Credit Fraud Detection Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a9cf928a",
   "metadata": {},
   "source": [
    "##  <p style=\"color:red\"> Goal :-</p>\n",
    "    \n",
    ">*    <font size='4'> It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8880bee",
   "metadata": {},
   "source": [
    "## <p style=\"color:red\"> DataSet Description :- </p>\n",
    "\n",
    ">* <font size='4'>The dataset contains transactions made by credit cards in September 2013 by European cardholders.\n",
    "This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. </font>\n",
    "\n",
    ">* <font size='4'> It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, author didn't provided the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise. </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2097457",
   "metadata": {},
   "source": [
    "### <p style=\"color:DarkSlateBlue\"> Note from Author:- </p>\n",
    "<font size='3.5' color=\"DarkSlateGray\">Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f28a994",
   "metadata": {},
   "source": [
    "## <p style=\"color:red\"> Importing Necessary Libraries :- </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da115922",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import xgboost as xgb\n",
    "df=pd.read_csv(\"creditcard.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "00092e6d",
   "metadata": {},
   "outputs": [],
   "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",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9f943d8e",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
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       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
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       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
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       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
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      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0ba17692",
   "metadata": {},
   "outputs": [
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       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "df.isnull().sum().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e43121b9",
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       "(284807, 31)"
      ]
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     "execution_count": 37,
     "metadata": {},
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   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6e038b2b",
   "metadata": {},
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1b4a935d",
   "metadata": {},
   "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": "code",
   "execution_count": 7,
   "id": "7a838106",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Class Distributions \\n (0: No Fraud || 1: Fraud)')"
      ]
     },
     "execution_count": 7,
     "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": [
    "sns.countplot('Class',data=df)\n",
    "plt.title('Class Distributions \\n (0: No Fraud || 1: Fraud)', fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "810522c4",
   "metadata": {},
   "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,
   "id": "261afb91",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scaled_amount</th>\n",
       "      <th>scaled_time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>...</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>...</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>...</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>...</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>...</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>...</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",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   scaled_amount  scaled_time        V1        V2        V3        V4  \\\n",
       "0       1.783274    -0.994983 -1.359807 -0.072781  2.536347  1.378155   \n",
       "1      -0.269825    -0.994983  1.191857  0.266151  0.166480  0.448154   \n",
       "2       4.983721    -0.994972 -1.358354 -1.340163  1.773209  0.379780   \n",
       "3       1.418291    -0.994972 -0.966272 -0.185226  1.792993 -0.863291   \n",
       "4       0.670579    -0.994960 -1.158233  0.877737  1.548718  0.403034   \n",
       "\n",
       "         V5        V6        V7        V8  ...       V20       V21       V22  \\\n",
       "0 -0.338321  0.462388  0.239599  0.098698  ...  0.251412 -0.018307  0.277838   \n",
       "1  0.060018 -0.082361 -0.078803  0.085102  ... -0.069083 -0.225775 -0.638672   \n",
       "2 -0.503198  1.800499  0.791461  0.247676  ...  0.524980  0.247998  0.771679   \n",
       "3 -0.010309  1.247203  0.237609  0.377436  ... -0.208038 -0.108300  0.005274   \n",
       "4 -0.407193  0.095921  0.592941 -0.270533  ...  0.408542 -0.009431  0.798278   \n",
       "\n",
       "        V23       V24       V25       V26       V27       V28  Class  \n",
       "0 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053      0  \n",
       "1  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724      0  \n",
       "2  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0  \n",
       "3 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458      0  \n",
       "4 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153      0  \n",
       "\n",
       "[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": "code",
   "execution_count": 10,
   "id": "17aee2eb",
   "metadata": {},
   "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",
    "from sklearn.model_selection import KFold, StratifiedKFold\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": "code",
   "execution_count": 11,
   "id": "aeb95134",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>V8</th>\n",
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       "      <th>V20</th>\n",
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       "      <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>191348</th>\n",
       "      <td>1.164675</td>\n",
       "      <td>0.523091</td>\n",
       "      <td>-1.290907</td>\n",
       "      <td>-1.725377</td>\n",
       "      <td>1.501748</td>\n",
       "      <td>-0.425998</td>\n",
       "      <td>-0.852586</td>\n",
       "      <td>1.330222</td>\n",
       "      <td>-1.797743</td>\n",
       "      <td>1.165274</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110751</td>\n",
       "      <td>0.088838</td>\n",
       "      <td>0.557202</td>\n",
       "      <td>0.222795</td>\n",
       "      <td>0.225586</td>\n",
       "      <td>-0.910638</td>\n",
       "      <td>-0.237145</td>\n",
       "      <td>0.219500</td>\n",
       "      <td>0.022445</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203328</th>\n",
       "      <td>12.622232</td>\n",
       "      <td>0.588318</td>\n",
       "      <td>-0.967767</td>\n",
       "      <td>2.098019</td>\n",
       "      <td>-5.222929</td>\n",
       "      <td>6.514573</td>\n",
       "      <td>-4.187674</td>\n",
       "      <td>2.114178</td>\n",
       "      <td>0.948701</td>\n",
       "      <td>-2.448427</td>\n",
       "      <td>...</td>\n",
       "      <td>1.147204</td>\n",
       "      <td>-0.843268</td>\n",
       "      <td>0.796739</td>\n",
       "      <td>1.314312</td>\n",
       "      <td>-0.352887</td>\n",
       "      <td>-1.770706</td>\n",
       "      <td>0.098133</td>\n",
       "      <td>0.956769</td>\n",
       "      <td>0.162777</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133850</th>\n",
       "      <td>1.045204</td>\n",
       "      <td>-0.048650</td>\n",
       "      <td>-0.862482</td>\n",
       "      <td>0.780102</td>\n",
       "      <td>1.233976</td>\n",
       "      <td>0.469258</td>\n",
       "      <td>-0.514055</td>\n",
       "      <td>-0.013420</td>\n",
       "      <td>0.334040</td>\n",
       "      <td>0.551068</td>\n",
       "      <td>...</td>\n",
       "      <td>0.189149</td>\n",
       "      <td>-0.038539</td>\n",
       "      <td>-0.451698</td>\n",
       "      <td>0.399874</td>\n",
       "      <td>0.206003</td>\n",
       "      <td>-0.785936</td>\n",
       "      <td>0.171344</td>\n",
       "      <td>-0.030913</td>\n",
       "      <td>0.063676</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276864</th>\n",
       "      <td>4.570391</td>\n",
       "      <td>0.970947</td>\n",
       "      <td>-1.374424</td>\n",
       "      <td>2.793185</td>\n",
       "      <td>-4.346572</td>\n",
       "      <td>2.400731</td>\n",
       "      <td>-1.688433</td>\n",
       "      <td>0.111136</td>\n",
       "      <td>-0.922038</td>\n",
       "      <td>-2.149930</td>\n",
       "      <td>...</td>\n",
       "      <td>0.650196</td>\n",
       "      <td>-0.870779</td>\n",
       "      <td>0.504849</td>\n",
       "      <td>0.137994</td>\n",
       "      <td>0.368275</td>\n",
       "      <td>0.103137</td>\n",
       "      <td>-0.414209</td>\n",
       "      <td>0.454982</td>\n",
       "      <td>0.096711</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248296</th>\n",
       "      <td>-0.307413</td>\n",
       "      <td>0.812780</td>\n",
       "      <td>-0.613696</td>\n",
       "      <td>3.698772</td>\n",
       "      <td>-5.534941</td>\n",
       "      <td>5.620486</td>\n",
       "      <td>1.649263</td>\n",
       "      <td>-2.335145</td>\n",
       "      <td>-0.907188</td>\n",
       "      <td>0.706362</td>\n",
       "      <td>...</td>\n",
       "      <td>0.354773</td>\n",
       "      <td>0.319261</td>\n",
       "      <td>-0.471379</td>\n",
       "      <td>-0.075890</td>\n",
       "      <td>-0.667909</td>\n",
       "      <td>-0.642848</td>\n",
       "      <td>0.070600</td>\n",
       "      <td>0.488410</td>\n",
       "      <td>0.292345</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        scaled_amount  scaled_time        V1        V2        V3        V4  \\\n",
       "191348       1.164675     0.523091 -1.290907 -1.725377  1.501748 -0.425998   \n",
       "203328      12.622232     0.588318 -0.967767  2.098019 -5.222929  6.514573   \n",
       "133850       1.045204    -0.048650 -0.862482  0.780102  1.233976  0.469258   \n",
       "276864       4.570391     0.970947 -1.374424  2.793185 -4.346572  2.400731   \n",
       "248296      -0.307413     0.812780 -0.613696  3.698772 -5.534941  5.620486   \n",
       "\n",
       "              V5        V6        V7        V8  ...       V20       V21  \\\n",
       "191348 -0.852586  1.330222 -1.797743  1.165274  ... -0.110751  0.088838   \n",
       "203328 -4.187674  2.114178  0.948701 -2.448427  ...  1.147204 -0.843268   \n",
       "133850 -0.514055 -0.013420  0.334040  0.551068  ...  0.189149 -0.038539   \n",
       "276864 -1.688433  0.111136 -0.922038 -2.149930  ...  0.650196 -0.870779   \n",
       "248296  1.649263 -2.335145 -0.907188  0.706362  ...  0.354773  0.319261   \n",
       "\n",
       "             V22       V23       V24       V25       V26       V27       V28  \\\n",
       "191348  0.557202  0.222795  0.225586 -0.910638 -0.237145  0.219500  0.022445   \n",
       "203328  0.796739  1.314312 -0.352887 -1.770706  0.098133  0.956769  0.162777   \n",
       "133850 -0.451698  0.399874  0.206003 -0.785936  0.171344 -0.030913  0.063676   \n",
       "276864  0.504849  0.137994  0.368275  0.103137 -0.414209  0.454982  0.096711   \n",
       "248296 -0.471379 -0.075890 -0.667909 -0.642848  0.070600  0.488410  0.292345   \n",
       "\n",
       "        Class  \n",
       "191348      0  \n",
       "203328      1  \n",
       "133850      0  \n",
       "276864      1  \n",
       "248296      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": "code",
   "execution_count": 12,
   "id": "4ce0c2a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of the Classes in the subsample dataset\n",
      "0    0.5\n",
      "1    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)\n",
    "plt.title('Equally Distributed Classes', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "74e6b1e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-SNE took 7.1 s\n",
      "PCA took 0.023 s\n",
      "Truncated SVD took 0.013 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": 14,
   "id": "25df4862",
   "metadata": {},
   "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": 15,
   "id": "43bf0ea7",
   "metadata": {},
   "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": 16,
   "id": "c3f8362c",
   "metadata": {},
   "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": 17,
   "id": "e0daa42c",
   "metadata": {},
   "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": 18,
   "id": "fb473448",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifiers:  LogisticRegression Has a training score of 94.0 % accuracy score\n",
      "Classifiers:  KNeighborsClassifier Has a training score of 93.0 % accuracy score\n",
      "Classifiers:  SVC Has a training score of 94.0 % accuracy score\n",
      "Classifiers:  DecisionTreeClassifier Has a training score of 91.0 % 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": 19,
   "id": "31cfd4f1",
   "metadata": {},
   "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": 20,
   "id": "00cfe28a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Cross Validation Score:  94.02%\n",
      "Knears Neighbors Cross Validation Score 93.65%\n",
      "Support Vector Classifier Cross Validation Score 94.02%\n",
      "DecisionTree Classifier Cross Validation Score 92.12%\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": 21,
   "id": "d08ddc63",
   "metadata": {},
   "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": 22,
   "id": "30498170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\Owner\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 22,
     "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": 23,
   "id": "dc761b1d",
   "metadata": {},
   "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": 24,
   "id": "d69a0401",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression:  0.9751082670803439\n",
      "KNears Neighbors:  0.9352983000452461\n",
      "Support Vector Classifier:  0.9741257837243875\n",
      "Decision Tree Classifier:  0.9199631568741516\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": 25,
   "id": "e7865267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Bd07whcAh+Dqgq/AtO36+mJd5FF8wvQzfubr/AlPwTTleUeDax/Hte70e3/LsbfjOyH3IWpu31HsGvjA8HN8gqX34liKPpPCxTwfFWjvZGLMK37FSV+L7c9gBrATG+r+X4mQBTwEn4NvLm4Rvj/An+L6vzaVZs4iIVD7GtwJIRESkejDG3AfcCzS31q6PbDUiIiJS1rSnV0RERERERKqscg29xpjXjTHbjDG/F/G4McY8Y4z5yxizzBjTrTzrExERERERkaqlvDu9bwBBj1DwOxlo7f+4AnihHGoSERERERGRKqpcQ6+19nsgPcQlpwFvWZ+fgRrGmAblU52IiFQH1tr7rLVG+3lFRESqh4q2p7cR8E/A1xv994mIiIiIiIiUWKU9ssgYcwW+JdAkJiZ2P+ywwyJckYiIiIhI+Urfm8OurGDHcoscPIPF4MVhvTjwfRjrxWk9OKwHp9fje8zrwVgvuXjItV4cFhxe34exNv9rY/M+TMj39RrLHutlR66b2nHRxEY5WJORvcNaW/dAvo+KFno3AU0Cvm7sv68Qa+3LwMsAPXr0sIsWLSr76kRERESk2nhvwd/MXBL0n6IVxo516aQARzavFelSKp2dzu/Z7fwl0mWUPmtx4sWBP5Di8X1tPb7gaj3E5XpIzPaQ6HKTkO0l3uUhMdtLvMtLvMsS57LEZ/tuY12GOFcUsS6Ic0GsCxzFhFZXNGTHGvbFGlzxDnITnLjiHGTHO3HFR+GKd5IdH012YjSuhGhc8dFkJ8ZwTNuTSNxRn/EPPMD/zZtHw4YNef7FFzn11FMxxmw40B9JRQu9nwDXGmM+AI4Edltrt0S4JhERERGphmYu2cSKLRm0a5ASsRqKC2b12kKdxFgSUmLLsaqqYcVWX9OsR70eEa4EwILX4/uw/luvu8DXHrBu8LiJyvEQt89N7D4PsfvcxGV7iN3nJTbbS1y2Jc5liPUHVF9QNfmBNdYFTm/o0JoTDa44f1CNc7C3ppO0OF9gzU6IwhUfjSshiuyEaFzxMb7P/WG2X7vBnNXunAP6KZx88snMmTOHxo0b89xzz3HppZcSFxd3QK8VqFxDrzHmfaAvUMcYsxG4F4gGsNa+CMwGBgF/AVnAJeVZn4iIiEh1MnXVVGavnR3pMoLaluFix15XRGvIsm4SmkaREMHQW7GCWdXSo14PBrUYxFltzjqwF/B6wJUJOXt8t6494Moo8HUm5GQW+HoP3r278WTswbtnD569+/Bm5eDJdeDNMfvdenIceHON/9b3tSfXQJGh1TeyycRE4UyIw5EUjzMpCUe9ZJwpKThSa+CsUQtnjdo4atTw3Zfse8yZnIwjJQVnUhImJubAfiYlZK3liy++YMCAATgcDoYMGcLQoUO56KKLiI0tvV/kGGttqb1YpGh5s4iIiEjJXTLnElamr6RtrbaRLqWQFVsyyHK5SYiN7MLEOomxHBLhLupBBTPZn9fjD6V7AgJrRjFfZ/734f/am7UHb9Y+PDkmP4zmhdNCQTXXiccd5b/O4HVZrCd0mSYqCkdyAs7kJJwpqThSUnGm1sCRmoozOQVHSjLO5BScKck4Am9TfSHWUU6h9UB5vV5mzJjBuHHjWLp0KTNnzmTIkCEhn2OMWWytPaDf/lS05c0iIiIiFboDWdmE6phmm3+Is03I2nBFOVdVvCz/suIpl/SOdCkSaV6vL2yG7KgG6bAGuy93L+BbMezJC6HBOqruKDyeWLzuKDy5Ub7HXAaPy4s3OxrrTgVSi645Kgpnir+7WjcVZ3Iy0YFBNSXVH1QLdFn9t45S7HJWJF6vl48++ojx48fz+++/06ZNG9566y0GDRpUpu+r0CsiIqVCIUVK06JSXtJZEZbKRkrGPt9k35T46EKPxdkmpHp6lndJYWnXIIXTuujkykrL6/UFzBBLfPfrqIbqsubsKfTy1kvh5cDuaDzeBLzeWDyeGLy5Ub4ua24MXldNPK5UvPvcePblYnPcoeuPivIH0GScKb7QGpUfSpODd1tT/1sqbGJjMSb0vtnqyOPxcMcddxAbG8t7773H8OHDcTqdZf6+Cr0iInJQ8sJuaYcUKZmqFuoSaEOqpydZG44tldf7Y106UE0n3DrhtC6NOO/IQyNdiVR01kLO3iKX9Ia8r9A+1j1A0dsofaHV4HXH4CERr03A443H64nF44nGk5uKN7cmHpfB6/LiyfbgzXbjycrBk5WNzc4J8Y14wZmLMznuv+5p7WRiA4NqQEAN1m018fEKraXA7Xbz7rvv8tprr/H5558THx/P119/zaGHHorD4Si3OhR6RUSqgLI+ViPU9NAsxyoAErylG1KkZKp1qAvDkc1rKfhJ1WQt5GaVbNlv0K6r/9Z6i39P48RGJ+N1JOOxCb7A6onF46n/33LgHIMnB7wuD559Hjz7cvFmufBkZePdk4U3a1+BF/UCe/0fgMMREEZTcdRJISZUlzXFH1jzQmtCgkJrBOXk5PD222/z4IMPsnbtWjp37szGjRtp3bo1zZo1K/d6FHpFRCj7pbll3YULtXyxNAQG24Lywm5Nj8JuJCnUiVQi1kLuvoMapLRfgA0zqBKbBLEpEJOEjUnCSyKe6Dp4HbF4omPw5jr/Wyrssvt1V71ZLjx79+HZsxdvpm/qMHiATP9HwfczvtCa3z2tQ3TDZOKSfcF0v/AabE9rQgKmHDuBUnr+/fdfevXqxYYNG+jRowdPPfUUgwcPjugvIRR6RaRSKatwWlpLc4sKt2UdSlPio8t4wuhBHusgIlLZWQvu7BABNIyOav59eyh2fC+AcUBMMsQm+wNrMsQkQXIDbHQSXhPvWx7sjvaF1Vynr7ua7cWT7fbvX3Xh2evrrnoyM/Hu3u273bPZ9z2FsH9oTSb60HrEBXRbg+1lzQ+tiYkKrdVIdnY2ixYtok+fPtSrV49BgwZx6qmnctJJJ1WIjruOLBKRMleSpbehltFC6I7jwSqNbuWCEEtM1YUTESln1oLbVYJBSkUs+837CCeoYvwh1R9Q9wus/wVYG5OE18blD1vy5oLHZf2B1YMny4V3b7YvoGZm4MnIxJOZgTcj039fpm9YVAiOxMSA7mleUE0pdN9+ITbvsaQkTDkMGJLKLSsri5dffpmJEyeya9cu/vnnH2rXrl0m76Uji0SkQpu5ZBMr/EdPFGe385f8IzSCqehLabXEVESkFOQF1YMapOS/31vMlF4ATJCAmgRJh/iWAwfe5w+0NiYJa2Pw5Jj8s1c9+9x49+XgydzzX1D9Ny+oZuDdvQ1P5hq8GRl49uwBT+gQ7UhI2G/Jb/Qh9XC0ahU8qBYcyJSUhInSP/WlbOzdu5cXX3yRRx99lK1bt9K3b1/uueceatWqmHMl9P8EkSou1HLg8pr2mmXdJDSNIiGM0OtM30K3Wu2ZfNLkMq9LRERKkTunQLe0pIOUArqu3tzw3jMghOZ/nlg3SJc1pVCotdFJWGJ83dV9bv9eVX9QzdiNNzMTz9b/uqzezPWFuq24QwdqEx+/X0c1qm5dYlq2DH3sTV6ITUrCRJfNlhiRg7VhwwZuvfVW+vfvz9ixYznmmGMiXVJICr0iFcSBTN8tbikwhF4OXNb7TPMkxEZRJzG8vaZta7VlUIuyPaBcRET83DkHOUgpINB6Qh0hEyAmqXBXNaFp4S5roQ5rga9jkvDm5ODJyPAF1Pxbf1DdlhdYM/BkbsS7O8O3LDjDd+vJzITc0OHaxMXtt6fVWasmMU2bFt9lzQutMTGl8IckEnm7d+/m2WefZePGjbz44ou0a9eOP//8kzZtSn+7WVlQ6BWpIEqyBDhPcUuBoZjlwDq7UUSk8vHkhjlIKVSH1X+fJ8zVPtGJhZf41mgSRke1wNcxSeD4b5+oNyfHF0IzMvfvsu7IC69b8GSsCr6nNSMDmxM6aJvoaBypqb7AmpKCs0YNYpo0Cd1lDRjM5FBolWpu586dPP300zz11FPs3r2b0047DbfbTVRUVKUJvKDQK1VAWZ9PWl7yAu+UK3sXe23ekmUtBRYRqSQ87iBLesMZrBSk6+rODu89oxMKB9CUxkUOVtq/o1rgvoCgGsjm5Pi6poFd1vS8ELvtv6BaqMvqC6/WVUzojo4udJxNdMOGAV3WVH9QLeLYm9iymmgvUvV9/vnnnHXWWWRmZjJ06FDuvvtuunXrFumyDohCr1QIBxNcQ03LrUzaNUjhtC6N9ruvqP24gcfraCmwiEgZ8XrCGJoU5vE17n3hvWdUfOEAmtKo0BCloF8H3heTBM7i/5lnc3Px7NnzX7d1WwaejDQ8Gev2Xyq8X5f1v26r3VfM9xUVld9lzQujUQ0ahN7TGnD8jYmNrRDHnYhUF9u2bSMtLY3DDz+cLl26MHjwYG6//XY6d+4c6dIOio4skoNSWl3Wgw2uFWWJbmmfIRvq7FidmSoiEoTXUyCQHuAgpZw9kJsV3ntGxYVY0lv0IKXCXdbksIJqIOt25x9fExhQCwXVjIz8Lut/j2Vis4r5Hp3OAt3TgKBaoMsarNtq4uMVWkUqgX///ZdHH32UF198ka5du/Ljjz9GuqRCdGSRRMyB7EMNpqyPeSntMFqUUCH1QOR1chVuRaRKyw+qoQYphTlYKXdveO/pjC0cPpPqQ+0SDFLKC7XOAx8GaD0evHv24EnLxLN7c/CBTCFCrLe40OpwFFryG9OseVhdVmdyMiYhQaFVpArbtGkTEydO5OWXXyYnJ4fzzz+fO++8M9JllTqFXjlo4e5DjaTZa2ezMn0lbWu1LdP3UUgVkWrD6/WFzQMepBQQYHP2hPeezpjCATTpEIhpUcSy3xCDlaJKZ0CR9XoLDWLa77bAXtaCe1q9e4r53o3xhdDAPa2HNiEuJTVI5zXIntaEBIzDUSrfq4hUPdOmTeO5555jxIgR3HnnnbRq1SrSJZUJhV6ptErSvc0LvBr4JCLVmtfr64SWZImvKzP4feEGVUd04cCZUAdqNitmkFKQr6NKfyiR9XrxZmXh3b278ECmcJYK79kDxWwVKxRaGzcmLiCwFtVldaSk4EhMVGgVkVKzfv16Hn74YXr37s1FF13EyJEjGTx4MC1atIh0aWVKoVcqhANZflySpcQ6+1VEKi1rIWdvCQcpBRuslBdUw5jl4YgqHEgTakGNQ0MPUgo2WKkMgur+Px6Ld29W8C5r3vE3eUfcBDv2JjPT98uAUD+OxMT99rRGN2yIs23b4PtcA89uTU7GkZSEcQaffCwiUl7WrFnDQw89xJtvvonD4aBJE99xl/Hx8VU+8IJCr0RQYNA9kL2wWkosIhWWtb4hSEWdjRr2YCX/rQ0dygAwzsIBNK4GpDYO3lENNVgpKhbKaR+ntRa7bx+ejIyALmvhbut+k4MDlwzv2QMeT8j3cCQk7LfkN/qQejhatdo/qAZ0WffrtiYlYaL0zyURqbwmTJjAfffdR3R0NFdffTW33XYbjRo1Kv6JVYj+Ky4HNYH5YIZYBe6zVYAVkYizFnL3HfwgpbwAG1ZQdRQOpHEpkNJw/yW+xQ1Sik32TRCOwMAhay02O7voLmtxS4UzM8HtDvkeJj7+v45qSipRdesS07Jl6GNv8rqtSUmY6AMfNCUiUhmtWLGChg0bUqNGDTp37swNN9zALbfcQoMGDSJdWkToyKJqLC/shnNc0E7n9+x2/hL0sTqJsRySUvLla9pnKyIHzVpwZ4d/Vmpx+1ht6I6hjwkdPsMdpBSbDNHxEQmqBXmzs0N2WfcLqrsLDmTKhNzckK9v4uKKPvYmVJc1L7TGlM7QKRGRqm758uVMmDCBqVOnct9993HPPfdEuqRSoyOLpESChd3A44KC7a9dUcpH8YD22YpUW9aC21WCQUohOqyuzBIE1SCBNLleyQYpxSZDdEKFCKqBvDk5BSYIZ4Q3kMkfXm1OTsjXN9HROFJ904KdKSk4a9QgpkmT0F3WgDDrUGgVESlTS5YsYfz48UyfPp3k5GTuuOMOrr766kiXVWEo9FZiB7osubiwG2x/rZYfi0h+UA1raFJG0ct+XZngDb2c1ccE2XOaBIl1wxikVLCjmgAVeAKuzckJb3JwoS6rL7xalyv0G0RHFzrOJrphw4ChS6n+oFrEsTexZTuMSkREDs5dd93FTz/9xD333MMNN9xArVpFr+CsjhR6K7GZSzYd0J7agmE3T94eWwVckSrEnRO8o1rSZb+uTPCGXsKaL6bgsl9/UC1qaFKhr/33RSdW6KAayObm4tmzJ7/b+t9e1mIGMvmPv7HZ2aHfICqqUBiNatAg9OTggKXCJjYWU8G60yIicuAWLFjAAw88wFNPPUWLFi14/vnnSU1NpUaNGpEurUJS6K3k2jVIYcqVvUv0HF9H92W+nLP//dpjK1JBuHMOcpBSQKD1hF62mi86sXBHtUbTMPatFlgGHJNUaYJqIOt25x9fE26X9b/HMrFZWaHfwOkstKc1tm5L//LflP26rMG6rSY+XqFVRET46aefGDduHF988QW1a9dm5cqVtGjRgqZNm0a6tApNobcCK2758oFOTg6cmhxIe2xFDoInN8xBSmF0WD3FLFXNE51QOJDWaFK4oxpqkFJsXlCt3OeIWo8H7549viW/u4vushbqtmZk4M3IwFtcaHU4Ci35jWnWPKwuqzM5GZOQoNAqIiIHzOv1csoppzBnzhwOOeQQJk6cyFVXXUVSUlKkS6sUFHoriGABt7ipyu0apHBal9BnbAUbSqWOroifxx1kSW84g5UyC9/nLmZ5ap6o+MJLelMaF7Pst4iOqrPq/Cfcer2+0BokoHozM/AU7LIW2NPq3bMn9BsY4wuhgXtaD21CXEpq4SXCwfa0JiRgKmEHW0REKi9rLYsXL6ZHjx44HA66d+/OiSeeyBVXXEFCQkKky6tUqs6/mCq5YPtzi9p7G0ywcAsEHUqljq5Ual7PQQxSKtBlde8L7z2j4gov6U1pVLJBSrFJvjBbhYJqIOv14s3Kwrt7d3gDmQK6rJ5Mf2gt5gi9QqG1cWPiAgJrwYnBgeHVkZio0CoiIpWCtZbPP/+ccePGMX/+fObPn0+vXr2YMGFCpEurtKrmv74qgYKd3bzAW5L9uYFBN1i4zftaQ6kk4rye4pf0httlzS1mGWqeqLjCATS5QZiDlArsY3VGl+3PpwKw1uLdmxWiyxpwxE2wY28yM8HrDfkejsTE/fa0RjdsiLNt26LPbk0J6LYmJWGclXsJtoiISCjWWmbNmsW4ceNYuHAhTZo04fnnn6dLly6RLq3SM7aY36xXBj169LCLFi2KdBklMvDVR9jinkdC7H+/d6iTGMshKeEfC1Ew6CrcSqnyevfvpB7MYKXcveG9pzO2+ABa7FE1ydUmqAay1mKzsgp0WUNMDg7ssvpviw2tCQn7LfkNGlQDuqz7dVuTkjBR+j2riIhIUTIzMzn00EOpUaMGd911FyNGjCBG55znM8Ysttb2KP7KwvQvkHKS15XdluFix14XWdGrIBra1TugPzdAXVwJIi+oHuwgpbzXCIcjuvBU38S6UKtF8CW+wY6mybsmqvr+h91ai83ODrIcOCO8pcKZmeAOffatiY//L6impBJVty4xLVsW3WUN7LYmJWGiq9cvEkRERMqS1+tl+vTpfPjhh3zwwQckJyczd+5c2rVrR7T+zi1VCr3l5I0lH7Mx6y9ysuoDkBLfhmMbDuTRE6+McGUScV6vrxNa4kFKQfa1hh1UowI6pP7bhNpQs1nhoUlFDVLK+zoq/NUJVZm1Futyheyy5gfVIF1WT2Ym5IY+B9fExe3XZXXWqklM06bFd1nzQqt+WywiIhJxHo+HqVOnMn78eFasWEHbtm3ZtGkTTZo0oXPnzpEur0pS6C0nO/a68Loa0Mk5JuzhVFKBWQs5e0MH0GBfFxqslBdUw9hm4IgqHD7ja0KNQ8Nc9hvYUY0FHZ9SiDcnxxdCD6DL6t29G1tcaI2OxpGamh9GnTVqENOkSfFdVn+IdSi0ioiIVGrr1q3j5JNPZuXKlbRr147333+fs846C6fmVpQphd4yUHApM0CW/ZuE2EOZckn4g6qklFnrG4JUZEc1MJyG6rr6b23o/Y8AGOd/YTMvgMbVgNTGJRiklNdRjVNQLYbNySl+cnDG7v+CaoFjb6yrmPNxo6MLHWcT3bBhQJc1L7AWcexNrLriIiIi1U1ubi6rVq2iffv2NGnShMMOO4wJEyYwbNgwHDpZoFwo9JaivLCbN2DKvbc5ACnx0SSYQzm24cBIllc5WQu5+0J0TMMcpJQXYMMKqo7CATQuBVIaBlniW0yHNTpeQbUEbG4unj17/N3WjPAGMmVm4PWf4WqzizkrNyqqUBiNatAg9OTggKXCJjYWoz9PERERCUNOTg5vvfUWDz74IHv37mXdunUkJCQwY8aMSJdW7Sj0HoSCZ+MGhl13Rhe61Ty5ei5lthbc2aHPTw1nkFJeoLWeMN7UBA+kyfWLGaQU5OvoBAXVA2Td7vzja8Ltsv4XYjOxWcUcR+R0FpocHFu3ZdAua7Buq4mPV2gVERGRMuVyuXj99dd5+OGH+fvvvzniiCN49tlniY+Pj3Rp1ZZC70HIG04VZ5sA4N4XEHb7VbKway24XSUYpBSsw5p3f2YJgmrBPafJkHRImB3VgGsUVEuF9Xjw7tlTbJd1v27r7v8GMnmLC60OR6ElvzHNmofVZXUmJ2MSEhRaRUREpEKbN28eV199Nb169eKll17ixBNP1L9fIkyh9yDkDadqZm7x3eGk/MNuXlAtdpBSwQ5rkC6rN/RxJ/ligpydmlg3+NCkogYp5QVV7WMoVdbr9YfWMLusBfa0evcUM/3ZGF8IDQiuMc2a+rqsBZcIB9vTmpio/+iLiIhIlZKVlcVLL73E3r17ufvuu+nbty/z5s2jV69e+ndPBaHQe5ASYqNKPpzKnXOQg5QCuqze0NNi88UkFe6oJtQpcFZqGIOVohMVVMuQ9Xrx7t37XxgtQZfVk+kPrTb0JOiCoTW6cWPiAgKrr8uaGnQgkyMxEaM/fxERERH27NnD888/z+OPP862bds45ZRTsNZijKF3bw2vrUgUesMUuH83bypztl3PId4asPjNknVZPTnhvWl0YuGOao2mJRuklHeroFIurLV492aF6LIGHHET7NibzEzfub0hOBIT/zunNSXFNz24bdv99rkWuVQ4MRGjkfgiIiIiB+XTTz/lkksuIS0tjYEDBzJ27Fj69OkT6bKkCAq9YZq9djYr01fStlZbdux1keVy051MTtm7BT69/r8LoxMKB9AaTUo2SCk/qCqclDdrLTYrq0CXtYjJwQW7rP7bYkNrQsJ+S36jD6mHo1Wr/YNqakrwgUxJSZgo/d9WREREpLzt2rWLPXv20LhxY1q1asWRRx7J2LFj6dWrV6RLk2LoX89FKDiZOS/wTj5pMme/NJ9Em8nrW8+CY0ZD90v+C6pO/UgjyVqLzc4u3GUNZ6mwv9uKO/TeZhMf7+uy+icGR9WtS0zLlkV3WQNDbFISJjq6nH4aIiIiInKw0tPTefrpp3n66acZMGAAU6dO5fDDD2fWrFmRLk3CpIRWQMGzdnvU6wFA21ptGdRiUP51Cd69vk9qtfB1cqVUWGuxLlfxk4OL6LJ6MjMhN/Q+ZxMXt9+eVmetmsQ0beofvpQa+tibpCRMTEw5/TREREREJFJ27NjBk08+ybPPPktmZiZnnHEGd911V6TLkgOg0FtA3jLmQ+M7kpvRmawNx+Y/9tEG+Ojb+azYksHJdfxHs8SmRKjSisubk+MLoRmZeDN2hzeQKcN/TUYGtrjQGhODIzXF10lNTsZZowYxTZoU32X1h1iHQquIiIiIFOPxxx/nkUceYfjw4dx111107Ngx0iXJAVLoDaJtrbZkbbiCFVsyqNmg8OPtGqQw4NAs2IFvWXMVY3NyQgfV3Rn7D18qcOyNdblCv0F0dKHjbKIbNgzYy5oSdHJw/gTh2Njy+UGIiIiISLWxZcsWHn30UU466SQGDhzI6NGjGTFiBIcffnikS5ODpNAbQrsGKUy5sohx4yvnwAIqZKfX5ubi2bMH7+6CXdYiBjIFdFk9mZnY7OzQbxAVVSiMRjVoUPzkYH+INbGxOrNMRERERCqEjRs3MnHiRF5++WXcbjd169Zl4MCB1KlThzp16kS6PCkFCr0HypXpu40r/dBr3e7842vC7bL+F2IzsVlZod/A6SzQPU0mtm7LoF3WYN1WEx+v0CoiIiIild7999/Pgw8+iNfrZcSIEdxxxx20atUq0mVJKVPoPVCuDN9tmMubsxYuJOfvv4PvaQ3osnozMvAWF1odjkJLfmOaNQ+ry+pMTsYkJCi0ioiIiEi1tG7dOho1akRMTAz16tXj4osvZsyYMTRv3jzSpUkZUeg9UCUIve4dO9hw4Yj/7jDGF0KT/wulMc2a+rqsBZcIF9rTmoojUaFVRERERKQkVq9ezYMPPsjbb7/Niy++yOWXX86oUaMiXZaUA4XeA+XKBOOE6IRiL3Vv2wZAgwnjST7xRByJiRiHo6wrFBERERGp9lauXMkDDzzAu+++S0xMDNdeey0nn3xypMuScqTQe6Bcmb4ubxgdV3daOgAxLVriTK56055FRERERCoiay3nnXcef/75JzfddBO33HIL9evXj3RZUs7UbjxQ2RlhD7HypKcBEFW7VllWJCIiIiJS7S1btowRI0awc+dOjDFMnjyZdevW8dhjjynwVlMKvX5TV03lkjmXsDJ9ZXhPcGWGfVxRXqfXWUuhV0RERESkLPz6668MHTqUzp07M3PmTH777TcAOnXqxCGHHBLh6iSSFHr9Zq+dzcr0lbSt1ZZBLQYV/wRXRtiTmz3paZjoaBxJSQdZpYiIiIiIBHK5XJx66ql0796db7/9lnvvvZf169dz/PHHR7o0qSCq9Z7eqaumMnvtbID8wDug5v189O0mVmzJoF2DEJ1cVwYkhbc8wp2WjrN2bU1cFhEREREpJWvXrqVFixbExsZSs2ZNxo8fz3XXXUdqamqkS5MKplp3evO6u0B+h3fmkv8C72ldGhX95LxBVmFwp6cRpaXNIiIiIiIH7fvvv2fAgAG0adOGNWvWAPDWW29x9913K/BKUNW60wu+sDv5pMn5X3/07XzaNUhhypW9Qz8xuwTLm/2dXhERERERKTlrLXPnzuX+++/nu+++45BDDuHhhx+mXr16kS5NKoFqH3oPmCsz7OnN7vQ0Ylu2LOOCRERERESqpq1bt3LiiSdSp04dnnrqKUaOHElCQkKky5JKQqHX770Ff++3tDkktws8rrA6vdZadXpFRERERErAWsucOXP46quvePzxx6lfvz6ff/45vXv3Ji4uLtLlSSVTrff0Bgp7Ly/4urwQ1pFF3r1ZWJdLZ/SKiIiIiBTDWsunn35Kz549GTRoENOmTSMtLQ2Afv36KfDKAanWnd5tGS527HVx9kvz8wNvsXt5wTe5GcIKvZ503/9JnbXU6RURERERKcqff/7Jueeey5IlS2jRogWvvfYaF154IdHR0ZEuTSq5ah16d+x1keVygyG8Dm+e/E5v8cub3f7fTKnTKyIiIiKyP4/Hw+bNm2nSpAkNGzYkLi6ON998k/POO4+oqGodVaQUVfv/JSXERjHlkjC6u4Gy8zq9xYdeT3o6oE6viIiIiEgej8fDlClTmDBhAsYYli9fTkpKCvPnz490aVIFaU/vgcjr9IYxvVmdXhERERERH7fbzVtvvUW7du04//zzcTqd3HPPPZEuS6q4at/pPSAlGGT1X6dXoVdEREREqrfp06dz0UUX0blzZ6ZNm8bpp5+Ow6E+nJQthd4D4Qp/ebM7LR1HUhKO2NgyLkpEREREpGLJycnhzTffJCoqiksuuYRhw4Yxa9YsTj75ZIwxkS5Pqgn9WuVAlGR6c1oaTi1tFhEREZFqJDs7m+eff55WrVpxxRVX8PHHHwMQFRXFoEGDFHilXFWrTu/UVVOZvXZ2/lFFWfZvEsyhJX8hVyY4oiGq+O6tOz2dKA2xEhEREZFqYsaMGVxzzTVs3ryZo48+mldffZUBAwZEuiypxqpV6J29djYr01ficTUgy+UmIfZQjm04sOQvlJ3hG2IVxm+oPGlpRDc9gGAtIiIiIlJJ7N27l5ycHGrWrElqaiqtW7fmnXfeoW/fvurqSsRVq9AL0LZWW7I2XAGGkh9VlMeVGdZ+XvB1euO7dj2w9xERERERqcAyMzN5/vnneeyxxzj//PN56qmn6NevH/369Yt0aSL5ql3oLRVhhl7r8eDZuVN7ekVERESkStm9ezeTJk3iiSeeID09nZNOOomzzz470mWJBKXQeyBcGRCbWuxlnt27wevVnl4RERERqVJGjx7Na6+9xuDBgxk7diw9e/aMdEkiRdL05gPhygir0+tJSwMgSp1eEREREanE0tLSGDt2LEuXLgXgzjvvZNGiRXz66acKvFLhqdN7IMJc3uxOSwfAqU6viIiIiFRC27dv54knnmDSpEns2bOHmjVr0rlzZ1q0aBHp0kTCVq1Cb/5RRVsyaNeg+DN2i5Q3vbkYnnR1ekVERESkcrr//vuZOHEi+/bt4+yzz+auu+6iQ4cOkS5LpMSq1fLmHXtdZLnctGuQwmldGh3Yi1hb8k5vbXV6RURERKTi27p1K9ZaAHJychg2bBgrVqzg/fffV+CVSqtadXoBEmKjDvyoIgB3Nnhzwwu96WngcOBMLX7olYiIiIhIpGzcuJFHHnmEV155henTpzNo0CAmTJigM3alSqgWofe9BX8zc8kmsqybhNiD/JZdmb7b2DCWN6el46xZE+N0Htx7ioiIiIiUgQ0bNvDwww/z+uuv4/V6ufjii2nXrh2AAq9UGeUeeo0xJwFPA07gVWvtwwUePxR4E6jhv2aMtXb2wbznzCWbWLElg4SmUdRJjD2YlypR6HWnpxFVS/t5RURERKTi8Xq99OvXj40bN3LZZZcxZswYmjZtGumyREpduYZeY4wTeA4YAGwEFhpjPrHWrgi47G7gQ2vtC8aYdsBsoNnBvne7BikkHMzwqjzZu323YR1ZlK79vCIiIiJSYaxatYrnn3+eiRMnEhMTw+TJk2nRogVNmjSJdGkiZaa8B1n1BP6y1q611uYAHwCnFbjGAnnpNBXYXI71FS+v0xvG9GZ1ekVERESkIvi///s/LrjgAg4//HBefvllFi9eDMBxxx2nwCtVXnmH3kbAPwFfb/TfF+g+4AJjzEZ8Xd7ryqe0MOUvb1anV0REREQqtszMTM455xzat2/Pxx9/zOjRo1m3bh29ex/EYFeRSqYiHll0LvCGtbYxMAh42xhTqE5jzBXGmEXGmEXbt28vv+pcGb7bYkKv1+XCu2ePzugVERERkXK3Y8cOAJKSktiyZQtjxoxh/fr1TJw4kXr16kW4OpHyVd6DrDYBgesnGvvvC3QZcBKAtXa+MSYOqANsC7zIWvsy8DJAjx49bFkVXEh+pzf0MUSedP8ZvVreLCIiIiLlZPHixYwfP55vv/2W9evXU7NmTebOnatJzFKtlXendyHQ2hjT3BgTA5wDfFLgmr+BEwCMMYcDcUA5tnKLkd/pTQp5mTvNF3qjtLxZRERERMrYggULGDx4MD169OC7775j9OjRREX5+lsKvFLdlWun11rrNsZcC3yO7zii1621fxhjxgGLrLWfAKOBV4wxN+EbanWxtbb8OrnFyc4AZyxEhT76yJOeBqjTKyIiIiJla/Xq1fTq1YvatWvz4IMPcs0115CSUgqnlohUEeV+Tq//zN3ZBe67J+DzFcDR5V1X2FyZ4U1uVqdXRERERMrId999x6JFixg9ejStW7fmww8/5OSTTyYpKfRqRJHqqCIOsqrYXJnhTW7O7/Qq9IqIiIjIwbPW8s0333DcccfRt29fnnzySbKysgA466yzFHhFiqDQW1KuDIgNr9NrYmNxJCaUQ1EiIiIiUpX9/vvv9OnThxNOOIG//vqLp59+mtWrV5OQoH9rihSn3Jc3V3rhdnrT0nDWrqXBASIiIiJyQKy17Nq1i5o1a5KamsrWrVt57rnnuPTSS4mLi4t0eSKVRrUIvTud37Pb+QvO9C20rdX24F7MlQk1mhZ7mTs9nSgtbRYRERGRErLW8sknnzBu3Dhq1qzJV199RZMmTVi1ahUOhxZqipRUtfh/zW7nL2Sbf2hbqy2DWgw6uBfLzghrkFVep1dEREREJBxer5dp06bRtWtXTj/9dHbv3s35559P3kEmCrwiB6ZadHoB4mwTJp80+eBfyJUR1vJm986dxLY9yK6yiIiIiFQbL730EldffTVt2rThrbfe4txzz80/a1dEDpz+X1QS1oa1p9daiyctjSh1ekVERESkCG63mylTplCzZk0GDRrEBRdcQI0aNRg+fDhOpzPS5YlUGVojURK5WWA9xU5v9u7di83J0XFFIiIiIlJIbm4ub7zxBocffjgXXHABkyf7ViMmJydz7rnnKvCKlDKF3pJwZfpui+n0etJ8Z/Sq0ysiIiIigaZPn07btm255JJLSEpKYvr06UyZMiXSZYlUaVreXBLZGb7bYjq97rR0AHV6RURERASXy4W1lri4OLKysqhduzZPP/00gwcP1vGWIuVAnd6SyOv0FjO92ZOuTq+IiIhIdZednc2kSZNo2bIlzz77LADnnXcev/zyC6eeeqoCr0g5UegtCVdepzf08ub8Tm9tdXpFREREqpusrCyeeuopWrRowXXXXUfz5s054ogjAN+xQwq7IuVLy5tLIszQm9fpddasWdYViYiIiEgFM2LECKZNm0bfvn1599136du3r4KuSASp01sS+YOsit/T60hOxhETUw5FiYiIiEgkZWZm8vDDD7Np0yYA7rzzTr7//nu+/fZb+vXrp8ArEmHq9JZEuNOb09OIqqX9vCIiIiJV2e7du3n22Wd58sknSU9Pp0aNGowaNYpu3bpFujQRCaBOb0lkh7+nV/t5RURERKomay3jx4+nadOmjB07lj59+rBw4UJGjRoV6dJEJAiF3pJwZUB0AjijQ17mSU/T5GYRERGRKmbPnj0AGGNYvXo1J5xwAr/++iszZ86kR48eEa5ORIqi0FsSrsxiu7zg7/TqjF4RERGRKmHbtm3cfvvtNGjQgCVLlgAwefJkpk2bRteuXSNbnIgUS3t6S8KVUWzotR4Pnp071ekVERERqeT+/fdfHn30UV544QWys7M555xzSE72/VvQ6XRGuDoRCZdCb0m4Moud3OzZtQusVadXREREpBJzuVx06tSJtLQ0LrjgAu68807atm0b6bJE5ABoeXNJhLG82Z3mO6NXnV4RERGRyuWff/7hoYcewlpLbGwsL7zwAitXruTNN99U4BWpxBR6SyI7A+KK6fSmpwOo0ysiIiJSSaxfv55Ro0bRsmVL7r33Xv744w8AzjjjDFq1ahXh6kTkYCn0lkQYy5vV6RURERGpHNLT07nsssto3bo1kydPZuTIkfz111906NAh0qWJSCnSnt6SCGN5syfN3+nVOb0iIiIiFVJWVhYJCQkkJibyww8/cPXVV3PbbbfRqFGjSJcmImVAoTdcXq9/enMxnd70NHA4cKamllNhIiIiIhKOFStWMGHCBObPn8+ff/5JbGwsf/zxB9HR0ZEuTUTKkJY3hyt3L2DD6vQ6a9XCOPSjFREREakIli1bxvDhw+nQoQOffPIJw4cPJycnB0CBV6QaUKc3XNkZvtvipjenpxNVS/t5RURERCqChQsX0rNnT5KTk7nzzju58cYbqVOnTqTLEpFypNAbLlem77a46c1paTg1xEpEREQkYhYuXMiqVas4//zz6dGjB88++yznnXcetdSYEKmWtAY3XHmht9g9velE6bgiERERkXL3888/M2jQIHr27Mldd92F2+3GGMO1116rwCtSjSn0hsu123db7J5edXpFREREytPy5csZOHAgvXv35pdffuGhhx5i2bJlREVpUaOIaHlz+MLo9Hqzs/Hu3atOr4iIiEgZs9bicrmIi4sjNzeXZcuW8eijjzJq1CiSkpIiXZ6IVCAKveHKD71Fd3o96Xln9KrTKyIiIlIWrLV8/fXXjBs3jubNm/Pmm2/SrVs3/v77b2JiYiJdnohUQFreHK4wpje703yhN6q2Or0iIiIipclay5w5czj66KMZMGAAa9eupVevXvmPK/CKSFEUesMVVqc3DUBHFomIiIiUsokTJ3LyySezadMmXnjhBdasWcNVV10V6bJEpBLQ8uZwuTIhJgkcziIvyev0OtXpFRERETkoXq+XmTNn0rhxY4444gjOP/98atWqxUUXXaSuroiUiDq94XLtLn5yszq9IiIiIgfF6/UydepUunTpwrBhw3juuecAaNy4MSNHjlTgFZESU+gNlyuz+DN609IxcXGYhIRyKkpERESk6pg5cyYdO3Zk+PDh5Obm8s477/Dqq69GuiwRqeS0vDlcrsywOr1RtWphjCmnokREREQqN7fbjTEGp9PJ//3f/2GM4YMPPuDMM8/E6Sx6W5mISLjU6Q1XdgbEFd/p1X5eERERkeLl5uYyefJkDjvsMKZMmQLAzTffzLJlyzj77LMVeEWk1Cj0hiuMTq/b3+kVERERkeBycnJ45ZVXaNOmDZdeeimpqanUq1cP8B075HDon6ciUrr0X5VwhbO8WZ1eERERkZAGDx7MFVdcwSGHHMJnn33GokWLOOGEEyJdlohUYQq94XJlQGxqkQ9ba3GnpxNVW51eERERkTz79u3jueeeIzMzE4BbbrmFOXPm8PPPP3PKKadoFoqIlDkNsgqH1wM5e0J2er2ZmZCbi7OWOr0iIiIiWVlZvPTSS0ycOJF///2X5ORkRowYwcCBAyNdmohUM+r0hsPl+81kqNDrTvOf0atOr4iIiFRjXq+XRx99lObNm3PzzTfTrl075s6dy4gRIyJdmohUU+r0hiMv9IaY3uxJTwdQp1dERESqpdzcXKKjo3E4HPzvf/+jS5cujB07lj59+kS6NBGp5hR6w6FOr4iIiEhQu3bt4plnnuGFF15g0aJFNGrUiM8++4yEhIRIlyYiAij0hseV4bsNEXrV6RUREZHqJD09naeeeoqnn36ajIwMhgwZQnZ2NoACr4hUKAq94cjv9BY9vdntD71RNWuUQ0EiIiIikZORkUHLli3ZtWsXZ5xxBnfffTddunSJdFkiIkFpkFU4wun0pqXjSE3FxMSUU1EiIiIi5Wfr1q1MnjwZgJSUFB588EGWL1/ORx99pMArIhWaQm84sosPve70NKJqaT+viIiIVC1btmzh5ptvpnnz5owcOZK///4bgKuuuooOHTpEuDoRkeIp9IYjnOnNaek4NcRKREREqogdO3Zw/fXX07x5c5555hmGDx/OihUrOPTQQyNdmohIiWhPbzhcmYCB6MQiL3GnpxHbomX51SQiIiJSBtxuN1FRURhjeOedd7jgggu44447aNlS/84RkcpJoTccrgzf0mZH0Y1xT1o6ziOOKMeiRERERErP2rVreeihh/j999+ZN28etWvX5u+//yYpKSnSpYmIHBQtbw6HKxNii17abN1uPLt2EaXjikRERKSSWb16NZdccglt2rThrbfeonv37vlHDynwikhVoE5vOPI6vUXw7NoF1mpPr4iIiFQqX3/9NQMHDiQmJoZrr72WW2+9lUaNGkW6LBGRUlXi0GuMSQJqA5uttbmlX1IFlB069LrT/Gf0qtMrIiIiFdwff/zB5s2bGTBgAH369GHs2LGMGjWK+vXrR7o0EZEyEfbyZmPMYGPMr8BuYA3Q0X//q8aY88qovorBlRl6cnN6GgBR6vSKiIhIBbVs2TLOOussOnTowI033oi1ltjYWO677z4FXhGp0sIKvcaY04GZwA7g9gLPWwdcVOqVVSSuzLA6vc7a6vSKiIhIxfL7778zdOhQOnfuzBdffMHYsWP5/vvvMcZEujQRkXIR7vLme4HJ1trLjTFRwMSAx34Hri71yioSV0bIQVb5nd5a6vSKiIhIxeD1enE4HKxfv565c+dy//33c/3111OjRo1IlyYiUq7CDb2HA7f5P7cFHtuJb49v1RVOpzcqCkdK0cFYREREpDzMmzeP8ePH07VrVx588EFOOeUUNmzYQIr+nSIi1VS4e3ozgDpFPNYM2F4q1VREHjfkZhXb6XXWrIEJcY6viIiISFn6/vvv6d+/P0cffTSLFi2iQYMGABhjFHhFpFoLN6V9CdxhjKkRcJ81xsQC1wL/K+3CKgxXhu82xCArd1q6JjeLiIhIxNxzzz0cd9xx/P777zz22GOsX7+e6667LtJliYhUCOEub74L+AVYCczGt8R5DNAJSAVOL4viKgRXpu821Dm9aWma3CwiIiLlxlrLl19+SYsWLWjVqhVnnHEGtWvXZuTIkSQkJES6PBGRCiWsTq+1dj3QDfgMGAB4gGOBn4EjrbWby6rAiMvr9Iba05uejlOdXhERESlj1lpmz55N7969OfHEE3nmmWcA6Ny5MzfccIMCr4hIEOF2erHWbgQuK8NaKqb8Tm+IPb3q9IqIiEgZmz17Nvfccw+LFy+madOmvPTSS1x0UdU+NVJEpDSEe07vN8aYw4p4rI0x5pvSLasCKSb0evftw5uVpU6viIiIlDqv15v/+Zw5c9i5cyevvfYaq1ev5oorriA2NjaC1YmIVA7hDrLqCxTV6kwGjiuVaiqi7NDLmz3p6QDq9IqIiEip8Xg8TJkyhc6dO/Pdd98B8MADD7By5UouvfRSoqOjI1yhiEjlUZIzdgqez5unJbCnFGqpmIqZ3uz2h151ekVERORgud1u3n33XTp06MA555yDx+PB7XYDkJycTFRU2DvTRETEr8j/chpjLgEu8X9pgZeNMZkFLosHOgBfl015FUAx05vdaWmAOr0iIiJycKy19O3bl59++okOHTowZcoUzjjjDJxOZ6RLExGp1EJ1er34pjR7AFPg67yPNOAFqvKAK1cGGCdEB5+G6Enzd3prq9MrIiIiJZOTk8O7776L2+3GGMOVV17JtGnTWLp0KcOHD1fgFREpBUV2eq21bwJvAhhjvgWustb+WV6FVRiuTF+X15igD7vT/Z3eWur0ioiISHhcLhdvvPEGDz30EBs2bCA5OZkhQ4Zw4YUXRro0EZEqJ9xzevtVy8AL/tAb6riidEx8PA6diyciIiLFyM3N5bnnnqNVq1aMGjWK+vXrM2vWLE499dRIlyYiUmWVaBqCMaYz0BaIK/iYtfat0iqqQsnOKHI/L/g6veryioiISCjWWowxOBwOJk2aRNOmTXn99dfp378/pojVZCIiUjrCCr3GmBrALKBX3l3+28CJzlUz9LoyipzcDL5Or/bzioiISDB79+7lxRdfZPLkycyfP5/k5GR++OEHateurbArIlJOwj2y6EGgNnAsvsA7FDgeeBdYC/Qsk+oqgrw9vUVwp6er0ysiIiL7yczM5JFHHqFZs2bccsstNGjQgDT/iQ916tRR4BURKUfhht4T8QXfn/1fb7TWzrXWjgC+Am4oi+IqBFfo5c2etDScOq5IRERE/LZs2UKzZs0YM2YMPXr04KeffuLLL7+kWbNmkS5NRKRaCjf0NgDWWms9QDYQmAKnA6eUdmEVRohBVtZaf6dXy5tFRESqs507dzJr1iwAGjRowNVXX82CBQv43//+x1FHHRXh6kREqrdwB1n9C9Twf74B6A3M9X/dqnRLqmBCLG/2ZmSA261Or4iISDWVlpbGk08+ybPPPovL5WLz5s3UqlWL8ePHR7o0ERHxC7fT+yP/DbF6G7jXGPOSMeY54FHg83Df0BhzkjFmpTHmL2PMmCKuGW6MWWGM+cMY8164r13q3Dngzi5ykJU7LR2AKA2yEhERqVbS0tIYM2YMzZo148EHH2TgwIEsWLCAWprzISJS4YTb6b0faOj//FF8Q63OBhKAT4DrwnkRY4wTeA4YAGwEFhpjPrHWrgi4pjVwB3C0tXanMeaQMGssfa5M320Ry5s96b6BFE79BSciIlIt5B09tHPnTp544gnOPPNM7rrrLtq3bx/p0kREpAhhhV5r7Rpgjf/zXGC0/6OkegJ/WWvXAhhjPgBOA1YEXDMSeM5au9P/ftsO4H1Kh2u377aI5c3q9IqIiFQPmzdv5tFHH2Xz5s1MmTKFVq1a8c8//1CvXr1IlyYiIsUId3lzkYwxXY0xH4d5eSPgn4CvN/rvC9QGaGOM+ckY87Mx5qSDrfGAqdMrIiJSrf3zzz9ce+21tGjRgmeffZbExETcbjeAAq+ISCURstPrX47cHTgUWGOt/S3gsR7AvcAgILOUa2oN9AUaA98bYzpaa3cVqO0K4AqAQw89tBTfPkB+6C2m01uzZtm8v4iIiETMJ598wplnngnAxRdfzJgxY2jRokWEqxIRkZIqstNrjGkMLADmAx8Ci4wxU4wxMcaYV/2PHQ88DrQM8/02AU0Cvm7svy/QRuATa22utXYdsApfCN6PtfZla20Pa22PunXrhvn2JZSd4bstIvR60tNwpqZioqPL5v1FRESkXK1Zs4bFixcD0KdPH0aNGsXq1at5+eWXFXhFRCqpUMubHwYOA8bi6+ZeCxwF/ARcCrwFtLDW3matTQvz/RYCrY0xzY0xMcA5+AZhBZqBr8uLMaYOvuXOa8N8/dKV1+mNSw36sDstHaf284qIiFR6q1at4qKLLqJt27Zcf/31ANSqVYtnnnmGpk2bRrg6ERE5GKGWN58A3GetfSzvDmPMSuAr4Flr7Q0lfTNrrdsYcy2+I46cwOvW2j+MMeOARdbaT/yPDTTGrAA8wK0lCNWly1VMpzctjSjt5xUREam0/vzzTyZMmMD7779PbGws119/PbfeemukyxIRkVIUKvTWBX4ucN98/+3UA31Da+1sYHaB++4J+NwCN/s/IquY0OtOTye2daGV1yIiIlLB5R099P333/Pxxx8zevRoRo8ereFUIiJVUKjlzQ4gp8B9eV9nlU05FYwrExzREBUX9GFPWhpRtdXpFRERqSyWLFnCGWecwQsvvAD4BlStX7+eiRMnKvCKiFRRxZ3Te6oxpkPA1w7AAkOMMV0CL7TWvl7KtUWeK9PX5TWm0EM2NxfP7t04a2lPr4iISEW3aNEixo8fzyeffEJKSgrHHXccADExMZTZQEwREakQigu9dxVx/z0FvrZA1Qu92RlF7+fdtQtAnV4REZEK7vbbb2fixInUrFmTcePGcd1111GjRo1IlyUiIuUkVOhtXm5VVFSuTIhLCfqQO913Rq86vSIiIhXPTz/9RJs2bahbty79+/enRo0aXHPNNaSkBP97XUREqq4i9/RaazeU5KM8iy43rkyIDf6XoyfNN1BanV4REZGK47vvvuOEE06gT58+TJo0CYABAwZwxx13KPCKiFRToQZZiWt30ZOb09TpFRERqSi+/vprjjvuOPr27cuKFSt44oknuP322yNdloiIVADF7emt3kJ1etPV6RUREakonn/+ef766y+eeeYZLr/8cuLj4yNdkoiIVBDq9IaSN705CHdaOkRF4dBSKRERkXJlreWzzz6jd+/e/Pnnn4Av9K5Zs4brrrtOgVdERPaj0FsUa0NOb3anpxFVqxYmyHFGIiIiUvq8Xi8zZsyge/funHrqqfz7779s2bIFgHr16hEXFxfhCkVEpCLS8uaiuF3gzS1yerMnLR1nbe3nFRERKQ9er5ejjjqKBQsW0LJlS15//XUuuOACoqOjI12aiIhUcCXq9BpjHMaYDsaY44wxiWVVVIXgyvTdFrGnN6/TKyIiImXD4/Hw+eefY63F4XBwxhln8NZbb/Hnn39yySWXKPCKiEhYwg69xphrgH+BpcA3QFv//TOMMdeXTXkR5Mrw3RZ5ZFE6Tg2xEhERKXVut5t33nmH9u3bc9JJJzFv3jwAbr31Vi688EKiorRQTUREwhdW6DXGjASeBmYAZwOBG1l/AM4o9coiLT/0FrWnN50oHVckIiJSanJzc3njjTc4/PDDufDCC4mNjWXq1Kn07t070qWJiEglFu6vSm8GHrfW3m6McRZ47E/g1tItqwLILjr0erOysFlZ6vSKiIiUopycHG677TaaNGnCxx9/zJAhQ3A4NHNTREQOTrihtznweRGP7QVqlEo1FUnent4gg6zc6TsB1OkVERE5CC6Xi8mTJ/PRRx8xZ84cEhMTWbBgAc2aNdPpCCIiUmrC/fXpDqBZEY+1BTaVSjUVSf4gq8KdXk96GoA6vSIiIgcgOzubSZMm0bJlS6666ir27t3Ltm3bAGjevLkCr4iIlKpwQ+9nwD3GmBYB91ljTB3gJnx7fauWEIOs3Gm+0BulI4tERERK5K+//qJFixZcd911NG/enC+//JJ58+bRsGHDSJcmIiJVVLih927ABfwOfAVY4Bng/wAPMK5MqoukEIOsPOnpADqySEREJAx79uxh/vz5gK+Te9JJJ/Htt9/y/fff079/f3V2RUSkTIW1p9dau8MY0wO4ETgRWON/7iTgSWttRplVGCmuTHDGQlRsoYfcab7Q61ToFRERKVJGRgbPPfccjz/+ONZa/vnnHxISEnj99dcjXZqIiFQjYR90Z63NBMb7P6q+7IwijyvypKXhSEjAER9fzkWJiIhUfLt37+bZZ5/liSeeYOfOnQwaNIixY8eSkJAQ6dJERKQaCvec3ieNMV3KuJaKxZUZdHIz+M7oVZdXREQkuD/++IOxY8dyzDHHsHDhQmbNmkWvXr0iXZaIiFRT4XZ6LwauN8b8H/AW8K61tupNbA7kygzZ6dXkZhEREZ8dO3bw5JNPkpuby8SJEznqqKNYuXIlbdq0iXRpIiIiYQ+yqgcMB/7Ct7x5gzHmK2PMCGNMYplVF0mujKCTm8HX6dUZvSIiUt1t27aN22+/nWbNmvHQQw+xZcsWrLUACrwiIlJhhBV6rbU51tpp1trTgQbA9UA88Aaw1RjzdplVGCmuzCJDrzq9IiJS3X344Yc0a9aMxx57jNNOO43ff/+dt99+W5OYRUSkwgm305vPWpturX3eWns00A/YCZxX6pVFmiv4ICvr9eLeuVOdXhERqXY2btzIX3/9BUD37t0566yzWLFiBe+++y7t2rWLcHUiIiLBlTj0GmMSjTEXGWO+xHdmbx1gWqlXFmlFTG/2ZmSA202UOr0iIlJN/P3331x99dW0bNmS0aNHA9CyZUvefPNN2rZtG+HqREREQgtrkJUxxgEMBC4ETsO3tPkn4CpgqrV2d5lVGAnWFjm92Z2ed0avOr0iIlK1rV+/ngcffJA33ngDgEsuuYQxY8ZEtigREZESCnd682agLr5BVg8D71hr15dVURGXuw+sJ2in15OWBqBOr4iIVHmTJ0/mzTffZOTIkdx+++0ceuihkS5JRESkxMJd3vwRcJS1tq21dkKVDrzg288LQQdZudP8nd7a6vSKiEjVsnLlSi666CJmzpwJwE033cTatWt57rnnFHhFRKTSCqvTa629tqwLqVBcmb7bYKE33d/praVOr4iIVA0rVqxgwoQJTJkyhbi4OI444ggAatSoQY0aNSJbnIiIyEEqMvQaY44FfrXW7vF/HpK19vtSrSyS8ju9wZY3+zu9NWuWZ0UiIiJl4uabb+app54iISGBW2+9lZtvvplDDjkk0mWJiIiUmlCd3rlAL+AX/+e2iOuM/zFnaRYWUdn+0Bt0kFUazho1MFHhbocWERGpWH777TcOO+ww4uPj6datG3feeSc33ngjderUiXRpIiIipS5UcusHrPB/fjxFh96qJ395c/BOr/bziohIZbRw4ULGjx/Pp59+yqRJk7jmmmu44IILIl2WiIhImSoy9Fprvwv4fG65VFNRhFje7E5P035eERGpVObPn8+4ceOYM2cONWvWZPz48Qq7IiJSbYQ1vdkYs9YY07mIxzoYY9aWblkRFmKQlTq9IiJSmVhruemmm1i0aBEPPfQQGzZs4O677yY1NTXSpYmIiJSLcI8sagbEFvFYHNC0VKqpKEIsb3anp6vTKyIiFZa1lrlz5zJo0CC2b9+OMYZ33nmHdevWMWbMGJKTC//dJiIiUpWFG3qh6D29PYBdB19KBZK9G6LiwRm93902Jwfv7t04ayv0iohIxWKt5auvvuK4446jX79+LFmyhJUrVwLQqlUrkpKSIlyhiIhIZIQ6sugm4Cb/lxb41BiTU+CyeKAW8EHZlBchrszgk5t37gIgSsubRUSkAnG5XBx//PHMmzePxo0bM2nSJC677DLi4uIiXZqIiEjEhZrevBb42v/5RcAiYHuBa1z4Jjy/WvqlRZArM/jk5vQ0AJxa3iwiIhFmreXXX3+le/fuxMbG0qNHD0aMGMHFF19MbGxRO5JERESqn1DTm2cCMwGMMQDjrLXryqmuyHJlBN/Pm5YOqNMrIiKR4/V6mTlzJuPGjWPp0qWsWLGCww47jKeffjrSpYmIiFRIYe3ptdZeUm0CL/g7vUEmN6vTKyIiEeL1epk6dSpdunRh2LBh7Nmzh8mTJ9OyZctIlyYiIlKhhdrTew/wqrV2s//zUKy1dnzplhZBrkxIrFvobnV6RUQkUrZv386IESNo1qwZ77zzDmeffTZRUcH/Gt+5cyfnnHsObVq34YgjjqBLly4cfvjhREdHB71eRESkKgu1p/c+YA6w2f95KBaoOqE3O6PoTm90NA4d9yAiImXM7Xbz3nvv8c033zB58mTq1avHvHnz6NSpE06nM+Rzk5OT+fP//mTN2jV89/1ctm/bwc6dO2nZsiXdu3enZ8+edO3alc6dO2uqs4iIVHmh9vQ6gn1eLRQ1vTnNd0avf4+ziIhIqcvNzeWdd97hgQceYM2aNXTq1ImdO3dSq1YtunbtGtZrREVFcd999/HAgw/Qr3+//NfdsX0H/2z6m+WTl5P+WDr//vsvDeo3oHOXzvQ6shfHHXccRx11VFl+eyIiIuUuVKe3erK2yEFWnrQ0ndErIiJlZvny5QwZMoT169fTrVs3ZsyYwamnnorDUfLfPV9wwQWMHTuWzZs207BRQ6Kjo2nQsAENGjbIv8bj8bBr5y62bdvOs5Oe5Y033sg/21dERKSqCOtvUWNMG2NMz4Cv440xDxljPjXGXFt25UVAzh7ABp/evHMnUbW0n1dEREpPdnY2f/75JwAtW7akffv2zJo1i0WLFnHaaacdUOAFiI6OZuzYsSz9bVmR1zidTmrXqU39+vXYu2cvH3zwwQG9l4iISEUW7t+kk4AzA75+ABgNNASeNMZcU9qFRYwr03cbbE9vWhpR6vSKiEgp2LdvH8888wwtW7Zk8ODBuN1uEhIS+Oyzzxg0aFCpbKW55JJLyNidwb9b/i3ymtzcXL76/GseeeSRsJdPi4iIVCbhht7OwE8AxhgHMAK43VrbHZgAXFE25UVAfugN0ulNT8epTq+IiByEvXv38sQTT9C8eXNuuOEGWrduzcsvv1zscKoDERMT4+v2/rq0yGv++Xsju3fvpk+fPqX+/iIiIhVBuKE3FUjzf94VqAl85P96LtCidMuKoOwM321c6n53e7OysPv2qdMrIiIH5X//+x+jR4+mffv2zJ07l7lz53L88ceX2ZDEyy67jPT0nWzbui3o4y1aNufoY4/m6D5H88Ybb5RJDSIiIpEUbujdCrTyfz4QWGOt/cf/dRLgLu3CIsblD70FOr3udN8Zver0iohISWRkZPDggw/yzDPPADBs2DB+/vlnvv76a4477rgyf/+4uDjuuOMOloTo9h7e7jAGDzmF22+/nYsvvph9+/aVeV0iIiLlJdzQ+wnwkDHmMXx7eacGPNYRWFvahUVMEaHXk+ZrdKvTKyIi4di5cyf3338/TZs25a677mLRokUAOBwOjjzyyHKt5corr2T7tu3s2L4DAJfLxczpn7Di9xX519SpW4dhw4eycNFCuvfozl9//VWuNYqIiJSVcEPvGOAz4ER8AfjBgMeGAF+Ucl2RU8QgK3eaOr0iIhKed999l2bNmnHfffdx3HHHsWjRIt56662I1ZOQkMBtt93Gkl+XYq3l+29/oEf3Hqxfu4G5X39Hbm4u4NsDfMLA46nfoB5HHHEE06ZNi1jNIiIipSWs0Gut3WutHWmt7WitvdRauzfgsaOstXeUXYnlrIhBVp50dXpFRKRoO3bsYOvWrQC0aNGCAQMG8NtvvzFjxgy6d+8e4erg6quvZsvmLXw/9weinE7effddlixZwmFtD2PGtJnsTN8JgDGGjp07MvDkAVx55ZVcd/11+aFYRESkMirR4X/GmFrGmFOMMRf6b6teAswuYk9vfqe36n3LIiJy4LZu3cptt91Gs2bNuPfeewHo3bs3H330EV26dIlscQGSkpIYPfpm1v61lpkzPyE2NpbExEQ++OAD7hl7DzOnf8Kqlavzr6/foD5nDB/GF198Tu/evfjnn39CvLqIiEjFFXboNcZMADYBnwJv+m83GWPGl1FtkeHKhJgkcOx/dIQnPQ1HYiKOuLgIFSYiIhXJ5s2buemmm2jevDmPP/44Q4cO5cYbb4x0WSHddtvtLF++nObNm+ffZ4xh1KhRfPvtt/y+9Hd+/P4n3G4PAHHxcZw46ETiEuPp0qULc+bMiVTpIiIiByys0GuMuRG4E3gH6Acc7r99B7jTGHN9WRVY7lwZwc/oTUvHWVv7eUVExOe+++7j2Wef5eyzz+bPP//k7bff5rDDDot0WSFFR0fTtGnToI9169aN5cuXU/+Q+nw641MyMnwrn4wxdO/Rjb4n9OXc887lzjvvxOPxlGfZIiIiByXcTu8o4Gn/vt7vrLUr/bcjgWeAq8uuxHJWROj1pKcRpaXNIiLV1oYNG7jqqqv45ZdfALj33ntZtWoVkydPpnXr1hGurnTUqFGDzz77jOuuvZaPp85g3dp1+Y81btKIM4YP44MpH9CvX1+2bQt+7q+IiEhFE27obQbMKuKxWf7HqwZXZqHJzaBOr4hIdbV27VpGjhxJq1ateO211/j1118BaNSoES1atIhwdaXPGMOtt97GrFmz+GX+Qn6etwCv1wtAYmIipwwZRI47l44dO/LDDz9EuFoREZHihRt604AORTzW3v941eDKDL68WZ1eEZFq56abbqJNmza8/fbbjBo1ijVr1jBq1KhIl1Uujj76aJYvX058bDyzPpnN3j2+gxscDgdH9u5Jr6OO5NRTT+Whhx7CWhvhakVERIoWbuj9GBjvn9ocBWCMiTLGnAuMA6rOQX7ZhZc3W68XT/pOnDquSESkylu9enV+Z7NevXpcd911rF27lmeffZYmTZpEuLryVbduXb7++mvOP+98pk/9mI3/bMx/rFmLZgw7aygvvvgCgwYNYufOnRGsVEREpGjhht47gCX4pjbvM8ZsBfYB7wJL8Q25qhpcmRC3//Jmz+7d4PEQVUvLm0VEqqrff/+dc845h7Zt2zJz5kwAxowZw5NPPknDhg0jXF3kOJ1Oxo8fzwcffMB333zP4oW/5nd2k1OSOXXoqWzfsZ2OHTuyePHiCFcrIiJSWFih11qbCRwLDAGeAD7x3w4GjrPW7imzCstbkD29nnT/Gb3q9IqIVDlLly7lrLPOomPHjsyaNYvbb7+dPn36RLqsCmfgwIEsXbqUnGwXc2Z9zr59+wBfKD762KPo1LUjxx9/PM8995yWO4uISIUSFepBY0wd4AKgFbATmGatvb08CosIrwdyCu/pdaf5tixHaZCViEiV4vV6GTZsGDt27GDs2LHccMMN1NZ/64vUqFEjfvppHrfddhtvv/02Jww8nvoN6gPQuk1r6tStw4MPPsi3c7/ljclvkJSUFOGKRUREQnR6jTFtgT/wdXSvBu4CFhpjTiun2spfjr9hXVSnV4OsREQqvQULFnDhhReyb98+HA4HU6dOZcOGDYwbN06BNwzR0dE8+eSTvPLKK3zxvy9ZumRZfme3Zs2anHbGEFb/tZrOnTvzxx9/RLhaERGR0MubJwDZQF8gEegI/IIvBFdNrkzfrTq9IiJVzrx58zjppJPo1asXs2fPzg9k3bp1o0aNGpEtrhIaOnQoixcvZsfWHXz9xde4XDmALxT3Pf44WrRuwVFHH8Wbb74Z4UpFRKS6CxV6jwTusdZ+b63dZ639A7gSaGaMqVs+5ZWz7AzfbYHQ60lLB2Nw6h9FIiKVzu7du+nfvz9HH300v/76K4888gjr16+nR48ekS6t0mvRogWLFi2i15G9+Xjqx+zYviP/sXbtD2fwkFO47bbbuPTSS8nOzo5gpSIiUp2FCr2NgJUF7lsJGKBqjrHM6/QWmN7sTk/DWbMmxumMQFEiIlJS1lrWrl0LQEpKCsnJyTz++OOsW7eO2267jeTkwuexy4GJi4vjtdde47HHHuOzT2ax4o//y3+sTt06DD1rKD//8jM9evRgzZo1EaxURESqq1Ch1wCeAvd5w3he5ZW/vLnAnt60dJy1akagIBERKQlrLZ9//jl9+vShQ4cObNu2DWMMH3/8MTfffDOJiYmRLrHKuvDCC5k/bz7r/lrH3G++Izc3F4DY2Bj6DzyBuvXq0KNHD6ZPnx7hSkVEpLoJOb0ZuN8YsyPga+O/HW+MSQ+431prLyrd0iLAtdt3G1uw05uuM3pFRCoway2zZ89m3Lhx/PLLLzRu3JjHHnuMlJSU4p8spaZdu3YsWbKESy69hBnTZtL/xBOoWbMmxhg6denEIfUO4YorrmDud3N5/LHHiY6OjnTJIiJSDYQKvX8Dhwe5fwPQvsB9VeNAviIGWXnS0og9/LAIFCQiIuFYvXo1gwcPpmnTprz00ktcdNFFxMbGRrqsaikpKYkPp3zICy+8wB133MHRxx5F6zatAajfoD7Dhg9lzpw5zJ83j48/nkHjxo0jXLGIiFR1RS5TttY2s9Y2D/OjRXkWXWaKGGSlTq+ISMXi9XqZPn06Y8eOBaBNmzZ8+eWXrF69miuuuEKBN8KMMVx99dV88803LPttOT99/xMej2/HVHx8PCedciJxCXF07tyZzz//PMLViohIVVc19+YeKFcmYCAmKf8um5ODNyMDZ22d0SsiEmkej4cpU6bQuXNnzjjjDD766CP27dsHQP/+/bVctoLp3r07y5cv55C69fjk40/JyPD9ctkYQ7ce3Tju+OM455xzuOuuu/JDsYiISGlT6A3kyvR1eR3//VjcO3cCqNMrIhJhixcvpkOHDpxzzjl4PB7ee+89fv/9d+Lj4yNdmoRQs2ZNZs2axTVXX8PHU2ewfu36/MeaHNqYM4YP4/0P3uf4449n27ZtkStURESqLIXeQK6MoPt5AXV6RUQiwO12s2nTJgAaNWpESkoKH374Ib///jvnnnsuTh0lVykYY7j99tv57LPP+Hn+AhbM/wWv13cgRGJSIqcMGYQrN5uOHTvy448/RrhaERGpahR6A7kyCk9uTvMNqY6qrU6viEh5ycnJ4bXXXqNt27acffbZWGupX78+CxYs4KyzzsLh0F9flVGfPn1Yvmw5sdGxzPpkNnv37gXA4XBwZO8jOfKongwePJiJEx/B2qoxI1NERCJP/2oIlLe8OYAn3dfpjaqlTq+ISFlzuVy89NJLtGnThssvv5xatWpx++23R7osKUWHHHII33zzDeedex7TPpzOxn825T/WvEVzhp51OpOee45TTjmFXbt2Ra5QERGpMhR6A2UXXt6c1+l1qtMrIlLmXn/9dUaNGkX9+vWZPXs2v/zyC6eeeirGmOKfLJWG0+lkwoQJfPD+B3z3zXcsXrg4v7ObkpLCkKGnsm37Vjp27MjixYsjXK2IiFR2JQq9xphOxphrjTH3GmPq++9rZYxJLu65lYIrE+L2X97sSU/DREfjSEoq4kkiInKg9u3bx9NPP83UqVMBuOiii/jiiy+YP38+J598ssJuFXfiiSeyZMkSsve5+Hz252TvywZ8ofjoY4+mQ+cOHH/88Tz//PNa7iwiIgcsrNBrjIk1xkwFfgOeAe4BGvofngjcFe4bGmNOMsasNMb8ZYwZE+K6M4wx1hjTI9zXPmhBlje709Jx1q6tf3iJiJSivXv38vjjj9O8eXNuvPFGZs+eDUBCQgIDBgzQf3OrkcaNGzN/3nxOPPEkpn04nX+3/Jv/WJu2rTlt2BAmTJjA2WefzZ49eyJYqYiIVFbhdnofAPoDFwL1gMB/jfwPODGcFzHGOIHngJOBdsC5xph2Qa5LBm4AFoRZX+kINsgqPU37eUVEStHbb79Ns2bNuOWWW+jUqRPff/89kydPjnRZEkHR0dE88/QzvPzyy3zxvy9ZtmR5fme3Zq2anH7maaxctZIuXbqwYsWKCFcrIiKVTbih91zgbmvte0B6gcfWAc3CfJ2ewF/W2rXW2hzgA+C0INeNBx4BssN83YPncUNuVqHQ6/F3ekVE5MDt3r07v0sXHx/PEUccwbx58/jiiy845phjIlydVBTDhg1j0aJFbN+6na+/+IacnBzAF4r7nnAczVs246ijevP2229HuFIREalMwg29tYH/C/EasWG+TiPgn4CvN/rvy2eM6QY0sdbOCvM1S0dOpu+24PJmdXpFRA7Yzp07ue+++2jatClPPvkkAGeeeSazZ8+md+/eEa5OKqKWLVuyaNEijux5JNOnfsyO7TvyH2vXoR2Dhgzilltu4bLLLiM7u/x+Ny4iIpVXuKF3HVDUv056AitLoxhjjAN4AhgdxrVXGGMWGWMWbd++/eDfPDvDdxsQeq216vSKiByAtLQ07r77bpo2bcr9999Pv379GDx4cKTLkkoiLi6O119/nUcnPspnn8zi/1b8mf9Y3bp1GXrWUOb/PJ8jjjiCtWvXRrBSERGpDMINvW8BY4wx5wPR/vusMaYfcBPwepivswloEvB1Y/99eZKBDsBcY8x6oBfwSbBhVtbal621Pay1PerWrRvm24fg8nd6A6Y3e/dmYV0uomqr0ysiUhIjR47kwQcf5KSTTmLp0qV8/PHHdO3aNdJlSSUzYsQI5v00jzWr1vDdN9/hznUDEBsbQ/8TT6D2IbXp3r07H3/8cYQrFRGRiizc0DsRmAW8Dez03/cj8BUwx1r7bJivsxBobYxpboyJAc4BPsl70Fq721pbx1rbzFrbDPgZGGKtXRTm6x84V+HlzZ70NACctdTpFREJ5d9//2X06NGsW7cOgAkTJrB8+XI+/PBDOnXqFOHqpDJr3749S5cupVXL1syYPpNdO3cBYIyhc5dODDhpAJePvJwbb7yR3NzcyBYrIiIVUlih11rrsdaeAxwHPA68iu/oouOtteeH+2bWWjdwLfA5vj3CH1pr/zDGjDPGDClx9aXJlbe8OTX/LneaL/Sq0ysiEtzmzZu54YYbaN68OU899RRz584FoF27drRv3z6yxUmVkZSUxNSpU7nzjjuZMX0mf636K/+xBg3rc8bwYcz63yyOPvpoNm3aFOKVRESkOooqycXW2h+AHw7mDa21s4HZBe67p4hr+x7Me5VI0E6vb1C1Or0iIvuz1nLzzTfzwgsv4Ha7GTFiBHfeeSetWrWKdGlSRRljuOaaazjyyCM5/fTT2bp1G72OOhKn00l8fDwnn3ISvy76lc6dO/P+++8zYMCASJcsIiIVRLjLm6u+7N2+24DQ6/aHXnV6RUR8tm7dCvgCSE5ODiNGjGD16tW8/vrrCrxSLnr06MHy5cupW7sun874jMwM3y+tjTF0P6I7x/Y7huHDhzN27Fg8Hk+EqxURkYogrNBrjPEaYzyhPsq60DIXZJCVJy2v06vQKyLV25o1a7jsssto3LgxCxYsAGDSpEm8/PLLNG/ePMLVSXVTs2ZNZs+ezVWjrmL61I9Zv259/mNNDm3CsOFDeefdd+jfvz+lcsKDiIhUauEubx4H2AL31QYG4juj941SrCkyXJlgHBCdkH+XOz0NR1ISjthwjyEWEalaVq1axQMPPMC7775LdHQ0V199NU2a+IbwG2MiXJ1UZ8YYxowZw1FHHcVZZ53Ftq3b6dGzOw6Hg6SkJAafdgq//LyQjh07Mm3aNI4++uhIlywiIhESVui11t4X7H5jjBP4FNhdijVFhivDt7Q54B9xvjN61eUVkerJ5XJx1FFHkZWVxfXXX8+tt95KgwYNIl2WyH6OPfZYli1bxplnnsnsT//H8f37kZCYgMPhoNdRR1Kv/iGccsop3HXXXdxyyy36ZY2ISDV0UHt6rbUe4HngxlKpJpJcmftNbgZfpzdKQ6xEpBpZvnw5o0ePxuv1Ehsby/vvv8+6det44oknFHilwqpXrx5z587l7OFnM+3D6Wza+N8E5+YtmjP0zNN55tlnOPXUU9m1a1fkChURkYgojUFWsUDlb4e6MvcbYgXq9IpI9fHbb78xbNgwOnXqxCuvvMKff/4JwIABA6hXr16EqxMpntPp5MEHH+Tdd9/l26/m8uuiX7HWtzMrJTWFIUOHsOXfLXTq1InffvstwtWKiEh5CneQ1aFBPloZY04HHgYWlWmV5SF7d6HQ605PV6dXRKq0bdu2MWTIELp168Y333zDPffcw/r162nXrl2kSxM5ICeffDJLlixh3959fD77C7L3ZQMQFeWkz3FH075TO/r27cuLL76YH4pFRKRqC7fTux5YV+BjJTDd//g1pV5ZeXNl7je52Xq9eNLV6RWRqmnHjh2Abwruli1bGDduHOvXr+f++++nlibWSyXXpEkT5s2bz4D+A5g2dTpb/92a/1ibtm0YMuxU7h93P+eedy579+6NYKUiIlIewp3efEmQ+7KBDcBC/97eys2VCbVb5n/p2b0bvF51ekWkSvnpp58YN24cy5YtY+3atcTHx/PLL79ouI9UOTExMUyaNIm+ffsycuRIunbvQodOHTDGUKtWLYaeeTo/fvcTXbt2ZebMmRx++OGRLllERMpIsZ1e/4TmJcBsa+2bAR9TrLU/V4nAC/9Nb/bzpKUBEKVOr4hUAd999x0nnHACffr0YcmSJYwePTr/MQVeqcrOPPNMFi5cyL+bt/LNl9+Qk5MDQHR0NH1POI5DmzWhd+/evPvuuxGuVEREyko4y5stvj27Xcu4lshyZULsf8ub3WnpADjV6RWRSm7BggX07duXFStW8OSTT7Ju3TpuueUW4uPjI12aSLlo1aoVixcvpkf3I/h46sek7fD9YtsYQ/uO7Rl06sncdPNNjBw5EpfLFeFqRUSktBUbeq21XuAfILHsy4kQdw64s/cLvZ50dXpFpHKy1jJnzhxeeOEFAHr27Ml7773H2rVrufHGG0lISIhwhSLlLz4+njfeeIOHHnqYT2d8xp8r/sx/rO4hdRl21lB+nPcjRxzRgw0bNkSwUhERKW3hDrJ6CbjRGBNTlsVEjCvTdxuwvDm/01tbnV4RqRystXz22WcceeSRnHzyyTzzzDO43W6MMZx77rnq7IoAl1xyCT/++COr/lzNd998jzvXDUBsbCz9B57A5i1b+PjjjyNcpYiIlKZwQ28y0BJYa4x51Rgz3hgzLuDj/jKssey5Mny3cQU6vQ4HztTUCBUlIhK+X375he7du3Pqqaeyfft2XnnlFZYuXUpUVLjzCkWqj44dO7Js2TJaNG/BzOkz2bVzFwCLFy6mTZs2XHvttZEtUERESlWR/xoyxqwFhlprlwJ3Bjx0aZDLLXBvKddWforo9Dpr1sQ4nREqSkQkNK/XS0ZGBjVq1CAxMZG9e/cyefJkzj//fKKjoyNdnkiFlpyczLRp05g0aRJ3j72bNm3bsGHdBpYtW6ZfFomIVDGhOr3NgFgAa62jmI/KnQzzOr2B05vT04jSWZUiUgF5PB7ef/99OnbsyJVXXglA+/bt+b//+z8uvvhiBV6RMBljuO666/jqy6/Yt3cfH374IfXr1490WSIiUsrCXd5cteV3evef3qz9vCJSkbjdbt555x3at2/PeeedB8CwYcPyH3c49J90kQNxxBFH8Pvvv9O3b99IlyIiImWguH8h2XKpItKChF5Pmjq9IlKxPPLII1x44YXExsYydepUli9fztlnnx3pskREREQqtOI2rdxvjNkRxutYa+1FpVFQRGTv9t0GDLJyp6vTKyKRlZOTw5tvvknbtm059thjufzyy2nfvj1DhgxRV1dEREQkTMX9q6kLcEyYH5VXgUFW3pwcvJmZOqNXRCLC5XLxwgsv0KpVK6644gree+89AOrVq8fpp5+uwCsSIcYYLrjggvyv3W43devWZfDgwQB88sknPPzwwyFfY/PmzZx55pllWmco9913H40aNaJLly60a9eO999/P/8xay0TJkygdevWtGnThn79+vHHH3/kP75nzx6uvPJKWrZsSffu3enbty8LFiwo9B6vv/46HTt2pFOnTnTo0IGZM2eWy/cWyjPPPMPhhx/O+eefv9/969evJz4+ni5dutClSxdGjRqV/9jixYvp2LEjrVq14vrrr8da3wLI22+/nU6dOjFixIj8a9955x2eeuqpcvleRKTkiuv0nm6t/aVcKokkVwY4oiAqDgBPuv+MXi1vFpFy9tZbb3HnnXeyadMmevfuzSuvvMLAgQMjXZaIAImJifz+++/s27eP+Ph4vvzySxo1apT/+JAhQxgyZEjI12jYsCEfffRRWZca0k033cQtt9zC6tWr6d69O2eeeSbR0dE899xzzJs3j6VLl5KQkMAXX3zBkCFD+OOPP4iLi+Pyyy+nefPmrF69GofDwbp161ixYsV+r71x40YeeOABfv31V1JTU9mzZw/bt28/qHrdbvdBT9R+/vnn+eqrr2jcuHGhx1q2bMmSJUsK3X/VVVfxyiuvcOSRRzJo0CDmzJnDUUcdxa+//sqyZcu4/PLLWb58Oa1atWLy5MnMmTPnoGoUkbKjdgH4Or2xKWAMAO60NACitLxZRMpBVlYWubm5AOzcuZMWLVrw1Vdf8dNPP3HiiSdi/P9tEpHIGzRoELNmzQLg/fff59xzz81/7I033sg/4/fiiy/m+uuv56ijjqJFixb5QXf9+vV06NAh//rTTz+dAQMG0KxZMyZNmsQTTzxB165d6dWrF+n+X8L37duXRYsWAbBjxw6aNWtWoucXpXXr1iQkJLBz507ANzdg0qRJJCQkADBw4ECOOuoo3n33XdasWcOCBQuYMGFC/mqT5s2bc8opp+z3mtu2bSM5OZmkpCQAkpKSaN68OQB//fUX/fv3p3PnznTr1o01a9ZgreXWW2+lQ4cOdOzYkSlTpgAwd+5cjjnmGIYMGUK7du3weDzceuutHHHEEXTq1ImXXnop6Pf0xBNP0KFDBzp06JDfeR01ahRr167l5JNP5sknnwz5M8mzZcsWMjIy6NWrF8YYRowYwYwZM3A4HOTm5mKtJSsri+joaB577DGuu+46Tc4XqcAUesEfegOPK1KnV0TK3p49e5g4cSLNmzfn7bffBuC6667ju+++44QTTlDYFamAzjnnHD744AOys7NZtmwZRx55ZJHXbtmyhR9//JHPPvuMMWPGBL3m999/Z/r06SxcuJC77rqLhIQEfvvtN3r37s1bb71VbD0H8/xff/2V1q1bc8ghh5CRkcHevXtp0aLFftf06NGDP/74gz/++IMuXbrgdIY+pbJz587Uq1eP5s2bc8kll/Dpp5/mP3b++edzzTXXsHTpUubNm0eDBg2YPn06S5YsYenSpXz11VfceuutbNmyJb++p59+mlWrVvHaa6+RmprKwoULWbhwIa+88grr1q3b770XL17M5MmTWbBgAT///DOvvPIKv/32Gy+++CINGzbk22+/5aabbipU87p16+jatSvHHXccP/zwAwCbNm3aryvcuHFjNm3aRHJyMoMGDaJr1640aNCA1NRUFixYwOmnnx7y5yIikaXT1wGyMwocV6ROr4iUnYyMjPyOTFpaGgMHDszv/Gi/rkjF1qlTJ9avX8/777/PoEGDQl6btwe/Xbt2bN26Neg1/fr1Izk5meTkZFJTUzn11FMB6NixI8uWLSu2ngN5/pNPPsnkyZNZtWrVfqG0NDidTubMmcPChQv5+uuvuemmm1i8eDGjR49m06ZNDB06FIC4ON+Wsh9//JFzzz0Xp9NJvXr1OO6441i4cCEpKSn07Nkzv0v8xRdfsGzZsvyO+e7du1m9enX+43mvNXToUBITEwHfkW4//PADXbt2LbLeBg0a8Pfff1O7dm0WL17M6aefvt8+5mBuu+02brvtNgAuv/xyxo0bx6uvvsoXX3xBp06duPvuuw/wpyciZaXIf11Zax3VYj8v+Dq9cYHHFeV1ehV6RaT0nXbaadx1110ceeSRzJ8/n88//5yePXtGuiwRCdOQIUO45ZZb9lvaHExsbGz+53lDkEJd43A48r92OBy43W4AoqKi8Hq9AGRnZ5f4+QXddNNN/PHHH0ybNo3LLruM7OxsUlJSSExMZO3atftdu3jxYtq3b0/79u1ZunQpHo8n5PcMvoFfPXv25I477uCDDz5g2rRpxT4nmLzwCr6f37PPPsuSJUtYsmQJ69atK5V5B7GxsdT2Nzm6d+9Oy5YtWbVqFY0aNWLjxo35123cuHG//dsAv/32G9Za2rZty9SpU/nwww9Zs2YNq1evPui6RKR0qaUAvkFWAcub3elpmNhYHIkJESxKRKqK9PR0xo0bx65duwCYMGECCxcuZNasWfTq1SuyxYlIiV166aXce++9dOzYsVzer1mzZixevBigVIdgDRkyhB49evDmm28CcOutt3L99dezb98+AL766it+/PFHzjvvPFq2bEmPHj2499578wP8+vXr8/c359m8eTO//vpr/tdLliyhadOmJCcn07hxY2bMmAH4ptRnZWVxzDHHMGXKFDweD9u3b+f7778P+kvAE088kRdeeCF//sGqVavYu3fvftccc8wxzJgxg6ysLPbu3cvHH3/MMceEPmBk+/bt+UF+7dq1rF69mhYtWtCgQQNSUlL4+eefsdby1ltvcdppp+333LFjxzJ+/Hhyc3PzX8PhcJCVlRXyPUWk/Gl5M/hDb9v8Lz1p6Thr19J+OhE5KDt27ODJJ5/k2WefJTMzk7Zt23L22Wdz9NFHR7o0ETkIjRs35vrrry+397vlllsYPnw4L7/8cqHBUQfrnnvu4bzzzmPkyJFcd9117Ny5k44dO+J0Oqlfvz4zZ84kPj4egFdffZXRo0fTqlUr4uPjqVOnDo8++uh+r5ebm8stt9zC5s2biYuLo27durz44osAvP3221x55ZXcc889REdHM3XqVIYOHcr8+fPp3LkzxhgmTpxI/fr1+fPPP/d73csvv5z169fTrVs3rLXUrVs3P0Dn6datGxdffHF+aL788stDLm0G+P777/PrcTgcvPjii9Tyz3R5/vnnufjii9m3bx8nn3wyJ598cv7zZsyYQY8ePWjYsCEAXbp0yT+mqXPnziX8UxCRsmaKWm5TmfTo0cPmTTUM5sjJZwCw4JIiltdMbAHtTofBTwDw9xVX4ElLp/m0yB4pICKVk9fr5Y477uC5554jKyuL4cOHc9ddd5VbV0hERESkqjHGLLbW9jiQ52p5MxSe3uzv9IqIlMSePXsA3/K2FStW5A9E+eCDDxR4RURERCJEoTc3Gzw5Bfb0phOlIVYiEqaNGzdy/fXX06BBg/wjNGbMmME777zD4YcfHuHqRERERKo3hV5Xpu82LhXwTQf0pKWp0ysixfr777+5+uqradmyJS+88ALDhw8nOjoaoNizLEWkcrPWcvzxx7Nhwwa6dOlCly5dqF+/Po0aNcr/etWqVfnHkRV0zz338NVXX4X1Xp988gkPP/xwWNeuX7+e9957L//rN954g2uvvTas5x6Mo446qthrLr/8clasWFHmtVQkS5YsYfbs2WX+Pk899dR+A7T69+/Pzp07y/x9RSoLhV5Xhu/W3+n17t2LzclRp1dEQtq9ezft2rXj1Vdf5ZJLLmH16tW89tprNG7cONKliUg5mD17Np07d6Zp06b5x+iMGjWKm266Kf/rmJiYIp8/btw4+vfvH9Z7DRkyhDFjxoR1bcHQW17mzZtX7DWvvvoq7dq1K4dqyl5Rx0EVFKnQe+GFF/L888+X+fuKVBYKvQVCryctDUCdXhEp5K+//uLxxx8HIDU1lZdffpk1a9bw4osv0qxZs8gWJyLl6t133y10hE0wHo+HkSNH0r59ewYOHJh/HNDFF1+cf/zQmDFjaNeuHZ06deKWW24p9BqB3dqpU6fSoUMHOnfuzLHHHlvo2jFjxvDDDz/QpUsXnnzyScB3jNBJJ51E69atue222/Kv/eKLL+jduzfdunXjrLPOyp9LEKhv377cdNNN9OjRg8MPP5yFCxcybNgwWrduzd13351/XVJSEgBz586lb9++nHnmmRx22GGcf/75+Ucc9e3bl7zBo0lJSdx66620b9+e/v3788svv9C3b19atGjBJ598Uuj7Bhg8eDBz584N+/mBtmzZwrHHHkuXLl3o0KEDP/zwAwBz5syhW7dudO7cmRNOOAHwHTN3+umn06lTJ3r16sWyZcsAuO+++7jwwgs5+uijufDCC9m+fTtnnHEGRxxxBEcccQQ//fTTfu+Zk5PDPffcw5QpU+jSpQtTpkwp8rUD5Q1AbNeuHUOHDuXII4/M/7kF+zN75pln2Lx5M/369aNfv36A7xcl77//fqHXFqm2rLWV/qN79+42lJ6vD7M9Xx8W/MG131l7b4q1a7+31lq7d/GvdkXbw2zm99+HfE0RqT7+/PNPe+GFF1qHw2Hj4uLs33//HemSRCTCDj30UJuRkbHffffee6999NFH879et26ddTqd9rfffrPWWnvWWWfZt99+21pr7UUXXWSnTp1qd+zYYdu0aWO9Xq+11tqdO3cWeq/Jkyfba665xlprbYcOHezGjRuLvPbbb7+1p5xyyn7Pbd68ud21a5fdt2+fPfTQQ+3ff/9tt2/fbo855hi7Z88ea621Dz/8sL3//vsLvd5xxx1nb7vtNmuttU899ZRt0KCB3bx5s83OzraNGjWyO3bssNZam5iYmP/+KSkp9p9//rEej8f26tXL/vDDD/mvtXDhQmuttYCdPXu2tdba008/3Q4YMMDm5OTYJUuW2M6dOxf6vq219pRTTrHffvtt2M8P9Nhjj9kJEyZYa611u902IyPDbtu2zTZu3NiuXbvWWmttWlqatdbaa6+91t53333WWmu//vrr/Ne79957bbdu3WxWVpa11tpzzz03/3vbsGGDPeywwwq9b8HvoajXDvToo4/aK664wlpr7fLly63T6bQLFy4M+WfWtGlTu3379v1ep1WrVvl/PiJVAbDIHmBe1Dm9eXt68zq96f5Oby11ekWquy1btjB69Gg++OAD4uPjuemmm7jllluoX79+pEsTkQhLT08nOTm52OuaN29Oly5dAOjevTvr16/f7/HU1FTi4uK47LLLGDx4MIMHDw75ekcffTQXX3wxw4cPZ9iwYWHVesIJJ5Ca6ptd0q5dOzZs2MCuXbtYsWJF/rnhOTk59O7dO+jzhwwZAkDHjh1p3749DRo0AKBFixb8888/1K69/5awnj175m/16NKlC+vXr6dPnz77XRMTE8NJJ52U/7qxsbFER0fTsWPHQj+jYEr6/COOOIJLL72U3NxcTj/9dLp06cLcuXM59thjad68OUD++bw//vgj06b5jrk8/vjjSUtLIyMjI/9nkXdu8VdffbXfHuWMjAz27NmT3/UOpqjXTklJ2e+aG264AYAOHTrQqVMnAH7++eew/8wADjnkEDZv3lzoz0ekOlLozd5/ebM7LR2AKP0HQqTaysrKIiEhgYSEBH788Uduv/12br75ZurWrRvp0kSkgoiKisLr9eJwhN4pFhsbm/+50+nMX94c+Dq//PILX3/9NR999BGTJk3im2++KfL1XnzxRRYsWMCsWbPo3r07ixcvLjbUFKzB7Xbz/+3dd1zV1f/A8ddhCCqK2xyluFA2Am5FxZUDV6ZoCZqmlWlafLUsV5qztMzRcGe5V2luSc2NuWcq5eDnAEUQQbic3x8XbiBDnCj3/Xw8fMD9fM49n/fnw0flfd/nnI/WmqZNm2ZrCGzK+y0sLNL0ZWFhkeHc1oyOdz9ra2uUUun6Td1nyjVOERcX91DvT61BgwZs376dtWvXEhQUxKBBgyhcuPADz/1++fPnN32flJTEnj17sLW1feh+HsXD/MzAeL1SEnQhzJ3M6b1v9Wap9Aphvg4ePEj79u2pVasWSUlJ2Nvbc+7cOcaOHSsJrxAiDUdHR86fP//Y/cTExBAVFUXLli2ZPHkyhw8fzrL9uXPnqFmzJqNGjaJ48eJcvHgxzf4CBQoQHR39wOPWqlWLP//8k7///huAO3fucObMmUc/kaegfPnyHDp0iKSkJC5evMi+ffseua9//vmHkiVL0rt3b3r16sXBgwepVasW27dvNz1qLjLSWPioX78+CxcuBIxzlIsVK5amEpuiWbNmTJ061fT60KFD6drc//PITt9169ZlyZIlAJw4cYKjR48CWf/M7j+O1pr/+7//k/UmhEgmld74KOPXVJVeiwIFsMhixUUhRO6yb98+Pv/8c3777Tfs7e354IMPuHfvHra2tqZHEAkhRGqtWrUiJCSESpUqPVY/0dHRtG3blri4OLTWfPXVV1m2Dw4O5uzZs2it8fPzw93dPc1+Nzc3LC0tcXd3JygoKNNqZvHixZk7dy4BAQHEx8cDMHr0aKpUqfJY5/Mk1a1bFwcHB5ycnKhWrRrVq1d/5L5CQkKYOHEi1tbW2NnZMX/+fIoXL873339Phw4dSEpKokSJEmzatIkRI0bQs2dP3NzcyJcvH/Pmzcuwz2+++Yb33nsPNzc3EhMTadCgATNnzkzTplGjRowbNw4PDw8+/vjjbPX97rvvEhgYiJOTE1WrVsXZ2Rl7e/ssf2Zvv/02LVq0oHTp0mzbto3Q0FBq1aqFlZX8qi8EgNLJK+q9yLy9vXXKqnYZqTmnIwB7eyxPv3PTMNgzEz67BsDlQYOIO36CihvWP5VYhRDPlw0bNtCiRQsKFy7MoEGDeP/9901z34QQIjPh4eF0796dTZs25XQoIpcxGAwkJCRga2vLuXPnaNKkCadPn87yEVj3GzBgAP7+/qYVqYXIDZRSoVpr70d5r3z8Ex9tqvKCsdJrKfN5hcjVtm/fzo0bN+jQoQN+fn58/fXX9OjRI1uL0gghBECpUqXo3bt3ukWIhHhcsbGxNGrUiISEBLTWTJ8+/aESXjAugCUJrxD/kaQ37naapNcQGUEemf8gRK6jtSYkJISRI0fyxx9/4O7uTvv27bGysqJ///45HZ4Q4gX0+uuv53QIIhcqUKAAWY1gzI7evXs/oWiEyB1kIav4aLD97xPaxIhILItIpVeI3GTv3r00aNCAxo0bc+bMGaZMmcKuXbtMK38KIYQQQojcSyq98dFgY0x6tcGA4dYtrIrKys1CvOi01sTHx2Nra0t0dDRhYWF8++23vPXWW8/s8RJCCCGEECLnSaU3/r/hzYaoKEhKkkqvEC8wrTW//vorNWrU4JNPPgHAz8+Pc+fO8d5770nCK4QQQghhZiTpjb9tqvQaIozP6JVKrxAvnqSkJFasWEH16tXx9/cnMjIST09PAJRSD70IiBBCCCGEyB0k6U21enNihPGh5FLpFeLFM2TIEDp27MidO3eYO3cup06d4s0338zpsIQQQgghRA4z7zm9WqdZvdkQKZVeIV4UBoOBxYsX4+HhgZOTEz179sTd3Z3OnTtjZWXe/7QJIYQQQoj/mHelN+EuaINp9WZTpVee0yvEcysxMZH58+fj5OREt27dmDVrFgBVq1alW7dukvAKIYQQQog0zDvpjY82fk0Z3hwZARYWWNrb52BQQojMLFy4EEdHRwIDA8mbNy/Lly9n4sSJOR2WEEIIIYR4jpl3SST+tvGraSGrSCyLFEFZmPdnAUI8T+7du4e1tTVKKQ4fPkzhwoVZvXo1bdq0kefsCiGEEEKIBzLv7O6+pDcxMgKrIjKfV4jnQVxcHNOnT6dSpUps3LgRgM8//5z9+/fj7+8vCa8QQgghhMgWM0960w5vNkREYimLWAmRo+7evcs333xDxYoVee+993jllVewT55yYGNjI8muEEIIIYR4KOY9vDkupdL735zevC6uORiQEOZNa029evU4ePAgDRs25KeffqJhw4aS6AohhBBCiEdm3klvSqXXNtWcXqn0CvFMRUdHM2fOHPr27UuePHn47LPPKFKkCA0aNMjp0IQQQgghRC4gSS+ATUGS4uNJionBqog8rkiIZyEqKopvv/2Wr776isjISCpWrEirVq1o165dTocmhBBCCCFyETOf0/vf8GZDZMozeqXSK8TTdO/ePUaOHEn58uX59NNPqV27Nnv27KFVq1Y5HZoQQgghhMiFzLzSexus8oKlNYkRxqTXqqhUeoV4GhISErC2tsba2ppVq1bh6+vLZ599hpeXV06HJoQQQgghcjHzTnrjbv+3cnNkBIA8skiIJ+z69et89dVX/PTTTxw9epRChQrx559/ki9fvpwOTQghhBBCmAEzH94cbVrEKqXSaymVXiGeiKtXrxIcHEz58uUZP3489erV486dOwCS8AohhBBCiGfGvCu98dFS6RXiKbhy5QqVKlUiPj6erl27MnToUKpWrZrTYQkhhBBCCDNk5pXe/4Y3J0ZEomxtUVKBEuKRXLx4kYULFwJQunRpxowZw6lTp1iwYIEkvEIIIYQQIseYedIbDTYpz+iNwKpIEZRSORyUEC+WsLAw+vbtS8WKFenduzc3b94EYODAgVSuXDmHoxNCCCGEEOZOkt7kpDcxMlLm8wrxEC5fvkyvXr2oXLkys2fP5q233uLkyZMULlw4p0MTQgghhBDCxLzn9KZevTkiAqvixXM4ICGef4mJiVhZWWEwGFiyZAl9+/blf//7Hy+//HJOhyaEEEIIIUQ65pv0am2c02v7X6XXRuYdCpGpkydPMmbMGCIiIvj999955ZVXuHLlCnZ2djkdmhBCCCGEEJky3+HN9+4AGmwKoLU2VnqLysrNQtzv2LFjdOnSBWdnZ1auXImLiwuJiYkAkvAKIYQQQojnnvlWeuNvG7/aFCApJgadkIBlEZnTK0Rqy5Yto1OnTtjZ2TFkyBAGDhxIcZkGIIQQQgghXiBmnPRGG7/aFMQQkfyMXqn0CkFoaCixsbHUr1+fZs2aMXLkSPr160cReYa1EEIIIYR4AZnv8OZUSW9iZCSAVHqFWdu7dy+tW7fG29ubTz75BICCBQsybNgwSXiFEEIIIcQLy3yT3rgo41ebAiRKpVeYsQMHDtCiRQtq1arFnj17+OKLL1i7dm1OhyWEEEIIIcQTIcObbQtiiPgXkEqvMC9JSUlYWFhw4sQJDh48yIQJE3jnnXdkcSohhBBCCJGrmG+l1zS8uQCJkcmV3sKFci4eIZ4BrTVbtmzB19eXyZMnA9C1a1cuXLhAcHCwJLxCCCGEECLXMeOk97/Vmw0RkVjY26Py5MnZmIR4SrTWbNiwgXr16tGkSRP+/vtvihY1jmywsrIif/78ORyhEEIIIYQQT4cZJ72pF7KKwEoW6hG52Pvvv0+LFi24ePEi06dP59y5cwQFBeV0WEIIIYQQQjx15j2n1zo/WFhiiIjEUpJekYtorfn111/x9vamdOnSdOnSBXd3dwIDA8kjIxqEEEIIIYQZMd9Kb1wU2BYEkEqvyDWSkpJYvnw5np6etG3blh9++AGAevXq0bt3b0l4hRBCCCGE2THfpDc+GmwKABgrvfK4IvGCW7p0KW5ubrz22mvcvXuX+fPnM3To0JwOSwghhBBCiBxlxsObb4NNAXRiIoZbt7CSxxWJF1DKY4cAVq1ahdaan3/+mddffx1LS8scjk4IIYQQQoic98wrvUqpFkqp00qpv5VSQzLYP0gpdUIpdUQptUUpVe6pBBIfDTYFMdy6BVpLpVe8UBISEpg7dy7VqlXjyJEjAEyfPp2jR48SEBAgCa8QQgghhBDJnmnSq5SyBKYBrwJOQIBSyum+Zn8B3lprN2AZMOGpBJM8vDkxIhIAq6JS6RXPv3v37jFr1iyqVq1Kjx49yJ8/P7GxsQDY29ubqr5CCCGEEEIIo2f9G3IN4G+t9Xmt9T1gEdA2dQOt9TatdWzyyz1A2acSSdxtY6U3MgJAVm8Wz72kpCSqV69Or169KFKkCL/++iuhoaHUqlUrp0MTQgghhBDiufWs5/SWAS6men0JqJlF+7eA359KJPHRYFtQKr3iuRYXF8eKFSsICAjAwsKC999/n1deeYUWLVqglMrp8IQQQgghhHjuPbcLWSml3gC8Ad9M9r8NvA3wyiuvPFznSUlwzzi82XBNKr3i+RMbG8v333/PhAkTCA8Pp1y5ctStW5c+ffrkdGhCCCGEEEK8UJ718ObLwMupXpdN3paGUqoJMBTw11rHZ9SR1vp7rbW31tq7ePHiDxfFvWjj15Q5vZaWWNrbP1wfQjwFcXFxTJo0CQcHBwYOHEjVqlXZtm0bderUyenQhBBCCCGEeCE966R3P1BZKeWglMoDdAHWpG6glPIEvsOY8F57KlHEpyS9xjm9lkUKo2QBIJGDtNam7ydPnoy7uzvbt29n69atNGzYUIYyCyGEEEII8Yie6fBmrXWiUqofsAGwBGZrrY8rpUYBB7TWa4CJgB2wNPkX/X+11v5PNJD4VJXeyJvyjF6RY6Kiopg6dSorVqxgz5492NracvjwYYoVK5bToQkhhBBCCJErPPM5vVrrdcC6+7YNS/V9k6ceRNxt41ebghgiIrCSZ/SKZ+zmzZt8/fXXTJkyhaioKNq0acPNmzcpWbKkJLxCCCGEEEI8Qc/tQlZPVUql17YgiZGR5C3rlrPxCLNy+vRpfHx8iI6Opn379nz22Wd4enrmdFhCCCGEEELkSuY5kTU+pdJbQCq94pm4du0aGzZsAKBKlSr06dOHw4cPs2LFCkl4hRBCCCGEeIrMtNJrTHqTdB6S7tzBUub0iqckPDycSZMmMWPGDGxtbbl8+TJ58+Zl4sSJOR2aEEIIIYQQZsFMK73G4c2GWAOAVHrFExceHs6AAQOoUKECU6ZMoVOnTuzevZu8efPmdGhCCCGEEEKYFTOt9BqT3sToOACp9IonRmuNUoorV64wffp03nzzTT755BMqVaqU06EJIYQQQghhlswz6Y27DXkKYLh5E5BKr3h8YWFhjB07FoDvvvsOLy8vLl26RMmSJXM4MiGEEEIIIcyb+Q5vti1IYkQkAJZFpdIrHs25c+d46623qFy5MnPnzsXW1hatNYAkvEIIIYQQQjwHzLPSGx9lXLk5MgIAqyJS6RUPb/78+fTs2RNra2veffdd/ve//1GmTJmcDksIIYQQQgiRipkmvdFgY6z0qrx5sciXL6cjEi+IEydOkJSUhIuLCw0bNuSDDz7go48+4qWXXsrp0IQQQgghhBAZMN/hzcmVXqnyiuw4evQonTt3xsXFhY8//hiAV155hUmTJknCK4QQQgghxHPMPJPeuNtgU4DEiEiZzyuydPjwYTp27Iibmxu///47H3/8MXPmzMnpsIQQQgghhBDZZL7Dm20Lkhh5GesSstiQSC/l0UPr1q1jy5YtDBs2jAEDBlBERgYIIYQQQgjxQjHPSm/ynF5DRCSW8rgikcru3bt59dVXWbx4MQD9+/cnLCyMkSNHSsIrhBBCCCHEC8j8kl5DIiTcQeexIzEyEqsiMrxZwM6dO2nWrBl16tRh//79xMXFAZA/f34KFSqUs8EJIYQQQgghHpn5DW++Fw1AUpItJCRIpVfQu3dvfvzxR0qUKMHEiRPp27cvdnZ2OR2WEEIIIZIlJCRw6dIl04fSQojcy9bWlrJly2Jtbf3E+jS/pDfemPQmxikArGQhK7OjtWbLli3UqlULOzs7WrZsibOzM2+//Tb55PFVQgghxHPn0qVLFChQgPLly6OUyulwhBBPidaaiIgILl26hIODwxPr1/yGN8fdBsBw1/jSUuZpmg2tNevXr6du3bo0bdqU2bNnA9C+fXs++OADSXiFEEKI51RcXBxFixaVhFeIXE4pRdGiRZ/4qA7zS3pTKr2xSYBUes2B1prffvuNmjVr8uqrr3L58mVmzJhBnz59cjo0IYQQQmSTJLxCmIen8XfdbJNew50EQCq95kApxaRJk7hx4wY//PADZ8+epW/fvtjY2OR0aEIIIYR4QTyJ9T4OHDhA//79M90fFhbGzz//nO3292vYsCGOjo64u7vj4+PDoUOHHifcJ2rNmjWMGzfuifT1f//3f3Tp0oWKFSvi5eVFy5YtOXPmDGFhYbi4uDyRYwAMGzaMzZs3A7Bjxw6cnZ3x8PDg8uXLvPbaa4/V9+zZs3F1dcXNzQ0XFxdWr179JEJ+aBcuXKBmzZpUqlSJzp07c+/evXRt7t27R48ePXB1dcXd3Z2QkBAAoqOj8fDwMP0pVqwYH3zwAQBz586lePHipn0//vijqb8WLVpQqFAhWrdu/SxOETDLOb3G4c2J0fEAWBUunJPRiKcgKSmJ5cuXM3HiRFauXEmZMmX4+eefKV68+BOdEC+EEEII8TC8vb3x9vbOdH9K0tu1a9dstc/IwoUL8fb2Zs6cOQQHB7Np06bHihnAYDBgaWn5WH34+/vj7+//2LForWnfvj2BgYEsWrQIgMOHD3P16lVefvnlx+4/tVGjRpm+X7hwIR9//DFvvPEGAMuWLct2P4mJiVhZ/Zd2Xbp0iTFjxnDw4EHs7e2JiYnh+vXrjxXr/cfIrsGDBzNw4EC6dOlC3759mTVrFu+8806aNj/88AMAR48e5dq1a7z66qvs37+fAgUKpPlgxcvLiw4dOphed+7cmW+//TbdMYODg4mNjeW777576HgflRlWepPn9EbHYmlvj5IkKNcwGAz8/PPPuLq68vrrr3P79m0uX74MQOnSpSXhFUIIIcQTdejQIWrVqoWbmxvt27fn5s2bAOzfvx83Nzc8PDwIDg42VR9DQkJM1a0//vjDVAXz9PQkOjqaIUOGsGPHDjw8PJg8eXKa9jExMaZqm5ubG8uXL88yttq1a5t+D7pz5w49e/akRo0aeHp6mqqKsbGxvP766zg5OdG+fXtq1qzJgQMHAGNl+8MPP8Td3Z3du3fz008/UaNGDTw8POjTpw8GgwGDwUBQUBAuLi64uroyefJkAL755hucnJxwc3OjS5cugLHy169fP8CY3Ddu3Bg3Nzf8/Pz4999/AQgKCqJ///7UqVOHChUqZJhYbtu2DWtra/r27Wva5u7uTv369dO0CwsLo379+lSvXp3q1auza9cuAMLDw2nQoAEeHh64uLiwY8eOTM8jKCiIZcuW8eOPP7JkyRI+++wzunXrlqaibDAYCA4OxsfHBzc3N1MiFxISQv369fH398fJySlNbNeuXaNAgQKm0QN2dnamRZv+/vtvmjRpgru7O9WrV+fcuXNorU33kaurK4sXL87wGJnFkhmtNVu3bjVVrQMDA1m1alW6didOnKBx48YAlChRgkKFCpnukxRnzpzh2rVr6X4OGfHz86NAgQIPbPckmWGlN3lOb9QdLGU+b64RFxeHl5cXJ06cwNnZmV9++YVOnTo99qeSQgghhHi+jPz1OCeu3H6ifTqVLsjwNs4P/b7u3bszdepUfH19GTZsGCNHjmTKlCn06NGDH374gdq1azNkyJAM3ztp0iSmTZtG3bp1iYmJwdbWlnHjxjFp0iR+++03ANMwUoDPP/8ce3t7jh49CmBKsDOzfv162rVrB8CYMWNo3Lgxs2fP5tatW9SoUYMmTZowY8YMChcuzIkTJzh27BgeHh6m99+5c4eaNWvy5ZdfcvLkScaPH8+ff/6JtbU17777LgsXLsTZ2ZnLly9z7NgxAG7dugXAuHHjuHDhAjY2NqZtqb3//vsEBgYSGBjI7Nmz6d+/vynZCg8PZ+fOnZw6dQp/f/90w4iPHTuGl5dXlucOxuRs06ZN2NracvbsWQICAjhw4AA///wzzZs3Z+jQoRgMBmJjYzl06FCG55GiV69e7Ny5k9atW/Paa68RFhZm2jdr1izs7e3Zv38/8fHx1K1bl2bNmgFw8OBBjh07lm4VYnd3d0qWLImDgwN+fn506NCBNm3aANCtWzeGDBlC+/btiYuLIykpiRUrVnDo0CEOHz7MjRs38PHxoUGDBumO8f3332cYi4ODAx4eHumGu0dERFCoUCFThbhs2bKmD0ruj3fNmjUEBARw8eJFQkNDuXjxIjVq1DC1WbRoEZ07d04zH3f58uVs376dKlWqMHny5CdeiX8Y5pf0xt0GZYHhZhRWMp/3hZaQkEBISAhNmzbF1taWTp064erqSvv27bGwML9BDEIIIYR4dqKiorh16xa+vr6AsUrWqVMnbt26RXR0NLVr1waga9eupiQ2tbp16zJo0CC6detGhw4dKFu2bJbH27x5s2k4L0DhTKbodevWjXv37hETE2NKcjZu3MiaNWuYNGkSYCwW/Pvvv+zcuZMBAwYA4OLigpubm6kfS0tLOnbsCMCWLVsIDQ3Fx8cHgLt371KiRAnatGnD+fPnef/992nVqpUp2XNzc6Nbt260a9fOlHintnv3blasWAHAm2++yf/+9z/Tvnbt2mFhYYGTkxNXr17N8ppkJSEhgX79+nHo0CEsLS05c+YMAD4+PvTs2ZOEhATatWuHh4cHFSpUyPA8smPjxo0cOXLEVJWOiori7Nmz5MmThxo1amT42B1LS0vWr1/P/v372bJlCwMHDiQ0NJQPP/yQy5cv0759e8D4vFqAnTt3EhAQgKWlJSVLlsTX15f9+/dTsGDBNMfILBYHB4fHmt/ds2dPTp48ibe3N+XKlaNOnTrpCkuLFi1iwYIFptdt2rQhICAAGxsbvvvuOwIDA9m6desjx/C4zC/pjY8GmwIkRkZiU6lSTkcjHsG9e/eYN28eX3zxBWFhYRw/fhwnJydGjBiR06EJIYQQ4il7lIrs82jIkCG0atWKdevWUbduXTZs2PBE+l24cCFeXl4EBwfz/vvvs2LFCrTWLF++HEdHx2z3Y2tra0pstNYEBgYyduzYdO0OHz7Mhg0bmDlzJkuWLGH27NmsXbuW7du38+uvvzJmzBhTdTo7Ui80qrVOt9/Z2Tlb82knT55MyZIlOXz4MElJSaYEskGDBmzfvp21a9cSFBTEoEGD6N69e4bnkR1aa6ZOnUrz5s3TbA8JCSF//vyZvk8pRY0aNahRowZNmzalR48efPjhh9k6Zmqpj5FZLJkpWrQot27dMs0HvnTpEmXKlEnXzsrKyjTkG6BOnTpUqVLF9Prw4cMkJiamqcAXTTWitlevXmk+2MgJ5lcOi48Gm4IYIiKwKiqV3hdJfHw8M2bMoFKlSrz99tuUKFGCtWvXUq1atZwOTQghhBBmxt7ensKFC7Njxw4AFixYgK+vL4UKFaJAgQLs3bsXIE11NrVz587h6urK4MGD8fHx4dSpUxQoUIDo6OgM2zdt2pRp06aZXmc1vFkpxeeff86ePXs4deoUzZs3Z+rUqaYk8q+//gKM1eYlS5YAxnmbmSWnfn5+LFu2jGvXrgEQGRnJP//8w40bN0hKSqJjx46MHj2agwcPkpSUxMWLF2nUqBHjx48nKiqKmJiYNP3VqVPHdF0WLlyYrXmgKRo3bkx8fDzff/+9aduRI0dMP4cUUVFRlCpVCgsLCxYsWIDBYADgn3/+oWTJkvTu3ZtevXpx8ODBDM8ju5o3b86MGTNISDA+GebMmTPcuXMny/dcuXIlzTEOHTpEuXLlKFCgAGXLljUN9Y6Pjyc2Npb69euzePFiDAYD169fZ/v27WmGFj9qLEopGjVqZPoQYd68ebRt2zZdu9jYWFM/mzZtwsrKKs085V9++YWAgIA07wkPDzd9v2bNmhz/fd0MK7230dYFMNy6hWURmdP7IomOjiY4OBh3d3d+/PFHmjZtKs/sE0IIIcQzERsbm2YI8qBBg5g3bx59+/YlNjaWChUqMGfOHMA4z7N3795YWFjg6+uLvb19uv6mTJnCtm3bsLCwwNnZmVdffRULCwssLS1xd3cnKCgIT09PU/tPP/2U9957DxcXFywtLRk+fHialXLvlzdvXj788EMmTpzIt99+ywcffICbmxtJSUk4ODjw22+/8e677xIYGIiTkxNVq1bF2dk5w1idnJwYPXo0zZo1IykpCWtra6ZNm0bevHnp0aMHSUlJAIwdOxaDwcAbb7xBVFQUWmv69+9PoUKF0vQ3depUevTowcSJEylevLjpumWHUoqVK1fywQcfMH78eGxtbSlfvjxTpkxJ0+7dd9+lY8eOzJ8/nxYtWpgqoiEhIUycOBFra2vs7OyYP38+ly9fTnce2dWrVy/CwsKoXr06WmuKFy+e4WJQqSUkJPDRRx9x5coVbG1tKV68ODNnzgSMH5706dOHYcOGYW1tzdKlS2nfvj27d+/G3d0dpRQTJkzgpZde4tSpU9mOJaM5vQDjx4+nS5cufPrpp3h6evLWW28BxkT1wIEDjBo1imvXrtG8eXMsLCwoU6ZMmmHMAEuWLGHdunVptn3zzTesWbMGKysrihQpwty5c0376tevz6lTp4iJiaFs2bLMmjUr29XpR6UyGjbwovH29tb3ryCWWs05xvkIe3ssh3ltSLx1l7MzLvPS8GEUvu9TCfH8uHPnDt999x0hISGsXr0apRQXLlygfPnykuwKIYQQZuTkyZM5Xil6GDExMaaVeceNG0d4eDhff/11DkeVnsFgICEhAVtbW86dO0eTJk04ffo0efLkyenQhJnL6O+8UipUa/1wz/BKZn6V3rjbJCYal8iWSu/zKSYmhunTpzNp0iSuX79O48aNiYqKolChQhkuBiCEEEII8TxZu3YtY8eOJTExkXLlyqWpcj1PYmNjadSoEQkJCWitmT59uiS8Ilcyv6Q3PhpDgjHZlTm9z5+DBw/SrFkzIiIiaNasGZ999hn16tXL6bCEEEIIIbKtc+fOdO7cOafDeKACBQqke96qELmRWS5klXjPmOtLpff5cOvWLdM/uM7OzrRs2ZLdu3ezYcMGSXiFEEIIIYQQj8UMK723Mdw1zgeVSm/OioyM5Ouvv+brr7+mUKFCnDt3DhsbG+bPn5/ToQkhhBBCCCFyCfOq9Cbeg8Q4Eu8CVlZYFCyY0xGZpRs3bjB06FDKly/PqFGjaNKkCatWrUr3kGshhBBCCCGEeFzmlfTeMz4jLDHWgFWRIrICcA7ZvXs3Y8eOpWXLlhw9epRly5bh4eGR02EJIYQQQgghciHzSnrjogAwxNzDsqjM531WwsPDGTRoEGPGjAGgdevWnD59mkWLFuHi4pLD0QkhhBBCPFjKI4gA1q1bR5UqVfjnn38YMWIE+fLl49q1axm2fdpCQkJQSvHrr7+atrVu3ZqQkJAs3zds2DA2b96cZZsRI0YwadKkdNvDwsKeye9w+/bto0GDBjg6OuLp6UmvXr2IjY1l7ty59OvX74kdp2XLlty6dQswPl+2WrVqdOvWjTVr1jBu3LhH7jcpKYn+/fvj4uKCq6srPj4+XLhw4QlF/XDWr1+Po6MjlSpVyvSc/vnnH/z8/HBzc6Nhw4ZcunTJtL169ep4eHjg7OxseqYwQMOGDXF0dMTDwwMPDw/T34OZM2fi6uqKh4cH9erV48SJE0//JLNgXnN646MBSIyJx6pY8RwOJve7dOkSEyZM4PvvvycxMZF3330XMD5UvHLlyjkcnRBCCCHEw9uyZQv9+/dnw4YNlCtXDoBixYrx5ZdfMn78+Cd6LIPBkK3pX2XLlmXMmDG0adMm232PGjXqcUJ7LNk5r6tXr9KpUycWLVpE7dq1AVi2bBnR0dFPPJ5169aZvp8+fTqbN2+mbNmyAPj7+2e7n8TERKys/kuvFi9ezJUrVzhy5AgWFhZcunSJ/PnzP1as9x8jOwwGA++99x6bNm2ibNmy+Pj44O/vj5OTU5p2H330Ed27dycwMJCtW7fy8ccfs2DBAkqVKsXu3buxsbEhJiYGFxcX/P39KV26NAALFy7E2zvt43O7du1K3759AVizZg2DBg1i/fr1j3Hmj8e8Kr3JSa/h9h2p9D5lP/zwAxUrVmTGjBm88cYbnD59mm+++SanwxJCCCGEeGTbt2+nd+/e/Pbbb1SsWNG0vWfPnixevJjIyMh07/npp5+oUaMGHh4e9OnTB4PBAMA777yDt7c3zs7ODB8+3NS+fPnyDB48mOrVq7N06VK++eYbnJyccHNzo0uXLhnG5e7ujr29PZs2bUq3LzQ0FF9fX7y8vGjevDnh4eEABAUFsWzZMsCY9FWtWhUvLy/69+9P69atTe8/ceIEDRs2pEKFCml+l0tMTKRbt25Uq1aN1157jdjYWMD4oYCnpyeurq707NmT+Pj4RzqvadOmERgYaEp4AV577TVKliyZpt2vv/5KzZo18fT0pEmTJly9ehWAP/74w1R99PT0JDo6mvDwcBo0aICHhwcuLi7s2LHDFNuNGzfo27cv58+f59VXX2Xy5MlpKsrXr1+nY8eO+Pj44OPjw59//gkYq+FvvvkmdevW5c0330wTW3h4OKVKlcLCwphylS1blsKFCwPGymv16tVxd3fHz88PMC7y2q5dO9zc3KhVqxZHjhzJ8BiZxZKZffv2UalSJSpUqECePHno0qULq1evTtfuxIkTNG7cGIBGjRqZ2uTJkwcbGxsA4uPjSUpKyvJ4AAVTrZ10586dHJ9WamaV3tsAJN6KwaqIrNz8pJ0/fx5ra2tefvllvLy86NGjB0OGDKF8+fI5HZoQQgghcovfh8D/HX2yfb7kCq9mPYw1Pj6edu3aERISQtWqVdPss7Ozo2fPnnz99deMHDnStP3kyZMsXryYP//8E2tra959910WLlxI9+7dGTNmDEWKFMFgMODn58eRI0dwc3MDoGjRohw8eBCA0qVLc+HCBWxsbExDcDMydOhQPvvsM5o2bWralpCQwPvvv8/q1aspXrw4ixcvZujQocyePdvUJi4ujj59+rB9+3YcHBwICAhI0++pU6fYtm0b0dHRODo68s477wBw+vRpZs2aRd26denZsyfTp0+nX79+BAUFsWXLFqpUqUL37t2ZMWMGH3zwwUOf17FjxwgMDMzyZwJQr1499uzZg1KKH3/8kQkTJvDll18yadIkpk2bRt26dYmJicHW1pbvv/+e5s2bM3ToUAwGgylRTzFz5kzWr1/Ptm3bKFasGHPnzjXtGzBgAAMHDqRevXr8+++/NG/enJMnTwLGZHHnzp3kzZs3TX+vv/469erVY8eOHfj5+fHGG2/g6enJ9evX6d27t+map3xYMnz4cDw9PVm1ahVbt26le/fuHDp0KN0xunbtmmEsBw4cYObMmfz4449p4rh8+TIvv/yy6XXZsmXZu3dvumvp7u7OihUrGDBgACtXriQ6OpqIiAiKFi3KxYsXadWqFX///TcTJ040VXkBevTogaWlJR07duTTTz81JbjTpk3jq6++4t69e2zduvWBP8unyewqvUmJCh0XL5XeJ+js2bP06NGDKlWqmD6prF69OjNnzpSEVwghhBC5grW1NXXq1GHWrFkZ7u/fvz/z5s1LM/x2y5YthIaG4uPjg4eHB1u2bOH8+fMALFmyhOrVq+Pp6cnx48fTzHns3Lmz6Xs3Nze6devGTz/9lOWw1gYNGgCwc+dO07bTp09z7NgxmjZtioeHB6NHjzbN00xx6tQpKlSogIODA0C6pLdVq1bY2NhQrFgxSpQoYaqkvvzyy9StWxeAN954g507d3L69GkcHByoUqUKAIGBgWzfvv2xzutBLl26RPPmzXF1dWXixIkcP34cgLp16zJo0CC++eYbbt26hZWVFT4+PsyZM4cRI0Zw9OhRChQokO3jbN68mX79+uHh4YG/vz+3b98mJsa4SK6/v3+6hBeMyeXp06cZO3YsFhYW+Pn5sWXLFvbs2UODBg1M17xIcjFu586dpmpx48aNiYiI4Pbt2+mOkVks3t7e6RLehzFp0iT++OMPPD09+eOPPyhTpoxpGPrLL7/MkSNH+Pvvv5k3b57pPli4cCFHjx5lx44d7NixgwULFpj6e++99zh37hzjx49n9OjRjxzXk2B2ld7EOGOeL8/ofXynTp1izJgx/Pzzz+TJk4d+/foRHByc02EJIYQQIjd7QEX2abGwsGDJkiX4+fnxxRdf8Mknn6TZX6hQIbp27cq0adNM27TWBAYGMnbs2DRtL1y4wKRJk9i/fz+FCxcmKCiIuLg40/7U8z7Xrl3L9u3b+fXXXxkzZgxHjx7NNEkcOnQoo0ePNu3XWuPs7Mzu3bsf+bxThrUCWFpakpiYCJBuuGp2hq8+zHk5OzsTGhpK27Zts+zz/fffZ9CgQfj7+xMSEsKIESMAGDJkCK1atWLdunXUrVuXDRs20KBBA7Zv387atWsJCgpi0KBBdO/e/YFxg3FRqj179mBra5vled3PxsaGV199lVdffZWSJUuyatUqmjVrlq1jZnaMrGLJSJkyZbh48aLp9aVLlyhTpky6dqVLl2bFihUAxMTEsHz5cgoVKpSuTcrQ8Ndee83UT4ECBejatSv79u1Ld027dOliGiGQU8yr0ht3G0O88ZQtZXjzY/v2229ZsWIFgwYN4sKFC0yZMiXDv0BCCCGEELlBvnz5WLt2LQsXLsyw4jto0CC+++47U2Lo5+fHsmXLTCvaRkZG8s8//3D79m3y58+Pvb09V69e5ffff8/weElJSVy8eJFGjRoxfvx4oqKiTNXFjDRr1oybN2+a5oI6Ojpy/fp1U9KbkJBgqoSmcHR05Pz584SFhQHGxZey499//zX1+/PPP1OvXj0cHR0JCwvj77//BmDBggX4+vo+0nn169ePefPmpRmGu2LFClOFMUVUVJTp98958+aZtp87dw5XV1cGDx6Mj48Pp06d4p9//qFkyZL07t2bXr16mYZaZ0ezZs2YOnWq6XXKsOOsHDx4kCtXrpjO+ciRI5QrV45atWqxfft200rOKcOb69evz8KFCwHjqtzFihVLMzf2UWPx8fHh7NmzXLhwgXv37rFo0aIMF+i6ceOGab7u2LFj6dmzJ2BMku/evQvAzZs32blzJ46OjiQmJnLjxg3AeG/99ttvplW9z549a+p37dq1Ob6IrXklvfHRJN6zBsBKhjc/tCNHjtCpUyfTMJXhw4cTFhbGxIkTeemll3I4OiGEEEKIp69IkSKsX7+e0aNHs2bNmjT7ihUrRvv27U2LNzk5OTF69GiaNWuGm5sbTZs2JTw8HHd3dzw9PalatSpdu3Y1DRO+n8Fg4I033sDV1RVPT0/69++frvJ2v6FDh5qqenny5GHZsmUMHjwYd3d3PDw82LVrV5r2efPmZfr06bRo0QIvLy8KFCiAvb39A6+Do6Mj06ZNo1q1aty8eZN33nkHW1tb5syZQ6dOnXB1dcXCwsK0gu/DnlfJkiVZtGgRH330EY6OjlSrVo0NGzakG5I8YsQIOnXqhJeXF8WKFTNtnzJlCi4uLri5uWFtbc2rr75KSEiI6dovXryYAQMGPPA8U3zzzTccOHAANzc3nJyc0jy2JzPXrl2jTZs2pjisrKzo168fxYsX5/vvv6dDhw64u7ubhn2PGDGC0NBQ3NzcGDJkSJokPjuxHDhwgF69eqVrb2Vlxbfffkvz5s2pVq0ar7/+Os7OzoDx0VUp93FISAiOjo5UqVKFq1evMnToUMA4N71mzZq4u7vj6+vLRx99hKurK/Hx8TRv3hw3Nzc8PDwoU6YMvXv3BozFMWdnZzw8PPjqq68yPZdnRWmtczSAJ8Hb21sfOHAg0/0153QEYG8JB26tXEn4zjxU3LyZPGWlKpkdBw8e5PPPP2fVqlUULFiQqVOnZnsoiBBCCCHE4zp58iTVqlXL6TByrZiYGOzs7NBa895771G5cmUGDhyY02EJM5bR33mlVKjW2juTt2TJzCq9t0lMMI59typSOIeDeTH06NEDLy8v0xyJsLAwSXiFEEIIIXKRH374AQ8PD5ydnYmKiqJPnz45HZIQT5SZLWQVjeFeHlQ+Cyzy5cvpaJ5b+/btw8vLC0tLS7y9valUqRL9+vXL1lAXIYQQQgjxYhk4cKBUdkWuZl6V3rjbJMZbyjN6M7F9+3aaNGlCzZo1Wbp0KWBcanzo0KGS8AohhBBCCCFeSOaV9MbfxhCnsJTHFZlordm6dSsNGzbE19eXY8eOMWnSJNq0aZPToQkhhBBCCCHEYzO74c2Jd62wLicrN6dISkri3Xff5fbt20yZMoXevXuTT4Z+CyGEEEIIIXIJ86v03kk060qv1prff/+dVq1aERMTg6WlJatXr+b8+fMMGDBAEl4hhBBCCCFErmJWSa+OiyYx5h5WRcyv0qu1Zs2aNdSoUYOWLVty/Phxzp07Bxifs2Zra5vDEQohhBBCPL/GjBmDs7Oz6Zmke/fuzbFYpkyZQmxsbLrtI0eO5OOPP06z7dChQw/9uKdbt24xffr0x4oRjI9C6tOnDxUrVsTLy4uGDRuarpudnd1j959i5syZzJ8/H4BTp07h4eGBp6cn586do06dOo/V92+//Yanpyfu7u44OTnx3XffPYmQH1pkZCRNmzalcuXKNG3alJs3b2bYbvDgwbi4uODi4sLixYtN2+vXr4+HhwceHh6ULl2adu3apXnf/v37sbKyYtmyZaZtLVq0oFChQrRu3fqpnNOzZDZJr9JJJMUlQJLGyswqvbdu3aJ69eq0bduWyMhIZs2axdmzZ3F3d8/p0IQQQgghnnu7d+/mt99+4+DBgxw5coTNmzfz8ssv50gsBoMh06Q3ICAgTaIDsGjRIgICAh7qGI+S9CYmJqbb1qtXL4oUKcLZs2cJDQ1lzpw53Lhx46H6zY6+ffuaHqm5atUqXnvtNf766y8qVqzIrl27st2P1pqkpCTT64SEBN5++21+/fVXDh8+zF9//UXDhg0fK9b7j5Fd48aNw8/Pj7Nnz+Ln58e4cePStVm7di0HDx7k0KFD7N27l0mTJnH79m0AduzYwaFDhzh06BC1a9emQ4cOpvcZDAYGDx5Ms2bN0vQXHBzMggULHjrW55HZJL0WJJEYZzxdSzOo9BoMBg4ePAhAoUKFqF69OvPmzeP06dP07NkTa2vrHI5QCCGEEOLFEB4eTrFixbCxsQGgWLFilC5dGoDy5cubErkDBw6YkqIRI0bw5ptvUrt2bSpXrswPP/wAQEhICA0aNKBVq1Y4OjrSt29fUxL0yy+/4OrqiouLC4MHDzYd387Ojg8//BB3d3fGjBnDlStXaNSoEY0aNUoTZ5UqVShcuHCaKvSSJUsICAjg3LlztGjRAi8vL+rXr8+pU6cAuHr1Ku3bt8fd3R13d3d27drFkCFDOHfuHB4eHgQHB6O1Jjg4GBcXF1xdXU2JdUhICPXr18ff3x8nJ6c0sZw7d469e/cyevRoLCyMv4M7ODjQqlWrNO1iYmLw8/OjevXquLq6snr1agDu3LlDq1atcHd3T1O1HDJkCE5OTri5ufHRRx+ZrvWkSZNYt24dU6ZMYcaMGaZrk7qiPHHiRHx8fHBzc2P48OEAhIWF4ejoSPfu3XFxceHixYum9tHR0SQmJlK0qDF3sLGxwdHRMdPrBvDVV1+ZKq1TpkzJ9BgZxZKV1atXExgYCEBgYCCrVq1K1+bEiRM0aNAAKysr8ufPj5ubG+vXr0/T5vbt22zdujVNpXfq1Kl07NiREiVKpGnr5+dHgQIFHhjbi8BsFrKyxIAhzhIgV1d6DQYDixcvZvTo0Zw7d44LFy5QunRpZs2aldOhCSGEEEI8tvH7xnMq8tQT7bNqkaoMrjE40/3NmjVj1KhRVKlShSZNmtC5c2d8fX0f2O+RI0fYs2cPd+7cwdPT05Tw7du3jxMnTlCuXDlatGjBihUrqFOnDoMHDyY0NJTChQvTrFkzVq1aRbt27bhz5w41a9bkyy+/BGD27Nls27aNYsWKpTtmQEAAixYtombNmuzZs4ciRYpQuXJl/Pz8mDlzJpUrV2bv3r28++67bN26lf79++Pr68vKlSsxGAzExMQwbtw4jh07xqFDhwBYvnw5hw4d4vDhw9y4cQMfHx8aNGgAwMGDBzl27BgODg5p4jh+/DgeHh5YWlpmeY1sbW1ZuXIlBQsW5MaNG9SqVQt/f3/Wr19P6dKlWbt2LQBRUVFERESwcuVKTp06hVKKW7dupemrZcuW9O3bFzs7O1NCnGLjxo2cPXuWffv2obXG39+f7du388orr3D27FnmzZtHrVq10rynSJEi+Pv7U65cOfz8/GjdujUBAQFYWFhkeN1Sqtl79+5Fa03NmjXx9fWlcOHCaY6RWSwNGjSgZcuW/Pjjj6YPVVJcvXqVUqVKAfDSSy9x9erVdNfS3d2dkSNH8uGHHxIbG8u2bdvSfRixatUq/Pz8KFiwIACXL19m5cqVbNu2jf3792f5s3qRmU+lVyeRGJ9c6S2a+yq9iYmJzJ8/HycnJ7p164alpSULFiygZMmSOR2aEEIIIcQLzc7OjtDQUL7//nuKFy9O586dmTt37gPf17ZtW/LmzUuxYsVo1KgR+/btA6BGjRpUqFABS0tLAgIC2LlzJ/v376dhw4YUL14cKysrunXrxvbt2wGwtLSkY8eO2Yq1c+fOLFu2jKSkJNPQ5piYGHbt2kWnTp3w8PCgT58+hIeHA7B161beeecd03Hs7e3T9blz504CAgKwtLSkZMmS+Pr6mhKkGjVqpEt4H4bWmk8++QQ3NzeaNGnC5cuXuXr1Kq6urmzatInBgwezY8cO7O3tsbe3x9bWlrfeeosVK1Y81AKsGzduZOPGjXh6elK9enVOnTrF2bNnAShXrly6hDfFjz/+yJYtW6hRowaTJk2iZ8+eQMbXbefOnbRv3578+fNjZ2dHhw4d2LFjR7pjZBXLunXr0iW891NKoZRKt71Zs2a0bNmSOnXqEBAQQO3atdN96PDLL7+kGe7+wQcfMH78eFM1Prcys0qv8YdpVST3VXrDwsLo0aMHrq6uLF++nHbt2uX6m1cIIYQQ5ieriuzTZGlpScOGDWnYsCGurq7MmzePoKAgrKysTMOT4+Li0rzn/sQk5XVm2zNja2v7wIppipdffhkHBwf++OMPli9fzu7du0lKSqJQoUKmyu2TlD9//gy3Ozs7c/jwYQwGQ5axL1y4kOvXrxMaGoq1tTXly5cnLi6OKlWqcPDgQdatW8enn36Kn58fw4YNY9++fWzZsoVly5bx7bffsnXr1mzFqbXm448/pk+fPmm2h4WFZXoOKVxdXXF1deXNN9/EwcEhWx943C/1MTKLJSslS5YkPDycUqVKER4enm4ocoqhQ4cydOhQALp27UqVKlVM+27cuMG+fftYuXKladuBAwfo0qWLaf+6deuwsrJKt9DVi85ssiILUlV6CxfO4WgeX3x8PN999x39+vUDoFKlSuzbt4+//vqLDh06SMIrhBBCCPGEnD592lSJA+OKyOXKlQOMc3pDQ0MB4zDg1FavXk1cXBwRERGEhITg4+MDGIc3X7hwgaSkJBYvXky9evWoUaMGf/zxBzdu3MBgMPDLL79kOoS6QIECREdHZxpvQEAAAwcOpEKFCpQtW5aCBQvi4ODA0qVLAWPSdfjwYcA4b3PGjBmAcZpcVFRUuv7r16/P4sWLMRgMXL9+ne3bt1OjRo0sr1nFihXx9vZm+PDhaK0BY4KZMlw5RVRUFCVKlMDa2ppt27bxzz//AHDlyhXy5cvHG2+8QXBwMAcPHiQmJoaoqChatmzJ5MmTTeeQHc2bN2f27NnExMQAxmG9165dy/I9MTExhISEmF6n/rlndN3q16/PqlWriI2N5c6dO6xcuZL69es/kVj8/f2ZN28eAPPmzaNt27bp2hgMBiIiIgDj0PojR46kWZxq2bJltG7dOs1TWy5cuEBYWBhhYWG89tprTJ8+PdclvGBOSa82zum1tC+IsnpxC9xxcXFMmzaNSpUq0bdvX0JDQ7l79y4AXl5eD/ykUAghhBBCPJyYmBgCAwNNCyidOHGCESNGADB8+HAGDBiAt7d3uoqmm5sbjRo1olatWnz22WemYas+Pj7069ePatWq4eDgQPv27SlVqhTjxo2jUaNGuLu74+XllWFiA/D222/TokWLdAtZpejUqRPHjx9PM4x14cKFzJo1C3d3d5ydnU0LRn399dds27YNV1dXvLy8OHHiBEWLFqVu3bq4uLgQHBxM+/btcXNzw93dncaNGzNhwgReeumlB163H3/8katXr1KpUiVcXFwICgpKV6Hs1q0bBw4cwNXVlfnz51O1alUAjh49So0aNfDw8GDkyJF8+umnREdH07p1a9zc3KhXrx5fffXVA2NI0axZM7p27Urt2rVxdXXltddey/KDAzB+ODBhwgQcHR3x8PBg+PDhpipvRtetevXqBAUFUaNGDWrWrEmvXr3w9PR8qFhatmzJlStX0r1nyJAhbNq0icqVK7N582aGDBkCGCu1vXr1AoyrTdevXx8nJyfefvttfvrpJ6xS5T0Pu5J3/fr16dSpE1u2bKFs2bJs2LAh2+993qiUT15eZN7e3vrAgQOZ7q85pyNFDDf4YfE/xFtVpeLv6zNt+zzbvXs3r732GleuXKFu3boMHz6cJk2aSKIrhBBCiFzt5MmTD/2s2Zw2YsSIDBdUCgkJYdKkSfz22285FJkQz7+M/s4rpUK11t6P0t+LW/J8SBYkYYizwKpc+lXunmd37twhPDycSpUqUaVKFTw8PPjpp59o2LChJLtCCCGEEEII8QDmk/RqA4nxltgUK57ToWRLdHQ006dPZ9KkSZQrV479+/dTtGjRdPMghBBCCCHE8ydl+PP9UhbDEkI8O2Yzp9eSJAzxls/9ys1RUVGMGTOG8uXLM2TIELy9vfnmm2+kqiuEEEIIIYQQj8Bskl6rxEQM8QrLos930rto0SI+/fRT6tSpw969e/n999+pU6dOToclhBBCCCGEEC8ksxnebHfX+Pw0q6JFcziStCIiIpg8eTKVKlUiKCiIoKAgfHx8qF69ek6HJoQQQgghhBAvPLOp9Ba8mwiA5XMyvPnatWsMGTKE8uXLM2bMGP766y8AbGxsJOEVQgghhBBCiCfEbJLeAncMwPNR6Z0+fToODg5MmDCB1q1bc/ToUb7++uucDksIIYQQQmTC0tISDw8PnJ2dcXd358svvyQpKemR+ho2bBibN2/OdP/MmTOZP3/+o4YKGJ9z6+HhgYeHB0WKFMHBwQEPDw+aNGnyWP3e7/fff8fb2xsnJyc8PT358MMPAeNCXpMmTXpix0k93S84OBhnZ2eCg4Mf+1rFxsbSrVs3XF1dcXFxoV69esTExDyJkB/avHnzqFy5MpUrV2bevHkZtjl8+LDp+b5t2rTh9u3bAGzatAkvLy/Tc4O3bt1qek9oaCiurq5UqlSJ/v37k/LI2sjISJo2bUrlypVp2rQpN2/efPonmVO01i/8Hy8vL52VGrM76C8GVNMnHKvquPPns2z7tFy+fFlHRkZqrbX+7bff9BtvvKFPnjyZI7EIIYQQQrxITpw4kdMh6Pz585u+v3r1qvbz89PDhg3LwYiyLzAwUC9dujTd9oSEhMfq9+jRo7pChQqm32kTExP19OnTtdZaDx8+XE+cOPGx+s9MwYIFdWJi4iO99/5z/uKLL/TAgQNNr0+dOqXj4uIeK75Hua4RERHawcFBR0RE6MjISO3g4GDKHVLz9vbWISEhWmutZ82apT/99FOttdYHDx7Uly9f1lobfy6lS5c2vcfHx0fv3r1bJyUl6RYtWuh169ZprbUODg7WY8eO1VprPXbsWP2///3voeN+WjL6Ow8c0I+YL5pNpTen5vRevHiRfv36UaFCBdOnXa1atWLBggVUrVr1mcYihBBCCCEeX4kSJfj+++/59ttv0VpjMBgIDg7Gx8cHNzc3vvvuO1Pb8ePH4+rqiru7O0OGDAEgKCiIZcuWATBkyBCcnJxwc3Pjo48+AtJWSQ8dOkStWrVwc3Ojffv2pmpcw4YNGTx4MDVq1KBKlSrs2LEjW7E3bNiQDz74AG9vb77++mtCQ0Px9fXFy8uL5s2bEx4eDsC5c+do0aIFXl5e1K9fn1OnTqXra8KECQwdOtT0O62lpSXvvPNOunY//PADPj4+uLu707FjR2JjYwFYunQpLi4uuLu706BBAwCOHz9OjRo18PDwwM3NjbNnzwJgZ2cHgL+/PzExMXh5ebF48eI01yqzmIOCgujbty81a9bkf//7X5rYwsPDKVOmjOm1o6MjNjY2AMyfPx83Nzfc3d158803AQgLC6Nx48a4ubnh5+fHv//+m+ExsnP9UtuwYQNNmzalSJEiFC5cmKZNm7J+/fp07c6cOWO6Vk2bNmX58uUAeHp6Urp0aQCcnZ25e/cu8fHxhIeHc/v2bWrVqoVSiu7du7Nq1SoAVq9eTWBgIACBgYGm7bmReSxkpSH/HQ2WllgUKPBMDhkWFsa4ceOYPXs2YPyL0KtXr2dybCGEEEKI3Or/vviC+JNZJxAPy6ZaVV765JOHek+FChUwGAxcu3aN1atXY29vz/79+4mPj6du3bo0a9aMU6dOsXr1avbu3Uu+fPmIjIxM00dERAQrV67k1KlTKKW4detWuuN0796dqVOn4uvry7Bhwxg5ciRTpkwBIDExkX379rFu3TpGjhyZ5ZDp1O7du8eBAwdISEjA19eX1atXU7x4cRYvXszQoUOZPXs2b7/9NjNnzqRy5crs3buXd999N82QWYBjx46ZhjNnpUOHDvTu3RuATz/9lFmzZvH+++8zatQoNmzYQJkyZUznPnPmTAYMGEC3bt24d+8eBoMhTV9r1qzBzs6OQ4cOAWmfh5xVzJcuXWLXrl1YWlqm6a9nz540a9aMZcuW4efnR2BgIJUrV+b48eOMHj2aXbt2UaxYMdPP7v333ycwMJDAwEBmz55N//79Tcli6mP4+fllGMuaNWs4cOAAo0aNShPH5cuXefnll02vy5Yty+XLl9NdS2dnZ1avXk27du1YunQpFy9eTNdm+fLlVK9eHRsbGy5fvkzZsmUz7Pfq1auUKlUKgJdeeomrV6+m/+HlEmaR9CqSyH8XrArkfWbPu/3oo4/49ddf6dWrF4MHD6ZcuXLP5LhCCCGEEOLZ2rhxI0eOHDFVb6Oiojh79iybN2+mR48e5MuXD4Ai9y2oam9vj62tLW+99RatW7emdevWafZHRUVx69YtfH19AWM1rlOnTqb9HTp0AMDLy4uwsLBsx9u5c2cATp8+zbFjx2jatCkABoOBUqVKERMTw65du9IcKz4+Ptv93+/YsWN8+umn3Lp1i5iYGJo3bw5A3bp1CQoK4vXXXzedS+3atRkzZgyXLl2iQ4cOVK5cOVvHeFDMnTp1SpfwAnh4eHD+/Hk2btzI5s2b8fHxYffu3WzdupVOnTpRrFgx4L+f3e7du1mxYgUAb775ZprKccoxsorF398ff3//bJ1TRlIS7c8//xx/f3/y5MmTZv/x48cZPHgwGzdufKh+lVLPLE/KCWaR9FpiIF8sWNrnf2rHOHPmDF988QWffPIJVapUYcKECUyZMiXNJytCCCGEEOLxPGxF9mk5f/48lpaWlChRAq01U6dONSVzKTZs2JBlH1ZWVuzbt48tW7awbNkyvv3223TV1KykDMO1tLQkMTEx2+/Ln9/4O7HWGmdnZ3bv3p1m/+3btylUqJCpmpoZZ2dnQkNDcXd3z7JdUFAQq1atwt3dnblz5xISEgIYq7p79+5l7dq1eHl5ERoaSteuXalZsyZr166lZcuWfPfddzRu3PiB55SUlJRlzCnnnBE7Ozs6dOhAhw4dsLCwYN26demSyexIOcaDYslImTJlTNcFjFXjhg0bpmtXtWpVU0J75swZ1q5dm+Y97du3Z/78+VSsWNHU76VLl9K0SRnOXbJkScLDwylVqhTh4eGUKFEi2/G+aMxiTq8FSeSLVVgVKvjE+z558iRvvPEG1apVY8mSJYSGhgLGIS+S8AohhBBC5D7Xr1+nb9++9OvXD6UUzZs3Z8aMGSQkJADGZOTOnTs0bdqUOXPmmOaw3j+8OSYmhqioKFq2bMnkyZM5fPhwmv329vYULlzYNF93wYIFpqrvk+Do6Mj169dNSW9CQgLHjx+nYMGCODg4sHTpUsCYHN8fGxhXUf7iiy84c+YMYEz2Zs6cma5ddHQ0pUqVIiEhgYULF5q2nzt3jpo1azJq1CiKFy/OxYsXOX/+PBUqVKB///60bduWI0eOZOtcshvz/f7880/TPOl79+5x4sQJypUrR+PGjVm6dCkRERHAfz+7OnXqsGjRIgAWLlxI/fr1n0gszZs3Z+PGjdy8eZObN2+ycePGdB+igPGxp2C81qNHj6Zv374A3Lp1i1atWjFu3Djq1q1ral+qVCkKFizInj170Fozf/582rZtCxirzimrRM+bN8+0PTcyi6TXUhvIdxesChd6Yn1qrQkKCsLZ2ZmVK1fy4YcfcuHCBQICAp7YMYQQQgghxPPh7t27pkcWNWnShGbNmjF8+HAAevXqhZOTE9WrV8fFxYU+ffqQmJhIixYt8Pf3x9vbGw8Pj3SP8ImOjqZ169a4ublRr149vvrqq3THnTdvHsHBwbi5uXHo0CGGDRv2xM4pT548LFu2jMGDB+Pu7o6Hhwe7du0CjAndrFmzcHd3N80jvZ+bmxtTpkwhICCAatWq4eLiwvnz59O1+/zzz6lZsyZ169ZNs5BrcHCw6VFBderUwd3dnSVLluDi4oKHhwfHjh2je/fu2T6f7MR8v3PnzuHr64urqyuenp54e3vTsWNHnJ2dGTp0KL6+vri7uzNo0CAApk6dypw5c3Bzc2PBggWZPnY0s1jWrFmT4c+wSJEifPbZZ/j4+ODj48OwYcNMQ6p79erFgQMHAPjll1+oUqUKVatWpXTp0vTo0QOAb7/9lr///ptRo0aZHlWVkiBPnz6dXr16UalSJSpWrMirr74KGBdR27RpE5UrV2bz5s2mhdZyI6WTn9P0IvP29tYpN0JGGs9qzuTJ//BS2+aUHPN4z8M9ffo0jo6OAHz88ccopRg4cCDFixd/rH6FEEIIIUTGTp48SbVq1XI6DCHEM5LR33mlVKjW2vtR+jOLSq/tvQTyJCqsihZ75D4OHDhA27ZtqVq1Kn/88QcAY8eO5YsvvpCEVwghhBBCCCGeU2aR9NrfMc6vsCxe8qHfu3fvXlq1aoWPjw87duxg1KhRD5ysL4QQQgghhBDi+WAWqzcXvGt8vpdVydIP9b64uDjatGlDUlISX3zxBe+99x4FCz75xbCEEEIIIYQQQjwd5pH03jEu4W5ZoswD2/7xxx/MnTuXH3/8EVtbW3799VecnZ2xs7N72mEKIYQQQohMaK1z9XNEhRBGT2PNKbMY3lwwNrnSWyLj4c1aa7Zs2YKvry8NGzZk/fr1/P333wDUrFlTEl4hhBBCiBxka2tLRETEU/llWAjx/NBaExERga2t7RPt1ywqvXbJSa9l8rLfqf3f//0fHTt2ZNeuXZQuXZpvvvmGXr16kTdv3mcdphBCCCGEyEDZsmW5dOkS169fz+lQhBBPma2tLWXLln2ifT7zpFcp1QL4GrAEftRaj7tvvw0wH/ACIoDOWuuwxzlmgdgk4vOARfInBlprLly4QIUKFShevDh2dnZMnz6dHj16PPFPFYQQQgghxOOxtrbGwcEhp8MQQrygnmnSq5SyBKYBTYFLwH6l1Bqt9YlUzd4CbmqtKymlugDjgc6Pc1y72CTu5IWkpCTWrFnDqFGj+PfffwkLC8POzo4NGzY8TvdCCCGEEEIIIZ5Tz3pObw3gb631ea31PWAR0Pa+Nm2BecnfLwP81GOuWpDvjoHfY6Lx9PSkffv23L59m4kTJ2JjY/M43QohhBBCCCGEeM496+HNZYCLqV5fAmpm1kZrnaiUigKKAjce9aCXrt1lzInLVKmSn/nz5xMQEICVlVlMZxZCCCGEEEIIs/bCZn5KqbeBt5NfxiilTj/gLcXOnDlzo3v37nTv3v0pRyfEU1GMx/jwR4gcJveveNHJPSxedHIPixed46O+8VknvZeBl1O9Lpu8LaM2l5RSVoA9xgWt0tBafw98n90DK6UOaK29HzpiIZ4Tcg+LF5ncv+JFJ/eweNHJPSxedEqpA4/63mc9p3c/UFkp5aCUygN0Adbc12YNEJj8/WvAVi0PZRNCCCGEEEII8QieaaU3eY5uP2ADxkcWzdZaH1dKjQIOaK3XALOABUqpv4FIjImxEEIIIYQQQgjx0J75nF6t9Tpg3X3bhqX6Pg7o9BQOne2h0EI8p+QeFi8yuX/Fi07uYfGik3tYvOge+R5WMnJYCCGEEEIIIURu9azn9AohhBBCCCGEEM9Mrkt6lVItlFKnlVJ/K6WGZLDfRim1OHn/XqVU+RwIU4gMZeP+HaSUOqGUOqKU2qKUKpcTcQqRmQfdw6nadVRKaaWUrCQqnivZuYeVUq8n/1t8XCn187OOUYisZON3iVeUUtuUUn8l/z7RMifiFCIjSqnZSqlrSqljmexXSqlvku/vI0qp6tnpN1clvUopS2Aa8CrgBAQopZzua/YWcFNrXQmYDIx/tlEKkbFs3r9/Ad5aazdgGTDh2UYpROayeQ+jlCoADAD2PtsIhchadu5hpVRl4GOgrtbaGfjgWccpRGay+e/wp8ASrbUnxgVjpz/bKIXI0lygRRb7XwUqJ/95G5iRnU5zVdIL1AD+1lqf11rfAxYBbe9r0xaYl/z9MsBPKaWeYYxCZOaB96/WepvWOjb55R6Mz7oW4nmRnX+DAT7H+IFj3LMMTohsyM493BuYprW+CaC1vvaMYxQiK9m5hzVQMPl7e+DKM4xPiCxprbdjfIJPZtoC87XRHqCQUqrUg/rNbUlvGeBiqteXkrdl2EZrnQhEAUWfSXRCZC07929qbwG/P9WIhHg4D7yHk4chvay1XvssAxMim7Lz73AVoIpS6k+l1B6lVFYVCSGetezcwyOAN5RSlzA+UeX9ZxOaEE/Ew/6+DOTAI4uEEI9PKfUG4A345nQsQmSXUsoC+AoIyuFQhHgcVhiH1TXEONpmu1LKVWt9KyeDEuIhBABztdZfKqVqAwuUUi5a66ScDkyIpyW3VXovAy+nel02eVuGbZRSVhiHdUQ8k+iEyFp27l+UUk2AoYC/1jr+GcUmRHY86B4uALgAIUqpMKAWsEYWsxLPkez8O3wJWKO1TtBaXwDOYEyChXgeZOcefgtYAqC13g3YAsWeSXRCPL5s/b58v9yW9O4HKiulHJRSeTBOzl9zX5s1QGDy968BW7U8rFg8Hx54/yqlPIHvMCa8Mo9MPG+yvIe11lFa62Ja6/Ja6/IY56X7a60P5Ey4QqSTnd8jVmGs8qKUKoZxuPP5ZxijEFnJzj38L+AHoJSqhjHpvf5MoxTi0a0Buiev4lwLiNJahz/oTblqeLPWOlEp1Q/YAFgCs7XWx5VSo4ADWus1wCyMwzj+xjhJukvORSzEf7J5/04E7IClyeuv/au19s+xoIVIJZv3sBDPrWzewxuAZkqpE4ABCNZay4gx8VzI5j38IfCDUmogxkWtgqQAJJ4XSqlfMH6wWCx53vlwwBpAaz0T4zz0lsDfQCzQI1v9yj0uhBBCCCGEECK3ym3Dm4UQQgghhBBCCBNJeoUQQgghhBBC5FqS9AohhBBCCCGEyLUk6RVCCCGEEEIIkWtJ0iuEEEIIIYQQIteSpFcIIcRzRSkVpJTSmfxp8hD9hCml5j7FUO8/Xuo4E5VSF5RSc5RSZZ/wcconHyMo1bYgpVTPDNqmXMvyTzKGB8TXMINr8a9SarpSqvAj9vmBUqrDk45VCCGEechVz+kVQgiRq3QCLt237UROBPIQ5gLfYfz/1QMYCdRRSnlore8+oWOEA7WBc6m2BSUfc/Z9bdcmtw1/Qsd+GP2B/UA+wA8YDLwMtHmEvj4AdgIrnlRwQgghzIckvUIIIZ5Xh7TWf+d0EA/pstZ6T/L3O5VS0RgT4Vd5Qgmb1joe2PPAhsa214HrT+K4j+BkqmuxVSlVAuillHpJa/1/ORSTEEIIMyTDm4UQQrxQlFLNlFLrlFLhSqlYpdQxpdSHSinLB7zvJaXUPKXUFaVUfPL7f0tOxlLa5FNKjU8emnwv+etQpdSj/n+5P/lrpeT+Syml5iulbiTHcEQp9cbDxHn/8GalVAjgC9RNNaQ4JHlfmuHNSqm1SqmDGVybUsnDkAem2uaglFqolLqeHMchpVT7R7wOACnHfSXVMXyUUsuUUpeUUneVUqeVUl8opfKmahMGlAO6pTq/uan2uyul1iilbib38adSqv5jxCmEECKXkUqvEEKI55WlUir1/1Naa20AKgBbgKlAHOANjACKA0Oy6G8BxuQpGLgIlMQ47DYfQPKxNgBOwOfAUaAW8BlQBPjwEc7BIfnrLaVUfuAPoDDwSXIMbwALlFL5tNbfZyfODLwL/ARYAn2St93OpO0C4BellJPWOvVQ8a7JX38GUEq9DOwFrgEDMVaLOwPLlVLttNZrsnHu9ysPGICwVNteAQ5hrIZHA87AMIw/4y7JbdoD64DDGH/OJMeDUqo6sAP4C+gNxAJ9gc1KqTpa69BHiFMIIUQuI0mvEEKI59Wp+17/CdTTWs9M2aCUUhiTnjzAR0qpT7TWSZn0Vxv4RGu9MNW2pam+DwDqAb5a6+3J27YYD8FwpdR4rfW1B8SskpPnlDm9kzAmYr8BPYDKQCOtdUhy+9+VUiWB0UqpWclJ/YPiTENrfUIpdRuwSjWcODOrMSbEbwIfp9r+JrBRa301+fUIQGG8FhHJ2zYkJ8OjgOwkvRbJ1yIvxqT9HWBK6muotV6e8n3yz/LP5PjmK6Xe01pHaK3/UkrFAzcyOL+JwL9AY631veR+NgDHMH5Y0S4bcQohhMjlZHizEEKI51V7wCfVn7fANBT3O6XUP8A9IAEYDRQCSmTcFWAcahyslBqglHJNTrJSawH8A+xSSlml/AE2AtYYq74P8klyPHeB3cnft9RaXwEaYJzzG3Lfe37CWKV2ymacjyx5Ma1lGIcKKwCllCvgjrEKnKIFxupq1H3XYgPgrpQqmI3DbcB4/reBlcB2jNVrE6VUweTh5OeA+OT2CzAm3JWz6jx5CLQvxg8EklLFqIDNGK+3EEIIIUmvEEKI59YxrfWBVH9OJ8+tXQO0xpjoNsaYEI9Jfo9tFv11Tn7v/4AjwGWl1LBU83VLYBxWnHDfn33J+4tmI+bZyfF4AsW01m5a6z+S9xUh41WU/y/V/uzE+bgWYFxFuWHy6zcxDi1elapNCaA76a/FxOT92bkW72G8Fk2AxUArjNXX1OZgHI78DdA0uf17yfuy+lmC8XpZJvd5f5z9gMJP8JoJIYR4gcnwZiGEEC+Sihjn8L6ptf4pZaNS6oGPwUkeVvse8J5SyhEIxPhIoevADCACuAC8nkkXYdmIL1xrfSCTfZGAYwbbX0q1PztxPq4/MA4JfkMp9QfG+bzL7nukUgTGYePjM+njSjaOcyblWiiltmKcm/yxUmqO1vqiUsoWaAuM0Fp/nfKm5MpzdtwCkoBpwPyMGmQx1F0IIYQZkaRXCCHEiyRlMaeElA1KKWug28N0orU+DXyilOoLuCRvXg90BGK01vfPJ34S/gA6KaXqaq3/TLW9K8YFo9I9gziTODMSDxTIThBaa62U+gljNXQlUIa0Q5vBeC1qA8efxPOFk485EOMKzkMwJvU2GCu1Cfc1D8qgi3iMc4NT93lHKbUD49Dsg5LgCiGEyIwkvUIIIV4kJzHOux2jlDJgTJgGZv0WUErZY5znuRDjAlkJGKuMhTHO2SV5Xw+Mi1d9iXG14DwYq8v+QDutdexjxD4XGACsUEoNBS5hTNabAn201oZsxpmRE8C7SqnOwDkgOjlhzswCjPOPZ2Ks+obct38YxmHd25VS32KschfGmHhX0Fr3zN4p/0drfUgptRx4Syk1Rmt9RSm1B/hQKRUO3AB6YkzCMzq/+kqp1hiHg9/QWocBgzDOFd6glJqFcfh4MaA6YKm1zmo1byGEEGZCkl4hhBAvDK31PaVUO+BbjENaIzHOo/0X+CGLt8ZhrDL2xjhvNwk4DXTTWq9O7jtBKdUcYyXybYyPG7qDMYlci3HRrMeJ/Y5SyheYAIzDWJk9Tdqh2g+MMxPjMQ6d/hGww1hVbphFLKeUUgcwDhUfq7XW9+3/VymV8iioLzAutBWBcVXkedk/63SGAR2AwRg/AAjAOGR7GsbFv5Ykb//tvvd9jPHnuwRjxXceEKS1PqiU8gGGY5wXbI9xGPhBjAm9EEIIgbrv/zkhhBBCCCGEECLXkFUNhRBCCCGEEELkWpL0CiGEEEIIIYTItSTpFUIIIYQQQgiRa0nSK4QQQgghhBAi15KkVwghhBBCCCFEriVJrxBCCCGEEEKIXEuSXiGEEEIIIYQQuZYkvUIIIYQQQgghci1JeoUQQgghhBBC5Fr/DyWl8DpWLpRDAAAAAElFTkSuQmCC\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": "code",
   "execution_count": 26,
   "id": "a5e66e78",
   "metadata": {},
   "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": "markdown",
   "id": "1350d998",
   "metadata": {},
   "source": [
    "# <p style=\"color:red\">Using Xgboost </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "536cbcff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"creditcard.csv\")\n",
    "target = 'Class'\n",
    "predictors = ['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\\\n",
    "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19',\\\n",
    "       'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28',\\\n",
    "       'Amount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c7f53e7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df,test_df = train_test_split(df, test_size=0.20, random_state=42, shuffle=True )\n",
    "train_df, valid_df = train_test_split(train_df, test_size=0.2, random_state=42, shuffle=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d342aa51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the train and valid datasets\n",
    "dtrain = xgb.DMatrix(train_df[predictors], train_df[target].values)\n",
    "dvalid = xgb.DMatrix(valid_df[predictors], valid_df[target].values)\n",
    "dtest = xgb.DMatrix(test_df[predictors], test_df[target].values)\n",
    "\n",
    "#What to monitor (in this case, **train** and **valid**)\n",
    "watchlist = [(dtrain, 'train'), (dvalid, 'valid')]\n",
    "\n",
    "# Set xgboost parameters\n",
    "params = {}\n",
    "params['objective'] = 'binary:logistic'\n",
    "params['eta'] = 0.039\n",
    "params['silent'] = True\n",
    "params['max_depth'] = 2\n",
    "params['subsample'] = 0.8\n",
    "params['colsample_bytree'] = 0.9\n",
    "params['eval_metric'] = 'auc'\n",
    "params['random_state'] = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d721f912",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:35:01] WARNING: C:/buildkite-agent/builds/buildkite-windows-cpu-autoscaling-group-i-0fc7796c793e6356f-1/xgboost/xgboost-ci-windows/src/learner.cc:767: \n",
      "Parameters: { \"silent\" } are not used.\n",
      "\n",
      "[0]\ttrain-auc:0.88014\tvalid-auc:0.87486\n",
      "[50]\ttrain-auc:0.91951\tvalid-auc:0.92951\n",
      "[100]\ttrain-auc:0.93605\tvalid-auc:0.93596\n",
      "[150]\ttrain-auc:0.97665\tvalid-auc:0.97069\n",
      "[200]\ttrain-auc:0.98816\tvalid-auc:0.98154\n",
      "[214]\ttrain-auc:0.98958\tvalid-auc:0.98045\n"
     ]
    }
   ],
   "source": [
    "model = xgb.train(params, \n",
    "                dtrain, \n",
    "                1000, \n",
    "                watchlist, \n",
    "                early_stopping_rounds=50, \n",
    "                maximize=True, \n",
    "                verbose_eval=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf4d13ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax) = plt.subplots(ncols=1, figsize=(8,5))\n",
    "xgb.plot_importance(model, height=0.8, title=\"Features importance (XGBoost)\", ax=ax, color=\"green\") \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e6d6cb88",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = model.predict(dtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "335f8fa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.972840048723485"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(test_df[target].values, preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c377b1b7",
   "metadata": {},
   "source": [
    "## <p style=\"color:red\"> Using IsolationForest Algorithm  and LocalOutlierFactor Algorithm from Scikit learn </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68e9a8e6",
   "metadata": {},
   "source": [
    "### <p style=\"color:Maroon\">Isolation Forest Algorithm : </p>\n",
    "\n",
    "One of the newest techniques to detect anomalies is called Isolation Forests. The algorithm is based on the fact that anomalies are data points that are few and different. As a result of these properties, anomalies are susceptible to a mechanism called isolation.\n",
    "\n",
    "This method is highly useful and is fundamentally different from all existing methods. It introduces the use of isolation as a more effective and efficient means to detect anomalies than the commonly used basic distance and density measures. Moreover, this method is an algorithm with a low linear time complexity and a small memory requirement. It builds a good performing model with a small number of trees using small sub-samples of fixed size, regardless of the size of a data set.\n",
    "\n",
    "Typical machine learning methods tend to work better when the patterns they try to learn are balanced, meaning the same amount of good and bad behaviors are present in the dataset.\n",
    "\n",
    "How Isolation Forests Work\n",
    "The Isolation Forest algorithm isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature. The logic argument goes: isolating anomaly observations is easier because only a few conditions are needed to separate those cases from the normal observations. On the other hand, isolating normal observations require more conditions. Therefore, an anomaly score can be calculated as the number of conditions required to separate a given observation.\n",
    "\n",
    "The way that the algorithm constructs the separation is by first creating isolation trees, or random decision trees. Then, the score is calculated as the path length to isolate the observation.\n",
    "\n",
    "\n",
    "### <p style=\"color:Maroon\"> Local Outlier Factor(LOF) Algorithm </p>\n",
    "The LOF algorithm is an unsupervised outlier detection method which computes the local density deviation of a given data point with respect to its neighbors. It considers as outlier samples that have a substantially lower density than their neighbors.\n",
    "\n",
    "The number of neighbors considered, (parameter n_neighbors) is typically chosen 1) greater than the minimum number of objects a cluster has to contain, so that other objects can be local outliers relative to this cluster, and 2) smaller than the maximum number of close by objects that can potentially be local outliers. In practice, such informations are generally not available, and taking n_neighbors=20 appears to work well in general."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4e11c040",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import classification_report,accuracy_score\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "from sklearn.svm import OneClassSVM\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 14, 8\n",
    "RANDOM_SEED = 42\n",
    "LABELS = [\"Normal\", \"Fraud\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7ffb5256",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('creditcard.csv',sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6cc27433",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Get the Fraud and the normal dataset \n",
    "\n",
    "fraud = data[data['Class']==1]\n",
    "\n",
    "normal = data[data['Class']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "01d6d441",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(492, 31) (284315, 31)\n"
     ]
    }
   ],
   "source": [
    "print(fraud.shape,normal.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0d644940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     492.000000\n",
       "mean      122.211321\n",
       "std       256.683288\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         9.250000\n",
       "75%       105.890000\n",
       "max      2125.870000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## We need to analyze more amount of information from the transaction data\n",
    "#How different are the amount of money used in different transaction classes?\n",
    "fraud.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0d272e52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    284315.000000\n",
       "mean         88.291022\n",
       "std         250.105092\n",
       "min           0.000000\n",
       "25%           5.650000\n",
       "50%          22.000000\n",
       "75%          77.050000\n",
       "max       25691.160000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ae89eb76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28481, 31)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Take some sample of the data\n",
    "\n",
    "data1= data.sample(frac = 0.1,random_state=1)\n",
    "\n",
    "data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6a636b8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8d5902f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Determine the number of fraud and valid transactions in the dataset\n",
    "\n",
    "Fraud = data1[data1['Class']==1]\n",
    "\n",
    "Valid = data1[data1['Class']==0]\n",
    "\n",
    "outlier_fraction = len(Fraud)/float(len(Valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fd605695",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0017234102419808666\n",
      "Fraud Cases : 49\n",
      "Valid Cases : 28432\n"
     ]
    }
   ],
   "source": [
    "print(outlier_fraction)\n",
    "\n",
    "print(\"Fraud Cases : {}\".format(len(Fraud)))\n",
    "\n",
    "print(\"Valid Cases : {}\".format(len(Valid)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7c9efa6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28481, 30)\n",
      "(28481,)\n"
     ]
    }
   ],
   "source": [
    "#Create independent and Dependent Features\n",
    "columns = data1.columns.tolist()\n",
    "# Filter the columns to remove data we do not want \n",
    "columns = [c for c in columns if c not in [\"Class\"]]\n",
    "# Store the variable we are predicting \n",
    "target = \"Class\"\n",
    "# Define a random state \n",
    "state = np.random.RandomState(42)\n",
    "X = data1[columns]\n",
    "Y = data1[target]\n",
    "X_outliers = state.uniform(low=0, high=1, size=(X.shape[0], X.shape[1]))\n",
    "# Print the shapes of X & Y\n",
    "print(X.shape)\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f7f9921f",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Define the outlier detection methods\n",
    "\n",
    "classifiers = {\n",
    "    \"Isolation Forest\":IsolationForest(n_estimators=100, max_samples=len(X), \n",
    "                                       contamination=outlier_fraction,random_state=state, verbose=0),\n",
    "    \"Local Outlier Factor\":LocalOutlierFactor(n_neighbors=20, algorithm='auto', \n",
    "                                              leaf_size=30, metric='minkowski',\n",
    "                                              p=2, metric_params=None, contamination=outlier_fraction),\n",
    "    \"Support Vector Machine\":OneClassSVM(kernel='rbf', degree=3, gamma=0.1,nu=0.05, \n",
    "                                         max_iter=-1)\n",
    "   \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2acb80a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(classifiers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cac87c7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isolation Forest: 73\n",
      "Accuracy Score :\n",
      "0.9974368877497279\n",
      "Classification Report :\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     28432\n",
      "           1       0.26      0.27      0.26        49\n",
      "\n",
      "    accuracy                           1.00     28481\n",
      "   macro avg       0.63      0.63      0.63     28481\n",
      "weighted avg       1.00      1.00      1.00     28481\n",
      "\n",
      "Local Outlier Factor: 97\n",
      "Accuracy Score :\n",
      "0.9965942207085425\n",
      "Classification Report :\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     28432\n",
      "           1       0.02      0.02      0.02        49\n",
      "\n",
      "    accuracy                           1.00     28481\n",
      "   macro avg       0.51      0.51      0.51     28481\n",
      "weighted avg       1.00      1.00      1.00     28481\n",
      "\n",
      "Support Vector Machine: 8516\n",
      "Accuracy Score :\n",
      "0.7009936448860644\n",
      "Classification Report :\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.70      0.82     28432\n",
      "           1       0.00      0.37      0.00        49\n",
      "\n",
      "    accuracy                           0.70     28481\n",
      "   macro avg       0.50      0.53      0.41     28481\n",
      "weighted avg       1.00      0.70      0.82     28481\n",
      "\n"
     ]
    }
   ],
   "source": [
    "n_outliers = len(Fraud)\n",
    "for i, (clf_name,clf) in enumerate(classifiers.items()):\n",
    "    #Fit the data and tag outliers\n",
    "    if clf_name == \"Local Outlier Factor\":\n",
    "        y_pred = clf.fit_predict(X)\n",
    "        scores_prediction = clf.negative_outlier_factor_\n",
    "    elif clf_name == \"Support Vector Machine\":\n",
    "        clf.fit(X)\n",
    "        y_pred = clf.predict(X)\n",
    "    else:    \n",
    "        clf.fit(X)\n",
    "        scores_prediction = clf.decision_function(X)\n",
    "        y_pred = clf.predict(X)\n",
    "    #Reshape the prediction values to 0 for Valid transactions , 1 for Fraud transactions\n",
    "    y_pred[y_pred == 1] = 0\n",
    "    y_pred[y_pred == -1] = 1\n",
    "    n_errors = (y_pred != Y).sum()\n",
    "    # Run Classification Metrics\n",
    "    print(\"{}: {}\".format(clf_name,n_errors))\n",
    "    print(\"Accuracy Score :\")\n",
    "    print(accuracy_score(Y,y_pred))\n",
    "    print(\"Classification Report :\")\n",
    "    print(classification_report(Y,y_pred))"
   ]
  }
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