credit-fraud-dealing-with-imbalanced-datasets.ipynb

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    "<h1 align=\"center\"> Credit Fraud Detector </h1>\n",
    "\n",
    "**Note:** There are still aspects of this kernel that will be subjected to changes. I've noticed a recent increase of interest towards this kernel so I will focus more on the steps I took and why I took them to make it clear why I took those steps.\n",
    "\n",
    "<h2>Before we Begin:  </h2>\n",
    "If you liked my work, please upvote this kernel since it will keep me motivated to perform more in-depth reserach towards this subject and will look for more efficient ways so that our models are able to detect more accurately both fraud and non-fraud transactions.\n",
    "\n",
    "<h2> Introduction </h2>\n",
    "In this kernel we will use various predictive models to see how accurate they  are in detecting whether a transaction is a normal payment or a fraud. As described in the dataset, the features are scaled and the names of the features are not shown due to privacy reasons. Nevertheless, we can still analyze some important aspects of the dataset. Let's start!\n",
    "\n",
    "\n",
    "<h2> Our Goals: </h2>\n",
    "<ul>\n",
    "<li> Understand the little distribution of the \"little\" data that was provided to us. </li>\n",
    "<li> Create a 50/50 sub-dataframe ratio of \"Fraud\" and \"Non-Fraud\" transactions. (NearMiss Algorithm) </li>\n",
    "<li> Determine the Classifiers we are going to use and decide which one has a higher accuracy. </li>\n",
    "<li>Create a Neural Network and compare the accuracy to our best classifier. </li>\n",
    "<li>Understand common mistaked made with imbalanced datasets. </li>\n",
    "</ul>\n",
    "\n",
    "\n",
    "<h2> Outline: </h2>\n",
    "I. <b>Understanding our data</b><br>\n",
    "a) [Gather Sense of our data](#gather)<br><br>\n",
    "\n",
    "II. <b>Preprocessing</b><br>\n",
    "a) [Scaling and Distributing](#distributing)<br>\n",
    "b) [Splitting the Data](#splitting)<br><br>\n",
    "\n",
    "III. <b>Random UnderSampling and Oversampling</b><br>\n",
    "a) [Distributing and Correlating](#correlating)<br>\n",
    "b) [Anomaly Detection](#anomaly)<br>\n",
    "c) [Dimensionality Reduction and Clustering (t-SNE)](#clustering)<br>\n",
    "d) [Classifiers](#classifiers)<br>\n",
    "e) [A Deeper Look into Logistic Regression](#logistic)<br>\n",
    "f) [Oversampling with SMOTE](#smote)<br><br>\n",
    "\n",
    "IV. <b>Testing </b><br>\n",
    "a) [Testing with Logistic Regression](#testing_logistic)<br>\n",
    "b) [Neural Networks Testing (Undersampling vs Oversampling)](#neural_networks)\n",
    "\n",
    "<h2>Correcting Previous Mistakes from Imbalanced Datasets: </h2>\n",
    "<ul>\n",
    "<li> Never test on the oversampled or undersampled dataset.</li>\n",
    "<li>If we want to implement cross validation, remember to oversample or undersample your training data <b>during</b> cross-validation, not before! </li>\n",
    "<li> Don't use <b>accuracy score </b> as a metric with imbalanced datasets (will be usually high and misleading), instead use <b>f1-score, precision/recall score or confusion matrix </b></li>\n",
    "</ul>\n",
    "\n",
    "\n",
    "<h2> References: </h2>\n",
    "<ul> \n",
    "<li>Hands on Machine Learning with Scikit-Learn & TensorFlow by Aurélien Géron (O'Reilly). CopyRight 2017 Aurélien Géron  </li>\n",
    "<li><a src=\"https://www.youtube.com/watch?v=DQC_YE3I5ig&t=794s\" > Machine Learning - Over-& Undersampling - Python/ Scikit/ Scikit-Imblearn </a>by Coding-Maniac</li>\n",
    "<li><a src=\"https://www.kaggle.com/lane203j/auprc-5-fold-c-v-and-resampling-methods\"> auprc, 5-fold c-v, and resampling methods\n",
    "</a> by Jeremy Lane (Kaggle Notebook) </li>\n",
    "</ul>"
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    "## Gather Sense of Our Data:\n",
    "<a id=\"gather\"></a>\n",
    "The first thing we must do is gather a <b> basic sense </b> of our data. Remember, except for the <b>transaction</b> and <b>amount</b> we dont know what the other columns are (due to privacy reasons). The only thing we know, is that those columns that are unknown have been scaled already.   \n",
    "\n",
    "<h3> Summary: </h3>\n",
    "<ul>\n",
    "<li>The transaction amount is relatively <b>small</b>. The mean of all the mounts made is approximately USD 88. </li>\n",
    "<li>There are no <b>\"Null\"</b> values, so we don't have to work on ways to replace values. </li>\n",
    "<li> Most of the transactions were <b>Non-Fraud</b> (99.83%) of the time, while <b>Fraud</b> transactions occurs (017%) of the time in the dataframe. </li>\n",
    "</ul>\n",
    "\n",
    "<h3> Feature Technicalities: </h3>\n",
    "<ul>\n",
    "<li> <b>PCA Transformation: </b>  The description of the data says that all the features went through a PCA transformation (Dimensionality Reduction technique) (Except for time and amount).</li>\n",
    "<li> <b>Scaling:</b> Keep in mind that in order to implement a PCA transformation features need to be previously scaled. (In this case, all the V features have been scaled or at least that is what we are assuming the people that develop the dataset did.)</li>\n",
    "</ul>"
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      "/opt/conda/lib/python3.6/site-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
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       "   Time        V1        V2        V3  ...         V27       V28  Amount  Class\n",
       "0   0.0 -1.359807 -0.072781  2.536347  ...    0.133558 -0.021053  149.62      0\n",
       "1   0.0  1.191857  0.266151  0.166480  ...   -0.008983  0.014724    2.69      0\n",
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       "3   1.0 -0.966272 -0.185226  1.792993  ...    0.062723  0.061458  123.50      0\n",
       "4   2.0 -1.158233  0.877737  1.548718  ...    0.219422  0.215153   69.99      0\n",
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "# Imported Libraries\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "import matplotlib.patches as mpatches\n",
    "import time\n",
    "\n",
    "# Classifier Libraries\n",
    "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",
    "import collections\n",
    "\n",
    "\n",
    "# Other Libraries\n",
    "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",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import NearMiss\n",
    "from imblearn.metrics import classification_report_imbalanced\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\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "df = pd.read_csv('../input/creditcard.csv')\n",
    "df.head()"
   ]
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       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.919560e-15</td>\n",
       "      <td>5.688174e-16</td>\n",
       "      <td>-8.769071e-15</td>\n",
       "      <td>2.782312e-15</td>\n",
       "      <td>-1.552563e-15</td>\n",
       "      <td>2.010663e-15</td>\n",
       "      <td>-1.694249e-15</td>\n",
       "      <td>-1.927028e-16</td>\n",
       "      <td>-3.137024e-15</td>\n",
       "      <td>1.768627e-15</td>\n",
       "      <td>9.170318e-16</td>\n",
       "      <td>-1.810658e-15</td>\n",
       "      <td>1.693438e-15</td>\n",
       "      <td>1.479045e-15</td>\n",
       "      <td>3.482336e-15</td>\n",
       "      <td>1.392007e-15</td>\n",
       "      <td>-7.528491e-16</td>\n",
       "      <td>4.328772e-16</td>\n",
       "      <td>9.049732e-16</td>\n",
       "      <td>5.085503e-16</td>\n",
       "      <td>1.537294e-16</td>\n",
       "      <td>7.959909e-16</td>\n",
       "      <td>5.367590e-16</td>\n",
       "      <td>4.458112e-15</td>\n",
       "      <td>1.453003e-15</td>\n",
       "      <td>1.699104e-15</td>\n",
       "      <td>-3.660161e-16</td>\n",
       "      <td>-1.206049e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>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      ...               Amount          Class\n",
       "count  284807.000000  2.848070e+05      ...        284807.000000  284807.000000\n",
       "mean    94813.859575  3.919560e-15      ...            88.349619       0.001727\n",
       "std     47488.145955  1.958696e+00      ...           250.120109       0.041527\n",
       "min         0.000000 -5.640751e+01      ...             0.000000       0.000000\n",
       "25%     54201.500000 -9.203734e-01      ...             5.600000       0.000000\n",
       "50%     84692.000000  1.810880e-02      ...            22.000000       0.000000\n",
       "75%    139320.500000  1.315642e+00      ...            77.165000       0.000000\n",
       "max    172792.000000  2.454930e+00      ...         25691.160000       1.000000\n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "03ddb929-5bc8-4af4-90cd-21dcbb57560d",
    "_kg_hide-input": true,
    "_uuid": "38bec67888aa534e9739e95ef9fac62d27a87021"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Good No Null Values!\n",
    "df.isnull().sum().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "01c007fa-0fcc-4eea-84ff-0861a2f8c533",
    "_kg_hide-input": true,
    "_uuid": "f6b96ff34855e3bf7af1f6979342b01c473e4e07"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Frauds 99.83 % of the dataset\n",
      "Frauds 0.17 % of the dataset\n"
     ]
    }
   ],
   "source": [
    "# The classes are heavily skewed we need to solve this issue later.\n",
    "print('No Frauds', 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": {
    "_cell_guid": "558c9b60-3f52-4da5-92fa-9fc4acbdbb3a",
    "_uuid": "c2bb0945a312508e908386fc87adc227f0afe0e0"
   },
   "source": [
    "**Note:**  Notice how imbalanced is our original dataset! Most of the transactions are non-fraud. If we use this dataframe as the base for our predictive models and analysis we might get a lot of errors and our algorithms will probably overfit since it will \"assume\" that most transactions are not fraud. But we don't want our model to assume, we want our model to detect patterns that give signs of fraud!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "657bc987-4b15-4cfa-b290-c39a2632e2ac",
    "_kg_hide-input": true,
    "_uuid": "337caaf6ed3f65beedb24a74eebb22d97ff52ba4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Class Distributions \\n (0: No Fraud || 1: Fraud)')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [\"#0101DF\", \"#DF0101\"]\n",
    "\n",
    "sns.countplot('Class', data=df, palette=colors)\n",
    "plt.title('Class Distributions \\n (0: No Fraud || 1: Fraud)', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3c9973d0-83bd-4b09-860e-c1f507f88310",
    "_uuid": "6894af2afdbfd5cd670d00b66f10ae49f1cab421"
   },
   "source": [
    "**Distributions:** By seeing the distributions we can have an idea how skewed are these features, we can also see further distributions of the other features. There are techniques that can help the distributions be less skewed which will be implemented in this notebook in the future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "cee315f2-325f-42b6-a640-736f10c272cc",
    "_kg_hide-input": true,
    "_uuid": "cfa51792bf6f8a6b318ae1bffcff4e922b1d1917"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(18,4))\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)])\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "72fdda5e-7f82-488d-a433-6157d6180bb8",
    "_uuid": "c5d6781e61c0ee84e72d26e8465bfd98ef91f3b9"
   },
   "source": [
    "<h2> Scaling and Distributing </h2>\n",
    "<a id=\"distributing\"></a>\n",
    "In this phase of our kernel, we will first scale the columns comprise of <b>Time</b> and <b>Amount </b>. Time and amount should be scaled as the other columns. On the other hand, we need to also create a sub sample of the dataframe in order to have an equal amount of Fraud and Non-Fraud cases, helping our algorithms better understand patterns that determines whether a transaction is a fraud or not.\n",
    "\n",
    "<h3> What is a sub-Sample?</h3>\n",
    "In this scenario, our subsample will be a dataframe with a 50/50 ratio of fraud and non-fraud transactions. Meaning our sub-sample will have the same amount of fraud and non fraud transactions.\n",
    "\n",
    "<h3> Why do we create a sub-Sample?</h3>\n",
    "In the beginning of this notebook we saw that the original dataframe was heavily imbalanced! Using the original dataframe  will cause the following issues:\n",
    "<ul>\n",
    "<li><b>Overfitting: </b>Our classification models will assume that in most cases there are no frauds! What we want for our model is to be certain when a fraud occurs. </li>\n",
    "<li><b>Wrong Correlations:</b> Although we don't know what the \"V\" features stand for, it will be useful to understand how each of this features influence the result (Fraud or No Fraud) by having an imbalance dataframe we are not able to see the true correlations between the class and features. </li>\n",
    "</ul>\n",
    "\n",
    "<h3>Summary: </h3> \n",
    "<ul>\n",
    "<li> <b>Scaled amount </b> and <b> scaled time </b> are the columns with scaled values. </li>\n",
    "<li> There are <b>492 cases </b> of fraud in our dataset so we can randomly get 492 cases of non-fraud to create our new sub dataframe. </li>\n",
    "<li>We concat the 492 cases of fraud and non fraud, <b>creating a new sub-sample. </b></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "d5d64bf0-2fbb-4096-a265-f68887bf2fde",
    "_kg_hide-input": true,
    "_uuid": "1501ec379c9b5c39c3857ba0febd0aedee9c30d5"
   },
   "outputs": [],
   "source": [
    "# Since most of our data has already been scaled we should scale the columns that are left to scale (Amount and Time)\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
    "\n",
    "# RobustScaler is less prone to outliers.\n",
    "\n",
    "std_scaler = StandardScaler()\n",
    "rob_scaler = RobustScaler()\n",
    "\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": 9,
   "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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       "        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>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  ...         V27       V28  Class\n",
       "0       1.783274    -0.994983 -1.359807  ...    0.133558 -0.021053      0\n",
       "1      -0.269825    -0.994983  1.191857  ...   -0.008983  0.014724      0\n",
       "2       4.983721    -0.994972 -1.358354  ...   -0.055353 -0.059752      0\n",
       "3       1.418291    -0.994972 -0.966272  ...    0.062723  0.061458      0\n",
       "4       0.670579    -0.994960 -1.158233  ...    0.219422  0.215153      0\n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 9,
     "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": [
    "### Splitting the Data (Original DataFrame)\n",
    "<a id=\"splitting\"></a>\n",
    "Before proceeding with the <b> Random UnderSampling technique</b> we have to separate the orginal dataframe. <b> Why? for testing purposes, remember although we are splitting the data when implementing Random UnderSampling or OverSampling techniques, we want to test our models on the original testing set not on the testing set created by either of these techniques.</b> The main goal is to fit the model either with the dataframes that were undersample and oversample (in order for our models to detect the patterns), and test it on the original testing set.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "c6c962cc-6f38-4a00-bcd7-ce9d91db954c",
    "_kg_hide-input": true,
    "_uuid": "9f7b5d920703b3a3c8c0f62bc6042e4615bc8324"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Frauds 99.83 % of the dataset\n",
      "Frauds 0.17 % of the dataset\n",
      "Train: [ 30473  30496  31002 ... 284804 284805 284806] Test: [    0     1     2 ... 57017 57018 57019]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 30473  30496  31002 ... 113964 113965 113966]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 81609  82400  83053 ... 170946 170947 170948]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [150654 150660 150661 ... 227866 227867 227868]\n",
      "Train: [     0      1      2 ... 227866 227867 227868] Test: [212516 212644 213092 ... 284804 284805 284806]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Label Distributions: \n",
      "\n",
      "[0.99827076 0.00172924]\n",
      "[0.99827952 0.00172048]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "print('No Frauds', 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')\n",
    "\n",
    "X = df.drop('Class', axis=1)\n",
    "y = df['Class']\n",
    "\n",
    "sss = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)\n",
    "\n",
    "for train_index, test_index in sss.split(X, y):\n",
    "    print(\"Train:\", train_index, \"Test:\", test_index)\n",
    "    original_Xtrain, original_Xtest = X.iloc[train_index], X.iloc[test_index]\n",
    "    original_ytrain, original_ytest = y.iloc[train_index], y.iloc[test_index]\n",
    "\n",
    "# We already have X_train and y_train for undersample data thats why I am using original to distinguish and to not overwrite these variables.\n",
    "# original_Xtrain, original_Xtest, original_ytrain, original_ytest = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Check the Distribution of the labels\n",
    "\n",
    "\n",
    "# Turn into an array\n",
    "original_Xtrain = original_Xtrain.values\n",
    "original_Xtest = original_Xtest.values\n",
    "original_ytrain = original_ytrain.values\n",
    "original_ytest = original_ytest.values\n",
    "\n",
    "# See if both the train and test label distribution are similarly distributed\n",
    "train_unique_label, train_counts_label = np.unique(original_ytrain, return_counts=True)\n",
    "test_unique_label, test_counts_label = np.unique(original_ytest, return_counts=True)\n",
    "print('-' * 100)\n",
    "\n",
    "print('Label Distributions: \\n')\n",
    "print(train_counts_label/ len(original_ytrain))\n",
    "print(test_counts_label/ len(original_ytest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "956d34b9-e562-4b70-a2f8-fbe060273a83",
    "_uuid": "cc554c4ffec656cb38d01c034f2cd338e1cb4565"
   },
   "source": [
    "## Random Under-Sampling:\n",
    "<img src=\"http://contrib.scikit-learn.org/imbalanced-learn/stable/_images/sphx_glr_plot_random_under_sampler_001.png\">\n",
    "\n",
    "In this phase of the project we will implement *\"Random Under Sampling\"* which basically consists of removing data in order to have a more <b> balanced dataset </b> and thus avoiding our models to overfitting.\n",
    "\n",
    "#### Steps:\n",
    "<ul>\n",
    "<li>The first thing we have to do is determine how <b>imbalanced</b> is our class (use \"value_counts()\" on the class column to determine the amount for each label)  </li>\n",
    "<li>Once we determine how many instances are considered <b>fraud transactions </b> (Fraud = \"1\") , we should bring the <b>non-fraud transactions</b> to the same amount as fraud transactions (assuming we want a 50/50 ratio), this will be equivalent to 492 cases of fraud and 492 cases of non-fraud transactions.  </li>\n",
    "<li> After implementing this technique, we have a sub-sample of our dataframe with a 50/50 ratio with regards to our classes. Then the next step we will implement is to <b>shuffle the data</b> to see if our models can maintain a certain accuracy everytime we run this script.</li>\n",
    "</ul>\n",
    "\n",
    "**Note:** The main issue with \"Random Under-Sampling\" is that we run the risk that our classification models will not perform as accurate as we would like to since there is a great deal of <b>information loss</b> (bringing 492 non-fraud transaction  from 284,315 non-fraud transaction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "f0acfc44-eb2a-4356-ad03-d0c12807acd7",
    "_kg_hide-input": true,
    "_uuid": "e3a2b89752681164f14c8273452fc66734d7f41b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .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>35719</th>\n",
       "      <td>0.368616</td>\n",
       "      <td>-0.545789</td>\n",
       "      <td>-0.887048</td>\n",
       "      <td>0.561889</td>\n",
       "      <td>0.906573</td>\n",
       "      <td>0.385667</td>\n",
       "      <td>1.739388</td>\n",
       "      <td>4.465832</td>\n",
       "      <td>-0.568384</td>\n",
       "      <td>1.012247</td>\n",
       "      <td>-0.073614</td>\n",
       "      <td>0.142165</td>\n",
       "      <td>-0.412621</td>\n",
       "      <td>-0.266949</td>\n",
       "      <td>0.133484</td>\n",
       "      <td>-0.225193</td>\n",
       "      <td>1.643363</td>\n",
       "      <td>-0.195570</td>\n",
       "      <td>-0.270580</td>\n",
       "      <td>1.003391</td>\n",
       "      <td>1.811364</td>\n",
       "      <td>0.198640</td>\n",
       "      <td>0.161127</td>\n",
       "      <td>0.431808</td>\n",
       "      <td>-0.380335</td>\n",
       "      <td>1.045194</td>\n",
       "      <td>0.340769</td>\n",
       "      <td>0.004459</td>\n",
       "      <td>-0.240767</td>\n",
       "      <td>0.153636</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154670</th>\n",
       "      <td>1.145812</td>\n",
       "      <td>0.209084</td>\n",
       "      <td>-2.296987</td>\n",
       "      <td>4.064043</td>\n",
       "      <td>-5.957706</td>\n",
       "      <td>4.680008</td>\n",
       "      <td>-2.080938</td>\n",
       "      <td>-1.463272</td>\n",
       "      <td>-4.490847</td>\n",
       "      <td>1.029246</td>\n",
       "      <td>-1.593249</td>\n",
       "      <td>-8.993811</td>\n",
       "      <td>7.864467</td>\n",
       "      <td>-10.649840</td>\n",
       "      <td>1.826591</td>\n",
       "      <td>-12.913632</td>\n",
       "      <td>-0.766330</td>\n",
       "      <td>-7.954098</td>\n",
       "      <td>-7.809635</td>\n",
       "      <td>-1.718511</td>\n",
       "      <td>3.832985</td>\n",
       "      <td>1.264954</td>\n",
       "      <td>1.089084</td>\n",
       "      <td>0.975398</td>\n",
       "      <td>-0.625530</td>\n",
       "      <td>-0.535181</td>\n",
       "      <td>0.247435</td>\n",
       "      <td>0.160400</td>\n",
       "      <td>0.969582</td>\n",
       "      <td>0.335041</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59856</th>\n",
       "      <td>-0.125900</td>\n",
       "      <td>-0.418872</td>\n",
       "      <td>1.205206</td>\n",
       "      <td>-0.041657</td>\n",
       "      <td>0.935974</td>\n",
       "      <td>1.145439</td>\n",
       "      <td>-0.547338</td>\n",
       "      <td>0.289337</td>\n",
       "      <td>-0.551764</td>\n",
       "      <td>0.125960</td>\n",
       "      <td>0.900694</td>\n",
       "      <td>-0.211548</td>\n",
       "      <td>-1.486680</td>\n",
       "      <td>0.373892</td>\n",
       "      <td>0.498039</td>\n",
       "      <td>-0.472276</td>\n",
       "      <td>0.205412</td>\n",
       "      <td>0.308328</td>\n",
       "      <td>-0.571202</td>\n",
       "      <td>0.173169</td>\n",
       "      <td>0.101632</td>\n",
       "      <td>-0.079117</td>\n",
       "      <td>-0.111767</td>\n",
       "      <td>-0.103876</td>\n",
       "      <td>-0.114961</td>\n",
       "      <td>-0.436178</td>\n",
       "      <td>0.536396</td>\n",
       "      <td>-0.358637</td>\n",
       "      <td>0.072760</td>\n",
       "      <td>0.031139</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14170</th>\n",
       "      <td>1.089779</td>\n",
       "      <td>-0.698951</td>\n",
       "      <td>-15.903635</td>\n",
       "      <td>10.393917</td>\n",
       "      <td>-19.133602</td>\n",
       "      <td>6.185969</td>\n",
       "      <td>-12.538021</td>\n",
       "      <td>-4.027030</td>\n",
       "      <td>-13.897827</td>\n",
       "      <td>10.662252</td>\n",
       "      <td>-2.844954</td>\n",
       "      <td>-9.668789</td>\n",
       "      <td>7.394419</td>\n",
       "      <td>-11.635630</td>\n",
       "      <td>1.423277</td>\n",
       "      <td>-8.640459</td>\n",
       "      <td>-0.674720</td>\n",
       "      <td>-7.695569</td>\n",
       "      <td>-13.684140</td>\n",
       "      <td>-4.777406</td>\n",
       "      <td>1.268343</td>\n",
       "      <td>1.501565</td>\n",
       "      <td>1.577548</td>\n",
       "      <td>-1.280137</td>\n",
       "      <td>-0.601295</td>\n",
       "      <td>0.040404</td>\n",
       "      <td>0.995502</td>\n",
       "      <td>-0.273743</td>\n",
       "      <td>1.688136</td>\n",
       "      <td>0.527831</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8842</th>\n",
       "      <td>-0.307413</td>\n",
       "      <td>-0.852912</td>\n",
       "      <td>-4.696795</td>\n",
       "      <td>2.693867</td>\n",
       "      <td>-4.475133</td>\n",
       "      <td>5.467685</td>\n",
       "      <td>-1.556758</td>\n",
       "      <td>-1.549420</td>\n",
       "      <td>-4.104215</td>\n",
       "      <td>0.553934</td>\n",
       "      <td>-1.498468</td>\n",
       "      <td>-4.594952</td>\n",
       "      <td>5.275506</td>\n",
       "      <td>-11.349029</td>\n",
       "      <td>0.374549</td>\n",
       "      <td>-8.138695</td>\n",
       "      <td>0.548571</td>\n",
       "      <td>-6.653594</td>\n",
       "      <td>-10.246755</td>\n",
       "      <td>-4.191066</td>\n",
       "      <td>0.991486</td>\n",
       "      <td>-0.158971</td>\n",
       "      <td>0.573898</td>\n",
       "      <td>-0.080163</td>\n",
       "      <td>0.318408</td>\n",
       "      <td>-0.245862</td>\n",
       "      <td>0.338238</td>\n",
       "      <td>0.032271</td>\n",
       "      <td>-1.508458</td>\n",
       "      <td>0.608075</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        scaled_amount  scaled_time         V1  ...         V27       V28  Class\n",
       "35719        0.368616    -0.545789  -0.887048  ...   -0.240767  0.153636      0\n",
       "154670       1.145812     0.209084  -2.296987  ...    0.969582  0.335041      1\n",
       "59856       -0.125900    -0.418872   1.205206  ...    0.072760  0.031139      0\n",
       "14170        1.089779    -0.698951 -15.903635  ...    1.688136  0.527831      1\n",
       "8842        -0.307413    -0.852912  -4.696795  ...   -1.508458  0.608075      1\n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since our classes are highly skewed we should make them equivalent in order to have a normal distribution of the classes.\n",
    "\n",
    "# Lets shuffle the data before creating the subsamples\n",
    "\n",
    "df = df.sample(frac=1)\n",
    "\n",
    "# amount of fraud classes 492 rows.\n",
    "fraud_df = df.loc[df['Class'] == 1]\n",
    "non_fraud_df = df.loc[df['Class'] == 0][:492]\n",
    "\n",
    "normal_distributed_df = pd.concat([fraud_df, non_fraud_df])\n",
    "\n",
    "# Shuffle dataframe rows\n",
    "new_df = normal_distributed_df.sample(frac=1, random_state=42)\n",
    "\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "77198464-c0f8-4694-ac0b-4b29b94d0da3",
    "_uuid": "b6818122806657e7accb8be1f4bf17086bb9b149"
   },
   "source": [
    "##  Equally Distributing and Correlating: \n",
    "<a id=\"correlating\"></a>\n",
    "Now that we have our dataframe correctly balanced, we can go further with our <b>analysis</b> and <b>data preprocessing</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "73454100-dc69-49fd-b1b2-f72e326bca5d",
    "_kg_hide-input": true,
    "_uuid": "68b42e92df59f10fbd3ba700389796c4506af604"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of the Classes in the subsample dataset\n",
      "1    0.5\n",
      "0    0.5\n",
      "Name: Class, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Distribution of the Classes in the subsample dataset')\n",
    "print(new_df['Class'].value_counts()/len(new_df))\n",
    "\n",
    "\n",
    "\n",
    "sns.countplot('Class', data=new_df, palette=colors)\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": [
    "<h3> Correlation Matrices </h3>\n",
    "Correlation matrices are the essence of understanding our data. We want to know if there are features that influence heavily in whether a specific transaction is a fraud. However, it is important that we use the correct dataframe (subsample)  in order for us to see which features have a high positive or negative correlation with regards to fraud transactions.\n",
    "\n",
    "### Summary and Explanation: \n",
    "<ul>\n",
    "<li><b>Negative Correlations: </b>V17, V14, V12 and V10 are negatively correlated. Notice how the lower these values are, the more likely the end result will be a fraud transaction.  </li>\n",
    "<li> <b> Positive Correlations: </b> V2, V4, V11, and V19 are positively correlated. Notice how the higher these values are, the more likely the end result will be a fraud transaction. </li>\n",
    "<li> <b>BoxPlots: </b>  We will use boxplots to have a better understanding of the distribution of these features in fradulent and non fradulent transactions. </li>\n",
    "</ul>\n",
    "\n",
    "\n",
    "**Note: ** We have to make sure we use the subsample in our correlation matrix or else our correlation matrix will be affected by the high imbalance between our classes. This occurs due to the high class imbalance in the original dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "9f353623-9435-4bb2-b854-b4a201ec7dd9",
    "_kg_hide-input": true,
    "_uuid": "e2f417c5d7c633a1e3cdfaa78acd6bd77a38400e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make sure we use the subsample in our correlation\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(24,20))\n",
    "\n",
    "# Entire DataFrame\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)\n",
    "\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)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "2f02c21f-daa3-4251-a8e9-acad09a5ce0f",
    "_kg_hide-input": true,
    "_uuid": "318d0e7e0443f99139be21c00a7abc663be26385"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n",
    "\n",
    "# Negative Correlations with our Class (The lower our feature value the more likely it will be a fraud transaction)\n",
    "sns.boxplot(x=\"Class\", y=\"V17\", data=new_df, palette=colors, ax=axes[0])\n",
    "axes[0].set_title('V17 vs Class Negative Correlation')\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df, palette=colors, 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, palette=colors, 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, palette=colors, ax=axes[3])\n",
    "axes[3].set_title('V10 vs Class Negative Correlation')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "b457b10e-c17c-4cb2-9719-6d4128377c9f",
    "_kg_hide-input": true,
    "_uuid": "7bfc46c028f8602ee949de83629082633aa47b2c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n",
    "\n",
    "# Positive correlations (The higher the feature the probability increases that it will be a fraud transaction)\n",
    "sns.boxplot(x=\"Class\", y=\"V11\", data=new_df, palette=colors, ax=axes[0])\n",
    "axes[0].set_title('V11 vs Class Positive Correlation')\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V4\", data=new_df, palette=colors, ax=axes[1])\n",
    "axes[1].set_title('V4 vs Class Positive Correlation')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V2\", data=new_df, palette=colors, ax=axes[2])\n",
    "axes[2].set_title('V2 vs Class Positive Correlation')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V19\", data=new_df, palette=colors, ax=axes[3])\n",
    "axes[3].set_title('V19 vs Class Positive Correlation')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "93e56c89-185e-40d2-9ccc-29b123feb5a6",
    "_uuid": "a721282c0f44ec8030bbad6d0220091bde8cbec8"
   },
   "source": [
    "## Anomaly Detection:\n",
    "<a id=\"anomaly\"></a>\n",
    "<img src=\"https://discourse-cdn-sjc1.com/business6/uploads/analyticsvidhya/original/2X/d/d11281b44c2e440b36aaf29156b5032105d2d06b.png\">\n",
    "\n",
    "\n",
    "Our main aim in this section is to remove \"extreme outliers\" from features that have a high correlation with our classes. This will have a positive impact on the accuracy of our models.  <br><br>\n",
    "\n",
    "\n",
    "### Interquartile Range Method:\n",
    "<ul>\n",
    "<li> <b>Interquartile Range (IQR): </b> We calculate this by the difference between the 75th percentile and 25th percentile. Our aim is to create a threshold beyond the 75th and 25th percentile that in case some instance pass this threshold the instance will be deleted.  </li>\n",
    "<li> <b>Boxplots: </b> Besides easily seeing the 25th and 75th percentiles (both end of the squares) it is also easy to see extreme outliers (points beyond the lower and higher extreme). </li>\n",
    "</ul>\n",
    "\n",
    "### Outlier Removal Tradeoff:\n",
    "We have to be careful as to how far do we want the threshold for removing outliers. We determine the threshold by multiplying a number (ex: 1.5) by the (Interquartile Range). The higher this threshold is, the less outliers will detect (multiplying by a higher number ex: 3), and the lower this threshold is the more outliers it will detect.  <br><br>\n",
    "\n",
    "**The Tradeoff: **\n",
    "The lower the threshold the more outliers it will remove however, we want to focus more on \"extreme outliers\" rather than just outliers. Why? because we might run the risk of information loss which will cause our models to have a lower accuracy. You can play with this threshold and see how it affects the accuracy of our classification models.\n",
    "\n",
    "\n",
    "### Summary:\n",
    "<ul>\n",
    "<li> <b> Visualize Distributions: </b> We first start by visualizing the distribution of the feature we are going to use to eliminate some of the outliers. V14 is the only feature that has a Gaussian distribution compared to features V12 and V10. </li>\n",
    "<li><b>Determining the threshold: </b> After we decide which number we will use to multiply with the iqr (the lower more outliers removed), we will proceed in determining the upper and lower thresholds by substrating q25 - threshold (lower extreme threshold) and adding q75 + threshold (upper extreme threshold). </li>\n",
    "<li> <b>Conditional Dropping: </b> Lastly, we create a conditional dropping stating that if the \"threshold\" is exceeded in both extremes, the instances will be removed. </li>\n",
    "<li> <b> Boxplot Representation: </b> Visualize through the boxplot that the number of \"extreme outliers\" have been reduced to a considerable amount. </li>\n",
    "</ul>\n",
    "\n",
    "**Note:** After implementing outlier reduction our accuracy has been improved by over 3%! Some outliers can distort the accuracy of our models but remember, we have to avoid an extreme amount of information loss or else our model runs the risk of underfitting.\n",
    "\n",
    "\n",
    "**Reference**: More information on Interquartile Range Method: <a src=\"https://machinelearningmastery.com/how-to-use-statistics-to-identify-outliers-in-data/\"> How to Use Statistics to Identify Outliers in Data </a> by Jason Brownless (Machine Learning Mastery blog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "9c690dfa-8fed-44e5-99f5-ff4eb6f87f16",
    "_kg_hide-input": true,
    "_uuid": "b6963900379db5b0d4adf92f8c7f959164e9119f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "\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)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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:[-19.2143254902614, -18.8220867423816, -18.4937733551053, -18.049997689859396]\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "V12 Lower: -17.3430371579634\n",
      "V12 Upper: 5.776973384895937\n",
      "V12 outliers: [-18.683714633344298, -18.047596570821604, -18.4311310279993, -18.553697009645802]\n",
      "Feature V12 Outliers for Fraud Cases: 4\n",
      "Number of Instances after outliers removal: 976\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "V10 Lower: -14.89885463232024\n",
      "V10 Upper: 4.920334958342141\n",
      "V10 outliers: [-24.403184969972802, -18.9132433348732, -15.124162814494698, -16.3035376590131, -15.2399619587112, -15.1237521803455, -14.9246547735487, -16.6496281595399, -18.2711681738888, -24.5882624372475, -15.346098846877501, -20.949191554361104, -15.2399619587112, -23.2282548357516, -15.2318333653018, -22.1870885620007, -17.141513641289198, -19.836148851696, -22.1870885620007, -16.6011969664137, -16.7460441053944, -15.563791338730098, -14.9246547735487, -16.2556117491401, -22.1870885620007, -15.563791338730098, -22.1870885620007]\n",
      "Feature V10 Outliers for Fraud Cases: 27\n",
      "Number of Instances after outliers removal: 947\n"
     ]
    }
   ],
   "source": [
    "# # -----> V14 Removing Outliers (Highest Negative Correlated with Labels)\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",
    "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('----' * 44)\n",
    "\n",
    "# -----> V12 removing outliers from fraud transactions\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",
    "v12_iqr = q75 - q25\n",
    "\n",
    "v12_cut_off = v12_iqr * 1.5\n",
    "v12_lower, v12_upper = q25 - v12_cut_off, q75 + v12_cut_off\n",
    "print('V12 Lower: {}'.format(v12_lower))\n",
    "print('V12 Upper: {}'.format(v12_upper))\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",
    "new_df = new_df.drop(new_df[(new_df['V12'] > v12_upper) | (new_df['V12'] < v12_lower)].index)\n",
    "print('Number of Instances after outliers removal: {}'.format(len(new_df)))\n",
    "print('----' * 44)\n",
    "\n",
    "\n",
    "# Removing outliers V10 Feature\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",
    "v10_iqr = q75 - q25\n",
    "\n",
    "v10_cut_off = v10_iqr * 1.5\n",
    "v10_lower, v10_upper = q25 - v10_cut_off, q75 + v10_cut_off\n",
    "print('V10 Lower: {}'.format(v10_lower))\n",
    "print('V10 Upper: {}'.format(v10_upper))\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",
    "new_df = new_df.drop(new_df[(new_df['V10'] > v10_upper) | (new_df['V10'] < v10_lower)].index)\n",
    "print('Number of Instances after outliers removal: {}'.format(len(new_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "66e44398-7c91-4cce-9778-4512cb838973",
    "_kg_hide-input": true,
    "_uuid": "ac80d9cfb07f1865094a8d460ae801750e93d694"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,(ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,6))\n",
    "\n",
    "colors = ['#B3F9C5', '#f9c5b3']\n",
    "# Boxplots with outliers removed\n",
    "# Feature V14\n",
    "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df,ax=ax1, palette=colors)\n",
    "ax1.set_title(\"V14 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "ax1.annotate('Fewer extreme \\n outliers', xy=(0.98, -17.5), xytext=(0, -12),\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            fontsize=14)\n",
    "\n",
    "# Feature 12\n",
    "sns.boxplot(x=\"Class\", y=\"V12\", data=new_df, ax=ax2, palette=colors)\n",
    "ax2.set_title(\"V12 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "ax2.annotate('Fewer extreme \\n outliers', xy=(0.98, -17.3), xytext=(0, -12),\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            fontsize=14)\n",
    "\n",
    "# Feature V10\n",
    "sns.boxplot(x=\"Class\", y=\"V10\", data=new_df, ax=ax3, palette=colors)\n",
    "ax3.set_title(\"V10 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "ax3.annotate('Fewer extreme \\n outliers', xy=(0.95, -16.5), xytext=(0, -12),\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            fontsize=14)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "74903f3b-dc6b-40ba-abc8-86c3df5ca46e",
    "_uuid": "0b365b10bd363c23068accc448509ced879f1670"
   },
   "source": [
    "<h2>Dimensionality Reduction and Clustering: </h2>\n",
    "<a id=\"clustering\"></a>\n",
    "\n",
    "<h3>Understanding t-SNE:  </h3>\n",
    "In order to understand this algorithm you have to understand the following terms: <br>\n",
    "<ul>\n",
    "<li> <b> Euclidean Distance </b></li>\n",
    "<li> <b>Conditional Probability</b> </li>\n",
    "<li><b>Normal and T-Distribution Plots</b> </li>\n",
    "</ul> \n",
    "\n",
    "**Note:** If you want a simple instructive video look at <a href=\"https://www.youtube.com/watch?v=NEaUSP4YerM\"> StatQuest: t-SNE, Clearly Explained </a> by Joshua Starmer\n",
    "\n",
    "\n",
    "<h3> Summary: </h3>\n",
    "<ul> \n",
    "<li>t-SNE algorithm can pretty accurately cluster the cases that were fraud and non-fraud in our dataset. </li>\n",
    "<li> Although the subsample is pretty small, the t-SNE algorithm is able to detect clusters pretty accurately in every scenario (I shuffle the dataset before running t-SNE)</li>\n",
    "<li> This gives us an indication that further predictive models will perform pretty well in separating fraud cases from non-fraud cases. </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "f83cde6b-90d0-4e9d-ac63-fb69780431b2",
    "_kg_hide-input": true,
    "_uuid": "af3027e7df67b75c92c88d597003632e285c9bff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-SNE took 7.0 s\n",
      "PCA took 0.032 s\n",
      "Truncated SVD took 0.0051 s\n"
     ]
    }
   ],
   "source": [
    "# New_df is from the random undersample data (fewer instances)\n",
    "X = new_df.drop('Class', axis=1)\n",
    "y = new_df['Class']\n",
    "\n",
    "\n",
    "# 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",
    "print(\"T-SNE took {:.2} s\".format(t1 - t0))\n",
    "\n",
    "# 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",
    "print(\"PCA took {:.2} s\".format(t1 - t0))\n",
    "\n",
    "# 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",
    "print(\"Truncated SVD took {:.2} s\".format(t1 - t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "07015ae5-f7ac-4d64-8f41-1e4b7c9dd2ac",
    "_kg_hide-input": true,
    "_uuid": "084f2a7421c2212082491d2a90e65d65c52b434a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(24,6))\n",
    "# labels = ['No Fraud', 'Fraud']\n",
    "f.suptitle('Clusters using Dimensionality Reduction', fontsize=14)\n",
    "\n",
    "\n",
    "blue_patch = mpatches.Patch(color='#0A0AFF', label='No Fraud')\n",
    "red_patch = mpatches.Patch(color='#AF0000', label='Fraud')\n",
    "\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])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "cb2c480a-090a-4cfb-b12e-3b74c325826c",
    "_uuid": "1b63bfd92008043cc1a336f924c835e73792f6d8"
   },
   "source": [
    "<h2> Classifiers (UnderSampling):  </h2>\n",
    "<a id=\"classifiers\"></a>\n",
    "In this section we will train four types of classifiers and decide which classifier will be more effective in detecting <b>fraud transactions</b>.  Before we have to split our data into training and testing sets and separate the features from the labels.\n",
    "\n",
    "## Summary: \n",
    "<ul>\n",
    "<li> <b> Logistic Regression </b> classifier is more accurate than the other three classifiers in most cases. (We will further analyze Logistic Regression) </li>\n",
    "<li><b> GridSearchCV </b> is used to determine the paremeters that gives the best predictive score for the classifiers. </li>\n",
    "<li> Logistic Regression has the best Receiving Operating Characteristic score  (ROC), meaning that LogisticRegression pretty accurately separates <b> fraud </b> and <b> non-fraud </b> transactions.</li>\n",
    "</ul>\n",
    "\n",
    "## Learning Curves:\n",
    "<ul>\n",
    "<li>The <b>wider the  gap</b>  between the training score and the cross validation score, the more likely your model is <b>overfitting (high variance)</b>.</li>\n",
    "<li> If the score is low in both training and cross-validation sets</b> this is an indication that our model is <b>underfitting (high bias)</b></li>\n",
    "<li><b> Logistic Regression Classifier</b>  shows the best score in both training and cross-validating sets.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "85ce8738-7599-4b06-a722-5c0ed073599b",
    "_kg_hide-input": true,
    "_uuid": "e3751d88766a982119e522e27a9c0c647f20af85"
   },
   "outputs": [],
   "source": [
    "# Undersampling before cross validating (prone to overfit)\n",
    "X = new_df.drop('Class', axis=1)\n",
    "y = new_df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "288a65b7-8b86-44b1-973d-38dbcfe82bbb",
    "_kg_hide-input": true,
    "_uuid": "fb0a479efaa7147d6702c2c24083f1118621863f"
   },
   "outputs": [],
   "source": [
    "# Our data is already scaled we should split our training and test sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# This is explicitly used for undersampling.\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": 23,
   "metadata": {
    "_cell_guid": "bccd5685-a979-451e-85b3-1cb968523540",
    "_kg_hide-input": true,
    "_uuid": "28f5178089d2d133b9e7478c1c7dc7a1f98aabee"
   },
   "outputs": [],
   "source": [
    "# Turn the values into an array for feeding the classification algorithms.\n",
    "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": 24,
   "metadata": {
    "_cell_guid": "7810d0b9-b4e5-4b7f-909b-c127365b167c",
    "_kg_hide-input": true,
    "_uuid": "8dd4ea07fd60973fccabc2d46af28a09b0de9178"
   },
   "outputs": [],
   "source": [
    "# Let's implement simple classifiers\n",
    "\n",
    "classifiers = {\n",
    "    \"LogisiticRegression\": LogisticRegression(),\n",
    "    \"KNearest\": KNeighborsClassifier(),\n",
    "    \"Support Vector Classifier\": SVC(),\n",
    "    \"DecisionTreeClassifier\": DecisionTreeClassifier()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "eb37c0f6-9cfe-48b6-92d3-475d5e6767a6",
    "_kg_hide-input": true,
    "_uuid": "fe129af379caccc5428cf1836e6c96bd32e68feb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifiers:  LogisticRegression Has a training score of 95.0 % accuracy score\n",
      "Classifiers:  KNeighborsClassifier Has a training score of 93.0 % accuracy score\n",
      "Classifiers:  SVC Has a training score of 92.0 % accuracy score\n",
      "Classifiers:  DecisionTreeClassifier Has a training score of 88.0 % accuracy score\n"
     ]
    }
   ],
   "source": [
    "# Wow our scores are getting even high scores even when applying cross validation.\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\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__, \"Has a training score of\", round(training_score.mean(), 2) * 100, \"% accuracy score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "a1c35773-f4c7-4caf-9911-532784c9eae0",
    "_kg_hide-input": true,
    "_uuid": "d15b1ab16737358806e34c48dc57aa238cf0cfd2"
   },
   "outputs": [],
   "source": [
    "# Use GridSearchCV to find the best parameters.\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "# Logistic Regression \n",
    "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "\n",
    "\n",
    "\n",
    "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n",
    "grid_log_reg.fit(X_train, y_train)\n",
    "# We automatically get the logistic regression with the best parameters.\n",
    "log_reg = grid_log_reg.best_estimator_\n",
    "\n",
    "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 best estimator\n",
    "knears_neighbors = grid_knears.best_estimator_\n",
    "\n",
    "# Support Vector Classifier\n",
    "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_\n",
    "\n",
    "# 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": "code",
   "execution_count": 27,
   "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:  94.05%\n",
      "Knears Neighbors Cross Validation Score 92.73%\n",
      "Support Vector Classifier Cross Validation Score 93.79%\n",
      "DecisionTree Classifier Cross Validation Score 91.41%\n"
     ]
    }
   ],
   "source": [
    "# Overfitting Case\n",
    "\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",
    "\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) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "38e430ef-0160-47a1-9b6f-11ff62c5ecc0",
    "_kg_hide-input": true,
    "_uuid": "eeb5736b279bb8fa3804689a175394f216ec4f72"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: [ 56959  56960  56961 ... 284804 284805 284806] Test: [    0     1     2 ... 57174 58268 58463]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 56959  56960  56961 ... 115109 116514 116648]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [113919 113920 113921 ... 170890 170891 170892]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [168136 168614 168817 ... 228955 229310 229751]\n",
      "Train: [     0      1      2 ... 228955 229310 229751] Test: [227842 227843 227844 ... 284804 284805 284806]\n",
      "NearMiss Label Distribution: Counter({0: 492, 1: 492})\n"
     ]
    }
   ],
   "source": [
    "# We will undersample during cross validating\n",
    "undersample_X = df.drop('Class', axis=1)\n",
    "undersample_y = df['Class']\n",
    "\n",
    "for train_index, test_index in sss.split(undersample_X, undersample_y):\n",
    "    print(\"Train:\", train_index, \"Test:\", test_index)\n",
    "    undersample_Xtrain, undersample_Xtest = undersample_X.iloc[train_index], undersample_X.iloc[test_index]\n",
    "    undersample_ytrain, undersample_ytest = undersample_y.iloc[train_index], undersample_y.iloc[test_index]\n",
    "    \n",
    "undersample_Xtrain = undersample_Xtrain.values\n",
    "undersample_Xtest = undersample_Xtest.values\n",
    "undersample_ytrain = undersample_ytrain.values\n",
    "undersample_ytest = undersample_ytest.values \n",
    "\n",
    "undersample_accuracy = []\n",
    "undersample_precision = []\n",
    "undersample_recall = []\n",
    "undersample_f1 = []\n",
    "undersample_auc = []\n",
    "\n",
    "# Implementing NearMiss Technique \n",
    "# Distribution of NearMiss (Just to see how it distributes the labels we won't use these variables)\n",
    "X_nearmiss, y_nearmiss = NearMiss().fit_sample(undersample_X.values, undersample_y.values)\n",
    "print('NearMiss Label Distribution: {}'.format(Counter(y_nearmiss)))\n",
    "# Cross Validating the right way\n",
    "\n",
    "for train, test in sss.split(undersample_Xtrain, undersample_ytrain):\n",
    "    undersample_pipeline = imbalanced_make_pipeline(NearMiss(sampling_strategy='majority'), log_reg) # SMOTE happens during Cross Validation not before..\n",
    "    undersample_model = undersample_pipeline.fit(undersample_Xtrain[train], undersample_ytrain[train])\n",
    "    undersample_prediction = undersample_model.predict(undersample_Xtrain[test])\n",
    "    \n",
    "    undersample_accuracy.append(undersample_pipeline.score(original_Xtrain[test], original_ytrain[test]))\n",
    "    undersample_precision.append(precision_score(original_ytrain[test], undersample_prediction))\n",
    "    undersample_recall.append(recall_score(original_ytrain[test], undersample_prediction))\n",
    "    undersample_f1.append(f1_score(original_ytrain[test], undersample_prediction))\n",
    "    undersample_auc.append(roc_auc_score(original_ytrain[test], undersample_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "bb72803c-3ea3-40cd-8ac3-399540ab7f5a",
    "_kg_hide-input": true,
    "_uuid": "a12fb2f7e104931bb78e1bd6cfc5a516c970708b"
   },
   "outputs": [],
   "source": [
    "# Let's Plot LogisticRegression Learning Curve\n",
    "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",
    "    # 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": 30,
   "metadata": {
    "_cell_guid": "5b8302aa-0207-455f-8c1a-78ff3e9b5141",
    "_kg_hide-input": true,
    "_uuid": "15b262baa0c61c288a5453031b4d7f80f5a7a5ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "780e485a-ea64-48a0-ad97-a7516b047f32",
    "_kg_hide-input": true,
    "_uuid": "fdd59bf2c7a8e61cfb401142570643e8a29cf86b"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "# Create a DataFrame with all the scores and the classifiers names.\n",
    "\n",
    "log_reg_pred = cross_val_predict(log_reg, X_train, y_train, cv=5,\n",
    "                             method=\"decision_function\")\n",
    "\n",
    "knears_pred = cross_val_predict(knears_neighbors, X_train, y_train, cv=5)\n",
    "\n",
    "svc_pred = cross_val_predict(svc, X_train, y_train, cv=5,\n",
    "                             method=\"decision_function\")\n",
    "\n",
    "tree_pred = cross_val_predict(tree_clf, X_train, y_train, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "57c211c6-e88f-4634-b321-4949df08815d",
    "_kg_hide-input": true,
    "_uuid": "cb2e4715e91e36f2029ef2a5c241991ff162cd9f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression:  0.9798658657817729\n",
      "KNears Neighbors:  0.9246195096680248\n",
      "Support Vector Classifier:  0.9746783159014857\n",
      "Decision Tree Classifier:  0.9173877431007686\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\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": "code",
   "execution_count": 33,
   "metadata": {
    "_cell_guid": "89b0b9b6-ef82-4b69-9517-e89a79696dbb",
    "_kg_hide-input": true,
    "_uuid": "9d57aad23f3f72f3c45bf80b089a65acbce2a9ab"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B778I1j6BYCDVKwTn5N7Uy9tcQlAx/TzB2cT/Iagy/wp4Luva7xO0+36N4O9cS3BG7hXOuVXhNcsIgvOJBIGyGVgFnOKcuy3Xv2t3nHPXmdnLwDkErcjlBKH1nwQ/EGjo4fa0eoKzmT9CsJe3mOA7uIfg79XTvmoRERnjrPsBiCIiImNX2C58DjDROddbFVFERERGMe3pFRERERERkTFrWEOvmd1iZnVhG1N3r5uZ/Y+ZvWZmL5rZvsO5PhERERERERlbhrvS+3OCcwG35Qhgt/DX6YTHJoiIiIiIiIjkYlhDr3PujwTDJrblWOA2F/grUGlmU4ZndSIiMl445851zpn284qIiIx9I21P7w4ERxWkrQmfExEREREREem3kXZkkXXzXLfjpc3sdIIWaEpKSvbbfffdh3JdIiIio0p9czubWjryvYxxIWGNJGnO9zK2kgpP6PCsu3+9GoT3t9bg/V3hkLy/iIxMBngph+8cXsrhOYefcniOzj97KTr/bI7OP6d/35uUOZpcivUdCWoKoxREPP4Vb13vnJuYy5pHWuhdQ3DwfdqOBOfsbcU5twRYAjB79my3YsWKoV+diIjICHfnM2/z4PPvsP6NesqBA6dV53tJo9pG/480+H/r8ZoWbzVQRXFqxvAsqh8mlBQwqbxgyN5/7vS5nDDjhCF7fxEZfKn2dlKb6kmu/w+pDe+S3FBHauM6kps2kGrYRDLeQKqxiWRTM8nmzaRa2khu7iDVmiTZlsIlevtBmsOLOfwCwyvw8YuieMUx/JIi/NJivNJS/PJyvPJK/MpqvKoJ+NUT8SZMwavejsef+weLrriCfz71FNtvvz033HQTRx99NGb2Vq5/55EWeh8CvmJmdwMHAg3OubV5XpOIiMio8eDz77BybZwDp1Vz7N47cOKBO+V7SaPaguVLaKpfy8zqmT1cNVvhT0SGhXMO19ZGsmETqfVrSa5fS6q+juSm9UGQbdhIqqGBZGMjqaaWMLS2ktzcQbI1SarN4ZK9fIg5/JjDixl+oYdXFKVgYhFecWEQWstK8cvK8Ssq8Sqr8Ssn4NVMwq/eLgiuVZOxguKc/n5HHHEEy5cvZ8cdd+T666/nC1/4AoWFA+8mGdbQa2Z3AXOACWa2BvguEAVwzt0EPALMBV4DWoAFw7k+EREZmdLVS+ndyrVxZk0p554vHZzvpQyrZauX8cjrjwz6+66qX8XM6pksPXzpoL+3iIw/zjlcSwvJeJzUhlqSG94lWV9LauP6sNK6MXitsZFkUzOpps0kW9pIbe4g2ZoIQmtv7cGew4+mMiqtESKTCvFLCvFKi/FLS/EyQ2vVBPzqSXg1k/EnTMEqt8NixTBEWyOyv4/f/e53fPzjH8fzPI455hg+9alPccopp1BQMHhdKsMaep1zn+vldQd8eZiWIyIig2yowukzbwSD/9Wq27tZU8o5du/RPQMylwC7ojbY5jR78uxBXcvM6pnMnT53UN9TREYvl0qRamkJqqkb1wfV1vo6UpuC0JrctJFUPE6yMU6qsZlkS9ge3NJBqjVBss1tY2JRF/MdXjSFXwB+2B4cq4jhFZdv2R5cEbQH+1UT8KomBqG1ZgpWMQkrKANvpM0s7pJKpXjggQdYuHAhL7zwAg8++CDHHHMMZ5555pB83khrbxYRkWE02CF1qMLpULfqDlWVMF8e2wiPLc/3KnKXS4CdPVktxiLSO5dMkmpqCtp/N9WTXP8uqfpakhvXkUqH1sY4yXhYaW1uIdmcUWlt70NojaTwow4/Bl6hh18YJTYlhl9ShldSjF9Wildejl9e0Vlp9aom4tdMxqvZHq98AhSUgz/2oloqleKXv/wlixYt4uWXX2bGjBncdtttzJ07tD9cHHvfpIjIGDRaKqgjYR/pSKoSSm4UYEVkW1wiEQTWxsZwX2vYHrxpPcmNYXtwQ0Nne3DmntbU5jC09sKLpoJKawy8Ao9oURS/OoZXXNoZWv3ysqDSWlGNV1WDXzURr2YKfs12WGkNFJZDZOiGyI1WyWSS8847j4KCAu68807mzZuH7/tD/rkKvSIiwyjX8DpaK6i5GGjVVVVCEZGRy7W3k2xsDPatxuNBYK2vCyqtG+tJbqonFW8g2dgUVGTTobUlqLS6jt5Cq8OLOvxYKngs9IgWRiisLsArLsEvLQr2tFaU45VX4FekK62Tgkpr9WSspBqKKiFSOCz7WseyRCLBHXfcwc9+9jMeffRRioqK+P3vf89OO+2EN4zt1wq9IiLDKD1Zd9aU8n7dNxLD6VB55PVHOocH5UIBVkRk6KTa2sI9q42kGuIkN67bchDTpo3BkTfxRpJNTaSaN5NsaQ3agzcncIneeoODIUydwbXAI1YcxauJ4ZeU45UU4ZeV4ZWV4VdUBEfeVIZH3lRPCiYHl1RDYQXEShVa86S9vZ1f/OIXXH755bz++uvstdderFmzht122433vOc9w74ehV4RGdeGeyrweJ2s2xfpCq+m5YqIDA3nHG7z5iCwpoPrpnqS9bUk69cHwTV95E1TY3BWa2eltZ1UaxKX7CW0picHx9LDmIxISRR/QhSvpDRjEFNZZ6U13R7s10zGKidhxVVBaC0oB2/oW19lcL377rscdNBBvPXWW8yePZurr76ao446CsvjDyAUekVkzOsp2A73VOB8TtYd6cOaMtuSNS1XRGRrzjlSzc3BftV4YzBwadOmoD24vo5UQzCIKai0xsNBTGGltaWdZGsCejnupnNycDSFFwuGMcVKI3iTYvglJcGe1tISvPIy/PJKvMqq4JzW6gn4VZOxyolYUUZojcSG58uRvGptbWXFihUccsghTJ48mblz53L00Udz+OGH5zXspllwStDoNnv2bLdixYp8L0NEcjTU1dbegu14aRtesHzBgNqGh4PakkVkLHOpVLBPNR1Y441d+1o3rie5aX1Xe3BjI8l0pbW5leTmdlKtiT4cd5NRZY25ILgWRvCKYvglBfglxXhlJfhl6T2tVWGlNTin1SufEATWwgoorIRo4fB8OTIqtbS0sGTJEhYvXsymTZv497//TU1NzZB8lpk955zLaeKkKr0i0qPhaP8d6mrrcO2HHemVVLUNi4gMTOfk4KamYBBTYyPJhngQWNNH3jRsJNWwKZww3ESyuYVkc7CnNdWa6PUzvEgKLxYceeNFU0RjDq8ygr99FK+4KAitpSX4nUfeVOFX1uBVT8KvnoSVVncF1sIKiJVoX6sMuubmZm666SauuuoqamtrmTNnDhdffDHV1cPTOddfCr0i48BAgutwtP+OlVA60o+9mVk9U23DIjKuufb2YLhSuJ81CK5NwQCm+nXhIKb6LduDm1o624NT7cnePiEYwJRuDY6miMZSFFb7QaW1uKDrnNaysiC0VlTiV4WDmKomhkOYKruqrQVl2tcqI85bb73FN7/5TT72sY9x0UUX8cEPfjDfS+qRQq/IOJDrxGAYW1ODBzoVuDeaGiwiMrS6Jgc3dbUHN8ZJNmwiWb8uqLjGw0FM8XhQkW1qCduDO3AdvYRWc50V1vSRN7FYCq/Gw98xildciF9aFAxkKi/DKy/Hr6wOK60T8SonhEOYKjOqreXgR4fnCxIZIg0NDVx77bWsWbOGm266iVmzZvHKK68wY8aMfC+tTxR6RUap/lRvR9LE4Hy2AKu9V0Qkf5xzuNbWLfezZj7WryO1aX04iGlT15E3GZVWl+htClPG+axhi3AklsKfCF5RFL+4MDzypiQ88qYSr6IqOPamaiJe5hCmzF/a1yrj1MaNG7nmmmu4+uqraWho4NhjjyWRSBCJREZN4AWFXpFBMdzH3kBX2/GMXV+mwf9bj9cW7wxNJQUsWL5kOJbWo3y2AKu9V0Qkd8Hk4JauoNrU2LWvNZ7e17qBVEM6tMZJNjaTbG7uHMREL8fdmOfC/awZ7cFRhz8xhV8UwysuwC8pCgYxlZYFrcGVVXiVNfhVE7DyCVhRRmtw+vfRYu1rFemnRx99lBNOOIHGxkY+9alPceGFF7Lvvvvme1k5UegV6UVfAu1Q73vd6P9xq2A7eSZMKCng7c0vASN3H2k2tQCLiOSHS6WC42622M+aefTNxs5Ka6phU1d7cHPYHtzS1r/JwWG1NVaQwisLJwgXF+KXFGUceRMOYqqoDtqDy2q2DqyFFRArA88bni9KZByrq6tjw4YN7LHHHuy9994cddRRfPvb32avvfbK99IGREcWybg2mIF2KPe99nbUjEKkiMjY55LJ8BibjAFMjfGgDbgxTmrTpmAQU0M9yYYGUvGGrvbg5lZSre29hlYv0tUa3HnkTfqxwAsHMRVlDGIqD468SVda05ODMwNrYWVwXquvWovISPXuu+9y1VVXcdNNN7HPPvvw5z//Od9L2oqOLBLJUV8GPI2UQU7aiyoiMrq5jo6uycEZ+1mTjWGAjTeEZ7XWB4OYGsNKbPNmUs2bSbV29PoZQYV1y8nBfqnDq07hxyxsDy7GKyvGLyvrOqe1shq/cgJWUpURWCu33NcaKRiGb0lEhtM777zD4sWLWbJkCe3t7Zx00kmcf/75+V7WoFPolREjPeCoLt7G+ua2YfnMFpegeOcIxb1MNX5sIzy2fFiW1K2hnDgsIiJ9k2pv3zKwZk8QjsdJbqwPKq2de1rTldbNuPZezmi1zONugvbgWCyFV+7wa8Ig21lpLcErLcUvrwz3tVZ3TQ4uygqr6fAaLdK+VhHZwn333cf111/P/PnzOf/889l1113zvaQhodArg2ogk3nTA44SzdMAKC8a+vH+xQURJpSM/J9cawCTiMjAdE4ObmzsGryUuZ81Hj7fsDE4p7VhU1B5bUzvad3ct+Nu0pODw2prQTSFV5XCnxS+VlSAX1KIV1qSUWmtxKusxquoCY+76SawFlZArFT7WkVkQN58802uvPJKDj74YE455RROO+00jjrqKKZPn57vpQ0phV4ZFOmwu63JvH2p3iY2TyMR35t9q44YEe3EIiIycjjncC0tWQOYwkFLmQG2YVPQHhzf1PV8WGkl2fNxN+a5rH2s4eTgmhTedmGQLYqEg5iK8crK8cvKOyutVlGz5eTg7Pbgwgrw/GH6xkREuvzrX//iiiuu4NZbb8XzPKZOnQpAUVHRmA+8oNAr/ZAe+tTdJOEWbzUAxakZVCQPoOWtD23x+j/6MgzKh2MPVdgVERmLtpgcnN7Xmg6u2ftaw0prKt4QtAc3h6E11ctxN/6WrcF+LEUsmsKb1FV59WMWtAd3VlrLg0prVQ1eWXU3gTV7X2tsmL4xEZHBcemll/K9732PaDTKWWedxbe+9S122GGHfC9rWCn0Sp/9/KW7WOuegujrQBBw09Jhtyr5oW7vHSnDoEREJDcumQyqqo2Nnfta00F1i4FM8fDom6xKa6plcx8mB3dVWL1oikjMEStI4Ze5jAFNltEeXBpUWyuDQUxeaVVGUM0Kq+kKbKRQ+1pFZMxbuXIl22+/PZWVley111589atf5dxzz2XKlCn5XlpeKPRKnzX4f8OLrGVfnbMqIjLqbDE5eKsBTBkBNh4n2bAxrLQG1dhUcwupltZeP8OLdoVWPxq2Bhel8Mq3bBn2iovCCcJl+BUVQXtwRVUwObi76mpR1r5WhVYRkW699NJLXHrppSxbtozvfe97XHzxxRx99NEcffTR+V5aXin0jgN9OYu2L1pcguKCqTo2R0QkD1Lt7d0PYOoczJQRYMPW4KA9uJFkUzOutb3Xz0gfc9O1nzVFYanDr+pqGfaiKfyiWFeltaICv7wCr6IaK+6hwpr+VVCufa0iIoPs+eefZ9GiRdx///2UlZVx3nnncdZZZ+V7WSOGQu8o1Z8g+0wf9tN2t083m2drmVA8NseYi4gMJeccrq2tM7CmGhuzBjJlTRCONwQDmdJ7Wpuace29nNFqdFVTo0m8mAv2s5al8Gu6BjT50RReoR9UWsvL8MuDQUxeWdXWk4O3GspUCYXl4A/9dH0REem7Cy64gL/85S9cfPHFfPWrX6W6uoc5OuOQQu8okw67fQmyaTN2fZlo+QsUl2/7aJ6V25i6vKX36tgcERmXnHO4zZu79rNmBtbGxq5BTPHGrmsaNmac5dqMS/RyRqsHfgEpDbctAAAgAElEQVT4kSReNIkf7mn1KzMHMYXV1hj4ZRl7WisqsbKqLScHF2VXXTN+Hy0cni9ORESGxDPPPMNll13G1VdfzfTp07nhhhuoqKigsrIy30sbkRR6R4nuwu6xe+9AtOqZXs/FXVm7AjbDpPJtB9rZ2qcrImOYS6VItbR07VHNbgduzJ4gHA9ag+MNYbBthlQvx9344BWAH03hRxJdk4OrHf524dmtsXTrMMEE4bLSYHpweSVWWokVVXXfErzVea0l2tcqIjIO/eUvf2HhwoX87ne/o6amhlWrVjF9+nR23nnnfC9tRFPoHSUefP4dVq6NbzUFecHy77KqfhUzq2du814FWhEZ7VwySaq5OasNOONxqwnCYYtw5yCm5t6Pu4laV/tvJIEfTRGLpfAnOrwdwsnBncHV4ZcU4JWWBvtZyyvxSrOrrNsIrJ37Wr1h+vZERGS0S6VSHHnkkSxfvpxJkyaxePFizjzzTEpLS/O9tFFBoXcEW7Z6WWcV981YnOKdoXhKOY9thMeWB9ekA6+GS4nISOYSia0rrN1UXDsDbDxOMh7vrLSmmlt6/QwvZkHbbxhao9FkEFAzqqxBa3AYYIui+KUZldaSyr4F1qKqILT6+keoiIgMHecczz33HLNnz8bzPPbbbz8+8YlPcPrpp1NcXJzv5Y0q+if2CPbz53/FmpbXKHRTaWlLUFyw9X9cM6tnap+tiAw5197eFVK3GMCUva+1a89r8BgObmrZ3OtneAUefoELAmokQTTSQWGhC467yWgNTk8Y9gs8vLJS/LJyvPKKYHLwNgNrVqAtKNe+VhERGZGcczz66KMsXLiQp59+mqeffpqDDjqISy+9NN9LG7UUekeAbU1ifj3ZBGzH7v65YHDsrK62ZhGR/ki1tWWF0a4q6xb7WeONJJsyBzMFFVjX1tbzBxhBCC1IV1qTxCLteMUp/MquycFdZ7i6ILyWluKVlYWhtSorrPZy9E20WPtaRURkzHDO8Zvf/IaFCxfy7LPPMnXqVG644Qb23nvvfC9t1FPoHQHS+3VnTSkHuo4Pitm77Fi8K/fMOzjPKxSRfNpicvA2BjClmrIDbGNni3CqqbH34258wy/wggFLsWAQU8TvwC9L4tVkTQ7ODLAlxfjlZVhZ5ZaTg7MDa3ehNVamfa0iIiKhpqYmPv/5z1NZWclPfvIT5s+fTywWy/eyxgSF3iHWl/N004H3ni8F4XbB8iU01a9l32odESQyFjjnSDW3bD2AqamXCcKdA5nikEz2+BkWsaA9OGadoTUW6cCrSuBPDve0ZrQGdwbYohh+RQVWUpYRWvsQWAsrta9VRERkAFKpFPfffz/33nsvd999N2VlZTz55JPMmjWLaFTnoQ8m/dvKEMuu4nZn1pRyjt17hy2e03AqkZHDpVKkmpoygmp82wOYtpogHCfV1NT7cTdRD7/Qw4tZ0P4bSRDzO/BqOvC327I1eIujbwp8vPIKvNKeBi9lV2DTfy6HyLbP7xYREZHBl0wmWbZsGYsWLWLlypXMnDmTd955h6lTp7LXXnvle3ljkkLvEElXeLOruJkTmTN1N5FZRAaHSySC0NqfAUxNTV1/bmoC1/NxN16BH1ZawYs5opEEXqwDf3Ib3o5ui7C6RcW1APyycqy4p8DaQ9twtEj7WkVEREaJN954gyOOOIJVq1Yxa9Ys7rrrLk444QR838/30sY0hd5BlNnK/Mwb9QDM2PVlmspfYMHyJQCsqF0BBGfnbosmMotsybW3bxlCex3AtGWATbX0ctyNgVcQCSut4Ieh1S/qwCtty2gHzgirmQG2tCwMreW9twRntw0XlCm0ioiIjGEdHR2sXr2a9773vUydOpXdd9+dSy+9lOOOOw5Psy2GhULvIMqs7M7Y9WWi5S/w9uaXYDNMKg9C7uzJs5k7fS4nzDghz6sVGT6ptrbu24G7HcC0dYB1m3s57sYMvyiCV9AVWmPRJF5ZO35FG1400dUenHFOa/o5r7goawhTd4F1G1XYgnLw9NNZERER2VJ7ezu33XYbl19+Oc3NzbzxxhsUFxfzwAMP5Htp445C7yBLtzIvWL6EVfVvKuTKqOecw7W29jiAadsBNqi0uvb2nj/EM/yiKF5hMIjJj6WIRJL4VR14NW34kfasI2/SATaovHoF0T6E1qwW4czzWiOajCgiIiKDo62tjVtuuYUrr7ySt99+m/33359rr72WoqKifC9t3FLoHQTpfbpvxuIALFhe3rkvV8OoJN86Jwc3dbOftYcA2zVBuBE6ej7uxiIeXlG0axBTNEU0msSfEO5p9du2aAnunBwcPlrEw4rSgbS8h8BauY3zWvUPERERERkZnnrqKc466ywOOuggbr75Zj7xiU9g2sqUVwq9g+CR1x9hVf0qYErnc9qXK4PFpVKkmps796impwEn41ntwI0ZE4SzBjL1etxNLIJfGMmotDpihUm8kg78HdrwvFb8aHLrycFhcPV8oCBdYe2mqtpbBTZWon2tIiIiMiq1tLRw880309zczIUXXsicOXN46qmnOOiggxR2RwiF3kFQF28j2TaFlrdOZ9aUcpYefnC+lyQjiEsmg6pqehBTPwYwJZuaSDU29mFycBSvKIJf6OPFIBJzxEqS+BUdQZXV24wXyQitmUffRFOYD0RLeh+8tK19rgVl2tcqIiIi40pTUxM33HADP/zhD6mrq+PII4/EOYeZcfDBygMjiULvAKSnNb+ebALg/d2ctyujn+vo2CKwbvM81m0E2FRzc6+f4RXF8AqD0OoXGNGYw69M4k3owPeTeNaKH2nLqLJuWW01D4gUbuMs1t4GM1UGLcW+DkEXERER6YuHH36YBQsWsGHDBg477DAuuugiDjnkkHwvS7ZBoXcA0tOay3eOMqGkgHvm6Sc6I1Gqvb0rsPZ5AFM6wDbhej3uxvCKY/iF4SCmAiMWA68mgT85geeD77XieZuDduDMKmsshRcJQ6sX6flom6Ksfa1bnddaOCzfp4iIiMh4tGnTJpqamthxxx3ZddddOfDAA7nooos46KCD8r006YVCbw4yB1cV7wx+wVomlc/M97LGJOccrq2tXwOYUo1BlTUdXF1bW88f4nv4xQWdlVavwCiIOrztkviRFL7vBYGVzXix5BZH3ngxF4RWA7CMc1oroXDC1pOCe2objhZrX6uIiIjICFNfX88111zDNddcw8c//nGWLVvGHnvswW9+85t8L036SKE3B9mDqzS0atucc7iWlq0HMGUH2MZu9rOGz/U+OdjHKy7oOvKmwCNSnMIvT+Ht7OP7ETzbjG8t+NFE1+Tg8Ogb811X1oyVZgTSmj4cfZPxeqwMdMC4iIiIyJiwfv16fvzjH3PttdfS2NjI8ccfzwUXXJDvZUkOFHpzMJ4GV7lUilRLy9ZhNJ4+zqabCcLpAUzhtb1PDo7iF8eCI28Kgj2tsXKHV+PwIwVBe7C14lkLfqS9s8IaPKa2nJ8UKerHWa2Z11QG57X6+p+EiIiIiMAPf/hDvv/97zNv3jwuuOAC3ve+9+V7SZIj/Rt+DtY3t9HSlmDWKBhc5ZLJoLraGVibejiPNb71BOE+TA62whh+Z6XVJ1JgxKod/iQfL1KC77cHR95Yc3j0TaqzyupFs0KrF8k6i3VyD4G1m7bhSMHQfqEiIiIiMiatXbuWq666isMPP5zDDjuMc845h/nz57PHHnvke2kyQAq9/ZCe1tziEhQXRLhnwdBXeF0i0Rk+exrAtNUk4XCCcKqpqdfP8IoLu9qDCzyiBR7+JIe3QwF+xMPz24NBTNaCT9MWVdbOycFp5gUV084wOmnbE4W7q8JGi7SvVURERESGzZo1a1i8eDFLliwhkUgwceJEDjvsMCZMmMCECRPyvTwZBAq9/ZCe1ly8c4QJJYNXUaz/xe1sfuGFrn2tnQG2sY+TgwuDQUxFUfxCn2ihF5xAs1MxXiS2ZaWV5mBfa/bk4EyxsoxAWtXH81rT+1pLta9VREREREaFSy65hMsvv5xUKsX8+fM577zz2HXXXfO9LBlkCr3bkK7qZlq5Ns6sKeUUTykf2Jsn2qG1AVobcM311F5xOX5pEdGaMrzCCLFiw6uM4Ecr8CLF+H47vt+KR1hpjbR3ThDumhycIVqcFUh36Ps+V+1rFREREZEx7I033mCHHXYgFosxefJkTj31VL7zne8wbdq0fC9NhojSzTakq7qzMgJueg/vYxvDJxJtUPdPaN3UGWJpbYDNWX/Ofr2jq3qbaPEgtR0TZ/yHql3D571o1pmsE3sJrJnPlWtfq4iIiIhIlldffZXLL7+cX/ziF9x0003813/9F2eccUa+lyXDQKE3S7rCmw6893ypa99ucD7vElbVr2Jm9Uz49dfh+Tu2fhPztq6gTpicFViD3yf+3QAP/YjI8Yvhox8LrokUal+riIiIiMggWLVqFZdddhl33HEHsViMr3zlKxxxxBH5XpYMI4XeUDrsPvNGPQAHTqvm2L13CIPuIwCsqF0BwOzJs4NzeZ++DSbuAUf+MOu81tI+h9ZE3eMARGbsC+VThuBvJiIiIiIyPjnnOPHEE3nllVf4+te/zrnnnst2222X72XJMFPoDaWru+mwe+KBOwGwYPl3Oyu76bB7wowTgpueuA4qp8J7PpDz53bU1gIQnTRpwH8HEREREZHx7sUXX+QHP/gB11xzDVVVVSxdupTtttuOSfr37XFLoTdDdjtz2szqmSw9fOnWN7TFoWZg090SdesgEsGvqRnQ+4iIiIiIjGd///vfWbRoEQ888ADl5eWceuqpfOQjH+H9739/vpcmeaazZQaiNR4MjhqARG0tkYkTMR3zIyIiIiLSb21tbRx99NHst99+PPHEE3z3u9/lzTff5CMf+Ui+lyYjhCq9A9EWD474GYBEXS2RSRMHaUEiIiIiIuPD66+/zvTp0ykoKKCqqopFixZx9tlnU1FRke+lyQij0LsN6QFWnZOas3W0QrJ9wJXejto6CqZPH9B7iIiIiIiMF3/84x9ZtGgRTzzxBKtWrWKXXXbhtttuy/eyZART6M2SDrtbTWrO1hYPHgdc6a2j5OCt9xGLiIiIiEjAOceTTz7JJZdcwh/+8AcmTZrElVdeyeTJk/O9NBkFFHqzpKu7W01qztYaht7C3NsnUi0tpBobiWiSnIiIiIjINtXW1vKJT3yCCRMmcPXVV3PaaadRXFyc72XJKKHQ241tTmvO1NYQPA6g0tt5XNFkhV4RERERkTTnHMuXL+fxxx/nhz/8Idtttx2PPvooBx98MIWFhflenowyGhmcq3Slt6As57dI1K0DIKK2DBERERERnHM8/PDDHHDAAcydO5f77ruPDRs2AHDooYcq8EpOFHpz1dYYPA5gkFWiLqj0qr1ZRERERMa7V155hX333ZdjjjmG+vp6fvazn/Hqq69SU1OT76XJKDcu25vvfOZtHnz+nS2eW7k2zqwp/QiwgzDIKlGbDr2q9IqIiIjI+JNMJvnPf/7D1KlT2X777SksLOTWW2/lxBNPJBIZl1FFhsC4rPQ++Pw7rFwb3+K5WVPKOXbvHfr+Jp2DrAawp7euDq+kBL+0JOf3EBEREREZbZLJJHfeeSfve9/7OPzww0mlUpSXl/P0008zf/58BV4ZVOP2v02zppRzz5e2PiroseV9fINBqfTWqbVZRERERMaNRCLBnXfeyWWXXcbq1avZc889ufDCC/O9LBnjxm3oHbDWOMRKwfNzfotEba2GWImIiIjIuHH//fdzyimnsNdee3HffffxyU9+Es8bl82nMowUenPV1jCgKi9Aoq6O4v1nD9KCRERERERGlvb2dm699VYikQgLFizguOOO4ze/+Q1HHHEEZpbv5ck4oR+rhJatXsaC5QtYVb+qbze0xge0n9elUnSsW6f2ZhEREREZc1pbW7nhhhvYddddOf300/nVr34FQCQSYe7cuQq8MqwUekOPvP4Iq+pXMbN6JnOnz+39hrb4gCq9yY0boaNDk5tFREREZEx54IEH2GWXXfjyl7/MTjvtxKOPPsqDDz6Y72XJOKb25gwzq2ey9PClfbu4NQ7F1Tl/VqKuDoDIZFV6RURERGR0a25upr29naqqKioqKthtt924/fbbmTNnjqq6kneq9OZqgJXejvCM3qjam0VERERklGpsbOT73/8+73nPe7jkkksAOPTQQ3nyySc59NBDFXhlRFClN1cD3NObqE1XetXeLCIiIiKjS0NDA9dddx0/+tGPqK+v5/DDD+czn/lMvpcl0i2F3ly1xaGgLOfbE3V1YEZkwoRBXJSIiIiIyNA755xz+NnPfsZRRx3FRRddxAEHHJDvJYlsk9qbc5Foh0QrFFTk/hZ1tfg1NVg0OogLExEREREZfBs2bOCiiy7ihRdeAOD8889nxYoVPPzwwwq8MuKp0puLtsbgcQDtzR21tdrPKyIiIiIj2rp16/jRj37EddddR1NTE1VVVey1115Mnz4930sT6TOF3ly0NQSPAxhklahbR3TKlEFakIiIiIjI4LrkkktYvHgxmzdv5jOf+QwXXHABe+65Z76XJdJvam/ORWs8eBzQIKtaIqr0ioiIiMgIUltbi3MOgPb2do477jhWrlzJXXfdpcAro5ZCby7awtCbY6U31d5OcuNGndErIiIiIiPCmjVrOPvss9l555357W9/C8Cll17KL37xC3bfffc8r05kYNTenIsBVnoTdesAiOq4IhERERHJo7feeosrr7ySW265hVQqxamnnsqsWbMAdMaujBnDHnrN7HDgGsAHfuqcuzLr9Z2AW4HK8JrvOOceGe519miAld5EXS0AkUkKvSIiIiKSH6lUikMPPZQ1a9bwxS9+ke985zvsvPPO+V6WyKAb1vZmM/OB64EjgFnA58xsVtZlFwL3Ouf2AT4L3DBYn3/nM2/zmZufZuXa+MDeqLPSm9uRRYnadOhVe7OIiIiIDJ/Vq1fzta99jfb2djzPY+nSpfzrX//ixhtvVOCVMWu49/QeALzmnHvdOdcO3A0cm3WNA9Il1ArgP4P14Q8+/w4r18aZNaWcY/feIfc36qz0luV0e0cYeqPa0ysiIiIiw+Cf//wnJ598MnvssQdLlizhueeeA+DDH/4wU6dOzfPqRIbWcLc37wD8O+PPa4ADs675HvA7MzsbKAE+NpgLmDWlnHu+dPDA3qS1AaLF4Edzuj1Rtw4rKMCryK1SLCIiIiLSF42NjZx22mnce++9FBUVcc4553DOOecwWbNlZBwZ7kpvd7vhXdafPwf83Dm3IzAX+IWZbbVOMzvdzFaY2Yp169YNwVJ70BYf2Bm94XFFGg4gIiIiIkNh/fr1AJSWlrJ27Vq+853v8Oabb7J48WIFXhl3hrvSuwbI7J/Yka3bl78IHA7gnHvazAqBCUBd5kXOuSXAEoDZs2dnB+eh1RrPubUZwtCr1mYRERERGWTPPfccixYt4oknnuDNN9+kqqqKJ598UsUWGdeGu9L7LLCbmU0zsxjBoKqHsq55G/gogJntARQCw1zK7UVbY87HFQF0rKsjqsnNIiIiIjJInnnmGY466ihmz57NH/7wB8455xwikaC+pcAr492wVnqdcwkz+wrwKMFxRLc45/5hZguBFc65h4BzgJ+Y2dcJWp9Pdc4NbyW3NwNob3bOkaitI3KoKr0iIiIiMnCvvvoqBx10EDU1NVx++eV8+ctfprw89wKNyFgz7Of0hmfuPpL13MUZv18JfGC419UvrXEo3z6nW1PxOK61lYj2UoiIiIhIjv7whz+wYsUKzjnnHHbbbTfuvfdejjjiCEpLS/O9NJERZ7jbm8eGAVR6E3XB1mQdVyQiIiIi/eGc43//93/58Ic/zJw5c/jxj39MS0sLACeccIICr8g2KPTmojUOhbkdN9RRG4TeyCSFXhERERHpm5dffplDDjmEj370o7z22mtcc801vPrqqxQXF+d7aSIj3rC3N496yQR0NOde6a2tBVB7s4iIiIj0yDnHpk2bqKqqoqKigtraWq6//nq+8IUvUFhYmO/liYwa4z70Llu9jEdef4RV9auYWT2z9xva4sFjjtObE+tU6RURERGRbXPO8dBDD7Fw4UKqqqp4/PHHmTp1KqtXr8bz1Kgp0l/j/n81mYF37vS5vd+QDr05Vno7amvxKyrwCgpyul9ERERExqZUKsV9993HPvvswyc/+UkaGho46aSTSB9kosArkptxX+kFmFk9k6WHL+3bxa0DrPTW1qm1WURERES2cvPNN3PWWWcxY8YMbrvtNj73uc91nrUrIrnT/4r6a4CV3kSdQq+IiIiIQCKR4J577qGqqoq5c+dy8sknU1lZybx58/B9P9/LExkz1CPRXwOu9NYSmTRxEBckIiIiIqNJR0cHP//5z9ljjz04+eSTWbo06DgsKyvjc5/7nAKvyCBT6O2vAVR6XSJBYsMGoqr0ioiIiIxL999/PzNnzmTBggWUlpZy//33c8899+R7WSJjmtqb+6utMXjMIfQmNmyAVIrIJIVeERERkfGira0N5xyFhYW0tLRQU1PDNddcw1FHHYWZ5Xt5ImOeKr391doQPObQ3tx5Rq+OKxIREREZ81pbW7nuuuvYZZdduPbaawE48cQT+dvf/sbRRx+twCsyTBR6+6stDn4BRPp/5FBHOvROVugVERERGataWlq4+uqrmT59OmeffTbTpk1j//33B4JjhxR2RYaX2pv7qzWe+xCrujoA7ekVERERGcPmz5/Pfffdx5w5c7jjjjuYM2eOgq5IHqnS219t8dyPK6qtg0gEv7p6kBclIiIiIvnS2NjIlVdeyTvvvAPA+eefzx//+EeeeOIJDj30UAVekTxTpbe/BlLpra0lMnEi5ulnDSIiIiKjXUNDA9deey0//vGPqa+vp7KykjPOOIN9990330sTkQxKX/01kErvujqiGmIlIiIiMqo551i0aBE777wzF110EYcccgjPPvssZ5xxRr6XJiLdUOjtrwFUejtq6zS5WURERGSUampqAsDMePXVV/noRz/K3//+dx588EFmz56d59WJyLYo9PZXWxwKKnK6NVFbS0RDrERERERGlbq6Or797W8zZcoUnn/+eQCWLl3Kfffdxz777JPn1YlIb7Snt79yrPSmmptJNTXpuCIRERGRUeLdd9/lqquu4sYbb6S1tZXPfvazlJWVAeD7fp5XJyJ9pdDbH6kktDfmtKe3Q8cViYiIiIwabW1tvP/972fDhg2cfPLJnH/++cycOTPfyxKRHKi9uT/aGoPHgrJ+35qoDUKv9vSKiIiIjEz//ve/ueKKK3DOUVBQwI033siqVau49dZbFXhFRrFxWeldtnoZj7z+CACr6lcxs7qP/yeWDr05tDcn6moBiExSpVdERERkJHnzzTe58sorueWWWwA4+uij2XPPPTn++OPzvDIRGQzjstL7yOuPsKp+FQAzq2cyd/rcvt3YFg8ec2hvTtSp0isiIiIyktTX1/PFL36R3XbbjaVLl3Laaafx2muvseeee+Z7aSIyiMZlpReCsLv08KX9u6k1DL05VHo7auvwSkrwS0v6fa+IiIiIDJ6WlhaKi4spKSnhT3/6E2eddRbf+ta32GGHHfK9NBEZAuM29Oaks9Lb/yOLdFyRiIiISH6tXLmSSy+9lKeffppXXnmFgoIC/vGPfxCNRvO9NBEZQuOyvTlnA6j0JurqdFyRiIiISB68+OKLzJs3jz333JOHHnqIefPm0d7eDqDAKzIOqNLbH20NwWNORxbVUrL//oO8IBERERHpybPPPssBBxxAWVkZ559/Pl/72teYMGFCvpclIsNIobc/cqz0ulSKRN06TW4WERERGQbPPvssq1ev5qSTTmL27Nlce+21nHjiiVRXV+d7aSKSB2pv7o+2OHhRiBT267bkxo2QSGhPr4iIiMgQ+utf/8rcuXM54IADuOCCC0gkEpgZX/nKVxR4RcYxhd7+aI0HVV6zft2WqE2f0TtxKFYlIiIiMq699NJLHHbYYRx88MH87W9/44orruDFF18kElFTo4iovbl/2uK57ecNQ29UlV4RERGRQeGco62tjcLCQjo6OnjxxRe56qqrOOOMMygtLc338kRkBFHo7Y90pbefEnXrANTeLCIiIjJAzjl+//vfs3DhQqZNm8att97Kvvvuy9tvv00sFsv38kRkBBpX7c0b/T/yZuwHrKpfldsb5FjpTdTWghmRmprcPldERERknHPOsXz5cj7wgQ/w8Y9/nNdff52DDjqo83UFXhHZlnEVehv8v9Fq/2Zm9UzmTp/b/zdoa8z5uCJ/Qg2mc+BEREREcrJ48WKOOOII3nnnHW688Ub+9a9/ceaZZ+Z7WSIyCoy79uZCN5Wlhy/N7eac25vriOq4IhEREZE+S6VSPPjgg+y4447sv//+nHTSSVRXV3PKKaeoqisi/TKuKr0D1taQY3tzHZFJk4ZgQSIiIiJjSyqVYtmyZey9994cd9xxXH/99QDsuOOOnHbaaQq8ItJvCr195VzQ3pxLpbe2lshkhV4RERGRnjz44IO8733vY968eXR0dHD77bfz05/+NN/LEpFRbty1N+esvQlcqt+V3lR7O8lNm3RckYiIiEg3EokEZobv+/zzn//EzLj77rv59Kc/je/7+V6eiIwBqvT2VWs8eOxnpTdRVweg9mYRERGRDB0dHSxdupTdd9+de+65B4BvfOMbvPjii3zmM59R4BWRQaPQ21dtYejtZ6U3UVsLQESDrERERERob2/nJz/5CTNmzOALX/gCFRUVTA474mKxGJ6nfz0VkcGl9ua+GmilV3t6RURERDjqqKN47LHHOOCAA7juuuuYO3cuZpbvZYnIGKYfpfVVZ6W3ol+3dYSV3qjam0VERGQc2rx5M9dffz2NjY0AnHvuuSxfvpy//vWvHHnkkQq8IiESn5kAACAASURBVDLkVOntq9aG4LG/ld7aOqygAK+if2FZREREZDRraWnh5ptvZvHixbz77ruUlZUxf/58DjvssHwvTUTGGVV6+yrXPb11dUQmT9ZPMUVERGRcSKVSXHXVVUybNo1vfOMbzJo1iyeffJL58+fne2kiMk6p0ttXbUFLDgVl/botUVtLZNLEIViQiIiIyMjR0dFBNBrF8zx++9vfsvfee3PRRRdxyCGH5HtpIjLOqdLbV61xMB9iJf26raOujqgmN4uIiMgYtWnTJhYuXMhOO+3EO++8A8Cvf/1rHn30UQVeERkRVOntq7Z4UOXtR5uyc66zvVlERERkLKmvr+fqq6/mmmuuIR6Pc8wxx9Da2gpAcXFxnlcnItJFobevWuP9HmKVisdxra06rkhERETGlHg8zi677MKmTZs4/vjjufDCC9l7773zvSwRkW6pvbmv2uI6rkhERETGrdraWpYuXQpAeXk5l19+OS+99BK//OUvFXhFZERT6O2rHCq9ido6ALU3i4iIyKi1du1avvGNbzBt2jROO+003n77bQDOPPNM9txzzzyvTkSkdwq9fdXWkNNxRaDQKyIiIqPP+vXr+e///m+mTZvG//zP/zBv3jxWrlzJTjvtlO+liYj0i/b09lVrHCb1N/QG7c2RiTqySEREREaHRCJBJBLBzLj99ts5+eSTOe+889hll13yvTQRkZwo9PZVW7zfld6O2lr8ykq8goIhWpSIiIjI4Hj99de54oorePnll3nqqaeoqanh7bffprS0NN9LExEZELU394Vzue3prVun1mYREREZ0V599VUWLFjAjBkzuO2229hvv/06jx5S4BWRsaBflV4zmwl8EKgBfu6cqzWzqcAG51zLUCxwROhoAZfs/57e2loimtwsIiIiI9Tvf/97Dvv/7N13WNXl/8fx580Qdy7cJbgTERK3pubKSWmOtEytLL+WlTkbmqnt4bfS5jdHprnKkZpZlmlq7m2au6Ge40BxgRy4f38c5GcmcDDgAL4e13WuMz6f8zkvqEt4875Hq1bkypWLxx9/nCFDhlCmTBlvxxIRSVceFb3GGH9gItADMIAFvgMcwHhgJ/BsBmX0vpho930aO71xTgcBt1bNgEAiIiIi12fnzp0cOXKEli1b0qhRI0aMGEG/fv0oWbKkt6OJiGQIT4c3jwEigb5AOdyF72WLgTvTOVfWEnvWfZ+GTq91uYg/cRL/4hreLCIiIt63bds2unTpQvXq1Xnqqaew1hIQEMCoUaNU8IpIjuZp0XsfMMJaOxE4ctWxA0BwuqbKamITO71pKHpdJ06AtRreLCIiIl61Y8cOOnbsSFhYGEuXLmXEiBGsWLECY0zqbxYRyQE8ndMbCOxI4XjudMiSYaav/Z35W/7ignWRN+A6FqyOOeO+T8PwZpcjcbuiEip6RUREJPMlJCTg4+PDoUOHWL58OS+++CJPPPEEhQoV8nY0EZFM5Wmn9zBQO5ljtYC96RMnY8zf8he7jkaTN8CPYvmuY/ug6+j0xjmdAPhr9WYRERHJRKtXr6ZNmzY8//zzALRr147Dhw8zcuRIFbwickPytOj9HHjOGHMP4Jv4mjXG1AeeBiZnQLZ0Va1UQaqVKkjxgtdR9F7HQlYuh7vo1fBmERERyQwrVqygRYsWNGzYkA0bNlCqVCkAjDEULJi2xThFRHIST8f6vgLUBGYD5xJf+xEoAMwF/pv+0bKQ65nT63CAvz++RYpkUCgRERERt5EjRzJmzBhKlCjBm2++Sb9+/ciXL5+3Y4mIZAkeFb3WWhfQ0RjTEvdKzcWBk8ASa+23GZgva4iJBgzk8nyDdpfTiV9gMYyPp810EREREc9Ya/nuu+8oX748FStW5J577qFo0aL07duXvHnzejueiEiW4uk+vcWBk9ba73Dvz3vlMR+gmLXWmQH5sobYaHeXNw0FbJzTgX+ghjaLiIhI+rHW8s033zB69GjWrl3LgAEDePfddwkLCyMsLMzb8UREsiRPq7ijQEQyx25LPJ5zxUSnaT4vuOf0+mkRKxEREUknixcvpnbt2rRr145jx47x0Ucf8cYbb3g7lohIlufpnN6UNnLzAxLSIUvWdbnTmwYup5N8DRtmUCARERG5EVzedghgyZIlREVF8emnn9KzZ0/8/f29nE5EJHtIttNrjMlvjCltjCmd+FKxy8+vuFUAegCOTEnrLTFn0tTpTTh/noRz5/ArHpiBoURERCSnio+PZ+bMmYSFhfHTTz8B8NJLL7Fnzx4efPBBFbwiImmQ0vDmQcAfiTcLfH3F88u334ABwKSMjellaez0xjm0R6+IiIikncvlYtq0aVSvXp17772X+Ph4XC4XAAUKFMDPz9NBeiIicllK/3IuBI7hHtr8PvA6cPCqc2KBXdbadRkTL4uIPQtFK3l8ust5eY9eFb0iIiLiGWstTZs2ZdWqVVSvXp2ZM2dyzz334Ovr6+1oIiLZWrJFr7V2I7ARwBhjgS+ttScyK1iWksaFrFxO92hvv+JavVlERESSd+nSJWbPnk23bt3w8/Pj0Ucf5emnn+buu+9OmssrIiL/jqf79H6U0UGytDQPb3YXvf4lVPSKiIjIP8XGxjJ58mReeeUVDh8+TIECBYiMjKRnz57ejiYikuN4PDHEGFMZ6ANUAXJfddhaa9ulZ7AsIy4G4i+lsdN7HJ/8+fHJly8Dg4mIiEh2ExcXx8cff8yrr77Kn3/+Sd26dXn//fdp06aNt6OJiORYHhW9xpgIYCXuVZpvAfYARYDiwBHg94wK6HWx0e77NHR6XQ6H9ugVERGRJNZajDH4+Pgwfvx4ypUrx8SJE2nRogXGpLQzpIiI/FueThZ5FVgEVMK9sNX91tqSQPvEawzLmHhZQExi0Zv7Jo/f4nI4tF2RiIiIcP78ed566y1CQ0M5e/Ysvr6+rFy5kpUrV9KyZUsVvCIimcDTojcMmAwkJD73BbDWLgZexr2yc84Ue8Z9n5Y5vU4n/lq5WURE5IZ19uxZXnvtNYKCghg8eDClSpXi5MmTABQrVkzFrohIJvJ0Tm8AcNZam2CMOQVcWdHtAmqke7KsIqnT61nRaxMScB0/ruHNIiIiN6ijR49SvXp1Tp06RevWrRkxYgQNGjTwdiwRkRuWp53eA0DpxMc7gd5XHLsfcKZjpqwljXN640+dApdL2xWJiIjcQKKioli0aBEApUqVon///qxdu5ZvvvlGBa+IiJd52un9BmgJzABeAb5O7Pi6gKLA4IyJlwWksdN7ebsiP21XJCIikuOdPHmScePG8d577xEbG8uRI0coUqQIY8aM8XY0ERFJ5FGn11r7rLX2ocTHS4DbgYnAHKCjtXacpx9ojGltjNljjNlnjBmezDldjTG7jDE7jTHTPb12hkhjp9fldDe9/TW8WUREJMc6efIkw4cPJygoiJdffplWrVqxdu1aihQp4u1oIiJyFY/36b2StfYX4Je0vs8Y4wtMwN01/hNYb4xZYK3ddcU5lYBngIbW2ihjjHdbppc7vQEFPDrd5XAXvRreLCIikvNc3nooKiqKt99+m86dO/Pcc88REhLi7WgiIpKM6yp6r2SMqQaMsNZ29+D0OsA+a+2BxPfOAO7CvRjWZX2BCdbaKABrrXfnC8eehVz5wcfXo9NdTgf4+OBXrFgGBxMREZHMcuTIEd544w2OHDnCzJkzqVixIn/88QclNLJLRCTLS3F4s3GrYYxpb4y59apjocaY2cA23Pv1eqIM8McVz/9MfO1KlYHKxphVxphfjDGtPbx2sqJ8V3Ao15vsObUn7W+OPZPm7Yr8ihbF+P3rvyeIiIiIl/3xxx88/vjjlC9fnvfee498+fLhcrkAVPCKiGQTyVZmxpiSwFdA3Ste+xx4CPgv0A+IA94HXvLw8661KZ29RqZKQFOgLLDSGFPdWnv6qnyPAI8A3HLLLSl+6BnfdcSYP6hZJIS25dt6GDVRTLTHi1iBe3izhjaLiIhkfwsWLKBz584A9O7dm+HDh1O+fHkvpxIRkbRKqR35KhCOu6DdBAQDQ4GfgPrATGCItfbPNHzen8DNVzwvCxy5xjm/WGvjgIPGmD24i+D1V55krf0Y+BigVq1aVxfO/5Db3syk1pPSEDVRbHSaOr0uhwP/smXT/jkiIiLidfv37+f06dNERETQqFEj+vXrx6BBgyhXrpy3o4mIyHVKaXhzS2C0tXaktXZe4grND+AueD+01nZPY8EL7sK1kjEm2BiTC7gXWHDVOfOAOwCMMcVwD3c+kMbPST9p7fQ6ndquSEREJJv57bff6NWrF1WqVOGJJ54AoEiRIrz77rsqeEVEsrmUit7iwKqrXrv8/Ivr+TBrrQt4HPgW+BWYZa3daYwZbYyJTDztW+CkMWYX8CPubvLJ6/m8dJGGTm9CbCzxp0/jr+HNIiIi2cLu3bu5//77ufXWW5k9ezZPPPEEc+bM8XYsERFJRykNb/YFYq967fLz89f7gdbaxcDiq14becVjCzydePO+NHR6L+/R61dcC1uIiIhkZZe3HlqxYgVz585l0KBBDBo0SItTiYjkQKktMdzKGFPxiuc+uBeeam2MqXrlidba6ekdLktIQ6c3qejVD0wREZEsacuWLYwZM4bmzZvTv39/evfuTceOHQkMDPR2NBERySCpFb2jk3l97FXPLZDzil7XJXDFeN7pdTgA8CuuH5wiIiJZyYYNGxgzZgwLFiygYMGCNGnSBIBcuXKp4BURyeFSKnpvTeHYjSE22n0fcJNHp8c53J1ef3V6RUREsoxhw4bx+uuvU7hwYUaPHs2AAQMoVKiQt2OJiEgmSbbotdbuycwgWVLMGfd9Gub0mty58Sno+WrPIiIikv5WrVpF5cqVCQwMpEWLFhQqVIjHHnuMgvoZLSJyw0lp9WZJ6vR6PrzZr3hxjDEZGEpERESS89NPP9G8eXMaNWrE+PHjAWjZsiXPPPOMCl4RkRuUit6UxJ513wcU8Oj0OKdD2xWJiIh4wbJly2jSpAlNmzZl165dvP322wwbNszbsUREJAtIbSGrG1tMYqfX4+HNx8kTGpqBgURERORa3n//ffbt28e7777Lww8/TJ48ebwdSUREsgh1elOShuHN1tqk4c0iIiKScay1LFy4kPr167N7927AXfTu37+fAQMGqOAVEZG/UdGbkqROb+qrNyecOYONjcWvhIpeERGRjJCQkMC8efOIiIigQ4cOHDt2jKNHjwJQokQJcufO7eWEIiKSFaV5eLMxpiJQFNhurb2Q/pGykKROb+pzeuOc2q5IREQkoyQkJNCgQQPWrl1LhQoVmDhxIvfffz/+/v7ejiYiIlmcx51eY8xDxpg/gT3AaqBq4utzjDH9Miifd8WcAf+84Jv6D1RX4h69Gt4sIiKSPuLj4/n222+x1uLj48M999zDZ599xu7du+nTp48KXhER8YhHRa8xpjfwMfAD0Au4ck+etUC3dE+WFcRGe75dkdMBgJ86vSIiIv+Ky+Xi888/JyQkhNatW7N69WoAhgwZQs+ePfHz0zqcIiLiOU87vUOAd6y1DwBfXHXsVxK7vjlOTHQaVm5Wp1dEROTfiIuLY/Lkydx666307NmTgIAAZs+eTf369b0dTUREsjFP/1RaAViUzLGzQOH0iZPFpKHTG+dw4Fu4MD65cmVwKBERkZzp0qVLDB06lJtvvpm5c+cSGRmJj4/W3BQRkX/H06L3FHBzMscqA0fTJ04Wk5ZOr8OpLq+IiEgaxMbGMmnSJObMmcOSJUvIly8fa9euJSgoCGNM6hcQERHxgKd/Pl0EPG+MubLwtcaYQsBTwPx0T5YVpGVOr8Oh7YpEREQ8EBMTw/jx46lQoQL/+c9/OH/+PM7EaULBwcEqeEVEJF15WvQ+l3juLmAhYIE3E5/7Ay9mSDpvS0OnN+64U9sViYiIpGLfvn2UL1+eAQMGEBwczHfffcfq1aspXbq0t6OJiEgO5VHRa611AjWBd4FA4C+gCDAFqGutjcqwhN4Ue9ajTq+NiyP+xEn8AtXpFRERudq5c+dYs2YN4O7ktm7dmh9//JEVK1bQokULdXZFRCRDebzmv7X2NO6O73MZFycLiXdB3HmPil7XiRNgrbYrEhERuUJ0dDQTJkzgrbfewlrLH3/8Qd68eZk4caK3o4mIyA3E0316XzbG5MxtiZITG+2+92B4c9J2RZrTKyIiwpkzZxg7dixBQUE8++yz1K1bl0WLFpE3b15vRxMRkRuQp3N6BwA7jTEbjDEDjDGBGRkqS7hc9HrQ6Y1zOADw1+rNIiIi7Ny5kxEjRnD77bezfv16Fi1aRL169bwdS0REblCeDm8uDnQC7gfeBt4yxizFPad3gbU2NoPyeU9MGjq9jsudXg1vFhGRG8+JEycYN24ccXFxvP766zRo0IA9e/ZQuXJlb0cTERHxeCGri9baadbaNkBZYDhQCpgJOIwxn2RgRu9IQ6fX5XSCvz++hQtncCgREZGsw+l0MmzYMIKCgnjllVc4evQo1loAFbwiIpJleDq8OYm11mGtfdtaGwE0B84CD6Z7Mm9LS6fX6cAvsBjGJ83fThERkWxp1qxZBAUF8eabb3LXXXexY8cOpk6dqpWYRUQky/F49ebLjDEBwN24hzq3AgywKJ1zeV+a5vQ68S+uoc0iIpKz/fnnn8TExFCxYkUiIiLo0qULzz77LFWqVPF2NBERkWR53Jo0xjQ1xnwKOIAvgBLAYKC0tTYyg/J5T1Kn96ZUT3U5nZrPKyIiOdbvv/9O//79qVChAoMGDQKgQoUKTJkyRQWviIhkeR51eo0xvwNlgD+ACcBn1to9GRnM62LPuO89mdPrcJCvYcMMDiQiIpK5Dh06xMsvv8zkyZMB6NOnD8OHD/duKBERkTTydHjzd7gL3Z8yMkyWEhMNfrnBL1eKp8WfO0/C+fP4a49eERHJYSZNmsSUKVPo27cvw4YN45ZbbvF2JBERkTTzdPXmh26oghfcc3o9XbkZbVckIiLZ3549e+jVqxfz588HYODAgRw4cIAJEyao4BURkWwr2U6vMaYOsMNaeyHxcYqstevSNZm3xZ6FgAKpnuZyOgDwC1SnV0REsqddu3YxduxYZs6cSe7cualduzYAhQoVolChQl5OJyIi8u+kNLz5F6AesC7xsU3mPJN4zDd9o3lZTLRn2xU5EoteDW8WEZFs6Omnn+a///0vefPmZciQITz99NMUL66faSIiknOkVPS2AX5NfNyW5IvenMnD4c1xicOb/fULgoiIZBObN2+matWq5MmTh5o1a/Lss8/y1FNPUaxYMW9HExERSXfJFr3W2m+veLwkc+JkITHRUCz1QtblcOKTPz8++fJlQigREZHrt379esaMGcPXX3/N+PHjeeyxx7j//vu9HUtERCRDebSQlTFmlzEmNJlj1Ywxu9I3VhYQGw0BHuzR63BoESsREcnS1qxZQ5s2bahTpw4///wzY8aMUbErIiI3DE+3LKoK5EnmWF4g5+1M7+mcXqdT2xWJiEiWZa1l4MCB7N+/n1deeYXHHnuMAgVSX6hRREQkp/Co05souTm9NYAz6ZAl60iIh0tnPZ7Tq5WbRUQkq7DWsnz5ctq2bcvx48cxxvD5559z8OBBhg8froJXRERuOCltWTQAGJD41AJzjDGxV52WBygNzMmYeF4Se9Z9n0qn1yYk4Dp+XMObRUTE66y1LFu2jNGjR7Ny5UpKlSrFnj17CAwMpGLFit6OJyIi4jUpDW8+AmxMfFwR2AOcvOqcWGAX8EH6R/Oi2Gj3fSqd3vhTp8Dl0nZFIiLiVbGxsTRr1ozVq1dTtmxZxo8fz0MPPUTu3Lm9HU1ERMTrUlq9+UvgSwBjDMBz1toDmZTLu2ISi95UOr1xl/fo1XZFIiKSyay1bNq0iYiICAICAqhVqxYPPPAAvXv3JiAgwNvxREREsgyPFrKy1nbP6CBZioedXpcjcY9eDW8WEZFMkpCQwPz58xk9ejRbt25l165dVK1alXfeecfb0URERLKklOb0DgU+s9YeS3ycEmutfSN9o3mRh51el9Nd9GpOr4iIZLSEhAS+/PJLxowZw/bt26lYsSKTJk2iQoUK3o4mIiKSpaXU6X0VWA4cS3ycEgvknKL38kJWqXV6nQ7w8cGvaNFMCCUiIjey48eP88ADDxAUFMTnn39Ot27d8PO79o/xqKgo7u1+L5UrVaZ27dqEh4dz66234u/vn8mpRUREvC+lojePtfbyas3J7dGbM8Um7sCUStEb53DgV7QoJplfOkRERK6Xy+Vi+vTp/PDDD0yaNIkSJUqwevVqatSoga+vb4rvLVCgALt/3c3+A/v5acVyjjtPEBUVRYUKFYiIiKBOnTrcdttthIWFkT9//kz6ikRERLwjpYWsYq/1+Ibg6fBmh1NDm0VEJF3FxcXx+eef89JLL7F//35q1KhBVFQURYoU4bbbbvPoGn5+fowaNYqXXn6JO1rckXTdE8dP8Mdfv7N90nZOvXmKY8eOUapkKcLCw6hXtx5NmjShQYMGGfnliYiIZDqPWpTGmPJAQWvtlsTnAcBwoDrwrbX2fxkX0Qtio8HHH/xS3urB5XTif/PNmRRKRERyuu3btxMZGcmhQ4eoWbMm8+bNo0OHDvj4+KT5Wvfffz8jRozgyF9HKF2mNP7+/pQqXYpSpUslnRMfH8/pqNM4ncd5b/x7TJ48mT179qTnlyQiIuJ1nv4UfR944IrnY4DngWrAh8aYR9M7mFfFRLu7vO6tmpLlcjjwKx6YSaFERCQniomJYffu3QBUqFCBkJAQFi1axIYNG7jrrruuq+AF8Pf3Z8SIEWzdvC3Zc3x9fSlarCglS5bg/LnzzJgx47o+S0REJCvz9CdpOLACwLg37e0NPGutDcG9yFW/DEnnLbHRqc7nTYiJIf7MGW1XJCIi1+XixYu8++67VKhQgfbt2+NyucibNy8LFy6kbdu2mFT+8OqJPn36EH0mmmNHjyV7TlxcHN9/u4zXXnvN4+HTIiIi2YmnRW8h4ETi43CgKDAr8fl3QM7aL+FypzcFruPHAfArrqJXREQ8d/78ed5++22Cg4N58sknqVSpEh9//HGqi1Ndj1y5crm7vZu2JnvOH7//yZkzZ2jUqFG6f76IiEhW4GnR6wTKJz5uCRy01h5OfJ4PiE/vYF7lQafX5XAA4Fe8eGYkEhGRHOKbb75h0KBBhISEsHz5cpYvX06zZs3SpbN7LQ899BCnTkXhdDivebx8hWAaNm5Iw0YNmTx5coZkEBER8SZPi96FwEvGmLHAYGDOFcdCgIPpHcyrYqIh900pnhKXWPT6l1DRKyIiyYuOjubll1/m3XffBaBTp0788ssvLFu2jCZNmmT45+fOnZtnnnmGLSl0e2+tVpX2ke0YNmwYvXv35uLFixmeS0REJLN4WvQOB5YD3YDvgbFXHOsK/JC+sbzMk06vM3F4s+b0iojINURFRfHiiy9Srlw5nnvuOTZs2ACAj48PdevWzdQsjz76KMedxzlx3D1TKTY2lvlfLWDXjl1J5xQLLEanrh1Zv2E9EbUi2LdvX6ZmFBERySgeFb3W2mhrbU9rbSVrbQ9r7bkrjtW21g7OuIhe4MmcXocDkzs3PgUKZFIoERHJLqZNm0ZQUBCjRo2iSZMmbNiwgc8++8xrefLmzcvQoUPZsmkr1lpW/LiSWhG1OHTgMMuX/URcXBzgngPcvFUzSpYqQe3atfnyyy+9lllERCS9pGkfBGNMAWNMc2NMF2NMM2NMzqv4EhI87PQ68CtRPMPmYImISPZy4sQJHIlTX8qXL0/Lli3ZvHkz8+bNIyIiwsvpoH///hw9cpQVy1fi5+vLtGnT2LJlC1WrVGXel/OJOhUFgDGG0LBQWrVpyaOPPsqAJwYkFcUiIiLZkcdFrzHmeeAosBSYiXuY81FjzHMZlM074s4DFgJSrufjnE78tXKziMgNz+FwMHToUIKCgnjhhRcAqF+/PnPmzCE8PNzL6f5f/vz5GTToaQ7sO8D8+QsICAggX758zJgxg5EjRjL/qwX8tmdv0vklS5Xknq6dWLr0W+rXr8cff/zhxfQiIiLXz6Oi1xjzGDAamAu0BW4D2iQ+H22M+U+GJcxsMdHu+1SHNzu1crOIyA3syJEjDBw4kODgYN566y06duzIU0895e1YKRo6dBjbt28nODg46TVjDP369ePHH39kx9Yd/LxiFS6Xe1OG3Hlyc2fbO8mdLw/h4eEsWbLEW9FFRESum6ed3seB9xPn9X5rrd2aeN8T+AAYkHERM1lsYtGbwvBmay0uh0OLWImI3MBGjRrFe++9R7du3di9ezdTp06latWq3o6VIn9/f8qVK3fNYzVr1mT79u2ULF6Sr+d9TXS0++ehMYaIWjVp2rwp3Xt059lnnyU+PmftVCgiIjmbp0VveWB+Msfm8/97+GZ/HnR6E86cwV66pO2KRERuIIcPH+Y///kP69atA+CFF17gt99+Y9KkSVSqVMnL6dJHoUKFWLhwIQMef5y5s+dx8MD/70hY9uYy3NO1EzNmzuCOO5ridF57318REZGsxtOi9xRQJZljVRKP5wxJnd7k9+mNc7h/0Gt4s4hIznfgwAH69u1LxYoV+fTTT9m0aRMAZcqUoXz5nPM338uMMQwZMpRFixaxbs16flm9loSEBADy5ctHu8i2XHLFERoaysqVK72cVkREJHWeFr3zgJcSV21OWq7YGNMRGJN4PGeIOeO+T6HT63K6V+fU8GYRkZxt4MCBVK5cmalTp9KvXz/2799Pv379vB0rUzRs2JDt27eTJyAPixYs5vy580DiPsP161CvQV06dOjAK6+8grXWy2lFRESS52nROxzYjXvV5gvGmMPGmAvAHGBP4vGcwYM5vS7n5U6vil4RkZxm7969SZ3NEiVKMGDAj1AXvgAAIABJREFUAA4cOMB7773HzTff7OV0mSswMJBly5ZxX4/7+Gr2XP7848+kY0Hlg+jUpSMffvgBbdu2JSoqyotJRUREkudR0WutPQM0ALoCHwMrgE+ALkBDa210hiXMbB7M6Y1L3IfRr3hgZiQSEZFMsGPHDu69916qVKnC/PnuZSyGDx/OuHHjKF26tJfTeY+vry9jxoxhxowZ/PTDCjau35TU2S1QsAAdOnbg+InjhIaGsnHjRi+nFRER+SeP9+m11sZba+dYa59MXMX5SWvtV9banLWEY2w0GF/wz5vsKS6HE9/ChfHJlSsTg4mISEbYunUrXbp0ITQ0lEWLFjFs2DAaNWrk7VhZTqtWrdi6dSuXYmJZsuhbLl68CLiL4oaNG1DjtlCaNWvGhAkTNNxZRESylBSLXmPMvcaYX4wxJ4wx+4wxLxlj/DIrnFfERLu7vP8/dfkfXE6n5vOKiOQACQkJdOrUiaVLlzJixAgOHTrEK6+8QmCgRvJcS5kyZVi1ajXt2rbjq1lzOXb0WNKxSpUrcdc9kbz88st06dqFc+fOeTGpiIjI/0u26DXGdAGmAyWBVcAF3HN3x2ZONC+JjU5xPi/g3qNXQ5tFRLKltWvX0rNnTy5evIiPjw+zZ8/m8OHDjB49mqJFi3o7Xpbn7+/PuHHj+OSTT1j6zXds3bItqbNbuHBh7ronkr379hIWFsbOnTu9nFZERCTlTu/TwCKgkrX2LmttDeA1YIAxxuNh0dnO5U5vCuKcTvzV6RURyVZWr15N69atqVevHosXL04qyGrWrEmhQoW8nC776dixIxs3buSE4wTLli4jNvYS4C6KmzZrQvlK5WnQsAFTpkzxclIREbnRpVS8VgE+sNbGXfHau0AeoFyGpvKm2LMpdnptXBzxJ09q5WYRkWzizJkztGjRgoYNG7Jp0yZee+01Dh06RK1atbwdLdsrX748GzZsoF7d+sydPZcTx08kHasWcivtI9sxdOhQHnzwQWJiYryYVEREbmQpFb2FgBNXvXY88b5wxsTJAmLPpLxd0YkTYC1+JYpnYigREUkLay0HDhwAoGDBghQoUIC33nqLgwcPMnToUAoUKODlhDlH7ty5+fTTT3nzzTdZuGARu3b+mnSsWGAxOnbpyC/rfqFWrVrs37/fi0lFRORGldow5eSWX8y5yzKmMrzZlbRdkYpeEZGsxlrLt99+S6NGjahevTpOpxNjDHPnzuXpp58mX7583o6YY/Xs2ZM1q9dwcN9Blv/wE3Fx7oFiAQG5aNGqOYElilGrVi2++uorLycVEZEbTWpF7ypjzKXLN+Bi4utrr3zdGBObwTkzTyoLWcU5nACa0ysikoVYa1m0aBH16tWjdevW/P7777z55psULJjyGg2SvqpVq8aWLVuoXKky876cT1RUFADGGGqE16BVm5Y88sgjPPHkE0lFsYiISEZLafuh1zItRVZhbeqdXqe76NWWRSIiWcfevXtp37495cqV46OPPqJXr14EBAR4O9YNKX/+/MyaOYsPPviAZ555hoaNG1CpciUASpYqSaeuHVmyZAlrVq9m7tx5lC1b1suJRUQkp0u26LXWPpOZQbKEuAtg41Oe0+t0gL8/vlrpU0TEaxISEpg3bx6bN29mzJgxVK5cme+++44mTZrg7+/v7Xg3PGMM/fv3p27dutx11104jzmp17Aevr6+5MmTh9bt7mTzxs2EhYUxffp07rzzTm9HFhGRHCznbj10PWKi3fcpdHrjHA78AwMxPvrWiYhktvj4eGbOnElYWBj33HMPc+bM4eJF98ybFi1aqODNYiIiIti+fTvFA0uwYO7XREe7f84aY6hZqyZNmjXh3nvv5bnnniM+Pt7LaUVEJKdS5Xal2MSiN8VO73ENbRYR8YKNGzdSvXp17r33XuLj45k+fTo7duwgT5483o4mKShcuDCLFi3isf6PMXf2PA4dOJR07OZbynJP1058MeMLmjVrhjNxCpGIiEh6UtF7paRO703JnuJyOLRys4hIJnG5XPz1118AlClThoIFCzJr1ix27NhB9+7d8fX19XJC8YQxhmHDhrFw4UJ+WbOWtWvWkZCQAEC+/PloF9mW2LgYQkND+fnnn72cVkREchoVvVeKPeO+T6nT63Boj14RkQx26dIlPv30U6pUqUK3bt2w1lKyZEnWrl1Lly5d8NEUk2ypUaNGbN+2nQD/ABYtWMz58+cB8PHxoW79utRtUIf27dvz+uuvYW3O3R1RREQyl35ruFIqc3rjz50n4cIFbVckIpJBYmNj+eijj6hcuTIPP/wwRYoUYdiwYd6OJemoePHi/PDDD/To3oMvZ33Fn3/8lXQsuHwwHbvczfgJE2jXrh2nT5/2YlIREckpVPReKZU5vS6nA0DDm0VEMsjEiRPp168fJUuWZPHixaxbt44OHTpgjPF2NElHvr6+jB07lhlfzOCnH35i4/qNSZ3dggULEtmxA87jDkJDQ9m4caOX04qISHbncdFrjClhjHnZGPOzMWaXMaZa4uv9jTG1Mi5iJoo9674PKHDNwy7H5aJXnV4RkfRw8eJF3nnnHWbPng1Ar169WLp0KWvWrKFNmzYqdnO4O++8ky1bthBzMZZvF39LzMUYwF0UN2zckOph1WnWrBnvv/++hjuLiMh186joNcZUBbYD/wEuAFWA3ImHqwBPefqBxpjWxpg9xph9xpjhKZzX2RhjM7WgjokGDOTKf83DrsRVJf01p1dE5F85f/48b731FsHBwTz11FMsXrwYgLx589KyZUsVuzeQsmXLsmb1Gu68szVfzvqKY0ePJR2rXKUSd3WKZOzYsXTr1o1z5855MamIiGRXnnZ63wQOAsFAW+DK30ZWAfU9uYgxxheYALQBqgHdL3eMrzqvAPAEsNbDfOkjNto9tDmZBVLiHO6iV8ObRUSu39SpUwkKCmLw4MHUqFGDFStWMGnSJG/HEi/y9/fn3Xfe5eOPP2bpN9+xbcv2pM5u4SKFubvzXez5bQ/h4eHs2rXLy2lFRCS78bTobQK8bK09DVw9vugYUMrD69QB9llrD1hrLwEzgLuucd4Y4HUgxsPrpo+Y6GQXsQL38GafAgXwyZs3E0OJiGR/Z86cSerS5cmTh9q1a7N69WqWLl3K7bff7uV0klV06tSJDRs2cNxxnGVLf+DSpUuAuyhu2rwJwRWCaNCgPlOnTvVyUhERyU7SspBVfDKvFwUueniNMsAfVzz/M/G1JMaY24CbrbUL05AtfVzu9CbD5XRquyIRkTSIiopi1KhRlCtXjnHjxgHQuXNnFi9eTP36Hg0SkhtMhQoV2LBhA3Xr1OWr2XM5cfxE0rFq1avRNrItgwcP5qGHHiImJnP/Ni4iItmTp0XvBqBnMsfuAX7x8DrXmqSV1Dk2xvgA44BBqV7ImEeMMRuMMRuOHz/u4cenIuZMip3eOKcDfw1tFhFJ1cmTJ3n++ecpV64cL774InfccQft27f3dizJJnLnzs3EiRN54/U3WLhgEb/u2p10LDAwkI5dOrLmlzXUrl2bAwcOeDGpiIhkB54WvS8B9xhjvga64C5UGxtjPgK6Ai97eJ0/gZuveF4WOHLF8wJAdWC5MeYQUA9YcK3FrKy1H1tra1lrawUGBnr48alIrdPrcGrlZhERD/Tt25eXX36Z1q1bs3XrVubOncttt93m7ViSzTzwwAOsXrWa/b/t56cffsIV5wIgICAXLe5sTtHiRYmIiGDu3LleTioiIlmZR0WvtfZ73MVtGDAdd8f2baAd0NVau8rDz1sPVDLGBBtjcgH3Aguu+Jwz1tpi1toga20Q7g5ypLV2g6df0L+Swpxem5CA6/hx/Eqo6BURudqxY8cYNGgQBw8eBGDs2LFs376dWbNmUaNGDS+nk+wsJCSErVu3UrFCJeZ9NZ/TUacBMMYQFl6Dlq1b8nDfh3nqqaeIi4vzcloREcmKPJ7Ta639CigH1ABaALcBt1hr56XhGi7gceBb4FdglrV2pzFmtDEmMk3JM0IKnd74kychPh6/4unUVRYRyQGOHDnCk08+SXBwMP/9739Zvnw5ANWqVSMkJMS74STHyJ8/P7Nnz+bZZ55l3lfz2ffbvqRjpUqX5J6unVj0zSIaNmzIX3/95cWkIiKSFaVlISus2w5r7Q/W2q3W2oS0fqC1drG1trK1toK19qXE10Zaaxdc49ymmdbltTbFTu/l7Yr81ekVEcFay8CBAylfvjwTJkyge/fu7Nmzhz59+ng7muRQxhgee+wxln2/jK2bt7Fq5Wri491rbObJk4c27VqTK7c/YWFhfPfdd15OKyIiWYlHRa8xpmtqt4wOmuFcMZAQl2yn1+VM3KNXRa+I3MAcDgfgLkAuXbrEAw88wN69e5k4cSIVK1b0cjq5EdSqVYvt27cTWDSQr+ct5Gz0WcD9/2RE7Qga33E7Xbt2ZcSIEUlFsYiI3Ng87fTOSOb2xRW37C0m2n2fTKfX5XT/ouen1ZtF5Aa0f/9+HnroIcqWLcvatWsBGD9+PB9//DHBwcFeTic3msKFC7N48WL+0+8/fDV7LocOHko6dvMtN9Opa0c+n/Y5LVq0IN12eBARkWzL06L31mvcGgGvAfsTH2dvse6/FCfX6Y1zOMDHB7+iRTMxlIiId/3222/06tWLKlWqMH36dPr378/NN7sX4TfmWrvQiWQOYwzDhw9nwYIF/LJqLet+WU9CgnvWVf78+Wl/VzsuxFwgNDSUVas8XW9TRERyIk9Xb95zjdtqa+2zwEzgPxkbMxPEnnHfpzC82a9YMYyfXyaGEhHxntjYWBo0aMDs2bN54oknOHDgAO+88w6lS5f2djSRJI0bN2bbtm34+fix+OtvuHD+AgA+Pj7Ua1CXOvVr065dO9544w2stV5OKyIi3pCmhayS8SPg/ZWX/63Uhjc7nBraLCI53vbt2xk0aBAJCQkEBATwxRdfcPDgQd5++21KlSrl7Xgi11SiRAmWL19Ot67d+HLWV/z15/+v4BxcPpiOne/m3ffepUOHDpw+fdqLSUVExBvSo+itBVxIh+t4V2xi0Ztcp9fh0CJWIpJjbd68mU6dOlGjRg0++eQTdu/eDUDLli0poX/7JBvw9fXl5ZdfZtq0afz4/XI2bdiU1NkteFNBIjtGcvTYUWrUqMHmzZu9nFZERDKTp6s3D73G7XljzAxgDPBVxsbMBKkuZOXEv4Q6vSKSszidTiIjI6lZsyY//PADI0eO5NChQ1SrVs3b0USuS5s2bdiyZQsXz1/k28VLibkYA4Cfny+NmjQkpEY1mjZtyocffqjhziIiNwhPJ6i+eo3X4oG/gHHAi+mWyFtS6PQmxMQQf+YMfsXV7RCRnOHEiRMUK1aMwoULc/ToUUaPHs2AAQMoVKiQt6OJ/Gs333wzq1ev4emnn2bGzBm0aNWcEiXdP8MrV6lMscBivDj6RZb/tJxP//cp+fLl83JiERHJSJ4Ob85zjVuAtTbIWjvcWnsxowJmmsud3oAC/ziUtEev5vSKSDa3atUq7rzzTkJDQ7l48SL+/v6sW7eOESNGqOCVHCVXrlyMHz+eDz/4kG8XL2X71u1Jnd0iRYrQsfPd7Nq1i9tuu41ff/3Vy2lFRCQjpVr0GmNyAaOA6tba2CtuCRmeLjPFRkOuAuDj+49DLkfiHr0a3iwi2dRPP/1E8+bNadSoEVu2bGHQoEFJx7T1kORknTt3Zv369Rw74uCH737g0qVLAPj7+9O0eRNuCbqZ+vXrM23aNC8nFRGRjJJq0WutvQQ8CeTssT8x0cnO541L7PT6azEXEcmG1q5dS9OmTdm1axfjxo3j4MGDDB48mDx58ng7mkimqFixIhs3bqRWRG3mzp7LyRMnAfcffEJCQ2jboQ0Dnx5I3759iY2N9XJaERFJb54Ob94K5OxVTWLPpLBys4Y3i0j2Ya1lyZIlfPDBBwDUqVOH6dOnc+DAAZ566iny5s3r5YQimS9PnjxMnjyZV155la/nLWT3rt1JxwKLB9KpS0d+Xv0ztWvX4vDhw15MKiIi6c3ToncoMMwY0yIjw3hVCp1el8OByZMHnwL/nO8rIpJVWGtZuHAhdevWpU2bNrz77ru4XC6MMXTv3l2dXRGgT58+/Pzzz/y2ey8//bACV5wLgICAAFq0as6Ro0eZO3eul1OKiEh68rTonQgUAr41xpw1xuw1xvx2xW1PBmbMHLHRyXd6jzvxL15c895EJMtat24dERERdOjQgePHj/PJJ5+wdetW/Pw8XaRf5MYRGhrKtm3bKB9cnvlfzed01GkANq7fSOXKlXn88ce9nFBERNKTp78NbQRy9mZ2sWehcPA1D8U5nBraLCJZTkJCAtHR0RQqVIh8+fJx/vx5Jk2axH333Ye/v7+344lkaQUKFODLL79k/PjxPD/ieSpXqczhg4fZtm2b/lgkIpLDePSvurX23owO4nWpDG/OEx6eyYFERK4tPj6eWbNmMXbsWKpXr87MmTMJCQnh119/xcfH0wE8ImKMYcCAAdSrV48+ffowa9YsSpYs6e1YIiKSzpL97cgYc8AYE5aZYbwqmeHN1lpcTqe2KxIRr3O5XHz++eeEhITQo0cPADp16pR0XAWvyPWpXbs2O3bsoGnTpt6OIiIiGSCl35CCgIBMyuFdrkvgirlmpzf+9GnspUv4a3iziHjZa6+9Rs+ePQkICGD27Nls376dbt26eTuWiIiISJamSSvg7vICBNz0j0OuxD16/bRHr4hkskuXLjFlyhSqVKlC48aNefjhhwkJCSEyMlJdXREREREPpfZbU85evOqymDPu+2t0epOK3uIqekUkc8TGxvLBBx9QsWJFHnnkEaZPnw5AiRIluPvuu1XwiniJMYaePXsmPXe5XAQGBtK+fXsAFixYwKuvvpriNY4cOULnzp0zNGdKRo0aRZkyZQgPD6datWp88cUXScestYwdO5ZKlSpRuXJl7rjjDnbu3Jl0/Ny5czz66KNUqFCBkJAQGjduzNq1a//xGRMnTiQ0NJQaNWpQvXp15s+fnylfW0pWrlxJSEgI4eHhXLx48W/HgoKCCA0NJTw8nFq1aiW9furUKVq2bEmlSpVo2bIlUVFRAHz55ZeEhIRw++23c/LkSQD279/Pvffm/CVwRLKr1Dq9LxpjTnhwHWut7ZUegbwiqdN7jaLX4QDQ6s0ikik+++wznn32Wf766y/q16/PJ598QqtWrbwdS0SAfPnysWPHDi5evEiePHn47rvvKFOmTNLxyMhIIiMjU7xG6dKlmTNnTkZHTdHAgQMZPHgwe/fuJSIigs6dO+Pv78+ECRNYvXo1W7duJW/evCxdupTIyEh27txJ7ty5efjhhwkODmbv3r34+Phw4MABfv31179d+88//+Sll15i06ZN3HTTTZw7d47jx4//q7zx8fH4+vr+q2tMmzaNwYMH06dPn2se//HHHylWrNjfXnv11Vdp3rw5w4cP59VXX+XVV1/ltdde46233uKXX35hxowZTJ8+nQEDBvD8888zZsyYf5VRRDJOau2CcOB2D2/ZV0xi0XuNTm9cYtHrXzwwMxOJyA3kwoULxMXFARAVFUX58uX5/vvvWbVqFXfeeaf2CBfJQtq0acOiRYsA+OKLL+jevXvSscmTJyft8du7d2+eeOIJGjRoQPny5ZMK3UOHDlG9evWk8++++246dOhAcHAw48eP5+233+a2226jXr16nDp1CoCmTZuyYcMGAE6cOEFQUFCa3p+cSpUqkTdv3qQO5muvvcZ7771H3rx5AWjVqhUNGjRg2rRp7N+/n7Vr1zJ27Nik0Sbly5enXbt2f7um0+mkQIEC5M+fH4D8+fMTHOzeEnLfvn20aNGCsLAwatasyf79+7HWMmTIEKpXr05oaCgzZ84EYPny5dxxxx306NGD0NBQAD7//HPq1KlDeHg4jz76KPHx8f/4mpYtW8Ztt91GaGgoDz74ILGxsfzvf/9j1qxZjB49mvvuuy/F78mV5s+fT69e7p5Or169mDdvHuBeNDA2NpYLFy7g7+/PypUrKVWqFJUqVfL42iKSuVIreu+21gZ7cCufKWkzSkqdXudxfIsUweTKlcmhRCSnO3fuHK+//jrBwcFMnToVgAEDBvDTTz/RvHlzFbsiWdC9997LjBkziImJYdu2bdStWzfZc48ePcrPP//MwoULGT58+DXP2bFjB9OnT2fdunU899xz5M2bl82bN1O/fn0+++yzVPP8m/dv2rSJSpUqUbx4caKjozl//jwVKlT42zm1atVi586d7Ny5k/Dw8FQ7rmFhYZQoUYLg4GD69OnD119/nXTsvvvu47HHHmPr1q2sXr2aUqVK8dVXX7Flyxa2bt3K999/z5AhQzh69CgA69at46WXXmLXrl38+uuvzJw5k1WrVrFlyxZ8fX2ZNm3a3z47JiaG3r17M3PmTLZv347L5eKDDz7g4YcfJjIykjfeeOMf7wH3sPVWrVoRERHBxx9/nPS6w+GgVKlSAJQqVQpn4pS3F154gTvvvJPvv/+e7t27M3bsWEaMGJHi90VEvEsLWUGKnV6Xw6GhzSKSrqKjo5M6MidPnqRVq1ZJnR/N1xXJ2mrUqMGhQ4f44osvaNu2bYrnXp6DX61aNRyJI8eudscdd1CgQAEKFCjATTfdRIcOHQAIDQ1l27Ztqea5nvePGzeOTz75hAMHDrBkyZIUr2+tTdMf4Hx9fVmyZAnr169n2bJlDBw4kI0bNzJo0CD++usvOnbsCEDu3LkB+Pnnn+nevTu+vr6UKFGCJk2asH79egoWLEidOnWSusTLli1j48aN1K5dG4CLFy9S/Krfz/bs2UNwcDCVK1cG3N3ZCRMm8NRTT6WYedWqVZQuXRqn00nLli2pWrUqjRs3Tvb8li1b0rJlSwCmTJlC27Zt2bNnD2+++SaFCxfmnXfeSeqWi0jWoN+uIMVOb5zToT16RSRd3XXXXTz33HPUrVuXNWvW8O2331KnTh1vxxIRD0VGRjJ48OC/DW2+loCA/9/50dprrw165Tk+Pj5Jz318fHC5XAD4+fmRkJAAuLuZaX3/1QYOHMiePXuYOXMmDzzwADExMRQsWJB8+fJx4MCBv527adMmqlWrRkhICFu3bk3KkRJjDHXq1OGZZ55hxowZfPnll8l+/cm9Du451Fee16tXL7Zs2cKWLVvYs2cPo0aN8vhaKSldujQAxYsXp2PHjqxbtw5wLx54uet89OjRfxTZFy5cYMqUKfTv359nnnmGiRMnEhERcc1usoh4l4pegNiz7vtkhjf7a+VmEfkXTp06xejRozl9+jQAY8eOZf369SxatIh69ep5OZ2IpNWDDz7IyJEjk+aaZrSgoCA2btwIkK6LYHXq1IlatWoxZcoUAIYMGcITTzyRtLrx999/z88//0yPHj2oUKECtWrV4oUXXkgqLvfu3fuPlZmPHDnCpk2bkp5v2bKFcuXKUbBgQcqWLZs0L/bynNjGjRszc+ZM4uPjOX78OCtWrLjmHwGbN2/OnDlzkoYYnzp1isOHD//tnKpVq3Lo0CH27dsHwNSpU2nSpEmK34Pz589z9uzZpMdLly5NGnkTGRmZ9L2ZMmUKd91119/e+/rrr/Pkk0/i7+/PxYsXMcbg4+PDhQsXUvxMEcl8yQ5vttbeOAVxzBnwzwu+f/922Lg44k+e1PBmEbkuJ06cYNy4cbz33nucPXuWKlWq0K1bNxo2bOjtaCLyL5QtW5Ynn3wy0z5v8ODBdO3alalTp9KsWbN0vfbIkSPp0aMHffv2ZcCAAURFRREaGoqvry8lS5Zk/vz55MmTB4D//e9/DBo0iIoVK5I3b16KFi3KG2+88bfrxcXFMXjwYI4cOULu3LkJDAzkww8/BNxF6KOPPsrIkSPx9/dn9uzZdOzYkTVr1hAWFoYxhtdff52SJUuye/fuv123WrVqjB07llatWpGQkJC02nS5cuWSzsmdOzeTJk2iS5cuuFwuateuTb9+/VL8+h0OR9KQa5fLRY8ePWjdujUAw4cPp2vXrnz66afccsstzJ49O+l9R44cYcOGDUnd5kGDBlGvXj0KFSqUVNiLSNZhrncoSFZSq1Yte3lVw2upO+keANb2+fLaJywYAL8thcF7/vZy3JEj7GvWnJKjX6Rw167plldEcraEhASeeeYZJkyYwIULF+jatSvPPfdcpnWFRERERHIaY8xGa22t1M/8pxunm5uSmOhrL2KVOITGv4SGN4tI6s6dOwe459Lt2rWLu+++m507dzJjxgwVvCIiIiJeotWbwb2Q1bUWsXK4i14NbxaRlPz555+8/vrrTJo0iW3bthEcHMy8efNS3dpDRERERDKeOr2QfKc3cXsBP3V6ReQafv/9d/r370+FChX44IMP6Nq1K/7+/gAqeEVyOGstzZo14/Dhw4SHhxMeHk7JkiUpU6ZM0vPffvstaVGkq40cOZLvv//eo89asGABr776qkfnHjp0iOnTpyc9nzx5Mo8//rhH7/03GjRokOo5Dz/8MLt27crwLFnJ6dOnef/99zP8c+bNm/e37+3gwYP54YcfMvxzRbILdXrB3em9qew/XnYdd4K/P76FC3shlIhkZWfOnKFatWpcunSJBx98kOHDhxMUFOTtWCKSSRYvXkxYWBjlypVjy5YtAIwaNYr8+fMzePBgwF2AJmf06NEef1ZkZCSRkZEenXu56O3Ro4fH108Pq1evTvWc//3vf5mQJHO4XC78/FL/Nfpy0du/f/8MzTNv3jzat29PtWrVABgwYAB9+/ZN94XPRLIrdXoh2U5vnMOBf2BgmjZlF5Gca9++fbz11lsA3HTTTXz88cfs37+fDz/8UAWvyA1m2rRp/9jC5lri4+Pp27cvISEhtGrVKmk7oN69eydtPzR8+HCqVatGjRo1kgrmK13ZrZ09ezbVq1drCDI8AAAgAElEQVQnLCyMxo0b/+Pc4cOHs3LlSsLDwxk3bhzgXmm4devWVKpUiaFDhyadu3TpUurXr0/NmjXp0qVL0roEV2ratCkDBw6kcePG3Hrrraxfv55OnTpRqVIlnn/++aTz8ufPD8Dy5ctp2rQpnTt3pmrVqtx3331JWxw1bdqUywuP5s+fn2HDhhEREUGLFi1Yt24dTZs2pXz58ixYsOAfXzdA+/btWb58ucfvv9LRo0dp3Lgx4eHhVK9enZUrVwKwZMkSatasSVhYGM2bNwfc2yHdfffd1KhRg3r16rFt2zbA/UeNRx55hFatWvHAAw8QHx/PkCFDqF27NjVq1OCjjz665n+P/fv3Ex4ezpAhQ7DWMmTIEKpXr05oaCgzZ878x3sAxowZQ9WqVWnZsiXdu3fnzTffBGD//v20bt2aiIgIbr/9dnbv3s3q1atZsGABQ4YMITw8nP3791OuXDlOnjzJsWPHrnl9kRuOtTbb3yIiImxK6kzsZOtM7JT8CWNLWrvk2X+8fOiBXvbgvd1TvLaI5Hy7d++2PXv2tD4+PjZ37tz2999/93YkEfGyW265xUZHR//ttRdeeMG+8cYbSc8PHjxofX197ebNm6211nbp0sVOnTrVWmttr1697OzZs+3Jkydt5cqVbUJCgrXW2qj/Y+/Ow6qqugeOf6+AgPOAmmYqponMs4AKKAIpzmWOSU5l5ZxT+WpqmZq+5VBmpqaVKTlbmpkDDqWimKHilEpOvKAYk3iBC/v3B3J+IOCUetW7Ps/Tg/fcM6xzrvaw7lp773/+KXKtr7/+Wr399ttKKaUcHR3VxYsXS9x3x44dKiwsrNCxtra2Kjk5Wd24cUPVqVNHnT9/Xl25ckU1b95cpaenK6WUmjZtmpo0aVKR8wUEBKjRo0crpZSaNWuWqlmzprp8+bLS6/Xq2WefVVevXlVKKVW2bFnt+hUqVFAXLlxQOTk5ysfHR+3evVs714EDB5RSSgFq06ZNSimlOnbsqIKDg1VWVpY6fPiwcnFxKXLfSikVFhamduzYcdfHFzRz5kz14YcfKqWUMhgMKjU1VSUmJqratWurs2fPKqWUSkpKUkopNWjQIDVx4kSllFLbtm3Tzvf+++8rd3d3lZGRoZRS6ssvv1QffPCBUkopvV6vPDw8tHPlO3funHJwcNBer1q1SrVq1UoZDAb1v//9Tz333HPq8uXLhY45cOCAcnFxURkZGSo1NVU1aNBA+3vVsmVLderUKaWUUvv27VMtWrRQSv3/36eC+vfvr1atWlXkWQjxpAIOqvvMF6W9OScbsjPAqmKRtwwJCVja2RkhKCHE4yA+Pp533nmHFStWYG1tzfDhwxk5ciTPPPOMsUMTQhjZtWvXKF++/B33s7W1xdXVFQAPD48iLc8VKlTAysqK/v37ExYWRtu2bW97vqZNm/Laa6/xyiuv0Llz57uKNSgoiIoV837Psbe35++//yY5OZnY2Fht3fCsrCx8fX2LPT6/tdrJyQkHBwdq1qwJQP369blw4QJVq1YttL+3tze1a+cNG3N1dSUuLo5mzZoV2qd06dLaerhOTk5YWlpiYWGBk5PTbdvC7/d4Ly8v+vbtS3Z2Nh07dsTV1ZXIyEj8/f2xtbUFoEqVKgDs2bOH1avzlrls2bIlSUlJpKSkaM8if93iLVu2EBMTo1XsU1JSOH36tHa+4uzZs4fu3btjZmZGjRo1CAgI4MCBA4Xa1/fs2UOHDh2067Rr1w7IWyHg999/p0uXLtq+mZmZJV6revXqXL58+TZPUQjTIUlvZlrez2JmbzYkJlIuoGjrkBDi6ZaRkUGZMmUoU6YMe/bsYcyYMYwYMYJq1aoZOzQhxGPC3Nyc3NxcSpW6/UgxS0tL7c9mZmZae3PB80RFRbFt2zZWrFjBZ599dtsJiObPn8/+/fvZuHEjrq6uHD58uEjSeacYDAYDSimCg4NZvnz5bY8teHypUqUKnatUqVIYDIa7ut6tLCwstOFjBc9b8Jz5zzifXq+/p+ML8vf3Z9euXWzcuJFXX32VUaNGUalSpWKHsKmb7dgF5e9XtmzZQvvNnTuX0NDQIvuXpLhz3+0+ubm5VKpUSRtDfid6vV5LnIUwdTKmV5/3zd2tY3pz0tPJzciQ5YqEMCGHDh2iU6dO+Pj4kJubS8WKFTlz5gxTp06VhFcIUUijRo04e/bsvz5Peno6KSkptGnThlmzZt0xoTlz5gxNmjRh8uTJ2NjYcOHChULvly9fnrS0tDte18fHh99++42//voLyPuy79SpU/d/Iw9BvXr1OHz4MLm5uVy4cIGoqKj7Ptfff/9N9erVGTBgAP369ePQoUP4+vqyc+dOzp07B+RV7yEvQV62bBmQN0bZxsaGChWKFkdCQ0P54osvyM7OBuDUqVNcv3690D63fh7+/v5ERESQk5PDlStX2LVrF97e3oWOadasGT/++CN6vZ709HQ2btwI5HUF2NrasnLlSiAvOf7zzz+LvU5+PCXNHi6EqZFKbwmVXm25ouqyXJEQT7uoqCg++OADfvrpJypWrMiwYcPIysrCyspKW4JICCEKCgsLIzIykgYNGvyr86SlpdGhQwf0ej1KKW3yqZKMGjWK06dPo5QiKCgIFxeXQu87Oztjbm6Oi4sLr732GpVLWIGiWrVqLFmyhO7du2stsh9++CEvvPDCv7qfB6lp06bY2tri5OSEo6Mj7u7u932uyMhIZsyYgYWFBeXKleObb76hWrVqLFiwgM6dO5Obm0v16tX59ddfmThxIn369MHZ2ZkyZcqwdOnSYs/Zv39/4uLicHd3RylFtWrVWLduXaF9qlatStOmTXF0dKR169Z8/PHH7N27FxcXF3Q6HR9//HGRITNeXl60b99emx3c09NTa09ftmwZb775Jh9++CHZ2dl069YNFxcXunXrxoABA5gzZw6rVq2iTp06/PXXX3h6et73MxPiaaK7mzaLx52np6fKnw2wOE2+fgmA/X1WF30zbg8sCYPeG6B+gLb5+t69nO/TlzrfLKXsLd/ACSGeHr/88gsvvvgilStXZsSIEQwePFj75UIIIUoSHx9P7969+fXXX40dingKpaenU65cOTIyMvD392fBggX3lPSvXbuWQ4cO8cEHHzzEKIV4tHQ6XbRS6r6+yZFKrz417+ct7c3ZNyu9FtLeLMRTZ9euXVy9epXOnTsTFBTE7Nmz6dOnz11NSiOEEAA1a9ZkwIABpKamFtv6KsS/8frrrxMbG4teryc8PPyeq9wGg4F33nnnIUUnxJNHkt7Mm0lvkfbmRADMa0h7sxBPA6UUkZGRTJo0iZ07d+Li4kKnTp0wNzdnyJAhxg5PCPEEeuWVV4wdgnhKff/99//q+IIzPAshZCKrApXewu2MhsRESlWoQCmZ9U6IJ97+/fvx9/enZcuWnDp1ilmzZvH7778XO2unEEIIIYR4ukilN/Pm7M23VnoTEzCvLrO1CvGkUkqRmZmJlZUVaWlpxMXF8dlnn9GvXz+srKyMHZ4QQgghhHhEpNKrTwVzKzAvXWhzdkIiFjJzsxBPHKUUP/74I97e3rz33nsABAUFcebMGd5++21JeIUQQgghTIwkvZmpRaq8kNfeLON5hXhy5ObmsmbNGtzd3Wnfvj3Xrl3Dzc0NAJ1OR+nSpe9wBiGEEEII8TSSpFefWmTmZpWTg+HKFcxl5mYhnhhjx47lpZde4vr16yxZsoQTJ07w6quvGjssIYQQQghhZDKmt5hKryEpCXJyMK8hSa8Qj6ucnBwiIiJwdXXF3t6evn374uLiQteuXTE3l/+1CSGEEEKIPFLpLabSa0i8AoCFtDcL8dgxGAx888032Nvb07NnTxYtWgSAnZ0dPXv2lIRXCCGEEEIUIklvcZXexAQAaW8W4jGzbNkyGjVqRHh4ONbW1qxevZoZM2YYOywhhBBCCPEYk5JIZlrRSm9CftIrlV4hjC0rKwsLCwt0Oh1//vknlStXZv369bRr107W2RVCCCGEEHcklV590UpvdmIilCqFuU1VIwUlhNDr9cybN48GDRqwZcsWAD744AMOHDhA+/btJeEVQgghhBB3xbST3twcyEor2t6ckIi5jQ06MzMjBSaE6bpx4wZz5szh+eef5+2336ZOnTpUrFgRAEtLS0l2hRBCCCHEPTHt9ubMtLyfxbQ3yxq9Qjx6SimaNWvGoUOHCAwM5LvvviMwMFASXSGEEEIIcd9MPOlNzftZZCKrRCzq1jFCQEKYnrS0NL7++msGDhxI6dKlGT9+PFWqVMHf39/YoQkhhBBCiKeAaSe9+ptJr1XRMb1lvDyNEJAQpiMlJYXPPvuMTz75hGvXrvH8888TFhZGx44djR2aEEIIIYR4ipj2mN5iKr25ej25KSkyc7MQD0lWVhaTJk2iXr16/Oc//8HX15d9+/YRFhZm7NCEEEIIIcRTSCq9UKjSa0hMBJAxvUI8YNnZ2VhYWGBhYcG6desICAhg/PjxeHh4GDs0IYQQQgjxFDPtpFer9FbUNv3/Gr3VjBGREE+dK1eu8Mknn/Ddd99x5MgRKlWqxG+//UaZMmWMHZoQQgghhDABpt3erE/J+1mg0pudkFfptZBKrxD/SkJCAqNGjaJevXpMnz6dZs2acf36dQBJeIUQQgghxCMjlV4oNKZX2puF+PcuX75MgwYNyMzMpEePHowbNw47OztjhyWEEEIIIUyQiVd6U8GsNFhYaZsMCQnorK0pVa6cEQMT4slz4cIFli1bBkCtWrWYMmUKJ06c4Ntvv5WEVwghhBBCGI1pJ72ZaUXW6M1OTMCienV0Op2RghLiyRIXF8fAgQN5/vnnGTBgAP/88w8Aw4cPp2HDhkaOTgghhBBCmDoTT3pTwbJ8oU2GhERpbRbiLly6dIn+/fvTsGFDFi9eTL9+/Th+/DiVK1c2dmhCCCGEEEJoTHtMrz610CRWkDem19rNzUgBCfH4MxgMmJubk5OTww8//MDAgQMZPXo0zz33nLFDE0IIIYQQogjTTnozUwu1NyulMCQmynJFQhTj+PHjTJkyhaSkJH7++Wfq1KnD5cuXKSfj34UQQgghxGPMtNub9alg9f9r9OYkJ6OysmS5IiEKOHr0KN26dcPBwYG1a9fi6OiIwWAAkIRXCCGEEEI89qTSW9xyRdUl6RUCYNWqVXTp0oVy5coxduxYhg8fTrVq0gkhhBBCCCGeHKad9N4ypteQkACAefXqxopICKOLjo4mIyOD5s2bExISwqRJkxg0aBBVqlQxdmhCCCGEEELcM9Ntb87NLVLpzb6Z9FrUkKRXmJ79+/fTtm1bPD09ee+99wCoUKECEyZMkIRXCCGEEEI8sUw36c1KB1ThSm9+e7O0bwoTcvDgQV588UV8fHzYt28fH330ERs3bjR2WEIIIYQQQjwQptvenJma97PgmN6ERMyqVEFXurSRghLi0cnNzaVUqVLExsZy6NAhPv74Y958802ZnEoIIYQQQjxVTLfSq7+Z9N4yptdcZm4WTzGlFNu2bSMgIIBPP/0UgB49enDu3DlGjRolCa8QQgghhHjqmG7SW0ylN/tKIhYyiZV4Ciml+OWXX2jWrBmtWrXir7/+omrVqgCYm5tTtmxZI0cohBBCCCHEw2HCSW9a3s8C6/QaEhJl5mbxVBo8eDAvvvgiFy5cYN68eZw5c4bXXnvN2GEJIYQQQgjx0JnumF59St5Py/IAqKwscpKSpL1ZPBWUUvz44494enpSq1YtunXrhouLC+Hh4ZSWMetCCCGEEMKEmHClt3B7s+HqVQDMZbki8QTLzc1l9erVuLm50aFDB7766isAmjVrxoABAyThFUIIIYQQJsd0k95bJrLS1uiV9mbxhFq5ciXOzs68/PLL3Lhxg2+++YZx48YZOywhhBBCCCGMynTbmzNTQWcGFmWAvPG8gLQ3iydK/rJDAOvWrUMpxffff88rr7yCmZmZkaMTQgghhBDC+B55pVen072o0+lO6nS6v3Q63dhi3h+h0+lidTpdjE6n26bT6eo+lED0qXlVXp0OAEOiJL3iyZGdnc2SJUto3LgxMTExAMybN48jR47QvXt3SXiFEEIIIYS46ZEmvTqdzgz4HGgN2APddTqd/S27/QF4KqWcgVXAxw8lmMzUQssVGRIT0FlYYFap0kO5nBAPQlZWFosWLcLOzo4+ffpQtmxZMjIyAKhYsaJW9RVCCCGEEELkedS/IXsDfymlziqlsoAVQIeCOyildiilMm6+3AfUfiiR5Fd6b8q+uVyR7mblV4jHTW5uLu7u7vTv358qVarw448/Eh0djY+Pj7FDE0IIIYQQ4rH1qMf0PgtcKPD6ItDkNvv3A35+KJFkpoJlgTV6ExOltVk8dvR6PWvWrKF79+6UKlWKwYMHU6dOHV588UX5gkYIIYQQQoi78KiT3uJ+S1fF7qjT9QI8gYAS3n8deB2gTp069x6JPhUqPae9NCQkYGlnd+/nEeIhyMjIYMGCBXz88cfEx8dTt25dmjZtyhtvvGHs0IQQQgghhHiiPOr25ovAcwVe1wYu37qTTqdrBYwD2iulMos7kVJqgVLKUynlWa1atXuPJDNFG9OrlCI7MRELWaNXGJler2fmzJnY2toyfPhw7Ozs2LFjB35+fsYOTQghhBBCiCfSo056DwANdTqdrU6nKw10AzYU3EGn07kBX5KX8CY+tEgKjOnNvX4dlZGBeXVpbxbGodT/Nzx8+umnuLi4sGvXLrZv305gYKC0MgshhBBCCHGfHml7s1LKoNPpBgG/AGbAYqXUMZ1ONxk4qJTaAMwAygErb/6if14p1f4BBwKZaVql15CQAIB5dan0ikcrJSWFuXPnsmbNGvbt24eVlRV//vknNjY2xg5NCCGEEEKIp8KjHtOLUmoTsOmWbRMK/LnVQw8iOwNUDliWBwokvdLeLB6Rf/75h9mzZzNr1ixSUlJo164d//zzDzVq1JCEVwghhBBCiAfokSe9jwV9at7Pm+3N2Yl5XdQWMnuzeAROnjyJl5cXaWlpdOrUifHjx+Pm5mbssIQQQgghhHgqPeoxvY+HzJtJr9benJf0SnuzeFgSExP55ZdfAHjhhRd44403+PPPP1mzZo0kvEIIIYQQQjxEJl7pzVun15CQQKkKFShlbW3EoMTTKD4+npkzZ/LFF19gZWXFpUuXsLa2ZsaMGcYOTQghhBBCCJNgopXelLyflvntzQmyXJF4oOLj4xk6dCj169dn1qxZdOnShb1792ItX6wIIYQQQgjxSJl4pfdme3PiFVmuSDwQSil0Oh2XL19m3rx5vPrqq7z33ns0aNDA2KEJIYQQQghhkkwz6S0ypjcBy+efN2JA4kkXFxfH1KlTAfjyyy/x8PDg4sWL1JDJ0YQQQgghhDAq02xvLlDpVTk5GK5eleWKxH05c+YM/fr1o2HDhixZsgQrKyuUUgCS8AohhBBCCPEYMN1Kr64UlC6H4coVyMmR5YrEPfvmm2/o27cvFhYWvPXWW4wePZpnn33W2GEJIYQQQgghCjDNpFefCpblQaeT5YrEPYmNjSU3NxdHR0cCAwMZNmwYI0eO5JlnnjF2aEIIIYQQQohimGZ7c2YqWN5crigxAUAmshK3deTIEbp27YqjoyPvvvsuAHXq1GHmzJmS8AohhBBCCPEYM9GkN63AzM03K70yplcU488//+Sll17C2dmZn3/+mXfffZevv/7a2GEJIYQQQggh7pKJtjen5LU3A9kJCWBmhnnVqkYOSjxO8pce2rRpE9u2bWPChAkMHTqUKlWqGDs0IYQQQgghxD0w0UpvaoHlihIxt7FBZ2Zm5KDE42Dv3r20bt2aiIgIAIYMGUJcXByTJk2ShFcIIYQQQognkGkmvfrUQu3N5jJzs8nbs2cPISEh+Pn5ceDAAfR6PQBly5alUqVKRo5OCCGEEEIIcb9Ms725YKU3MQGLunWNHJAwpgEDBrBw4UKqV6/OjBkzGDhwIOXKlTN2WEIIIYQAsrOzuXjxovaFtBDi6WZlZUXt2rWxsLB4YOc0vaRXqUKV3uyERMp4eRs5KPEoKaXYtm0bPj4+lCtXjjZt2uDg4MDrr79OmTJljB2eEEIIIQq4ePEi5cuXp169euh0OmOHI4R4iJRSJCUlcfHiRWxtbR/YeU2vvdmgh9xssKxA7o0b5KamSnuziVBKsXnzZpo2bUpwcDCLFy8GoFOnTgwbNkwSXiGEEOIxpNfrqVq1qiS8QpgAnU5H1apVH3hnh+klvfrUvJ9WFf5/uaLqslzR00wpxU8//USTJk1o3bo1ly5d4osvvuCNN94wdmhCCCGEuAuS8AphOh7Gv3fTS3ozbya9lhXzlisCLGSN3qeaTqdj5syZXL16la+++orTp08zcOBALC0tjR2aEEIIIR5zD2Kej8uXL/Pyyy+X+H5ycjLz5s276/1v9dprr2Fra4urqysuLi5s27btX8X7oM2fP59vvvnmgZzr1KlTtGnThgYNGtC4cWNeeeUVEhISiIyMpG3btg/kGgD9+/cnNjYWgJUrV9K4cWNatGjBwYMHGTJkyL8695QpU3BwcMDZ2RlXV1f279//IEK+Z9euXSM4OJiGDRsSHBzMP//8U+x+Y8aMwdHREUdHR22FE4DmzZvj6uqKq6srtWrVomPHjgD8888/dOrUCWdnZ7y9vTl69Kh2zOzZs3F0dMTBwYFZs2Y93BssSCn1xP/n4eGhbsd7cWflvbhz3osLB5V6v4JSJzer5B9/UrGN7JT+r79ue7x4suTk5KgffvhBeXl5qYsXLyqllLp06ZLKysoycmRCCCGEuFexsbFGvX7ZsmUf+jXOnTunHBwc7vv48PBwtXLlSqWUUtu3b1cNGjR4IHFlZ2c/kPM8KDdu3FANGjRQGzZs0LZt375dHTlyRO3YsUOFhYU9lOuGhoaq7du339extz7D33//Xfn4+Ci9Xq+UUurKlSvq0qVL/yq++/2cRo0apaZOnaqUUmrq1Klq9OjRRfb56aefVKtWrVR2drZKT09XHh4eKiUlpch+nTt3VkuXLlVKKTVy5Eg1ceJEpZRSx48fVy1btlRKKXXkyBHl4OCgrl+/rrKzs1VQUJA6depUsbEV9+8eOKjuM180wUpvSt5PywoYblZ6pb356ZCTk8P333+Pk5MTr7zyCqmpqVy6dAmAWrVqPdAZ4IQQQghhuv7++2+CgoJwdnYmKCiI8+fPA3DmzBl8fHzw8vJiwoQJWpU4Li4OR0dHAI4dO4a3tzeurq44Oztz+vRpxo4dy5kzZ3B1dWXUqFGF9s/JyWHkyJE4OTnh7OzM3Llzbxubr6+v9vsPQHR0NAEBAXh4eBAaGkp8fDwABw4cwNnZGV9fX0aNGqVdb8mSJXTp0oV27doREhICwIwZM/Dy8sLZ2Zn3338fgOvXrxMWFoaLi0uhCuDYsWOxt7fH2dmZkSNHAjBx4kRmzpwJwOHDh/Hx8cHZ2ZlOnTpp1cXAwEDGjBmDt7c3L7zwArt37y5yb99//z2+vr60a9dO29aiRQst9nxRUVH4+fnh5uaGn58fJ0+eLPHZl3QfgYGBHDx4kMmTJ7Nnzx4GDhzIqFGjClWUr1+/Tt++ffHy8sLNzY3169eX+AzzxcfHY2Njo3Uc2tjYUKtWLe0z8fPzw8XFBW9vb9LS0tDr9fTp0wcnJyfc3NzYsWPHPX1Ot7N+/XrCw8MBCA8PZ926dUX2iY2NJSAgAHNzc8qWLYuLiwubN28utE9aWhrbt2/XKr2xsbEEBQUBYGdnR1xcHAkJCRw/fhwfHx/KlCmDubk5AQEBrF279o5xPgimN3tzoTG9CejKlKGULE/zxNPr9Xh4eBAbG4uDgwPLly+nS5cumJmZGTs0IYQQQjwgk348Ruzl1Ad6TvtaFXi/ncM9HTNo0CB69+5NeHg4ixcvZsiQIaxbt46hQ4cydOhQunfvzvz584s9dv78+QwdOpSePXuSlZVFTk4O06ZN4+jRoxw+fBjIS5LzLViwgHPnzvHHH39gbm7OtWvXbhvb5s2bteQjOzubwYMHs379eqpVq0ZERATjxo1j8eLF9OnThwULFuDn58fYsWMLnWPv3r3ExMRQpUoVtmzZwunTp4mKikIpRfv27dm1axdXrlyhVq1abNy4EYCUlBSuXbvG2rVrOXHiBDqdjuTk5CLx9e7dm7lz5xIQEMCECROYNGmS1uZqMBiIiopi06ZNTJo0ia1btxY69ujRo3h4eNz2/iEv0dq1axfm5uZs3bqV9957j9WrVxf77Ddt2lTkPgqaMGEC27dvZ+bMmXh6ehIZGam9N2XKFFq2bMnixYtJTk7G29ubVq1aFXmGBYWEhDB58mReeOEFWrVqRdeuXQkICCArK4uuXbsSERGBl5cXqampWFtbM3v2bACOHDnCiRMnCAkJ4dSpU3f9Ofn7+9OmTRsWLlyoJdf5EhISqFmzJgA1a9Yk8eZ8RwW5uLgwadIkRowYQUZGBjt27MDe3r7QPmvXriUoKIgKFSpox6xZs4ZmzZoRFRXF33//zcWLF3F0dGTcuHEkJSVhbW3Npk2b8PT0vOPn+SCYXtKbmZb307IC2YmJWFSvLpMjPKGys7OJjIwkODgYKysrunTpgpOTE506daJUKdNrYhBCCCHEo7F3717WrFkDwKuvvsro0aO17fnVsh49emiVzoJ8fX2ZMmUKFy9epHPnzjRs2PC219q6dSsDBw7E3Dzv1/Zbk6h8o0aNYvTo0SQmJrJv3z4ATp48ydGjRwkODgbyqsY1a9YkOTmZtLQ0/Pz8tFh/+ukn7VzBwcHadbZs2cKWLVtwc3MDID09ndOnT9O8eXNGjhzJmDFjaNu2Lc2bN8dgMGBlZUX//v0JCwsrMsY2JSWF5ORkAgICgLzqYpcuXbT3O3fuDICHh0ehxP9epaSkEB4ezunTpyOWQfUAACAASURBVNHpdGRnZwPFP3snJ6ci93G3tmzZwoYNG7Qqtl6v16r+BZ9hQeXKlSM6Oprdu3ezY8cOunbtyrRp0/Dw8KBmzZp4eXkBaAnknj17GDx4MJCXzNetW1dLeu/mc/L392fTpk33/AzzhYSEaBXoatWq4evrq/1dzLd8+XL69++vvR47dixDhw7F1dVVq1Cbm5vTuHFjxowZQ3BwMOXKlcPFxaXIuR4WE0x68yeyKo8hIVFam59AWVlZLF26lI8++oi4uDiOHTuGvb09EydONHZoQgghhHiI7rUi+6jcSwGlR48eNGnShI0bNxIaGsrChQupX79+ifsrpe7q/DNmzKBz587MmTOH8PBwoqOjUUrh4ODA3r17C+1b0oRF+cqWLVvo+u+++26xq15ER0ezadMm3n33XUJCQpgwYQJRUVFs27aNFStW8Nlnn7F9+/Y7xp4vv+XXzMwMg8FQ5H0HBwd27tx5x/OMHz+eFi1asHbtWuLi4ggMDASKf/YtW7Ys9j7uhlKK1atX06hRo0Lb9+/fX+gZ3srMzIzAwEACAwNxcnJi6dKluLu7F/s55w1lLd7dfk4lqVGjBvHx8dSsWZP4+Hiql5AXjRs3jnHjxgF5z7DgFzVJSUlERUUValOuUKECX3/9tRaXra2ttuZuv3796NevHwDvvfcetWvXvut4/w3TK4fpCya9CbJG7xMkMzOTL774ggYNGvD6669TvXp1Nm7cSOPGjY0dmhBCCCFMiJ+fHytWrABg2bJlNGvWDAAfHx9Wr14NoL1/q7Nnz1K/fn2GDBlC+/btiYmJoXz58qSlpRW7f0hICPPnz9eSwNu1N5cqVYqhQ4eSm5vLL7/8QqNGjbhy5YqW9GZnZ3Ps2DEqV65M+fLltYpwSbEChIaGsnjxYtLT0wG4dOkSiYmJXL58mTJlytCrVy9GjhzJoUOHSE9PJyUlhTZt2jBr1iytXTtfxYoVqVy5sjZe99tvv9WqvnejR48e/P7771orMuS1cx85cqTQfikpKTz77LNA3tjXfMU9++Lu426FhoYyd+5cLTH9448/7njMyZMnOX36tPb68OHD1K1bFzs7Oy5fvsyBAweAvHGyBoMBf39/li1bBuTNXH3+/PkiSXZ+LMV9TrfTvn17li5dCsDSpUvp0KFDkX1ycnJISkoCICYmhpiYmELjlFeuXEnbtm2xsrLStiUnJ5OVlQXAwoUL8ff31yrX+TGdP3+eNWvW0L1799vG+KCYZqW3dHmUrhSGxERZrugJkpaWxqhRo3BxcWHhwoUEBwdLa7oQQgghHqqMjIxC1agRI0YwZ84c+vbty4wZM6hWrZpW1Zo1axa9evXiv//9L2FhYVSsWLHI+SIiIvjuu++wsLDgmWeeYcKECVSpUoWmTZvi6OhI69atefvtt7X9+/fvz6lTp3B2dsbCwoIBAwYwaNCgEuPV6XT85z//4eOPPyY0NJRVq1YxZMgQUlJSMBgMDBs2DAcHBxYtWsSAAQMoW7YsgYGBxcYKeUn38ePH8fX1BfLac7/77jv++usvRo0aRalSpbCwsOCLL74gLS2NDh06oNfrUUrx6aefFjnf0qVLGThwIBkZGdSvX197dnfD2tqan376iWHDhjFs2DAsLCxwdnZm9uzZWmIGMHr0aMLDw/nkk09o2bLlbZ/9gQMHitzH3Ro/fjzDhg3D2dkZpRT16tUr1CZenPT0dAYPHkxycjLm5uY0aNCABQsWULp0aSIiIhg8eDA3btzA2tqarVu38tZbbzFw4ECcnJwwNzdnyZIlxS67WdLnVL169RLH9I4dO5ZXXnmFRYsWUadOHVauXAnAwYMHmT9/PgsXLiQ7O1tr+a5QoQLfffddoZbkFStWFBkTfvz4cXr37o2ZmRn29vYsWrRIe++ll14iKSkJCwsLPv/8cypXrnzXz/vf0N2uZP6k8PT0VAcPHizx/SZfvwTA/j6rYd3bcHYHhj6/cdrXjxrvvUuV3r0fVajiHly/fp0vv/ySyMhI1q9fj06n49y5c9SrV0+SXSGEEMJEHD9+/Inp6srIyMDa2hqdTseKFStYvny5NqPv4yY9PV2bXXratGnEx8drkyYJYWzF/bvX6XTRSqn7mvnKBCu9KbcsVyTtzY+b9PR05s2bx8yZM7ly5QotW7YkJSWFSpUqaeMBhBBCCCEeN9HR0QwaNAilFJUqVWLx4sXGDqlEGzduZOrUqRgMBurWrVuoDViIp43pJb361LzlivKTXmlvfqwcOnSIkJAQkpKSCAkJYfz48do4GSGEEEKIx1nz5s35888/jR3GXenatStdu3Y1dhhCPBKmN5FVZqq2XBGAhUxkZXTJycnkt6c7ODjQpk0b9u7dyy+//CIJrxBCCCGEEOJfMc1Kb5X6GP6Xl/Sa29gYOSDTde3aNWbPns3s2bOpVKkSZ86cwdLSkm+++cbYoQkhhBBCCCGeEiZb6TUkJGBWtSq60qWNHZHJuXr1KuPGjaNevXpMnjyZVq1asW7dOszMzIwdmhBCCCGEEOIpY3pJb/6Y3sREGc9rJHv37mXq1Km0adOGI0eOsGrVKlxdXY0dlhBCCCGEEOIpZFpJryETcjK1Mb0W1STpfRTi4+MZMWIEU6ZMAaBt27acPHmSFStW4OjoaOTohBBCCCFKlr+sD8CmTZto2LAh58+fZ+LEiZQpU4bEm/PE3LrvwxYZGYlOp+PHH3/UtrVt25bIyMjbHjdhwgS2bt16230mTpzIzJkzi2yPi4t7JL+7RUVF4e/vT6NGjbCzs6N///5kZGSwZMmS265RfK/atGlDcnIyAHPmzKFx48b07NmTDRs2MG3atPs+b25uLkOGDMHR0REnJye8vLw4d+7cgwr7npw7d44mTZrQsGFDunbtSlZWVpF9srKy6NOnD05OTri4uGh/hzIyMggLC8POzg4HB4ci6/ECrFq1Cp1OR8HlY2NiYvD19cXBwQEnJyf0ev1Du7+7ZVpJb2Za3k+rihgSEjCXSaweqosXLzJkyBBsbW2ZM2cOCTdnzNbpdDRs2NDI0QkhhBBC3L1t27YxePBgNm/eTJ06dQCwsbHhv//97wO/Vk5Ozl3tV7t2ba2ocLfyh5YZw93cV0JCAl26dGH69OmcPHmS48eP8+KLL5KWlvbA49m0aROVKlUCYN68eWzatIlly5bRvn37YhO8khgMhkKvIyIiuHz5MjExMRw5coS1a9dq17lft17jbo0ZM4bhw4dz+vRpKleuzKJFi4rs89VXXwFw5MgRfv31V9555x1yc3MBGDlyJCdOnOCPP/7gt99+4+eff9aOS0tLY86cOTRp0qRQnL169WL+/PkcO3aMyMhILCws7iv2B8m0kl59CgDKrAw5165Je/ND9NVXX/H888/zxRdf0KtXL06ePMmcOXOMHZYQQgghxD3bvXs3AwYMYOPGjTz//PPa9r59+xIREcG1a9eKHPPdd9/h7e2Nq6srb7zxhpbwvfnmm3h6euLg4MD777+v7Z8/10mzZs1YuXIlc+bMwd7eHmdnZ7p161ZsXC4uLlSsWJFff/21yHvR0dEEBATg4eFBaGgo8fHxALz22musWrUKyEv67OzsaNasGUOGDKFt27ba8bGxsQQGBlK/fv1Cv8MZDAbCw8Nxdnbm5ZdfJiMjA8j7UsDNzQ0nJyf69u1LZmbmfd3X559/Tnh4OL6+vkBeseTll1+mxi3Fqh9//JEmTZrg5uZGq1attOLKzp07cXV1xdXVFTc3N9LS0oiPj8ff3x9XV1ccHR3ZvXu3FtvVq1cZOHAgZ8+epX379nz66aeFKspXrlzhpZdewsvLCy8vL3777Tcgrxr++uuvExISQu/evQvFFh8fT82aNSlVKi/Vql27NpUrVwZg8+bNuLu74+LiQlBQEJA3uWvHjh1xdnbGx8eHmJiYYq+Rk5PDqFGj8PLywtnZmS+//LLYvxf5lFJs376dl19+GYDw8HDWrVtXZL/Y2FgtlurVq1OpUiUOHjxImTJlaNGiBQClS5fG3d2dixcvaseNHz+e0aNHY2VlpW3bsmULzs7OuLi4AFC1atXHYt4e05q9OTMVAMN1BYB5dUl6H6SzZ89iYWHBc889h4eHB3369GHs2LHUq1fP2KEJIYQQ4mnw81j435EHe85nnKB1ya2smZmZdOjQgcjISOzs7Aq9V65cOfr27cvs2bOZNGmStv348eNERETw22+/YWFhwVtvvcWyZcvo3bs3U6ZMoUqVKuTk5BAUFERMTAzOzs4AWFlZsWfPHgBq1arFuXPnsLS01Fpwi/Of//yH//znPwQHB2vbsrOzGTx4MOvXr6datWpEREQwbtw4Fi9erO2j1+t544032LVrF7a2tnTv3r3QeU+cOMGOHTtIS0ujUaNGvPnmmwCcPHmSRYsW0bRpU/r27cu8efMYNGgQr732Gtu2beOFF16gd+/efPHFFwwbNuye7+vo0aOEh4eXeL/5mjVrxr59+9DpdCxcuJCPP/6Y//73v8ycOZPPP/+cpk2bkp6ejpWVFQsWLCA0NJRx48aRk5OjJer55s+fz+bNm9mxYwc2NjYsWbJEe2/o0KEMHz6cZs2acf78eUJDQzl+/DiQ98XCnj17sLa2LnS+V155hWbNmrF7926CgoLo1asXbm5uXLlyhQEDBmjPPP/Lkvfffx83NzfWrVvH9u3b6d27N4cPHy5yjQULFlCxYkUOHDhAZmYmTZs2JSQkBFtbW1xdXbVj8iUlJVGpUiXMzfNSvtq1a3Pp0qUiz9LFxYX169fTrVs3Lly4QHR0NBcuXMDb21vbJzk5mR9//JGhQ4cC8Mcff3DhwgXatm1bqBX+1KlT6HQ6QkNDuXLlCt26dWP06NF3/DwfNtNKevV5SW92el57gKzR+2CcPn2ajz76iG+//ZbevXuzePFi3N3dmT9/vrFDE0IIIYT4VywsLPDz82PRokXMnj27yPtDhgzB1dWVd955R9u2bds2oqOj8fLyAuDGjRtUv1ls+eGHH1iwYAEGg4H4+HhiY2O1pLdr167aOZydnenZsycdO3akY8eOJcbXvHlzAK16CXmJ6dGjR7VEOCcnh5o1axY67sSJE9SvXx9bW1sAunfvzoIFC7T3w8LCsLS0xNLSkurVq2uV1Oeee46mTZsC0KtXL+bMmUNwcDC2tra88MILQF5F8fPPP9eS3vu5rzu5ePEiXbt2JT4+nqysLO0+mjZtyogRI+jZsyedO3emdu3aeHl50bdvX7Kzs+nYseM9TaC6detWYmNjtdepqalaq3X79u2LJLyQl1yePHmS7du3s337doKCgli5ciUZGRn4+/trsVapUgWAPXv2sHr1agBatmxJUlISKSkpRa6xZcsWYmJitEp9SkoKp0+fxtbWtkjCC3mV3lvpdLoi2/r27cvx48fx9PSkbt26+Pn5aYky5FX3u3fvzpAhQ6hfvz65ubkMHz680JcDBffds2cPBw4coEyZMgQFBeHh4aFVko3FtJLe/Epvat4AbhnT+++cOHGCKVOm8P3331O6dGkGDRrEqFGjjB2WEEIIIZ5Wt6nIPiylSpXihx9+oFWrVnz00Ue89957hd6vVKkSPXr0YN68edo2pRTh4eFMnTq10L7nzp1j5syZHDhwgMqVK/Paa68VmuSnbNmy2p83btzIrl272LBhAx988AHHjh0rlIgUNG7cOKZMmaK9r5TCwcGBvXv3lnhfxSVEBVlaWmp/NjMz08aU3po06XS6O57rXu7LwcGB6OhoOnTocNtzDh48mBEjRtC+fXsiIyOZOHEiAGPHjiUsLIxNmzbh4+PD1q1b8ff3Z9euXWzcuJFXX32VUaNGFWlJLklubi579+4tNrkteF+3srS0pHXr1rRu3ZoaNWqwbt06goODi006b5ecFryGUoq5c+cSGhp6V7Hb2NiQnJyMwWDA3NycixcvUqtWrSL7mZub8+mnn2qv/fz8Cs2/8/rrr9OwYUPtS4y0tDSOHj1KYGAgAP/73/9o3749GzZsoHbt2gQEBGBjYwPkTRZ26NAhoye9Jjam92bSm5zX0iDtzf/OZ599xpo1axgxYgTnzp1j1qxZPPvss8YOSwghhBDigSpTpgw//fQTy5YtK3YioBEjRvDll19qiWFQUBCrVq3SZna+du0af//9N6mpqZQtW5aKFSuSkJBQaFKggnJzc7lw4QItWrTg448/Jjk5mfT09BLjCwkJ4Z9//uHPP/8EoFGjRly5ckVLerOzszl27FihY+zs7Dh79ixxcXFA3uRLd+P8+fPaeZcvX06zZs2ws7MjLi6Ov/76C4Bvv/2WgICA+7qvQYMGsXTpUvbv369t++677/jf//5XaL+UlBTt986lS5dq28+cOYOTkxNjxozB09OTEydO8Pfff1O9enUGDBhAv379OHTo0F3dK+Q9288++0x7XVxF9VaHDh3i8uXL2j3HxMRQt25dfH192blzpzaTc357s7+/P8uWLQPyZuW2sbGhQoUKRc4bGhrKF198QXZ2NpDXSnz9+vUS49DpdLRo0UKrDC9durTYLxMyMjK08/z666+Ym5tjb28P5LXPp6SkMGvWLG3/ihUrcvXqVeLi4oiLi8PHx4cNGzbg6elJaGgoMTExZGRkYDAY2Llzp3YuYzKtpPdmpTf7Whq60qUx+5ezqJmamJgYunTpwq5du4C88QdxcXHMmDGDZ555xsjRCSGEEEI8PFWqVGHz5s18+OGHrF+/vtB7NjY2dOrUSZu8yd7eng8//JCQkBCcnZ0JDg4mPj4eFxcX3NzccHBwoG/fvlqb8K1ycnLo1asXTk5OuLm5MXz48DvO/jtu3DhtkqHSpUuzatUqxowZg4uLC66urvz++++F9re2tmbevHm8+OKLNGvWjBo1alCxYsU7PofGjRuzdOlSnJ2duXbtGm+++SZWVlZ8/fXXdOnSBScnJ0qVKsXAgQPv675q1KjBihUrGDlyJI0aNaJx48bs3r27SBI4ceJEunTpQvPmzbWqIsCsWbNwdHTExcUFa2trWrduTWRkpDax1erVq7VxqXdjzpw5HDx4EGdnZ+zt7e9q+F5iYiLt2rXD0dERZ2dnzM3NGTRoENWqVWPBggV07twZFxcXre174sSJ2jXGjh1bKIkvqH///tjb2+Pu7o6joyNvvPGG9kVLSS3b06dP55NPPqFBgwYkJSXRr18/ADZs2MCECRO0eN3d3WncuDHTp0/n22+/BfJayKdMmUJsbCzu7u64urqycOHC29575cqVGTFiBF5eXri6uuLu7k5YWNgdn9nDprtTO8KTwNPTUxVcG+pWTb5+CYD9tt4Q+RGX0gdw4/CfNNhadKY7UdShQ4f44IMPWLduHRUqVGDu3Ll33RIihBBCCPFvHD9+nMaNGxs7jKdSeno65cqVQynF22+/TcOGDRk+fLixwxKi2H/3Op0uWinleT/nM71Kr0VZDIlXpLX5LvXp0wcPDw9trERcXJwkvEIIIYQQT4GvvvoKV1dXHBwcSElJ4Y033jB2SEI8FKY1kZU+BawqYEhIwNJevjEsSVRUFB4eHpiZmeHp6UmDBg0YNGjQXbW8CCGEEEKIJ8Pw4cOlsitMgslVelXp8mRfuYJFdZm5+Va7du2iVatWNGnShJUrVwLw9ttvM27cOEl4hRBCCCGEEE8k00p69ankliqPysiQ9uablFJs376dwMBAAgICOHr0KDNnzqRdu3bGDk0IIYQQQggh/jXTam/OTMOQmbfGlqzRmyc3N5e33nqL1NRUZs2axYABAyhTpoyxwxJCCCGEEEKIB8K0Kr2ZqRgyLQCwqGGalV6lFD///DNhYWGkp6djZmbG+vXrOXv2LEOHDpWEVwghhBBCCPFUMa2kV59K9g0zAJNrb1ZKsWHDBry9vWnTpg3Hjh3jzJkzQN4C5lZWVkaOUAghhBDi8TNlyhQcHBxwdnbG1dWV/fv3Gy2WWbNmkZGRUWT7xIkTeffddwttO3z48H0t9XT48GE2bdp03zHmO3XqFG3atKFBgwY0btyYV155hYSEBCIjI2nbtu2/Pn++/v37ExsbC8DKlStp3LgxLVq04ODBgwwZMuRfnftx+eyvXbtGcHAwDRs2JDg4mH/++afY/caMGYOjoyOOjo5ERERo25s3b46rqyuurq7UqlWLjh07FjruwIEDmJmZsWrVKgB27Nih7e/q6oqVlRXr1q17eDf4CJhYe3Mqhut5fzSl9ubk5GRatGjB4cOHqV+/PosWLeLVV1/FwsLC2KEJIYQQQjy29u7dy08//cShQ4ewtLTk6tWrZGVlGSWWnJwcZs2aRa9evYp05nXv3p3WrVszdepUbduKFSvo0aPHPV/n8OHDHDx4kDZt2tz1MQaDAXPz/08r9Ho9YWFhfPLJJ9o8MTt27ODKlSv3HM+dLFy4UPvzokWLmDdvHi1atADA0/Pul3S99R4exmd/6zXu1rRp0wgKCmLs2LFMmzaNadOmMX369EL7bNy4kUOHDnH48GEyMzMJCAigdevWVKhQgd27d2v7vfTSS3To0EF7nZOTw5gxYwgNDdW25ecNkJdwN2jQgJCQkHuO+3FiMpVenVKQnYHhei6lKlak1FNe2czJyeHQoUMAVKpUCXd3d5YuXcrJkyfp27evJLxCCCGEEHcQHx+PjY0NlpaWANjY2FCrVi0A6tWrx9WrVwE4ePAggYGBQF7V9dVXX6Vly5Y0bNiQr776CoDIyEj8/f3p1KkT9vb2DBw4kNzcXACWL1+Ok5MTjo6OjBkzRrt+uXLlmDBhAk2aNGHKlClcvnyZFi1aaEldvkaNGlGpUqVClcgffviBbt26AbBlyxZ8fX1xd3enS5cupKenA3kVPj8/P1xcXPD29iYlJYUJEyYQERGBq6srERERXLt2jY4dO+Ls7IyPjw8xMTHafb7++uuEhITQu3fvQvF8//33+Pr6FpoYtUWLFjg6OhbaLyoqCj8/P9zc3PDz8+PkyZMAHDt2DG9vb1xdXXF2dub06dNcv36dsLAwXFxcClUyAwMDOXjwIJMnT2bPnj0MHDiQUaNGFaooX79+nb59++Ll5YWbmxvr168HYMmSJXTp0oV27doVSepu99nf+tzS0tLQ6/X06dMHJycn3Nzc2LFjR4nXmDFjBl5eXjg7O/P+++9zJ+vXryc8PByA8PDwYquusbGxBAQEYG5uTtmyZXFxcWHz5s2F9klLS2P79u2FKr1z587lpZdeonoJXbCrVq2idevWT/wQSJOp9JYiB4Ds1GwsqlczcjQPT05ODhEREXz44YecOXOGc+fOUatWLRYtWmTs0IQQQggh/pXpUdM5ce3EAz2nXRU7xniPKfa9kJAQJk+ezAsvvECrVq3o2rUrAQEBdzxnTEwM+/bt4/r167i5uREWFgbkJXmxsbHUrVuXF198kTVr1uDn58eYMWOIjo6mcuXKhISEsG7dOjp27Mj169dxdHRk8uTJACxevJgdO3ZgY2NT5Jrdu3dnxYoVNGnShH379lG1alUaNmzI1atX+fDDD9m6dStly5Zl+vTpfPLJJ4wdO5auXbsSERGBl5cXqamplClThsmTJ3Pw4EE+++wzAAYPHoybmxvr1q1j+/bt9O7dW6sCRkdHs2fPHqytrQvFcvToUTw8PO787O3s2LVrF+bm5mzdupX33nuP1atXM3/+fIYOHUrPnj3JysoiJyeHTZs2UatWLTZu3AhASkpKoXNNmDCB7du3M3PmTDw9PYmMjNTemzJlCi1btmTx4sUkJyfj7e1Nq1atgLyKbkxMDFWqVCl0vpI++6ysrCLPzdramtmzZwNw5MgRTpw4QUhICKdOnSpyjS1btnD69GmioqJQStG+fXt27dqFv78/bdq0YeHChVpynS8hIYGaNWsCULNmTRITE4s8SxcXFyZNmsSIESPIyMhgx44d2NvbF9pn7dq1BAUFUaFCBQAuXbrE2rVr2b59OwcOHCj2M1qxYgUjRowo+UN8QphMpbcUed+kGVJuYP4UrtFrMBj45ptvsLe3p2fPnpiZmfHtt99Sw4TauIUQQgghHqRy5coRHR3NggULqFatGl27dmXJkiV3PK5Dhw5YW1tjY2NDixYtiIqKAsDb25v69etjZmZG9+7d2bNnDwcOHCAwMJBq1aphbm5Oz5492bVrFwBmZma89NJLdxVrt27dWLVqFbm5uaxYsYLu3bsDsG/fPmJjY2natCmurq4sXbqUv//+m5MnT1KzZk28vLwAqFChQrGtt3v27OHVV18FoGXLliQlJWkJZ/v27YskvPciJSWFLl264OjoyPDhwzl27BgAvr6+fPTRR0yfPp2///4ba2trnJyc2Lp1K2PGjGH37t1UrFjxrq+zZcsWpk2bhqurK4GBgej1es6fPw9AcHBwkYQXSv7sS3puBZ+TnZ0ddevW1ZLegtfYsmULW7Zswc3NDXd3d06cOMHp06cBtMT+foSEhNCmTRv8/Pzo3r07vr6+RT7P5cuXa38vAIYNG8b06dMxMzMr9pzx8fEcOXKkUOvzk8pkKr1mKq/Sa0i+jqXL05cIxsXFaS0Vq1evpmPHjpQqZTLfaQghhBDCBJRUkX2YzMzMCAwMJDAwECcnJ5YuXcprr72Gubm51p6s1+sLHaPT6Yp9Xdx2pVSJ17aysioxIbnVc889R7169di5cyerV69m7969QN5kpsHBwSxfvrzQ/jExMUXiKU5x8eUfV7Zs2WKPcXBwYOfOnXc89/jx42nRogVr164lLi5OaxHv0aMHTZo0YePGjYSGhrJw4UJatmxJdHQ0mzZt4t133yUkJIQJEybc8Rr597B69WoaNWpUaPv+/ftLvAco/rN3d3cv9rnd7nMseA2lFO+++y5vvPHGXcUOUKNGDeLj46lZsybx8fEltiKPGzeOcePGAXnPsGHDhtp7SUlJREVFsXbtWm3bwYMHnc/VNAAAIABJREFUtRb4q1evsmnTJszNzbX25x9++IFOnTo9FcMiTSYrKkUOKhcM/6Rh/hS0N2dmZvLll18yaNAgABo0aEBUVBR//PEHnTt3loRXCCGEEOJfOnnypFaFg7xJnurWrQvkjemNjo4GYPXq1YWOW79+PXq9nqSkJCIjI7WqYFRUFOfOnSM3N5eIiAiaNWtGkyZN2LlzJ1evXiUnJ4fly5eX2EJdvnx50tLSSoy3e/fuDB8+nOeff57atWsD4OPjw2+//cZff/0FQEZGBqdOncLOzo7Lly9rba1paWkYDIYi1/D392fZsmVA3rhkGxsbrT22JD169OD333/XWpEBNm/ezJEjRwrtl5KSwrPPPgtQqIJ+9uxZ6tevz5AhQ2jfvj0xMTFcvnyZMmXK0KtXL0aOHKnNXXM3QkNDmTt3rpaY/vHHH3c8pqTPvqTnVvA5nTp1ivPnzxdJsvNjWbx4sTau+tKlS8W2KxfUvn17li5dCsDSpUsLTUSVLycnh6SkJCDvC42YmJhC45RXrlxJ27ZtC63Ycu7cOeLi4oiLi+Pll19m3rx5hcb73loZfpKZTGZkRi6GzFKQm4vFE9zyq9fr+fzzz2nQoAEDBw4kOjqaGzduAODh4XFX39gJIYQQQog7S09PJzw8HHt7e5ydnYmNjWXixIkAvP/++wwdOpTmzZsXqcZ6e3sTFhaGj48P48eP11pWfX19GTt2LI6Ojtja2tKpUydq1qzJ1KlTadGiBS4uLri7uxeb1AC8/vrrtG7dushEVvm6dOnCsWPHtOodQLVq1ViyZAndu3fXJqM6ceIEpUv/X3t3Hh5Vle57/PuSIGGeBLsxyiCTGEjEIGBsERsRAZsG8YKKCAg02ngEcYiojErbOACKgCg2At0g0gdEZEbh+qgM4YqCDIKYKxg5yBTmKVnnj12prlQGihAy/j7PkydVtdde+62dRchba7qCDz/8kMcff5zo6GjuvPNOTp8+TevWrdm6dat/IasRI0aQkJBAkyZNiI+P9ydf2SldujSLFi3irbfeol69ejRq1Ijp06dn6KF85plneO6554iLiyMlJcX/+ocffkhUVBQxMTFs376dnj17snnzZv/iVi+//DIvvPDCBeNI8+KLL3Lu3DmaNGlCVFQUL7744gXPyepnn9V9e+yxx0hJSaFx48b+odBpi2AFatu2LQ888AAtW7akcePGdO3a1f8hQ/v27UlKSspwTnx8PCtWrKBevXqsWLGC+Ph4wOup7du3LwDnzp3jD3/4A40aNaJ///7MmjUr3fDmwCHvoUhMTGTPnj0hzWEvDCy7rvjCIjY21iUkJGR5vPk/7qViymE+/vY7EldUI3LS25S/4448jDB3fP3113Tt2pWkpCTi4uIYPnw4bdq0UaIrIiIiRda2bdtytN9sfhkxYgTlypXjqaeeSvf66tWree2111i0aFE+RSZSeGT2797MNjrnQt+HKkDxmdNLCudPeZ/CFaaFrE6cOMGvv/5K3bp1qV+/PjExMcyaNYvbb79dya6IiIiIiMgFFJukt4RL5dwpbzR3YZjTe+zYMSZNmsRrr71GzZo12bBhA1WrVk03N0JERERECpa04c/B0hZEEpG8V4zm9KZw/kwpCAsjvGrV/A4nS8nJybz88svUqlWL+Ph4YmNjefPNN9WrKyIiIiIikgPFJukt4Ut6w6tVw0Jc+j0/zJkzhxdeeIFbbrmFdevWsWTJEm655Zb8DktERERERKRQKjbDm8NcKudPhxOexb5W+eXgwYOMGzeOunXr0qtXL3r16kWzZs1o2rRpfocmIiIiIiJS6BWrnt5zJ0tQ8qqCkfTu37+f+Ph4atWqxcsvv+zfL6xUqVJKeEVERERERHJJsUp6z59ILRArN0+aNInatWszduxYOnbsyObNm5kwYUJ+hyUiIiIiQcLCwoiJieGGG24gOjqaN954g9TU1BzVNWzYMFauXJnl8SlTpjBjxoychgrA5s2biYmJISYmhipVqlC7dm1iYmJo06bNJdWbGeccY8eOpUGDBv59df/5z38CcOutt7Jp06Zcuc66desYPHgwAKdOneKOO+4gJiaGefPm0bt3b3bs2JHjurdt20arVq2IiYnh+uuv59FHH82VmHNi2rRp1KtXj3r16jFr1qxMy3zzzTe0aNGCxo0b06lTJ44fPw54HWq33347ZcuWZdCgQf7yR44c8beHmJgYqlatmmE7rTlz5mBmufbzKoiKzfDmUmdTSD2dmm/Dm5OSkihdujSVK1emZs2adOnSheeff56GDRvmSzwiIiIicmGlS5f2JwP79+/ngQceIDk5mZEjR150XaNGjcr2+IABA3IUY6DGjRv74+3VqxcdO3aka9euGcqdP3+e8PBLSwXefvttPv/8cxISEihfvjxHjhxh4cKFl1RnZpo3b07z5s0B2LhxY7oELbP3lp3g9z1w4ECeeeYZOnTogHOOLVu2XHK8Obm3Bw4cYMyYMWzcuJGUlBRiY2O55557qFixYrpyffr0YeLEicTFxTF16lRef/11hg8fTpkyZfyjR3ft2uUvX6lSpXTJbHR0NF26dPE/P3r0KJMmTSI2Nkfb3xYaxaant+LxFADC83h48549exg4cCB16tThtddeA6BDhw7MnDlTCa+IiIhIIVK9enWmTp3KxIkTcc6RkpLC008/TbNmzWjSpAnvvPOOv+zYsWNp3Lgx0dHRxMfHA14SOm/ePADi4+Np1KgRTZo08fe8jRgxwv/34qZNm2jRogVNmjShc+fOHD58GPC2Pnr22We5+eabqV+/Pl988UXI8a9cuZI2bdrQvXt3brzxRgA++OADbr75ZmJiYnjsscf8vdhLliyhZcuWNG3alG7dunHixIkM9Y0ZM4YpU6ZQvnx5wEuwevbsmaFc//79iY2N5YYbbkiX+D/99NP+e/Dss88CXq9jVFQU0dHRtG7d2h/3n//8Z5KSkujVqxcJCQnExMSQmJiYrkc5q5gjIyMZPXo0cXFxzJ8/P11sv/76K5GRkQCYGY0bNwa8xHXw4MFERUXRpEkTJk2aBMCKFSuIiYmhcePG9OvXj7Nnz2Z6jZ07d3LXXXdx0003cdttt/HDDz9k+7NZsmQJ7dq1o1KlSlStWpU77riD5cuXZyj3448/EhcXB8Cdd97Jv//9bwDKlStHXFwcERERWV5j27ZtJCcn07JlS/9rQ4cOZejQoZQqVSrb+Aq7YtPTW/FEKlCCklflzfDmxMREXnnlFd5//33A+yXXt2/fPLm2iIiISFG0b8wYzmzbnqt1lrq+Ib8bOjTk8nXq1CE1NZX9+/fz8ccfU7FiRTZs2MCZM2eIi4ujbdu2bN++nQULFrBu3TrKlCnDoUOH0tVx6NAh5s+fz/bt2zEzjhw5kuE6PXv25K233qJVq1YMGzaMkSNHMn78eMBLyNavX8/ixYsZOXJktkOmg61du5atW7dy7bXXsmXLFubPn89XX31FeHg4/fv3Z86cObRp04ZXXnmFVatW+XsQJ0yYwNCA+3T48GHOnTtHzZo1L3jNV155hSpVqnD+/Hlat25N165dqVq1KosXL+b7779Pdw9GjhzJ6tWrueqqqzLclxo1ajBlyhQmTpzIggUL0h3bv39/tjGXLVuWL7/8MkNsTz75JLfddpv/Z9e7d28qVqzI5MmTSUpK4ttvvyUsLIxDhw5x8uRJ+vTpw+rVq7nuuut48MEHmTp1KgMHDsxwjdatW/Pee+9x3XXX8eWXXzJw4ECWL1/O/Pnz2bx5M8OGDUsXxy+//MI111zjfx4ZGckvv/ySId6GDRvy6aef0qFDBz766CP27NlzwfufZvbs2XTv3t2/FeqGDRvYv38/7dq146WXXgq5nsKoeCS9zlHhmAPIs+HNTz31FJ988gl9+/bl2WefDekXgoiIiIgUfM55f1cuX76c7777zt97m5yczM6dO1m5ciW9e/emTJkyAFSpUiXd+RUqVCAiIoK+ffvSoUMHOnbsmO54cnIyR44coVWrVgA8/PDD3Hffff7jacNTb7rpJhITEy8q9pYtW3LttdcCXg/qhg0b/ENbT506xTXXXEOZMmXYunWrf9vMs2fPcuutt2Z6D0Ixe/Zspk2bxvnz50lKSmLr1q106tSJEiVK0K9fv3T3IC4ujp49e3LfffelG4Z7IV999VW2MXfr1i3T8/r27cvdd9/NsmXLmD9/PlOnTmXTpk2sXLmSQYMGEebb6rRKlSps3LiRevXqcd111wHeBxPTpk3zJ71p1zhy5Ahr167l3nvv9V/n/PnzAHTu3JnOnTtniMM5509G0wQ/B5g+fTpPPPEEw4YNo1OnTpQsWTK0G4TXi/7RRx8BkJqaypAhQ7KcO1zUFIukN4xUyntzvAm/TD29P/zwA2PGjGHo0KHUr1+fsWPHMn78eP9wCRERERG5NBfTI3u57N69m7CwMKpXr45zjrfeeou77rorXZmlS5dmmrCkCQ8PZ/369axatYo5c+YwceJEPvvss5BjSBuKGhYW5k+mQlW2bFn/Y+ccffr0YfTo0enKzJ8/n3bt2jFz5sws66lSpQolS5bk559/9ifRmdm5cycTJkxg/fr1VKpUiR49enD69GlKlixJQkICK1asYM6cOUyePJnly5fz7rvvsm7dOhYtWkR0dDTfffddSO/LOZdtzIHvO9jVV19Nnz596NOnDw0bNmTbtm2ZJqEXSvTTruGc48orr7yohaEiIyNZu3at//nevXuJiorKUK5Ro0asWLECgK1bt7J06dKQ6t+4cSPh4eFER0cDXmK+detWbrvtNgD27dtH+/bt+fTTT/1D34uSYjGntwQplDthlIi4grBy5XK17m3bttGjRw+uv/565s6dy8aNGwFv6IsSXhEREZGi47fffmPAgAEMHDgQM+Ouu+5i8uTJnDt3DvA6QU6cOEHbtm15//33OXnyJECG4c3Hjx8nOTmZ9u3bM378+AzJUcWKFalcubJ/vu7MmTP9vb65qU2bNsydO5cDBw4AcPDgQX7++WduueUW1qxZw+7duwE4ceIEO3fuzHB+fHw8jz32GMeOHQO8ROrdd99NV+bo0aOUL1+eChUq8Ouvv7Js2TIAjh07xtGjR+nYsSPjxo3zb9+5e/duWrRowejRo6lcuXKmQ3wzE2rMwZYuXer/4CApKYnDhw9To0YN2rZty+TJk0lJ8dYFOnToEI0aNWLnzp3+a8yaNSvTn0vlypX5/e9/758/nJqayrfffpttHO3atWPJkiUcOXKEgwcPsmrVKtq2bZuh3P79+/11vvTSSyEvfjZ79mzuv/9+//MqVapw4MABEhMTSUxMJDY2lsWLFxfJhBeKSU9vCZdCueMQXqVCrtXpnKN3797MmDGD0qVLM2TIEIYMGcJVeTRnWEREREQuv1OnThETE8O5c+cIDw/noYce4sknnwS8obGJiYk0bdoU5xzVqlVjwYIFtGvXjk2bNhEbG8sVV1xB+/btGTNmjL/OY8eO0alTJ06fPo1zjnHjxmW47gcffMCAAQM4efIkderU4R//+Eeuv7fGjRszfPhw2rRpQ2pqKiVLlmTKlCk0a9aMadOm0a1bN/9CTWPGjKFevXrpzn/88cc5ceIEN910E1dccQUlS5bkmWeeSVemadOmNGrUiKioKOrUqeNfhCk5OZkuXbpw5swZUlNTeeONNwAYPHgwP/30E8452rZtS1RUFPv27bvge7nqqqtCijnYkiVLeOKJJ4iIiMDMGD9+PNWqVeMvf/kLO3fupEmTJoSHh/Poo48yYMAApk2bRpcuXUhJSaF58+b069cv03rnzJnDo48+yogRIzh79iw9evQgOjo6yzm91apV47nnnvMPNR81apR/5ebevXvzxBNPEBMTw8yZM5k6dSrOOe677z4eeughfx2RkZGcPHmSc+fOMW/ePFatWkWDBg1wzjF37tyLGk1Q1NjFjMcvqGJjY11CQkKWx1tPa8fQWYnUr9aAmnM/vqRr7dixgwYNGgDw3HPPYWYMHjyYatWqXVK9IiIiIpLRtm3buP766/M7DBHJQ5n9uzezjc65HO2tVIyGN0N49ZwnpgkJCXTq1ImGDRuyZs0aAP72t78xZswYJbwiIiIiIiIFVLFIesNSz1PuBJSs/ruLPnfdunV06NCBZs2a8cUXXzBq1Cj/BHAREREREREp2IrFnN7yp1IITzHCa9S4qPNOnz7NPffcQ2pqKmPGjOGvf/0rFSrk3rxgERERERERubyKRdJb+bi3ol54jayXU0+zZs0apk+fznvvvUdERASffPIJN9xwA+VyedVnEREREQlNZtvHiEjRdDnWnCoWw5srH/OWIQ///dWZHnfOsWrVKlq1asXtt9/O0qVL2bVrFwDNmzdXwisiIiKSTyIiIjh48OBl+UNYRAoW5xwHDx4kIiIiV+stFj29lY57+2uVzGQ7oX379nHvvffy1VdfUaNGDd5880369u1L6dKl8zpMEREREQkSGRnJ3r17+e233/I7FBHJAxEREURGRuZqnXme9JpZO2ACEAa855x7Jeh4KWAGcBNwEOjmnEu8lGtW9CW94b5Vlp1z/PTTT9SpU4dq1apRrlw5Jk2aRO/evXP9UwURERERybmSJUtSu3bt/A5DRAqxPE16zSwMeBu4E9gLbDCzhc65rQHFHgEOO+fqmll34O9At0u5bsXjKRwvAy4sjI8XLGDUqFH8/PPPJCYmUq5cOZYtW3Yp1YuIiIiIiEgBlddzem8GdjnndjvnzgJzgE5BZToBH/gezwP+aJe4ckG5YyksOnWMG2+8kc6dO3P06FFeffVVSpUqdSnVioiIiIiISAGX18Obrwb2BDzfCzTPqoxz7ryZJQNVgQM5vWjSvlOM2v4L9euXZcaMGdx///2EhxeL6cwiIiIiIiLFWl5nfpn12AYvxRdKGcysP9Df9/S4me24wLWv/OGHHw707NmTnj17XjhSkYLnSi7hwx+RfKb2K4Wd2rAUdmrDUtg1yOmJeZ307gWuCXgeCSRlUWavmYUDFYFDwRU556YCU0O9sJklOOdiLzpikQJCbVgKM7VfKezUhqWwUxuWws7MEnJ6bl7P6d0A1DOz2mZ2BdAdWBhUZiHwsO9xV+Azp43ZREREREREJAfytKfXN0d3ILAMb8ui951z35vZKCDBObcQmAbMNLNdeD283fMyRhERERERESk68nw1J+fcYmBx0GvDAh6fBu67DJcOeSi0SAGlNiyFmdqvFHZqw1LYqQ1LYZfjNmwaOSwiIiIiIiJFVV7P6RURERERERHJM0Uu6TWzdma2w8x2mVl8JsdLmdmHvuPrzKxW3kcpkrkQ2u+TZrbVzL4zs1VmVjM/4hTJyoXacEC5rmbmzEwriUqBEkobNrP/4/td/L2Z/SuvYxTJTgh/S1xrZp+b2Te+vyfa50ecIpkxs/fNbL+ZbcniuJnZm772/Z2ZNQ2l3iKV9JpZGPA2cDfQCLjfzBoFFXsEOOycqwuMA/6et1GKZC7E9vsNEOucawLMA8bmbZQiWQuxDWNm5YH/AtblbYQi2QulDZtZPeA5IM45dwMwKM8DFclCiL+HXwDmOuduxFswdlLeRimSrelAu2yO3w3U8331ByaHUmmRSnqBm4FdzrndzrmzwBygU1CZTsAHvsfzgD+ameVhjCJZuWD7dc597pw76Xu6Fm+va5GCIpTfwQCj8T6wOZ2XwYmEIJQ23A942zl3GMA5tz+PYxTJTiht2AEVfI8rAkl5GJ9Itpxz/xdvB5+sdAJmOM9aoJKZ/f5C9Ra1pPdqYE/A872+1zIt45w7DyQDVfMkOpHshdJ+Az0CLLmsEYlcnAu2YTO7EbjGObcoLwMTCVEov4frA/XN7EszW2tm2fVIiOS1UNrwCKCHme3F21Hl8bwJTSRXXOzfy0A+bFl0mWXWYxu8PHUoZUTyQ8ht08x6ALFAq8sakcjFybYNm1kJvGklvfIqIJGLFMrv4XC8YXW34422+cLMopxzRy5zbCKhCKUN3w9Md869bmYtgZm+Npx6+cMTuWQ5yuWKWk/vXuCagOeRZByy4S9jZuF4wzqy60IXySuhtF/MrA3wPPAn59yZPIpNJBQXasPlgShgtZklAi2AhVrMSgqQUP+O+Ng5d8459xOwAy8JFikIQmnDjwBzAZxzXwMRwJV5Ep3IpQvp7+VgRS3p3QDUM7PaZnYF3uT8hUFlFgIP+x53BT5z2qxYCoYLtl/f0NB38BJezSOTgibbNuycS3bOXemcq+Wcq4U3L/1PzrmE/AlXJINQ/o5YALQGMLMr8YY7787TKEWyFkob/hn4I4CZXY+X9P6Wp1GK5NxCoKdvFecWQLJz7tcLnVSkhjc7586b2UBgGRAGvO+c+97MRgEJzrmFwDS8YRy78Hp4u+dfxCL/EWL7fRUoB3zkW3/tZ+fcn/ItaJEAIbZhkQIrxDa8DGhrZluBFOBp59zB/Ita5D9CbMNDgHfNbDDesNBe6gCSgsLMZuNNH7nSN+98OFASwDk3BW8eentgF3AS6B1SvWrjIiIiIiIiUlQVteHNIiIiIiIiIn5KekVERERERKTIUtIrIiIiIiIiRZaSXhERERERESmylPSKiIiIiIhIkaWkV0RECgwz62VmLouvNhdZV1/feZGXK96g670UFO9hM1tnZrm+NZ6Zhfuu8ULAa13MbFAmZdv4yt6a23FkE1/doHuRYma/mtlMM7s6h3U2NbMRZlYpt+MVEZGirUjt0ysiIkXGfcDeoNe25kcgOdDS970q8Bdgtpld4ZybkVsX8O3F2RLYE/ByF+BWYHxQ8fW+mL7PretfhJeAT4FSvhiGAQ3NrKVz7vxF1tUUb7/G6cCR3AxSRESKNiW9IiJSEG1yzu3K7yBywjm3Nu2xmS0HdgCDgFxLeoOvc4FyR4GQyl4GPwbEucbMSgEjgBggIZ9iEhGRYkbDm0VEpFAxs9JmNsHMvjezE75hswvNrEEI5z5kZpt85yWb2Xdm1jeoTGsz+8zMjvu+lphZo5zE6pw7B2wC6gbUX9HMJvniPmtmO8zsiaAYKpjZRDPbY2ZnzOx/zGyFmdX3HU83vNnMZgEPAjUDhhTv8h1LN7zZzKaaWZKZhQVdM8J3T14LeK26mb3jK3/WzLaZ2SM5uRc+/8/3/dqga79kZt+Y2VEzO2Bmq8zs5oDjfYF3fU9/CniPkQH343nfvTxjZr+Y2au+JFtERIo59fSKiEhBFGZmgf9HOedciu9xad/XKGAf3jDivwJfm1lD59z+zCo0s1bAB3jDf4cAYUAjoHJAmU7Av4GFwAN4Hw7HA1+YWRPn3C85eC+18Q3H9SWaS4AmwIt4Q47/BIw3s6rOuWG+cyYA7YDngV3AlXhDlytmcY3hvjLRQGffa6ezKDsD6Af8EVge8HonoAIw0xdrJeBLoCTesOREoD3wrm+49uSQ3n16tXzffwx6vQbwOt6Q9nLAw3j3vKlz7nvgY6AO8BzeMO5ffeel/axnA3cDr+D1at+A1z6uBbrlIE4RESlClPSKiEhBtD3o+Zd4SR/OuUNA/7QDvkRyGfAbXoLzVhZ1tgQOOOeeDHjNn/SZmeElm6ucc10CXl8N7AYGA09dKPCAZL0qMBBvKO/rvtfu8cXxkHNuVloMZlYOeMbMxvveX0tgpnPu/YCq/zurazrnfjSzA8CZEIY9fwn8BDxE+qT3IWCzc+5b3/PBQCQQ5ZxLS1JXmlllYISZTQ34ICIrJXz3oxTQAi9p/TDgGmnx90l7HPDz3A70AYY4534zs92+It845xIDyrcGugIPOuf+FRDnEWC6mb3knNt8gThFRKQI0/BmEREpiDoDzQK+0g2pNbPuZrbezJKB88BxvN7f7IY4bwCqmdkMM+tgZsG9pg2BmsA/fcNlw30J23FgHXBbiLGf833tA54G3sDrscVXx3lgTtA5s/ASw+YBsT5iZvFmdpOZ5dr/184557teZzMrC2Bm1YC7SD/vuB3wFfD/g+7HMqA62d/rNNPw7sVxYCVeT+7DwYXMrK2ZrTazg3j35yxez24o12iH16s9PyjOtIT+DyHUISIiRZiSXhERKYi2OOcSAr52pB0ws854w1m3APfjJYrNgENARFYVOudW4fUE1wIWAAfMbLmZRfmKVPd9/4D/JK5pX+3wem5DkZao1wXKO+eGOOfO+I5VwettDl65eF/AcYDH8Oaw9sNb8Gm/mb1uZqVDjOFCZgBl8YYKg3cfSwD/CihTHbiDjPditu94KPdjJN69uB2Y7HucrifezJrhrfCcjNez28JXbgvZ/DyD4owATgbFmXQRcYqISBGm4c0iIlLYdAe2Bw2JjQAuuH+rc24uMNc3nPgO4O/AEjO7FjjoK/YM8Hkmp5/J5LXMrpHdqsSHgCvNLDwo8f2d7/tBXx3H8OYSx5tZLbwtnP6G16P5PJfIObfLzNYCPfDm8PbAG9adFFDsIN6WSE9mUgV4q1JfSGLA/VhjZhWAvmY2xTmXtqhVV7z3dW/gPTGzKsD/hHCNg3gJb6ssjidl8bqIiBQTSnpFRKSwKYM3BDZQTy5i9JJz7jiw0Mzq4s23rYy3D/AeoJFz7tVcijXYGry5svcCHwa8/iBe4rcuk1gTgVfN7CEgKvh4gDN4Q7xDNRN40zcnthnenN5AS/H2GU50zh24iHqz8yzeex+Ot3AW/Ofn6dIKmVlbvMWttgWcm/ahQ/B7XIq3MFlZ59yaXIpTRESKECW9IiJS2CwFJvq21lmCl7D9FTia3Ulm9jLeUNfP8Vb/vRZvoakE3+JRmNlA4L99Pccf4fUi/g64BdjtnJtwibEvAr7GWwH5d3hJXUegFzDaOXfYF8c6vIWrtgAngNZ4KxK/k03dW4E+ZtYf+AY45Zzbkk35OcA4vOT3BDA/6PhreD3MX5jZOOAHoDze3OdbnHOduUjOuV9Tqu3qAAABe0lEQVTMbAowyMxinHOb8H6eA4F/mNkHvvpfIGMP7Vbf94G+LZrOAd8651aa2Ud4c3rfANb7ytXCW216SMBCXCIiUgwp6RURkcJmCnA1XqL4GF7vaEe8eaHZWYeXXHXCmzu7Hy/hejGtgHNuoW9ro6F4izCVxkuQ1/Kfuaw55pxLMbO78YYqx+Ml4T8Bg4IS6i/whnHXwdtaaTfwhHPu7WyqnwrcjDdkuxLetkB1syrsnDtkZouBP+OtFH0i6PgRM2uJ1ys7FK/n9QjeqsofhfymM/ob3lzlF/GGNH9qZoOBQXhJ9ma87aJGB8Wz0cxGA32BAXg9+9fgLY51P/BfQG+8hPk03hZLaat6i4hIMWbeIo4iIiIiIiIiRY9WbxYREREREZEiS0mviIiIiIiIFFlKekVERERERKTIUtIrIiIiIiIiRZaSXhERERERESmylPSKiIiIiIhIkaWkV0RERERERIosJb0iIiIiIiJSZCnpFRERERERkSLrfwGcXjDzKF0V7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_fpr, log_tpr, log_thresold = roc_curve(y_train, log_reg_pred)\n",
    "knear_fpr, knear_tpr, knear_threshold = roc_curve(y_train, knears_pred)\n",
    "svc_fpr, svc_tpr, svc_threshold = roc_curve(y_train, svc_pred)\n",
    "tree_fpr, tree_tpr, tree_threshold = roc_curve(y_train, tree_pred)\n",
    "\n",
    "\n",
    "def graph_roc_curve_multiple(log_fpr, log_tpr, knear_fpr, knear_tpr, svc_fpr, svc_tpr, tree_fpr, tree_tpr):\n",
    "    plt.figure(figsize=(16,8))\n",
    "    plt.title('ROC Curve \\n Top 4 Classifiers', fontsize=18)\n",
    "    plt.plot(log_fpr, log_tpr, label='Logistic Regression Classifier Score: {:.4f}'.format(roc_auc_score(y_train, log_reg_pred)))\n",
    "    plt.plot(knear_fpr, knear_tpr, label='KNears Neighbors Classifier Score: {:.4f}'.format(roc_auc_score(y_train, knears_pred)))\n",
    "    plt.plot(svc_fpr, svc_tpr, label='Support Vector Classifier Score: {:.4f}'.format(roc_auc_score(y_train, svc_pred)))\n",
    "    plt.plot(tree_fpr, tree_tpr, label='Decision Tree Classifier Score: {:.4f}'.format(roc_auc_score(y_train, tree_pred)))\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([-0.01, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "    plt.annotate('Minimum ROC Score of 50% \\n (This is the minimum score to get)', xy=(0.5, 0.5), xytext=(0.6, 0.3),\n",
    "                arrowprops=dict(facecolor='#6E726D', shrink=0.05),\n",
    "                )\n",
    "    plt.legend()\n",
    "    \n",
    "graph_roc_curve_multiple(log_fpr, log_tpr, knear_fpr, knear_tpr, svc_fpr, svc_tpr, tree_fpr, tree_tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f56e6936-314c-42d4-8ea2-0cb2386ad382",
    "_uuid": "d6e62d64e9d9aa70223576a1df91a008aa6c2664"
   },
   "source": [
    "## A Deeper Look into LogisticRegression:\n",
    "<a id=\"logistic\"></a>\n",
    "In this section we will ive a deeper look into the <b> logistic regression classifier</b>.\n",
    "\n",
    "\n",
    "### Terms:\n",
    "<ul>\n",
    "<li><b>True Positives:</b> Correctly Classified Fraud Transactions </li>\n",
    "<li><b>False Positives:</b> Incorrectly Classified Fraud Transactions</li>\n",
    "<li> <b>True Negative:</b> Correctly Classified Non-Fraud Transactions</li>\n",
    "<li> <b>False Negative:</b> Incorrectly Classified Non-Fraud Transactions</li>\n",
    "<li><b>Precision: </b>  True Positives/(True Positives + False Positives)  </li>\n",
    "<li><b> Recall: </b> True Positives/(True Positives + False Negatives)   </li>\n",
    "<li> Precision as the name says, says how precise (how sure) is our model in detecting fraud transactions while recall is the amount of fraud cases our model is able to detect.</li>\n",
    "<li><b>Precision/Recall Tradeoff: </b> The more precise (selective) our model is, the less cases it will detect. Example: Assuming that our model has a precision of 95%, Let's say there are only 5 fraud cases in which the model is 95% precise or more that these are fraud cases. Then let's say there are 5 more cases that our model considers 90% to be a fraud case, if we lower the precision there are more cases that our model will be able to detect. </li>\n",
    "</ul>\n",
    "\n",
    "### Summary:\n",
    "<ul>\n",
    "<li> <b>Precision starts to descend</b> between 0.90 and 0.92 nevertheless, our precision score is still pretty high and still we have a descent recall score. </li>\n",
    "\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_cell_guid": "b4eaea18-ec79-4cb2-9a92-8d70a7f593bf",
    "_kg_hide-input": true,
    "_uuid": "0daaa7137ab61d6fd88e5fcc0849acc94c693df0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def logistic_roc_curve(log_fpr, log_tpr):\n",
    "    plt.figure(figsize=(12,8))\n",
    "    plt.title('Logistic Regression ROC Curve', fontsize=16)\n",
    "    plt.plot(log_fpr, log_tpr, 'b-', linewidth=2)\n",
    "    plt.plot([0, 1], [0, 1], 'r--')\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "    plt.axis([-0.01,1,0,1])\n",
    "    \n",
    "    \n",
    "logistic_roc_curve(log_fpr, log_tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_cell_guid": "d59c7621-5adb-4339-9a04-54630f679665",
    "_uuid": "f6d54ac036fa499104d269dd52d704c71629c1b0"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precision, recall, threshold = precision_recall_curve(y_train, log_reg_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_cell_guid": "06d9e8b8-3ba5-4c1e-8480-af5a8e04f851",
    "_kg_hide-input": true,
    "_uuid": "b19df81d0a5178a260d7518f9cca804646839c01"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "Overfitting: \n",
      "\n",
      "Recall Score: 0.90\n",
      "Precision Score: 0.76\n",
      "F1 Score: 0.82\n",
      "Accuracy Score: 0.81\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "How it should be:\n",
      "\n",
      "Accuracy Score: 0.65\n",
      "Precision Score: 0.00\n",
      "Recall Score: 0.29\n",
      "F1 Score: 0.00\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score, precision_score, f1_score, accuracy_score\n",
    "y_pred = log_reg.predict(X_train)\n",
    "\n",
    "# Overfitting Case\n",
    "print('---' * 45)\n",
    "print('Overfitting: \\n')\n",
    "print('Recall Score: {:.2f}'.format(recall_score(y_train, y_pred)))\n",
    "print('Precision Score: {:.2f}'.format(precision_score(y_train, y_pred)))\n",
    "print('F1 Score: {:.2f}'.format(f1_score(y_train, y_pred)))\n",
    "print('Accuracy Score: {:.2f}'.format(accuracy_score(y_train, y_pred)))\n",
    "print('---' * 45)\n",
    "\n",
    "# How it should look like\n",
    "print('---' * 45)\n",
    "print('How it should be:\\n')\n",
    "print(\"Accuracy Score: {:.2f}\".format(np.mean(undersample_accuracy)))\n",
    "print(\"Precision Score: {:.2f}\".format(np.mean(undersample_precision)))\n",
    "print(\"Recall Score: {:.2f}\".format(np.mean(undersample_recall)))\n",
    "print(\"F1 Score: {:.2f}\".format(np.mean(undersample_f1)))\n",
    "print('---' * 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_cell_guid": "89d88863-536f-4eea-a0cd-d2c77f407dd6",
    "_kg_hide-input": true,
    "_uuid": "f041ab92c183d2aa29569fc048ee6af4e6ee81f0"
   },
   "outputs": [],
   "source": [
    "undersample_y_score = log_reg.decision_function(original_Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_cell_guid": "3f54604f-396c-421e-ae93-305ad0103591",
    "_kg_hide-input": true,
    "_uuid": "c501d9226855a510a136bbf06794c702497e5b28"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.03\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import average_precision_score\n",
    "\n",
    "undersample_average_precision = average_precision_score(original_ytest, undersample_y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      undersample_average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_cell_guid": "2442a51e-0263-48a3-99f9-06f47bdc04f1",
    "_kg_hide-input": true,
    "_uuid": "2edd5461ff5253f12955ac02106c323f7aabe49f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'UnderSampling Precision-Recall curve: \\n Average Precision-Recall Score =0.03')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(original_ytest, undersample_y_score)\n",
    "\n",
    "plt.step(recall, precision, color='#004a93', alpha=0.2,\n",
    "         where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2,\n",
    "                 color='#48a6ff')\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('UnderSampling Precision-Recall curve: \\n Average Precision-Recall Score ={0:0.2f}'.format(\n",
    "          undersample_average_precision), fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e70e913a-173b-401b-96ee-93ddda7374c0",
    "_uuid": "d901eb00581cc890075a93d292935304e5b63355"
   },
   "source": [
    "### SMOTE Technique (Over-Sampling):\n",
    "<a id=\"smote\"></a>\n",
    "<img src=\"https://raw.githubusercontent.com/rikunert/SMOTE_visualisation/master/SMOTE_R_visualisation_3.png\", width=800>\n",
    "<b>SMOTE</b> stands for Synthetic Minority Over-sampling Technique.  Unlike Random UnderSampling, SMOTE creates new synthetic points in order to have an equal balance of the classes. This is another alternative for solving the \"class imbalance problems\". <br><br>\n",
    "\n",
    "\n",
    "<b> Understanding SMOTE: </b>\n",
    "<ul>\n",
    "<li> <b> Solving the Class Imbalance: </b> SMOTE creates synthetic points from the minority class in order to reach an equal balance between the minority and majority class. </li>\n",
    "<li><b>Location of the synthetic points: </b>   SMOTE picks the distance between the closest neighbors of the minority class, in between these distances it creates synthetic points. </li>\n",
    "<li> <b>Final Effect:  </b> More information is retained since we didn't have to delete any rows unlike in random undersampling.</li>\n",
    "<li><b> Accuracy || Time Tradeoff: </b> Although it is likely that SMOTE will be more accurate than random under-sampling, it will take more time to train since no rows are eliminated as previously stated.</li>\n",
    "\n",
    "</ul>\n",
    "\n",
    "### Cross Validation Overfitting Mistake:\n",
    "## Overfitting during Cross Validation:  \n",
    "In our undersample analysis I want to show you a common mistake I made that I want to share with all of you. 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. <b>In the following section you will see amazing precision and recall scores but in reality our data is overfitting!</b>\n",
    "### The Wrong Way:\n",
    "<img src=\"https://www.marcoaltini.com/uploads/1/3/2/3/13234002/2639934.jpg?401\"><br>\n",
    "\n",
    "As mentioned previously, if we get the minority class (\"Fraud) in our case, and create the synthetic points before cross validating we have a certain influence on the \"validation set\" of the cross validation process. Remember how cross validation works, let's assume we are splitting the data into 5 batches, 4/5 of the dataset will be the training set while 1/5 will be the validation set. The test set should not be touched! For that reason, we have to do the creation of synthetic datapoints \"during\" cross-validation and not before, just like below: <br>\n",
    "\n",
    "\n",
    "### The Right Way:\n",
    "<img src=\"https://www.marcoaltini.com/uploads/1/3/2/3/13234002/9101820.jpg?372\"> <br>\n",
    "As you see above, SMOTE occurs \"during\" cross validation and not \"prior\" to the cross validation process. Synthetic data are created only for the training set without affecting the validation set.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**References**: \n",
    "<ul>\n",
    "<li><a src=\"https://www.marcoaltini.com/blog/dealing-with-imbalanced-data-undersampling-oversampling-and-proper-cross-validation\"> \n",
    "DEALING WITH IMBALANCED DATA: UNDERSAMPLING, OVERSAMPLING AND PROPER CROSS-VALIDATION </a></li> \n",
    "\n",
    "<li> <a src=\"http://rikunert.com/SMOTE_explained \"> SMOTE explained for noobs  </a></li>\n",
    "<li> <a src=\"https://www.youtube.com/watch?v=DQC_YE3I5ig&t=794s\"> Machine Learning - Over-& Undersampling - Python/ Scikit/ Scikit-Imblearn </a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_cell_guid": "cc175ddc-ddd7-4087-ae1f-dd6fac664d58",
    "_kg_hide-input": true,
    "_uuid": "96f8d3f4160d65f12af4c7106739c4ad46d1e76b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of X (train): 227846 | Length of y (train): 227846\n",
      "Length of X (test): 56961 | Length of y (test): 56961\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "accuracy: 0.9694005888966659\n",
      "precision: 0.06547023328181797\n",
      "recall: 0.9111002921129504\n",
      "f1: 0.1209666729570652\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "\n",
    "\n",
    "print('Length of X (train): {} | Length of y (train): {}'.format(len(original_Xtrain), len(original_ytrain)))\n",
    "print('Length of X (test): {} | Length of y (test): {}'.format(len(original_Xtest), len(original_ytest)))\n",
    "\n",
    "# List to append the score and then find the average\n",
    "accuracy_lst = []\n",
    "precision_lst = []\n",
    "recall_lst = []\n",
    "f1_lst = []\n",
    "auc_lst = []\n",
    "\n",
    "# Classifier with optimal parameters\n",
    "# log_reg_sm = grid_log_reg.best_estimator_\n",
    "log_reg_sm = LogisticRegression()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "rand_log_reg = RandomizedSearchCV(LogisticRegression(), log_reg_params, n_iter=4)\n",
    "\n",
    "\n",
    "# Implementing SMOTE Technique \n",
    "# Cross Validating the right way\n",
    "# Parameters\n",
    "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "for train, test in sss.split(original_Xtrain, original_ytrain):\n",
    "    pipeline = imbalanced_make_pipeline(SMOTE(sampling_strategy='minority'), rand_log_reg) # SMOTE happens during Cross Validation not before..\n",
    "    model = pipeline.fit(original_Xtrain[train], original_ytrain[train])\n",
    "    best_est = rand_log_reg.best_estimator_\n",
    "    prediction = best_est.predict(original_Xtrain[test])\n",
    "    \n",
    "    accuracy_lst.append(pipeline.score(original_Xtrain[test], original_ytrain[test]))\n",
    "    precision_lst.append(precision_score(original_ytrain[test], prediction))\n",
    "    recall_lst.append(recall_score(original_ytrain[test], prediction))\n",
    "    f1_lst.append(f1_score(original_ytrain[test], prediction))\n",
    "    auc_lst.append(roc_auc_score(original_ytrain[test], prediction))\n",
    "    \n",
    "print('---' * 45)\n",
    "print('')\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)))\n",
    "print('---' * 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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       0.10      0.86      0.19        98\n",
      "\n",
      "    accuracy                           0.99     56961\n",
      "   macro avg       0.55      0.92      0.59     56961\n",
      "weighted avg       1.00      0.99      0.99     56961\n",
      "\n"
     ]
    }
   ],
   "source": [
    "labels = ['No Fraud', 'Fraud']\n",
    "smote_prediction = best_est.predict(original_Xtest)\n",
    "print(classification_report(original_ytest, smote_prediction, target_names=labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_cell_guid": "f0e671f7-7ed1-4188-b9bf-e509f050b134",
    "_kg_hide-input": true,
    "_uuid": "a8dcc4bba95aed7fbc8b9e39ceeeec6902d1865c"
   },
   "outputs": [],
   "source": [
    "y_score = best_est.decision_function(original_Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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_ytest, y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_cell_guid": "54e926f4-2a5d-4bb1-b74c-8cd79da7b6e5",
    "_kg_hide-input": true,
    "_uuid": "7be0445ac80df7ca252ec350b026d6275669aea6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'OverSampling Precision-Recall curve: \\n Average Precision-Recall Score =0.75')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(original_ytest, y_score)\n",
    "\n",
    "plt.step(recall, precision, color='r', alpha=0.2,\n",
    "         where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2,\n",
    "                 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": "code",
   "execution_count": 45,
   "metadata": {
    "_cell_guid": "d5c6fe5b-f086-4151-aba5-3c758677be0f",
    "_kg_hide-input": true,
    "_uuid": "787ec6bb25c3dc379c12a57619f5cc3e41afa42e"
   },
   "outputs": [],
   "source": [
    "# SMOTE Technique (OverSampling) After splitting and Cross Validating\n",
    "sm = SMOTE(ratio='minority', random_state=42)\n",
    "# Xsm_train, ysm_train = sm.fit_sample(X_train, y_train)\n",
    "\n",
    "\n",
    "# This will be the data were we are going to \n",
    "Xsm_train, ysm_train = sm.fit_sample(original_Xtrain, original_ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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 :14.371394634246826 sec\n"
     ]
    }
   ],
   "source": [
    "# We Improve the score by 2% points approximately \n",
    "# Implement GridSearchCV and the other models.\n",
    "\n",
    "# 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",
    "t1 = time.time()\n",
    "print(\"Fitting oversample data took :{} sec\".format(t1 - t0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a250e819-cdd4-43f5-b0a4-eb8f232199a0",
    "_uuid": "feb07b601c9ec79be1fe96cbbadf4ac838f7f7a8"
   },
   "source": [
    "# Test Data with Logistic Regression:\n",
    "<a id=\"testing_logistic\"></a>\n",
    "## Confusion Matrix:\n",
    "**Positive/Negative:** Type of Class (label) [\"No\", \"Yes\"]\n",
    "**True/False:** Correctly or Incorrectly classified by the model.<br><br>\n",
    "\n",
    "**True Negatives (Top-Left Square):** This is the number of **correctly** classifications of the \"No\" (No Fraud Detected) class. <br><br>\n",
    "\n",
    "**False Negatives (Top-Right Square):** This is the number of **incorrectly** classifications of the \"No\"(No Fraud Detected) class. <br><br>\n",
    "\n",
    "**False Positives (Bottom-Left Square):** This is the number of **incorrectly** classifications of the \"Yes\" (Fraud Detected) class <br><br>\n",
    "\n",
    "**True Positives (Bottom-Right Square):** This is the number of **correctly** classifications of the \"Yes\" (Fraud Detected) class.\n",
    "\n",
    "\n",
    "### Summary: \n",
    "<ul>\n",
    "<li> <b>Random UnderSampling:</b> We will evaluate the final performance of the classification models in the random undersampling subset. <b>Keep in mind that this is not the data from the original dataframe. </b> </li>\n",
    "<li> <b>Classification Models: </b> The models that performed the best were <b>logistic regression </b> and <b>support vector classifier (SVM)</b>  </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_cell_guid": "13a7d31c-2586-4946-aaa3-60090cd5680b",
    "_kg_hide-input": true,
    "_uuid": "d0e37500506d1b942431ac5bfabedcfea30275ce"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x864 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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",
    "# Other models fitted with UnderSampling\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)\n",
    "\n",
    "\n",
    "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",
    "\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=14, rotation=90)\n",
    "ax[0, 0].set_yticklabels(['', ''], fontsize=14, 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=14, rotation=90)\n",
    "ax[0][1].set_yticklabels(['', ''], fontsize=14, 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=14, rotation=90)\n",
    "ax[1][0].set_yticklabels(['', ''], fontsize=14, 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=14, rotation=90)\n",
    "ax[1][1].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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       0.90      0.99      0.94        91\n",
      "           1       0.99      0.90      0.94        99\n",
      "\n",
      "    accuracy                           0.94       190\n",
      "   macro avg       0.94      0.94      0.94       190\n",
      "weighted avg       0.95      0.94      0.94       190\n",
      "\n",
      "KNears Neighbors:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      1.00      0.93        91\n",
      "           1       1.00      0.86      0.92        99\n",
      "\n",
      "    accuracy                           0.93       190\n",
      "   macro avg       0.93      0.93      0.93       190\n",
      "weighted avg       0.94      0.93      0.93       190\n",
      "\n",
      "Support Vector Classifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.99      0.93        91\n",
      "           1       0.99      0.88      0.93        99\n",
      "\n",
      "    accuracy                           0.93       190\n",
      "   macro avg       0.94      0.93      0.93       190\n",
      "weighted avg       0.94      0.93      0.93       190\n",
      "\n",
      "Support Vector Classifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.99      0.93        91\n",
      "           1       0.99      0.87      0.92        99\n",
      "\n",
      "    accuracy                           0.93       190\n",
      "   macro avg       0.93      0.93      0.93       190\n",
      "weighted avg       0.93      0.93      0.93       190\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\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": "code",
   "execution_count": 49,
   "metadata": {
    "_cell_guid": "9103c5ed-df9d-4441-91dc-1104a51f06ff",
    "_kg_hide-input": true,
    "_uuid": "49c94105ad280d1ca16271daf7f9395041016c5c"
   },
   "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>0.942105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Oversampling (SMOTE)</td>\n",
       "      <td>0.987079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Technique     Score\n",
       "0  Random UnderSampling  0.942105\n",
       "1  Oversampling (SMOTE)  0.987079"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final Score in the test set of logistic regression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Logistic Regression with Under-Sampling\n",
    "y_pred = log_reg.predict(X_test)\n",
    "undersample_score = accuracy_score(y_test, y_pred)\n",
    "\n",
    "\n",
    "\n",
    "# Logistic Regression with SMOTE Technique (Better accuracy with SMOTE t)\n",
    "y_pred_sm = best_est.predict(original_Xtest)\n",
    "oversample_score = accuracy_score(original_ytest, y_pred_sm)\n",
    "\n",
    "\n",
    "d = {'Technique': ['Random UnderSampling', 'Oversampling (SMOTE)'], 'Score': [undersample_score, oversample_score]}\n",
    "final_df = pd.DataFrame(data=d)\n",
    "\n",
    "# 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",
    "# Note how high is accuracy score it can be misleading! \n",
    "final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5ec3ca7c-fae2-4e0a-991b-bb689a8c4456",
    "_uuid": "d709eac19181e9b7026e2f9e9f780a207bc8c19a"
   },
   "source": [
    "## Neural Networks Testing Random UnderSampling Data vs OverSampling (SMOTE): \n",
    "<a id=\"neural_networks\"></a>\n",
    "In this section we will implement a simple Neural Network (with one hidden layer) in order to see  which of the two logistic regressions models we implemented in the (undersample or oversample(SMOTE)) has a better accuracy for detecting fraud and non-fraud transactions. <br><br>\n",
    "\n",
    "### Our Main Goal:\n",
    "Our main goal is to explore how our simple neural network behaves in both the random undersample and oversample dataframes and see whether they can predict accuractely both non-fraud and fraud cases. Why not only focus on fraud? Imagine you were a cardholder and after you purchased an item your card gets blocked because the bank's algorithm thought your purchase was a fraud. That's why we shouldn't emphasize only in detecting fraud cases but we should also emphasize correctly categorizing non-fraud transactions.\n",
    "\n",
    "\n",
    "### The Confusion Matrix:\n",
    "Here is again, how the confusion matrix works:\n",
    "<ul>\n",
    "<li><b>Upper Left Square: </b> The amount of <b>correctly</b> classified by our model of  no fraud transactions. </li>\n",
    "<li> <b>Upper Right Square:</b> The amount of  <b>incorrectly </b> classified transactions as fraud cases, but the actual label is <b> no fraud </b>. </li>\n",
    "<li><b>Lower Left Square:</b> The amount of <b> incorrectly </b> classified transactions as no fraud cases, but the actual label is <b>fraud </b>. </li>\n",
    "<li><b> Lower Right Square:</b> The amount of  <b>correctly</b> classified by our model of fraud transactions. </li>\n",
    "</ul>\n",
    "\n",
    "### Summary (Keras || Random UnderSampling):\n",
    "<ul>\n",
    "<li><b>Dataset: </b> In this final phase of testing we will fit this model in both the <b>random undersampled subset</b>  and <b> oversampled dataset (SMOTE) </b>in order to predict the final result using the <b>original dataframe testing data.</b> </li>\n",
    "<li>  <b>Neural Network Structure: </b> As stated previously, this will be a simple model composed of one input layer (where the number of nodes equals the number of features) plus bias node, one hidden layer with 32 nodes and one output node composed of two possible results 0 or 1 (No fraud or fraud). </li>\n",
    "<li> <b>Other characteristics:</b> The learning rate will be 0.001, the optimizer we will use is the AdamOptimizer, the activation function that is used in this scenario is \"Relu\" and for the final outputs we will use sparse categorical cross entropy, which gives the probability whether an instance case is no fraud or fraud (The prediction will pick the highest probability between the two.) </li>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 51,
   "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": 52,
   "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": 53,
   "metadata": {
    "_cell_guid": "0067f625-d734-4c15-9526-6efb1c47dc2c",
    "_kg_hide-input": true,
    "_uuid": "98a36722723d4f7285eb9de158b12be9694a603f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 605 samples, validate on 152 samples\n",
      "Epoch 1/20\n",
      " - 0s - loss: 0.4640 - acc: 0.7455 - val_loss: 0.3672 - val_acc: 0.8684\n",
      "Epoch 2/20\n",
      " - 0s - loss: 0.3475 - acc: 0.8579 - val_loss: 0.2970 - val_acc: 0.9342\n",
      "Epoch 3/20\n",
      " - 0s - loss: 0.2830 - acc: 0.9107 - val_loss: 0.2592 - val_acc: 0.9342\n",
      "Epoch 4/20\n",
      " - 0s - loss: 0.2364 - acc: 0.9388 - val_loss: 0.2336 - val_acc: 0.9211\n",
      "Epoch 5/20\n",
      " - 0s - loss: 0.2038 - acc: 0.9421 - val_loss: 0.2161 - val_acc: 0.9211\n",
      "Epoch 6/20\n",
      " - 0s - loss: 0.1798 - acc: 0.9488 - val_loss: 0.1980 - val_acc: 0.9211\n",
      "Epoch 7/20\n",
      " - 0s - loss: 0.1621 - acc: 0.9504 - val_loss: 0.1890 - val_acc: 0.9276\n",
      "Epoch 8/20\n",
      " - 0s - loss: 0.1470 - acc: 0.9521 - val_loss: 0.1864 - val_acc: 0.9276\n",
      "Epoch 9/20\n",
      " - 0s - loss: 0.1367 - acc: 0.9554 - val_loss: 0.1838 - val_acc: 0.9276\n",
      "Epoch 10/20\n",
      " - 0s - loss: 0.1281 - acc: 0.9603 - val_loss: 0.1826 - val_acc: 0.9211\n",
      "Epoch 11/20\n",
      " - 0s - loss: 0.1218 - acc: 0.9537 - val_loss: 0.1795 - val_acc: 0.9211\n",
      "Epoch 12/20\n",
      " - 0s - loss: 0.1134 - acc: 0.9570 - val_loss: 0.1856 - val_acc: 0.9211\n",
      "Epoch 13/20\n",
      " - 0s - loss: 0.1071 - acc: 0.9587 - val_loss: 0.1852 - val_acc: 0.9276\n",
      "Epoch 14/20\n",
      " - 0s - loss: 0.1015 - acc: 0.9620 - val_loss: 0.1790 - val_acc: 0.9211\n",
      "Epoch 15/20\n",
      " - 0s - loss: 0.0966 - acc: 0.9587 - val_loss: 0.1842 - val_acc: 0.9276\n",
      "Epoch 16/20\n",
      " - 0s - loss: 0.0910 - acc: 0.9636 - val_loss: 0.1813 - val_acc: 0.9276\n",
      "Epoch 17/20\n",
      " - 0s - loss: 0.0871 - acc: 0.9620 - val_loss: 0.1831 - val_acc: 0.9276\n",
      "Epoch 18/20\n",
      " - 0s - loss: 0.0835 - acc: 0.9636 - val_loss: 0.1822 - val_acc: 0.9276\n",
      "Epoch 19/20\n",
      " - 0s - loss: 0.0791 - acc: 0.9702 - val_loss: 0.1822 - val_acc: 0.9276\n",
      "Epoch 20/20\n",
      " - 0s - loss: 0.0751 - acc: 0.9752 - val_loss: 0.1877 - val_acc: 0.9211\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f056fd8e278>"
      ]
     },
     "execution_count": 53,
     "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": 54,
   "metadata": {
    "_cell_guid": "9a30cd49-23c3-4417-8ce1-b6e82890481c",
    "_kg_hide-input": true,
    "_uuid": "e82f40ef343b53b71d6fcfa317e872278db27114"
   },
   "outputs": [],
   "source": [
    "undersample_predictions = undersample_model.predict(original_Xtest, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_cell_guid": "a47a91a0-2cf3-436f-a1cd-bb207ba94997",
    "_kg_hide-input": true,
    "_uuid": "028270a5b2ba0e4c85100f287fec64343ad900ea"
   },
   "outputs": [],
   "source": [
    "undersample_fraud_predictions = undersample_model.predict_classes(original_Xtest, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, 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": 57,
   "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",
      "[[55148  1715]\n",
      " [    8    90]]\n",
      "Confusion matrix, without normalization\n",
      "[[56863     0]\n",
      " [    0    98]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "undersample_cm = confusion_matrix(original_ytest, undersample_fraud_predictions)\n",
    "actual_cm = confusion_matrix(original_ytest, original_ytest)\n",
    "labels = ['No Fraud', 'Fraud']\n",
    "\n",
    "fig = plt.figure(figsize=(16,8))\n",
    "\n",
    "fig.add_subplot(221)\n",
    "plot_confusion_matrix(undersample_cm, labels, title=\"Random UnderSample \\n Confusion Matrix\", cmap=plt.cm.Reds)\n",
    "\n",
    "fig.add_subplot(222)\n",
    "plot_confusion_matrix(actual_cm, labels, title=\"Confusion Matrix \\n (with 100% accuracy)\", cmap=plt.cm.Greens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9d7cd385-9270-426e-b537-c80501dce889",
    "_uuid": "be2b0e76445ecb745b6e49e69993dd1de7839eb4"
   },
   "source": [
    "### Keras || OverSampling (SMOTE):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": 59,
   "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": 60,
   "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",
      " - 3s - loss: 0.0641 - acc: 0.9771 - val_loss: 0.0152 - val_acc: 0.9977\n",
      "Epoch 2/20\n",
      " - 3s - loss: 0.0130 - acc: 0.9972 - val_loss: 0.0070 - val_acc: 0.9995\n",
      "Epoch 3/20\n",
      " - 2s - loss: 0.0078 - acc: 0.9987 - val_loss: 0.0044 - val_acc: 1.0000\n",
      "Epoch 4/20\n",
      " - 2s - loss: 0.0060 - acc: 0.9990 - val_loss: 0.0030 - val_acc: 1.0000\n",
      "Epoch 5/20\n",
      " - 3s - loss: 0.0046 - acc: 0.9992 - val_loss: 0.0036 - val_acc: 0.9999\n",
      "Epoch 6/20\n",
      " - 2s - loss: 0.0035 - acc: 0.9993 - val_loss: 0.0012 - val_acc: 1.0000\n",
      "Epoch 7/20\n",
      " - 2s - loss: 0.0038 - acc: 0.9994 - val_loss: 0.0017 - val_acc: 1.0000\n",
      "Epoch 8/20\n",
      " - 2s - loss: 0.0028 - acc: 0.9995 - val_loss: 0.0027 - val_acc: 0.9999\n",
      "Epoch 9/20\n",
      " - 2s - loss: 0.0022 - acc: 0.9996 - val_loss: 0.0022 - val_acc: 1.0000\n",
      "Epoch 10/20\n",
      " - 2s - loss: 0.0021 - acc: 0.9996 - val_loss: 0.0017 - val_acc: 1.0000\n",
      "Epoch 11/20\n",
      " - 2s - loss: 0.0020 - acc: 0.9996 - val_loss: 0.0013 - val_acc: 1.0000\n",
      "Epoch 12/20\n",
      " - 2s - loss: 0.0018 - acc: 0.9997 - val_loss: 2.8322e-04 - val_acc: 1.0000\n",
      "Epoch 13/20\n",
      " - 3s - loss: 0.0018 - acc: 0.9996 - val_loss: 0.0035 - val_acc: 0.9994\n",
      "Epoch 14/20\n",
      " - 3s - loss: 0.0018 - acc: 0.9997 - val_loss: 9.4907e-04 - val_acc: 1.0000\n",
      "Epoch 15/20\n",
      " - 3s - loss: 0.0014 - acc: 0.9998 - val_loss: 1.5897e-04 - val_acc: 1.0000\n",
      "Epoch 16/20\n",
      " - 2s - loss: 0.0015 - acc: 0.9997 - val_loss: 5.9093e-04 - val_acc: 1.0000\n",
      "Epoch 17/20\n",
      " - 3s - loss: 0.0016 - acc: 0.9997 - val_loss: 3.7523e-04 - val_acc: 1.0000\n",
      "Epoch 18/20\n",
      " - 2s - loss: 0.0014 - acc: 0.9998 - val_loss: 2.7042e-04 - val_acc: 1.0000\n",
      "Epoch 19/20\n",
      " - 3s - loss: 0.0020 - acc: 0.9997 - val_loss: 2.2361e-04 - val_acc: 1.0000\n",
      "Epoch 20/20\n",
      " - 2s - loss: 0.0012 - acc: 0.9998 - val_loss: 1.8081e-04 - val_acc: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f056234b470>"
      ]
     },
     "execution_count": 60,
     "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": 61,
   "metadata": {
    "_cell_guid": "09628790-3d4b-4516-b355-4177e5ad9329",
    "_kg_hide-input": true,
    "_uuid": "222a3f8e7614c4e0241e0f0c85b0ecd45fd3cca6"
   },
   "outputs": [],
   "source": [
    "oversample_predictions = oversample_model.predict(original_Xtest, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "_cell_guid": "374217c9-a4b7-4691-918b-6a91bb1c02e3",
    "_kg_hide-input": true,
    "_uuid": "9ede2c436251f560615087202cff031936f3a6e5"
   },
   "outputs": [],
   "source": [
    "oversample_fraud_predictions = oversample_model.predict_classes(original_Xtest, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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",
      "[[56851    12]\n",
      " [   33    65]]\n",
      "Confusion matrix, without normalization\n",
      "[[56863     0]\n",
      " [    0    98]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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mVqN+BQyPiNdn58MR0RoR70XE1E4uF0Az8CVwFnB7qQyS+gB3AA+TnoX7Z+Dvkn5akGdH4AzS0x7WyvLeKWnpbP+2wC+ALYAjgQvbKuqSvgGcBuw9p88ArgZJTXPx6RaXADtLWngunc+qxBVdA0DSQNJDx4+IiMMiYlhEvBkRD0TEzqQLb1vefSSNkjQ5+7tX0bFC0t6Srs96hV+XtEvhfmAN4PdZ3uPL9WpmadsXvP+9pDclfSXpPUmXF+ybYeiypHkknS7p/axl9FFJ6xfsb2vN3VTSY5ImSRohqV8FP9mdwPzARgVpuwCPATPchCUNkvSQpI+yVti7Ja1UkOWN7O/jWXmGFn4fSUdJGgOMydKnD6uStEL2G/+y6HyTJa1TwfcwMzOrB78Abml7I2lLSZ9Kasne983uoecW5DlR0r3Z6+lxhqRlgAezbOOy9EsLztUk6U+Sxkv6QNIpksrGzBHxeUTsGxEXkN2rS9gXGBsRB0XEixHxD+Ay4PCCPIcBl0bEP7I8BwHv8nVnw0rA0IgYERH/BD4B+mT7/gRcGREvlCtnwe/SLOmirOf4C0mvSjqy+DtKGqI0su+rLJa6tGDfNyWdK+ndLMZ6MauoI2k3SZ8VHWuGHvS2PJK2kvQcMBlYSdL3JN2T/fafKPVQf7/oWCXPLWm+7DPbF+XfXKmnfXGAiHgOGEsaIWc55oqutdkZ+Bz4e6mdbT2lkn5MarE8HViVVAE+R6mVsdDvgZtJFdprgYslfSfbtwTwMnBq9vqUSgqo1Op5OLA/0JfU6zy8nY+cBOwI7EFqGX0WuEvSEkX5/gwcDfQDPgSukjoc9jOFNPRlj4K0PYCLSuSdj/R7DSRVjCcCtyrroc3SAQaRfo/CC++GwOrZvk2LDxwRLwOHAmdJWlbSosClwIkR8WgH38HMzKzmKfW8rQyMKEh+COgBtDWQbwSMBzYuyLMRMLTEId8G2npSVyHde39dsH9nYCqwLnAgcAgpnpgT3wfuKUq7GxggqVsWE/QvkeeerBwAT2f5F5LUH5gXGJU1bG9MquxWogl4B/gZqfJ8LKmzY/e2DJL2Ac4n9X6uDmwFPJ/tE6nBf8PsMyuTKumTKzx/mx7AccA+2THeBL4BXAH8gBQfjQTuKKgglz13RHwO/JMZYzOy97dFxPsFacOzY1iOtVS7AFYz+gKvRcSUDvIdDlwREW1zUF/JLrZHAbcW5LsiIq4EkPQ70g3kB8CbEfGepKnAZxHxXpankjky3yG1bN6TlfMtZrzpTSdpPlIL6K8i4vYsbV9gE+AA0oW1ze8i4sEszwnAMKAX5Vtl21wMjJB0ALA8aYj3DRRdYCPiX0Vl253UCjswO1fb0O0P236PAl8Ce0TEV+UKEREXSNoSuIpUUX+N2Z/DZGZmVmuWBkSKAQCIiM8kPUmq4D1KqtSeBRydNWhPJK0BcmTxwSKiVdKE7O0HETG+KMsLEfH77PUrSiPXNiVVombXt4H7itLeJ8Xii5C+X3OWVpxns6zcd0u6Engc+AIYAnxGqpDuS5pSdQhpDZSDIuLhUgXJYqjfFySNzkaz/ZyvG+x/B5weEX8ryPdE9nczUsV9lYh4MUubnSHlzVk5nyhIe6Awg6SDSI0Sg4ArKzj3P4BHJfWKiHckLQRsB+xQdO6xdN4aMVaj3KNrbSpduGAl4H9FacNILWqFnml7kc2HGQfM6YJW15Na/97IhtzsIGmeMnmXBboVljUiWoFH2isr6cJHJWXNLrBPk24MewLXRMSk4nxZT+vVkl6T9AnpptVEunF35Ln2KrkFfkWa97wBsEv2Xc3MzPJg3uzvl0XpQ/l6CtGGpJ6+4VnaeqTRV+2N/CrnmaL3Y5nzGAageO6sSqSXyjM9LSKOj4jlImK1iLiR1NHwCKlifwKpQf8Y4PqCkWMzkbSv0nStcdkw40PJ4hKlhb96AfeX+fhawLsFFc3ZNZVscbCCci0m6XxJr0iaCHxK+u3bYqZ2zx0RI0gj+IZkSb8APiL9t1HoC77+78pyyhVda/MKsGx7F8UCpRY5KE4r7hkO2v/vbVr2t3Bxp24zHCDibWAF0hCXT0hDn5/Iem+Llbp5VFLWtn2V/n/j4qw8P89el3IrsGiWb23SRXoqUMlv/XmF5VgVWIDUENCrws+YmZnVg7Ye14WK0ocC60lamTTk9YksbWNSZffhCkaqlTKrMUwl3iP16hZajBQPfEj6jq1l8hT38gIgaXnSKLKjSN/5vxHxbkTcQ4oxVijzuR1JU6ouBX4IrAmcw9dxSUedHx3tn1YiT7cS+b4q0TB/Gamn9VDSkO01SSPsKi0bwIV8PQx7D9K85+LzLMzXI+osp1zRtTZXk+aSHlhqp6QFs5cvAusX7V4f6HDxgw60XWwK58+uWZwpIr6MiNsj4lDShXAVUqttsVGkuSKFi081k4a7zGlZC11LGrY8JiIeK96ptBLzSsCfIuK+rAXyG8w4baBtTstsrTaY/dtcTprrfDZwhaRvzs6xzMzMatBrpAbu4hFZDwHzkIYnD8sqM0P5uqI7tJ1jztG9dzY8QjYEucDmwIiImBIRk0kV9c1L5JlpCHI2V/V84PCImEiK6bsV7OtG+e+2PvBYRJwVEU9GxCjSSDgAsrms71BibZDMk8ASmnFhzULjgJ5FschMMV07Zft7Fus9T+rRLYwNOzo3pCHOvSQdSFp/5ZISeVbNjmU55jm6BkBEPCbpJOBkpYdo/4vUgtaHNCx3FOmZYyeThsM8QVogYRBp0YY5WrkuIr6Q9ChwlKTXSL2Tfy7MI2k30n+zj5HmpOxIanV9tcTxPldaefEvksaTVjY+FFic1GrZKSLiU0m9SK2wpXxEaqXdS9LbpN7Wk0ktuG0+IA2h+aGk0cCX2U2rUudl5/g96Ua3CanCu+ssHMPMzKwmRcQ0SfeRKkE3FKS3zdPdhbSoJKQK5VKk+GWm+bkF3iT11G4t6Vbgi4j4rJ387cp6lbuT5tvOL2nNrIxtQ3PPAw6UdDqpgroesBtpRFibv5Eaq4eTpl7tCyyZfbbYnsDHEfHv7P0w4ASlp0usToqPXi5T3FeA3bL1PUYBO5GGfn9UkOdE4DRJ75MemdQT2DQiTiUNaX4M+JekQ7PjLQfMFxE3Zfs+B/4s6TTSwqT7lylLqbLtIukxUgfMScy4yFVH5yYiJkq6njTy778RMUOcKKknaeGv31ZYJqtT7tG16SLiKNLFrh/pWW8vkBZ2eIuscphdRA4iVRpfIC0ytX9E3FrqmLOobRGnx0k3geOK9n9MurA/BDxHWpzgJxHxBqUdBVxHaskbSbZ6cUS8Wyb/bMme31vy5hgR00gV8tWzMp9NWuDhq4I8U4GDSfNsx5JWq66IpF1Jz+7dOWsR/oo0H2V7ST9v/9NmZmZ14wJgR838rNUHST2XQyGN/CItTvUV7czPjYh3gD+QKnTvk+KdOXEH8BTpnt8/e/1UwfneIK1cvAEpJjkWOLhwwcqIuJa0wvNxWZ71ga0i4s3CE2WPyTmOglF42dzUPwM3khYO3TUivihT1vNJ8dHVpJhrGVKlcLqIOJe0eOdepPjlLtIourbYZktSZfxK0mi/M8iGF0fEBFInyOak+bJ7k2KfSuxBenzjE8A1pGlhowvK1e65C1yUpZV6GsZg4K2IeKjCMlmdUh0+U9rMzMzMGoykR4BzIuKKapfFals2D/l8YMnihUKzHvPTI+LqqhTO5hr36JqZmZlZPdgHx67WDkk9s2HkvwX+UaKSuxhp+PucPCrK6oR7dM3MzMzMrO5JOp40LHwYMDgiPqluiayaXNE1MzMzMzOzXPHwDzMzMzMzM8sVV3TNzMzMzMwsV/wc3Qr1bFYs2K3apbA5seTKa1S7CDYHRr/1NuPHf6i5db7l5muKSa2VTe149yvujohBXVwkM7Muo+5NQQ+HhfWs3/KrVrsINgfeHP0W48ePn2txjhbpEUyeVvkHPp1Sd7GOr2gVWrAb7L2Mf656dvyw+6pdBJsDA9bfbK6eb1JrsE+F/58//uWpi3RxcczMulaPFlh7sWqXwubA/+4aVu0i2BxYb+315+4JJ0+DdRavPP+9Y+ou1nHNzcysBCltZmZmZrkjcj+J1RVdM7Mycn79NzMzs0aW8xZ9V3TNzMpoyvf138zMzBqWch/ouKJrZlaCyH1Dp5mZmTUqD102M2tcOb/+m5mZWSPLeYu+K7pmZmXk/PpvZmZmjSzncY4rumZmZeT8+m9mZmaNSniOrplZI/IcXTMzM8u1nMc5ruiamZWR8+u/mZmZNbKct+i7omtmVoqgOd/XfzMzM2tUIveBjiu6ZmYleOiymZmZ5VrO4xxXdM3Mysj59d/MzMwalnLfou+KrplZGU2KahfBzMzMrPN51WUzs8aV78u/mZmZNbScBzqu6JqZlSByf/03MzOzRuahy2ZmjSnn138zMzNrZDmPc1zRNTMrI+er7puZmVmj8uOFzMwak4cum5mZWa7lfOiaK7pmZqXkf9V9MzMza2RN1S5A13JF18ysDNdzzczMLJeU/xZ9V3TNzMrI+ePlzMzMrJHlPM5xRdfMrATP0TUzM7Ncy3mPbs5HZpuZzb62UT0dbZUdS6MlPStppKQRWdrCku6V9Gr2d6EsXZLOlDRK0jOS+hUcZ0iW/1VJQwrS+2fHH5V9Nt93LzMzM5szTbOwdaAW4xxXdM3MylCF2yzYOCLWjIgB2fujgfsjoi9wf/YeYEugb7btDZwL6YYB/AFYGxgI/KHtppHl2bvgc4NmrWhmZmbWMETlLfqVt53XVJzjiq6ZWQltj5erZJsDg4HLsteXAdsVpF8eyaPAgpKWAH4I3BsREyLiI+BeYFC275sR8UhEBHB5wbHMzMzMZlZpoDP7wU5V4xxXdM3MypiFRs5FJI0o2PYucbgA7pH0RMH+xSPiXYDs72JZei/g7YLPjsnS2ksfUyLdzMzMrLRZ69HtKNapuTjHi1GZmZUxC+2X4wuG6ZSzXkSMlbQYcK+kl2bx1DEb6WZmZmYzm/X5Vx3FOjUX57hH18ysBCk9XqiSrRIRMTb7+wFwI2nuyfvZcByyvx9k2ccASxV8vDcwtoP03iXSzczMzEoQUuVbR2oxznFF18ysjM5ajErSfJK+0fYa2AJ4DrgFaFtRcAhwc/b6FuCX2aqE6wATsyE/dwNbSFooW5xhC+DubN+nktbJViH8ZcGxzMzMzGbSWRXdWo1zPHTZzKyMSntrK7A4cGN2o2gBro6IuyQ9DlwnaU/gLWCHLP8dwFbAKGASsDtAREyQ9P+Ax7N8J0TEhOz1fsClwLzAndlmZmZmVlInPoiwJuMcV3TNzEoQnTfkJSJeB9Yokf4hsGmJ9AAOKHOsi4GLS6SPAFad48KamZlZ7glomoWabms7+2o1znFF18ysjDl8dJCZmZlZbRI0NeV7FqsrumZmZXTikB4zMzOzGlLZIlP1zBVdM7MS0pAeP6HHzMzM8inn9VxXdM3Mysn3gB4zMzNrVAL36JqZNSTlv6XTzMzMGpRc0TUza0idueqymZmZWa0RruiamTWknDd0mpmZWQNzj66ZWQMS0JLv67+ZmZk1KCGam/Id6Liia2ZWRs4bOs3MzKyBuUfXzKxBeY6umZmZ5ZIXozIza0xp2f1ql8LMzMysa+Q9znFF18ysDPfompmZWR75ObpmZo1KkPM1GszMzKyBuaJrZtaAlG1mZmZm+SNXdM3MGpV7dM3MzCyXvBiVmVljEtCc7+u/mZmZNSgBTTlv0XdFN2cOuf9Vvvr8M6K1lWmtU7lg+3UAGLjLAQzceT+mTZ3Kq/+5k3tPOYamlhZ+9McLWGLltWhqbubpm69k2AUntXuclX/4UzY68HcsuuxK/ONn6zL2uSeq9l0b1R77Hsxtd97LYosuwnMjHgLgiN8ez6133k33bt1Z9rvLcMl5Z7LgggtUuaT1r0lR7SKYmVmBN654hE+/+JzWaa1MbZ3K9w7YGoADB+/OgYN3Y2rrVG5/7AGOuvBEWppbuPCwk+nXdzVampu5/N4b+Ms1ZwOwwHzf5MLDTmbVZVYgCPY45Tc8+uKTnDDkcAav+0OmxTQ++Hg8u518GO9++H41v7IB99x1D4cfdiTJfZNxAAAgAElEQVStra3stscQjjjq8GoXKRfco2t157Jfbsakjz+c/n6ZtTdkxU225dwf9aN1ymTmW3hRAFYZtD0t3bpz7o/WoluPeTng9md47vZr+fidN0seB+CDV5/n2oN/xrb/d87c+0I2g9122YkD99mTX+514PS0zTfZkD+fcBwtLS0cddwJ/PmUM/jrH39fxVLmQ74v/2Zm9Wnjw3fgw08+mv5+ozXWZfC6W7D6PpszecpkFl3wWwDssME2zNOtO6vvvRnzztODFy58kH8+eDNvvj+GM/b/P+4aMZQd/t8+dGvpRs955gXg5OvP4/eXnQLAQdvtwe93OYT9zjhm7n9Jm661tZVDDj6M2++6lV69e7H+Oj9gm223ZqWVV6p20epeU84run56RgP43k77MOwfJ9E6ZTIAn08YB0BE0K3nfDQ1N9PSY15ap0zmq88+afdY419/iQ/feKXLy2zlbbD+uiy88EIzpG2x2ca0tKR2q3UG9mfMO2OrUbRcEWmObiWbmZlVz37b7spfrjmbyVmcMy5rpA+C+Xr0pLmpmXm792Dy1Cl8MukzvtFzfjZYbW0uuvOfAEyZOoWJn6f459NJn00/7nw95iXCI3uq7fHhI1h22e/S57t96N69Ozv8bHtuu+W2aher/ik9R7fSrR65opszEcGuF93J3v96jP4/+xUA31pmeZYesD6/uvZ/7HbF/Sy56gAAXrj7X0yZ9Dm/eehtDn3gdR6++DS+mPhR2eNYfbj48qvZcotNq12MXFCFm5mZzR0RwT1/uZoRZ9/BXlvtDMDyvb/LD1Zbm0fPvJWhp97AgOXXAOCG/97O519O4t1rn+Stq4ZzyvXn89GnH/PdJZZm3MQJXHLE33jy3Lv4x2En07PHvNPP8cfdj+Stq4az8yY/nt67a9UzduxYei/Ve/r7Xr178c7Yd6tYonxQtupypVs96rKKrqSQdGrB+8MlHT8Ln99N0jhJI7Pt8i4q5/GScjPQ/+JfbMj5Px3IVXttw/d+sR/fGbA+Tc3NzPvNhbhwx/W496Sj2eH0qwHotdpApk2bxqkbLM0Zm/Xl+7sfwkK9+5Q9jtW+E0/6Gy0tLey80/bVLkr9q7A31z26Zo3Lsc7ct96hP6b//luy5bG7csCPhvCD1dampamZheZfgHUO3pYjLvgj1x13LgADV1yT1mnTWHKn/vT55ff5zfZ70+fbS9PS3EK/vqty7q1X0G+/QXz+5SSO3vGA6ec47pKTWHrngVz1wI0cOHj3an1Vy5TqVa/Xilet0Sz8rx51ZY/uV8BPJC0yB8e4NiLWzLZfFu+U5DnGRT79ILVwfT5hHC/ddxO9Vv8en7z/Di/eeyMA7zz7ODFtGj0XWoTVttmJUQ/dzbSpU/l8wjjefvIRlly1f9njWG277MpruO3Oe7nq4nN9A+gEIl0gK9nMrGE51pnL2haGGvfxh9z4v7sYuMKajBn/Hv8edicAj788kmkxjUUWWJhfbLIdd40YytTWqYz7+EP+9/zjDFh+dcaMe5cx495l+EtPAannt1/f1WY619UP3MRP199y7n05K6lXr16MeXvM9PfvjHmHJZf4dhVLlB/u0Z19U4ELgEOLd0j6jqT7JT2T/V260oNKGirpT5L+A/xa0raSHpP0lKT7JC2e5Zuh9VLSc5KWyV4fK+llSfcBK8zZ16wd3ebtSff55p/+etn1NueDV57npftuoc/aGwPwrWX60tytO5M+Gs/Ed9+izzobT8/fe42BjH/95bLHsdp11z3389fT/s4t111Bz549q12c3Ghuqmwzs4blWGcu6tljXuafd77pr7fovwHPjX6Zmx6+i03WWg+Avr360L2lO+MnTuCtD8ayyZrrTs+/zkr9eOnt13j/o3G8PW4sy/f+LgCbrrU+L7z5KgDL9eoz/Xw/+v4WvPT2a3PzK1oJA77Xn1GjXmP0G6OZPHky1193A1tvu3W1i5ULTU2qeKtHXd1KeDbwjKSTitLPAi6PiMsk7QGcCWxX4vM7SmobM3tGRFySvV4wIjYEkLQQsE5EhKRfAUcCvylXIEn9gZ2AtUjf/0mg5DNyJO0N7A2wQB20p87/rcXZ8awbAGhqbubZ265h1LB7aO7WjcEnXsj+tzxF65Qp3HT0HgA8fvW5DP7Thex/60gk8dS/L+P9V55lod59Sh4HYMXNBrPVcafTc+FF+cV5N/PeS09z5a98sZmbfj5kb4Y+9D/GfziB3n1X5/+OO5I/n3IGX301mc23TUOW1xk4gPPO9LyiOVWfl3Uzm8vqNtYpjHPo0VzJd62qxRdclBuPvxCAluZmrn7wJu4eMZRuLd24+Den8uwF9zF56hSGnHwIAGfffCmXHPE3nvvH/Ujikruv49k3XgTgoLN/x1XH/J3uLd15/d032f2U9HP+Zc9jWKH3d5kWwZvvj2Ffr7hcdS0tLZx2xqlsu9VgWltbGbLbL1l5lZWrXay6lxaZyneko65aTU7SZxExv6QTgCnAF8D8EXG8pPHAEhExRVI34N2IWKTo87sBAyLiwKL0ocAfIuI/2fvVgFOBJYDuwBsRMSibI/NZRJyS5XsO2IZ0k1k4In6fpf8NGNuWr5wleyj2XqYOartW1vFPeuGCejZg/c0Y8eTIuXZFXm0Bxb/Xq6y7dvk7pz0REQO6uEhmVmPyFOvom92DtRebw1/EqumLu/xUjHq23trr88SIJ+danNNjqQViqUPWqTj/qMPvqbtYZ24Mujsd2BOYr508s1rb/rzg9d+BsyJiNWAfoEeWPpUZv1+PgtdeK97MOpTnJffNrFM51jGzuuM5unMoIiYA15FuAG0eJg2pAdgZGDYHp1gAeCd7PaQgfTTQD0BSP6Bt0sV/gR9LmlfSN4Bt5+DcZpZXwjVdM6uIYx0zq0d+jm7nOBUoHK5zMLC7pGeAXYFfz8Gxjweul/QQML4g/V/AwpJGAvsBrwBExJPAtcDILM9Dc3BuM8uxPF/8zazTOdYxs7qS9x7dLpt0GhHzF7x+H+hZ8H40sEkHn78UuLRE+kZF728Gbi6R7wtgizLHPhE4sb3zm5nV64XdzOYOxzpmVq8aYTEqr65kZlaKhJrzfQMwMzOzxlWvjw2qlJ8AaWZWQpqi23nDeSQ1Z8/AvC173yd7Luarkq6V1D1Lnyd7Pyrbv0zBMY7J0l+W9MOC9EFZ2ihJR3fm72BmZmZ5VPmw5XqNdVzRNTMro5Pn6P4aeLHg/V+B0yKiL/ARXy9isyfwUUQsB5yW5UPSyqSFbVYBBgHnZDeUZtJzPLcEVgZ+nuU1MzMzK6sL5ujWVKzjiq6ZWRmddfGX1BvYGrgwey/S3L0bsiyXkZ57CTA4e0+2f9Ms/2Dgmoj4KiLeAEYBA7NtVES8HhGTgWuyvGZmZmYltc3RzXOs44qumVkpmoUNFpE0omDbu+hopwNHAtOy998CPo6Iqdn7MUCv7HUv4G2AbP/ELP/09KLPlEs3MzMzK2sWHy9Ud7GOF6MyMytjFobqjI+IAWWOsQ3wQUQ8IWmjtuQSWaODfeXSSzVYRok0MzMzs+lmcdXluot1XNE1Myujk1bdXw/4kaStgB7AN0mtngtKaslaMnsDY7P8Y4ClgDGSWoAFgAkF6W0KP1Mu3czMzKy0znu8UE3GOh66bGZWghBqaqpoa09EHBMRvSNiGdICCw9ExM7Ag8D2WbYhfP2MzFuy92T7H4iIyNJ3ylYq7AP0BYYDjwN9s5UNu2fnuKWzfgczMzPLIYmmpsq39tRqrOMeXTOzUmZtReXZcRRwjaQ/Ak8BF2XpFwFXSBpFat3cCSAinpd0HfACMBU4ICJaASQdCNwNNAMXR8TzXVpyMzMzq2ttj1HsYlWNdVzRNTMro7NvABExFBiavX6dtIpgcZ4vgR3KfP5E4MQS6XcAd3RiUc3MzCznuqKiW0uxjiu6ZmZldH1Dp5mZmVl1zIUe3apyRdfMrJyc3wDMzMysceU9zHFF18ysjLzfAMzMzKxBSe7RNTNrROkB6fm+AZiZmVljmkuLUVWVK7pmZmXk/QZgZmZmjSvvcY4rumZmJQl18Nw4MzMzs7okOnw+br1zRdfMrIycN3SamZlZA3OPrplZI/IcXTMzM8sp4cWozMwaUlqkodqlMDMzM+saruiamTWqnN8AzMzMrHHlPcxxRdfMrIy8t3SamZlZg2qAKVqu6JqZlZHz67+ZmZk1spwHOq7ompmVIvx4ITMzM8slAc05j3Nc0TUzK0EINTVVuxhmZmZmXcCrLpuZNa6c3wDMzMysQQmach7nuKJrZlaKny9kZmZmOZXCnHzHOa7ompmVJCQPXTYzM7N8ynuUU7aiK+mb7X0wIj7p/OKYmdWQnLd0mjU6xzpm1sgaeejy80CQerbbtL0PYOkuLJeZWfXl/AZgZo51zKwxNfTQ5YhYam4WxMyspkioqbnapTCzLuRYx8walkRzzp8uUdG3k7STpN9mr3tL6t+1xTIzqz41qaLNzOqfYx0zayQiVQQr3epRh+WWdBawMbBrljQJOK8rC2VmVhOkyjYzq2uOdcysETVJFW/1qJJVl9eNiH6SngKIiAmSundxuczMqs+rLps1Csc6ZtZwGnaOboEpSs/YCABJ3wKmdWmpzMyqTcr9DcDMpnOsY2YNReR/1eVKuivOBv4FLCrp/4BhwF+7tFRmZrXAQ5fNGoVjHTNrOJqFrR512KMbEZdLegLYLEvaISKe69pimZnVAFdizRqCYx0zazz1O/e2UpUMXQZoBqaQhvR40pqZ5V56vpwvd2YNxLGOmTUMyUOXkXQs8E9gSaA3cLWkY7q6YGZmVSWh5qaKNjOrb451zKwRNTc1VbzVo0p6dHcB+kfEJABJJwJPAH/uyoKZmVWde3TNGoVjHTNrKPU897ZSlVR03yzK1wK83jXFMTOrITkf0mNm0znWMbOGk/ehy2UrupJOI81TmQQ8L+nu7P0WpNUIzcxyzI8XMss7xzpm1rgaezGqttUGnwduL0h/tOuKY2ZWI9JqVJ1zKKkH8F9gHtJ194aI+IOkPsA1wMLAk8CuETFZ0jzA5UB/4ENgx4gYnR3rGGBPoBU4OCLuztIHAWeQFtS5MCL+0imFN8s3xzpm1pDSExLzHeeUrehGxEWz+V3NzPKh8+bofgVsEhGfSeoGDJN0J3AYcFpEXCPpPNKF/dzs70cRsZyknUjP89xR0srATsAqpEVz7pO0fHaOs4HNgTHA45JuiYgXOusLmOWRYx0za2Sd2KNbk3FOJasuLyvpGknPSHqlbZudX8DMrJ5IqmjrSCSfZW+7ZVsAmwA3ZOmXAdtlrwdn78n2b6p0osHANRHxVUS8AYwCBmbbqIh4PSImk1pPB8/p9zdrFI51zKwRaRa29tRqnFNJd8WlwCWk77glcF12cDOz/JJQc3NFG7CIpBEF294zH07NkkYCHwD3Aq8BH0fE1CzLGKBX9roX8DZAtn8i8K3C9KLPlEs3s8pcimMdM2sgAlqamire6CDWqcU4p5JVl3tGxN2STomI14DjJD1UwefMzOpb5UN6xkfEgPYyREQrsKakBYEbgZVKZWs7c5l95dJLNVpGiTQzK82xjpk1mFledLPdWKcW45xKKrpfZV3Jr0naF3gHWKyCz5mZ1bcuWI0wIj6WNBRYB1hQUkvWmtkbGJtlGwMsBYyR1AIsAEwoSG9T+Jly6WbWMcc6ZtZQRGVDe2dVLcU5lXy/Q4H5gYOB9YC9gD0q+JyZWd0Slc3PraQ1VNKiWQsnkuYFNgNeBB4Ets+yDQFuzl7fkr0n2/9ARESWvpOkebKVDPsCw4HHgb6S+kjqTlrI4ZZO+BnMGoVjHTNrLKp8LZKOYp1ajXM67NGNiMeyl58Cu3aU38wsNzpv1eUlgMskNZMaGK+LiNskvQBcI+mPwFNA2wqwFwFXSBpFauHcCSAinpd0HfACMBU4IBsqhKQDgbtJy+5fHBHPd1bhzfLOsY6ZNaJOXHW5JuOcshVdSTfSztjniPhJRwc3M6tbnfgc3Yh4BlirRPrrpJUEi9O/BHYoc6wTgRNLpN8B3DHHhTVrII51zKxRic6r6NZqnNNej+5Zs3KgvFty5TU4fth91S6Gmc1Fs7hIg5nVH8c6mX7Lr8r/7hpW7WKY2VyU9zinbEU3Iu6fmwUxM6stgvToIDPLKcc6Zta4RHPnTdGqSZWsumxm1nhEZ87RNTMzM6sZUqfO0a1JruiamZWkLnm8kJmZmVktUMnH1uZHxRVdSfNExFddWRgzs5riHl2zhuJYx8waSd7n6HYYxUkaKOlZ4NXs/RqS/t7lJTMzqzapss3M6ppjHTNrNEI0qfKtHlXSXXEmsA3wIUBEPA1s3JWFMjOrPqUe3Uo2M6t3jnXMrOGIpoq3elTJ0OWmiHizqGu7tYvKY2ZWGzrxObpmVvMc65hZw6nXntpKVVLRfVvSQCAkNQMHAa90bbHMzGpAkx8vZNYgHOuYWUNJQ5frs6e2UpVUdPcjDelZGngfuC9LMzPLMc+/NWsgjnXMrLH48UIQER8AO82FspiZ1Zact3SaWeJYx8waUd5XXe6woivpH0AUp0fE3l1SIjOzWuA5umYNw7GOmTUaAU11ushUpSoZunxfwesewI+Bt7umOGZmtULu0TVrHI51zKzByD26EXFt4XtJVwD3dlmJzMxqRc5vAGaWONYxs0bU8BXdEvoA3+nsgpiZ1Rz36Jo1Ksc6ZpZ7TTR4RVfSR3w9b6UJmAAc3ZWFMjOrPq+6bNYoHOuYWaNJS5HkO85pt6Kr9O3XAN7JkqZFxEyLNZiZ5Y7wc3TNGoBjHTNrSBLNOR+51u63yy70N0ZEa7b5wm9mDSJbjKqSzczqlmMdM2tEAprUVPFWjyop9XBJ/bq8JGZmtUaqbDOzeudYx8wajqSKt3pUduiypJaImAqsD+wl6TXgc1IDQESEbwhmll/CvbVmOedYx8wamRp4MarhQD9gu7lUFjOz2lKnLZhmVjHHOmbWoERTzuOc9iq6AoiI1+ZSWczMaojco2uWf451zKwhicbu0V1U0mHldkbE37qgPGZmtSPnLZ1m5ljHzBpXI/foNgPzQ86r+mZmpUh+vJBZ/jnWMbPGJNGkfMc57VV0342IE+ZaSczMao2HLpvlnWMdM2tI6fFC+W7j63COrplZw2ryZdAs5/x/cjNrWPX62KBKtVfR3XSulcLMrBbl/AZgZo51zKxxNeW8ra9sRTciJszNgpiZ1RR51WWzvHOsY2aNSjR2j66ZWWPL+Q3AzMzMGpVQzhv0XdE1Mysn5zcAMzMza1x5H7rsKM7MrCRBU0tlW0dHkpaS9KCkFyU9L+nXWfrCku6V9Gr2d6EsXZLOlDRK0jOS+hUca0iW/1VJQwrS+0t6NvvMmcr7eCQzMzObbRI0qanirf1j1Wac44qumVkpafJKZVvHpgK/iYiVgHWAAyStDBwN3B8RfYH7s/cAWwJ9s21v4FxINwzgD8DawEDgD203jSzP3gWfGzSnP4GZmZnllZAq3zpQk3GOK7pmZiVli1FVsnUgIt6NiCez158CLwK9gMHAZVm2y4DtsteDgcsjeRRYUNISwA+BeyNiQkR8BNwLDMr2fTMiHomIAC4vOJaZmZnZTJpQxVt7ajXO8RxdM7NyKp+ju4ikEQXvL4iIC0oeUloGWAt4DFg8It6FdJOQtFiWrRfwdsHHxmRp7aWPKZFuZmZmNpM0cG2W+jwrinVqKc5xRdfMrJzKp7mOj4gBHR9O8wP/Ag6JiE/aGQpUakfMRrqZmZlZCUKzthhVh7FOrcU5HrpsZlZS5w1dBpDUjXTxvyoi/p0lv58NxyH7+0GWPgZYquDjvYGxHaT3LpFuZmZmVlInztGtyTjHFV0zs1JEp1V0s5UBLwJejIi/Fey6BWhbUXAIcHNB+i+zVQnXASZmQ3/uBraQtFC2OMMWwN3Zvk8lrZOd65cFxzIzMzObiWbhf+0ep0bjHA9dNjMrSZ35HN31gF2BZyWNzNJ+C/wFuE7SnsBbwA7ZvjuArYBRwCRgd4CImCDp/wGPZ/lOiIgJ2ev9gEuBeYE7s83MzMyspE58EmFNxjmu6JqZldPU3CmHiYhhlJ5fArBpifwBHFDmWBcDF5dIHwGsOgfFNDMzswYhieZOatCv1TjHFV0zs5I6tUfXzMzMrKYo57NYXdE1MytFQFO+bwBmZmbWuDpx6HJNckXXzKycnN8AzMzMrDEJZvXxQnXHFV0zs5I8dNnMzMzySjTlvEHfFV0zs3Jc0TUzM7Occo+umVkjEh66bGZmZrnlObpmZg1J0ORLpJmZmeWPEE3qnMco1ipHcWZm5XjospmZmeVUk4cum5k1InnospmZmeWTPHTZzKwxCffompmZWS41wuOFHMU1mC+//JKBG2zBGmtvxCoD1ucPf/wrAHvu92vWWHsjVh+4IdvvvDufffZZlUtq7fn444lsv/PurLjW91mp37o88tjjHH/iSfRabjXWXGcj1lxnI+64695qF7P+qamyzczMatY9d93D6iuvySorrMbJfz2l2sWxCpx15tn0X2MA/VYfwN/POAuAp0c+zQbrbsTa/ddhvbXX5/HhI6pcyvonqeKtHjlCazDzzDMPD9zxb55+bCgjH3mQu+59gEeHj+C0v/6Rpx8byjPD/8PSvXtz1nkXVbuo1o5fH/FbBm2+CS899QhPPzqUlVZYHoBDD9yXkY8OZeSjQ9lq0OZVLmW9y4YuV7KZmVlNam1t5ZCDD+Pm227kqWef4Pprr+fFF16sdrGsHc8/9zyXXHQJDz3yX4Y/+Sh33n4no14dxbFHH8exvzuGx554lN/94TiOPfq4ahe1zgnRVPFWj+qz1DbbJDH//PMDMGXKFKZMmYIkvvnNbwAQEXzx5Zd123LTCD755FP++79H2XPILgB0796dBRdcoMqlyin36JqZ1bXHh49g2WW/S5/v9qF79+7s8LPtue2W26pdLGvHSy+9zMC1B9KzZ09aWlr4wQY/4OabbkESn3z6KQATP/mEJZb8dpVLWv+apIq3euQIrQG1tray5jobsdgyK7H5Jhux9vf6A7D7Pgfx7T6r8NIrr3LQfr+qbiGtrNffGM2ii3yL3fc5iLW+vzG/2v8QPv/8cwDOOv8iVh+4IXvsezAfffRxlUta55Q9XqiSzczMatLYsWPpvVTv6e979e7FO2PfrWKJrCOrrLIywx76Hx9++CGTJk3irjvvZsyYdzj5byfx26OOZblllueYI3/LCSeeUO2i1jUBTWqqeKtHNV1qSa2SRhZsy3TBOZaR9FxnH7eWNTc3M/LRoYx55RmGP/Ekzz2fhvBccv7fGfvas6y0wvJce8NNVS6llTO1tZUnRz7DfnvtzlOPPMh8PXvyl1PPZL9f7cZrzz3OyEcfZIlvL85vjvl9tYta/5pU2WZmNhsc53S9iJgpzaPWatuKK63Ib444jG0GbcuPttqO1ddYjZbmZi44/0JOOvWvjBr9Cied+lf222u/ahe1zmmW/leParqiC3wREWsWbKMLd0pyV8ocWHDBBdjoB+tx170PTE9rbm5mx58O5l83e1hPreq95BL07rXk9J747X+8LU+OfIbFF1+M5uZmmpqa2Gv3XRk+4qkqlzQHPHTZzLqW45wu1qtXL8a8PWb6+3fGvMOSS3jIa63bbY8hPPL4w9w39B4WWmghluu7HFddfhXb/XgwAD/d/ieMePyJKpey/nkxqhojaTdJ10u6FbhH0vyS7pf0pKRnJQ3O8s3QginpcEnHZ6/7S3pa0iPAAVX5IlUybtx4Pv54IgBffPEF9z34H1ZYfjlGvfY6kFo+b73jHlZcvm81i2nt+Pa3F2ep3kvy8iujALh/6EOsvOIKvPvue9Pz3HjLHay6yorVKmJOyBVdM5vrHOd0rgHf68+oUa8x+o3RTJ48meuvu4Gtt9262sWyDnzwwQcAvPXW29x80y38bKcdWGLJJXjoPw8BMPSBoSzXd9lqFrHuCWiahf/Vo1pvKZxX0sjs9RsR8ePs9feB1SNiQtba+eOI+ETSIsCjkm7p4LiXAAdFxH8knVwuk6S9gb0Bli6Y31HP3n3vfYbsfSCtrdOYNm0aP/vpYLYetDk/2HwbPvnkMyKCNVZbhXPPKPuzWA34+yl/Zuc99mXy5Cl8t893uOS8Mzn48N8y8pnnkMQy31mK88/0IxTmiJ+ja2Zdr2binKWWXmrOvkmNamlp4bQzTmXbrQbT2trKkN1+ycqrrFztYlkHfr7DzkyYMIFu3Vo4/cy/sdBCC3H2eWdxxGFHMHXqVOaZpwdnnXtWtYtZ35T/Yfy1XtH9IiLWLJF+b0RMyF4L+JOkDYBpQC9g8XIHlLQAsGBE/CdLugLYslTeiLgAuABgQL81Z57kUYdWX20VnnrkwZnS/3f/HVUojc2uNddYjRHD7psh7YqLzqlSafLKjw4ysy5XM3FO/wH9chHnlDJoq0EM2mpQtYths+D+/9w7U9p666/Lw8P/V4XS5FX9zr2tVK1XdMv5vOD1zsCiQP+ImCJpNNADmMqMQ7N7ZH8F5PZibmadKd83ADOrWY5zzKzL5b1HNw/j8hYAPsgu/hsD38nS3wcWk/QtSfMA2wBExMfAREnrZ/l2nuslNrP60NRc2WZm1nUc55hZp/Mc3fpwFXCrpBHASOAlgOyGcALwGPBGW3pmd+BiSZOAu+dyec2sLqRbgJlZlTnOMbOukfMe3Zqu6EbE/CXSLgUuLXg/nrRoQ6nPnwmcWSL9/7d371GSlOUdx7+/XVblpkAUMMIJykU0RO6XY0RRYVkFAiIoqOCFAKIQ1GCCIFG8RIySCEajoAQ4nBDFSOAoSNBzRFDQJYSb4W5EV4ncFLkpCE/+6FrTjjO7PbMzVHf197Onz3RXV1c9sz2n3uepet+3/hPYvG/R+1csUkmd1PEGQFK7zHMktccxuh025BsAABF3SURBVJI0noKFriRJ6qyuj9G10JWkSdl1WZIkdZdXdCVpXHX8TKckSRpfFrqSNK4sdCVJUgf1Rmh1O8+x0JWkKdl1WZIkdZGTUUnSmIr3yJUkSd0UmJdun9C30JWkKXX7TKckSRpfXtGVpHHk7YUkSVJHjcMY3W5fr5akGQtk3mCP5W0pOS3JnUmu71u2VpKLk9zS/FyzWZ4kJye5Ncm1Sbbq+8wbm/VvSfLGvuVbJ7mu+czJ6XrLJUmSVlCm9W+5WxvCXMdCV5KmkGSgxwBOBxZNWHY08I2q2hj4RvMa4BXAxs3jEOCfmljWAt4HbA9sB7xvaYPRrHNI3+cm7kuSJOl3zGahyxDmOha6kjSleQM+lq2qvgXcO2HxnsAZzfMzgL36lp9ZPVcAayR5JrArcHFV3VtVPwcuBhY17z21qi6vqgLO7NuWJEnSpAY9oT/ISf1hzHUcoytJk8p0xug+PcmVfa9PqapTlvOZdarqDoCquiPJ2s3yZwE/7ltvSbNsWcuXTLJckiRpStOcjGrkch0LXUmaSga+vdDdVbXNbO11kmU1g+WSJEmTCpnu7YVGLtex67IkTWbprMuDPGbmZ01XHJqfdzbLlwDr9623HvDT5Sxfb5LlkiRJy5BpPGak1VzHQleSJjVgkTvzQvd8YOlsgm8EzutbfmAzI+EOwH1Nt5+LgIVJ1mwmZlgIXNS8d3+SHZoZCA/s25YkSdLvy+yO0Z1Cq7mOXZclaUqzcy4wydnATvTGtyyhN6PgCcAXkxwE/AjYt1n9AuCVwK3AQ8CbAarq3iQfBBY3632gqpZO+nAYvdkOVwYubB6SJElTmuYY3WVvawhzHQtdSZrKLN2Otqr2n+Ktl0+ybgFvn2I7pwGnTbL8SmCzFYlRkiSNl9ksdIcx17HQlaRJBaY3SYMkSdJICCvUJXkkWOhK0pS63QBIkqTxNZtXdIeRha4kTSbAvIFvLyRJkjRSpnl7oZFjoStJk1qhGZUlSZKGmld0JWlsdbsBkCRJ48kxupI0zjrepUeSJI0vr+hK0tjqdgMgSZLGWbfzHAtdSZqUY3QlSVJ3dT3LsdCVpCl1vQmQJEnjyjG6kjSuOt4ASJKkcdbtPMdCV5ImEyx0JUlSR8XJqCRpfHW7AZAkSeMpseuyJI0pJ6OSJEkaVRa6kjQlC11JktRNdl2WpHHlFV1JktRRFrqSNLa63QBIkiR1lYWuJE3FK7qSJKmjnIxKksZSIPPaDkKSJGkOeHshSRpj3W4AJEnSOOt2nmOhK0mSJEljJHS9zLXQlaTJjcGN1CVJ0vjqep5joStJU+p2AyBJksZZt/McC11JmlScdVmSJHVW17McC11JmlLXmwBJkjS+up3nWOhK0lS8vZAkSeqkOEZXksZWxxsASZI0nnqzLnc7z7HQlaRJjcPE+5IkaXx1O8+x0JWkyQSv6EqSpM7qepZjoStJU+p6EyBJksaVY3QlaVx1+/gvSZLGVveHaFnoStKUut0ASJKk8dX1LMd7Z0jSVJLBHpIkSSMn03gsZ0vJoiQ3Jbk1ydFzFvI0eEVXkiYV76MrSZK6KbM3RjfJfOBTwC7AEmBxkvOr6r9nZQczZBYnSVOanbOckiRJw2TpfXQH/bcc2wG3VtUPquoR4F+BPef6d1ieVFXbMYyEJHcBt7cdxxx6OnB320Foxsbh+/ujqnrGE7WzJF+j9/86iLuratFcxiNJc8k8RyOg69/hMOc5AE8BftX3+pSqOqXZ1j7Aoqr68+b1AcD2VXX4bMU7E3ZdHtAT+YfXhiRXVtU2bcehmfH7m30WrpLGiXmOhp3f4eya5Txnsku+rV9NteuyJEmSJGmmlgDr971eD/hpS7H8loWuJEmSJGmmFgMbJ3l2kicB+wHntxyTXZf1W6e0HYBWiN+fJElTs50cfX6HQ6qqfpPkcOAiYD5wWlV9v+WwnIxKkiRJktQtdl2WJEmSJHWKha6mLcmCtmOQJEmaK+Y60uiz0NW0JNkUeFuStduORZIkabaZ60jdYKGr6Xp+89g3yXRuMq0nWJLntx2DJEkjyFxnRJjraFksdDWQJPMAqurLwG3AC4HXJFmz1cA0qSRPBj6e5Ky2Y5EkaRSY64wWcx0tj4WuBlJVjwMkOQLYid6tqfYG3uDZzuGSZF5V/RrYH3hGkn9oOyZJkoaduc7oMNfRILyPrgaWZD1gH2D3qro/yb7ALsCjSc6pqnvajVDw/w01sDNwE7BXkpWq6ogWw5IkaeiZ64wGcx0Nwiu6mo5fAAuAhQBVdQ7wc+Aw4NVJ5rcYm/okeS3wQeBU4EhggySfaTcqSZKGnrnOiDDX0fJY6Gq5kuyX5PCqegA4HfiTJC9u3l4MXAGcW1WPtRWjfs9KwGlVdR1wAfAuYIckn203LEmSho+5zkgy19Ey2XVZg7gTOD7JPcAl9MarfCjJT4DNgb2q6q42A1RPkm2AO4C7gI8k+UJV/Ri4JcmlwPOSrFtV/9tqoJIkDRdznRFhrqNBparajkFDKsnzgJ9U1S+TvAT4JHACcA6wIb2p96+qqh+2F6WWasYVHQ3cQ+97Ogg4HDgU2IjeOJbDHV8kSVKPuc5oMdfRdFjoalLNgf8vge8BZzcTMrwUOA84rqpOajVATSrJrsDLgQfoNdb70Js5cg3gPVV1bXvRSZI0PMx1RpO5jgZloSsAkqT6/hiSBHg9sB1wNfCl5mznacCmwKKq+mU70apfkr2BF1bVUc3rnYE9gJ8BJ1XVg0kWVNWjbcYpSVKbzHVGl7mOZsIxuvqdA3+SA4D5wD1VdVaSR4AdgfWacSsrAft54G/PxIYauAU4JsnxVfW+qvp6kk2BQ+jdDuETwG9aCVaSpCFgrjNazHU0G5x1WfQd+N8FHNwsfk+S91TVF4EvA6sCrwY+WlU/aidSTWio120mW7gOOAB4WZIPN6v+CPgucEZVPTqhsZAkaayY64wOcx3NFrsuj7EJB5JNgeOAA4Fj6J3ZfAC4tqre36yzSlU91FK46pPkKHrjU/4AOLWqTk2yCfAV4DZ6E2jsUVU3tRimJEmtMtcZXeY6WlF2XR5TSeZV1ePN8z2bxUcBu9G7SfpOwJuB45oxD8cCD7cRq36voT6U3oH9JUnOBP4+yVOr6sQkmwPbA7dW1ZI2Y5YkqU3mOqPFXEezzUJ3TPUd+F8EvBXYu6oeTrIKcF5VPZbkceDzwD83n/HyfwsmHPifAVwDXJjkHcAqwK7ABUlWraoPAN9sLVhJkoaEuc7oMNfRXLDQHTPNtPnrVtXZSV4BvAP4ZlUtPYN5H/C3Sf4I2B3YubkJt1rSd+A/iN7YoX2AlendK+6oqroxyfnAK5OcVFX3tRetJEntMtcZPeY6mgtORjVGmvuOnUhv8D7AYnoH+y2TrANQVRfSG7tyMbCwqm5rI1b1JmDoe74jsDdwQDN26D5641Nem+QIoIB9PfBLksaZuc5oMdfRXLLQHRPN/cY+BxxcVd9O8kx6V/QPBZ4EvHPpwaaqLquq86vqlvYiHm9JdgPOT7J2kjWBFwFb05s4g6r6DfAtYAHwOuDvPBstSRpn5jqjxVxHc81Zl8dAkicDf0XvAPI64DHgq8CHq+qCZizEp+nddPv4qrqrtWBFkkXAsfS+n681y1YB/gJ4DvAvVfXNvvVXraoH24hVkqRhYK4zWsx19ETwiu4YqKpfA2cDFwAnAd8DTm8O/PObg/3bgTWAtBepkqxF73s6saq+lmSjJGfQ+16+BNwAvCbJLks/44FfkjTuzHVGh7mOnigWumOiqm6ld9+xG4CfAJcufauZUv9O4MDmp1pSVfcCewB/k+QFwGeBa6rqweY7/HdgCb3JGFZuMVRJkoaKuc5oMNfRE8Wuy2MmyQb0BvpvApxVVZe1GpAm1XTpuQA4pqpOSLJSM1aFZpbI+5uGQpIk9THXGQ3mOpprFrod1H8vsr5l86vqseb5RvTOpG0DnFxV320hTC1H02Xnk8D2VXVfczb60bbjkiSpbeY63WCuo7lk1+WOmXDD7Y2bmewWNDdFXwC/7dpzAfAd4PYWw9UyVNXFwDuB7yVZywO/JEnmOl1irqO55BXdjkryNuAtwI3A+sBuVfXAhG4hnjUbAUn2BN5H76x0TTyDLUnSODLX6Q5zHc0FC92OSLJ6Vd3fPN+RXjeQvYCfAh8DdgJ2qKqHWwtSM5Zktap6oO04JElqi7lOt5nraLbZdbkDkmwIHJdk22bRz4HvVNUPgUer6kjgenqNgUaQB35J0jgz1+k+cx3NNgvdbnga8DjwqiRbAPcCuybZva/rx8+ABW0FKEmStALMdSRNi12XR1iSNarqF83zPwb2A1YGPg5sBJwLnAjMB14N7FdVN7cUriRJ0rSY60iaKa/ojqgkO9Oboe6kphvPvcCngAeAI4FbgV2AAlYHXu+BX5IkjQpzHUkrwiu6I6rptnMF8AhwDL0D/keBTYG7gLWBT1TVj1sLUpIkaYbMdSStiJXaDkAzU1VXJ9kKuAT4JbAQeCmwNb1xLFsA85L8Nb1JGjyjIUmSRoa5jqQV4RXdEdd05fk6cGRVnZ5kPrA5vcbgvKq6odUAJUmSVoC5jqSZsNDtgKYB+A/g2Kr6dNvxSJIkzSZzHUnTZdflDqiqxc2EDYuT/KqqTms7JkmSpNliriNpuryi2yFJtgQeqqqb2o5FkiRptpnrSBqUha4kSZIkqVO8j64kSZIkqVMsdCVJkiRJnWKhK0mSJEnqFAtdSZIkSVKnWOhKkiRJkjrFQldzIsljSa5Ocn2Sc5KssgLb2inJV5rnf5bk6GWsu0aSt81gH+9PctSgyyesc3qSfaaxrw2SXD/dGCVJ0nAwz1nm+uY5GgoWuporD1fVFlW1GfAI8Nb+N9Mz7b+/qjq/qk5YxiprANNuACRJkqbBPEcacha6eiJcCmzUnOG7IcmngauA9ZMsTHJ5kquaM6KrASRZlOTGJJcBey/dUJI3JfnH5vk6Sc5Nck3zeCFwArBhc5b1Y816706yOMm1SY7v29axSW5K8nXgucv7JZIc3GznmiT/NuHs7c5JLk1yc5Ldm/XnJ/lY374PXdH/SEmSNHTMc8xzNIQsdDWnkqwEvAK4rln0XODMqtoSeBB4L7BzVW0FXAm8K8lTgFOBPYAdgXWn2PzJwCVVtTmwFfB94GjgtuYs67uTLAQ2BrYDtgC2TvLiJFsD+wFb0mtgth3g1/lyVW3b7O8G4KC+9zYAXgLsBnym+R0OAu6rqm2b7R+c5NkD7EeSJI0A8xzzHA2vldoOQJ21cpKrm+eXAp8H/hC4vaquaJbvADwf+HYSgCcBlwObAv9TVbcAJDkLOGSSfbwMOBCgqh4D7kuy5oR1FjaP/2per0avQVgdOLeqHmr2cf4Av9NmST5Er9vQasBFfe99saoeB25J8oPmd1gIvKBvXMvTmn3fPMC+JEnS8DLPMc/RkLPQ1Vx5uKq26F/QHOQf7F8EXFxV+09YbwugZimOAB+pqs9O2Mc7ZrCP04G9quqaJG8Cdup7b+K2qtn3EVXV31CQZINp7leSJA0X8xzzHA05uy6rTVcAf5pkI4AkqyTZBLgReHaSDZv19p/i898ADms+Oz/JU4H76Z3FXOoi4C19Y2KelWRt4FvAq5KsnGR1et2Hlmd14I4kC4DXT3hv3yTzmpifA9zU7PuwZn2SbJJk1QH2I0mSRp95jtQir+iqNVV1V3PG8OwkT24Wv7eqbk5yCPDVJHcDlwGbTbKJI4FTkhwEPAYcVlWXJ/l2etPaX9iMX3kecHlzpvUB4A1VdVWSLwBXA7fT63a0PMcB323Wv47fbWhuAi4B1gHeWlW/SvI5emNarkpv53cBew32vyNJkkaZeY7UrlTNVs8JSZIkSZLaZ9dlSZIkSVKnWOhKkiRJkjrFQleSJEmS1CkWupIkSZKkTrHQlSRJkiR1ioWuJEmSJKlTLHQlSZIkSZ3yf/5B1VjlJkrFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oversample_smote = confusion_matrix(original_ytest, oversample_fraud_predictions)\n",
    "actual_cm = confusion_matrix(original_ytest, original_ytest)\n",
    "labels = ['No Fraud', 'Fraud']\n",
    "\n",
    "fig = plt.figure(figsize=(16,8))\n",
    "\n",
    "fig.add_subplot(221)\n",
    "plot_confusion_matrix(oversample_smote, labels, title=\"OverSample (SMOTE) \\n Confusion Matrix\", cmap=plt.cm.Oranges)\n",
    "\n",
    "fig.add_subplot(222)\n",
    "plot_confusion_matrix(actual_cm, labels, title=\"Confusion Matrix \\n (with 100% accuracy)\", cmap=plt.cm.Greens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ee0932c1-e9dc-4135-93af-1972d07d3d0f",
    "_uuid": "1b03ca4b3e37985bea49686abd466fdd9a7d84d3"
   },
   "source": [
    "### Conclusion: \n",
    "Implementing SMOTE on our imbalanced dataset helped us with the imbalance of our labels (more no fraud than fraud transactions). Nevertheless, I still have to state that sometimes the neural network on the oversampled dataset predicts less correct fraud transactions than our model using the undersample dataset. However, remember that the removal of outliers was implemented only on the random undersample dataset and not on the oversampled one. Also, in our undersample data our model is unable to detect for a large number of cases non fraud transactions correctly and instead, misclassifies those non fraud transactions as fraud cases. Imagine that people that were making regular purchases got their card blocked due to the reason that our model classified that transaction as a fraud transaction, this will be a huge disadvantage for the financial institution. The number of customer complaints and customer disatisfaction will increase.  The next step of this analysis will be to do an outlier removal on our oversample dataset and see if our accuracy in the test set improves. <br><br>\n",
    "\n",
    "**Note:** One last thing, predictions and accuracies may be subjected to change since I implemented data shuffling on both types of dataframes. The main thing is to see if our models are able to correctly classify no fraud and fraud transactions. I will bring more updates, stay tuned!\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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