHQOgVp1cF76Qgt2iLOsJ/cv6qqio0bN5KZmcnmzZvZvHkze/fu/aFA4HQ6ad++PSkpKXTo0IHWrVufdElOTiYiomFzXQTql4FQyhPGGAoLCykoKODo0aMUFBRQUFBA3uF8dh3cT1ZONodyDnE8Jx+7vPKH+zmcDlI6ppCamkrPnj3p3bs3vXr1OmmYWX1owUgp3zqbBaMTttnHeNu9gx3mOOFYZFit6CMt6G0lNskcR4GaIyC08kRdGWOoqvAMZSkssCk8Yig65hlqhkBCcyGxtUXzZOsnPRSaopdNeXkZRwryyc8/RF7+IfIOHyIvL4e8/EMcOLiPgzn7sW1P4cqyHHTt0p20HheS1vNCevW8kDbJ7fWHcXVOqdlTrqLMkJvlJv+ATUWZZwhpXKLnrINxiZ6zDvq6gHT8+HHWr1/PpZdeesYJ5GtT3zxxxp89RMQBvAwMB7KBNSKywBizudpqNwNHjTFdRWQ88AxwnYikAeOBXkBbYImIdDPG+PwImJuby8GDB6koL6O8tJTK8jLKy8qoLC+nvLyMirIyKkpLKC0poriwkMLCIoqKCikqKqKwuJSisnKKKqqocP+0mh8dHU2HDh3odn4Kw1NS6NixI6mpqfTo0cPnk1Appc4+iYxGLrwELrwEU1aMydqK2bcFk7sP890yTNWPRQBi4iEuEYlrDvHNkeh4HJHR9ImIps+l/ZArh4AznJKKSrbtzWJvdg5ZBw6QtT+brH37Wbt2LSUlJSc9/mOPPcb48eObuNVnT7DkiS+++IKNGzdSUFJISUkJJSUllJaUUlJSTElxKaWlJZSWlFBaVIzbVfvDO5PiCEtOJLpLK3oP7kP3tilktOtGWtdutG/f/qQfDJRS557uVjOmSDq7TCGf2wdZZeeynEPghiQi6CCxtJYoEiSceMJpJuHEEU4kDiLEQTTOgDnjmi8FS544HWMMtg3GpsZfg6vK03G5ssKmstxQXmZTVmRTUmx7JoQ2YDBEx0PzNhDXXIhLFBxOz3bdBoq9PY2MMRhjKCpy4XK7cLlcuF0/LrtcVZ5LjduqXFVUlJdTXl5KWXlZtb+eS0lJMYVFxzheeIzCouMUFh6jsrLiJ+1MSEikVYtkOqd24/Kh/0FKxy506tiF9u06NWj4i1KhLCJK6NjDSYfunjOpFeTaHMv39D46ISwCIqOFyBghMloIjwJnmOB0eiaUdzgFy/JMJn9iUnmRE5efFpsSEhK47LLLmqyNdeknOwDYaYzZDSAi7wHXANUP8NcAj3uX5wIviad11wDvGWMqgD0istO7vZW+Cf9H7838K6/MnnPG9cIsIS7cSXy4g7hwJ3FRkSS3bkZ8fAKxCc1okdyWVp270apTF1q1akXLli21KKRUCJOoWKRHOvTwFNqNbcOxPEzefjiSgykqgMICzNE82LcVc+JctjVEAhd6LwC0B9onAUmUVLnJLakkr7SS3NIK+vauf/fRABcUeWLJkiX885//xIqOwIqJwIqOxBETgRUTiaNtJFZ0AmExkTSPi8KZGIszMZZmzZvRsnkL2jRvScfmrekS2ZzOVjyticLSX1WVUrUQEbpKAl2tBG4xPdhjithsjpJlithnitlhH6cEV633vUCa80hY3yaOuEkERZ6YNGkSa9as+aFoAycKRd7/vYWfE9fDj+sFIofDSWRkFFGRUcTExBIf14zk1u3odl4aCXHNiI9vRmJiEq1atqFVy2RatmhNZGSUv8NWKuiIeIrAcYkWKT3AVeUpIBUfN5SXeC7HD9vkl9d/2z8bGkZUrP8+c9alYNQO2F/t/2wg41TrGGNcInIcSPJev6rGfdvVfAARuRW41ftvsYhsq1P0gaEFcNjfQfhAKLQjFNoA2o5Acnba8LePGnPvFF+F4UOaJ04vFN4LNWmbgoO2KTj80KY5wH83bluBmCMgcPJEoL9+NL7G0fgaL9Bj1PgapwX1zBNNOxPfKRhjXgVe9XccDSEiawN1rHh9hEI7QqENoO0IJKHQhlCheSKwaJuCg7YpOIRim/yhLnki0J9rja9xNL7GC/QYNb7G8cbXqT73qcssSQeADtX+b++9rtZ1RMQJJOCZrK4u91VKKRXcNE8opZQ6Hc0TSikVhOpSMFoDnCciqSISjmfSuQU11lkA3OhdHgt8bjwDehcA40UkQkRSgfOAb3wTulJKqQCheUIppdTpaJ5QSqkgdMYhad4xxHcCi/GcBnOWMWaTiDwBrDXGLABeA972TkJXgCcJ4F1vDp4J7VzAHU19RoMmEJRDJGoRCu0IhTaAtiOQhEIbzjrNE2cUiq8jbVNw0DYFh1Bs00kCKE8E+nOt8TWOxtd4gR6jxtc49Y5PAnlmf6WUUkoppZRSSinV9OoyJE0ppZRSSimllFJKnUO0YKSUUkoppZRSSimlTqIFowYSkedEZKuIfCci80SkWbXbHhKRnSKyTUR+7s84T0dErhWRTSJii0h6tes7iUiZiGR6L6/4M84zOVU7vLcFxb6oSUQeF5ED1fbBVf6Oqa5E5Bfe53uniDzo73gaSkT2ishG7/O/1t/xqOATCnmiplDJG9WFYg6pLpjzSU2hkl+q01xz9onIFO9xOFNEPhWRtt7rRUT+4n09fSciff0UX625IpCOq4GezwI9NwVTngnUnBEMx/9AO56LyCwRyROR76td11xEPhORHd6/iWfckDFGLw24AFcCTu/yM8Az3uU04FsgAkgFdgEOf8d7ijb0BLoDS4H0atd3Ar73d3w+aEfQ7Ita2vQ4cL+/42hA3A7v89wZCPc+/2n+jquBbdkLtPB3HHoJ3kso5Ila2hQSeaOObQra/VSjfUGZT2ppR8jklxrt0lxz9p/j+GrLdwOveJevAj4GBLgIWO2n+E6VKwLmuBro+SzQc1Mw5ZlAzBnBcvwPtOM5cAnQt/p7AHgWeNC7/OCJ9/LpLtrDqIGMMZ8aY1zef1cB7b3L1wDvGWMqjDF7gJ3AAH/EeCbGmC3GmG3+jqOxTtOOoNkXIWQAsNMYs9sYUwm8h2c/KHXOCYU8UVOo5I3qNIcEDc0vqkGMMYXV/o0BTpzx5xrgLeOxCmgmIm38EN+pckXACPR8Fui5SfNMo+nxvwGMMcvwnHGyumuAN73LbwK/PNN2tGDkGzfh+YUCoB2wv9pt2d7rgk2qiGwQkS9FZIi/g2mgYN8Xd3q7/s6qU3fBwBDsz3l1BvhURNaJyK3+DkYFvVDMEzWFQt6oLpT2UzDmk5pCaX9Up7mmCYjIUyKyH/gKwZr2AAADhUlEQVQ18Kj36kB8TVXPFRCYx9Vgy2eB+ByeEKjPX6DljEB9nmoKhuN5a2NMjnf5END6THdwnt14gpuILAGSa7npEWPMfO86jwAuYHZTxlZXdWlDLXKAjsaYIyLSD/hQRHrV+IWmSTWwHQHtdG0CZgBT8Bx4pgDT8CRo1XQGG2MOiEgr4DMR2eqt1Cv1g1DIEzWFSt6oLhRzSHWaT4Ka5hofONN73BjzCPCIiDwE3Ak8FkjxedepmSua9Lga6Pks0HNTMOUZzRlnTVAdz40xRkTMmdbTgtFpGGOGne52EZkIXA1cYbwDAYEDQIdqq7X3XucXZ2rDKe5TAVR4l9eJyC6gG+C3ybsa0g4CbF/UVNc2ichMYOFZDsdXAvo5rw9jzAHv3zwRmYenO2zAHvSVf4RCnqgpVPJGdaGYQ6oL0XxSU9Dsj/rQXOMb9XiPzwYW4SkYNdlrqiG5oqmPq4GezwI9NwVTngnCnBEUx/8gOZ7nikgbY0yOdwhu3pnuoEPSGkhEfgE8AIwyxpRWu2kBMF5EIkQkFTgP+MYfMTaUiLQUEYd3uTOeNuz2b1QNErT7osYY+tHA96daN8CsAc4TkVQRCQfG49kPQUVEYkQk7sQynskeg2UfqAARynmiphDKG9WFxH4K4nxSU0jkl+o01zQNETmv2r/XAFu9ywuA34rHRcDxakM1mjK+WnNFIB1XgzWfBdJzeAoB9/wFaM4I+ON/EB3PFwA3epdvBM7Y+017GDXcS3hmtP9MRABWGWNuM8ZsEpE5wGY8XTbvMMa4/RjnKYnIaOBFoCXwkYhkGmN+jmdG9SdEpAqwgduMMTUnzAoYp2pHMO2LWjwrIn3wdAfdC0zybzh1Y4xxicidwGI8ZzSYZYzZ5OewGqI1MM/73nYC7xhjPvFvSCoIBX2eqClU8kZ1IZpDqgvKfFJTCOWX6jTXNI0/iUh3PMemLOA27/WL8JwpbSdQCvzOP+HVnisIrONqQOezQM9NQZZnAi5nBMnxP+CO5yLyLjAUaCEi2Xh6Vv4JmCMiN+M5Ho4743Z+7FGolFJKKaWUUkoppZQOSVNKKaWUUkoppZRSNWjBSCmllFJKKaWUUkqdRAtGSimllFJKKaWUUuokWjBSSimllFJKKaWUUifRgpFSSimllFJKKaWUOokWjJRSSimllFJKKaXUSbRgpJRSSimllFJKKaVO8n+cazfZAKgEIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "f, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(20, 6))\n",
    "\n",
    "v14_fraud_dist = new_df['V14'].loc[new_df['Class'] == 1].values\n",
    "sns.distplot(v14_fraud_dist,ax=ax1, fit=norm, color='#FB8861')\n",
    "ax1.set_title('V14 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
    "\n",
    "v12_fraud_dist = new_df['V12'].loc[new_df['Class'] == 1].values\n",
    "sns.distplot(v12_fraud_dist,ax=ax2, fit=norm, color='#56F9BB')\n",
    "ax2.set_title('V12 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
    "\n",
    "v10_fraud_dist = new_df['V10'].loc[new_df['Class'] == 1].values\n",
    "sns.distplot(v10_fraud_dist,ax=ax3, fit=norm, color='#C5B3F9')\n",
    "ax3.set_title('V10 Distribution \\n (Fraud Transactions)', fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "2e19fe33-f85a-4ffd-8e4a-807d0e0fb992",
    "_kg_hide-input": true,
    "_uuid": "21e43406e62a9561fba2f065ce15a8d87a1bf389"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quartile 25: -9.692722964972385 | Quartile 75: -4.282820849486866\n",
      "iqr: 5.409902115485519\n",
      "Cut Off: 8.114853173228278\n",
      "V14 Lower: -17.807576138200663\n",
      "V14 Upper: 3.8320323237414122\n",
      "Feature V14 Outliers for Fraud Cases: 4\n",
      "V10 outliers:[-18.049997689859396, -18.8220867423816, -18.4937733551053, -19.2143254902614]\n",
      "아웃라이어가 제거된 데이터의 크기: 980\n"
     ]
    }
   ],
   "source": [
    "# V14\n",
    "v14_fraud = new_df['V14'].loc[new_df['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v14_fraud, 25), np.percentile(v14_fraud, 75)\n",
    "print('Quartile 25: {} | Quartile 75: {}'.format(q25, q75))\n",
    "\n",
    "v14_iqr = q75 - q25\n",
    "print('iqr: {}'.format(v14_iqr))\n",
    "\n",
    "v14_cut_off = v14_iqr * 1.5\n",
    "v14_lower, v14_upper = q25 - v14_cut_off, q75 + v14_cut_off\n",
    "print('Cut Off: {}'.format(v14_cut_off))\n",
    "print('V14 Lower: {}'.format(v14_lower))\n",
    "print('V14 Upper: {}'.format(v14_upper))\n",
    "\n",
    "outliers = [x for x in v14_fraud if x < v14_lower or x > v14_upper]\n",
    "print('Feature V14 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
    "print('V10 outliers:{}'.format(outliers))\n",
    "\n",
    "new_df = new_df.drop(new_df[(new_df['V14'] > v14_upper) | (new_df['V14'] < v14_lower)].index)\n",
    "print('아웃라이어가 제거된 데이터의 크기: {}'.format(len(new_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quartile 25: -8.67303320439115 | Quartile 75: -2.893030568676315\n",
      "iqr: 5.780002635714835\n",
      "Cut Off: 8.670003953572252\n",
      "V12 Lower: -17.3430371579634\n",
      "V12 Upper: 5.776973384895937\n",
      "V12 outliers: [-18.047596570821604, -18.4311310279993, -18.553697009645802, -18.683714633344298]\n",
      "Feature V12 Outliers for Fraud Cases: 4\n",
      "아웃라이어가 제거된 데이터의 크기: 976\n"
     ]
    }
   ],
   "source": [
    "# V12\n",
    "v12_fraud = new_df['V12'].loc[new_df['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v12_fraud, 25), np.percentile(v12_fraud, 75)\n",
    "print('Quartile 25: {} | Quartile 75: {}'.format(q25, q75))\n",
    "\n",
    "v12_iqr = q75 - q25\n",
    "print('iqr: {}'.format(v12_iqr))\n",
    "\n",
    "v12_cut_off = v12_iqr * 1.5\n",
    "v12_lower, v12_upper = q25 - v12_cut_off, q75 + v12_cut_off\n",
    "print('Cut Off: {}'.format(v12_cut_off))\n",
    "print('V12 Lower: {}'.format(v12_lower))\n",
    "print('V12 Upper: {}'.format(v12_upper))\n",
    "\n",
    "outliers = [x for x in v12_fraud if x < v12_lower or x > v12_upper]\n",
    "print('V12 outliers: {}'.format(outliers))\n",
    "print('Feature V12 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
    "\n",
    "new_df = new_df.drop(new_df[(new_df['V12'] > v12_upper) | (new_df['V12'] < v12_lower)].index)\n",
    "print('아웃라이어가 제거된 데이터의 크기: {}'.format(len(new_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quartile 25: -7.466658535821848 | Quartile 75: -2.5118611381562523\n",
      "iqr: 4.954797397665596\n",
      "Cut Off: 7.4321960964983935\n",
      "V10 Lower: -14.89885463232024\n",
      "V10 Upper: 4.920334958342141\n",
      "V10 outliers: [-19.836148851696, -15.1237521803455, -14.9246547735487, -14.9246547735487, -16.3035376590131, -16.2556117491401, -17.141513641289198, -16.7460441053944, -15.346098846877501, -15.2318333653018, -24.403184969972802, -16.6011969664137, -18.9132433348732, -15.2399619587112, -15.563791338730098, -15.124162814494698, -20.949191554361104, -15.2399619587112, -24.5882624372475, -23.2282548357516, -22.1870885620007, -22.1870885620007, -15.563791338730098, -18.2711681738888, -22.1870885620007, -22.1870885620007, -16.6496281595399]\n",
      "Feature V10 Outliers for Fraud Cases: 27\n",
      "아웃라이어가 제거된 데이터의 크기: 948\n"
     ]
    }
   ],
   "source": [
    "# V10\n",
    "v10_fraud = new_df['V10'].loc[new_df['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v10_fraud, 25), np.percentile(v10_fraud, 75)\n",
    "print('Quartile 25: {} | Quartile 75: {}'.format(q25, q75))\n",
    "\n",
    "v10_iqr = q75 - q25\n",
    "print('iqr: {}'.format(v10_iqr))\n",
    "\n",
    "v10_cut_off = v10_iqr * 1.5\n",
    "v10_lower, v10_upper = q25 - v10_cut_off, q75 + v10_cut_off\n",
    "print('Cut Off: {}'.format(v10_cut_off))\n",
    "print('V10 Lower: {}'.format(v10_lower))\n",
    "print('V10 Upper: {}'.format(v10_upper))\n",
    "\n",
    "outliers = [x for x in v10_fraud if x < v10_lower or x > v10_upper]\n",
    "print('V10 outliers: {}'.format(outliers))\n",
    "print('Feature V10 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
    "\n",
    "new_df = new_df.drop(new_df[(new_df['V10'] > v10_upper) | (new_df['V10'] < v10_lower)].index)\n",
    "print('아웃라이어가 제거된 데이터의 크기: {}'.format(len(new_df)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extream outlier를 제거하고 난 후 다시 box plot을 그려보면 수정된 데이터를 기반으로 IQR이 재계산 되므로 아웃라이어가 물론 발생하긴 합니다. \n",
    "\n",
    "하지만 기존데이터에서 outlier를 제거하였기 때문에, 재계산된 outlier를 제거하지는 않겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "66e44398-7c91-4cce-9778-4512cb838973",
    "_kg_hide-input": true,
    "_uuid": "ac80d9cfb07f1865094a8d460ae801750e93d694"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'V10 Feature \\n Reduction of outliers')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,(ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,6))\n",
    "\n",
    "# Boxplots with outliers removed\n",
    "# V14\n",
    "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df,ax=ax1)\n",
    "ax1.set_title(\"V14 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "\n",
    "# 12\n",
    "sns.boxplot(x=\"Class\", y=\"V12\", data=new_df, ax=ax2)\n",
    "ax2.set_title(\"V12 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "\n",
    "# V10\n",
    "sns.boxplot(x=\"Class\", y=\"V10\", data=new_df, ax=ax3)\n",
    "ax3.set_title(\"V10 Feature \\n Reduction of outliers\", fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이런방법으로 양의 상관관계를 가지는 feature와 label이 0인 데이터에 대해서도 outlier 제거를 수행합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Fraud detection\n",
    "## 4.1 차원축소 및 클러스터링"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [t-SNE](https://www.youtube.com/watch?v=NEaUSP4YerM) (t-SNE는 정확하게 클러스터를 구분하는 알고리즘 중의 하나입니다)\n",
    "- PCA\n",
    "- SVD\n",
    "- KMeans clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_df.drop('Class', axis=1)\n",
    "y = new_df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-SNE 소요 시간 1.9e+01 s\n"
     ]
    }
   ],
   "source": [
    "# T-SNE Implementation\n",
    "t0 = time.time()\n",
    "X_reduced_tsne = TSNE(n_components=2, random_state=42).fit_transform(X.values)\n",
    "t1 = time.time()\n",
    "\n",
    "print(\"T-SNE 소요 시간 {:.2} s\".format(t1 - t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA 소요 시간 0.0055 s\n"
     ]
    }
   ],
   "source": [
    "# PCA Implementation\n",
    "t0 = time.time()\n",
    "X_reduced_pca = PCA(n_components=2, random_state=42).fit_transform(X.values)\n",
    "t1 = time.time()\n",
    "\n",
    "print(\"PCA 소요 시간 {:.2} s\".format(t1 - t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_cell_guid": "f83cde6b-90d0-4e9d-ac63-fb69780431b2",
    "_kg_hide-input": true,
    "_uuid": "af3027e7df67b75c92c88d597003632e285c9bff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Truncated SVD 소요 시간 0.0041 s\n"
     ]
    }
   ],
   "source": [
    "# TruncatedSVD\n",
    "t0 = time.time()\n",
    "X_reduced_svd = TruncatedSVD(n_components=2, algorithm='randomized', random_state=42).fit_transform(X.values)\n",
    "t1 = time.time()\n",
    "\n",
    "print(\"Truncated SVD 소요 시간 {:.2} s\".format(t1 - t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_cell_guid": "07015ae5-f7ac-4d64-8f41-1e4b7c9dd2ac",
    "_kg_hide-input": true,
    "_uuid": "084f2a7421c2212082491d2a90e65d65c52b434a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13275cc88>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(24,6))\n",
    "f.suptitle('Clusters using Dimensionality Reduction', fontsize=14)\n",
    "\n",
    "blue_patch = mpatches.Patch(color='red', label='No Fraud')\n",
    "red_patch = mpatches.Patch(color='blue', label='Fraud')\n",
    "\n",
    "# t-SNE scatter plot\n",
    "ax1.scatter(X_reduced_tsne[:,0], X_reduced_tsne[:,1], c=(y == 0), cmap='coolwarm', label='No Fraud', linewidths=2)\n",
    "ax1.scatter(X_reduced_tsne[:,0], X_reduced_tsne[:,1], c=(y == 1), cmap='coolwarm', label='Fraud', linewidths=2)\n",
    "ax1.set_title('t-SNE', fontsize=14)\n",
    "\n",
    "ax1.grid(True)\n",
    "\n",
    "ax1.legend(handles=[blue_patch, red_patch])\n",
    "\n",
    "\n",
    "# PCA scatter plot\n",
    "ax2.scatter(X_reduced_pca[:,0], X_reduced_pca[:,1], c=(y == 0), cmap='coolwarm', label='No Fraud', linewidths=2)\n",
    "ax2.scatter(X_reduced_pca[:,0], X_reduced_pca[:,1], c=(y == 1), cmap='coolwarm', label='Fraud', linewidths=2)\n",
    "ax2.set_title('PCA', fontsize=14)\n",
    "\n",
    "ax2.grid(True)\n",
    "\n",
    "ax2.legend(handles=[blue_patch, red_patch])\n",
    "\n",
    "# TruncatedSVD scatter plot\n",
    "ax3.scatter(X_reduced_svd[:,0], X_reduced_svd[:,1], c=(y == 0), cmap='coolwarm', label='No Fraud', linewidths=2)\n",
    "ax3.scatter(X_reduced_svd[:,0], X_reduced_svd[:,1], c=(y == 1), cmap='coolwarm', label='Fraud', linewidths=2)\n",
    "ax3.set_title('Truncated SVD', fontsize=14)\n",
    "\n",
    "ax3.grid(True)\n",
    "\n",
    "ax3.legend(handles=[blue_patch, red_patch])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Classification (Random under sampling 데이터를 이용)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 4.2.1 학습하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(948, 31)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_cell_guid": "85ce8738-7599-4b06-a722-5c0ed073599b",
    "_kg_hide-input": true,
    "_uuid": "e3751d88766a982119e522e27a9c0c647f20af85"
   },
   "outputs": [],
   "source": [
    "X = new_df.drop('Class', axis=1)\n",
    "y = new_df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_cell_guid": "288a65b7-8b86-44b1-973d-38dbcfe82bbb",
    "_kg_hide-input": true,
    "_uuid": "fb0a479efaa7147d6702c2c24083f1118621863f"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Random under sampling이 1:1의 비율로 적용되었기 때문에 train_test_split을 사용합니다.\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_cell_guid": "bccd5685-a979-451e-85b3-1cb968523540",
    "_kg_hide-input": true,
    "_uuid": "28f5178089d2d133b9e7478c1c7dc7a1f98aabee"
   },
   "outputs": [],
   "source": [
    "X_train = X_train.values\n",
    "X_test = X_test.values\n",
    "y_train = y_train.values\n",
    "y_test = y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_cell_guid": "7810d0b9-b4e5-4b7f-909b-c127365b167c",
    "_kg_hide-input": true,
    "_uuid": "8dd4ea07fd60973fccabc2d46af28a09b0de9178"
   },
   "outputs": [],
   "source": [
    "classifiers = {\n",
    "    \"LogisiticRegression\": LogisticRegression(),\n",
    "    \"KNearest\": KNeighborsClassifier(),\n",
    "    \"Support Vector Classifier\": SVC(),\n",
    "    \"DecisionTreeClassifier\": DecisionTreeClassifier(),\n",
    "    \"RandomForestClassifier\": RandomForestClassifier()\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_cell_guid": "eb37c0f6-9cfe-48b6-92d3-475d5e6767a6",
    "_kg_hide-input": true,
    "_uuid": "fe129af379caccc5428cf1836e6c96bd32e68feb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifiers:  LogisticRegression - training score of 93.0 % accuracy score\n",
      "Classifiers:  KNeighborsClassifier - training score of 92.0 % accuracy score\n",
      "Classifiers:  SVC - training score of 93.0 % accuracy score\n",
      "Classifiers:  DecisionTreeClassifier - training score of 89.0 % accuracy score\n",
      "Classifiers:  RandomForestClassifier - training score of 92.0 % accuracy score\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "for key, classifier in classifiers.items():\n",
    "    classifier.fit(X_train, y_train)\n",
    "    training_score = cross_val_score(classifier, X_train, y_train, cv=5)\n",
    "    print(\"Classifiers: \", classifier.__class__.__name__, \n",
    "          \"- training score of\", round(training_score.mean(), 2) * 100, \"% accuracy score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Search for hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Logistic Regression parameter\n",
    "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "\n",
    "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n",
    "grid_log_reg.fit(X_train, y_train)\n",
    "log_reg = grid_log_reg.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- K nearest neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "knears_params = {\"n_neighbors\": list(range(2,5,1)), 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']}\n",
    "\n",
    "grid_knears = GridSearchCV(KNeighborsClassifier(), knears_params)\n",
    "grid_knears.fit(X_train, y_train)\n",
    "knears_neighbors = grid_knears.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_params = {'C': [0.5, 0.7, 0.9, 1], 'kernel': ['rbf', 'poly', 'sigmoid', 'linear']}\n",
    "grid_svc = GridSearchCV(SVC(), svc_params)\n",
    "grid_svc.fit(X_train, y_train)\n",
    "\n",
    "# SVC best estimator\n",
    "svc = grid_svc.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Decisiontree classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_cell_guid": "a1c35773-f4c7-4caf-9911-532784c9eae0",
    "_kg_hide-input": true,
    "_uuid": "d15b1ab16737358806e34c48dc57aa238cf0cfd2"
   },
   "outputs": [],
   "source": [
    "# DecisionTree Classifier\n",
    "tree_params = {\"criterion\": [\"gini\", \"entropy\"], \"max_depth\": list(range(2,4,1)), \n",
    "               \"min_samples_leaf\": list(range(5,7,1))}\n",
    "grid_tree = GridSearchCV(DecisionTreeClassifier(), tree_params)\n",
    "grid_tree.fit(X_train, y_train)\n",
    "\n",
    "# tree best estimator\n",
    "tree_clf = grid_tree.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Randomforest classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DecisionTree Classifier\n",
    "rf_params = {\"criterion\": [\"gini\", \"entropy\"], \"max_depth\": list(range(2,4,1)), \n",
    "               \"min_samples_leaf\": list(range(5,7,1))}\n",
    "grid_rf = GridSearchCV(RandomForestClassifier(), rf_params)\n",
    "grid_rf.fit(X_train, y_train)\n",
    "\n",
    "# tree best estimator\n",
    "rf_clf = grid_rf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_cell_guid": "7f327bcd-335f-4e49-af07-fc4214dbcbdc",
    "_kg_hide-input": true,
    "_uuid": "1b2108bf377b924ed8a6efe580d9e162a132cd9e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Cross Validation Score:  93.28%\n",
      "Knears Neighbors Cross Validation Score 92.49%\n",
      "Support Vector Classifier Cross Validation Score 93.8%\n",
      "DecisionTree Classifier Cross Validation Score 91.15%\n",
      "Randomforest Classifier Cross Validation Score 92.22%\n"
     ]
    }
   ],
   "source": [
    "# 모델 비교\n",
    "log_reg_score = cross_val_score(log_reg, X_train, y_train, cv=5)\n",
    "print('Logistic Regression Cross Validation Score: ', round(log_reg_score.mean() * 100, 2).astype(str) + '%')\n",
    "\n",
    "knears_score = cross_val_score(knears_neighbors, X_train, y_train, cv=5)\n",
    "print('Knears Neighbors Cross Validation Score', round(knears_score.mean() * 100, 2).astype(str) + '%')\n",
    "\n",
    "svc_score = cross_val_score(svc, X_train, y_train, cv=5)\n",
    "print('Support Vector Classifier Cross Validation Score', round(svc_score.mean() * 100, 2).astype(str) + '%')\n",
    "\n",
    "tree_score = cross_val_score(tree_clf, X_train, y_train, cv=5)\n",
    "print('DecisionTree Classifier Cross Validation Score', round(tree_score.mean() * 100, 2).astype(str) + '%')\n",
    "\n",
    "rf_score = cross_val_score(rf_clf, X_train, y_train, cv=5)\n",
    "print('Randomforest Classifier Cross Validation Score', round(rf_score.mean() * 100, 2).astype(str) + '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2.2 학습 도중 성능평가하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Learning curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "def plot_learning_curve(estimator1, estimator2, estimator3, estimator4, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(20,14), sharey=True)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "\n",
    "    # First Estimator\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator1, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    ax1.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"#ff9124\")\n",
    "    ax1.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
    "    ax1.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
    "             label=\"Training score\")\n",
    "    ax1.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
    "             label=\"Cross-validation score\")\n",
    "    ax1.set_title(\"Logistic Regression Learning Curve\", fontsize=14)\n",
    "    ax1.set_xlabel('Training size (m)')\n",
    "    ax1.set_ylabel('Score')\n",
    "    ax1.grid(True)\n",
    "    ax1.legend(loc=\"best\")\n",
    "    \n",
    "    # Second Estimator \n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator2, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    ax2.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"#ff9124\")\n",
    "    ax2.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
    "    ax2.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
    "             label=\"Training score\")\n",
    "    ax2.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
    "             label=\"Cross-validation score\")\n",
    "    ax2.set_title(\"Knears Neighbors Learning Curve\", fontsize=14)\n",
    "    ax2.set_xlabel('Training size (m)')\n",
    "    ax2.set_ylabel('Score')\n",
    "    ax2.grid(True)\n",
    "    ax2.legend(loc=\"best\")\n",
    "    \n",
    "    # Third Estimator\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator3, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    ax3.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"#ff9124\")\n",
    "    ax3.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
    "    ax3.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
    "             label=\"Training score\")\n",
    "    ax3.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
    "             label=\"Cross-validation score\")\n",
    "    ax3.set_title(\"Support Vector Classifier \\n Learning Curve\", fontsize=14)\n",
    "    ax3.set_xlabel('Training size (m)')\n",
    "    ax3.set_ylabel('Score')\n",
    "    ax3.grid(True)\n",
    "    ax3.legend(loc=\"best\")\n",
    "    \n",
    "    # Fourth Estimator\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator4, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    ax4.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"#ff9124\")\n",
    "    ax4.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
    "    ax4.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
    "             label=\"Training score\")\n",
    "    ax4.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
    "             label=\"Cross-validation score\")\n",
    "    ax4.set_title(\"Decision Tree Classifier \\n Learning Curve\", fontsize=14)\n",
    "    ax4.set_xlabel('Training size (m)')\n",
    "    ax4.set_ylabel('Score')\n",
    "    ax4.grid(True)\n",
    "    ax4.legend(loc=\"best\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Users/kakao/.pyenv/versions/3.6.3/lib/python3.6/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=42)\n",
    "plot_learning_curve(log_reg, knears_neighbors, svc, tree_clf, X_train, y_train, (0.87, 1.01), cv=cv, n_jobs=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Train set AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression:  0.9768201314616755\n",
      "KNears Neighbors:  0.9240404968805704\n",
      "Support Vector Classifier:  0.9736101827094473\n",
      "Decision Tree Classifier:  0.9115767045454546\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "log_reg_pred = cross_val_predict(log_reg, X_train, y_train, cv=5, method=\"decision_function\")\n",
    "knears_pred = cross_val_predict(knears_neighbors, X_train, y_train, cv=5)\n",
    "svc_pred = cross_val_predict(svc, X_train, y_train, cv=5, method=\"decision_function\")\n",
    "tree_pred = cross_val_predict(tree_clf, X_train, y_train, cv=5)\n",
    "\n",
    "print('Logistic Regression: ', roc_auc_score(y_train, log_reg_pred))\n",
    "print('KNears Neighbors: ', roc_auc_score(y_train, knears_pred))\n",
    "print('Support Vector Classifier: ', roc_auc_score(y_train, svc_pred))\n",
    "print('Decision Tree Classifier: ', roc_auc_score(y_train, tree_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f56e6936-314c-42d4-8ea2-0cb2386ad382",
    "_uuid": "d6e62d64e9d9aa70223576a1df91a008aa6c2664"
   },
   "source": [
    "___\n",
    "# Undersample & Oversample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "_cell_guid": "3f54604f-396c-421e-ae93-305ad0103591",
    "_kg_hide-input": true,
    "_uuid": "c501d9226855a510a136bbf06794c702497e5b28"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import average_precision_score, precision_recall_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Overfitting during Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=red>**It is simple, if you want to undersample or oversample your data you should not do it before cross validating. Why because you will be directly influencing the validation set before implementing cross-validation causing a \"data leakage\" problem.**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Wrong Way:\n",
    "<img src=\"https://www.marcoaltini.com/uploads/1/3/2/3/13234002/2639934.jpg?401\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test data는 절대 손대면 안되지만, 위의 경우는 test data가 이미 학습셋에 포함되어 있습니다. 즉 data leakage 문제가 발생합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Right Way:\n",
    "<img src=\"https://www.marcoaltini.com/uploads/1/3/2/3/13234002/9101820.jpg?372\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SMOTE**는 cross validation이 수행되는 도중에 oversampling을 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e70e913a-173b-401b-96ee-93ddda7374c0",
    "_uuid": "d901eb00581cc890075a93d292935304e5b63355"
   },
   "source": [
    "**References**: \n",
    "- https://www.marcoaltini.com/blog/dealing-with-imbalanced-data-undersampling-oversampling-and-proper-cross-validation\n",
    "- http://rikunert.com/SMOTE_explained\n",
    "- https://www.youtube.com/watch?v=DQC_YE3I5ig&t=794s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. SMOTE Technique (Over-Sampling):\n",
    "Reference : https://www.hindawi.com/journals/mpe/2013/694809/\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/rikunert/SMOTE_visualisation/master/SMOTE_R_visualisation_3.png\", width=800>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oversampling은 class imbalance problem을 해결하는 방법중 하나로, undersampling과 달리, majority class에 맞추어 oversampling을 수행합니다.\n",
    "\n",
    "SMOTE(Synthetic Minority Over-sampling Technique):\n",
    "\n",
    "- **Solving the Class Imbalance**: SMOTE creates synthetic points from the minority class in order to reach an equal balance between the minority and majority class.\n",
    "- **Location of the synthetic points**: SMOTE picks the distance between the closest neighbors of the minority class, in between these distances it creates synthetic points.\n",
    "- **Final Effect**: More information is retained since we didn't have to delete any rows unlike in random undersampling.\n",
    "- **Accuracy || Time Tradeoff**: Although it is likely that <U>SMOTE will be more accurate than random under-sampling</U>, it will take more time to train since no rows are eliminated as previously stated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross validation을 수행하기 직전, stratified data의 크기를 살펴봅니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "227846\n",
      "227846\n",
      "56961\n",
      "56961\n"
     ]
    }
   ],
   "source": [
    "print(len(original_X_train))\n",
    "print(len(original_y_train))\n",
    "print(len(original_X_test))\n",
    "print(len(original_y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_lst = []\n",
    "precision_lst = []\n",
    "recall_lst = []\n",
    "f1_lst = []\n",
    "auc_lst = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "log_reg_sm = LogisticRegression()\n",
    "rand_log_reg = RandomizedSearchCV(LogisticRegression(), log_reg_params, n_iter=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Cross validation using SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_cell_guid": "cc175ddc-ddd7-4087-ae1f-dd6fac664d58",
    "_kg_hide-input": true,
    "_uuid": "96f8d3f4160d65f12af4c7106739c4ad46d1e76b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9620536705396671\n",
      "precision: 0.06354928925426508\n",
      "recall: 0.9136319376825707\n",
      "f1: 0.11729894352224483\n"
     ]
    }
   ],
   "source": [
    "for train, test in sss.split(original_X_train, original_y_train):\n",
    "    pipeline = imbalanced_make_pipeline(SMOTE(), rand_log_reg)\n",
    "    model = pipeline.fit(original_X_train[train], original_y_train[train])\n",
    "    best_est = rand_log_reg.best_estimator_\n",
    "    prediction = best_est.predict(original_X_train[test])\n",
    "    \n",
    "    accuracy_lst.append(pipeline.score(original_X_train[test], original_y_train[test]))\n",
    "    precision_lst.append(precision_score(original_y_train[test], prediction))\n",
    "    recall_lst.append(recall_score(original_y_train[test], prediction))\n",
    "    f1_lst.append(f1_score(original_y_train[test], prediction))\n",
    "    auc_lst.append(roc_auc_score(original_y_train[test], prediction))\n",
    "    \n",
    "print(\"accuracy: {}\".format(np.mean(accuracy_lst)))\n",
    "print(\"precision: {}\".format(np.mean(precision_lst)))\n",
    "print(\"recall: {}\".format(np.mean(recall_lst)))\n",
    "print(\"f1: {}\".format(np.mean(f1_lst)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_cell_guid": "41dd6215-2927-4de3-999a-724272aea2b6",
    "_kg_hide-input": true,
    "_uuid": "d109652d1e170d0f9938d64f29aa33d93c941cdc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "   No Fraud       1.00      0.99      0.99     56863\n",
      "      Fraud       0.11      0.86      0.20        98\n",
      "\n",
      "avg / total       1.00      0.99      0.99     56961\n",
      "\n"
     ]
    }
   ],
   "source": [
    "labels = ['No Fraud', 'Fraud']\n",
    "smote_prediction = best_est.predict(original_X_test)\n",
    "print(classification_report(original_y_test, smote_prediction, target_names=labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_cell_guid": "f0e671f7-7ed1-4188-b9bf-e509f050b134",
    "_kg_hide-input": true,
    "_uuid": "a8dcc4bba95aed7fbc8b9e39ceeeec6902d1865c"
   },
   "outputs": [],
   "source": [
    "y_score = best_est.decision_function(original_X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "average_precision_score : https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "_cell_guid": "77bed8fa-1117-4bc0-a740-bd1bd97012a4",
    "_kg_hide-input": true,
    "_uuid": "f9213b24dd2fb3eb04f9b59c3b715dcb167664b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.75\n"
     ]
    }
   ],
   "source": [
    "average_precision = average_precision_score(original_y_test, y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "_cell_guid": "54e926f4-2a5d-4bb1-b74c-8cd79da7b6e5",
    "_kg_hide-input": true,
    "_uuid": "7be0445ac80df7ca252ec350b026d6275669aea6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'OverSampling Precision-Recall curve: \\n Average Precision-Recall Score =0.75')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M/M5MxiJJmj3sIiJJUxDlCZrPprT4DlAeNPMWSkvod2cwNEnSLGOCLUlTs40yDNzvUYYnvIcyFvdbJ7hRTZK0D7KLiCRJktRB3qwgSZIkdZAJtiRJktRBJtjSHBARfxoRGRFfnOlYZpNqn4y9RiLi5oj4REQcMc1xrK5iOGM3ljmjWmZ11wJrv+3m/daIiPUR8eWIeNh0x1IT21kRkS3zMiLOmsKyT4mIiyPijogYiIh11XS3HnY0q0TEqoi4ICI2R8SWiPj3iHjQFJY7q+WYaH4NtNRd26be87v3yaS5x5scpbnhldX7syLigMzcMKPRzC7nAR+l/D57FGVM4ydGxKOm8cl0d1Kehnfjbizz1WqZuqcJTofzuH+/nQi8i/J49BMzc9MMxbTHqgTv3yljSr+eMu73kZTxpp8FfHrmouu+iFhEGbd6kDJWewLvBr4VEY+oxi5v52OUJzo2W1zNu7Cm/tcoY7I3u76mnrTPMsGWZrmIeALlSYUXURKF0ykPoJjOGHopN0WPTOd2p+j2zLy8+v93qyctnkd5EMy/1y0QEfMzc7BTAVTrunzSirsucy/1jyGfLq37bQvlSZCnUp4iONe8CfgJ8Jsto7r8S0RM29XaTh9bu+G1wNHA8Zm5porl55QHEb2O+5+4Ok5mrqM8jfOXIuIVlBzhX2oWWd907EiqYRcRafZ7FTBK+QN6WzX9SxHxK9Ul2tNaF4yIcyLi3ojob5p3ZkT8rLqEvj4i/jki9m9ZLiPiLyPiLRFxMzAEnBgRCyLigxHxi4jYFhF3RcR/RMRDarb9tIj4SbWdNRHxmog4LyLWttRbFBFnV907hqr3P30ASdEV1fux1frHLn8/PCK+FhHbKMPrjW3/BRFxeUTsiIhNEfH5usvqEfHaiPhxROyMiI0RcVlEPLEqG9dFpPpevhERG6plboqIc5rKx3URiYj+iHh3dRl+qHp/d8v3N7at10XEuyLiziru/4gH1jXmx9X7Lp89Ivoi4q0RcV1EDFbdL94fEQta6i2OiPdFxI1Vvbsi4gsRcXBVfmBEfDQibqj29W0R8ZmI6MgjyIH9gXvqhkzMzF0eZV/Fck4Vw2D1fn5EzG+qc2pE/KD67jZHxJci4viW9VwaEd+NiOdWx/og5cmrU95vHXQacPlYcl197puB71GeZrm7XkV5QujXOhOetG8xwZZmseoP/kuAb2TmHZQWxpMi4qFjdTLzCsrl2Ze3LDuvWvZz1SO0iYj3UR7/fAnlD/KfUFos/zNKK3WzMygPVvnj6v0OYD6wlHLp+dnA/wEWAD+IiEOatn0CpQvENuClwNsoj5r+9ZYY+yh/wF9DeXz7MymXq/8c+Ovd2VdNjqreW7s5fJnyCPDTKI8FJyJ+F/gCcA3lEeSvAx4OXBYRS5vi/BvgXEoS+mLKvv42LcloU/0l1ecapezHZ1K6YEx21fBfKA+v+STwHEpL/Jupb0V8K+Uk4nco+/YJlONjT62u3lu7uXwK+DPgM5Tv/L3Aq2nqclEda98A3lDF/Bzu76axoqq2P+UBPW+lHHN/AhwHfK9DSed/A6dUJySPiIioqxQRK4DvU342PkC5KvT/KI9dn1fVOZX7j9+XUI7zh1Na+ltPCB4MfBj4O+AZwDer+ZPut3YiordK0Cd7NX/GhwG/qFnd1cAJk22zZfurgKdSHq1ed9XqudVJ0mB1cmr/a6lVZvry5WuWvijJXAKnV9PHV9Pva6n3p8BOYL+mec+v6j6uml5NSfje3rLsk6p6z2+al5SEeuEk8fUCi4CtwB82zf8MpfvDoqZ5h1ISrLVN815RbevXaj7PEHDQJNtP4C8piesC4PHAtcB24LCqzllVvTe2LLsE2Ax8vGX+UdW2/6CaPrbabx+YII7V1TbOqKZPqqYfMcEyZ1R1VlfTD6+mz2qp92fN62ra1qUt9f64mn/YFI6r1v32K8BVwA+A/qZ6T67qvrJl+ZdV8x9VTf9ONX3abhzbvcCqarnfbJp/FpA18Z41yfoOopz0ZPXaDHwJeHFLvXdV3+ejJ1jXlZSuFX0tx8Vw83EAXAo0xvbD7u63CbZ/adPnmOh1ctMyQ7T8XqjmvxsYmer3Ui3z1nbHL+VE4pXVZ/ytplhfvjvb8OVrb3/Zgi3Nbq8CtlASBTLzesqjul8eu3ah+BSldflFTfNeAVyfmf9dTT+dctXq082tYNX6tlKeUtjs4qy5STAiXhwRP4yITcAIJZldQkn+xzweuCgzd4zNyMw7KS2HzU4FbgG+3xLT1yktio+fYN+MeRsl8dlJSRCHgWdlafFv1joCyxMoT2Rs3R+3Addx//54GmW/nTuFWMb8D6UF/aMR8fKqRXAyY9trbYUem35Ky/yLWqavqt4fBBARPS2tna2/75v3239TvsPTsrraUTmVkrhdUPP9NMd8CnBXZtbdEPdLEfF/onRP2kY5dm6tio6fYLEpycx7MvPXgMcBbwe+Q/nu/jUi/qmp6inAFZn5kzYxLgYeA/xrNrXe5v3dLVq/h7WZ+dOWeVPdb+28jnLSM9nrR5OsZ0+9EvhJZv68tSAz35CZn8zM72TmBcBvUE5I3tulWKQ5yQRbmqWqLhfPoFyqnh8RyyNiOaVLw+GUP2wAZOYtlNa7V1TLLqdclj6/aZUHVe9rKIlV82spcEBLCONGt4iI5wL/Smkl/m3gVyl/6O+ltISOOZTyKPFWd7dMH0QZ6aE1nrGTgtaY6ny8iuHRwMrMfERmXlZTr/XzjO2PS2q2f2LTtsfed7kJbCKZuZlyif0O4Bzg1ij91l84wWJj/eBb47yrpXzMfS3TYzfWjX0PH2fXz/Txlvpj++3JlFbjBwGfa+l2cBCl28T2lnWNfbfN++j2+o9VRMQbKPviEuAFlER47ASqY/2SM/OKzPyLzHwOcASly8ZrIuLhTbFO9F2uAIL60V3uYvz3UFdvqvutnTXAT6fw2ta0zEbu747TbP+qbEoi4nHAQ6jvljROZo4CnweOiIhDp7odaW/nKCLS7PUyymX006tXq1dR+r2OOR/4p4g4kpKYz2PX1tCxof1Oof4PbuvQf+NuFqP0p16TmWeMzYhyA15d0nEQ4x1cs82bKV1h6qxtM3+XbWXmlVOo1/p5xj7vGZR+qq22Vu/rq/fD2Y2hyKpWzRdWLZcnUS67/1tEPDIz6/rKjiXMh7BrP+hDWsqn6ix2HW1mfUt58377bpVYv4Ny2f/z1fwNlG49T26zjbGrBOspXVwm8lLgm5n5R2MzIuKoCeo/YJm5KSI+TDkZPYHSR3k95btsZyPlWDmkpuwQxn8PdT8nU91v7XyT8S3ldZ5K6aIB5RiuG8f8BMo9BlP1KsrJwGd2Y5kxdftC2ieZYEuz16so3SfOqCl7M/CbEbE0M8cSwc9TEqqXUW6q+07Vsj3mG5T+og/KzG+wZxZRLu03ewXlRKDZ5ZQxuxeNdROpWreexK4tfhcDLwS2ZeZ1exjTnvo+JYk+NjMnaq27hLLfzgT+aIJ6tapuBpdHxJ9TbrB8KPU3o327en8ppX/0mLGHpFy6m9tdy9ROUMacTRmp5u0RcUFmJuX7eTOlb/83J1j268BLI+K5mfkfbeosonR3ava/dyO+CUXEoVU3pFZjI9yMlX0d+LPqROdnrZUzc3tE/Ah4UUScVbXQUp24PpHSB3kyU91v7byOclVpMs0nfBcCfxMRR2fmTVBGnKH8zL1lKhutblZ9KfCfWYaRnMoyfZQbQW/NzLsmqy/tK0ywpVkoIh5N6aZwVmZeWlO+gNLP87eATwBk5paI+DLwe5QuGq9tXiYzb4yIs4G/r4Ybu4zSyraK0j/7Y5n5rUlCuxh4fkR8EPgKpWX2DYwfsePdVWxfq0bgmE8ZGeRuSrI65tOUJOubEfF+4GeUlvdjKMno85v7cXdStb/+BPhIRBwI/CflxrjDKa2Hl2bmZ6r99kHgTdXIIhdSbpJ7HHBdZv5r67oj4jmUhPxLlBb6xcDvUxL6H7SJ5xcR8VngrCpp+T6ln/ifA5/NzKvqluuUzNwZEe+hnKS9APhCZl5axXRBRHyA0nWnQbnR8lnAmzPzBsqVktcCn42I91L69S+lXEn5UHXydDHw5oh4W7WeX6ccI51ycUTcRvl+rgcWUr7HN1H2+feqeh+kdG+6JCLeTem7vpIylN3vViesf07pmvWVKEMrLqE8wGgz8P7JAtmN/dZu+T15aMs/UUZu+XJEjN0Y+xeUewo+OlapOlG4EXhXZr6rZR3PoVyNqj3hjIjTKfvpomq9B1N+3zyG+qts0r5rpu+y9OXL1/gX8CFKEndkm/Ieyg1il7bMfzblD+suI4q01HkFpYV5O6UP57WUpOqIpjoJvLvNdt9NucS9g5KkP5rSUnpeS92nU/qJDgI3UVrlvki5eaq53gJKd4brqrr3UcayPoumURzafJbaOFvqnFXVq10XJeH5FqV1dQflBsWPAye01Ptd4OdNMV4KPKEqW82uo4gcT+mrfjPlJOZeSlLyq03rO4OmUUSqefOq/XsL5TL9LdV088geY9t6TUt8J9MyssTu7rdq+2spD2yJpu/8jZSTnwFKkvkz4K/YddSaJZShFW+h3OB3J3AB1UgwlIT3H6p9sZVygnYULSOEsOejiLyEMr75jdX3uJPSNeI9wNKWugdRblq9s4r1NkpSOb+pzqmUxHxn9Zm/THmIS/N6LgW+O8HP6KT7rcO/Nx5EuUdjS7WPv9R8fLUcP+P2Z/UZNwDz2qz/8ZSnRd5dHZ+bKFd4ntGNz+PL11x+jf0ClaSuqsaGXgN8NTNfPdPxSJLULXYRkdQVEfF3lG4OdwCHUVrzVlAeKCNJ0l7LBFtStyyg3Dh3MOUy/H8DT8uasXUlSdqb2EVEkiRJ6iAfNCNJkiR10JzrIrJy5cpcvXr1TIchSZKkvdyPfvSj9Zl54O4uN+cS7NWrV3PllVN5aJskSZK05yLilslrjWcXEUmSJKmDTLAlSZKkDjLBliRJkjrIBFuSJEnqIBNsSZIkqYNMsCVJkqQOMsGWJEmSOsgEW5IkSeogE2xJkiSpg0ywJUmSpA7qWoIdER+PiHsi4hdtyiMiPhwRayLi5xHxmG7FIkmSJE2XbrZgnwecOkH5M4HjqteZwD90MRZJkiRpWnQtwc7MbwP3TVDlecAns7gcWB4Rh0664uHhDkUoSZIkdd5M9sE+HLitaXpdNW+ciDgzIq6MiCs33n0H2RidlgAlSZKk3TUnbnLMzHMz86TMPGnF8mXQsBVbkiRJs9NMJti3A6uapo+o5kmSJElz1kwm2BcCr6xGE3k8sDkz75zBeCRJkqQHrK9bK46IzwInAysjYh3wDqAfIDP/EbgIeBawBtgB/O9uxSJJkiRNl64l2Jl5+iTlCfxet7YvSXPWyAhkjp/faMDQEESMLxsaql9mzLJl0N/fuRglSW11LcGWpFlpYKA+ER0ZaZ+87tzZfn333UcGwATJbavRURgchBwZv73MciN3m2Q5G0NEXYxAY3hH2zji4CPoeejjph6jJGmPmWBL6r7MktjWGRxsP779tm3QU3OrSCZs3kz21pSNjsLgADSG6tfZGIZs1BeN7ATqyxjeBgHVP80Lwcg2csmi+uXaGR2G/l6YN3/X+VGtc2E/9NT8ih4dJRb0Q/SOL4uEeTVx3LiW3DoCmGBL0nQwwZb2VaOj7RPbrVtLd4RWjQZs3gx9Nb86BgbIndvrW2UbCaMD7RPb4S1Vi21ra24DhjfBvHkt8xNGR0iGYMGCms82AvN6Yd7C+rKF/URPXXeJhIUL65PX/oXQ3yaJjl56eufXl80C2b9hpkOQpH2KCbY0V+zYUd9tYGCglNXZtIlstOnPOzRYWmVbk1ogc5gc2lK/zsYgjO6AeTUJ6sgQuXge9LUkxAEwBMuWEDXJa/YGsWhZ/fb6D4Q2yWv09NauT3uZzPb9y9t1+RkdLWV1V0CgnJgtWdK5GCWpiQm21A2Z5Q98nU2b6luOR0fJe+4pg2e2tgAPDpYW4MZg7Sobg5ugsRN6WpLNTBjcSi5fTG3rcIwQS5fXx7l4UX2LbQT0L4DaFmDoid0f/bO+R7FmXKNRfyUjs32/9LEuP3X9xLdvr58PsGUL2VPWHPiwAAAgAElEQVR3FSPv7/JTt+zoMFD/s9YYGYQcGr9OgN4+ep70bKKuG44kPUD+ZpEmMzhY30K2ZUv7hGHDBnJ4W32XiMYQjYEN1N6MNjpI9g6VbgrjyoZgv0VEX02XiP4FsOTQNv1yY1Z3X9A0GB6BjQOwdu2u8zNh40ayr+YYHhmB4aEJ+rIPlasjNXJke/sn7g5vg/n13XNydBAWLa7Z1kg58Zxfc8I3Ogrze4i+mmM8AhYsGt+X/e4N5Prby2czwZbUBf5m0b6jXWvc8DDcfXd9En3ffeTIDhhp03K8/bZqdPfWPsejZAzCiv3HLxQQhyyBhfVdIqJvoa1q6qwcJYe3MXrzpS3zq77sMQwLa5LXkWFY1A91J3XZIBbNr09Q+xbB/MX1J5/R077LT/QQe3AFZLctGCHDfumSuse/4tq7bNxYWpxbDQ+Td90BI/V9lRtDG2HwXuhv+cOfSY7shIP2I3rnjV/woBWw7ODaRKIkC/YP1syLxzwKBjbAvDYnddE7PYntXNGuv/fwcBnKsc62bfXzG41SVndjMJSxyVet2v0YJc1qJtiavepamwHuuIPcupVxXSwyYesmGtvXtRnneAe5aBCWrBhftqSHOOD42pa6IGxR1pwXCw6Y6RBmj63by++Xa68l+1q6Y23fDqODkPX9unNkR+3JSI4Ol25hdRqD5R6J1tFwRhswmvSs+E1iyco9+SSSZimzBs2czPYjY6xbR25eX/9HLhs0Nv8Puajm8I2EgxYSKw6tKQu7X0gqie7IdkY3/oTSubvF6CAs3a9mwYT+UWJRm9FHFi2D1oQdyhWu3n6Ilt89GzaRN13fvr+6pDnLTEPdlVluBqxLou+6i9x0dxkdo0Zj61rywKX13S+OOZSeFYd1OlpJ+4KjjiAedCAx0zf/9vSRjqEj7ZVMsNUZ27aVUQdabdpE3nULjcHN9csN3EsevpRoHV4O4LAj6Fl6SGfjlCSY+eRa0l7NBFtTNzJSn0QPDZHXXkUObqxdLIc3kwf3EAuWji/sP4JYtL83WEmSpL2GCbamJpP8+c9gZ5skemQ7jSWbiGU1NxD2LieWHNDm0dSSJEl7FxNs7erWW8mN62ufgcLAZkZzLbFfzc0/PT3EitXEPB89LEmS9m0m2PuiwUHYurW2KDfcS2PDT8hF9a3NcfDBhDcXStIDNzYU6foNMNTyO3f79jLmdrtHyy9fDisd2k+arUyw90W3305j3bVko2b0jmyQS+fRc8zDpj8uSdqXbNsBI9torLmi/iFAg5tgXjLuSbFDw7B4OT1PegnRLgGXNKNMsPdWO3bAnXfWl23fTo7cDUcdWh5b3CLmH9zl4CRJrDoEBu8mD15K9Nd0r+vbD+YtHf97+qZbyR33UfrymWBLs5EJ9t5q0yZGb/spZP3weLkkiCUrHb1DkmZKXx89D3nsHizYU3ubjKTZwwR7LhsagnVtHgu+c2d5gMsJx9Q+WSwIk2tJkqQuMMGey7Zto3HH9eTQHdAz/jJhLoCIPiJqHuIiSZKkrjDBngu2bIHR0fHzt28nRwfhyENg2YHjigNMriVJkqaZCfZsNzBAXnsVjYH19eXD26B3pYm0JEnSLGGCPdtlwsgOcvEmYsX4Vmp6V8KimqcnSpIkaUaYYM8WGzaUB8C0Gh4GIBYsJvZz+DxJkqTZzgR7Nsgkb7yBxtabqX1GeY7AgTWPJ5ckSdKsY4I9WzSGyGUNYtWR9eU986Y3HkmSJO0RE+zplAmNRv18gOgh+hZNb0ySJEnqKBPs6bRmDbnhjvoHwzQGoX/6Q5IkSVJnmWBPoxzYTmPnDeTypbXlsf/KaY5IkiRJnWaCPc1yXj89q46b6TAkSXPVtu0wsBOuuZqMnvHlhxxK7L//9Mcl6ZdMsCVJmmtGBxi974dAS4K9dQcxuJre/Z8zI2FJKkywO+2++2B9m6cuDg1NbyySpL3Pw44jdu5HLD1sXFH+4loYGZiBoCQ1M8HutI0bGb3th2RfzUNjAFY4Sogk6QHo66tNriXNHibY3dAzTDz0RKLH3StJkrSvqbk7QpIkSdKeMsGWJEmSOsg+DHsiE3burC8bHZ3eWCRJkjSrmGDviXvuIW++Dkbb3MjYE9MbjyRJkmYNE+w9MTpKY9s68uBeoqfm+eYLDoTonf64JEmSNONMsB+AOPBwos9h9yRJknQ/b3KUJEmSOsgEW5IkSeogE2xJkiSpg0ywJUmSpA7yJsd2BgbIm2+GRs241kNDQE57SJIkSZr9TLDb2bGDvPdGGo07obdmyL2lvUR4AUCSJEm7MsGeQOYosepwYr+DZzoUSZIkzREm2JIk7QtGR8urbv6tt5LZputjfz9x9NEQPqVYmioTbEmS9hZDwzA4CHffvev8TPK2W2F4W+1iObyF3Hk7uXD+rgXDo8TQCD0Hv5hYcmCXgpb2PibYkiTtLbIBozsZvfrS8WWjA2TfNth/5fiynh7igIfRM2/prvM3bKKx5pr6G/4ltWWCLUnS3mJeH9kzQhxdk0RHEP0LiN7548skdZQJtiRJe4k48eHYU1qaeV0dZy4iTo2I6yNiTUS8pab8QRHxrYj4SUT8PCKe1c14JEmSpG7rWoIdEb3AR4BnAicAp0fECS3V/gz4t8x8NPBS4JxuxSNJkiRNh252EXkcsCYzbwKIiM8BzwOuaaqTwLLq//sBd3Qxnnrr19cPW7Rjx7SHIkmSpLmvmwn24cBtTdPrgF9tqXMW8PWIeAOwGHha3Yoi4kzgTICjD665cWNPDQyQN15LY3ubvL6xE/oO69z2JEmStNeb6ZscTwfOy8z3R8QTgPMj4uGZ2WiulJnnAucCnPSQY9qMhL8HMmFkgDxglFh56PjyCOhf1LHNSZIkae/XzQT7dmBV0/QR1bxmrwZOBcjMH0TEAmAlcE8X4xoneuYR85dNXlGSJEmaRDdHEbkCOC4ijoqIeZSbGC9sqXMr8BsAEfFQYAFwbxdjkiRJkrqqawl2Zo4Arwe+BlxLGS3k6oh4V0ScVlX7I+C1EfEz4LPAGZnZuS4gkiRJ0jTrah/szLwIuKhl3tub/n8N8KRuxiBJkvbQWJvX5i3QWDC+fP58WLhwemOS5oCZvslRkiTNVpu3wMhOGtdfAf3j71WK+fOIxz2N6OmdgeCk2csEW5Ik1Vu2FO4cJFfuJBYv3bXszvU01m+h967joa9mxK0lS2CRI3Fp32SCLUmS6h24P3HA0yF6iWi5bevWLWQO07jmMuip6T6ybDm9Jz1jeuKUZhkTbEmS1Fb09NcXrFwBN98Mx+4PvS0t1WvvILfeSmYSEd0PUpplTLAlSdLuO/wQ4rBn1ifQ87aQIwPTH5M0S3RzHGxJkrQXs3VaqmcLtiRJmh127oTh4fqynp5y46Q0B5hgS5Kk6bN5M2zfPn5+o0HeeTs5sKHtonHiScTyg7oYnNQZJtiSJKmztm2HgQH4n/+Blm4kuWkjjS03Q46MXy5HyKWjxIr9d50/MEjeehs9O44ywdacsG8k2NdfT27bDLQ8hT2TzJFxP/ySJOkBagzQuOPbtUV54CLioAfVlkVP//iRS7btgN57Ox2h1DX7RIKdm+5hdMf1sHD++MJ5QSzzbFiSpI455EDYtBEe/EhgfCNW4A2S2rvtEwk2mbB0ET2rj5/pSCRJ2vsdvJKeg1fOdBTSjHGYPkmSJKmDTLAlSZKkDjLBliRJkjrIBFuSJEnqIBNsSZIkqYNMsCVJkqQOMsGWJEmSOsgEW5IkSeogE2xJkiSpg0ywJUmSpA4ywZYkSZI6qG+mA5AkSZpYlrc77oTG0vHF/f1w8MHTG5I0ARNsSZI0u+0YIEd2wPr/YXTLHbuWZUIGPUufQyxaPjPxSS1MsCVJ0uy2fBlkgzyin1i5eteyDZvItTfC6OCMhCbVMcGWJEmz27x+ep709PqyvkHSW8o0y3hESpIkSR1kgi1JkiR1kAm2JEmS1EEm2JIkSVIHmWBLkiRJHWSCLUmSJHWQCbYkSZLUQXvXONiNxkxHIEmSpH3c3pNgr1lDrr8DyPFlowPQP+0RSZIkaR+01yTYuXM7jR3Xk/stHl84L4j9D5j+oCRJkrTP2WsSbEhyfj89Rx4/04FIkiRpH+ZNjpIkSVIHmWBLkiRJHWSCLUmSJHWQCbYkSZLUQSbYkiRJUgeZYEuSJEkdtBcN0ydJkvY5w8Pl/ZZbYcnO8eWLFsFBB01vTNrnmWBLkqS5a+t2GN5K47YrYf7CXctGRqF3Hj0nv4TonTcj4WnfZIItSZLmrgP3h7tHyYccQiw6ZNey2+8m71kHOQKYYGv6mGBLkqS5a/kyev7Xs+rLevtJYnrjkfAmR0mSJKmjTLAlSZKkDjLBliRJkjrIBFuSJEnqIBNsSZIkqYO6mmBHxKkRcX1ErImIt7Sp8+KIuCYiro6Iz3QzHkmSJKnbujZMX0T0Ah8Bng6sA66IiAsz85qmOscBbwWelJkbI8JHLUmSJGlO62YL9uOANZl5U2YOAZ8DntdS57XARzJzI0Bm3tPFeCRJkqSu62aCfThwW9P0umpeswcDD46I70XE5RFxat2KIuLMiLgyIq7cuGlLl8KVJEmSHriZvsmxDzgOOBk4HfiniFjeWikzz83MkzLzpBXLl01ziJIkSdLUdTPBvh1Y1TR9RDWv2TrgwswczsybgRsoCbckSZI0J3Uzwb4COC4ijoqIecBLgQtb6nyJ0npNRKykdBm5qYsxSZIkSV015VFEIuJw4MjmZTLz2+3qZ+ZIRLwe+BrQC3w8M6+OiHcBV2bmhVXZKRFxDTAK/ElmbtizjyJJkiTNvCkl2BFxNvASYCwRBkigbYINkJkXARe1zHt70/8TeFP1kiRJkua8qbZgPx84PjMHuxmMJEmSNNdNNcG+CegHTLAlSdLcMdqA4WHI4fFlfX0QMf0xaa831QR7B/DTiPgmTUl2Zv5+V6KSJEl6oNZvhMYQ+aPvQc+C8eUr9ice+qjpj0t7vakm2BcyfgQQSZKk2W10J42Fd0Pfol3n37uJGOql5yGPJGzFVodNKcHOzH+phtp7cDXr+sy6ay2SJEmzxPFHw73DxOEPJXr6dy0buJXGjvUzE5f2elMdReRk4F+AtUAAqyLiVRMN0ydJkjSjFsynZ9UjZjoK7YOm2kXk/cApmXk9QEQ8GPgs8NhuBSZJkiTNRVNNsPvHkmuAzLwhIvonWkCSJGlO2rQJtm+vL4uAAw+EftMgtTfVBPvKiPgY8Klq+mXAld0JSZIkqcu27YCBAVi3btxQfXnnHTS23MT9z9ZrLkzi2EfSs/rE6YlTc9JUE+z/A/weMDYs33eAc7oSkSRJUtclNIZo3PCt8WNhN0bJA/qIQ4/cdf7oKPzsF+TgfdMXpuakqY4iMgh8oHpJkiTNbREwugMedhTQM764p3f8yCMxSrbOk2pMmGBHxL9l5osj4iogW8sz01tzJUnS3POIhxCNY8Yn0VIHTNaC/cbq/TndDkSSJGk6mVyrW8ZfE2mSmXdW/10P3JaZtwDzgUcCd3Q5NkmSJGnOmTDBbvJtYEFEHA58HXgFcF63gpIkSZLmqqkm2JGZO4AXAOdk5ouAh3UvLEmSJGlumnKCHRFPoIx//dVqXm93QpIkSZLmrqkm2H8AvBX4YmZeHRFHA9/qXliSJEnS3DTVcbAvAy5rmr6J+x86I0mSJKky2TjYH8rMP4iI/6B+HOzTuhaZJEmSNAdN1oJ9fvX+N90ORJIkSdobTJhgZ+aPqv9eCezMzAZARPRSxsOWJEmS1GSqNzl+E1jUNL0QuKTz4UiSJElz21QT7AWZuW1sovr/ognqS5IkSfukKY0iAmyPiMdk5o8BIuKxwM7uhTWBTNiyBfqGdp3faMxIOJIkSVKzqSbYfwB8PiLuAAI4BHhJ16KaROOqHxA983aZl9mA/pihiCRJkqRiquNgXxERDwGOr2Zdn5nD3QtrAo0R8sCd0Dtu1EBi8WEzEJAkSZJ0vykl2BGxCHgTcGRmvjYijouI4zPzK90Nr008+x9B9C2ciU1LkiRJE5rqTY6fAIaAJ1TTtwPv7kpEkiRJ0hw21QT7mMz8K2AYIDN3UPpiS5IkSWoy1QR7KCIWUj0uPSKOAQa7FpUkSZI0R011FJF3ABcDqyLi08CTgDO6FZQkSZI0V02aYEdEANcBLwAeT+ka8sbMXN/l2CRJkqQ5Z9IEOzMzIi7KzBOBr05DTJIkSdKcNdU+2D+OiF/paiSSJEnSXmCqfbB/FXh5RKwFtlO6iWRmPqJbgUmSJElz0VQT7Gd0NQpJkiRpLzFhgh0RC4DfBY4FrgL+OTNHpiMwSZIkaS6arA/2vwAnUZLrZwLv73pEkiRJ0hw2WReRE6rRQ4iIfwb+u/shSZIkSXPXZC3Yw2P/sWuIJEmSNLnJWrAfGRFbqv8HsLCaHhtFZFlXo5MkSZptdg7Ajh3j5/f0wIIF0x+PZp0JE+zM7J2uQCRJkma1RkI24O515Pbv1td5+GOJ/Q6Y3rg060x1mD5JkqR9W0CODsDgBhpHLN+1bHCIvP0OeravMsGWCbYkSdKU9PURJzwc+kaIRQftWrZtB9y1YWbi0qxjgi1JkjRFsWz/mQ5Bc8Bko4hIkiRJ2g0m2JIkSVIHmWBLkiRJHWSCLUmSJHWQCbYkSZLUQSbYkiRJUgeZYEuSJEkdZIItSZIkdVBXE+yIODUiro+INRHxlgnqvTAiMiJO6mY8kiRJUrd1LcGOiF7gI8AzgROA0yPihJp6S4E3Aj/sViySJEnSdOlmC/bjgDWZeVNmDgGfA55XU+8vgLOBgS7GIkmSJE2LbibYhwO3NU2vq+b9UkQ8BliVmV+daEURcWZEXBkRV27cvK3zkUqSJEkdMmM3OUZED/AB4I8mq5uZ52bmSZl50or9lnQ/OEmSJGkPdTPBvh1Y1TR9RDVvzFLg4cClEbEWeDxwoTc6SpIkaS7rZoJ9BXBcRBwVEfOAlwIXjhVm5ubMXJmZqzNzNXA5cFpmXtnFmCRJkqSu6lqCnZkjwOuBrwHXAv+WmVdHxLsi4rRubVeSJEmaSX3dXHlmXgRc1DLv7W3qntzNWCRJkqTp4JMcJUmSpA4ywZYkSZI6yARbkiRJ6iATbEmSJKmDTLAlSZKkDurqKCKSJEn7hGyU97W3wsD88eX9/bBq1fj52iuZYEuSJD1QOwbI4W2waQ2jg7fvWjbagOERevZ/MbF4/5mJT9PKBFuSJOmBOmA53NRLHrOcWHHsrmUbNpE3XQeN4ZmJTdPOBFuSJOmB6uuj5wm/Xl/W00d629s+xW9bkiRJ6iBbsCVJkrpp+w6gQf7sx7BgxfjyJUuJhzxs2sNS95hgS5IkddPwMAxvpTG8FvrX71q2bQexYZSeY48h+hbMSHjqPBNsSZKkbjrycGATsepoYt7SXctuv4vGnWuBnP641DUm2JIkSd3U10fPMY+c6Sg0jbzJUZIkSeogE2xJkiSpg0ywJUmSpA4ywZYkSZI6yARbkiRJ6iATbEmSJKmDTLAlSZKkDjLBliRJkjrIBFuSJEnqIBNsSZIkqYNMsCVJkqQOMsGWJEmSOsgEW5IkSeogE2xJkiSpg0ywJUmSpA4ywZYkSZI6yARbkiRJ6iATbEmSJKmDTLAlSZKkDjLBliRJkjrIBFuSJEnqIBNsSZIkqYNMsCVJkqQOMsGWJEmSOsgEW5IkSeogE2xJkiSpg0ywJUmSpA4ywZYkSZI6yARbkiRJ6iATbEmSJKmDTLAlSZKkDjLBliRJkjrIBFuSJEnqIBNsSZIkqYNMsCVJkqQOMsGWJEmSOsgEW5IkSeogE2xJkiSpg0ywJUmSpA7qaoIdEadGxPURsSYi3lJT/qaIuCYifh4R34yII7sZjyRJktRtXUuwI6IX+AjwTOAE4PSIOKGl2k+AkzLzEcAFwF91Kx5JkiRpOnSzBftxwJrMvCkzh4DPAc9rrpCZ38rMHdXk5cARXYxHkiRJ6rpuJtiHA7c1Ta+r5rXzauA/6woi4syIuDIirty4eVsHQ5QkSZI6a1bc5BgRLwdOAv66rjwzz83MkzLzpBX7LZne4CRJkqTd0NfFdd8OrGqaPqKat4uIeBrwp8BTMnOwi/FIkiRJXdfNFuwrgOMi4qiImAe8FLiwuUJEPBr4KHBaZt7TxVgkSZKkadG1BDszR4DXA18DrgX+LTOvjoh3RcRpVbW/BpYAn4+In0bEhW1WJ0mSJM0J3ewiQmZeBFzUMu/tTf9/Wje3L0mSJE23WXGToyRJkrS3MMGWJEmSOsgEW5IkSeogE2xJkiSpg0ywJUmSpA4ywZYkSZI6yARbkiRJ6iATbEmSJKmDTLAlSZKkDjLBliRJkjrIBFuSJEnqIBNsSZIkqYNMsCVJkqQOMsGWJEmSOsgEW5IkSeogE2xJkiSpg0ywJUmSpA4ywZYkSZI6yARbkiRJ6qC+mQ5AkiRpn3XfZhgdhh/9kOydP7585YHE6mOnPSw9MCbYkiRJM2XxIti8g9HRNRALdi3bvI3YsZCeI48hImYmPu0RE2xJkqSZcvQq4siDid5548tuvJXGzg3TH5MeMPtgS5IkzaDa5Fpzmgm2JEmS1EEm2JIkSVIHmWBLkiRJHWSCLUmSJHWQCbYkSZLUQSbYkiRJUgeZYEuSJEkdZIItSZIkdZAJtiRJktRBPipdkiRpNtq2HQZ2wo03QsT48oMOgqVLpz8uTcoEW5IkabZqDNK47bLxCfbOnbDlaHof/cyZiUsTMsGWJEmajR75UGLocOhfPL7sqmtgZPv0x6QpMcGWJEmapWLestr5OdKA9RvIH1/RUpAwMgIHHkzst3z8gj09sHx5fZcTdYwJtiRJ0pyT5PBmRoeu3XX2aAM23kds2g/m1yTYQM8jnkjsf+g0xLjvMsGWJEmaY+Ixj6JdG3TefScZm4hFLV1LdgyQa64nB04gMMHuJhNsSZKkvUgcfGibBHoH9C2Gtqm5OsVxsCVJkqQOsgVbkiRpX9BolPcbb4btvePL+/vhyCO9AbIDTLAlSZL2Bf195PB22H4To7ffvmvZSAOGRug58CXE4gNmJr69iAm2JEnSvmDhAuKJTy1D+UVLC/b6jeRN10JjZGZi28uYYEuSJO0jInrr73GMIMNb8zrFPSlJkiR1kAm2JEmS1EEm2JIkSVIHmWBLkiRJHWSCLUmSJHWQCbYkSZLUQQ7TJ0mSpOK2dbBkcPz8RYtg5crpj2eOMsGWJEna1y2YD43tNG7/CfT271rWSMig56RnEz3945ddtKg8Zl2/ZIItSZK0r1uyiPiVX4MchdYHztx1L3nrGho/u4zoXTB+2eil56gTx8/PhN5eWFCzTCbMnw89Nb2Ve3raJ+wR5TXLdTXBjohTgb8FeoGPZeb7WsrnA58EHgtsAF6SmWu7GZMkSZLGi9559QWHHU7OH4LePpLRXcvu2kBu2khed+/4xHdkuCTSCw4Yv84cBZKe/v1qNjgK9EC7eFYeRvTWpLCNBixbdn8czfHU/X/sfd68ciLQQV1LsCOiF/gI8HRgHXBFRFyYmdc0VXs1sDEzj42IlwJnAy/pVkySJEnaTRH0rDy6vmzF4TC8DaImQR0dIbfeSfSNTzdzxw4Y2U6jvzG+bMv60oLdu2jXgp2DsHUTse2G8d1YhoYhG9C/H/TUpLfD22B+TaJPQmOYnvnLaz9eT+zZgCDdbMF+HLAmM28CiIjPAc8DmhPs5wFnVf+/APj7iIjMzAnXPDpAMvsvD0iSJO31ok062dtPLH9Q/SJL6hNagDjwsPbbypGSSNfkgbnzPmIslrFUcuxtMKGv+ebNqmBgAIZ2Mtq3vWZbDeb306YZfWLdTLAPB25rml4H/Gq7Opk5EhGbgQOA9c2VIuJM4EyAgOHlxz79RpKJk3DtU4ZGWTGvl40zHYdmF48L1fG4UB2PC9XZOcTqPVluTtzkmJnnAucCRMSVW3fmSTMckmaZ+P/t3X/IXQUdx/H3p8wMZpMahUxzQhO0WTMkVkEFxlCh+Uc/aDB0ZT/+6HdmFAmtIoKigrLfTKxBoeuPeKRiVFpSbMPBY8MZxcgQLTDThJiZrW9/nLN4lLvnHu0+59x7n/cLHji/7uH7wJdzv/fc7z3f5NBjj5sXeiLzQqOYFxrFvNAoSQ49ndet5KCZ+4Gzl6yf1W4beUyae/praX7sKEmSJM2klSyw7wA2Jjk3yanAW4GFJx2zAFzVLr8JuHVs/7UkSZI0xVasRaTtqX4vsI/mMX03VNWRJJ8GDlXVArAb2JPkKPAQTRE+zrdXKmbNNPNCo5gXGsW80CjmhUZ5WnkRbxhLkiRJk7OSLSKSJEnSqmOBLUmSJE3Q1BbYSS5N8vskR5N8bMT+Zye5qd1/MMmG/qNU3zrkxYeT3J3kcJJfJDlniDjVr3F5seS4NyapJD6KaxXokhdJ3tJeM44k+X7fMap/Hd5HXpTktiSL7XvJ5UPEqf4kuSHJA0nuOsn+JPlKmzOHk7x83DmnssBeMmb9MuACYHuSC5502P/GrANfphmzrjnWMS8WgYur6qU000E/32+U6lvHvCDJ6cAHgIP9RqghdMmLJBuBjwOvrqqXAB/sPVD1quP14jrg5qq6iObhC1/vN0oN4Ebg0mX2XwZsbP/eBXxj3AmnssBmyZj1qvoXcGLM+lJXAN9tl38IXJLE+enzbWxeVNVtVXWsXT1A8/x1zbcu1wuAz9B8EP9nn8FpMF3y4p3A16rqYYCqeqDnGNW/LnlRwHPb5bXAn3uMTwOoqttpnmZ3MlcA36vGAeCMJGcud85pLbBHjVlff7JjqurfwIkx65pfXfJiqauBn65oRJoGY/Oi/Trv7Kr6cZ+BaVBdrhfnAecl+U2SA0mWu4Ol+dAlL3YBO5LcB/wEeF8/oWmKPdX6YzZGpUtPVZIdwMXAa4eORcNK8gzgS8DOgUPR9DmF5kITdd4AAAMXSURBVCvf19F823V7kgur6u+DRqWhbQdurKovJnklzbyOTVX1n6ED0+yY1jvYjlnXKF3ygiSvBz4BbKuqx3qKTcMZlxenA5uAXyb5E7AFWPCHjnOvy/XiPmChqh6vqnuAP9AU3JpfXfLiauBmgKraD5wGrOslOk2rTvXHUtNaYDtmXaOMzYskFwHfoimu7adcHZbNi6p6pKrWVdWGqtpA05u/raoODROuetLlfeRHNHevSbKOpmXkj30Gqd51yYt7gUsAkpxPU2D/tdcoNW0WgCvbp4lsAR6pqr8s94KpbBFZwTHrmmEd8+ILwBpgb/ub13urattgQWvFdcwLrTId82IfsDXJ3cBx4Nqq8pvQOdYxL64BvpPkQzQ/eNzpDbz5luQHNB+217W9958EngVQVd+k6cW/HDgKHAPeNvac5owkSZI0OdPaIiJJkiTNJAtsSZIkaYIssCVJkqQJssCWJEmSJsgCW5IkSZogC2xJmiFJjie5M8ldSW5JcsaEz78zyfXt8q4kH5nk+SVpNbDAlqTZ8mhVba6qTTQzAN4zdECSpCeywJak2bUfWH9iJcm1Se5IcjjJp5Zsv7Ld9tske9ptb0hyMMlikp8neeEA8UvSXJrKSY6SpOUleSbNOOfd7fpWYCPwCiDAQpLXAH8DrgNeVVUPJnlee4pfA1uqqpK8A/gozQQ7SdL/yQJbkmbLc5LcSXPn+nfAz9rtW9u/xXZ9DU3B/TJgb1U9CFBVD7X7zwJuSnImcCpwTz/hS9L8s0VEkmbLo1W1GTiH5k71iR7sAJ9r+7M3V9WLq2r3Muf5KnB9VV0IvBs4bUWjlqRVxAJbkmZQVR0D3g9ck+QUYB/w9iRrAJKsT/IC4FbgzUme324/0SKyFri/Xb6q1+Alac7ZIiJJM6qqFpMcBrZX1Z4k5wP7kwD8A9hRVUeSfBb4VZLjNC0kO4FdwN4kD9MU4ecO8T9I0jxKVQ0dgyRJkjQ3bBGRJEmSJsgCW5IkSZogC2xJkiRpgiywJUmSpAmywJYkSZImyAJbkiRJmiALbEmSJGmC/gsC2HRyyyUiqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(original_y_test, y_score)\n",
    "\n",
    "plt.step(recall, precision, color='r', alpha=0.2,where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2,color='#F59B00')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('OverSampling Precision-Recall curve: \\n Average Precision-Recall Score ={0:0.2f}'.format(\n",
    "          average_precision), fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "_cell_guid": "d5c6fe5b-f086-4151-aba5-3c758677be0f",
    "_kg_hide-input": true,
    "_uuid": "787ec6bb25c3dc379c12a57619f5cc3e41afa42e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(454904, 30)\n",
      "(454904,)\n"
     ]
    }
   ],
   "source": [
    "sm = SMOTE(ratio='minority', random_state=42)\n",
    "Xsm_train, ysm_train = sm.fit_sample(original_X_train, original_y_train)\n",
    "\n",
    "print(Xsm_train.shape)\n",
    "print(ysm_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "_cell_guid": "7af62152-e7e3-45c8-9a56-69467ede59a6",
    "_kg_hide-input": true,
    "_uuid": "a25f7cc327bbaeae985cb0d2f9a0c8e2c2009aa3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting oversample data took :31.33479905128479 sec\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "t0 = time.time()\n",
    "log_reg_sm = grid_log_reg.best_estimator_\n",
    "log_reg_sm.fit(Xsm_train, ysm_train)\n",
    "\n",
    "t1 = time.time()\n",
    "print(\"Fitting oversample data took :{} sec\".format(t1 - t0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. NearMiss Technique (Under-sampling)\n",
    "\n",
    "Reference : http://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf?attredirects=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`undersample_pipeline = imbalanced_make_pipeline(NearMiss(), rand_log_reg)`을 사용하면 됩니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.3 Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a250e819-cdd4-43f5-b0a4-eb8f232199a0",
    "_uuid": "feb07b601c9ec79be1fe96cbbadf4ac838f7f7a8"
   },
   "source": [
    "## 7.1 Confusion Matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(190, 30)\n",
      "(190,)\n"
     ]
    }
   ],
   "source": [
    "# Under-sampling 되었던 데이터\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Logistic Regression fitted using SMOTE technique\n",
    "y_pred_log_reg = log_reg_sm.predict(X_test)\n",
    "\n",
    "# Random sunder ampling\n",
    "y_pred_knear = knears_neighbors.predict(X_test)\n",
    "y_pred_svc = svc.predict(X_test)\n",
    "y_pred_tree = tree_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "_cell_guid": "13a7d31c-2586-4946-aaa3-60090cd5680b",
    "_kg_hide-input": true,
    "_uuid": "d0e37500506d1b942431ac5bfabedcfea30275ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,''), Text(0,1.5,'')]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x864 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_reg_cf = confusion_matrix(y_test, y_pred_log_reg)\n",
    "kneighbors_cf = confusion_matrix(y_test, y_pred_knear)\n",
    "svc_cf = confusion_matrix(y_test, y_pred_svc)\n",
    "tree_cf = confusion_matrix(y_test, y_pred_tree)\n",
    "\n",
    "fig, ax = plt.subplots(2, 2,figsize=(22,12))\n",
    "\n",
    "sns.heatmap(log_reg_cf, ax=ax[0][0], annot=True, cmap=plt.cm.copper)\n",
    "ax[0, 0].set_title(\"Logistic Regression \\n Confusion Matrix\", fontsize=14)\n",
    "ax[0, 0].set_xticklabels(['', ''], fontsize=25, rotation=90)\n",
    "ax[0, 0].set_yticklabels(['', ''], fontsize=25, rotation=360)\n",
    "\n",
    "sns.heatmap(kneighbors_cf, ax=ax[0][1], annot=True, cmap=plt.cm.copper)\n",
    "ax[0][1].set_title(\"KNearsNeighbors \\n Confusion Matrix\", fontsize=14)\n",
    "ax[0][1].set_xticklabels(['', ''], fontsize=25, rotation=90)\n",
    "ax[0][1].set_yticklabels(['', ''], fontsize=25, rotation=360)\n",
    "\n",
    "sns.heatmap(svc_cf, ax=ax[1][0], annot=True, cmap=plt.cm.copper)\n",
    "ax[1][0].set_title(\"Suppor Vector Classifier \\n Confusion Matrix\", fontsize=14)\n",
    "ax[1][0].set_xticklabels(['', ''], fontsize=25, rotation=90)\n",
    "ax[1][0].set_yticklabels(['', ''], fontsize=25, rotation=360)\n",
    "\n",
    "sns.heatmap(tree_cf, ax=ax[1][1], annot=True, cmap=plt.cm.copper)\n",
    "ax[1][1].set_title(\"DecisionTree Classifier \\n Confusion Matrix\", fontsize=14)\n",
    "ax[1][1].set_xticklabels(['', ''], fontsize=25, rotation=90)\n",
    "ax[1][1].set_yticklabels(['', ''], fontsize=25, rotation=360)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "_cell_guid": "bd4529fd-f38a-4dd1-8b63-467a15a2167d",
    "_kg_hide-input": true,
    "_uuid": "1380d639d3b9087ec767ed6db391fc4b8c01e765"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.91      0.98      0.95       107\n",
      "          1       0.97      0.88      0.92        83\n",
      "\n",
      "avg / total       0.94      0.94      0.94       190\n",
      "\n",
      "KNears Neighbors:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.90      0.99      0.94       107\n",
      "          1       0.99      0.86      0.92        83\n",
      "\n",
      "avg / total       0.94      0.93      0.93       190\n",
      "\n",
      "Support Vector Classifier:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.90      0.97      0.94       107\n",
      "          1       0.96      0.87      0.91        83\n",
      "\n",
      "avg / total       0.93      0.93      0.93       190\n",
      "\n",
      "Support Vector Classifier:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.89      0.94      0.91       107\n",
      "          1       0.92      0.84      0.88        83\n",
      "\n",
      "avg / total       0.90      0.90      0.90       190\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print('Logistic Regression:')\n",
    "print(classification_report(y_test, y_pred_log_reg))\n",
    "\n",
    "print('KNears Neighbors:')\n",
    "print(classification_report(y_test, y_pred_knear))\n",
    "\n",
    "print('Support Vector Classifier:')\n",
    "print(classification_report(y_test, y_pred_svc))\n",
    "\n",
    "print('Support Vector Classifier:')\n",
    "print(classification_report(y_test, y_pred_tree))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "성능 평가를 하면서, 범하기 쉬운 실수는 다음과 같습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "_cell_guid": "9103c5ed-df9d-4441-91dc-1104a51f06ff",
    "_kg_hide-input": true,
    "_uuid": "49c94105ad280d1ca16271daf7f9395041016c5c"
   },
   "outputs": [],
   "source": [
    "# Final Score in the test set of logistic regression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression with Random Under-Sampling\n",
    "y_pred = log_reg.predict(X_test)\n",
    "undersample_score = accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression with SMOTE Technique\n",
    "y_pred_sm = best_est.predict(original_X_test)\n",
    "oversample_score = accuracy_score(original_y_test, y_pred_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'Technique': ['Random UnderSampling', 'Oversampling (SMOTE)'], 'Score': [undersample_score, oversample_score]}\n",
    "final_df = pd.DataFrame(data=d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>Technique</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random UnderSampling</td>\n",
       "      <td>0.936842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Oversampling (SMOTE)</td>\n",
       "      <td>0.988220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Technique     Score\n",
       "0  Random UnderSampling  0.936842\n",
       "1  Oversampling (SMOTE)  0.988220"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Move column\n",
    "score = final_df['Score']\n",
    "final_df.drop('Score', axis=1, inplace=True)\n",
    "final_df.insert(1, 'Score', score)\n",
    "\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.99      0.99     56863\n",
      "          1       0.11      0.86      0.20        98\n",
      "\n",
      "avg / total       1.00      0.99      0.99     56961\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# SMOTE\n",
    "print(classification_report(original_y_test, y_pred_sm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.99      0.99     56863\n",
      "          1       0.12      0.87      0.20        98\n",
      "\n",
      "avg / total       1.00      0.99      0.99     56961\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(original_y_test, log_reg.predict(original_X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Neural Networks with Random Under-Sampling Data vs OverSampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "_cell_guid": "e774e22e-8ce0-4c2e-99fa-9f3c6a915b6d",
    "_kg_hide-input": true,
    "_uuid": "35be99c61da4054c952e1955a5e809d003966975"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Activation\n",
    "from keras.layers.core import Dense\n",
    "from keras.optimizers import Adam\n",
    "from keras.metrics import categorical_crossentropy\n",
    "\n",
    "n_inputs = X_train.shape[1]\n",
    "\n",
    "undersample_model = Sequential([\n",
    "    Dense(n_inputs, input_shape=(n_inputs, ), activation='relu'),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(2, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "_cell_guid": "c249283c-f4d9-43f4-a859-ccd5a7661cf7",
    "_kg_hide-input": true,
    "_uuid": "ccdae6b84326551e1ff5199c44f7d53ccd3179d9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 30)                930       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 32)                992       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 2)                 66        \n",
      "=================================================================\n",
      "Total params: 1,988\n",
      "Trainable params: 1,988\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "undersample_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "_cell_guid": "6327357a-b8ca-4aa4-8764-48673b2d6c9d",
    "_kg_hide-input": true,
    "_uuid": "e2ec864b9ef6f530df28688a703bcc8f2243baa1"
   },
   "outputs": [],
   "source": [
    "undersample_model.compile(Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "_cell_guid": "0067f625-d734-4c15-9526-6efb1c47dc2c",
    "_kg_hide-input": true,
    "_uuid": "98a36722723d4f7285eb9de158b12be9694a603f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 606 samples, validate on 152 samples\n",
      "Epoch 1/20\n",
      " - 0s - loss: 0.6521 - acc: 0.5693 - val_loss: 0.4532 - val_acc: 0.6776\n",
      "Epoch 2/20\n",
      " - 0s - loss: 0.3948 - acc: 0.8168 - val_loss: 0.3172 - val_acc: 0.9013\n",
      "Epoch 3/20\n",
      " - 0s - loss: 0.2940 - acc: 0.9191 - val_loss: 0.2540 - val_acc: 0.9408\n",
      "Epoch 4/20\n",
      " - 0s - loss: 0.2414 - acc: 0.9257 - val_loss: 0.2195 - val_acc: 0.9342\n",
      "Epoch 5/20\n",
      " - 0s - loss: 0.2061 - acc: 0.9274 - val_loss: 0.1964 - val_acc: 0.9342\n",
      "Epoch 6/20\n",
      " - 0s - loss: 0.1819 - acc: 0.9323 - val_loss: 0.1807 - val_acc: 0.9276\n",
      "Epoch 7/20\n",
      " - 0s - loss: 0.1633 - acc: 0.9389 - val_loss: 0.1734 - val_acc: 0.9276\n",
      "Epoch 8/20\n",
      " - 0s - loss: 0.1504 - acc: 0.9472 - val_loss: 0.1672 - val_acc: 0.9276\n",
      "Epoch 9/20\n",
      " - 0s - loss: 0.1393 - acc: 0.9488 - val_loss: 0.1642 - val_acc: 0.9211\n",
      "Epoch 10/20\n",
      " - 0s - loss: 0.1317 - acc: 0.9521 - val_loss: 0.1623 - val_acc: 0.9276\n",
      "Epoch 11/20\n",
      " - 0s - loss: 0.1248 - acc: 0.9521 - val_loss: 0.1610 - val_acc: 0.9211\n",
      "Epoch 12/20\n",
      " - 0s - loss: 0.1189 - acc: 0.9554 - val_loss: 0.1598 - val_acc: 0.9276\n",
      "Epoch 13/20\n",
      " - 0s - loss: 0.1121 - acc: 0.9587 - val_loss: 0.1619 - val_acc: 0.9276\n",
      "Epoch 14/20\n",
      " - 0s - loss: 0.1061 - acc: 0.9637 - val_loss: 0.1628 - val_acc: 0.9276\n",
      "Epoch 15/20\n",
      " - 0s - loss: 0.1005 - acc: 0.9637 - val_loss: 0.1655 - val_acc: 0.9276\n",
      "Epoch 16/20\n",
      " - 0s - loss: 0.0959 - acc: 0.9670 - val_loss: 0.1669 - val_acc: 0.9276\n",
      "Epoch 17/20\n",
      " - 0s - loss: 0.0941 - acc: 0.9620 - val_loss: 0.1728 - val_acc: 0.9211\n",
      "Epoch 18/20\n",
      " - 0s - loss: 0.0889 - acc: 0.9670 - val_loss: 0.1711 - val_acc: 0.9211\n",
      "Epoch 19/20\n",
      " - 0s - loss: 0.0861 - acc: 0.9719 - val_loss: 0.1721 - val_acc: 0.9276\n",
      "Epoch 20/20\n",
      " - 0s - loss: 0.0792 - acc: 0.9752 - val_loss: 0.1740 - val_acc: 0.9276\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x144cbf1d0>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "undersample_model.fit(X_train, y_train, validation_split=0.2, batch_size=25, epochs=20, shuffle=True, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "_cell_guid": "9a30cd49-23c3-4417-8ce1-b6e82890481c",
    "_kg_hide-input": true,
    "_uuid": "e82f40ef343b53b71d6fcfa317e872278db27114"
   },
   "outputs": [],
   "source": [
    "undersample_predictions = undersample_model.predict(original_X_test, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "_cell_guid": "a47a91a0-2cf3-436f-a1cd-bb207ba94997",
    "_kg_hide-input": true,
    "_uuid": "028270a5b2ba0e4c85100f287fec64343ad900ea"
   },
   "outputs": [],
   "source": [
    "undersample_fraud_predictions = undersample_model.predict_classes(original_X_test, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "_cell_guid": "44216511-5fa7-404f-9b40-2c72f30c1ca7",
    "_kg_hide-input": true,
    "_uuid": "b0681d10d7f3e68a6b91864670b7aa04cacd362f"
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "# Create a confusion matrix\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, fontsize=14)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "_cell_guid": "ee16d514-2134-4cfd-b4d8-1ddb183960f0",
    "_kg_hide-input": true,
    "_uuid": "003c84c96d49bebdb5f09970102d89e3db5ff2f1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[54422  2441]\n",
      " [    7    91]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "undersample_cm = confusion_matrix(original_y_test, undersample_fraud_predictions)\n",
    "labels = ['No Fraud', 'Fraud']\n",
    "\n",
    "fig = plt.figure(figsize=(8,4))\n",
    "\n",
    "fig.add_subplot(111)\n",
    "plot_confusion_matrix(undersample_cm, labels, title=\"Random UnderSample \\n Confusion Matrix\", cmap=plt.cm.Reds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9d7cd385-9270-426e-b537-c80501dce889",
    "_uuid": "be2b0e76445ecb745b6e49e69993dd1de7839eb4"
   },
   "source": [
    "### Keras with OverSampling (SMOTE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "_cell_guid": "e7c29164-751a-4ccd-b517-527debf38fdf",
    "_kg_hide-input": true,
    "_uuid": "7130856ed8a6f87fe86b72c5142ff27ccf4eef1a"
   },
   "outputs": [],
   "source": [
    "n_inputs = Xsm_train.shape[1]\n",
    "\n",
    "oversample_model = Sequential([\n",
    "    Dense(n_inputs, input_shape=(n_inputs, ), activation='relu'),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(2, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "_cell_guid": "25beccc6-fe0b-4c85-a439-81aaf5cdc019",
    "_kg_hide-input": true,
    "_uuid": "937a0a57f3dae8172fb6c88b60944257e8198ae7"
   },
   "outputs": [],
   "source": [
    "oversample_model.compile(Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "_cell_guid": "888c3acd-3d8d-4f1b-b68a-bef304feca14",
    "_kg_hide-input": true,
    "_uuid": "5ddbf33763fb33393d7969fdbd7338aa8e708c43"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 363923 samples, validate on 90981 samples\n",
      "Epoch 1/20\n",
      " - 2s - loss: 0.0689 - acc: 0.9764 - val_loss: 0.0234 - val_acc: 0.9936\n",
      "Epoch 2/20\n",
      " - 2s - loss: 0.0156 - acc: 0.9964 - val_loss: 0.0138 - val_acc: 0.9992\n",
      "Epoch 3/20\n",
      " - 2s - loss: 0.0086 - acc: 0.9984 - val_loss: 0.0078 - val_acc: 1.0000\n",
      "Epoch 4/20\n",
      " - 1s - loss: 0.0060 - acc: 0.9989 - val_loss: 0.0028 - val_acc: 1.0000\n",
      "Epoch 5/20\n",
      " - 1s - loss: 0.0046 - acc: 0.9991 - val_loss: 0.0025 - val_acc: 1.0000\n",
      "Epoch 6/20\n",
      " - 1s - loss: 0.0037 - acc: 0.9993 - val_loss: 9.2678e-04 - val_acc: 1.0000\n",
      "Epoch 7/20\n",
      " - 2s - loss: 0.0031 - acc: 0.9994 - val_loss: 0.0026 - val_acc: 0.9998\n",
      "Epoch 8/20\n",
      " - 1s - loss: 0.0030 - acc: 0.9994 - val_loss: 8.5292e-04 - val_acc: 1.0000\n",
      "Epoch 9/20\n",
      " - 2s - loss: 0.0025 - acc: 0.9995 - val_loss: 0.0236 - val_acc: 0.9961\n",
      "Epoch 10/20\n",
      " - 2s - loss: 0.0027 - acc: 0.9995 - val_loss: 6.4186e-04 - val_acc: 1.0000\n",
      "Epoch 11/20\n",
      " - 1s - loss: 0.0020 - acc: 0.9996 - val_loss: 9.9438e-04 - val_acc: 1.0000\n",
      "Epoch 12/20\n",
      " - 1s - loss: 0.0018 - acc: 0.9997 - val_loss: 4.3128e-04 - val_acc: 1.0000\n",
      "Epoch 13/20\n",
      " - 1s - loss: 0.0018 - acc: 0.9997 - val_loss: 7.7375e-04 - val_acc: 1.0000\n",
      "Epoch 14/20\n",
      " - 1s - loss: 0.0016 - acc: 0.9997 - val_loss: 2.8814e-04 - val_acc: 1.0000\n",
      "Epoch 15/20\n",
      " - 1s - loss: 0.0021 - acc: 0.9996 - val_loss: 8.4319e-04 - val_acc: 1.0000\n",
      "Epoch 16/20\n",
      " - 1s - loss: 0.0014 - acc: 0.9997 - val_loss: 6.7655e-04 - val_acc: 1.0000\n",
      "Epoch 17/20\n",
      " - 1s - loss: 0.0012 - acc: 0.9998 - val_loss: 5.4540e-04 - val_acc: 1.0000\n",
      "Epoch 18/20\n",
      " - 1s - loss: 0.0016 - acc: 0.9997 - val_loss: 4.4604e-04 - val_acc: 1.0000\n",
      "Epoch 19/20\n",
      " - 1s - loss: 0.0012 - acc: 0.9998 - val_loss: 0.0031 - val_acc: 0.9992\n",
      "Epoch 20/20\n",
      " - 1s - loss: 0.0012 - acc: 0.9998 - val_loss: 1.5322e-04 - val_acc: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1455c7fd0>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oversample_model.fit(Xsm_train, ysm_train, validation_split=0.2, batch_size=300, epochs=20, shuffle=True, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "_cell_guid": "09628790-3d4b-4516-b355-4177e5ad9329",
    "_kg_hide-input": true,
    "_uuid": "222a3f8e7614c4e0241e0f0c85b0ecd45fd3cca6"
   },
   "outputs": [],
   "source": [
    "oversample_predictions = oversample_model.predict(original_X_test, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "_cell_guid": "374217c9-a4b7-4691-918b-6a91bb1c02e3",
    "_kg_hide-input": true,
    "_uuid": "9ede2c436251f560615087202cff031936f3a6e5"
   },
   "outputs": [],
   "source": [
    "oversample_fraud_predictions = oversample_model.predict_classes(original_X_test, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "_cell_guid": "9a58d39f-9149-4279-bce3-fc8372e55f93",
    "_kg_hide-input": true,
    "_uuid": "a18452b7051e4905f32b27940f006d0bc4bc2d5e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[56847    16]\n",
      " [   26    72]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "oversample_smote = confusion_matrix(original_y_test, oversample_fraud_predictions)\n",
    "labels = ['No Fraud', 'Fraud']\n",
    "\n",
    "fig = plt.figure(figsize=(8,4))\n",
    "\n",
    "fig.add_subplot(111)\n",
    "plot_confusion_matrix(oversample_smote, labels, title=\"OverSample (SMOTE) \\n Confusion Matrix\", cmap=plt.cm.Oranges)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}