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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Importation des librairies nécessaires"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "# SCIKIT-LEARN: SELECTION DE VARIABLES\n",
+ "\n",
+ "from sklearn.feature_selection import SelectKBest\n",
+ "from sklearn.feature_selection import chi2, f_classif\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "\n",
+ "# SCIKIT-LEARN: PRE-PROCESSING\n",
+ "from sklearn.preprocessing import LabelEncoder, OrdinalEncoder # Encodage des variables catégorielles ordinales\n",
+ "from sklearn.preprocessing import LabelBinarizer, OneHotEncoder # Encodage des variables catégorielles nominales\n",
+ "from sklearn.preprocessing import StandardScaler # Normalisation des variables numériques\n",
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "from sklearn.preprocessing import RobustScaler\n",
+ "from sklearn.preprocessing import label_binarize\n",
+ "from sklearn.compose import ColumnTransformer\n",
+ "from sklearn.impute import SimpleImputer # Imputation\n",
+ "from sklearn.impute import KNNImputer \n",
+ "from sklearn_pandas import DataFrameMapper\n",
+ "\n",
+ "# SCIKIT-LEARN: MODELES\n",
+ "from sklearn.linear_model import LogisticRegression, RidgeClassifier, Lasso, ElasticNet\n",
+ "from sklearn.metrics import f1_score, accuracy_score, classification_report, confusion_matrix, recall_score, precision_score, roc_curve, auc\n",
+ "\n",
+ "# SMOTE\n",
+ "from imblearn.over_sampling import SMOTE\n",
+ "\n",
+ "# SCIKIT-LEARN: VALIDATION CROISEE + OPTIMISATION\n",
+ "from sklearn.model_selection import train_test_split # Séparer en données train et test\n",
+ "from sklearn.model_selection import cross_val_score # Validation croisée pour comparison entre modèles\n",
+ "from sklearn.model_selection import validation_curve # Courbe de validation : visulaisr des scores lors du choix d'un hyper-paramètre\n",
+ "from sklearn.model_selection import GridSearchCV # Tester plusieurs hyper-paramètres\n",
+ "from sklearn.model_selection import learning_curve # Courbe d'apprentissage : visualisation des scores du train et du validation sets en fonction des quanitiés des données\n",
+ " \n",
+ "## EVALUATION\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "from sklearn.metrics import f1_score\n",
+ "from sklearn.metrics import confusion_matrix\n",
+ "from sklearn.metrics import ConfusionMatrixDisplay\n",
+ "from sklearn.metrics import classification_report\n",
+ "\n",
+ "# SCHIKIT-LEARN: PIPELINE AND TRANSFORMATEURll\n",
+ "from sklearn.pipeline import make_pipeline\n",
+ "from sklearn.compose import make_column_transformer\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "\n",
+ "# YELLOWBRICK\n",
+ "from yellowbrick.model_selection import LearningCurve\n",
+ "from yellowbrick.model_selection import ValidationCurve"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Importation des modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Configuration du chemin d'accès\n",
+ "import sys\n",
+ "import os\n",
+ "sys.path.append(os.path.abspath(os.path.join('..', 'src')))\n",
+ "\n",
+ "# Importations des modules nécessaires\n",
+ "import data_loader\n",
+ "import preprocessing\n",
+ "import modeling\n",
+ "import evaluation\n",
+ "import cleaning"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Chargement des données"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Distribution des classes dans le dataset équilibré :\n",
+ "SMK_stat_type_cd\n",
+ "1.0 174951\n",
+ "2.0 174951\n",
+ "3.0 174951\n",
+ "Name: count, dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "data_path = '../data/smoking_driking_dataset_Ver01.csv'\n",
+ "target_column = 'SMK_stat_type_cd' \n",
+ "output_path = '../data/balanced_smoking_drinking_dataset.csv'\n",
+ "\n",
+ "# Équilibrer le dataset\n",
+ "df_balanced = data_loader.balance_dataset(data_path, target_column, output_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Nettoyage des données"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Type des variables"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "intToFloat = ['age', 'height', 'weight']\n",
+ "floatToInt = ['hear_left', 'hear_right', 'urine_protein', 'SMK_stat_type_cd']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sex</th>\n",
+ " <th>age</th>\n",
+ " <th>height</th>\n",
+ " <th>weight</th>\n",
+ " <th>waistline</th>\n",
+ " <th>sight_left</th>\n",
+ " <th>sight_right</th>\n",
+ " <th>hear_left</th>\n",
+ " <th>hear_right</th>\n",
+ " <th>SBP</th>\n",
+ " <th>...</th>\n",
+ " <th>LDL_chole</th>\n",
+ " <th>triglyceride</th>\n",
+ " <th>hemoglobin</th>\n",
+ " <th>urine_protein</th>\n",
+ " <th>serum_creatinine</th>\n",
+ " <th>SGOT_AST</th>\n",
+ " <th>SGOT_ALT</th>\n",
+ " <th>gamma_GTP</th>\n",
+ " <th>SMK_stat_type_cd</th>\n",
+ " <th>DRK_YN</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Female</td>\n",
+ " <td>70.0</td>\n",
+ " <td>155.0</td>\n",
+ " <td>40.0</td>\n",
+ " <td>61.0</td>\n",
+ " <td>0.9</td>\n",
+ " <td>0.9</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>124.0</td>\n",
+ " <td>...</td>\n",
+ " <td>141.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>13.2</td>\n",
+ " <td>2</td>\n",
+ " <td>0.8</td>\n",
+ " <td>30.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>24.0</td>\n",
+ " <td>1</td>\n",
+ " <td>N</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Male</td>\n",
+ " <td>75.0</td>\n",
+ " <td>160.0</td>\n",
+ " <td>65.0</td>\n",
+ " <td>82.0</td>\n",
+ " <td>0.5</td>\n",
+ " <td>0.5</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>177.0</td>\n",
+ " <td>...</td>\n",
+ " <td>88.0</td>\n",
+ " <td>116.0</td>\n",
+ " <td>13.3</td>\n",
+ " <td>4</td>\n",
+ " <td>0.9</td>\n",
+ " <td>18.0</td>\n",
+ " <td>28.0</td>\n",
+ " <td>29.0</td>\n",
+ " <td>1</td>\n",
+ " <td>N</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>Female</td>\n",
+ " <td>45.0</td>\n",
+ " <td>150.0</td>\n",
+ " <td>55.0</td>\n",
+ " <td>81.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>175.0</td>\n",
+ " <td>...</td>\n",
+ " <td>201.0</td>\n",
+ " <td>104.0</td>\n",
+ " <td>13.8</td>\n",
+ " <td>1</td>\n",
+ " <td>1.0</td>\n",
+ " <td>29.0</td>\n",
+ " <td>25.0</td>\n",
+ " <td>18.0</td>\n",
+ " <td>1</td>\n",
+ " <td>N</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>Female</td>\n",
+ " <td>70.0</td>\n",
+ " <td>155.0</td>\n",
+ " <td>65.0</td>\n",
+ " <td>85.0</td>\n",
+ " <td>0.8</td>\n",
+ " <td>0.7</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>166.0</td>\n",
+ " <td>...</td>\n",
+ " <td>147.0</td>\n",
+ " <td>169.0</td>\n",
+ " <td>11.4</td>\n",
+ " <td>1</td>\n",
+ " <td>0.9</td>\n",
+ " <td>28.0</td>\n",
+ " <td>24.0</td>\n",
+ " <td>20.0</td>\n",
+ " <td>1</td>\n",
+ " <td>Y</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>Female</td>\n",
+ " <td>45.0</td>\n",
+ " <td>155.0</td>\n",
+ " <td>55.0</td>\n",
+ " <td>75.5</td>\n",
+ " <td>0.7</td>\n",
+ " <td>0.9</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>95.0</td>\n",
+ " <td>...</td>\n",
+ " <td>123.0</td>\n",
+ " <td>176.0</td>\n",
+ " <td>12.7</td>\n",
+ " <td>1</td>\n",
+ " <td>0.8</td>\n",
+ " <td>23.0</td>\n",
+ " <td>23.0</td>\n",
+ " <td>29.0</td>\n",
+ " <td>1</td>\n",
+ " <td>N</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>524848</th>\n",
+ " <td>Male</td>\n",
+ " <td>35.0</td>\n",
+ " <td>170.0</td>\n",
+ " <td>65.0</td>\n",
+ " <td>80.2</td>\n",
+ " <td>0.7</td>\n",
+ " <td>0.6</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>120.0</td>\n",
+ " <td>...</td>\n",
+ " <td>55.0</td>\n",
+ " <td>51.0</td>\n",
+ " <td>14.1</td>\n",
+ " <td>1</td>\n",
+ " <td>0.5</td>\n",
+ " <td>24.0</td>\n",
+ " <td>31.0</td>\n",
+ " <td>47.0</td>\n",
+ " <td>3</td>\n",
+ " <td>Y</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>524849</th>\n",
+ " <td>Male</td>\n",
+ " <td>40.0</td>\n",
+ " <td>175.0</td>\n",
+ " <td>60.0</td>\n",
+ " <td>74.0</td>\n",
+ " <td>1.2</td>\n",
+ " <td>0.8</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>122.0</td>\n",
+ " <td>...</td>\n",
+ " <td>109.0</td>\n",
+ " <td>125.0</td>\n",
+ " <td>14.8</td>\n",
+ " <td>1</td>\n",
+ " <td>1.2</td>\n",
+ " <td>22.0</td>\n",
+ " <td>17.0</td>\n",
+ " <td>69.0</td>\n",
+ " <td>3</td>\n",
+ " <td>Y</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>524850</th>\n",
+ " <td>Male</td>\n",
+ " <td>30.0</td>\n",
+ " <td>165.0</td>\n",
+ " <td>75.0</td>\n",
+ " <td>84.0</td>\n",
+ " <td>0.9</td>\n",
+ " <td>1.2</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>126.0</td>\n",
+ " <td>...</td>\n",
+ " <td>139.0</td>\n",
+ " <td>240.0</td>\n",
+ " <td>16.5</td>\n",
+ " <td>1</td>\n",
+ " <td>0.5</td>\n",
+ " <td>37.0</td>\n",
+ " <td>57.0</td>\n",
+ " <td>123.0</td>\n",
+ " <td>3</td>\n",
+ " <td>Y</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>524851</th>\n",
+ " <td>Male</td>\n",
+ " <td>30.0</td>\n",
+ " <td>165.0</td>\n",
+ " <td>70.0</td>\n",
+ " <td>83.5</td>\n",
+ " <td>1.2</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>160.0</td>\n",
+ " <td>...</td>\n",
+ " <td>71.0</td>\n",
+ " <td>51.0</td>\n",
+ " <td>15.6</td>\n",
+ " <td>1</td>\n",
+ " <td>0.8</td>\n",
+ " <td>23.0</td>\n",
+ " <td>25.0</td>\n",
+ " <td>75.0</td>\n",
+ " <td>3</td>\n",
+ " <td>Y</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>524852</th>\n",
+ " <td>Male</td>\n",
+ " <td>55.0</td>\n",
+ " <td>175.0</td>\n",
+ " <td>70.0</td>\n",
+ " <td>84.0</td>\n",
+ " <td>0.7</td>\n",
+ " <td>1.2</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>130.0</td>\n",
+ " <td>...</td>\n",
+ " <td>99.0</td>\n",
+ " <td>214.0</td>\n",
+ " <td>16.3</td>\n",
+ " <td>1</td>\n",
+ " <td>1.1</td>\n",
+ " <td>19.0</td>\n",
+ " <td>27.0</td>\n",
+ " <td>25.0</td>\n",
+ " <td>3</td>\n",
+ " <td>Y</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>524853 rows × 24 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " sex age height weight waistline sight_left sight_right \\\n",
+ "0 Female 70.0 155.0 40.0 61.0 0.9 0.9 \n",
+ "1 Male 75.0 160.0 65.0 82.0 0.5 0.5 \n",
+ "2 Female 45.0 150.0 55.0 81.0 1.0 1.0 \n",
+ "3 Female 70.0 155.0 65.0 85.0 0.8 0.7 \n",
+ "4 Female 45.0 155.0 55.0 75.5 0.7 0.9 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "524848 Male 35.0 170.0 65.0 80.2 0.7 0.6 \n",
+ "524849 Male 40.0 175.0 60.0 74.0 1.2 0.8 \n",
+ "524850 Male 30.0 165.0 75.0 84.0 0.9 1.2 \n",
+ "524851 Male 30.0 165.0 70.0 83.5 1.2 1.0 \n",
+ "524852 Male 55.0 175.0 70.0 84.0 0.7 1.2 \n",
+ "\n",
+ " hear_left hear_right SBP ... LDL_chole triglyceride \\\n",
+ "0 1 1 124.0 ... 141.0 63.0 \n",
+ "1 2 2 177.0 ... 88.0 116.0 \n",
+ "2 1 1 175.0 ... 201.0 104.0 \n",
+ "3 1 1 166.0 ... 147.0 169.0 \n",
+ "4 1 1 95.0 ... 123.0 176.0 \n",
+ "... ... ... ... ... ... ... \n",
+ "524848 1 1 120.0 ... 55.0 51.0 \n",
+ "524849 1 1 122.0 ... 109.0 125.0 \n",
+ "524850 1 1 126.0 ... 139.0 240.0 \n",
+ "524851 1 1 160.0 ... 71.0 51.0 \n",
+ "524852 1 1 130.0 ... 99.0 214.0 \n",
+ "\n",
+ " hemoglobin urine_protein serum_creatinine SGOT_AST SGOT_ALT \\\n",
+ "0 13.2 2 0.8 30.0 9.0 \n",
+ "1 13.3 4 0.9 18.0 28.0 \n",
+ "2 13.8 1 1.0 29.0 25.0 \n",
+ "3 11.4 1 0.9 28.0 24.0 \n",
+ "4 12.7 1 0.8 23.0 23.0 \n",
+ "... ... ... ... ... ... \n",
+ "524848 14.1 1 0.5 24.0 31.0 \n",
+ "524849 14.8 1 1.2 22.0 17.0 \n",
+ "524850 16.5 1 0.5 37.0 57.0 \n",
+ "524851 15.6 1 0.8 23.0 25.0 \n",
+ "524852 16.3 1 1.1 19.0 27.0 \n",
+ "\n",
+ " gamma_GTP SMK_stat_type_cd DRK_YN \n",
+ "0 24.0 1 N \n",
+ "1 29.0 1 N \n",
+ "2 18.0 1 N \n",
+ "3 20.0 1 Y \n",
+ "4 29.0 1 N \n",
+ "... ... ... ... \n",
+ "524848 47.0 3 Y \n",
+ "524849 69.0 3 Y \n",
+ "524850 123.0 3 Y \n",
+ "524851 75.0 3 Y \n",
+ "524852 25.0 3 Y \n",
+ "\n",
+ "[524853 rows x 24 columns]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cleaning.convertType(df_balanced, intToFloat, floatToInt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "sex object\n",
+ "age float64\n",
+ "height float64\n",
+ "weight float64\n",
+ "waistline float64\n",
+ "sight_left float64\n",
+ "sight_right float64\n",
+ "hear_left int64\n",
+ "hear_right int64\n",
+ "SBP float64\n",
+ "DBP float64\n",
+ "BLDS float64\n",
+ "tot_chole float64\n",
+ "HDL_chole float64\n",
+ "LDL_chole float64\n",
+ "triglyceride float64\n",
+ "hemoglobin float64\n",
+ "urine_protein int64\n",
+ "serum_creatinine float64\n",
+ "SGOT_AST float64\n",
+ "SGOT_ALT float64\n",
+ "gamma_GTP float64\n",
+ "SMK_stat_type_cd int64\n",
+ "DRK_YN object\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_balanced.dtypes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cont_features = df_balanced.select_dtypes(include=['float64']).columns\n",
+ "cat_features = df_balanced.select_dtypes(include=['object', 'int64']).columns\n",
+ "cat_features = cat_features.drop(['SMK_stat_type_cd', 'DRK_YN'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(Index(['age', 'height', 'weight', 'waistline', 'sight_left', 'sight_right',\n",
+ " 'SBP', 'DBP', 'BLDS', 'tot_chole', 'HDL_chole', 'LDL_chole',\n",
+ " 'triglyceride', 'hemoglobin', 'serum_creatinine', 'SGOT_AST',\n",
+ " 'SGOT_ALT', 'gamma_GTP'],\n",
+ " dtype='object'),\n",
+ " Index(['sex', 'hear_left', 'hear_right', 'urine_protein'], dtype='object'))"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cont_features, cat_features"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Séparation données train / test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X = df_balanced.drop(columns=['SMK_stat_type_cd', 'DRK_YN'])\n",
+ "y = df_balanced['SMK_stat_type_cd']\n",
+ "\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(SMK_stat_type_cd\n",
+ " 1 139961\n",
+ " 2 139961\n",
+ " 3 139960\n",
+ " Name: count, dtype: int64,\n",
+ " SMK_stat_type_cd\n",
+ " 3 34991\n",
+ " 1 34990\n",
+ " 2 34990\n",
+ " Name: count, dtype: int64)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_train.value_counts(), y_test.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Sélection de variables"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Validation croisée pour avoir la meilleure valeur de k features à retenir"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best k: 22\n",
+ "Best cross-validation score: 0.645635760602923\n"
+ ]
+ }
+ ],
+ "source": [
+ "def preprocess_data_without_selecting(X_train, X_test, cont_features, cat_features):\n",
+ " \"\"\"\"\n",
+ " Renvoie les données prétraitées pour l'entraînement et le test, ainsi que le preprocessor utilisé.\n",
+ " \"\"\"\n",
+ " \n",
+ " # Preprocess numerical features\n",
+ " cont_preprocessor = make_pipeline(StandardScaler())\n",
+ " \n",
+ " # Preprocess categorical features\n",
+ " cat_preprocessor = make_pipeline(OrdinalEncoder())\n",
+ " \n",
+ " preprocessor = make_column_transformer((cont_preprocessor, cont_features),\n",
+ " (cat_preprocessor, cat_features),\n",
+ " remainder='drop')\n",
+ " \n",
+ " X_train_prep = preprocessor.fit_transform(X_train)\n",
+ " X_test_prep = preprocessor.transform(X_test)\n",
+ " \n",
+ " return X_train_prep, X_test_prep, preprocessor\n",
+ "\n",
+ "def plot_and_select_best_k(X_train, y_train, preprocessor, k_values):\n",
+ " \"\"\"\n",
+ " Trace la courbe de validation pour SelectKBest et renvoie le meilleur k et le meilleur score.\n",
+ " Modèle utilisé : LogisticRegression simple\n",
+ " \"\"\"\n",
+ "\n",
+ " scores = []\n",
+ " best_k = None\n",
+ " best_score = -np.inf\n",
+ "\n",
+ " for k in k_values:\n",
+ " selector = SelectKBest(score_func=f_classif, k=k)\n",
+ " X_train_selected = selector.fit_transform(X_train, y_train)\n",
+ " \n",
+ " model = LogisticRegression()\n",
+ " cv_scores = cross_val_score(model, X_train_selected, y_train, cv=5, scoring='f1_weighted')\n",
+ " mean_cv_score = np.mean(cv_scores)\n",
+ " scores.append(mean_cv_score)\n",
+ " \n",
+ " if mean_cv_score > best_score:\n",
+ " best_score = mean_cv_score\n",
+ " best_k = k\n",
+ " \n",
+ " plt.figure(figsize=(10, 6))\n",
+ " plt.plot(k_values, scores, marker='o')\n",
+ " plt.xlabel('Number of Features (k)')\n",
+ " plt.ylabel('Cross-Validation Score')\n",
+ " plt.title(f'Validation Curve for SelectKBest (Best k={best_k})')\n",
+ " plt.grid(True)\n",
+ " plt.show()\n",
+ " \n",
+ " return best_k, best_score\n",
+ "\n",
+ "# Prétraiter les données\n",
+ "X_train_prep, X_test_prep, preprocessor = preprocess_data_without_selecting(X_train, X_test, cont_features, cat_features)\n",
+ "\n",
+ "# Définir les valeurs de k à tester\n",
+ "k_values = range(1, X_train_prep.shape[1] + 1)\n",
+ "\n",
+ "# Trouver le meilleur k\n",
+ "best_k, best_score = plot_and_select_best_k(X_train_prep, y_train, preprocessor, k_values)\n",
+ "\n",
+ "print(f\"Best k: {best_k}\")\n",
+ "print(f\"Best cross-validation score: {best_score}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<font color='red'>**Commentaire**</font> \n",
+ "\n",
+ "On aperçoit une certaine stabilisation à partir de k=12. Nous choisissons donc ce k afin de réduire la dimension (le score optimal est obtenu pour k=22, càd toutes les features)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sélection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def preprocess_data(X_train, X_test, cont_features, cat_features, target, k):\n",
+ " \"\"\"\n",
+ " Preprocess the data and select the top k features using SelectKBest.\n",
+ " \"\"\"\n",
+ "\n",
+ " # Preprocess numerical features\n",
+ " cont_preprocessor = make_pipeline(StandardScaler())\n",
+ " \n",
+ " # Preprocess categorical features\n",
+ " cat_preprocessor = make_pipeline(OrdinalEncoder())\n",
+ " \n",
+ " preprocessor = make_column_transformer((cont_preprocessor, cont_features),\n",
+ " (cat_preprocessor, cat_features),\n",
+ " remainder='drop')\n",
+ " \n",
+ " # Fit and transform the training data\n",
+ " X_train_prep = preprocessor.fit_transform(X_train)\n",
+ " X_test_prep = preprocessor.transform(X_test)\n",
+ " \n",
+ " # SelectKBest to select the top k features\n",
+ " selector = SelectKBest(score_func=f_classif, k=k) \n",
+ " X_train_selected = selector.fit_transform(X_train_prep, target)\n",
+ " X_test_selected = selector.transform(X_test_prep)\n",
+ " \n",
+ " # Get the selected feature names\n",
+ " selected_features = selector.get_support(indices=True)\n",
+ " feature_names = preprocessor.get_feature_names_out()\n",
+ " selected_feature_names = [feature_names[i] for i in selected_features]\n",
+ "\n",
+ " print(\"Selected feature names:\", selected_feature_names)\n",
+ " \n",
+ " return X_train_selected, X_test_selected, selected_feature_names"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Selected feature names: ['pipeline-1__age', 'pipeline-1__height', 'pipeline-1__weight', 'pipeline-1__waistline', 'pipeline-1__DBP', 'pipeline-1__HDL_chole', 'pipeline-1__triglyceride', 'pipeline-1__hemoglobin', 'pipeline-1__serum_creatinine', 'pipeline-1__SGOT_ALT', 'pipeline-1__gamma_GTP', 'pipeline-2__sex']\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Nombre de caractéristiques à sélectionner\n",
+ "k = 12\n",
+ "\n",
+ "X_train_prep, X_test_prep, feature_names = preprocess_data(X_train, X_test, cont_features, cat_features, y_train, k)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Modélisation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_confusion_matrix(y_true, y_pred, classes, cmap='Blues', figsize=(10, 7)):\n",
+ " \"\"\"\n",
+ " Plots a confusion matrix using matplotlib.\n",
+ " \n",
+ " Parameters:\n",
+ " y_true : array-like of shape (n_samples,)\n",
+ " True labels.\n",
+ " y_pred : array-like of shape (n_samples,)\n",
+ " Predicted labels.\n",
+ " classes : array-like of shape (n_classes,)\n",
+ " List of class labels.\n",
+ " cmap : str, default='Blues'\n",
+ " Colormap for the heatmap.\n",
+ " figsize : tuple, default=(10, 7)\n",
+ " Figure size.\n",
+ " \"\"\"\n",
+ " # Calculer la matrice de confusion\n",
+ " cm = confusion_matrix(y_true, y_pred)\n",
+ " \n",
+ " # Créer une figure et un axe\n",
+ " fig, ax = plt.subplots(figsize=figsize)\n",
+ " \n",
+ " # Créer une heatmap\n",
+ " cax = ax.matshow(cm, cmap=cmap)\n",
+ " \n",
+ " # Ajouter une barre de couleur\n",
+ " fig.colorbar(cax)\n",
+ " \n",
+ " # Annoter la heatmap avec les valeurs de la matrice de confusion\n",
+ " for (i, j), val in np.ndenumerate(cm):\n",
+ " ax.text(j, i, f'{val}', ha='center', va='center', color='black')\n",
+ " \n",
+ " # Définir les labels des axes\n",
+ " ax.set_xticks(np.arange(len(classes)))\n",
+ " ax.set_yticks(np.arange(len(classes)))\n",
+ " ax.set_xticklabels(classes, rotation=45)\n",
+ " ax.set_yticklabels(classes)\n",
+ " \n",
+ " # Définir les labels des axes et le titre\n",
+ " plt.xlabel('Predicted Labels')\n",
+ " plt.ylabel('True Labels')\n",
+ " plt.title('Confusion Matrix')\n",
+ " \n",
+ " # Afficher la heatmap\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def evaluate_model_multiclass(y_true, y_pred, class_labels):\n",
+ " \"\"\"\n",
+ " Evaluate a multi-class classification model using confusion matrix, classification metrics, \n",
+ " and ROC AUC.\n",
+ "\n",
+ " Args:\n",
+ " y_true (array): True class labels.\n",
+ " y_pred (array): Predicted class labels.\n",
+ " class_labels (array): List of class labels.\n",
+ " \"\"\"\n",
+ " # Plot confusion matrix\n",
+ " plot_confusion_matrix(y_true, y_pred, classes=class_labels)\n",
+ "\n",
+ " # Calculate classification metrics\n",
+ " # precision = precision_score(y_true, y_pred, average='weighted')\n",
+ " # recall = recall_score(y_true, y_pred, average='weighted')\n",
+ " f1 = f1_score(y_true, y_pred, average='weighted')\n",
+ "\n",
+ " # Display classification metrics\n",
+ " print(\"*** Classification Metrics ***\")\n",
+ " # print(\"Precision =\", precision)\n",
+ " # print(\"Recall =\", recall)\n",
+ " print(\"F1 Score =\", f1)\n",
+ " print(\"******************************\")\n",
+ "\n",
+ " # Binarize the output\n",
+ " y_onehot_test = label_binarize(y_true, classes=class_labels)\n",
+ " y_score = label_binarize(y_pred, classes=class_labels)\n",
+ "\n",
+ " # ROC AUC for multi-class classification\n",
+ " fpr = {}\n",
+ " tpr = {}\n",
+ " roc_auc = {}\n",
+ "\n",
+ " for i in range(len(class_labels)):\n",
+ " fpr[i], tpr[i], _ = roc_curve(y_onehot_test[:, i], y_score[:, i])\n",
+ " roc_auc[i] = auc(fpr[i], tpr[i])\n",
+ "\n",
+ " # Plot ROC curve for each class\n",
+ " plt.figure(figsize=(8, 6))\n",
+ " for i in range(len(class_labels)):\n",
+ " plt.plot(fpr[i], tpr[i], label='Class %d (AUC=%0.3f)' % (i, roc_auc[i]))\n",
+ "\n",
+ " plt.plot([0, 1], [0, 1], 'k--')\n",
+ " plt.xlim([0.0, 1.0])\n",
+ " plt.ylim([0.0, 1.05])\n",
+ " plt.title('Multi-class ROC Curve')\n",
+ " plt.xlabel('False Positive Rate')\n",
+ " plt.ylabel('True Positive Rate')\n",
+ " plt.legend(loc=\"lower right\")\n",
+ " plt.show()\n",
+ "\n",
+ " # Print AUC scores for each class\n",
+ " print(\"AUC scores for each class:\", roc_auc)\n",
+ "\n",
+ " # Calculate micro-average ROC AUC\n",
+ " fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_onehot_test.ravel(), y_score.ravel())\n",
+ " roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
+ "\n",
+ " # Plot micro-average ROC curve\n",
+ " plt.figure(figsize=(8, 6))\n",
+ " plt.plot(fpr[\"micro\"], tpr[\"micro\"], label='micro-average ROC curve (AUC = %0.3f)' % roc_auc[\"micro\"], color='darkorange')\n",
+ " plt.plot([0, 1], [0, 1], 'k--')\n",
+ " plt.xlim([0.0, 1.0])\n",
+ " plt.ylim([0.0, 1.05])\n",
+ " plt.title('Micro-averaged ROC Curve')\n",
+ " plt.xlabel('False Positive Rate')\n",
+ " plt.ylabel('True Positive Rate')\n",
+ " plt.legend(loc=\"lower right\")\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Régression logistique "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Modèles\n",
+ "logreg = LogisticRegression(penalty=None, tol=10e-6, random_state=42, solver='lbfgs')\n",
+ "logregRidge = LogisticRegression(C=0.1, penalty='l2', tol=10e-6, random_state=42, solver='lbfgs')\n",
+ "logregLasso = LogisticRegression(C=0.1, penalty='l1', tol=10e-6, random_state=42, max_iter=1000, solver='saga')\n",
+ "logregElasticNet = LogisticRegression(C=0.1, penalty='elasticnet', l1_ratio=0.7, tol=10e-6, random_state=42, max_iter=1000, solver='saga')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ ],
+ "text/plain": [
+ "LogisticRegression(penalty=None, random_state=42, tol=1e-05)"
+ ]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Créer et entraîner un modèle de régression logistique\n",
+ "\n",
+ "logreg.fit(X_train_prep, y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x700 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "*** Classification Metrics ***\n",
+ "F1 Score = 0.645040329810779\n",
+ "******************************\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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K1atXM3DgQN5//30efvjhy3rdWq2Wu+66ixUrVvxupzkwMJDVq1fz9ttvs2DBAiorK+nUqROvvvoq999//2V+Z4UQLYlKuXBaQgghhBBCiFZEanSFEEIIIUSrJImuEEIIIYRolSTRFUIIIYQQrZIkukIIIYQQolWSRFcIIYQQQrRKkugKIYQQQohWqc300T1w4ACKouDk5OToUIQQQgghxCVUV1ejUqno169fgzxem9nRVRTF9p9o/RRFoaqqSt7vNkLe77ZF3u+2Rd7vtqWhc7U2s6Pr5OREVVUVXbp0+cORoaJ1KCsrIzExUd7vNkLe77ZF3u+2Rd7vtuXw4cN2o7uvVpvZ0RVCCCGEEG2LJLpCCCGEEKJVkkRXCCGEEEK0SpLoCiGEEEKIVkkSXSGEEEII0SpJoiuEEEIIIVolSXSFEEIIIUSrJImuEEIIIYRolSTRFUIIIYQQrZIkukIIIYQQolWSRFcIIYQQQrRKkugKIYQQQohWSRJdIYQQQgjRKkmiK4QQQgghWiVJdIUQQgghRKvULBLdqqoqxowZw549e+pcc+zYMSZMmEDfvn0ZP348CQkJTRihEEIIIYRoaRye6FZWVjJnzhxOnTpV55qysjIee+wxBgwYwLp16+jXrx/Tp0+nrKysCSMVQgghhBAtiUMT3aSkJB544AHS09P/cN2GDRtwcXHhueeeIzw8nBdffBF3d3d++OGHJopUCCGEEEK0NA5NdPfu3cv111/PmjVr/nDdoUOH6N+/PyqVCgCVSkVERAQHDx5sgiiFEEIIIURjyiut5Kt1X1JakNegj6tt0Eerp4kTJ17WutzcXLp06WJ3m16v/8Nyh7qUl5fX+z6i5bnwPsv73TbI+922yPvdtsj73fqUVZk4eLaQ/Zn5HE9NJSzuKwbt30X7kxmYV60DP0ODPZdDE93LVV5ejrOzs91tzs7OVFVV1fux0tLSGigq0RLI+922yPvdtsj73bbI+90ymSwKKUWVHMsr52jNf5nFpdxbdoh7Tu5iePwxNJXVtvWqBn7+FpHouri4/C6praqqQqfT1fuxOnXqhKura0OFJpqp8vJy0tLS5P1uI+T9blvk/W5b5P1uORRFIbWglP2ZBezPzGf/mQIOnS2kvNoMKAxUZfJUxg767tuP7lwR6ah5Q+PCHKpRQvypvvtOfIOCGjSmFpHoBgYGYjQa7W4zGo0EBATU+7FcXV1xc3NrqNBEMyfvd9si73fbIu932yLvd/Nz7nw5e9ON7MvIY296HvsyjOSX2W9MBmlLePr8bm46/Ct+CacBUIDv1E4s0eqoREXQ3XcSs2I1KpWKw4cPN2iMLSLR7du3L5988gmKoqBSqVAUhfj4eGbMmOHo0IQQQgghWr3zFdXsz8wjLj2PvRnW5Da9oPSSa3UaE5NUx7jzxC7a/XIYTa3kt0Cl4h19AHuKrTXXTk5OdB88zNZwoKE120Q3NzcXT09PdDodo0eP5u233+bVV1/loYceYvXq1ZSXl3PHHXc4OkwhhBBCiFalymTm8NlC4jKMxKXnEZduJDGnCEW59HqdVsOADj6M8s3lurjv8N7wEy4Z9t0TzMEGEq4fwpu/HiE3Lx+Aa665hk8++YQ+ffo02mtptonu0KFDWbx4MePGjcPDw4PY2Fj+9re/8cUXX9CtWzeWLFkiH2EIIYQQQlwFi0XhlLGYvTUJ7b6MPA6cyafKbLnkerVKRe92PgwM1TMw1EBv33LU21dTsfYrXOOSUFkuZsMWV2e0o28mYNI0PtyxhyVLltiuPfrooyxYsKDRc7lmk+ieOHHiD7++9tpr+eqrr5oyJCGEEEKIVuVMUZmtrjYu3UhcRh7FFdV1rg/TezAwxGBNbEMM9Av2BcpJ2bkW40fLYct+1OcrqJ2uKhE9MET9idAHH0Xr5cWiRYtsSa6/vz8xMTGMGjWqcV9ojWaT6AohhBBCiIZTWF5lS2gvJLdZxXX3I/b3cGFgiIFBodbEdkAHPQYPa4crk7matFO72f/KJ6jW/4guNRePWve1BPji8cD9dHrkGdy7drN73Keeeoovv/ySLl26EBMTc0XNBK6UJLpCCCGEEC1cRbWZg1n5tl3auPQ8TuYW17ne3VnLgBA9A0P0DAg1MChET6ivu92hMEWxcDbvJKlffUrF2m9w33MKV9PFkgbFWYvTyOF0mPokhtvuQqXRAJCdnY2iKLRv3x4AT09PNmzYQEBAQKMdOquLJLpCCCGEEC2I2WLheE4xe9Oth8X2ZRg5lFWAyXLp02JatYprg3xtJQiDQg10D/BCo1Zfcn1xuZGkX74m/98rcdscj1NBGU61F/S5hoDJUwl+6BG0vr529/3+++95+umn6d27N+vWrUNd8xyBgYEN8dLrTRJdIYQQQohmSlEU0gtK2ZuRx76a3dr9mXmUVJrqvM81/l41NbXWA2PXBfmhc9L84fNUVpeRkrKbrFWfov32J9xOZONdOw69N14T7id06kzcev6+S0JpaSnz589n2bJlAPz000/88ssvDB069Iped0ORRFcIIYQQopnIK620tfW6UFebU1JR5/r2Xq4MDNHX1NUaGBCix8fV+bKey2wxkWlM5PT3n1P15fd4/nIKr6qLCbSiUeNy23A6/OkJfG8fg9rJ6ZKPc+DAAaZPn05SUpI1pvbt+fDDDx2e5IIkukIIIYQQDlFWZSI+M/9iv9oMIyl5JXWu99I52XZpB9Qkt8He9WvPpSgKeSVnSN73PYWrVuOx5SCuueepPVxZ1S2MwMmP0G7iVJz8/et8LLPZTExMDIsWLcJksibIY8aM4d1338XPz69ecTUWSXSFEEIIIRqZyWwhIbuw5qCYNbE9eq4Qcx11tc4aNf2C/awHxkINDArV09XghVp9ZYe5SisLSUr9ley1y3H5/lfcEzKxS0W9PfEaP54OU5/ArW+//3lorKioiMjISHbv3g2Au7s7ixcvZtKkSU1+4OyPSKIrhBBCCNGAFEUhJa/Eelgsw8i+9Dziz+RTXm2+5HqVCnoEeDMw9GK/2mvb++Cs/eO62v+l2lxJWu4R0jd/gfmrzXjtOoFfrZ65ilqF7uZhBE+Zge9d96J2cbnsx/b09ESrtaaRERERxMbGEh4eflXxNgZJdIUQQgghrsK58+W2etq9NV0Q8suq6lwf6utuLUGo6YLQv4MeT92l61/ry6JYyC5MJvnQZs6v/RKvzYfxOltot0YdFkrg5EcJmDQF5/ZBV/Q8arWajz76iBUrVjB79myc6qjfdTRJdIUQQgghLtP5imr2Z1r71O7NsCa36QWlda73c3NmQIi19GBgqIGBIXoCPV3rXH+lCkrPkZT+Kzlfr8ZtQxzuB0+jq10V4e6G97jxBE15DPdBg+tdXrBnzx5ee+01li1bhpeXF2A9dPbcc8814KtoeJLoCiGEEEJcQpXJzOGzhRcPi6UbScwpQrl0WS2uThoigv3sDouF6T0arWa1vKqElJwDZOxYj+qb7XjvPE5AaaXdGt3QIbSPno7vvePQuNXv4BqAyWTizTff5O2338ZisTBv3jw++uijhnoJjU4SXSGEEEK0eRaLwiljMXtrEtp9GXkcOJNPldlyyfVqlYre7XysNbU1h8V6Bfqg1Vx6CENDMVmqycxPJPnYdsq//AbvLQno0/PsY+sQRGDUVPwjp+DSsdMVP1dqairTp09n3759gLUu95Zbbrma8JucJLpCCCGEaHPOFJXZ6movjM0trnVQ67+F6T1sNbUDQwz0C/bF3aVp6lIVRSHn/GmSz+wl99t1eG6KxzMuFa/aHRt0LvjcO5bAyY/iOWw4qjqmnl3u861atYp58+ZRUmJtdzZ48GA+/vhjQkNDr/blNClJdIUQQgjRqhWWV9kS2gvJbVZxeZ3r/T1cGBhiqBnCoGdABz0GD10TRmx1viKP5JwDpO/+Dufvf8F7eyJBRfZx6wYOoF30NPzGTUBTUzt7NQoLC5k9ezbr168HQKPRMG/ePJ555hk0mqvrAuEIkugKIYQQotWoqDZzMCvftksbl57HydziOte7O2utvWpD9AwINTAoRE+or7vDesFWmso5bTxC8qmfqF6/GZ8tCbRPzrFbo2kXiP+kaPwjp6Drek2DPv9HH31kS3LDwsKIjY2lf//+DfocTUkSXSGEEEK0SGaLhcRzRbaENi7DyOGsAkx1DGHQqlVcG+RrK0EYFGqge4AXmqv4mL8hWCxmzhSeIjkrjvzN3+O1+RB+vyWjNtWqD3Z2wueuewiY/Ce8RoxE1Ui7q3PmzOH777+nf//+LFq0CA8Pj0Z5nqYiia4QQgghmj1FUTidX8LejIuHxfZl5FFaZarzPtf4e9XU1FoPjF0X5IfOqXl8/K4oCvmlWSTlxJO5fytuG/bive0YIfn2rcp0ffsSMPkR9BMeQtsIY3VPnjyJs7MznTp1sj6fTscPP/yAp6dngz+XI0iiK4QQQohmx1hSQVxGHr+mZLPjeDon1idj/K/WWbW193JlYE1LrwvtvXxcnZsw4stTWllESu5BUlJ2ww+78NmcQOjxs3Zr1Ho9/g9FYoiaglvvPo0Sh6IofPbZZ8yfP5+ePXuyYcMG29CH1pLkgiS6QgghhHCw0spqDpwpIC7j4mGxlLySOtd76Zxsu7QX+tUGe9e/R2xTqTZXkZ53lOTsfRTt3I7P5iO0++UU6spau9FaLd6334F/5BS8R9+JuhEnjRmNRmbNmsUPP/wAwJEjR9i/fz+DBw9utOd0FEl0hRBCCNFkTGYLCdmFtVp75ZGQXYiljikMzho11/i4cGOXIIaEtWNQqJ6uBi/UasccFrtcimIhuyiFpJx4so7swmPTAXy2HsU357zdOl3PnvhH/Qn9AxNxCgxs9Li2bt3KzJkzycmxHnC75ppr+OSTT+jTp3F2jh1NEl0hhBBCNApFUUjJK2FvupG4DCP70vOIP5NPebX5kutVKugR4G0dlVvTr7aLtwvJp07Qo0cP3K5gsldTKyzLITknntT0PWi2xeG7OYGwI5l2a9Te3hgemIghKhq3fv2bpMNDeXk5CxcuZMmSJbbbpk2bxoIFC3B1bfiRxM2FJLpCCCGEaBDnzpfbdmr3puexL8NIfllVnetDfd2tJQg1XRD6d9DjqbP/yL6srKyxw75qFdWlpOYeIvncfsr27MFncwIddp1AU15rAIVKhdetIzFETsF3zD2odU3Xl7egoIAxY8aQmJgIgL+/PzExMYwaNarJYnAUSXSFEEIIUW/nK6rZn2ktPdibYU1u0wtK61zv5+bMgBDrqNyBoQYGhugJ9Gy5O4lmi4nM/OMk5cSTfTIO761H8NlylMAzBXbrXMK7YIiMxjAxCufgDg6J1cfHh7CwMBITExk5ciQxMTEEBAQ4JJamJomuEEIIIf5QlcnM4bOFtYYwGEnMKaKOslpcnTREBPvZHRYL03s4bAhDQ1EUhdzz6STnxJOWFY/LriP4bE6g64HTqGr17lV7eOA3/gEMkdF4DL7BIa/bYrGgrukPrFKpePfdd7ntttuYPHlyi38f6kMSXSGEEELYWCwKJ3OLbQltXIaRg2cKqDJbLrlerVLRp72PdbpYqHXHtlegD1qNY4cwNKTzFfmk5Bwg6dx+qo8cw3dzAp13JKIpsW935jlsOIaoKfjeOw6Nu7uDooUNGzawePFi1q9fj19N7129Xk90dLTDYnIUSXSFEEKINuxMUZn1sNiFLggZeRRXVNe5PkzvYaupHRhioF+wL+4ujdcKy1GqTBWkGY+QnBOP8XQCPj8eI2DLUXRpRrt1ziGhGCZNRj9pMrrOYQ6K1qq0tJT58+ezbNkyAJ555hmWL1/u0JgcTRJdIYQQoo0oKKu0TRTbW1OGcLa4vM71/h4uDAwx1Axh0DOggx6DR9MdompqFsVMVkESyTnxpOccxu23k/hsTqBbXCqqWjvaKp0O33vH4R8ZjefwW1A5eIQwwMGDB3nsscdISkoCoH379kydOtXBUTmeJLpCCCFEK1RRbeZgVj5x6caaDgh5nMwtrnO9u7PWWn4QomdAqIFBIXpCfd3bRD1nfkkWyTnxJOceRDmZis/mBLr8eAxtkf0vAe7XD8YwKRq/8Q+g9fZ2ULT2zGYzMTExLFq0CJPJOoDi7rvv5p133rGVLbRlkugKIYQQLZzZYiHxXJEtoY3LMHI4qwCT5dKnxbRqFX2DfBlQU4IwKNRA9wAvNM1gZ7KplFUVk5JzkOSceIrPpeK9/ThBWxJwPXXObp1Tu/boJ0ZimBSNa7fuDor20jIzM3n88cfZvXs3AO7u7ixevJhJkya1iV9QLockukIIIUQLoigK6QWl7K05LHahFKG0ylTnfa7x97ImtCEGBoTquS7ID52Tpgmjbh5M5irS84+RnBNPlvEE7vGp+Gw5Srtfk1DXGmKhcnLC5657MERF433rKFTa5pkurVq1ypbkRkREEBsbS3h4uIOjal6a5zsnhBBCCACMJRXE2dXVGsn9r9P+tbX3crXW1NZ0QRgQosfH1bkJI25eFMXCueI0a0sw4xFUp89aW4JtO4ZTXondWre+/TBERqN/4GG0er2DIr58zzzzDNu2bWPYsGE899xzODm1vkOBV0sSXSGEEKKZKK2s5sCZAuIyjLYJYyn/lYzV5q1zsrX1upDYBns3/zG5TaGoPJfknAMk58RTnn8Or59O0GFLAm7HsuzWafUG9A/WjOPt09dB0V6ePXv24OnpSc+ePQFwcnLiu+++Q9tMd5ybA/nOCCGEEA5gMltIyC60JbRx6XkkZBdiqWMKg7NGTb9gP2v3g5oJY10NXqjVUot5QUV1KWnGwyTlxGMsSsftSAa+mxMI/fkk6spapR0aDd6jRmOInILPHXehdm7eO94mk4m33nqLt956i27durFt2zZ0NSOEJcn9Y/LdEUIIIRqZoiik5JXYSg/2pecRfyaf8lp1obWpVNAz0PviYbEQA33a++CsbXt1tf+L2WIis+AEyTnxZOYfR3M2H5+tR+m6JQHnc/ZdJnTdemCIisbwcCROge0cFHH9pKamMn36dPbt2wdYD6AdO3aMiIgIB0fWMkiiK4QQQjSwc+fLbTu11k4IRvLLqupcH+rrzsCaUbkDQvT076DHUyf1lnVRFAVjSSbJOfGk5h6iqqQIr59PErIlAY9DGXZrNd7e+N3/EIaoaNz7D2wx3QgURWHVqlXMmzePkhJr+crgwYP5+OOPCQ0NdXB0LYckukIIIcRVOF9Rzf5Ma+nB3gxrcpteUFrnej83Z1vpwYXa2kBP1yaMuOUqqSgkJfcASTnxFJfl4JqYhX5zAl4/nUBT+xcJlQqvW27FEDkF37vvRe3asr6/BQUFzJkzh/Xr1wOg0WiYN28ezzzzDBqN7OrXhyS6QgghxGWqMpk5fLaQuJqpYnHpRhJziqijrBZXJw0RwX627geDQg2E6T1azK5ic1BtrqTAlMaOk3HklKShzSvBZ+tRumw5iktmvt1al7Bw6zjeiVG4hLTMXc+8vDxuuukmzp49C0BYWBixsbH079/fwZG1TJLoCiGEEJdgsSiczC22JbRxGUYOnimgqtYo2NrUKhV92vvYuiAMCtXTK9AHrabtDGFoKBbFwtlC6yje08YELJUVeG5LJnRLAh7701DVGoShdnfH7777MURG43HjsBb/S4Rer2f48OGsXr2ayMhIFi1ahIeHh6PDarEk0RVCCCGAM0Vl1sNiF7ogZORRXFFd5/owvQcDaw6LDQwx0C/YF3cXqau9GgWl2TWjeA9QXlmMLikH/y0JeG9PRHu+wm6t543D0EdG43ff/WhaeCJYWVmJi4uL7evXXnuNe+65h9GjRzswqtZBEl0hhBBtTkFZpW2i2N6aMoSzxeV1rvf3cGFgiME6iCFUz4AOegweuiaMuPUqrzpPSu4hknPiyS/NQlNYhs+PxwjanIAuzWi31rlDCPqJURgmTUYX3sVBETccRVH47LPP+Mc//sHmzZtp3749AF5eXpLkNhBJdIUQQrRqFdVmDmblE5durOmAkMfJ3OI617s7a63lByF6BoQaGBSiJ9TXvcV/JN6cmMzVZOQnkpwTz5mCkyimajzjUgnZkoDnnhRUtcpDVC4uKEOH03H6E/jffgeqVnIYy2g0MmvWLH744QcAZs+ezerVqx0cVesjia4QQohWw2yxkHiuyJbQxmUYOZxVgMly6dNiWrWKvkG+F/vVhhroHuCFRi11tQ1NURRyitNIqhnFW22uwCXNSMCWBLx/PIZTQZndevcBAzFETsH1rrs5mXUWjx49Wk2Su3XrVmbOnElOTg4A3bp1Y/78+Q6OqnWSRFcIIUSLpCgKp/NL2HvhsFi6kf2Z+ZRWmeq8zzX+XrYBDANC9VwX5IfOqXUkT81VcbmxZhTvAUoq81Gfr8B753F8NifgdjLbbq02IBDDw5OsCW4P65jbsrIyyDrriNAbXEVFBQsWLGDJkiW226ZNm8aCBQtwbWEt0FoKSXSFEEK0CMaSCuIy8vg1JZsdx9M5sT4ZY2llnevbe7laa2pruiAMCNHj49q8R722FpWmMtJyj5CcE0/O+dNgtuB+MJ0OmxPw/OUU6loT4VROTvjcMQZDVDRet92O2ql1Hug7duwY06ZNIzExEQB/f38++OADRo4c6eDIWjdJdIUQQjQ7pZXVHDhTQFyG0TZhLCWvpM713jonW1uvC4ltsLdbE0YsLBYzZwpOkJRzgIz8Y1gUM85ZBQRsPorP1qM4Gc/brXft0xdDZDT6Bx7Gyd/fQVE3nW3bttmS3FGjRhETE4N/G3jdjiaJrhBCCIeqNls4ml1oS2jj0vNIyC7EUscUBmeNmmt8XBjaNYjBndsxKFRPV4MXarUcFmtqiqKQV3qG5HPxpBoPUVFdirqsCq9dJ/DZnID70TN26zV+fugfmGgdx9u3n4Oidownn3yS3bt3M3LkSKZOnSqHG5uIJLpCCCGajKIoJOedJy7delAsLj2PA2fyKa/1UXZtKhX0DPS+eFgsxEC4twvJp07Qo0cP3Nxk19YRSiuLSMk9QHJOPIVlOaAouB3JJGhzAt4/n0Rdu/+wWo33yNsxRE7B584xqGv1i23Nvv/+e9q1a2ebaKZWq1m1apUkuE1MEl0hhBCNJru43JbQxmXksS/DSH5ZVZ3rQ33dGVgzKndAiJ7+HfR46uxrNsvKyuq4t2hM1eZK0vOOkpQTz9nCZEDBKacY/y3W0gTns4V263Vdu2GIikb/cCTO7YMcErMjlJaWMn/+fJYtW0bnzp3ZuXOnbbKZJLlNTxJdIYQQDaK4oor9mfnsS89jb4a1C0JGYd1JqZ+bMwNCrKNyL9TWBnrKyfPmxKJYyC5KsY3iNVmqUFVW4/1LkrU04WA6qlolJhovL/zGP4ghcjLugwa3ucTuwIEDTJ8+naSkJMDaZeH06dP06tXLwZG1XZLoCiGEqLcqk5nDZwtrhjBYa2sTc4qoo6wWVycNEcF+tu4Hg0INhOk92lwi1FIUlp0jKSeelJyDlFUVgaLgevysdfd25wnUpfbjeL1uHoEhago+d49F0wbLScxmM++//z6LFy/GZLK2txszZgzvvvsufn5+Do6ubZNEVwghxB+yWBRO5hazN8PIvpra2oNnCqiqNb2qNo1aRe92PrYuCINC9fQK9EGrkSEMzVlFdYltFG9eifUQmTavBP2Px/DdcgyXdPtxvC6dOqOfNBnDpMm4hHZ0RMjNQmZmJjNmzOCXX34BwN3dncWLFzNp0iT5Ra4ZkERXCCGEnTNFZeytGcAQl57Hvsw8imsfLvovYXoPBtaUIAwIMdAv2Bd3l9bZC7W1MVmqycw/TnJOPJkFJ1AUC6pqM557kvHdnIDH/jS7cbxqNzd8x47HEBmN59CbULXxCXK5ubkMGzaMoqIiACIiIoiNjSU8PNzBkYkLJNEVQog2rKCskn0Z1nG5e9ONxGXkcba4vM71AR46BobqGVjTBWFABz0GD10TRiyulqIo5J5Pt47izT1EldlahqBLzsFncwK+O06gLiq1u4/HkBswRE7Bb9wENJ6ejgi7WfL392fChAksXbqU2bNn89xzz+HUSgdetFSS6AohRBtRUW3mYFZ+TV2tNbk9mVtc53oPFy39O+htAxgGhugJ9XWXj2NbqPMVebZRvOcr8gDQFJXht/04fluP4ZJkP47XKSgYw8RIDJOi0XW9xhEhN0tFRUV4e3vbvl64cCH3338/gwYNcmBUoi6S6AohRCtktlhIPFdkS2jjMowczirAZLn0aTGtWkXfIF+7w2LdA7zQtPGPplu6KlMFacbDJOfEc644zXqj2YLHvlT8thzD47ckVKZa43hdXPAdc691HO8tt6HSaBwTeDNkMpl46623iI2NZfv27XTq1AkAV1dXSXKbMUl0hRCihVMUhdMFpcRl5NXU1RrZn5lPaZWpzvt08/diQM0AhoGhevoG+aFzkqSmNbAoZrIKTpGUE09G/jHMFuufA+f0PHw3J+C3/QTqPPudfLeIAfhHRuM34SG0vr6OCLtZS01NZfr06ezbtw+Av/zlL6xdu9bBUYnLIYmuEEK0MMaSiotJbc1ubW5JZZ3rg7xcbd0PBoRYd2x9XJ2bMGLR2BRFIb/0LMk58aTkHqSiugQAdWklvjuOY9h2Audj6Xb30foHoH9oIobIKbj16u2IsJs9RVFYtWoV8+bNo6TE+j0dPHgwb7/9toMjE5dLEl0hhGjGSiurOXCmgLgMY00nhDxS80vqXO+tc7K19bpQWxvs3fb6mrYVZZXFpOQeJDknnoKymhpbi4L7wXT0W4/jsfsEqsqLk+hUWi3eo+/EEDkF79vvQC0Hp+pUUFDAnDlzWL9+PQAajYZ58+bxzDPPoJGSjhZDEl0hhGgmqs0WjmYX2gYwxKXnkZBdiKWOKQzOGjX9gv2s3Q9q2nt1NXihVsthsdas2lxFet5RknPiOVuYhIL1z4fT2UJ8txxD/+MJ1Nl5dvdx7dnbOo73wUk4BQQ4IuwW5eeff2bGjBlkZWUBEBYWRmxsLP3793dwZKK+JNEVQggHUBSF5LzzxNUMYIhLz+PAmXzKq82XXK9SQc9AbwbU1NQOCjHQp70PzlrZWWoLFMVCdlEqyTnxpOUdwWS27tKqy6vw3nUS/x+TcD6YZHcfja8v+gkPY4iKxu26COmWUQ+nTp2yJbmRkZEsWrQIDw8PB0clroQkukII0QSyi8ttCW1cRh77Mozkl1XVuT7U152BNd0PBoTo6d9Bj6dOPmZuawrLckjJOUBy7gFKKwutNyoKbkfP4L/1FB4/HYOyWn2P1Wq8bx2JIXIKPnfdjVonPY6vxJQpU9i3bx+jR4/m7rvvdnQ44ipIoiuEEA2suKKK/Zn57EvPY2+GtQtCRmFZnev93JztamoHhugJ9HRtwohFc1JRXUpqrrUlmLEkw3a7NrcY/bYT6LedQJVh3/PWpUtXDJHRGCZG4RwU3NQht2iKovDZZ5/RpUsXhg0bBoBKpeLDDz90cGSiIUiiK4QQV6HKZObw2cKaIQzW2trEnCLqKKvF1UlDRLCfNaGtmTAWpveQj5XbOLPFZDeK16JYS1hUVSa8fkkmcHsKTnuPUfsPltrDA7/xD2CIjMZj8A3yZ+gKGI1GZs2axQ8//ED79u35+eef8ZX2aq2KJLpCCHGZLBaFk7nF7M0wsq+mtvbgmQKqzJZLrteoVfRu52PrgjAoVE+vQB+0GhnCIKw7icaSDJJz4knNPUylqezCBXQns2m3PRX3Hw9DsX2XDc+bbsYQGY3vvePQuLs7IPLWYevWrcycOZOcnBwAPD09ycvLk0S3lZFEVwgh6nCmqKympZe1tnZfZh7FFdV1rg/TezAw5GK/2n7Bvri7SF2tsFdSUUBy7gGSc+IpLjfabtcUlOK/Mxn91uOQZN/z1jm0I4ZJkzFMmoxLp85NHXKrUlFRwYIFC1iyZInttmnTprFgwQJcXaVkqLWRRFcIIYCCskr2ZVjH5e6tGcRwtri8zvUBHjpb6cHAUD0DOugxeMjBH3FpVaYKTuclkHRuP+eKU223q6rNeMWdpv3ONDQ/HwLzxa4baldXfO8dhyEyGs+bbkYl45iv2rFjx5g2bRqJiYkA+Pv788EHHzBy5EgHRyYaiyS6Qog2p6LazMGs/Jq6WmtyezK3uM71Hi5a+nfQ2x0WC/V1l5pI8YcsipmswiSSc+JJzzuG2XLx0wCXlFyCd2bgtiUeJb/Q7n7u1w/GP3IKvuMmoPX2buKoW6+cnBxGjhxJebn1F9iRI0cSExNDgPQVbtUk0RVCtGpmi4XEc0W2hDYuw8jhrAJMlkufFtOqVfQN8mVgTVuvQaEGugd4oZHdNHGZbKN4cw5SXn3edrvmfDmBP2fit+UYyrFTAFz4U+jUrj36iZEYJkXj2q27A6Ju/QICApgxYwb/93//xyuvvMLUqVPll9U2QBJdIUSroSgKafklxGXk1dTVGtmfmU9planO+3Tz92JAzQCGgaF6+gb5oXOSIQyifsqqzpOae5CknHgKSs9evGC24HvoLO12pKHesR+qqmzJrcrZGZ+77sEQORnvW0eh0so/yQ0tMzOTDh062L6eO3cuDz/8MF26dHFgVKIpyU+VEKLFMpZUEJeRxy/JZ9l5IoMT65MxllbWuT7Iy9XW/WBAiHXH1sfVuQkjFq2JyVxNev4xknPiySo4aRvFC6DLLCRkVxa6H+KwnMu1u5/bdREYIqPRT3gIrV7f1GG3CaWlpcyfP581a9awbds2evToAYCzs7MkuW2MJLpCiBahtLKa+DP5Fw+LpeeRml9S53pvnZOtrdeF2tpgb7cmjFi0Ropi4VxxmnUUr/EI1eaLv1ipSysJ3puLz+YELAcSALjQeE6rN6B/aCKGqCm49b7WAZG3HQcOHGD69OkkJVlHIv/tb3/jiy++cHBUwlEk0RVCNDvVZgtHswttCe2+jDwSsgux1DGFwVmj5hofF4Z2DWJw53YMCtXT1eCFWi31d6JhFJcbSc6JJznnACWVBRcvWBT0ifm035EGW39FKa+wJbdoNPjcfgeGyCl4j74TtbN8etCYzGYz77//PosXL8ZkspYr3X333bzzzjsOjkw4kiS6QgiHUhSF5LzzNYfFrIltfGY+FSbzJderVNAz0JsBNTW1g0IMhHu7kHzqBD169MDNTXZtRcOorC4j1WgdxZt73r6vrWtOOR13Z+O8YQ/mjExq/wqm694TQ1Q0hocm4RTYrmmDbqMyMzOZMWMGv/zyCwDu7u4sXryYSZMmyYGzNk4SXSFEk8ouLieuJqG9MDK3oLyqzvWhvu4MrOl+MCBET/8Oejx19kMYysrKGjts0UaYLSbOFJwkOSeejPxE2yheAHWFidADRXhvPoLp133W9TXXNN7e+N3/EIaoaNz7D5Tkqgl98803zJo1i+Jia4vAiIgIYmNjCQ8Pd3BkojmQRFcI0WiKK6rYn5nPvvQ89mZYuyBkFNadlPq5OdvV1A4M0RPoKZOKRONSFIW8kkySbKN4S2tfJDClgsAdqSgbf8JSUoKth4dKhdeI2zBETsF3zD2oZaqWQ1RVVVFcXIxarWb27Nk899xzODnJREJhJYmuEKJBVJnMHD5bWDOEwbpTm5hTRB1ltbg6aYgI9rMmtDUTxsL0HrITJppMaWUhyTnWUbxF5fadETwKFTr+moPTd7upTk6hdiGNS3gXDJMmo58YhUuHkKYNWvzO/fffz5EjRxg9ejRDhgxxdDiimXFooltZWcnChQvZvHkzOp2OqVOnMnXq1Euu3bJlC//4xz/Izs6me/fuzJ8/n169ejVxxEIIAItF4WRuMXszjOxLtw5hOHimgCqz5ZLrNWoVvdv5WEflhljbe/UK9EGrkSEMomlVmyo5nZdAck48Z4tSoFZ1rdYEnY5U4LXpIJU//QIWCxdmmand3fEbNwFDZDQeNwyVX8gcxGQy8dZbb9G3b1/uuOMO2+0LFy50YFSiOXNoovvGG2+QkJDAsmXLyMrKYu7cuQQFBTF69Gi7dadOneLZZ5/l5ZdfJiIigs8++4zp06ezZcsWXOWjIiEalaIonCkqqzWEIY99mXkUV1TXeZ9wvWfNVDFrCUK/YD/cnOUDJOEYFsVCdmEySTnxpOclYKo1ihdFIeislsAfkzB/9yPmwgJqd2L2vHEYhqgp+I4dj8bDo8ljFxedPn2aZ555hri4OPR6PT///DOBgYGODks0cw77l6esrIy1a9fyySef0KtXL3r16sWpU6f4/PPPf5fo7t69my5dujB27FgA5syZw+eff05SUhJ9+vRxQPRCtF4FZZU1o3JrEtuMPM4Wl9e5PsBDZ+1+UHNYbGCIAb27SxNGLMSlFZSes47izT1AWVWx3TWfch2hv+Wi/fYnKo8lUvs4pHOHEPQTozBERqMLkwNNjqYoCps3b+ajjz6itNRaP921a1eqq+v+ZVuICxyW6B4/fhyTyUS/fv1st/Xv35+PP/4Yi8WCutZceR8fH5KSkti/fz/9+vVj3bp1eHh4EBoa6ojQhWg1yqtNHDxTwL4MI3vTrYntKeP5Otd7uGjp30FvOyw2KNRAiI+bfIwrmo3yqhJScw+SnHOAvNIzdteccSYs0YTHDweo2LYds8lkq71V6XT43nMfhsjJeA0fgUojY6Cbg8LCQmbNmsV3330HgEajYd68eTzzzDNo5D0Sl8FhiW5ubi6+vr4412qgbTAYqKyspLCwED8/P9vtd955Jz/++CMTJ05Eo9GgVquJjY3F29u73s9bXl73zpRoPS68z/J+X2S2KJzILWZfZgH7z+QTf6aAhOwiTJZLnxbTqlX0aedN/w5+RAT7MiDYj2v8PdH81xCG5vA9lve7bfnv99tsMZFVdJK0vENkFyfZjeJVoSbY6Ib/tpOYv92C2ZhL7T8lrv0H4PNQJN5jx6Hx9rE+bmXdY6RF09m9ezfPPPMMZ8+eBaBjx4588MEH9OvXj0p5j1otRVEadPPEYYlueXm5XZIL2L6uqrLvqVlQUEBubi4vvfQSffv2ZdWqVTz//PN89dVX6Os5JzwtLe2q4hYtS1t9vxVF4WxpNUfzyknMr+BoXjnH88spN9XRAgHo6OVMTz9Xeupd6aXX0dVXh4vtsFg55J/hZH7TxH+l2ur73RYpisLR5H0Umk9TaM7g4rExK/cSHf4/ZeK26TdUJ47blSbgp4dRd6C64y4qO4VxDjiXdRayzjblSxB/IC8vj6ioKFt5wujRo3niiSfQ6XQkJiY6ODrR2P47P7waDkt0XVxcfpfQXvhap9PZ3f7WW29xzTXXMGnSJABeeeUV7rjjDr788ksee+yxej1vp06d5ABbG1BeXk5aWlqbeb+NpZXEnymw9qw9U0D8mQKMpXXveLT31NG/gx/9g33p38GXfkG++Li23PGkbe39bstKKvNJOhdPWt4hqpRSu2uuag86nrLg8cN+KjZvRam166dycsJj1B34PjwJjxEjUWnlcGRz9+yzz/Lxxx/z97//nW7dusnPdxtx6tSpBn08h/2kBwYGUlBQgMlkQlvzF05ubi46nQ4vLy+7tUePHiUqKsr2tVqtpnv37mRlZdX7eV1dXWVEaBvSGt/v0spq4s/kE1fT1isuPY/U/JI613vrnKyHxGoNYgj2bl3fkwta4/stoNJUTlrNKN6c4tN217QaZzqXGtBvO0HFutVUn8m0L03o0xdDZDT6ByfiZDA0beDisimKQkJCgt0B82effZbo6Gg8PT1JTEyUn+82oqHPfDgs0e3RowdarZaDBw8yYMAAAPbv30+fPn3sDqIBBAQEkJycbHdbamqqdFwQrV612cLR7EL2XmjrlZFHQnYhljqmMLho1VwX5GcdwFCT2HY1eKFWy2Ex0bJYLGbOFFpH8abnJWJRTHbXPSu86Xq0DO13Oyj99RdqH6HU+unxe3Ai/lHRuF17XZPGLerPaDQya9Ystm7dyqZNm2yH1DUaDYGBgTLiW1wVhyW6rq6ujB07lgULFrBo0SJycnJYunQpixcvBqy7u56enuh0Oh544AHmzZtH79696devH2vXriUrK4v77rvPUeEL0eAURSE57zx70/PYV7NTG5+ZT4XJfMn1KhX0DPRmYIiBAaF6BoUY6NPeB2etnEQWLZOiKOSXZtWM4j1IRbV9aYKPzp/Op7U4f/cbJd9+S2VFxcWet2o13qNGY4icgs8dd6F2kRZ3LcHWrVuZOXMmOTk5ALz99tusXLnSwVGJ1sShRUrPP/88CxYsIDo6Gg8PD5566ilGjRoFwNChQ1m8eDHjxo3jzjvvpLS0lNjYWLKzs+nRowfLli2r90E0IZqT7OJyW+nBhZG5BeVVda7v6OteM4TBwMBQAxHBfnjqZJ67aPlKK4tIyT1Ick48hWXn7K7pnNwJqw7Cb9sJStd+TFVaqt3BMt013TFERaN/OBLndu2bNnBxxSoqKliwYAFLliyx3fboo4+yYMECxwUlWiWHJrqurq68/vrrvP7667+7duLECbuvJ0yYwIQJE5oqNCEaVHFFlfWgWHoeezOMxKUbySis++M4Pzdna5/amt3agSF6Aj3lEIZoParNVaTnHSU5J56swiRqj+JVq7SEuocTtD8Py1ebOL9zBwW1ynXUnl5Ybh5B58dnoh82XPo4tzDHjh1j2rRptu4J/v7+xMTE2Da6hGhIcuxUiAZWZTJz+GwhcelG205tYk4RdZTV4uqkoX8Hfc2BMeuObWc/D/nHW7Q6imIhuyiFpJx4ThsTMFnsP8EI8OxIpywXXL7/laJ1H1FUXGuamUqF180jMERG43LrKE6cPo1bjx7yc9LCLF++nLlz59r64I4cOZKYmBgCAgIcHJlorSTRFeIqWCwKJ3OL2ZthZF9NF4SDZwqoMlsuuV6jVtG7nQ8DQ/UMCDEwKFRPr0AftBr1JdcL0RoUluXYRvGWVhbZXfPU+RGm6ojPtuOUrP6Q0pPHqV2Z69I5DMOkyegnRuES2hFADie1YAEBAVRWVqLT6XjllVeYOnWq/LIiGpUkukJcJkVROFNURlyGdVRuXHoe+zLzKK6oe956uN6zpq7W2gWhX7Afbs7yYydav4rqUlJzD5GcE4+xJNPumpNGR2fvHrQ/mE/1lxsp2vpXjOaLhy7Vbm743nc/hshoPG8chkotvwi2FqNHj2bBggWMGjWK7t27Ozoc0QbIv7hC1KGgrJJ9GXkXE9uMPM4W1z1iNsBDZys9GBCiZ2CIAb27nPwWbYfZYiIjP5HknHgyC06gKBc/2VChJtj3GjrmuuH01S4K1s7CmJ9nd3+PITdiiJqC3333o/H0bOrwRQMrLS1l/vz53HDDDXZnbGbNmuXAqERbI4muEEB5tYmDZwrYl2Fkb7o1sT1lPF/neg8XLf076G0DGAaFGgjxcZOP4ESboygKuefTSc6JJ9V4mCqT/S+Deo9gwrRheP14jKJVMRQeOWR33SkoGMPEKAyR0ei6dG3K0EUjOnDgANOnTycpKYl169Zx/fXXExoa6uiwRBskia5oc8wWC4nniqwJbYb1sNjhrAJMlkufFtOqVfQN8mVgzU7toFAD3QO80MjHqaINO1+RT0rOAZJy4jlfYb8z6+bsRWe/a2l/pJCKj9ZTuPE5yqovlvioXFzwHXMvhqhovG65DZVGej+3Fmazmffff5/FixdjMlmHfNx88814eHg4ODLRVkmiK1o1RVFIyy+xdT+ISzeyPzOf0ipTnffp5u9lG8AwMFRP3yA/dE7yD7EQVaYK0oxHSM6J51xxqt01rdqJjvrehBZ7o/r6R/JXz+DsuWy7NW4RA/CPjMZvwkNofX2bMnTRBDIzM5kxYwa//PILAO7u7ixevJhJkybJp13CYSTRFa2KsaSCuIw8fkk+y84TGZxYn4yxtLLO9UFerjWlB9YuCANC9Pi4OjdhxEI0bxbFTFZBknUUb/5RzJbavySqaO8dTmddFzy3H6Xg3++Ssy/O7v5a/wD0D0/CMCkat169mzZ40WTWrVvHnDlzKK5pCRcREUFsbCzh4eEOjky0dZLoihartLKa+DP5xNWUIMSl55GaX1Lnem+dU02vWoOttjbY260JIxai5cgvySI5J57k3INUVNv/XHm7+hNm6Ee7xGJK/vUfCr59hqKKCtt1lVaL9+i7MERNwXvUaNROMsGvNcvKymLmzJlUVFSgVquZPXs2zz33HE7yvotmQBJd0SJUmy0czS5k74W2Xhl5JGQXYqljCoOLVk1XbxeGdg1iSFg7Bobo6WrwQq2Wj8+EqEtZVTEpOdZRvAVl9mUHLlp3wvz7Elqmx/zVJvL+/QjpmRl2a1x79bGO431gIk4yAKDNCAoKYuHChXzwwQfExsYyePBgR4ckhI0kuqLZURSF5Lzz7E3PY1/NTm18Zj4VJvMl16tU0DPQm4E143IHhRgI93Yh+dQJevTogZub7NoKUReTuYr0/GPWUbwFp1DsRvFqCPHrQWf37rjtPEz+394hc/cuu/trfH3RT3gYQ1Q0btdFSC1mG2Aymfj555+5+eabbbc9+uijPPTQQ3hKWzjRzEiiKxwuu7jcVnpw4dBYQXlVnes7+rrbuh8MDDUQEeyHp87+IzKZnCRE3ayjeFNJzonndF4C1Wb7OnZ/z1DC/PsRcLKE4vdWk/fVk+SW1ppXplbjfetIDJFT8LnrbtQ6XRO/AuEoqampTJ8+nf379/PNN99w4403AqBSqSTJFc2SJLqiSRVXVLE/M982gCEu3UhGYd1JqZ+bs/WwWM1u7cAQPYGerk0YsRCtR1FZLsm58STnHKC0stDumoeLL+EBEYRWB1D55XcYP59CSkqy3Rpd12swREajfzgS56DgJoxcOJqiKKxatYp58+ZRUmKt2f70009tia4QzZUkuqLRVJrMHM4qYF+Gdac2LiOP4zlF1FFWi6uThv4d9DUHxqw7tp39POSjUCGuQkV1KWnGwyTlxGM8b19T66RxoZPhWsI8e+L00wHyXnmblO3bqP1Dqvb0xG/8Axgio/G4foj8PLZBhYWFzJ49m/Xr1wOg0WiYN28ezzzzjGMDE+IySKIrGoTFonAyt5i9GRcOixk5eKaAKrPlkus1ahW92/kwsKat16BQPb0CfdBqZAiDEFfLbDGRWXDCOoo3/zgW5WJ9u3UUb1fC/PuhTyuj4IPPOfufxzAXFdk9hudNN2OImoLvPfehcXdv6pcgmoldu3bx+OOPk5WVBUBYWBixsbH079/fwZEJcXkk0RX1pigKZ4rK7A6L7cvMo7iius77hOs9a+pqrW29+gX74eYsf/yEaCiKomAsybSO4s09RKXJviTIz7094QERdLC0p2zdeowrJpN3/JjdGufQjhgmTcYwaTIunTo3ZfiiGXr//fdZuHAhSs0Of2RkJIsWLZIpZ6JFkUxD/E8FZZXWqWIZFw+LnS0ur3N9gIfOVnowIETPwBADeneXJoxYiLajpKKQlFzrKN7i8ly7a65OnoQF9CPMpxfqXQcwLn6bk5s2gvniDq/a1RXfseMxREbjOWw4KhltLWr069cPAF9fX959913uvvtuB0ckRP1JoivslFebOHimwO6w2Cnj+TrXe7hoGWCrqzUwKNRAiI+b1PEJ0YiqTZWk5VlH8WYXpdhd06id6KjvRXhABN4ZpeR/vIL01VMx5Rnt1nkMHoIhcgp+4yag8fJqyvBFM3Vh5/bC39/Dhg3jvffeY8SIEQQFBTkyNCGumCS6bZjZYiHxXBF7a00WO3K2AJPl0qfFnDRqrm3vUzNZzMDAUD3dA7zQyA6QEI3Oolg4W5hU0xLsKGaLfalQO+8wwgMiCFa15/yXX2FcOYmsQwfs1ji1D0I/MRLDpGhcr+nWlOGLZs5oNDJr1ixGjRrFlClTbLdHRkY6LighGoAkum2EoiicLii1lR7EpRvZn5lPaZWpzvt08/eqNS5XT98gP3ROmiaMWghRUJpdM4r3AOVV9p+ueLkaCA+IIMy3D+bdcRhff5NjG75FqbrYh1rl7IzPXfdgiIrGe8RIVFr5a1/Y27p1KzNnziQnJ4edO3dy44030rVrV0eHJUSDkL/xWiljSYWt9GBvhvXQWG5JZZ3rg73dLh4WCzHQP0SPj6tzE0YshLigvOo8KbnWUbz5pWftrrlo3ejs35fwgH54ZJVh/GQZyf+Opjrbfp1bv/4YJk1GP+EhtHp9U4YvWoiKigoWLFjAkiVLbLdFRkbSoUMHB0YlRMOSRLcVKK2sJv5MPnG1ShBS80vqXO+tc7JNFrtQWxvsLWNyhXAkk7majJpRvGcKTqFwsTWfWqWhg193wgMiaK9pT9FX68hdOZG0Pb/ZPYbW4I/+oYkYIqNx631tU78E0YIcO3aMadOmkZiYCIC/vz8ffPABI0eOdHBkQjQsSXRbmGqzhYSzhbaENi7DyNHsIix1TGFw0arpF+xnd1isi94TtVoOiwnhaIqikFOcRlJOPGnGw78bxWvwDKFLQAQd/XpT/Wscxrff4Mj6dVjKa3U90WjwGX0nhsgpeN9+B2pn+SRG1M1isRAbG8vChQupqilxGTlyJDExMQQEBDg4OiEaniS6zZiiKCTnnbceFquprY3PzKfCZL7kepUKegZ61xwUs9bW9mnvg7NW6mqFaE6Ky40k5xwgOecAJZX5dtfcXXwID+hHuH8EupwSjP9axsl/R1KVftpunWuPXhiiotE/OAmnwMCmDF+0YNnZ2SxevJiqqip0Oh2vvPIKU6dOlU45otWSRLeZOZVbzIp9KbZDYwXlVXWu7ejrbitBGBhqICLYD0+dUxNGK4S4XJWmMtJyrS3Bcs7bJ61ajTOd9H0ID4jAXxtA4Tdfc3bFQ5zftdNuncbHB7/7H8IQFY17xABJTkS9BQUF8cYbb/DRRx+xZMkSunfv7uiQhGhUkug2M3cs2XbJ+lo/N2dr6UGIgQGhegaG6An0dHVAhEKIy2WxmDlTcIKknANk5B/7r1G8Ktr7dKVLQAQhfj2oiNuH8b3XObRuLZbztborqFR4jbgNQ+QUfO++F7VO54BXIlqq0tJSfvjhB8aPH2+77cEHH2T8+PE4OcnGiGj9JNFtRvLLKm1Jbp/2Ptzatb1twlhnPw/ZvRGiBVAUhbzSMySfiycl9xCVplK76z5ugXQJ6E+Y/3Vo885jXL6C459PpOLUSbt1LuFdrF0TJkbh0iGkKV+CaCUOHDjA9OnTSUpKwtPTk1GjRgHWgRCS5Iq2QhLdZiQl7+JO7jtjB3JLl3YOjEYIUR+llUWk5B4gOSeewrIcu2s6Jw/C/K8jPCACH40vhd9/S8bKhyj+cStYanVX8PDAb9wEDJHReAy5UX65FVfEbDbz/vvvs3jxYkwma6/0H374wZboCtGWSKLbjKTkXfy4Mlzv6cBIhBCXo9pcSXreUZJy4jlbmAxc7H6iUWsJ8etJl4AI2vt0oeLgQYwxizn9n9WYCwrsHsdz6E0Yoqbge+84NB4eTfwqRGuSmZnJjBkz+OWXXwBwd3dn8eLFTJo0ycGRCeEYkug2I6k1O7pOGjXB3lJ/K0RzZFEsZBclk3zOOorXZLE/MBro1ZnwgAg6Gfqgyi8m7/PPSVyxjPJjCXbrnENCMUyMQj9pMrqw8KZ8CaKVWrduHXPmzKG4uBiAiIgIYmNjCQ+XP1+i7ZJEtxlJybfu6HbydUejVjs4GiFEbYVl50jKiScl5wBlVcV21zx1eroERBAW0A93jSdFmzZyeuViin7YgGK6OGZbpdPhe899GKKi8Ro+ApX8nIsG8ve//51//OMfAKjVambPns1zzz0ntbiizZNEtxm5sKPbWcoWhGgWKqpLSMk9RHJOPHklZ+yuOWt0NaN4I/D3DKX8aALG/1tE0up/Y8q1r9F1HzgIQ+QU/MY/gNbHpwlfgWgrRo8ezXvvvUdQUBCxsbEMHjzY0SEJ0SxIotuMJNfU6IbppUZPCEcxWarJzD9Ock48mQUnUJSLh8VUKjUdfLsRHhBBB7/uUHievFWrObZyGWUH9ts9jlNgO/QTIzFMisa1e4+mfhmilTOZTKjVatQ1nwoMGDCAZcuWMXToULy8vBwcnRDNhyS6zUS12UJ6QRkgB9GEaGqKopB7Pt06ijf3EFXmCrvreo8OdAnoR2f/vrioXSn+cQtpKxZR+N16lKqLNboqJyd87rwbQ1Q03rfdjkorf8WKhpeamsr06dMZM2YMs2bNst1+5513OjAqIZon+Vu4mUgvKMWiWE9sd5YdXSGaxPmKPNso3vMVeXbX3Jy9raN4A/rh4xZIxamTGJcsxvjvlVRn2ZcxuF17HYbIaPweeBgng6EpX4JoQxRFYdWqVcybN4+SkhIOHTrEqFGjZLqZEH9AEt1monZrMSldEKLxVJkqSDMeJjknnnPFaXbXtGpnOhp6Ex4QQTvvMJSSUvLXriVx5WeU/PqL/Vo/PX4PTsQ/Khq3a69ruhcg2qSCggLmzJnD+vXrAdBqtcydO5euXbs6ODIhmjdJdJuJlFpjfzv7SaIrREOyWMycKTxFck486XnHsCimWldVBPl0ITwgglB9L7QqLed//om0la9S8PWXWMrKLi7VaPAeNRrDpGh87hyD2tm5yV+LaHt27drFjBkzOHv2LADh4eHExsYSERHh4MiEaP4k0W0mUozWHV2DuwteOvnHU4irpSgK+aVZJOdYR/FWVJfYXfdxCyC8ZhSvu4s3lafTyHnjdfI+X05lWqrdWl23HhiiotE/NAnndu2b8mWINqyqqopFixYRExODUlPaFhUVxauvvoqHDBYR4rJIottMXNjRlYNoQlydsspiUnIPkJQTT2HZObtrOid3OvtfR5eACPzcg7CUl1Pw1VdkrPiM8zu3263VeHnhd/9DGKKicR8wSMbxiiaXn5/PypUrURQFX19f3n33Xe6++25HhyVEiyKJbjNxsYeu/JYuRH2ZzFUUmE6z89R+cs6notQaxatWaQjV9yQ8IIJgn2tQqdSU7v2N0yteIX/dF5iLaw1/UKnwunmEdRzv3WNRu8qEQuE47dq149133+Vf//oXH374IUFBQY4OSYgWRxLdZkBRFOmhK0Q9KYqF7KJUknPiSTMesY7irb54PcCro20Ur4vWjaqzWZx7922MK5dTcfK43WO5dA7DMGky+kmTcQkJbeJXIoSV0Wjkq6++Ytq0abbbxowZw1133SWfKAhxhSTRbQYKyqsorrD+C93ZT0oXhPgjhWU5pOQcIDk3ntLKIrtr7s4+dAnsT3hAP7xcDVgqKyn89jtOr/yMoi2bwHJx+IPa3R2/++7HEBmNxw1DZRyvcKitW7cyc+ZMcnJy0Ov1jBs3znZNklwhrpwkus1AslFaiwnxRyqqS0nNtbYEM5Zk2F1z0ugI8ekJ573p32so7u7ulB46wOkVfyfvi39jzs+3W+9xw1AMUVPwGzsejaf8Yikcq6KiggULFrBkyRLbbUePHrVLdIUQV04S3WYgJe/iaXA5jCaEldlishvFa1HMtmsq1AT7XkN4QAQhfj2oqqzm2K+/kL/kI1K/WE35kUN2j+UU3AHDxCgMkdHowrs09UsR4pKOHTvGtGnTSExMBMDf358PPviAkSNHOjgyIVoPSXSbgdR8646us0ZNkLccfhFtl3UUbwYpufGk5h6m0lRmd93PPYjwgAjC/Pvi6uyJYjJRtGkj2Z8tRdm8kWzTxf64KhcXfO8eiyEqGq+bb0Wl0TT1yxHikiwWC0uWLGHhwoVUVlYCMHLkSGJiYggICHBwdEK0LpLoNgMXdnQ7+XmgkTpB0Qadr8i31t3mHKC4wmh3zc3ZizB/6yheX/d2AJQnHiNj5WcYV32OKce+hZh7/wEYIqfgd/+DaH19m+w1CHG5XnjhBVupgk6n45VXXmHq1KlSiytEI5BEtxmQ1mKiLaoyVXDaeISknHjOFdsPaNCqnQjV96ZLQATtfMJRq9SYCgvJ+WcsxpWfUbovzm69xuCP+daRhD/5NH4R/ZvyZQhRb5MnT2bZsmVcc801xMbG0r17d0eHJESrJYluM2BrLSajf0UrZ1HMZBUm1YziPYrZYj+Kt713GOEBEXTU98ZJ64JiNlP841aMK5dT8O3XKBUVF1drtXjfMQb/qGicbryJ40lJ6Lr3aPoXJcT/UFpairOzM05OTgD07NmTdevWERERgYuLi4OjE6J1k0TXwapMZjIKrXWI4QY5iCZap/zSs9ZRvDkHKa8+b3fNy9WfLgERhPn3w0PnA0BFchLnVi4jb9VKqjLtuyy49r7WOo73gYk4+fsDUFZmX8srRHNx4MABpk+fzr333suLL75ou33IkCEOjEqItkMSXQdLLyzFUjPDvLPs6IpWpKzqPKm5B0nKiaeg9KzdNRetG539+9IlIAK9RwdUKhXmkhJyV3yGccVnlPzys916jZ8f+gkPY4iKxq1vP6llFM2e2WwmJiaGRYsWYTKZeOeddxg3bhw9esinDkI0JUl0Hax2a7EwaS0mWjiTuZr0/GMk58STVXDyd6N4Q/y6W0fx+nZDo9aiKAolu3dhXLmM/K/+g6W09OKDqdV43zYKQ+QUfO66G7V8xCtaiMzMTB5//HF2794NgLu7O4sXL5ZaXCEcQBJdB6ud6MqOrmiJFMXCueI02yjeanOl3XV/z1DbKF6dkzsAlRnpnPv3CoyfL6cyJdluva7rNRgio9E/HIlzUHCTvQ4hGsK6deuYM2cOxcXFAERERBAbG0t4eLiDIxOibZJE18FSag6i+Xu44KlzcnA0Qly+4nIjyTnxJOccoKSywO6au4sP4QERhAf0w9vVWkdrKS8n76tVGFd8RvGOH0Gptdvr6Yl+/IPoIyfjcf0QKU0QLU5xcTHz5s1j9erVAKjVambPns1zzz1nO4QmhGh6V5zoVlVVkZmZSWhoKIqiyA/yFbqwoysT0URLUFldRqrROoo393y63TUnjQudDH0ID4gg0KsTKpXaWpoQtwfjimXkf7kGc1GR3X08h9+CIWoKvvfch8bNrSlfihANqrq6mh07dgAQEhJCbGwsgwcPdmxQQoj6J7qKovD222+zYsUKqqur2bRpE++88w6urq4sWLBAEt56Sq3Z0ZWyBdFcmS0mzhScIDknnoz84/81ildFUM0o3lC/Hmg1zgBUn8vGuGolxpXLqTh+zO7xnDt2wjBpMoZJk3Hp2KkpX4oQjUav1/Phhx+yevVq3nzzTby8vBwdkhCCK0h0V6xYwfr16/nb3/7Gyy+/DMBtt93GwoULMRgMzJ49u8GDbK0URSEl37qjKwfRRHOiKAp5JZkk5cSTmnvod6N4fd3b0yUggs7+fXFztv6DbqmqIv+7rzCu/IyizT+A+WJCrHZ1xXfseAyR0XgOG45KJgCKFi41NZVVq1bx/PPP20ptbrnlFm655RYHRyaEqK3eie6aNWt46aWXGDlyJK+88goAd955J05OTixevFgS3XrIL6uiuKIakKloonkorSwkOecAyTnxFJXn2l1zdfIkLOA6wv374ecRZLu97MghjCuWkbfm35jy7Mf3egweYh3HO24CGtnhEq2AoiisWrWKefPmUVJSQocOHZg8ebKjwxJC1KHeiW5mZuYl+wB2796d3NzcS9xD1OXCRDSQHV3hONWmSk7nJZCcE8/ZohSo1RJMo9YSqu9FeEAEQT5dUKs0AJjy8sj7YhXGlcsoO3TA7vGc2gdhmBSFYVI0uq7XNOVLEaJRFRYWMnv2bNavXw+AVqu1dVcQQjRP9U50g4ODOXLkCB06dLC7/aeffiIkJKTBAmsLUmoluuGyoyuakEWxkF2YTFJOPOl5CZgs1XbXA7060yUggo6GPjhrdQAoJhOFW7/HuGIZhRu+Ram+eB+VszM+Y+7FPyoarxEjUWk0Tfp6hGhsu3bt4vHHHycrKwuAsLAwYmNj6d+/v4MjE0L8kXonuo888ggLFy4kNzcXRVH49ddfWbNmDStWrGDevHmNEWOrlVrTccFZoybIS06ci8ZXUHrOOoo39wBlVfY7UV46A+EB/QgL6Ienzs92e/mJ4xhXfkbeqs+pzrafcObWr7+15+2Eh9D6+SFEa1NVVcWiRYuIiYlBqWmJFxkZyaJFi/DwkA0KIZq7eie648ePx2Qy8X//939UVFTw0ksv4efnxzPPPMPDDz/cGDG2Whdai3X280Ctlr6honGUV5WQmnuQ5JwD5JWesbvmrHWls6Ev4QER+HuG2A7VmIqKyP/yC4wrP6N07x67+2gN/ugfmoghcgpuvfs02esQwhH+8pe/sGLFCgB8fX159913ufvuux0clRDictU70c3KymLChAk8+OCD5OfnoygKer0ek8nE4cOHufbaaxsjzlYpNb+mtZiULYgGZrJUk5mfSNK5eM4UnETBYrumUqkJ8bWO4u3g1x2N2vrXgGKxULzjR3JXLqPwm6+wlJdfvI9Wi/foOzFMisZ79J2opY2gaCOefvpp1q1bx4ABA/jwww8JCgr633cSQjQb9U50b731Vnbv3o2fnx9+tT6qzMzMJCoqikOHDjVogK1Zcp60FhMNR1EUcopPk5wbT2ruYarNFXbXDR4hhAdE0Nn/WtsoXoDKtFSMK5dh/PcKqtJP293HtWdvDFHR6B+YiFNgYJO8DiEcyWg04uHhgU5nrU0PCwtj06ZNdO/eHbW0xROixbmsRPfzzz9n6dKlgPUf0/Hjx//uB764uFh+062HKpOZjMJSQA6iiatTXJ5nq7s9X5Fvd83dxZtw/wjCAvrh4xZgu91cWkrB119iXLmM87t22t1H4+ODfsLDGKKicevXX8bxijZj69atzJw5k/vuu4/Fixfbbu/Zs6cDoxJCXI3LSnTHjRtHQUEBiqLw4YcfMnr0aNzd3e3WuLu7M2rUqEYJsjU6XVBKzbkGOsuOrqinSlM5aTWjeHOK7XdhtRpnOumto3jbeXdGpbL+UqooCiW/7sa4chn569ZiKSm5eCeVCq9bR2KInILvmHtQ1+xmCdEWlJeXs3DhQpYsWQJAbGwsU6ZMoVu3bg6OTAhxtS4r0XV1dWXmzJkAqFQqHnnkEVxdXRs1sNbuwkE0gDDZ0RWXwWIxc6bwJMk58aTnJWJRTLZrKlS09+liHcWr74VTzShegKozmRj/vQLj58upTDpl95gu4V0wREZjmBiFc7B9y0Ah2oJjx44xbdo0EhMTAfD39ycmJkaSXCFaiXrX6M6cOROTycS5c+cw14z4VBSFqqoqjhw5wj333NPgQbZGKfkXe+h29pNEV1yaoijkl2bVjOI9SEV1qd11H7fAmlG81+Hu4m273VJRQcF332Bc+RnFP24Fy8XDaGoPD/zGTcAQGY3HkBulNEG0SRaLhdjYWBYuXEhVVRUAI0eOJCYmhoCAgP9xbyFES1HvRPfnn39m7ty55Ofn/+6aTqeTRPcypRitO7oBHjo8XOQEu7BXWllESu5BknPiKSw7Z3dN5+RBmL+1JZife5AtUVUUhbID+63jeNeuwlxYaHc/z2HDMURNwffecWj+q/RIiLbEaDQyffp0tm/fDlj/7XrllVeYOnWq/OInRCtT70T3H//4Bz179iQqKoqnn36at956i6ysLN5//3274n3xxy7s6IZLfa6oUW2uIj3vKMk58WQVJlF7FK9apSVU35PwgAiCfbqiVl+cPFZ97hx5az7HuHI55ccS7B7TOSQUw6TJ6CdNRtc5rKleihDNmrOzMykpKQD07t2bJUuW0L17dwdHJYRoDPVOdJOSkli0aBHdu3enR48euLm5ERUVhZubG//617+47bbbGiPOVufCVDTpodu2KYqF7KIUknLiOW1MwGSpsrse4NXJNorXRXuxLt5SXU3RDxswrvyMok0bUUy16nV1OnzvHYd/ZDSew29BJS2RhLDj5eVFbGws3377LfPnz8fFxcXRIQkhGkm9E12NRoOnp3UXsmPHjpw8eZIhQ4YwePBgXn/99QYPsDVSFMV2GE0OorVNhWU5tpZgpZVFdtc8dXrCA/oRHtAPT53e7lpZwhHrON7V/8ZkzLW75j7oegyRU/Ab/wBab2+EEFYHDx5k2bJlvP3227bWmIMGDWLQoEEOjkwI0djqneh27dqVH3/8kaioKMLCwti/fz/R0dFkZ2c3RnytUl5pJecrqwHo7CelC21FRXUJqbmHSMo5QF5Jpt01Z42OTv596RIQgb9nqF2doCk/n7y1qzGuXEbZgf1293MKbId+YiSGSdG4du/RJK9DiJbCbDYTExPDokWLMJlMdOnShSeffNLRYQkhmlC9E93HHnuMWbNm4eTkxJgxY4iJieGxxx7jxIkTDB48uDFibHWS8y52XJAd3dbNbDGRkZ9Ick48mQUnUBT7UbwdfLvZRvFq1RcPJSpmM0XbNmNcuZzC79ajVF0saVA5OeFz590YoqLxvu12VNp6/xgL0eplZmby+OOPs3v3bsDa693X19fBUQkhmlq9/4W87bbbWLt2LRqNhvbt2/PPf/6TTz/9lFtvvZVZs2Y1RoytTu0euuEG2dFtbRRFIfd8Osk58aQaD1NlKre7rvcIto7iNfTF1dn+F52KUyfJXfkZef9eSfXZLLtrbn37YYiMRv/Aw2j19iUNQoiL1q1bx5w5cyguLgYgIiKC2NhYwsPDHRyZEKKpXdFWUK9evWz/X7vO6ejRo/j4+DRIYK1Zar410XXRqmnvKYM3WovzFfmk5BwgKSee8xV5dtfcnL0IC+hHuH8Evu6BdtfMxcXkr1uLceVnlPz2q901rd6A/sGJ1nG8ffo2+msQoiUrLi5m7ty5rFmzBgC1Ws3s2bN57rnncHKSNo5CtEWXnegePnyYjRs3otVqueuuu+xasVRWVvLuu++yYsUKEhIS/uBRBEBKTelCZz8P1Grp2diSVZkqSDMeITknnnPFqXbXtGonOup7Ex4YQTvvcNSqi90PFIuF87t2Yly5jIL167CUlV28o0aD96jRGCKn4HPHXaidnRFC/G/PP/+8LckNCQkhNjZWSuqEaOMuK9HdsGEDf/7zn3F2dkar1fLpp5/y6aefMnDgQA4cOMBzzz1HRkYG48aNq9eTV1ZWsnDhQjZv3oxOp2Pq1KlMnTr1kmtPnDjBggULOHr0KB07duTFF19ssX+BXWwtJmULLZFFMZNVkGQdxZt/FLPFVOuqivbe4YQH9KOjoTdOGvu2RZWn0zB+vpy8f6+gMs0+MdZ164EhKhrDw5E4BbZrglciROvywgsvsHHjRkaOHMmbb76Jl5eXo0MSQjjYZSW6n3zyCbfddhtvvfUWarWa1157jXfffZepU6fy9NNP065dOz799FOGDBlSryd/4403SEhIYNmyZWRlZTF37lyCgoIYPXq03brz588zdepURowYwWuvvcb69euZOXMmmzZtQt8CaxVTakoXwmT0b4tzriiNHSc+p7zqvN3t3q4BhAdEEB5wHe4uPnbXzGVlFHzzFcYVn3F+53a7axpvb/zufwhD5GTcBwySqUxC1MPp06fp0KGDreVlcHAwu3btIjg42MGRCSGai8tKdNPS0li0aBHONR+hzpo1ixtuuIH58+dzzz33MH/+fNzc3Or1xGVlZaxdu5ZPPvmEXr160atXL06dOsXnn3/+u0T3q6++ws3NjQULFqDRaJg1axY7d+4kISGB4cOH1+t5Ha3SZCajsBSQg2gtjdliYtfJNbYk10XrbhvFq/cItktSFUWhZM+vGFcuI//LL7Ccr5UYq1R43XIrhsgp+N59L2pXqdMWoj4URWHz5s189NFHjB07lpiYGNs1SXKFELVdVqJbXl6Ov7+/7WsvLy9bre78+fOv6ImPHz+OyWSiX79+ttv69+/Pxx9/jMVisTX1Bti7dy+33norGs3FsadffvnlFT2vo50uKEWpmezaWXZ0W5TjZ3+lpLIAgEGdx9C9/RC7UbwAVWezyPv3Cowrl1Nx6oTdNZewcOs43olRuISENlncQrQmhYWFzJo1i++++w6ANWvW8PTTT9OlSxcHRyaEaI4u+zDaf3+kqlKpePDBB6/4iXNzc/H19bXtEgMYDAYqKyspLCzEz8/PdntGRgbXXnstf/3rX/nxxx8JDg5m7ty59O/fv97PW15e/r8XNaLELKPt/4PctZTVPoQkGsyF97mh3u9KUzkH07cBoHcPpqNPPyoqKgGwVFZyftMGCletpGT7NrBc7JWrdnPH656x+DwcidvgG1CpVJhB3vcG1tDvt2iedu/ezTPPPMPZs2cB63TODz74gKCgIPmZasXk57ttURSlQcv4rqrTvE6nu+L7lpeX2yW5gO3rqlrN8cGaFCxZsoTJkyfzySef8P333/PII4+wceNG2rdvX6/nTUtLu+KYG8JvJ/Nt/1+Rk0liftYfrBZXq6He77PVh6g2VwDgXX0NiYmJcOoEyobvYNsmqOnXadO3H6rRd6HcfCvFbm4UAxw/3iCxiLo5+udbNI7q6mqWLVvGF198gVLzkdjo0aN54okn0Ol01p9H0erJz3fb8d/54dW47ET3wIEDeHt7275WFIXDhw//bvTvwIEDL+vxXFxcfpfQXvj6vxNojUZDjx49bAMpevbsye7du1m/fj0zZsy43JcAQKdOnXB1YE1k+enDQDYBHi5E9On1P9eLK1NeXk5aWlqDvN8llQUcPbYOgA6WYDrvPkzBqs+pPGbfSs8puAPeDz6MzwMTcQmTxvRNqSHfb9G8ZGVl8cgjj3DkyBEAfHx8+Pvf/063bt3k/W4j5Oe7bTl16lSDPt5lJ7pPPfWU7TfpC5599lm7r1Uq1WX/Zh0YGEhBQQEmkwltzQjT3NxcdDrd71rC+Pv7ExYWZndbp06dbB9f1Yerq2u9D841pPQi665gF4OXQ+NoKxri/Y5LX4/mbD7Bn+zAa08q2aaL7cRULi743nMfhqhovIaPQKXR/MEjicbm6J9v0fCCg4NtmyDDhw/nww8/xMfHh8TERHm/2xh5v9uGhu4+dFmJ7rZt2xr0SQF69OiBVqvl4MGDDBgwAID9+/fTp08fu4NoANdddx1xcXF2t6WkpDBmzJgGj6uxXeyhKwfRWoLc8xmknjtA+IKv0KVdrK92HzAQQ+QU/O5/EK1MAxSi0bi6uvLJJ5+wY8cOnnjiCdRqtdTjCiEu22Uluo3RrsXV1ZWxY8eyYMECFi1aRE5ODkuXLmXx4sWAdXfX09MTnU7HQw89xMqVK4mJieGee+7h66+/JiMjg3vvvbfB42pMiqKQkm9tMxXmJ63FmjtFUdiX+j0+mxNsSa5h8p9oN/MZXHtK2YkQjWHr1q0sX76cpUuX2j7t6927N71793ZwZEKIlkj9v5c0nueff55evXoRHR3NwoULeeqppxg1ahQAQ4cOZcOGDYA10f7nP//J9u3bGTNmDNu3b2fJkiUEBgY6Mvx6M5ZWUlJp/dhbdnSbv4z8Y+SeO0nA8t0AuPbsTaeYjyXJFaIRlJeXM2/ePB544AG+++473n77bUeHJIRoBa6q68LVcnV15fXXX+f111//3bUTJ+x7kPbv359169Y1VWiNIiXv4tCAMEl0mzWLxcy+tB/Q/ycOpwLrgI+QV1+TGlwhGsHRo0eZNm0ax2s6k/j7+9v1WBdCiCvl0B3dtia5pj4XIFwvpQvN2clzcZRlpGD4j7U23OvWkXiPHP0/7iWEqA+LxcJHH33ErbfeaktyR44cya5du2yf7gkhxNVw6I5uW5Nas6Or02po5yktUpqrKlMFB9O3ErB8N+pKE6hUhLz6+08dhBBX7uzZszz55JPs2LEDsLaVfOWVV5g6dWqDn7oWQrRdV5To5uTk8MUXX5CSksKLL75IXFwc11xzze9agAl7KbU6LqjV8hd5c5VwZifKiRR8tlj75Bom/wm33tc6OCohWpdXXnnFluT27t2bJUuW0L17d8cGJYRodepdunD69GnuvvtuvvrqKzZv3kxZWRkbNmxg/PjxHDp0qDFibDVS82sSXT+pz22uSiuLOJq5i3af7ESlgNrNjeC/LnR0WEK0Oi+//DLt2rVj5syZbNmyRZJcIUSjqHei+9prr3HbbbexdetWnJycAPjHP/7BiBEjeOuttxo8wNbkwmE0OYjWfB04vRnXPSfxOHAagHbP/BnndvUbMy2E+L2DBw9SUFBg+9pgMPDbb7/x8ssv4+Li4sDIhBCtWb0T3fj4eP70pz/Z1VBptVqeeOIJjh071qDBtSaVJjOZRdYm53IQrXnKLz1L0tl9BP5zJwBO7drT7uln/8e9hBB/xGw288477zBq1CieeeYZuwmb/z0FUwghGlq9a3QtFgsWi+V3t5eWlqKR1kt1Sssv4cLf79JDt3nal7oR3x8Oo0vPAyD4pZfRuLs7OCohWq7MzExmzJjBL7/8AsCPP/5ISkoK4eHhDo5MCNFW1HtHd+jQocTGxtolu4WFhbz55psMHjy4QYNrTVJqtRYLkx3dZudMwUmysxIIWFEzHKL3tRgmTXZwVEK0XOvWrWPo0KG2JDciIoIdO3ZIkiuEaFL1TnTnzZtHQkICQ4cOpbKykscff5xbbrmFzMxM5s6d2xgxtgqptRJdOYzWvFgUC/tSN2D4Yi/aQmt5Scii12U4hBBXoLi4mMcff5xHH32U4uJi1Go1zz77LBs3bpQkVwjR5OpduhAYGMjXX3/Nd999R2JiIhaLhYcffph7770XDw9J4OqSkm89iNbO0xU3Z2lf3Jwk58Rz/vQpuq7bB4D3qNF4jxjp4KiEaHnS0tK47777OH3aepgzJCSE2NhY+bRPCOEw9c643nvvPcaNG8eECRMaI55WK9loTXTDpT63WTGZqzhwejOBy3ahrjKBWk3I32U4hBBXIigoCD8/P06fPs2ECRN488035cCZEMKh6p3ofvvtt3z88cdEREQwbtw4Ro8ejbsc2PmfbD10pT63WTmWtRvL0VN4b7N2DPGPfgTXnr0cHJUQLYeiKLYuPM7OzsTGxnLw4EHuv/9+B0cmhBBXUKO7detWPv/8c6655hreeusthg4dynPPPcevv/7aGPG1Coqi2A6jSQ/d5qO8qoQjGdsJ/OcO63AIDw+C5//N0WEJ0SIoisK///1v7rrrLiorK223d+nSRZJcIUSzUe9EF6ynZ//2t7+xa9cu3nnnHRRF4cknn2TEiBENHV+rkFtSQWmVCZDWYs3JoYytuPySiMehDADaz/4LToHtHByVEM1fYWEhU6dOZebMmfz222/8/e9/d3RIQghxSVd1Kio/P5/U1FQyMjKorKykY8eODRVXq5KSX6u1mJ+ULjQHRWW5nMj4lfALwyGCggl8araDoxKi+du1axePP/44WVlZAISFhTF27FjHBiWEEHWod6JbUlLCpk2b+Pbbb4mLiyMoKIj77ruPd955h/btZVTqpVw4iAYQbpAd3eZgf9pGfDYexCUzH4AOf3sFjZubg6MSovmqqqpi8eLFvP/++7bpZpGRkSxatEg67gghmq16J7o33HADTk5OjBo1imXLljFgwIDGiKtVuXAQTafV0M7T1cHRiHNFqWRmHKDrSmsje7drr0P/cKSDoxKi+Tp58iTTp0/n0KFDAPj6+vLuu+9y9913OzgyIYT4Y/VOdBcuXMjo0aNxdZWE7XLVPoh24XSycAxFUYhL3YBhzR60ReUAhCx6A5X6isrVhWgTYmJibEnu8OHD+fDDDwkKCnJwVEII8b9dVqIbFxdHv3790Gq1dOjQgYSEhDrXDhw4sMGCay1S86ylC3IQzfHSjIcpSjlKl6/2A+B9x1143SyHKIX4I6+++ip79uxh8uTJPPHEE6jlF0MhRAtxWYluVFQUu3fvRq/XExUVhUqlstVo1aZSqUhMTGzwIFu6izu6chDNkcwWE/vTNhHw6S7U1WbQaAh55TVHhyVEs7Nt2zZ69epFu3bWLiReXl78/PPPODs7OzgyIYSon8tKdLdt24avr6/t/8Xlq6g2c6a4DJCpaI52/OyvmI4cw2e79Zcx/z9Nw7V7DwdHJUTzUVFRwYIFC1iyZAkjRozgiy++sO3eSpIrhGiJLuvzp+DgYNtfdh988AHe3t4EBwfb/efu7s6rr77aqMG2RGn5JVzY/JapaI5TaSrjUPo22n2yAwC1pyfBL7zk2KCEaEaOHTvGrbfeypIlSwA4cuQIGRkZDo5KCCGuzmXt6O7fv9/2F97XX39Nr169ftdOJjk5WaajXYJ9D13Z0XWUwxnbcdl1BPcjmQC0f3YuTgEBDo5KCMezWCzExsaycOFCqqqqABg5ciQxMTEEyM+IEKKFu6xEV6VSMW/ePNv/X2oKjpubG4888kjDRtcKXDiIBtBJEl2HOF+RT2L6LsL+9RMAzh1CaPfk0w6OSgjHO3v2LE8++SQ7duwAQKfT8corrzB16lTpECOEaBUuK9GNiIjg+PHjAHTv3p2ff/4Zg8HQqIG1FhcOorX3csXN+aoG0YkrFH96Ez7fxeNypgCA4AV/Ry3t8UQbd+rUKe644w7y861DU3r37s2SJUvo3r27gyMTQoiGU+8eMcePH5cktx6Sa3Z0w6U+1yHyS7M4nboH/8+tZTVu/fqjf+BhB0clhOOFhYXZktqZM2eyZcsWSXKFEK3OZW0xTp48mQ8++AAvLy8mT578h2uXL1/eIIG1Fqk1O7rSQ7fpKYrCoTNb8F+9B22xDIcQoqqqytY9QaPR8PHHH5OUlMTNN9/s2MCEEKKRXFaiW7vrQlBQkNRuXSZFUUjJt+7oykG0pnfecpbCpMN0WR8PgM+Ye/AaNtzBUQnR9MxmM++//z5r165ly5YtuLu7A9ChQwc6dOjg4OiEEKLxXFaiu3jxYtv/v/aaNNi/XDklFZRVmQFpLdbULIqF7OrDBF4YDqHV0uHlxf/7jkK0MpmZmcyYMYNffvkFsI5xf+ONNxwclRBCNI0r+gw3Pj7edoDh66+/Zvr06cTGxl5yWlpbduEgGkCYlC40qRRjPOrEk3jvtB6iDHhkOq7XdHNwVEI0rXXr1jF06FBbkhsREcH06dMdHJUQQjSdeie6q1evZtKkSZw4cYLjx4/z/PPPU11dzWeffcaHH37YGDG2WMm1WotJott0qk2VHM3aQeCSHQCovbwIev6vjg1KiCZUXFzM448/zqOPPkpxcTFqtZpnn32WjRs3Eh4e7ujwhBCiydQ70V22bBnz589nyJAhbNiwga5du7J06VLeeOMN1q1b1xgxtlgXDqK5Omlo5yntrJrKkTM7cd55EPdjZwAI+svzOEmnENFG7Nmzh+HDh7NmzRoAQkJC+O6773jxxRdxcnJycHRCCNG06p3oZmZmMmLECAB2797NTTfdBEB4eDhGo7Fho2vhUmp2dMP0HnKAr4mUVhZxNG0HgTXDIZxCQgl8/CkHRyVE01m7di2nT58GYMKECezatYvBgwc7OCohhHCMeie6er2enJwccnNzSUxM5MYbbwSkv+6lpNaM/+3sJwfRmsrB9C14fxOHy9lCAAJf/Btqnc6xQQnRhF5++WX69+/PkiVLiI2NxcvLy9EhCSGEw9R7VNddd93Fn//8Z1xdXWnXrh2DBg1iw4YNvPLKK9x///2NEWOLdeEwmtTnNo2C0mySk3bTtWY4BD164XWf/JkUrZeiKKxevZobb7yR0NBQwDqOffPmzfIpkhBCcAWJ7rPPPku7du3IyMhg0qRJaDQa8vLyeOihh3jqKfmI+IKKajNnisoASXSbyr60Dfiv+gVtSQUAqidmyT/2otUqLCxk9uzZrF+/nuuvv55vv/0Wrdb6V7r8uRdCCKt6J7pqtZqoqCi72/77a3GxbAEgTHroNrqsglPkHNtLl28OAOB51z2UXnudY4MSopHs2rWLxx9/nKysLAByc3PJzs6W4Q9CCPFfrqiP7rZt23jggQe47rrrGDBgAA899BBbtmxp6NhatBS71mKS6DYmi2IhLm0DgUt/Qm2ygFZL4F8XODosIRpcVVUVCxYsYOzYsbYkNyoqih07dkiSK4QQl1DvRHfz5s3MnDmTgIAAZs+ezcyZM9Hr9Tz99NNs27atMWJskVJrDYvo5OfuwEhav5ScA1TE7cd710kAAh97ApewLg6OSoiGdfLkSW6//Xbef/99FEXB19eXZcuW8d577+HhIeVRQghxKfUuXfjoo4948sknmTlzpu22KVOm8MEHH/Dxxx9z6623NmiALVVKvnVHN8jLFVenen+bxWUymauJT9tEuyXbAdD4+BA090WqHByXEA3p+PHj3HrrrZSXlwMwfPhwPvzwQ4KCghwcmRBCNG/13tFNSUnh7rvv/t3tY8aM4eTJkw0SVGtwseOClC00pmNZP6Pdthe342cBCHruBbR6vYOjEqJhdevWjWHDhuHk5MTLL7/Ml19+KUmuEEJchnpvNQYEBHD69Gk6duxod/vp06fx9JSk7oLawyJE4yivKiEhZSsdl+4CwLlTZwKmP+ngqIRoGMXFxbYeuCqVipiYGLKzs+nTp4+DIxNCiJaj3ju6Y8aMYcGCBezcuZOSkhJKSkrYuXMnCxcu5M4772yMGFscRVFkR7cJHMrYhufXv+GcXQRAyMJFqF1cHByVEFenoqKCefPmMXToUAoLC223+/v7S5IrhBD1VO8d3ccff5yTJ08yffp0W69GRVG4+eabmTNnToMH2BKdO19BebUZgM6yo9soispzSTq5gy6rfgPAfdD1+I6T4RCiZTt27BjTpk0jMTERgIULF/LOO+84OCohhGi56p3ouri48NFHH5GcnMzJkydRFIVu3boRHh7eGPG1SHatxfwk0W0M+9N+wPD5bjQllQCELHpTmuSLFstisRAbG8vChQupqrIepRw5ciTPP/+8gyMTQoiW7bIT3ezsbLZs2YKzszPDhw8nPDxckts6pMiwiEZ1riiN7MM/0+XbgwD43nc/noNvcGxQQlyhs2fP8uSTT7Jjxw4AdDodr7zyClOnTpVf3oQQ4ipdVqK7b98+Hn30USoqrKNV3dzceP/99xk6dGijBtdSpRitO7puzhoCPXUOjqZ1URSFuLTvCVz6EyqzBZWTEx0WvurosIS4Ihs2bGDWrFnk5+cD0KdPH2JjY+nevbuDIxNCiNbhsg6jvffeewwZMoSffvqJ3bt3M2zYMF577bXGjq3FurCjG+bnKTsyDSzNeISyX3/Fa/cpAAJmPIkuTD5ZEC3T3r17bUnuzJkz2bx5syS5QgjRgC5rR/fYsWOsWbOGgIAAAF544QVuvvlmSkpKZCLPJVyYiiYH0RqW2WIiPnUDgZ/sAEDj40vQX15wbFBCXIUXXnjBdrh3+PDhjg5HCCFancva0S0rK8PHx8f2dWBgIE5OThQVFTVWXC2a9NBtHMfP/oZq06+4ncgGIOj5+Wj9/BwclRCXx2w2895773HixAnbbc7Ozvz73/+WJFcIIRrJZSW6iqL87iN4jUaDxWJplKBasvJqE1nF1jGdYX5yEK2hVJrKOJK8mcBPrcMhXMLCCZj2uIOjEuLyZGZmcu+997Jw4UKmT59u66wghBCicdV7YIT4YxfKFgDCDJLoNpTDGTtw//JnnHOKAejw8mLUzs6ODUqIy7Bu3TqGDh3KL7/8AoBWq7XV5QohhGhcl91ebOnSpbi6utq+NplMLF++HG9vb7t1M2fObLjoWiC71mLSQ7dBnK/I52TiFsJrhkN4DLkB33vvc3BUQvyx4uJi5s2bx+rVqwFQq9XMnj2b5557DicnJwdHJ4QQbcNlJbpBQUFs3LjR7jZ/f3+2bdtmd5tKpWrziW5qrWERnSTRbRAHTm9Gv/JnNGXWj3tlOIRo7vbs2cOMGTM4ffo0ACEhIXz88ccMGTLEwZEJIUTbclmJ7o8//tjYcbQaKTWlC8HebuicNA6OpuUzns8kM/5Hunx/CAC/+x/EY+D1Do5KiLodPXqUu+66y3aGYcKECbz55pt4eXk5ODIhhGh7pEa3gV1IdKXjwtVTFIV9aRsuDodwdqbDgr87Oiwh/lDPnj0ZO3Ysnp6eLFmyhNjYWElyhRDCQS67RldcnoutxeQg2tXKzE+keNdOOv+aBEDgE0/h0qmzg6MSwp6iKJw9e5agoCDAWsL19ttvU1RURGhoqIOjE0KItk12dBuQoiiyo9tALIqZfSnf0+7CcAg/Pe3//LxjgxLivxQWFjJ16lRuueUWcnJybLd7e3tLkiuEEM2AJLoNKPt8ORUmMwCd5SDaVTmVvQ827ML11DkAgp//K9paQ0uEcLRdu3YxdOhQ1q9fT25uroxFF0KIZuiqEl1pem4vpXYPXSlduGLVpkoOntpAwGc1wyHCu+D/6HQHRyWEVVVVFQsWLGDs2LFkZWUBEBkZycsvv+zgyIQQQvy3K6rRXbVqFZ988gnZ2dls2rSJf/7znwQGBvLEE080dHwtin2iKzu6VyrhzE+4f/ETzrnWeueQv7+OWvqOimbg5MmTTJ8+nUOHrF1AfH19effdd7n77rsdHJkQQohLqfeO7rfffsvbb7/NfffdZ2t6Hh4ezscff8zSpUsbPMCW5MJBNHdnLQEeOgdH0zKVVRZzPGEjhi/2AOB54zB8xtzj4KiEgGXLlnHLLbfYktzhw4eza9cuSXKFEKIZq3eiu3TpUl588UWeeuop1Grr3SdPnsxLL73EmjVrGjzAlqT2QTQZaHBlDqRvxm/FzovDIRbLcAjRPOTm5lJeXo6TkxMvv/wyX375pa3TghBCiOap3oluamoqAwYM+N3t119/PWfPnm2QoFqqC1PR5CDalSkozSY9bjO+Gw4D4PfgRNwjfv9nTQhHmD17NpMmTWLr1q3MnDnT9ou+EEKI5qvef1MbDAZSU1N/d/uBAwcICAhokKBaqpT8Czu6chDtSuxL20jgv3agsiioXFzo8LdXHB2SaKMqKip44YUXbGUKABqNhpiYGPr06ePAyIQQQtRHvRPdBx98kJdffplt27YBkJKSwqpVq3j11VcZN25cgwfYUpRVmThbXA7IQbQrkVV4isLtW/DckwJA4MyncQnt6OCoRFt07Ngxbr31Vj7++GMee+wxysrKHB2SEEKIK1TvrgvTpk3j/PnzzJkzh8rKSqZPn45Wq+Whhx5ixowZjRFji5CaL63FrpSiWNiX/B2BF4ZD6PW0nzPXsUGJNsdisRAbG8vChQttrRM7depERUUFbm5uDo5OCCHElbii9mJz5szh8ccfJykpCUVRCAsLw8Ojbe9iXui4ALKjW1/JOQcwf7MN12TrZKkOLy5A6+3t4KhEW5Kdnc2TTz7J9u3bAdDpdLz88ss88sgjchhSCCFasHonuhcapAPo9XoAiouLKS4uBmizp5BTazouqFTQ0VcS3ctlMldz8OR3BC37GQDdNd0w/OlRB0cl2pINGzYwa9Ys8vPzAejduzdLliyhe/fuDo5MCCHE1ap3ojtixIg/3OFITEy8qoBaqgsH0YK93NA5aRwcTctxLGs3ulU/4mS0fv9C/v6aDIcQTebIkSNERkbavp45cyYvvvgiLi4uDoxKCCFEQ6l3ort8+XK7r81mM6mpqXz22WfMmzevwQJraS6ULkjZwuWrqC7h2OFv6fTFXgA8b7oZ7zvGODgq0Zb06dOHyZMns2XLFj788ENuvvlmR4ckhBCiAdU70R00aNDvbhsyZAghISHExMQwYsSIBgmspbk4LEIOol2ug+nb8P1sO5qKalCpCFn0htRDikZlNps5deqUXVnCq6++yksvvYSfn58DIxNCCNEYGqzjeadOnTh+/HhDPVyLYrEothpd2dG9PEXluaT99j2+m44AoH84EvfrIhwclWjNMjMzuffeexk9ejSZmZm2293d3SXJFUKIVuqqDqNdUFJSQmxsLB06dGiQoFqa7PPlVJjMAHSWHd3LEp/2AwH/3G4dDqHTEfzSy44OSbRi69atY86cObZDszExMbz++usOjkoIIURja5DDaIqi4ObmxptvvtlggbUkF8oWQHZ0L8e54jSMW76nU5x1wl67p2bj0iHEwVGJ1qi4uJi5c+eyZs0aANRqNXPmzOEvf/mLgyMTQgjRFK76MBqAk5MT11xzDe7u7g0SVEuTkl+rh66fJLp/RFEU9iV9R7tPdgKg9fen/ZznHByVaI327NnDjBkzOH36NAAhISHExsYyePBgB0cmhBCiqdS7Rnf58uXo9XoGDRpk+69fv35XlORWVlbywgsvMGDAAIYOHcrSpUv/530yMzPp168fe/bsqffzNZaUmtZYHi5a/D10Do6meTudd4TqrzaiS80FIHj+QjSeUu4hGtY777zDXXfdZUtyJ0yYwK5duyTJFUKINqbeO7q//fZbg/WYfOONN0hISGDZsmVkZWUxd+5cgoKCGD16dJ33WbBgQbObPX9hRzfMz1O6BvwBs8VEfOK3tLswHKJ7D/yjpzo4KtEaubm5YbFY8PT05K233mLChAmODkkIIYQD1DvRve+++3jrrbd48skn6dixI87Ozlf0xGVlZaxdu5ZPPvmEXr160atXL06dOsXnn39eZ6L7zTffUFpaekXP15gudFzoLPW5f+jE2d9w+XwTTvnW9zDk1ddRaa9oCrUQf+ixxx7j3LlzTJkyhdDQUEeHI4QQwkHqnWXs3LmT9PR0Nm3adMnrlzsZ7fjx45hMJvr162e7rX///nz88cdYLBbUavuqioKCAt58802WLl3KmDHNa6hAirQW+58qTeUkHFxPx7U1wyFuuRXvUXc4OCrRGhQUFDB79mxuvvlmevToAYBKpeKll15ycGRCCCEcrd6J7uOPP94gT5ybm4uvr6/djrDBYKCyspLCwsLf9bV87bXXuO++++ja9f/bu/e4nM//geOvu3MShYTIuWKSxn7Kl8lhzjZM5jBsDnNYDsPMqYbJ2WxoW6wtjTlkYeR82mLGxuQYiijnVKLSXd2f3x/pnlYoqrvD+/l49Hi4r/tzeN/3Jd73db+v66r/SvdNTk5+pfP/K0mdxu2HGdesbm5c5MoqiorQ6L1Y/LAPvZQ0UKmo7Dk73/viaZnXLsh7CN07cuQI48aN4/bt2xw7dgw3NzcqV66s67BEAZPf79JF+rt0URQlX8tAc5XoNmjQgMOHD1OxYkV69uyZLzdOTk7OVvaQ+VitVmdp/+OPPzhx4gTbt29/5ftGRka+8jWeFhH/WPtng0exXLigfs7RpZNak8i1C9uosydjcwg6deWqvgHkcvT/VeR3f4uiITU1FX9/fwIDA1EUBQBnZ2eio6O5f/++jqMThUV+v0sX6e/S42XLYnOSq0Q38z+S/GRsbJwtoc18bGLy78oFjx8/xsvLi88//zxL+8uqVasWpqamr3ydTFcu3ASuANDKyYH6lWQFgf/68+pmrL8/hEoBlakp9ecvxrBqtQK9Z3JyMpGRkfne30L3wsPDmTBhAmfOZHxwsrCwYM6cOdjb20t/lxLy+126SH+XLpcvX87X6+lsJpC1tTVxcXGkpaVh8GRC0r179zAxMaFcuXLa406fPk1UVBRjx47Ncv7w4cPp0aMHs2fnbUctU1NTypQp8+ov4ImbiakAqFTgUK0Sxgb6+XbtkiDmUTSxe3+l5slIAKqOm0j5uvUK7f753d9CdxRFwd/fnxkzZmi/wmzdujU+Pj5YWFhw4cIF6e9SRvq7dJH+Lh3ye/WqXCe6O3fupGzZF0+26tGjR66u16BBAwwMDDh16hTNmjUD4MSJEzg6OmaZiNa4cWP27NmT5dwOHTowZ84c/ve//+U2/AJz5X7G0mLVy5eRJPc/FEXh78vbsF51CAADa2uqjJ+k26BEsXX69GkmTpwIZHyt5enpyahRo9DT05PaeCGEEDnKdaI7Z86cFx6jUqlyneiamprSo0cPZs6cydy5c7l79y4//PAD8+bNAzJGd83NzTExMaFmzZrZzre2tqZixYq5Db/A/LvigpQs/Fd0XBgpm7ZR8VpGzWR1z9no5+LDkhA5cXJyYvTo0ezfv59Vq1bRqFEjXYckhBCiiMt1onvkyJF8TyynTp3KzJkzGTx4MGXLlmXMmDF06NABgJYtWzJv3jx69eqVr/fMb1djZWmxnGiUdE6c20LlgCebQzR8jUoDP9BtUKJYSU5O5vTp0zRv3lzb5unpyfTp06VOTwghRK7kKtEtqN2+TE1NWbBgAQsWLMj23MWLF5953vOeK0wajaItXZAR3awu3/kbw5+CMYzL+ErZdu5CVPpS2iFy59y5cwwfPpzr169z6NAh6tXLqOvOr10ZhRBClA56Lz6kYFZdKAluPUwmJU0DQO0KMqKbKTU9hdN//0KlX/4GoFz7DpRv31HHUYniQKPR8M0339CuXTvCwsJISkrip59+0nVYQgghiqlcjej27NlTRlJykDmaC1K68LSz0b9T3m93xuYQenrUmJN9xF6I/7p16xYff/wxhw4dAjKWGfziiy8YMmSIbgMTQghRbOUq0c2cICayypyIBlK6kCkpJYHw3wOpue8cAJUGfUiZRo46jkoUdcHBwYwbN47Y2FgAGjVqxMqVK3FwcNBxZEIIIYqzXJUuiJxdfZLomhsbUslMRrwB/rm2h0q++zI2hyhTBpsZM3UdkijiZsyYwcCBA7VJroeHB3v37pUkVwghxCvT2YYRJUGEdiJa2QKbsFecxCXe4fa2jdieug5A1U8+xahKVR1HJYq6Bg0aAFC1alV8fHxwc3PTbUBCCCFKDEl0X0HmiG5tqc8F4ET4Nir7HQLAoEoVqoydoNuARJGkKEqWD4b9+/fnwYMH9O3blwoVKugwMiGEECWNlC68giuxT0Z0K0h97s34cBLXb8LkesbXz9U/n4O+mZmOoxJFTXR0NO+88w779u3TtqlUKkaPHi1JrhBCiHwnI7ovKTEllTsPHwOy4oKiaDhxNojKP/0BgEkjRyr1H6jjqERRExQUxIQJE0hISODSpUscPnyYSpUq6TosIYQQJZgkui8pc0c0kBUXrtw7hb7/Vgzin2wOMW+RbA4htBISEpgyZQrr168HQE9Pj4EDB1K+fHkdRyaEEKKkk0T3JUVkWVqs9I7opqWnEvrXRmyCTgBQrkMnyrdpr+OoRFFx7NgxRo4cybVr1wCoUaMGvr6+uLi46DgyIYQQpYHU6L6kq09WXFCpoKZl6a1FvXDrCOYrd6CnztgcwlY2hxBAWloa8+fPp2vXrtok193dnZCQEElyhRBCFBoZ0X1JmZtF1LAww8igdH5N/zg1kYsHfqbm/vMAWH0wFNOGr+k4KlEUnD9/niVLlqDRaDA3N2fJkiX07t1b12EJIYQoZSTRfUlXntTo1qlQessWQq/to9J3ewFQmZlhM32mbgMSRUbjxo359NNPOXToEN999x22tra6DkkIIUQpJKULL+mqdrOI0jkRLSE5hugtazA7HQVAtYmfYWhtreOohK7Ex8eze/fuLG0TJkxg27ZtkuQKIYTQGUl0X4JGo2hXXSitE9FOhG/H+vvfADCoWhVrj/G6DUjoTEhICC1btmTw4MGcOXNG225gYIC+rL4hhBBChyTRfQk3E5JISdMApXNXtLsJ13j40zqMozM2h6gxay76ZcroOCpR2NRqNTNnzqRHjx7cvHkTtVrNrl27dB2WEEIIoSU1ui/hyv3Su4auoij8fWYTVmufbA7R2ImKfQfoOCpR2C5dusSIESMIDQ0FwMLCgq+++oq3335bx5EJIYQQ/5IR3ZeQJdEtZZPRrt0/C36/YPAgGYCa8xaj0pO/RqWFoij8+OOPtGnTRpvktm7dmsOHD0uSK4QQosiREd2XcDU2YyJaORNDKpoZ6ziawpOuSePUn+uouvnJ5hCdu1KudRsdRyUKk4eHB+vWrQPAyMiIGTNmMHr0aPTkw44QQogiSP53egkRMU9WXKhQFpVKpeNoCs/F28cw892GXmo66OvL5hClUIcOHQCws7Nj7969eHh4SJIrhBCiyJIR3ZeQueJC7VJUn5uSlkzYngBqHLwAgNWQ4ZjaO+g4KlHQUlNTMTQ01D5+55138PX1pVu3bpiamuowMiGEEOLFZCjmJWTW6JampcVORx2kwncZ66SqypphM9VLxxGJgnb+/Hnc3NzYunVrlnZ3d3dJcoUQQhQLkujm0aOUVO4+egyUnqXFHj2OIzrwR8zORgNg8+k0DCtX1nFUoqBoNBq+/fZb2rZty4ULFxg/fjx3797VdVhCCCFEnknpQh5lli0A1C0lpQsnw3dg9f1BAAyq22A9eqyOIxIF5datW3z88cccOnQIABMTE2bMmIGVlZVuAxNCCCFegiS6eZQ5EQ1KR+nC/Uc3iPdfTdWb8QDYzpqHnnxtXSIFBwczbtw4YmMzNgJp1KgRK1euxMFBarGFEEIUT1K6kEeZI7p6KhW2FmY6jqZgKYrCidP/bg5h6uxMBfe+Oo5K5LfExEQ++eQTBg4cqE1yPTw82Lt3ryS5QgghijUZ0c2jzIloNSzKYGSgr+NoCtaNuIuk+67D4GFGTXLNeUtkc4gS6Nq1a9q1catWrYqPjw9ubm66DUoIIYTIB5Lo5tGV+0/W0C3hZQsaJZ1//lhD5V//AaB8t+6Yt3xTx1GJgtCwYUM8PT05duwYX331FRUqVNB1SEIIIUS+kOG5PLqqXVqsZE9EC79zAtNvN2dsDmGgj+0XsjlESREdHc2GDRuytI0ePZrVq1dLkiuEEKJEkRHdPEjXaLQ1uiV5RDc1PYXzO3/A5reLAFgNG4lJfTsdRyXyQ1BQEBMmTODRo0fUqlWL5s2bA5SqHf6EEEKUHjKimwc3HySjTtcAULtCyR3RPRv9O5bf7gBAVc4cmymeOo5IvKqEhARGjRrFsGHDSEhIAODUqVO6DUoIIYQoYDKimwdXnlpDt6SO6CapE7i2biU2528CYPPpdAwrVdJxVOJVHDt2jJEjR3Lt2jUAatSoga+vLy4uLjqOTAghhChYMqKbB5kT0aDk1uieCt+Fld8BAAyqV8d6lIeOIxIvKy0tjfnz59O1a1dtkuvu7k5ISIgkuUIIIUoFGdHNg8yJaOVNDKlQxkjH0eS/uMQ73P9+FVVuPQDA9osF6JmY6Dgq8bKGDBnC9u3bATA3N2fJkiX07t1bx1EJIYQQhUdGdPMgQru0mHmJnLxzMjSQSj8fBcC0aVMq9O6j44jEqxg8eDAALi4uhISESJIrhBCi1JER3TzIHNGtXQLrc2/Fh6P+9ifKPXqyOcT8L0tkMl+SPXz4kLJly2r7rV27dmzatInWrVujr1+yNzcRQgghciIjunlwJfbJiG6FkpXoKoqGk4cDqLDtyeYQb7+Duev/dByVyIuQkBBcXV1Zu3Ztlva2bdtKkiuEEKLUkkQ3lx4+TuXeoxQAapewiWhX7oVivGITemkaMDSQzSGKEbVazcyZM+nRowc3b95k6tSp3Lt3T9dhCSGEEEWClC7k0tWnlharW4JKF9I0qZzbvpKqhy8BUHn4KEzq1tNxVCI3Ll26xIgRIwgNDQXA0tKSr776CisrKx1HJoQQQhQNMqKbSyV1abHzN45Q/puMmfmyOUTxoCgKP/74I23atNEmua1btyYkJITu3bvrODohhBCi6JAR3Vy68mQimp5Kha2lmY6jyR+PUxO5usaHamG3ALCZ4olBhQo6jko8T0xMDGPHjmXXrl0AGBkZMWPGDEaPHo2ennxuFUIIIZ4miW4uZY7o2lqWwVC/ZCQUoeG7qPT9fgAMatpiPeJjHUckXiQ+Pp7ff/8dADs7O1atWoWjo6OOoxJCCCGKJkl0cylz+986FUpG2UJCcgz3fL/F+k4CADW/WIiesbGOoxIvUq9ePebOncvZs2eZNWsWpqamug5JCCGEKLIk0c2lzDV061QqGRPRTpz6hUrrnmwO8UYzLHu+q+OIRE7Onz/Pb7/9xqhRo7RtgwYN0mFEQgghRPEhiW4upGs0RJagEd27CddIWeFH2cSM5dJqzV8qm0MUMRqNBl9fX2bNmoVaraZBgwa4ubnpOiwhhBCiWJFENxduPEhGna4Biv+uaIqi8M/v/lTYfgqA8j17Ura5q26DElncvn2bjz/+mIMHDwJgYmLC7du3dRyVEEIIUfxIopsLJWlpsWv3z2KwbD2qdA0YGmI7WzaHKEp27NjB2LFjiY2NBcDR0RFfX18cHBx0HJkQQghR/JSM5QMKWObSYgB1ivGIbromjbNbv6XcH5cBqDxyNCa16+g4KgGQmJjIJ598wvvvv69Ncj08PNizZ48kuUIIIcRLkhHdXLgamzGia2FqRIUyxXdlgos3j2Lu8ysAKovy2EyeoeOIRKbBgwdz4MABAKpWrYqPj4/U5AohhBCvSEZ0cyFzRLc4j+aq0x4TsXoZZS5l1HpWn+qFgaWljqMSmSZNmoSenh7du3cnJCREklwhhBAiH8iIbi5k1ujWrlB8E93TEXuo4LcPAINaNak8fNQLzhAF6fbt21SuXFm7m5mLiwv79++ncePGsgKGEEIIkU9kRDcX/h3RLZ4T0R49jueOzzKM7mYk7DW9F6NnZKTjqEqvoKAgXFxcWLlyZZZ2JycnSXKFEEKIfCSJ7gskPFYT82S92eK6tNg/pwKpuP4PAEya/x+Wb/fQbUClVEJCAqNGjWLYsGEkJCQwZ84c7t+/r+uwhBBCiBJLEt0XuBr774oLdYvhiO79RzdI/Hol+klqAGov+EpGDXXg2LFjtG7dmg0bNgBQo0YNAgMDqVixoo4jE0IIIUouSXRfoDgvLaYoCicP/UiF4FMAlH/3Xco2+z/dBlXKpKWlMW/ePLp27cq1a9cAcHd3JyQkBFdX2ahDCCGEKEgyGe0FrsRk1LXq66moYWGm42jy5kbcJfS++gmVRgEjQ2rK5hCF6vr16wwbNoy///4bAHNzcxYvXoy7u7uOIxNCCCFKB0l0X+DKk9IFWwszDPWLzwC4Rknn9OblWP0ZAUDlUWMwrllLt0GVMnp6ely+nLE5h4uLC9999x22trY6jkoIIYQoPSTRfYHiuobu5Vt/UXb5ZgBUluWx+XSajiMqfapXr87SpUuJiIhg/Pjx6Ovr6zokIYQQolSRRPcFrmauoVuMEt3UdDXhP3xJ5fA7AFSfPgsDCwvdBlUKHD58mN9++43p06dr23r06KG7gIQQQohSThLd50jXaIiMSwSK14oLZyP2Yvn9XgD069Si8tAROo6oZFOr1cybN49ly5ahKApOTk5069ZN12EJIYQQpZ4kus8RHZ9EaroGgNrFJNFNUj/k1vIvsXoyia6W9xL0DA11HFXJdenSJUaMGEFoaCgAFhYWUqIghBBCFBHFZ3aVDlx5ag3dOsVk+99T/wRSIXNzCFcXLLu9reOISiZFUfjxxx9p06aNNslt3bo1hw8fpnPnzjqOTgghhBAgI7rPdeVJfS4Uj8lo8Ul3ePilDxWSUwGoI5tDFIiYmBjGjh3Lrl27ADAyMmLGjBmMHj0aPT357CiEEEIUFZLoPsfVJysuWJgaYVnGWMfRvNjJ/X5Y7jwNQHl3d8xeb6bjiEqmoUOHEhISAoCdnR2rVq3C0dFRx1EJIYQQ4r9k+Ok5MpcWq1sMRnNvxUfAUv+MzSGMjWRziAL0xRdfYGRkxPDhwzl48KAkuUIIIUQRJSO6z3FFu7RY0Z6IpigaTm/6igrHrwBQ+eOxGNeQjQnyy6VLl6hTpw4GBhm/Lo0bN+avv/6iRo0aOo5MCCGEEM8jI7rPod0soohPRIu4fRLTZZsAUFWwoPok2RwiP2g0Gr799lvefPNNvvrqqyzPSZIrhBBCFH2S6D7Dg2Q195NSgKK9WUSaJpXL3y/E9Mo9AKrPmI1+uXI6jqr4u3XrFr1792b69Omo1WqWLVtGXFycrsMSQgghRB5IovsMV59eWqwIly5ciDhA+e/3AKBftzbWQz7ScUTFX3BwMK1ateLQoUMANGrUiD179mBpaanbwIQQQgiRJ5LoPkNm2QIU3cloj1MTif5qIYZPYq09bykqAym7flmJiYl88sknDBw4kNjYWAA8PDzYu3cvDg4OOo5OCCGEEHklWdEzZE5E09dTUcPCTMfR5Oz0P79gueHJ5hAtXbHo3FXHERVfYWFhDBo0iPDwcACqVq2Kj48Pbm5uug1MCCGEEC9NRnSfIXNEt6alGQb6Re9tSki+T/yir9F/nIqiUlFnwTLZHOIVVKxYkQcPHgDQrVs3QkJCJMkVQgghijkZ0X0G7dJiRXTFhX/2f4/F7iebQ/Rxx8zJWccRFW9WVlasWLGCO3fuMGDAAPnQIIQQQpQAkug+Q+ZktKI4Ee1uwnXSF69CpVFQTIyoPXuhrkMqdoKCgggJCeHLL7/UJrVvvfWWjqMSQgghRH6SRDcH6RoNkbGZu6IVrURXURRCNyzG4u9IACp7jMPIprpugypGEhIS+Oyzz9iwYQMAzZs3p2/fvjqOSgghhBAFQRLdHETFJ5GmUYCit4butbunMfl6Y8aDSpbUmCibQ+TWsWPHGDlyJNeuXQMyNn2oVauWboMSQgghRIHR6SyrlJQUpk2bRrNmzWjZsiU//PDDM489dOgQ77zzDs7OznTv3p39+/cXWFyZ9bkAdYpQoqvRpHPxu7mYRMYAUMPzC/TNi9aIc1GUlpbGvHnz6Nq1qzbJdXd3JyQkBBcXFx1HJ4QQQoiCotMR3YULF3L27FlWr17NzZs3+eyzz6hWrRqdOnXKclxYWBgeHh5MnjyZ1q1bc/jwYcaNG8emTZsKZH3Tp9fQLUo1umERBzH32wWAfv3aWA8epuOIir7IyEjGjRvHiRMnADA3N2fx4sW4u7vrODIhhBBCFDSdJbpJSUkEBgayatUqXnvtNV577TUuX77M2rVrsyW627dvx8XFhUGDBgFQs2ZNDhw4wM6dOwsk0c2ciGZpaoSFqVG+X/9lqNMeE/XlPCrEJgJQZ/7XsjlELkyaNEmb5Lq4uPDdd99ha2ur46iEEEIIURh0VroQFhZGWloazs7/LovVtGlTQkND0Wg0WY7t2bMnkyZNynaNhw8fZmvLD5mlC3UrFZ3R3NMnf8Fi4xEAjN/8H+U7dNZxRMXD/PnzMTc3Z/r06Wzbtk2SXCGEEKIU0dmQ4L1797C0tMTI6N8R00qVKpGSkkJ8fDwVKlTQttetWzfLuZcvX+bo0aMvNVs+OTn5hceE30sAwLa8KUlJSXm+R35LUj8gbsESLFLSUFQqqs9cmKvXURr9/fffODo6aj8s2djYcPToUSwsLEhJSdFxdKKgZP4+yO9F6SD9XbpIf5cuiqLk61r2Okt0k5OTsyS5gPaxWq1+5nmxsbGMGTOG119/nXbt2uX5vpGRkS88JjPRNdckc+HChTzfI7/dCttM5T0Zm0NoOnXkqr4BFIG4ipLU1FT8/f0JDAzE3d2d4cOHA//2961bt3QYnSgsufn9FiWH9HfpIv1devw3P3wVOkt0jY2NsyW0mY9NTExyPCcmJoYPP/wQRVFYtmwZenp5r7yoVasWpqamz3w+PllNgvo8AE3r16RBg9p5vkd+ik28RcIn61ApoJgY02DB1xhWqarTmIqa8PBwJkyYwJkzZwDYu3cvkydPJi4u7oX9LUqG5ORkIiMjpb9LCenv0kX6u3S5fPlyvl5PZ4mutbU1cXFxpKWlYfBkUtW9e/cwMTGhXLly2Y6/c+eOdjJaQEBAltKGvDA1NaVMmTLPfP5i7GPtnx2qVHjusQVNURQurVmO+cmMJbEqjx1P+Tp1X3BW6aEoCv7+/syYMUP7lVbr1q3x8fHBwsKCuLi4F/a3KFmkv0sX6e/SRfq7dMjPsgXQ4WS0Bg0aYGBgwKlTp7RtJ06cwNHRMdtIbVJSEsOGDUNPT481a9ZgbW1dYHFdif13gpuuJ6NFx5zH8Kt1GQ8qWVJjwlSdxlOUxMTEMGDAACZOnKgtg5k9eza//PIL1apV03V4QgghhCgCdDaia2pqSo8ePZg5cyZz587l7t27/PDDD8ybNw/IGN01NzfHxMQEX19frl+/zk8//aR9DjJKHMzzecOEq0/W0DXQU1G9vO4+OWoUDRd8ZmN5/T4ANT73Rr9s0dm8QpdOnTpF3759uXv3LgB2dnasWrUKR0dHHUcmhBBCiKJEpzujTZ06lddee43Bgwcza9YsxowZQ4cOHQBo2bIlO3bsAGD37t08fvwYd3d3WrZsqf3x9vbO95giniwtVtOyLAb6unt7Lkf8Tlm/nQDo2dfFetBQncVS1NSuXVtbqD58+HAOHjwoSa4QQgghstHpjgOmpqYsWLCABQsWZHvu4sWL2j/v2rWr0GLK3BWttg63/k1NV3Nt8Wws4zKWNqu7YBkqfX2dxVMUPL3cSPny5Vm5ciUPHz7krbfe0nFkQgghhCiqZGut/8gsXaijw0T33Ikgygf+AYCxW0ss2nfUWSy6ptFo8PX15ejRo6xevVqb7Lq4uOg4MiGEEEIUdZLoPiUtXcO1uIxEt25F3UxES1I/JGbeAsqnpKHoqai3YLlO4igKbt26xccff8yhQ4cA8PPzY9iwYboNSgghhBDFhk5rdIua6AdJpGkUQHelC6f2rKLcvoz1YMv170uZ10pn7WlwcDCtWrXSJrmOjo60bNlSt0EJIYQQoliRRPcpETH/Li1Wp0Lhj+jGJd5BPX9FxuYQpsbUnbWo0GPQtcTERD755BMGDhxIbGwsAB4eHuzZswcHBwcdRyeEEEKI4kRKF55yJfaR9s+6qNE9vWYBZqeuA2A17hMMrasUegy69M8//zBixAjCw8MBqFq1Kj4+Pri5uek2MCGEEEIUSzKi+5SrT5YWq1DGiPKm+bfPcm7cun8J/a/WZDyoXAHbT0rf5hCzZs3SJrndunUjJCREklwhhBBCvDRJdJ+SubRYYU9EUxQN55Z5YRyV8VV9jc/nom9mVqgxFAXLly+natWqLFu2jNWrV7/0Ns9CCCGEECClC1lcjdXNGroRV/7A7PtgAPQc6mH9/oeFen9d2bFjB61bt8bsSVJfo0YNTp48ibGxsY4jE0IIIURJICO6T8mcjFanEEd00zSpXF3ghcGD5Ix7Lyz5m0MkJCQwevRo3n//fby8vLI8J0muEEIIIfKLjOg+EZeUQlyyGoDaFQpvRPf831sotyljcwijtq2wbNuh0O6tC8eOHWPkyJFcu3YNgH379vHgwQPKly+v48iEEEIIUdLIiO4TV3Ww4kJKahL3vL3RU6eh6OtRf8GKQrmvLqSlpTF//ny6du2qTXLd3d0JCQmRJFcIIYQQBUJGdJ/InIgGhTcZLXTXSsrtPwuA+YC+lGnwWqHct7BdvXqVESNG8PfffwNgbm7OkiVL6N27t44jE0IIIURJJonuE1efJLoGeiqqW5Qp8Ps9SIohad7XmAFKGRPqfV4yN4f4888/6dOnD48eZby/Li4ufPfdd9ja2uo4MiGEEEKUdFK68ETEkzV0a1Uoi75ewb8tpwPmYXY6CgCr8Z9gaG1d4PfUBUdHR6pUqYKBgQEzZsxg27ZtkuQKIUQp8eDBA+bPn0/btm1xcnKic+fO+Pv7o9FotMfY29tz7NixQo3r/PnzuLu74+TkxLvvvsvZs2efe7yiKAwcOJCIiIgs7cuXL8fe3p6jR49mO2fgwIEsX748W/uxY8ewt7fP0paSksKKFSvo2LEjjRs3pn379ixbtozHjx/n6XX5+/vTqlUrnJ2dmTZtGsnJyc889tKlS7z//vs4OzvTsWNHtm/fnuX5X375hU6dOuHs7Iy7uzsnTpzIEu8XX3yBq6srrq6ueHl5kZSUBEBcXBw9e/YkJSUlT7EXFEl0n7jyJNEtjIlod2MjUC0NAECxrojt+JK1OURaWpr2z2ZmZnz//ffs3LmTCRMmoF/CV5QQQgiRIS4uDnd3d86ePYu3tzfbt29nzJgx+Pr64u3trbO4kpKS+Oijj2jWrBlBQUE4OzszYsQIbaKWk82bN1OtWjXq1q2bpX379u3Y2tqyZcuWl45HrVYzaNAg9uzZw9SpUwkODtYODI0fPz7X19m9ezcrVqxg9uzZrF69mtDQUBYtyvnbYrVazciRI2nYsCFbt25l+PDhTJkyhTNnzgDw+++/M3v2bEaPHs2WLVv43//+x0cffcSdO3cAWLFiBcePH2flypX4+vry999/8+WXXwJgaWlJmzZtWLly5Uu/J/lJEt0nMiejFfTSYoqicParGRjfiAOgxsy56Jcp+FKJwqBWq5k5cya9evUiPT1d2964cWOaNm2qw8iEEEIUtiVLlmBkZISfnx+urq7UqFGDLl264O3tzdq1a7l69apO4tqxYwfGxsZMnjyZunXrMn36dMzMzNi1a1eOxyuKwrfffku/fv2ytJ87d47r168zatQo9uzZQ2Ji4kvF4+fnR1RUFAEBAbi5uVGjRg3c3NxYvnw5hw4d4siRI7m6TkBAAIMHD6ZNmzY0btyYWbNm8csvv+Q4qhseHs6NGzcYN24ctra29O7dGzs7O44fPw5kJPY9evTg7bffpmbNmowfP55KlSrx22+/AfDbb7/x3nvv4ejoSOPGjenXrx9//vmn9vr9+vUjICDguR8eCovU6AJp6RquxWX8Ba1bwCsuREYew/T7jK8HVA3rU2VAydgc4tKlS4wYMYLQ0FAAfHx8GDt2rI6jEkKIkulBspqwuw8K9Z4OlctT3tQoV8eq1WqCg4OZPHlytvXR27Rpg7+/PzY2NtnOu3PnDt7e3hw9epTk5GTq16/Pp59+qr1GQEAAP/74IzExMdSvX59p06bRrFkzAL788kuCgoJISEjAyckJLy8v6tevn+0eoaGhNG3aFJVKBYBKpeL111/n1KlT9OrVK9vxhw8fJjk5GScnpyzt27dvx8HBgY4dO+Ll5cWePXvo2bNnrt6fp23evJlevXphYWGRpd3BwYE1a9bQoEEDgGzlDpk8PDwYPXo0Z86cwcPDQ9vepEkTUlNTCQsLw9nZOcs5masdBQYGMmjQIEJDQ7ly5QoNGzYEYNiwYdrNnJ728GHGt98WFhbs3r2b7t27A7Bnzx5tnABWVlbUqlWLbdu28d577+Xl7ch3kugCUfGJpGsUAGoX4IiuRpPOlbnTKZeQ8emq7sIVqAqhHrggKYqCv78/M2bM0H5qfPPNN3n33Xd1HJkQQpRMD5LV1PHeTPyTtd8Li4WpEVem98xVsnv9+nWSkpJwdHTM9pxKpcLFxSXH8yZNmkS5cuVYv349iqKwePFi5s6dy6xZswgLC2PhwoWsWLGCevXqERAQwPjx4/n999/Zv38/GzZswMfHh8qVK7N06VKmTp3Kpk2bst3j3r171KtXL0tbxYoVuXz5co4xhYSE4Orqqk2MIeP/vp07d9KrVy/MzMxwdXVl8+bNeU50k5OTuXbtWo7vE6BN4iEj4c5JmTJlSEhIICUlhcqVK2vbDQwMsLCw4Pbt29nOsbGxYcKECSxevJiFCxeSnp7OmDFjcHV1BeC117KuAvX7778TGRmp7bfJkyczZswYmjdvDoCdnR3ffvttlnNatGhBSEiIzhPd4p1l5ZOI+4Wzhu6Fv7dSdlPGX1TD9m9i6dauwO5VGGJiYhgwYAATJ04kOTkZQ0NDZs+eTVBQUI6f1IUQQpQOCQkJQMZykrmlKArt27fH09OTunXrUq9ePQYMGKCdAHbz5k1UKhXVqlWjevXqjB8/nkWLFqHRaLhx4waGhoZUq1YNW1tbPD09mTJlSo73SU5Oxsgoa7JuZGSEWp3zB4fz589nq809ceIEt27don379gB06NCB48ePc+PGjVy/Xsjb+2RlZZXjj5mZmXbSWm5fV2pqKleuXOG9994jMDCQqVOnsmrVqhwnBV6/fp2pU6fSvXt3bQJ8/fp1qlatyurVq/Hz8yMlJYX58+dnOa9evXqcP38+d29EAZIRXf6diAYFNxlNnfaYO3NmY56ajqKvh9384r05xL59+/Dw8ODu3btAxqe5VatWPfNTqRBCiPxR/snIalEuXcj8Gv7Bg9zHqFKp6NevHzt27ODkyZNcvXqVs2fPaldocHV1xc7Oju7du9OwYUPatWuHu7s7BgYGdO3alTVr1tCuXTuaNGlC+/btn7lWu7GxcbbkT61WY2JikuPxsbGxWFpaZmkLDg7GxsZG+1V/u3bt8PLyYuvWrYwePRrIGFF9enWJTBqNBgODjPQrL+/Tf8sPMo0YMYI+ffpoX8d/X5epqWm2c7Zs2cLZs2fZvn07KpWK1157jfDwcFatWqUdpYWMdfA//PBDatSowZw5cwB49OgR06dPx9/fX1vOMXfuXN5//33Gjh2rHVW2sLDg/v37L3xdBU0SXf5dQ7diGeNc/xLn1ZmdKzE/kLF8Sdn3+1LGoWGB3KcwKIrCypUrtUnusGHDmDlzJmVKyKQ6IYQo6sqbGtG8ppWuw3gmW1tbzM3NOXfuHI0bN872/KhRoxg4cCAtWrTQtmk0GoYMGUJCQgJdunShbdu2pKamautOTU1NCQwM5Pjx4xw8eJCgoCDWrVtHUFAQ1tbW7Ny5kyNHjnDw4EH8/PzYuHEjW7ZsyZboWVtbExMTk6UtJiYmy9f+T1OpVFkmWKenp7Nr1y7i4uK0iW5m/E8nuubm5tqa1qc9fPhQO4JrbGxM/fr1OXfuHJ07d8527LRp02jRogXdunV75soO5cuXp1y5chgbGxMTE6MdfU5LSyM+Ph4rq+x/T86dO4ednV2WcowGDRpw8uRJ7ePLly/zwQcfUKNGDb7//nvtB4ErV66QlJSEg4OD9tiGDRui0Wi4ffu29n3UaDToFYHyTN1HUARcebLiQt1KBTOa++hxPI+8M5bdUMxMqT9zSYHcp7CoVCqWL19OgwYNWL9+PQsXLpQkVwghhJaBgQFdunRh7dq12UYZDxw4wIEDB7IlluHh4fz111/4+/szcuRI3NzctAMqiqIQGhqKr68vLi4uTJ06lV27dpGSksKJEyc4dOgQgYGBuLm5MWvWLLZu3UpkZCSXLl3KFpuTkxP//PMPiqJor33y5Mlsk80yVaxYkfj4eO3jo0ePEhsby7Jly9iyZYv2Z8qUKURGRmqTRXt7e06dOpXteqGhoVkS5Lfffls7ie5pYWFhbN68WZsU16xZM8cfCwsL9PT0cHR0zLLW7alTpzAwMMiSkGaqXLky4eHhWdquXr1K9erVAbh79y5DhgyhZs2a+Pn5UbZs2SznAlnOv3LlCoD2fMhYXq5SpUo5vKOFSxJd4Kp2Dd2CmYgW+uMcypyNBqDSJ59gmMOnq6JMo9Hg7++f5asVa2trQkJC6NChgw4jE0IIUVSNGTOGR48eMXToUI4fP87169cJDAxkypQpDBo0KNuEsHLlyqGnp0dwcDA3btxg165d2g0XUlNTMTExwcfHh8DAQKKjowkODiYpKQl7e3s0Gg0LFy5k7969REdHExQUhKmpKbVq1coWV6dOnUhISMDb25vw8HC8vb1JTk7OcUQVMkYrL168qH0cHBxM/fr16dChA3Z2dtqf/v37Y2FhoR15fffdd4mIiGDOnDlEREQQERFBQEAAP//8M4MHD9Zeb9CgQVhZWTFw4EB+++03oqKi2LlzJyNHjqRt27a8+eabuXq/+/fvj5+fH/v27eP06dPMnDmTPn36aEe04+PjtSPM3bt3JyoqikWLFnH9+nW2bNnCxo0bGThwIAALFixAo9Hg7e1NUlIS9+7d4969eyQmJlKlShVatWqFp6cnZ8+e5cyZM3h6etK1a1cqVKigjefixYtZEnqdUUqJ06dPK3///beSmJiY7bkK09crehMClOnBJ/P9vjH3ryoh9Ssqx830lWN1rZX05OR8v0dBunXrltKrVy/F0tJSGT58uK7DybXExMRn9rcoeaS/Sxfp7+Lj5s2bytSpU5VWrVopjo6OSteuXZWffvpJSUtL0x5jZ2en/Pnnn4qiKMr69euVVq1aKU2aNFF69uypbNu2TWnYsKHy888/K4mJicqWLVuUDh06KI0aNVI6dOigbN++XXsdPz8/pU2bNkqjRo2Ut99+Wzly5Mgz4woNDVV69OihODo6Kr1791bOnTv3zGMPHz6stGzZUtFoNEpKSorStGlT5ccff8zx2Pnz5yvNmjVTHj9+rCiKopw5c0YZMmSI0qxZM8XJyUnp1auXsmfPnmznxcXFKXPmzFHatGmjODo6Kh06dFCWL1+uJOcxZ/D19VVcXV2Vpk2bKlOnTtXGoSiK8v777yufffaZ9vGJEyeU9957T2nSpInSpUsX5ddff1UURVE0Go3SuHFjxc7OLtvPsmXLFEVRlPj4eGXKlCmKq6ur0qJFC8XT01N59OhRllj69OmjbNq0KU/xK0pG35w+fTrP5z2LSlGejN2XcGfOnEGtVtOgQYMsX7PHJaVQyXMjACv7uDC0efY1917Fb579KbM04/o2K1dSrf+QfL1+QdqxYwdjx44lNjYWgEaNGrF9+3bKlSun48heLCkpiQsXLmTrb1EySX+XLtLfpYuu+zs9PZ2OHTsyb9483njjjUK/f3EUHR1Nr169OHjwYI7r8T7P6dOnUalU+Ta5vdSXLlzJsrRY/pYuRF39C6Pvf8148Fp9qvb9IF+vX1ASExP55JNPeP/997VJroeHB3v37i0WSa4QQgiRX/T19fnoo49Yv369rkMpNjZu3Ei/fv3ynOQWBEl0Y/9NdOvmY6KrUTSEz5mCwcOMte3qLfYpFptD/PPPP7Rp04bVq1cDULVqVYKCgpg9e3a23W2EEEKI0qB3797cvHlTu6aveLa4uDgOHTrEyJEjdR0KIMuLaSeiGerrYVM++1pzL+vSX79SJihjcwiDDq2xbNU2365dUH7//Xd69+5NWloaAN26deOrr77KUlwuhBBClDZ6enqsW7dO12EUC5aWlvz666+6DkOr6A8xFrCIJ4luLUsz9PNpxDU1Xc2t2TPRe7I5hH0x2RyiefPmODg4YGZmxrJly1i9erUkuUIIIYQotmRE90mNbu18LFs4G+xL2UMZm0OYDepHGbsG+Xbt/Pbo0SPt+njGxsb4+fmhp6eXbbtDIYQQQojiptSP6GZORqtTMX82i0hKSeDhnMUAaMqaUt9rcb5cN78lJCQwatQo3n777SyLedevX1+SXCGEEEKUCKU60U1N13A9PhHIv4lop/3mYHr+BgCVJnyCURHcHOLYsWO0bt2aDRs2cOrUKXx8fHQdkhBCCCFEvivViW5UfCLpmoxlhGvnw4hubFwU6Ut/AEBTzYraY6e98jXzU1paGvPmzaNr165cu3YNAHd3d4YOHarjyIQQQggh8l+prtGNiHmo/XN+lC6cW/wZJrfiAagxyxs9E5NXvmZ+uXr1KiNGjODvv/8GwNzcnCVLltC7d28dRyaEEEIIUTBK9Yju02vo1q7waonujciTGPptzXjQ2J6qfT98pevlF0VRWLduHa1bt9YmuS4uLoSEhEiSK4QQokA9ePCA+fPn07ZtW5ycnOjcuTP+/v5oNBrtMfb29hw7dkwn8f3999+0a9fuhccpisLAgQOzraO7fPly7O3tOXr0aLZzBg4cyPLly7O1Hzt2DHt7+yxtKSkprFixgo4dO9K4cWPat2/PsmXLePz4cZ5ej7+/P61atcLZ2Zlp06aRnJz8zGMfPHjAxIkTcXZ25s033yQgICDL8+vWraNdu3a8/vrrDB06lKioqByvM2vWLAYOHKh9HBERwcCBAykqG++W6kQ3c8WFSmbGlDMxeunrKIqGy7M/Rf9RCgB1F61ApVLlS4z5Yffu3Tx69AgDAwNmzJjBtm3bsLW11XVYQgghSrC4uDjc3d05e/Ys3t7ebN++nTFjxuDr64u3t7euw+PixYuMGzcuVwnZ5s2bqVatWrbJ2tu3b8fW1pYtW7a8dBxqtZpBgwaxZ88epk6dSnBwsPb/6vHjx+f6Ort372bFihXMnj2b1atXExoayqJFi555/MSJE4mOjmbDhg1MmzaNxYsXExISAkBISAiLFi1ixowZ/PLLL5QpU4aPP/442zVOnjyZbX3hunXrUq1aNTZv3pzr2AtSqU50rzxZQ/dVJ6KF/70D06CMvxz6HVtT4X9tXjm2/KJSqVi6dCmtWrVi586dTJgwAX19fV2HJYQQooRbsmQJRkZG+Pn54erqSo0aNejSpQve3t6sXbuWq1ev6iy29evX07dvXypWrPjCYxVF4dtvv6Vfv35Z2s+dO8f169cZNWoUe/bsITEx8aVi8fPzIyoqioCAANzc3KhRowZubm4sX76cQ4cOceTIkVxdJyAggMGDB9OmTRsaN27MrFmz+OWXX3Ic1Q0LC+OPP/5g8eLF2NnZ0alTJ3r37s3JkycB+O2332jZsiVt2rShdu3aeHh4cPHiRWJjY7XXUKvVeHl50aRJk2zX79+/P999912RGNUt1TW6V2Mz19B9+bKFdE0aNz6fjlmaBsVAH4cF3+RXeC9FrVazdOlShg4dSqVKlYCMXUq2bt2q07iEEELkH3XaYx4k3y3Ue5Y3rYyRQe7mnqjVaoKDg5k8eXK27ePbtGmDv78/NjY22c67c+cO3t7eHD16lOTkZOrXr8+nn36qvUZAQAA//vgjMTEx1K9fn2nTptGsWTMAvvzyS4KCgkhISMDJyQkvLy/q16+fY3y///47CxYs4NGjR6xY8fxNnQ4fPkxycjJOTk5Z2rdv346DgwMdO3bEy8uLPXv20LNnz1y9P0/bvHkzvXr1wsLCIku7g4MDa9asoUGDjLX4/1vukMnDw4PRo0dz5swZPDw8tO1NmjQhNTWVsLAwnJ2ds5xz/PhxHBwcqFGjhrbNy8tL+2cLCwuCg4OJiIigZs2abNmyBRsbG8qXL689ZuXKldjb21OrVi2OHz+e5fqNGzcmKSmJI0eO0LJly7y9Ifms1Ca6iqJoJ6O9ykS0s9t8Mfv9HABlBvelTL2c/yIWhkuXLjFixAhCQ0MJDQ1l7dq1RaqEQgghxKtTpz1m01/zUafnrX7zVRnpm9D7jSm5SnavX79OUlISjo6O2Z5TqVS4uLjkeN6kSZMoV64c69evR1EUFi9ezNy5c5k1axZhYWEsXLiQFStWUK9ePQICAhg/fjy///47+/fvZ8OGDfj4+FC5cmWWLl3K1KlT2bRpU473+eabjEGpoKCgF76WkJAQXF1ds/x/qigKO3fupFevXpiZmeHq6srmzZvznOgmJydz7dq1HN8nQJvEQ0bCnZMyZcqQkJBASkoKlStX1rYbGBhgYWHB7du3s50TFRVF9erV8fPzY+3atRgZGfHBBx/Qt29fIKO++OjRo3Tp0gV9fX1MTU1Zu3at9hvhiIgI1q1bx9atW3PcGjmzj0NCQiTR1ZW4ZDUPHqcCULvCy5UuPFYnkvDFAkwAjXkZ7L2+zMcIc09RFPz9/ZkxY4b2K4rk5GQSExO1u54JIYQQhSUhIQHIWOEntxRFoX379nTs2JEqVaoAMGDAAD766CMAbt68iUqlolq1alSvXp3x48fTpk0bNBoNN27cwNDQkGrVqlGtWjU8PT25cuVKvryW8+fPZ0vWTpw4wa1bt2jfvj0AHTp0wNPTkxs3buQ4Uv0seXmfrJ6zLn/mdYyMss43MjIyyrIpVKakpCT++OMP0tLS+Prrr7l06RKzZ8/G0tKSjh07cvfuXVJSUli8eDE1a9bkm2++4dNPP2XTpk0YGRnh5eXFmDFjtN8c56RevXq5LrsoSKU20c3cEQ1efkT3zKrZmITdBKDixE8wzEWtT36LiYlh7Nix7Nq1C8j4S+3p6cmoUaPQ0yvVJdhCCFEiGRlkjKwW5dKFzK/hHzx4kOvrq1Qq+vXrx44dOzh58iRXr17l7Nmz2hUaXF1dsbOzo3v37jRs2JB27drh7u6OgYEBXbt2Zc2aNbRr144mTZrQvn37fFtZKDY2FktLyyxtwcHB2NjY0LBhQwDatWuHl5cXW7duZfTo0UDGiOrTq0tk0mg0GBhkpF95eZ/+W36QacSIEfTp0wcgW1KrVqsxNTXNdo6+vj7p6eksXryYMmXK4OjoSFhYGBs2bKBjx458/vnndOjQge7duwMZ9dZubm7s37+fhIQE0tPTee+9954br4WFBffv33/h6ypopTjR/XcN3ZeZjPYg/gbqL1dhBGhsrKgzpvA3h9i3bx8eHh7cvZvxj52dnR2rVq165lcgQgghSgYjAxOszIvu6jm2traYm5tz7tw5GjdunO35UaNGMXDgQFq0aKFt02g0DBkyhISEBLp06ULbtm1JTU3V1p2ampoSGBjI8ePHOXjwIEFBQaxbt46goCCsra3ZuXMnR44c4eDBg/j5+bFx40a2bNmSY6KXFyqVivT0dO3j9PR0du3aRVxcnDbRzYz/6UTX3Nychw8fZrvew4cPtSO4xsbG1K9fn3PnztG5c+dsx06bNo0WLVrQrVu3Z67sUL58ecqVK4exsTExMTHalSHS0tKIj4/PcSS4cuXKVKlShTJlymjbateurS2POHfuHCNHjtQ+Z2ZmRs2aNblx4wa///47Z8+e5fXXXwcgNTWV9PR0nJ2dCQ4Oplq1atr3oygMuJXaRDdzaTEjfT2qlc/7L8HZhZMxupPxVUH12XPR+0+xfUHbt2+f9hMcwPDhw5k5c+Yr/0ILIYQQr8rAwIAuXbqwdu1a3n333SxfqR84cIADBw4wceLELOeEh4fz119/cfToUSpUqADA2rVrgYyyhtDQUE6dOsWoUaNwcXFh4sSJtGjRghMnTlCmTBlu3rxJ//79cXNzw8PDg5YtW3Lp0qVsk8jyqmLFisTHx2sfHz16lNjYWJYvX06tWrW07UeOHGH+/PmcPHmS119/HXt7ew4ePJjteqGhoVkS5Lfffht/f38++ugjypUrp20PCwtj8+bNdOzYEYCaNWs+N05HR0dOnDhB8+bNATh16hQGBgY4ODhkO9bJyYmVK1dmSbqvXLmiLbuoXLkyERERvPnmm0DGyHB0dDTVq1dn8eLFWdb3/emnnwgNDWXx4sVZaoTj4uKeW9pQWHSfauvIldiMT1m1KpRFP4+fOG5HhqLvtwUApYk91fp8kM/RvZibmxv/93//h5WVFevXr2fBggWS5AohhCgyxowZw6NHjxg6dCjHjx/n+vXrBAYGMmXKFAYNGkS9evWyHF+uXDn09PQIDg7mxo0b7Nq1S7vhQmpqKiYmJvj4+BAYGEh0dDTBwcEkJSVhb2+PRqNh4cKF7N27l+joaIKCgjA1Nc2SiL6shg0bcvHiRe3j4OBg6tevT4cOHbCzs9P+9O/fHwsLC+3I67vvvktERARz5swhIiKCiIgIAgIC+Pnnnxk8eLD2eoMGDcLKyoqBAwfy22+/ERUVxc6dOxk5ciRt27bVJpsv0r9/f/z8/Ni3bx+nT59m5syZ9OnTR5sbxMfHa0eYW7RoQe3atfnss8+IiIhgx44dBAYGapdQc3d357vvvuPgwYNcuXIFT09PzMzMaNu2LdbW1tSsWVP7U758eUxMTKhZs6a2JAMy1il+OqHXlVI7onsl5uWWFlMUhUszJ2CamLE5RL2FPoWysoFGoyEmJkb7acnAwIDvv/8eIyOjLJ+ghBBCiKLAysqKdevWsXz5ciZNmkR8fDy2traMHTs225q0AFWqVGHmzJn4+Pjw5ZdfUrt2bWbMmMFnn31GZGQk77zzDt7e3nzzzTfMnj2batWqsWjRIurWrUvdunUZO3Ys8+bN4969e9SpU4dvvvkmy3JYL6tVq1ZMmTIFRVFITU1l7969WZbxymRsbEyvXr3YtGkT06dPp0qVKvz0008sXbqUvn37kpqaSt26dVm4cCGtW7fWnmdiYsLq1avx8fFh1qxZxMTEULVqVXr37s2wYcNynWN07dqVGzdu4OXlhVqtpkOHDnz66afa58eMGYONjQ3z589HX1+flStX8vnnn9OrVy8sLS2ZMmWKdpe4oUOHAjBnzhzi4+NxdnbG398/21Jxz6IoCv/88w/9+/fP1fEFSaUUhdV8C8GZM2dQq9U0aNCAMmXKUNc7iMjYREa1sGPFu81zfZ0rfwUT074nqnQNep3daBq4rwCjznDr1i0+/vhjbt68yYEDB7LU1IicJSUlceHCBW1/i5JN+rt0kf4uXXTd3+np6XTs2JF58+bxxhtvFPr9i6Pjx4/j6enJzp0781yne/r0aVQqVb7NNyqVpQup6RquxyUBULdS7ieiaTTpRHlORZWuQTHUx2H+twUVolZwcDCtWrXi0KFDXLp0ie+++67A7ymEEEKIDPr6+nz00UesX79e16EUGxs2bGDYsGFFYjKa7iPQgetxiWieDGTXrpD70oXzv/pS5vB5AEw/6IdZ3Zx3XMkPiYmJfPLJJwwcOFC75Z6Hh0eOe00LIYQQouD07t2bmzdvEhERoetQiryIiAhu3ryZb8u7vapSWaP79NJidXK5tFiKOom4L+ZnbA5RrgwOnksKKLqMmZIfffQR4eHhAFStWhUfHx/c3NwK7J5CCCGEyJmenl6OO4CJ7OrWrVuk3qtSOaIb8dRmEbkd0T2zajYmFzM2h7CcOB7DCvm/OYSiKHz99dd06NBBm+R269aNkJAQSXKFEEIIIfKoVI7oXn0yomtV1hhzE8MXHv8w/jbqJSsxBNKrV6bemBkFEpdKpSIiIoK0tDTMzMyYN28eAwYMKJRVHYQQQgghSppSmeheic0Y0c3tjmhnF0zC8G7G5hA2X3ij95+9pPPT3LlzSUpKYurUqdrdTYQQQgghRN6VytKFzF3RclO2cC/yDCq/IAA0zg5U7/1BvsWRkJDAxIkTiY6O1raVLVuW77//XpJcIYQQQohXVOpGdBVFIeJJ6UJuJqKFfT4ekyQ1APUWrci3MoJjx44xcuRIrl27xuXLl9m8eTP6+vr5cm0hhBBCCFEKR3Rjk9UkPE4FXrwrWuRfuzDe/DsAqq5uVHRxe+X7p6WlMW/ePLp27cq1a9eAjFUVUlJSXvnaQgghhBDiX6Uu0Y2MTdT++XkjuhpFQ9T0yag0ChpDfRrkw+YQV69epUuXLixatAiNRoO5uTkrV67E19dXdvcRQghR4jx48ID58+fTtm1bnJyc6Ny5M/7+/mg0Gu0x9vb2HDt2rFDjOnToEO+88w7Ozs50796d/fv3P/d4RVEYOHBgtnV0ly9fjr29PUePHs12zsCBA1m+fHm29mPHjmFvb5+lLSUlhRUrVtCxY0caN25M+/btWbZsGY8fP87T6/L396dVq1Y4Ozszbdo0kpOTn3nsgwcPmDhxIs7Ozrz55psEBARon2vbti329vbZflasWAFkrPU/Y8YMXFxcePPNN1m5cqX23Li4OHr27FlkBvBKXelCZNy/iW7d54zohm1dhckfGZtDmHzYD7PaL785hKIorFu3jilTpvDoUUZ9sIuLC9999x22trYvfV0hhBCiqIqLi+O9996jcuXKeHt7U716dc6cOcMXX3xBVFQUnp6eOokrLCwMDw8PJk+eTOvWrTl8+DDjxo1j06ZNODg45HjO5s2bqVatWrb5M9u3b8fW1pYtW7bg6ur6UvGo1WoGDRpEcnKydiJ6REQE3t7enD9/Ptc7ou7evZsVK1awaNEiKlasyNSpU1m0aBFeXl45Hj9x4kQePnzIhg0buHLlCpMnT6Z27dq0atWKTZs2kZ6enuXaX331FT179gTA09OTc+fO4ePjg6IoTJ48GUNDQz788EMsLS1p06YNK1euZMyYMS/1nuSnUjeie/XJiK6Rvh7VyuU8ipqa+pjYmXMASC9vRkOvL1/pnrt378bDw4NHjx6hr6/P9OnT2bZtmyS5QgghSqwlS5ZgZGSEn58frq6u1KhRgy5duuDt7c3atWu5evWqTuLavn07Li4uDBo0iJo1azJgwACaN2/Ozp07czxeURS+/fZb+vXrl6X93LlzXL9+nVGjRrFnzx4SExNzPP9F/Pz8iIqKIiAgADc3N2rUqIGbmxvLly/n0KFDHDlyJFfXCQgIYPDgwbRp04bGjRsza9YsfvnllxxHdcPCwvjjjz9YvHgxdnZ2dOrUid69e3Py5EkAKlSogJWVFVZWVpiYmODj48Nnn32GjY0NsbGxBAcHM2vWLJo2bUqzZs2YNGkSfn5+2uv369ePgIAAkpKSXuo9yU+ldkS3doWy6OnlPLHszHezMA6/BYDFpHEYWlR4pXt26NCB1q1bExUVha+vL02bNn2l6wkhhCjd0h484PGlsEK9p4mdAwbly+fqWLVaTXBwMJMnT8bY2DjLc23atMHf3x8bG5ts5925cwdvb2+OHj1KcnIy9evX59NPP9VeIyAggB9//JGYmBjq16/PtGnTaNasGQBffvklQUFBJCQk4OTkhJeXF/XrZ/82tmfPnqSmpmZrf/jwYbY2gMOHD5OcnIyTk1OW9u3bt+Pg4EDHjh3x8vJiz5492hHPvNi8eTO9evXCwsIiS7uDgwNr1qyhQYMGANnKHTJ5eHgwevRozpw5g4eHh7a9SZMmpKamEhYWhrOzc5Zzjh8/joODAzVq1NC2PWvk18/PDysrK959910A7UpRT78f9vb23Lt3j+joaKpXr46VlRW1atVi27ZtvPfee7l8JwpGqUt0M0d0nzURLTH+Lo+//C5jcwhba+p/nPfNIdRqNTdu3KB27dpAxtaBK1euxNTUlLJlc7cTmxBCCJGTtAcPOP1aXdLj4wv1vvoWFjQ+F5GrZPf69eskJSXh6OiY7TmVSoWLi0uO502aNIly5cqxfv16FEVh8eLFzJ07l1mzZhEWFsbChQtZsWIF9erVIyAggPHjx/P777+zf/9+NmzYgI+PD5UrV2bp0qVMnTqVTZs2ZbvHf8sPLl++zNGjR+nbt2+OMYWEhODq6ppl1SVFUdi5cye9evXCzMwMV1dXNm/enOdENzk5mWvXruX4PgHaJB4yEu6clClThoSEBFJSUqhcubK23cDAAAsLC27fvp3tnKioKKpXr46fnx9r167FyMiIDz74INt7kJyczJo1a5g9ezZ6ehlFABUrZuwMe+fOHWrVqgXArVsZg4NxcXFUr14dgBYtWhASEiKJbmHLHNF91kS0s/MnYngv41Nd1S/moJ/HzSEuXbrEiBEjiI+P57fffqNcuXIAWFlZvULUQgghRPGRkJCxyZK5ee42ZoKM5LF9+/Z07NiRKlWqADBgwAA++ugjAG7evIlKpaJatWpUr16d8ePH06ZNGzQaDTdu3MDQ0JBq1apRrVo1PD09uXLlygvvGRsby5gxY3j99ddp165djsecP3+eli1bZmk7ceIEt27don379kDGN7eenp7cuHEjx5HqZ8nL+/S8PCLzOkb/yVmMjIxQq9XZjk9KSuKPP/4gLS2Nr7/+mkuXLjF79mwsLS3p2LGj9rgdO3ZQpkwZOnTooG2zsbGhSZMmeHt7s2jRIlJTU7WT1J4eKa9Xrx7btm174esqaKUq0VUUhegHGfUiOU1Ei4k8h+KX8ekvvakDtr0+yNO1/f39mTFjhrYext/fn7Fjx7564EIIIcQTBuXL0/hcRJEuXcj8Gv7Bgwe5vr5KpaJfv37s2LGDkydPcvXqVc6ePatdocHV1RU7Ozu6d+9Ow4YNadeuHe7u7hgYGNC1a1fWrFlDu3btaNKkCe3bt6d3797PvV9MTAwffvghiqKwbNky7Yjlf8XGxmJpaZmlLTg4GBsbGxo2bAhAu3bt8PLyYuvWrYwePRrIGFF9enWJTBqNBgMDgzy/T/8tP8g0YsQI+vTpA5AtqVWr1ZiammY7R19fn/T0dBYvXkyZMmVwdHQkLCyMDRs2ZEl0d+/eTZcuXbTxZlq4cCFjx47FxcUFc3NzJkyYwD///JPlW2sLCwvu37//wtdV0EpVopuugEbJ+HPtHEZ0L3qOwyg549NI3QW53xwiJiaGsWPHsmvXLiDjE5SnpyejRo3Kn8CFEEKIpxiUL0/ZN5rrOoxnsrW1xdzcnHPnztG4ceNsz48aNYqBAwfSokULbZtGo2HIkCEkJCTQpUsX2rZtS2pqqrbu1NTUlMDAQI4fP87BgwcJCgpi3bp1BAUFYW1tzc6dOzly5AgHDx7Ez8+PjRs3smXLlhwTvTt37jBo0CAgo+63QoVnz8VRqVRZViBIT09n165dxMXFaRPdzPifTnTNzc1zrPt9+PChdgTX2NiY+vXrc+7cOTp37pzt2GnTptGiRQu6devGli1bcoyvfPnylCtXDmNjY2JiYrSlGWlpacTHx+c4Ely5cmWqVKmSZWnT2rVrZymPUKvVHD9+XDui/rSaNWuydetW7t+/j7m5OdevX0dPT49q1apleT+e9eGhMOk+gkKUlpnlAnX+M6Ib/dceDLf+lvGgexuscrk5xL59+2jZsqU2ybWzs2Pv3r18/PHHRaKDhRBCiMJmYGBAly5dWLt2bbZRxgMHDnDgwIEs9aQA4eHh/PXXX/j7+zNy5Ejc3Ny4e/cukPGtaWhoKL6+vri4uDB16lR27dpFSkoKJ06c4NChQwQGBuLm5sasWbPYunUrkZGRXLp0KVtsSUlJDBs2DD09PdasWYO1tfVzX0vFihWJf6oe+ujRo8TGxrJs2TK2bNmi/ZkyZQqRkZHalQvs7e05depUtuuFhoZmSZDffvtt7SS6p4WFhbF582ZtUlyzZs0cfywsLNDT08PR0ZETJ05ozz916hQGBgY5Lpnm5OTEjRs3siTiV65cyVJ2cfHiRdLS0rJ9UMn8QHLx4kUqVqyIkZERhw4domHDhllGdOPi4qhUqdLz3tpCUaoysTTl30S3doV/O0NRFCKnTcrYHMLIAId537zwWoqiMHXqVPr06aP9RRw+fDgHDx58ZlG5EEIIUVqMGTOGR48eMXToUI4fP87169cJDAxkypQpDBo0iHr16mU5vly5cujp6REcHMyNGzfYtWuXdsOF1NRU7TJXgYGBREdHExwcTFJSEvb29mg0GhYuXMjevXuJjo4mKCgIU1NT7WSpp/n6+nL9+nUWLFgAwL1797h3794zV11o2LAhFy9e1D4ODg6mfv36dOjQATs7O+1P//79sbCw0I68vvvuu0RERDBnzhwiIiKIiIggICCAn3/+mcGDB2uvN2jQIKysrBg4cCC//fYbUVFR7Ny5k5EjR9K2bVvefPPNXL3f/fv3x8/Pj3379nH69GlmzpxJnz59tCPa8fHx2tfYokULateuzWeffUZERAQ7duwgMDAwyxJqly9fpnr16tnqfvX09DAxMWHJkiVERkayb98+fHx8GDlyZJbjLl68mCWh15XSVbrwZES3clkTyhobatsvbVmF8dGMzSGMhvTFvNaLN4dQqVTa0gYrKytWrFjBW2+9VQBRCyGEEMWPlZUV69atY/ny5UyaNIn4+HhsbW0ZO3ZstjVpAapUqcLMmTPx8fHhyy+/pHbt2syYMYPPPvuMyMhI3nnnHby9vfnmm2+YPXs21apVY9GiRdStW5e6desyduxY5s2bx71796hTpw7ffPMN5XOoKd69ezePHz/G3d09S3vPnj2ZP39+tuNbtWrFlClTUBSF1NRU9u7dm2UZr0zGxsb06tWLTZs2MX36dKpUqcJPP/3E0qVL6du3L6mpqdStW5eFCxfSunVr7XkmJiasXr0aHx8fZs2aRUxMDFWrVqV3794MGzYs12WUXbt25caNG3h5eaFWq+nQoQOffvqp9vkxY8ZgY2PD/Pnz0dfXZ+XKlXz++ef06tULS0tLpkyZkmVCXkxMTI7vH8CsWbPw9PSkZ8+eVKxYkRkzZmTLgU6ePKmtHdYllaI8NcxZgp05c4ZbD5LovPkSrjWtODy2EwBpqSkca1oXoyu3Sbcsy+tnrmCUy3VzHz9+zMyZM5k4caKsqlDEJCUlceHCBRo0aCDbK5cC0t+li/R36aLr/k5PT6djx47MmzePN954o9DvXxxFR0fTq1cvDh48iJmZWZ7OPX36NCqVKt++HS9VpQvpT3L6p9fQPfftLIyuZKwxV37i2Gcmubdv32bQoEFZ9rk2MTFh/vz5kuQKIYQQJZS+vj4fffQR69ev13UoxcbGjRvp169fnpPcglCqEt3MyWiZE9GSH9wjacm3Gc/Vssb+45z33Q4ODqZly5Zs376dESNG5LijihBCCCFKpt69e3Pz5s0sg10iZ3FxcRw6dChbza6ulKoa3cwajdoVMmYwnvWegMH9J5tDzJ6DnqFhluMTExOZMWMGq1ev1ra5urpSSqo9hBBCCEHGBKx169bpOoxiwdLSkl9//VXXYWiVqkQ3U52KZYmLvIDmh03oAWnNGlCz5wdZjvnnn38YMWIE4eHhAFStWhUfHx/c3NwKPV4hhBBCCJF3pap0IVPdSuaEzRiD3uNUFBXUWbhMO6sxPT2dr776io4dO2qT3G7duhESEiJJrhBCCCFEMVLqRnSNDfRQLhxG/9eMzSGU7m2w/r822ud3797N7NmzATAzM2PevHkMGDAg18t7CCGEEEKIoqHUJbo1LcpwbfpEjDQKGmMDGs7zyfJ8586d6datGzdv3sTX11e7lZ4QQgghhCheSl2i2+fRMYyOXQDAcMh7KJZVOHPmjHa9NpVKxYoVKzA1NcXwP5PThBBCCCFE8VG6El0F2m4JACDNsiwpnQbSunVrUlJSCAkJoWLFikDGNoRCCCGEEKJ40+lktJSUFKZNm0azZs1o2bIlP/zwwzOPPX/+PO7u7jg5OfHuu+9y9uzZPN+vvCaJMtfukg5satacHu/15dq1a9y+fZtNmza9wisRQgghhBBFjU4T3YULF3L27FlWr17N559/zooVK9i1a1e245KSkvjoo49o1qwZQUFBODs7M2LECJKSkvJ0vzLJSdxExSdmFvz4219oNBrMzc1ZuXIlI0aMyK+XJYQQQgghigCdJbpJSUkEBgYyffp0XnvtNd566y2GDRvG2rVrsx27Y8cOjI2NmTx5MnXr1mX69OmYmZnlmBQ/z2MNjDYsy8VUDQAuLi6EhITQu3fvfHlNQgghhBCi6NBZohsWFkZaWhrOzs7atqZNmxIaGopGo8lybGhoKE2bNtUu8aVSqXj99dc5depUnu75QKUiWaXCwMCAGTNmsG3bNmxtbV/5tQghhBBCiKJHZ5PR7t27h6WlJUZGRtq2SpUqkZKSQnx8PBUqVMhybL169bKcX7FiRS5fvpzr+6WmpmJlZcXatWupVKkSxsbGnDt37tVfiCiSMrdpvnz5sqyBXApIf5cu0t+li/R36ZKampqv/ayzRDc5OTlLkgtoH6vV6lwd+9/jnkelUmFoaEj16tVfMmJRnKhUqmx/Z0TJJf1dukh/ly7S36WLSqUqGYmusbFxtkQ187GJiUmujv3vcc/zdImEEEIIIYQo+XRWo2ttbU1cXBxpaWnatnv37mFiYpJtHVtra2tiYmKytMXExFC5cuVCiVUIIYQQQhQ/Okt0GzRogIGBQZYJZSdOnMDR0RE9vaxhOTk58c8//2jrdBRF4eTJkzg5ORVmyEIIIYQQohjRWaJrampKjx49mDlzJqdPn2bfvn388MMPDBo0CMgY3X38+DEAnTp1IiEhAW9vb8LDw/H29iY5OZnOnTvrKnwhhBBCCFHEqZTMYVIdSE5OZubMmezZs4eyZcsydOhQPvjgAwDs7e2ZN28evXr1AuD06dN8/vnnREREYG9vz6xZs2jYsKGuQhdCCCGEEEWcThNdIYQQQgghCopOtwAWQgghhBCioEiiK4QQQgghSiRJdIUQQgghRIlUohLdlJQUpk2bRrNmzWjZsiU//PDDM489f/487u7uODk58e6773L27NlCjFTkh7z096FDh3jnnXdwdname/fu7N+/vxAjFfkhL/2dKTo6GmdnZ44dO1YIEYr8lJf+vnjxIv369aNx48Z0796dP//8sxAjFfkhL/29d+9eOnfujLOzM/369ePcuXOFGKnIT2q1mm7duj333+hXzddKVKK7cOFCzp49y+rVq/n8889ZsWIFu3btynZcUlISH330Ec2aNSMoKAhnZ2dGjBhBUlKSDqIWLyu3/R0WFoaHhwfvvvsuW7ZsoW/fvowbN46wsDAdRC1eVm77+2kzZ86U3+tiKrf9/fDhQ4YMGUK9evXYtm0bb731Fh4eHty/f18HUYuXldv+vnz5MhMnTmTEiBFs3bqVBg0aMGLECJKTk3UQtXgVKSkpTJgwgcuXLz/zmHzJ15QSIjExUXF0dFT+/PNPbZuPj4/y/vvvZzs2MDBQadu2raLRaBRFURSNRqO89dZbyi+//FJo8YpXk5f+XrRokTJ06NAsbUOGDFG+/PLLAo9T5I+89HemrVu3Kn379lXs7OyynCeKvrz09+rVq5X27dsraWlp2rZevXophw4dKpRYxavLS3//+OOPSs+ePbWPHz58qNjZ2SmnT58ulFhF/rh8+bLy9ttvK927d3/uv9H5ka+VmBHdsLAw0tLScHZ21rY1bdqU0NBQNBpNlmNDQ0Np2rQpKpUKAJVKxeuvv55llzZRtOWlv3v27MmkSZOyXePhw4cFHqfIH3npb4C4uDgWLVrE7NmzCzNMkU/y0t/Hjx+nXbt26Ovra9t++eUXWrduXWjxileTl/62sLAgPDycEydOoNFoCAoKomzZstja2hZ22OIVHD9+nObNm7Nhw4bnHpcf+ZrBqwRalNy7dw9LS0uMjIy0bZUqVSIlJYX4+HgqVKiQ5dh69eplOb9ixYrPHT4XRUte+rtu3bpZzr18+TJHjx6lb9++hRaveDV56W+A+fPn07NnT+rXr1/YoYp8kJf+joqKonHjxnh6enLgwAFsbGz47LPPaNq0qS5CFy8hL/3dpUsXDhw4QP/+/dHX10dPTw9fX1/Kly+vi9DFS+rfv3+ujsuPfK3EjOgmJydn+SUBtI/VanWujv3vcaLoykt/Py02NpYxY8bw+uuv065duwKNUeSfvPT3H3/8wYkTJxg9enShxSfyV176OykpiZUrV2JlZcWqVat44403GDp0KLdu3Sq0eMWryUt/x8XFce/ePby8vNi4cSPvvPMOU6dOlZrsEio/8rUSk+gaGxtne+GZj01MTHJ17H+PE0VXXvo7U0xMDIMHD0ZRFJYtW4aeXon561/i5ba/Hz9+jJeXF59//rn8Phdjefn91tfXp0GDBowdO5aGDRvy6aefUqtWLbZu3Vpo8YpXk5f+Xrx4MXZ2dgwYMIBGjRrxxRdfYGpqyi+//FJo8YrCkx/5Won5n97a2pq4uDjS0tK0bffu3cPExIRy5cplOzYmJiZLW0xMDJUrVy6UWMWry0t/A9y5c4cBAwagVqsJCAjI9lW3KNpy29+nT58mKiqKsWPH4uzsrK35Gz58OF5eXoUet3g5efn9trKyok6dOlnaatWqJSO6xUhe+vvcuXM4ODhoH+vp6eHg4MDNmzcLLV5RePIjXysxiW6DBg0wMDDIUqB84sQJHB0ds43cOTk58c8//6AoCgCKonDy5EmcnJwKM2TxCvLS30lJSQwbNgw9PT3WrFmDtbV1IUcrXlVu+7tx48bs2bOHLVu2aH8A5syZw7hx4wo5avGy8vL73aRJEy5evJil7cqVK9jY2BRGqCIf5KW/K1euTERERJa2q1evUr169cIIVRSy/MjXSkyia2pqSo8ePZg5cyanT59m3759/PDDDwwaNAjI+HT4+PFjADp16kRCQgLe3t6Eh4fj7e1NcnIynTt31uVLEHmQl/729fXl+vXrLFiwQPvcvXv3ZNWFYiS3/W1iYkLNmjWz/EDGqEDFihV1+RJEHuTl97tv375cvHiR5cuXc+3aNb7++muioqJ45513dPkSRB7kpb/79OnDxo0b2bJlC9euXWPx4sXcvHmTnj176vIliHyU7/naq66FVpQkJSUpkydPVpo0aaK0bNlS+fHHH7XP2dnZZVl3LTQ0VOnRo4fi6Oio9O7dWzl37pwOIhavIrf93bFjR8XOzi7bz2effaajyMXLyMvv99NkHd3iKS/9/ffffys9e/ZUGjVqpLzzzjvK8ePHdRCxeBV56e+NGzcqnTp1Upo0aaL069dPOXv2rA4iFvnlv/9G53e+plKUJ+PBQgghhBBClCAlpnRBCCGEEEKIp0miK4QQQgghSiRJdIUQQgghRIkkia4QQgghhCiRJNEVQgghhBAlkiS6QgghhBCiRJJEVwghhBBClEiS6AohhBBCiBJJEl0hRLE1cOBA7O3tc/zJ3PL5RY4dO4a9vT3R0dEFEmN0dHS22Bo2bIirqyvjx4/n5s2b+Xavtm3bsnz5ciBjT/jNmzdz//59AIKCgrC3t8+3e/1X5vWf/mnQoAFvvPEGH374IefPn8/T9W7evElwcHABRSuEKC0MdB2AEEK8is6dOzN9+vRs7aampjqI5tmWL1+Os7MzABqNhqioKKZPn86IESP49ddfUalUr3yPTZs2YWxsDMBff/3FlClT2L9/PwBdunShVatWr3yPFzl8+LD2z+np6Vy9epW5c+cydOhQ9u3bh5mZWa6u89lnn2FjY0PXrl0LKlQhRCkgia4QolgzMTHByspK12G8UPny5bPEaW1tjYeHB5MmTeLixYs4ODi88j0qVKig/fN/d3c3MTHBxMTkle/xIv/tiypVquDl5cX777/Pn3/+Sbt27Qo8BiGEyCSlC0KIEu3BgwfMmDGDVq1a8dprr+Hq6sqMGTNITk7O8fjIyEiGDh1K06ZNcXZ2ZujQoVy8eFH7/MOHD/H09MTFxYWmTZsyaNAgzpw581Kx6evrA2BoaAjArVu3mDRpEv/73/9o0qQJQ4cOJSwsTHv8/fv3GTt2LM2bN6dx48b07duX48ePa5/PLF04duwYgwYNAqBdu3YEBQVlKV2YMmUK7u7uWWK5ceMGDg4O/PHHHwCcPHmSAQMG0LhxY9zc3Jg1axaPHj16qdeZOcpsYJAxtqLRaPD19aVjx440atSI119/nWHDhnH9+nUgoyTl+PHjbN68mbZt2wKgVqtZtGgRrVq1wtnZmT59+mQZPRZCiJxIoiuEKNGmTJnC+fPnWbFiBbt372bq1Kls2bKFDRs25Hj8hAkTsLa25pdffiEwMBA9PT08PDyAjFHS4cOHExUVha+vLxs3bqRJkyb069cvTzWoGo2GCxcu8O233+Lg4EDt2rV59OgR/fr1486dO3z77besX78eExMT3n//fW7cuAHAzJkzSUlJYc2aNWzbto3atWszevRokpKSslzf2dlZW6sbGBhIly5dsjzfq1cvTp8+rU0sAbZt20aVKlVwcXEhLCyMDz/8kFatWvHrr7+yePFizp07x5AhQ7KNFL9IVFQUixYtolq1arzxxhsABAQE4Ofnx5QpU9i9ezc+Pj5ERkYyf/584N8yj86dO7Np0yYApk6dypEjR1i8eDGbN2+mc+fOjBw5kkOHDuUpHiFE6SKlC0KIYm3btm3s3r07S1vTpk35/vvvAfjf//7HG2+8oR3NrF69OmvWrOHSpUs5Xu/69eu0aNECGxsbDA0NmTt3LleuXEGj0XDs2DFOnTrFn3/+iYWFBZCRGJ88eZKAgABtopaT4cOHa0dw1Wo1iqLQrFkzvvjiC/T09Pj111+Ji4sjKChIW4KwZMkS2rdvz9q1a5k8eTLXr1/Hzs6OGjVqYGJiwvTp0+nevbv2upmMjIwoX748kFHO8N+ShTfeeIMaNWrw66+/apP4bdu28c4776Cnp4efnx//+9//GDlyJAC1atXSxnL8+HGaN2/+zNeZWYcMkJqaiqGhIS1btmTevHmUKVMGAFtbWxYsWECbNm0AsLGxoVOnTuzatQsACwsLDA0NMTExoUKFCly7do3t27ezZcsWGjRoAMCHH35IWFgYfn5+uLm5PTMeIUTpJomuEKJYa9u2LZMmTcrS9nRi179/fw4cOMDmzZuJjIwkPDyc6Oho6tSpk+P1PvnkE+bOncvPP//M//3f/9GqVSu6deuGnp4e586dQ1EUbYKWSa1Wk5KS8tw458yZg5OTE5DxFX7FihWzxHnp0iVq1aqVpc7WxMSExo0ba5NyDw8PPv30U3bv3k3Tpk1p2bIl3bp105YG5JZKpaJHjx5s27YNDw8Pzp8/T3h4ON988w0A58+f59q1a1mS1kwRERHPTXS3bNkCZJRZfPXVV9y/f5/x48dTvXp17TFt27YlNDSUr7/+mqtXr3L16lXCw8OxtrbO8ZqZo+X9+/fP0p6amkq5cuXy9NqFEKWLJLpCiGLNzMyMmjVr5vicRqNhxIgRXL58mW7dutGlSxdee+01PD09n3m9AQMG0KlTJ3777TeOHj3KsmXL+Pbbb9myZQsajYayZcsSFBSU7TwjI6Pnxmltbf3MOCH75LGnX0Nmbetbb71FSEgIISEh/PHHH/z444+sWLGCjRs3Ur9+/efe/7969uzJihUrOHPmDDt27OD111/XxqfRaOjevbt2RPdpTyfiOcm8Rs2aNfH19cXd3Z2hQ4eyefNmLC0tAVi5ciU+Pj707NkTV1dXPvjgA/bv3//M5cQy35u1a9dmW7VBT08q8IQQzyb/QgghSqwLFy7w+++/8/XXXzNp0iTefvttbG1tuX79eo6J5f3795k9ezapqan06tWLRYsW8euvv3Lv3j2OHz+OnZ0djx49IjU1lZo1a2p/Vq1apV3G62XZ29sTGRmpXfcWICUlhbNnz1KvXj3UajXz5s0jKiqKLl26MGfOHPbt24eenl6OdaovWq7MxsaG5s2bs3v3bnbu3EmvXr20z9WvX5/w8PAsrzEtLY158+Zx69atXL8mU1NTFi9eTExMDLNnz9a2f/fdd3z88cfMnDmT9957jyZNmhAZGfnMZD8zib93716WmDIn2QkhxLNIoiuEKLEqVaqEgYEBO3fuJCoqijNnzjB+/Hju3buHWq3Odnz58uU5dOgQM2bM4MKFC0RFRbF+/XoMDQ1p1KgRrVq1okGDBnzyySf8+eefXLt2jXnz5hEUFETdunVfKdbu3btjYWHB+PHjOX36NGFhYUyaNImkpCTee+89jIyMOHPmDJ6enpw6dYro6GiCgoJISkrKscQgsx42LCyMxMTEHO/Zs2dPfv75Z+Lj4+ncubO2fciQIZw/f55Zs2YRERHBP//8w8SJE4mMjKRWrVp5el0ODg4MGzaMHTt2cODAAQCqVq3KkSNHCA8P58qVKyxdupQ9e/Zk6RMzMzNu3LjB7du3qV+/Pm3atOHzzz/nwIEDREVFsWrVKnx9fbG1tc1TPEKI0kUSXSFEiWVtbc38+fM5cOAAXbp0Ydy4cVhbW/PBBx9w9uzZbMcbGBiwatUq9PT0+OCDD+jatSt//PEHK1euxNbWFn19fX744QcaNWrE+PHjefvtt/nrr79YsWIFrq6urxSrubk5a9asoVy5cnzwwQf079+fx48fs27dOmrUqAHA0qVLqVGjBqNGjaJTp06sX7+exYsX06xZs2zXs7Ozo3Xr1owfP/6ZK0x07NgRgPbt21O2bFlte5MmTfj++++5cOECPXv2ZNSoUdSuXRt/f/8XlmjkZPTo0dSpU0e7RNnChQt5/Pgx7777Lu+//z6XLl1i1qxZ3L9/X7tTXN++fbl06RJvv/026enpLF26lA4dOuDl5UWXLl3YsmUL3t7e9OzZM8/xCCFKD5WS17VihBBCCCGEKAZkRFcIIYQQQpRIkugKIYQQQogSSRJdIYQQQghRIkmiK4QQQgghSiRJdIUQQgghRIkkia4QQgghhCiRJNEVQgghhBAlkiS6QgghhBCiRJJEVwghhBBClEiS6AohhBBCiBJJEl0hhBBCCFEi/T+HBGhhrBgZmAAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 800x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "AUC scores for each class: {0: 0.8280716258175201, 1: 0.6842080347719217, 2: 0.6787572649739427}\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Evaluer le modèle sur les données train\n",
+ "y_train_pred = logreg.predict(X_train_prep)\n",
+ "evaluate_model_multiclass(y_train, y_train_pred, logreg.classes_)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x700 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "*** Classification Metrics ***\n",
+ "F1 Score = 0.6438332427049808\n",
+ "******************************\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "AUC scores for each class: {0: 0.8281442473674488, 1: 0.6823466502170134, 2: 0.6777637801347342}\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Evaluer le modèle sur les données test\n",
+ "y_test_pred = logreg.predict(X_test_prep)\n",
+ "evaluate_model_multiclass(y_test, y_test_pred, logreg.classes_)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<font color='red'>**Commentaire**</font> \n",
+ "\n",
+ "Ajoutons une pénalisation pour tenter d'améliorer le modèle."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Régression logistique + pénalité"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dict_models_pen = {\n",
+ " 'Logistic Regression (Ridge)': logregRidge, \n",
+ " 'Logistic Regression (Lasso)': logregLasso,\n",
+ " 'Logistic Regression (Elastic Net)': logregElasticNet\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ "*** Logistic Regression (Ridge) ***\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x700 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "*** Classification Metrics ***\n",
+ "F1 Score = 0.64382415467185\n",
+ "******************************\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "AUC scores for each class: {0: 0.8281442473674488, 1: 0.6823180706228436, 2: 0.6777709246248917}\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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dOjBu3Dj+++8/atasSUREBB999BFly5a951UCAGrVqsWSJUuYOnUqzZs359y5c3z22WdERUVZZv3KlSvHiy++yIcffsiNGzfw8PBg/fr1lhtwpDSApTTDjRgxgg4dOpCUlMSCBQvYvXu3pdmuV69efPvtt/Tp04c33niDpKQkPvroo1Q3bcioOnXq0KFDB1auXMnPP//M888/T/78+QkMDMTX15dOnTrx+uuvU6NGDeLi4vjll1/49ttvadmyJW+++abldVxdXZk6dSp+fn74+PjQvXt3KlasyJkzZ/jyyy/Zs2cPM2fOtMxw380LL7zA77//zuTJk9m9ezdt2rQhf/787Nmzh88//xw3NzdLQV6oUCHq1KnD4sWLqVChAoUKFWLRokVcv349zfKYe+nQoQNDhgwhKSkp1S8/dnZ2DBs2jPHjx2NnZ0fz5s2JiYnh448/5uzZs6luuiEixlKhKyIZ9tRTT1GwYEFKlSp1zyUHdypQoABffPEFH3zwAZMmTSI5OZknnniCRYsWWa5IcDePP/44X331FR9++CFjxozBxsaGWrVqsWjRIsts6718/PHHvP/++4wePRpHR0eqVKnCJ598wpQpU9i+fTs9evSw7NuxY0dCQkJo166dpbhMERAQQFBQEF9//TVnzpyhaNGivPDCCwwdOtTSdHU3nTt3JjIyku+++46vvvqKkiVL0rRpU1599VXGjRvHkSNHcHd3Z9y4ceTPn58FCxZw9epVGjduzMCBA5k7d66lKPP29uazzz5jzpw5DBkyBAcHBzw9Pfn8888tl/lyc3NjyZIllq/ZxcXFsu74Ubz11lts2LCBDz74wHLljZRr1H7xxRd8++23REZG4uTkhIeHBx988MFdZ/q9vb1ZtmwZCxYsICgoiKioKAoXLkzNmjVZunQptWvXfmCWyZMn07BhQ7755hvGjx9PbGwspUuXxsfHhz59+liugAEwdepUJk2axNixYylQoAAvvfQS9erVS3WHt/tp2rQprq6ulCtXznJptRQ+Pj64uLjw6aefsnTpUvLnz0/dunWZMWPGff8+i0j2sjHpgn8iIoaJjo7mt99+o0mTJqmKtGnTprF8+XLLsgQREck4zeiKiBjI2dmZ999/n+rVq9OrVy/y58/Prl27+OKLL/D19TU6nohIrqYZXRERgx04cIBZs2axa9cu4uLiKF++PF27dqVbt24ZvsuXiIjcokJXRERERPIkXV5MRERERPIkFboiIiIikiep0BURERGRPMlqrrqwc+dOTCbTI104XURERESyzo0bN7CxsaFOnTqZ8npWM6NrMpksH5L3mUwmEhISdL6thM63ddH5ti4639Yls2s1q5nRdXBwICEhgSpVqqT79o+Se127do0DBw7ofFsJnW/rovNtXXS+rcuePXsy9bKKVjOjKyIiIiLWRYWuiIiIiORJKnRFREREJE9SoSsiIiIieZIKXRERERHJk1ToioiIiEiepEJXRERERPIkFboiIiIikiep0BURERGRPEmFroiIiIjkSSp0RURERCRPUqErIiIiInmSCl0RERERyZNU6IqIiIhInqRCV0RERETypBxR6CYkJNCuXTu2bdt2z33279+Pj48PtWvX5sUXX2Tfvn3ZmFBEREREchvDC934+HiGDx/O4cOH77nPtWvX6N+/P/Xr12f58uXUqVMHX19frl27lo1JRURERCQ3MbTQDQ8P5+WXX+bEiRP33W/16tXky5ePkSNH4u7uzrvvvouLiwtr1qzJpqQiIiIiktsYWuj+9ddfNGzYkKVLl953v927d1OvXj1sbGwAsLGxoW7duuzatSsbUoqIiIhIljKZuHp0KyTEZOrL2mfqq2XQq6++mq79zp8/T5UqVVJtK1q06H2XO9xLXFxchp8juU/Kedb5tg4639ZF59u66HznYUk3sD29FbtjP2EX8RNOMSeJf3IlTvkKZdohDC100ysuLg5HR8dU2xwdHUlISMjwax07diyTUkluoPNtXXS+rYvOt3XR+c4bbJPiKHjxDwpHbabQhS3YJ96awbW1BftMXmuQKwrdfPnypSlqExIScHJyyvBrVaxYEWdn58yKJjlUXFwcx44d0/m2Ejrf1kXn27rofOcBceexO7Yau4ifsIv8BZuk6wAcirJl+hYX/tfJBkf31iRVbIct5TL10Lmi0C1ZsiRRUVGptkVFRVGiRIkMv5azszP58+fPrGiSw+l8Wxedb+ui821ddL5zmeijEL7C/HFqK5iSLQ+ZTLBwX1HGrrEhLiGJIo1eZ9qImdgDNnv2ZGqMXFHo1q5dm/nz52MymbCxscFkMvHPP/8wYMAAo6OJiIiIiMkE53beKm6j9qbdp3AVooq3YcjCw6zZ/BcADg4OlKtQKcti5dhC9/z587i6uuLk5ESbNm2YOXMm77//Pl27duXrr78mLi6O559/3uiYIiIiItYp6Qb8F3qruL1yMu0+jz0JVTpBlU5s2HkKPz8/zp07B0DVqlWZP38+Xl5eWRYxxxa63t7eBAQE0KVLFwoUKEBQUBDvvfce33zzDdWqVSM4OFhvYYiIiIhkpxuxcGytubA9+hNcv5T6cVt7KNfcXNy6dwDXsly/fh1/f3+Cg4Mtu/Xt2xd/f/8sr+VyTKF78ODB+35eq1Ytvv/+++yMJCIiIiLXzsORH83F7Yn1kHg99eMOBaDS8+bittIL4FQ41cMffvihpcgtXrw4gYGBtG7dOlui55hCV0RERERyiOgjEL7yrs1kAOQvAe4dzcVt+RZgf+8rYb3xxht89913VKlShcDAwIe6mMDDUqErIiIiYu3S2UxGlc7m4rZUQ7C1u+tLnTlzBpPJRKlSpQBwdXVl9erVlChRwnKX2+yiQldERETEGmWwmYwi1eEBheqqVat48803qVmzJsuXL8fW1nwHiJIlS2Z2+nRRoSsiIiJiLRKu3momi1h1j2ayFrc1k5VJ18vGxsYyduxYQkJCAPjtt9/4/fff8fb2ztz8GaRCV0RERCQvu3buVjPZ8fWQFJ/6cYcC5iayKp3MTWV3NJM9yM6dO/H19SU8PByAUqVKMXfuXMOLXFChKyIiIpL3RB+5tSThv62AKfXj+UtClZvNZOVagH2+DB8iKSmJwMBApkyZQmJiIgDt2rVj1qxZFClS5FG/gkyhQldEREQktzOZ4Nw/tzWT7Uu7j9vjqZvJbGwf+nCXL1+me/fubN26FQAXFxcCAgLo1q1btjec3Y8KXREREZHcKOkGRP5mLmyPrLxHM1mD25rJPB7YTJZerq6u2Nuby8i6desSFBSEu7t7prx2ZlKhKyIiIpJb3N5MdvQniI9O/fhDNpNllK2tLR9//DGLFy9m2LBhODg4ZMlxHpUKXREREZGcLIubydJj27ZtTJ06lZCQEAoWLAiYm85GjhyZ6cfKTCp0RURERHKaS+Hm5QhZ2EyWHomJiUyfPp2ZM2eSnJzM6NGj+fjjj7PkWFlBha6IiIiI0bK5mSw9IiIi8PX1Zfv27YB5XW7z5s2z9JiZTYWuiIiIiBFubyYLXwFXI9Puk0XNZPdjMplYsmQJo0eP5urVqwA0atSIefPmUb58+Sw/fmZSoSsiIiKSXXJIM9m9REdHM2zYMFauXAmAnZ0do0ePZujQodjZ2WVrlsygQldEREQkK8WeNTeTHVl592YyR1dzM5l7xyxrJkuvjz/+2FLkVq5cmaCgIOrVq2dYnkelQldEREQks10Kv7Uk4dTvpGkmc3nMXNhW6QTlmmdZM1lGDR8+nFWrVlGvXj2mTJlCgQIFjI70SFToioiIiDwqkwnO7rhV3F74N+0+blVvayZrkOXNZOlx6NAhHB0dqVixIgBOTk6sWbMGV1dXY4NlEhW6IiIiIg8j6QZE/nqzuF1592ayUg3BvZO5uC3qkc0B781kMrFw4ULGjh1LjRo1WL16teWmD3mlyAUVuiIiIiLpl3AVjq252Uy26i7NZA5Q/rZmsgKlDQh5f1FRUQwZMoQ1a9YAsHfvXnbs2EGjRo0MTpb5VOiKiIiI3I+lmWwFHN9w72aylDuT5StkRMp02bBhA35+fpw7dw6AqlWrMn/+fLy8vAxOljVU6IqIiIjcKZc2k91LXFwcEyZMIDg42LKtX79++Pv74+zsbGCyrKVCV0RERCSXNpOlx6VLl2jXrh0HDhwAoHjx4gQGBtK6dWuDk2U9FboiIiJinXJxM1lGFC5cmMqVK3PgwAFatWpFYGAgJUqUMDpWtlChKyIiItYj4coddya7nPrxXNBMlh7JycnY2ppnnG1sbJg1axbPPvssPXv2xCYbbiOcU6jQFRERkbwtDzWTpcfq1asJCAhg5cqVFClSBICiRYvSq1cvg5NlPxW6IiIikvdcOmxejpBHmsnSIzY2lrFjxxISEgLA0KFDWbRokcGpjKVCV0RERHI/kwnObr+tmWx/2n3cqsHjN5vJHnsy1zSTpceuXbvo378/4eHhAJQqVYrevXsbnMp4KnRFREQkd0pKgJM3m8mOrISr/6Xdp1Sjm+ttO+baZrL7SUpKIjAwkClTppCYmAhA+/bt+eijjyzLFqyZCl0RERHJPRKuQMTNO5NFrLpHM1nLm8Vt+1zbTJYekZGRDBw4kK1btwLg4uJCQEAA3bp1s6qGs/tRoSsiIiI5mn3CBez2fw4nVsOJDeaZ3Ns5ukKltjebydrk+may9FqyZImlyK1bty5BQUG4u7sbnCpnUaErIiIiOc+lwxC+gnyHllPrzDZs0jSTlYIqN5vJyjbLE81kGTV06FA2btxIkyZNGDlyJA4ODkZHynFU6IqIiIjxTMl33JnM3Exmd/s+RTzMhW0ebCZLj23btuHq6kqNGjUAcHBw4KeffsLeXuXcveg7IyIiIsZIRzNZUskGnHZpQNEGvXEuUyf7M+YAiYmJzJgxgxkzZlCtWjU2btyIk5MTgIrcB9B3R0RERLJPhprJOhBvW4izBw5QxK2aEWkNFxERga+vL9u3bwfMDWj79++nbt26BifLHVToioiISNaKPWO+M1n4inQ0kz0P+QreeuzatexMmmOYTCaWLFnC6NGjuXr1KgCNGjVi3rx5lC9f3uB0uYcKXREREcl8N5vJzHcm+4O0dyZTM9m9XLp0ieHDh7Ny5UoA7OzsGD16NEOHDsXOzu4Bz5bbqdAVERGRR3ePZrJUrLyZLD0uXLjAM888w+nTpwGoXLkyQUFB1KtXz+BkuZMKXREREXk4SQlwcjOEr7zPncka3yxuO0IR61xnmxFFixaladOmfP3113Tv3p0pU6ZQoEABo2PlWip0RUREJP0SrkDEz+ZZ26OrICEm9eN2jreaySq3hwKljEiZq8THx5Mv362lG1OnTqVDhw60adPGwFR5gwpdERERub/YM3Dkh5vNZBvv0kxWECrfbCar2CZ1M5nck8lkYuHChXz44YesW7eOUqXMvxQULFhQRW4mUaErIiIiaV08dGu97ek/uXszWSfzR7lm5plcSbeoqCiGDBnCmjVrABg2bBhff/21wanyHhW6IiIiYm4mO7P9VnF78UDafYpUv62ZrL6ayR7Shg0b8PPz49y5cwBUq1aNsWPHGpwqb1KhKyIiYq0szWQrbjaTnUq7j5rJMs3169fx9/cnODjYsq1fv374+/vj7OxsYLK8S4WuiIiINYmPgWNr1EyWzfbv30+/fv04cMA8U168eHHmzJlDq1atDE6Wt6nQFRERyevUTGa4jRs3Worc1q1bExgYSPHixQ1Olfep0BUREcmLHtRMVqA0uHdUM1k2GTx4MFu3bqVVq1b07t0bGxsboyNZBRW6IiIieYGayXKUVatW8dhjj1nuaGZra8uSJUtU4GYzFboiIiK51QObyWygdOObM7dqJssOsbGxjB07lpCQECpVqsSvv/5qubOZitzsp0JXREQkN0lXM9mz5llb9/bg8pgRKa3Szp078fX1JTw8HDBfZeH48eN4enoanMx6qdAVERHJ6R7UTJavEFS62UxWqQ04uhqR0molJSUxe/ZsAgICSExMBKBdu3bMmjWLIkWKGJzOuqnQFRERyYkuHry53nblfZrJOt1sJmuqZjKDREZGMmDAAH7//XcAXFxcCAgIoFu3blqqkAOo0BUREckJTMlw5u/bmsnC0u5TtMatZrKS9dRMZrDz58/TpEkTLl++DEDdunUJCgrC3d3d4GSSQoWuiIiIUZIS4OSmWzO3safv2OFmM1mVTuaGsiJVDQgp91K8eHF8fHxYsGABw4YNY+TIkTg4OBgdS26jQldERCQ7xcdAxM/m4jZitZrJcpnLly9TqFAhy+cTJkzgpZdeokGDBgamkntRoSsiIpLVrp5O3UyWfCP142omy/ESExOZMWMGQUFBbNq0iYoVKwLg7OysIjcHU6ErIiKSFSzNZCtuNpPdQc1kuUZERAS+vr5s374dgLfffptly5YZnErSQ4WuiIhIZlAzWZ5jMplYsmQJo0eP5urVqwA0atSImTNnGpxM0kuFroiIyMNSM1medenSJYYPH87KlSsBsLOzY/To0QwdOhQ7OzuD00l6qdAVERHJCDWT5XlbtmxhwIABnDplvqVy5cqVCQoKol69egYnk4xSoSsiIvIgaiazKocPH7YUud27d2fKlCkUKFDA4FTyMFToioiI3M2FMDiy8j7NZGVuLUlQM1me8tprr7F9+3batGlD+/btjY4jj0CFroiICJibyU7/dauZ7NLBtPsU9byjmUy3eM3tTCYTCxcupEqVKjRp0gQAGxsb5s6da3AyyQwqdEVExHolxt9qJjvywz2ayZ66Wdx2BLfHDQgpWSUqKoohQ4awZs0aSpUqxZYtW3BzczM6lmQiFboiImJd4i/f0Ux2JfXjdo5QoZX5Grfu7cGlpBEpJYtt2LABPz8/zp07B4CrqysXLlxQoZvHqNAVEZG87+qp25rJfrl7M1nlduaZ24rPqZksD7t+/Tr+/v4EBwdbtvXr1w9/f3+cnZ0NTCZZQYWuiIjkTRfCbi5JWAGnt6V9PKWZrEonKNsU7ByyN59ku/3799OvXz8OHDgAQPHixZkzZw6tWrUyOJlkFRW6IiKSN6iZTO7j3LlztGrViri4OABatWpFYGAgJUqUMDiZZCUVuiIiknulaiZbCbFn7thBzWRiVqJECQYMGMAnn3zCpEmT6N27Nzb6RSfPU6ErIiK5S/xl3M6uxTFyKpxYe5dmsnxQ4Vk1kwmRkZGULVvW8vmoUaN45ZVXqFKlioGpJDup0BURkZzvtmYy5xO/UFnNZHIfsbGxjB07lqVLl7Jx40aqV68OgKOjo4pcK6NCV0REcqZ7NJOlvNmc7FIG28c7qZlMUtm5cye+vr6Eh4cD8N577/HNN98YnEqMokJXRERyhvQ0kxWryY0KbQmnJhWe7Ex+F5fsTik5VFJSErNnzyYgIIDExEQA2rdvz0cffWRwMjGSCl0RETFOeprJyjxtnrV17whuVbhx7RrXDhzQFRPEIjIykgEDBvD7778D4OLiQkBAAN26dVPDmZVToSsiItkr/jIcXW0ubO96Z7J85juTVelkXnerZjK5jx9++IEhQ4YQExMDQN26dQkKCsLd3d3gZJITqNAVEZGsd+W/W3cmO7npLncmK3xHM1kBA0JKbpSQkEBMTAy2trYMGzaMkSNH4uCg9dpipkJXREQyn8kEF8Nurbc981fafQqUve3OZM+omUweyksvvcTevXtp06YNjRs3NjqO5DCGFrrx8fFMmDCBdevW4eTkRO/evendu/dd912/fj0ffvghZ86cwcPDg7Fjx+Lp6ZnNiUVE5J5MyearI1iayQ6l3adYzVvFbYm6WmcrGZKYmMiMGTOoXbs2zz//vGX7hAkTDEwlOZmhhe4HH3zAvn37CAkJ4dSpU4waNYrSpUvTpk2bVPsdPnyYESNGMHHiROrWrcvChQvx9fVl/fr1ODs7G5ReRETMzWS/3Gwm+yFdzWQiD+P48eMMHTqUv//+m6JFi7JlyxZKltT6bbk/wwrda9eusWzZMubPn4+npyeenp4cPnyYL7/8Mk2hu3XrVqpUqUKnTp0AGD58OF9++SXh4eF4eXkZkF5ExIqlNJOFrzA3k924mvrx25vJ3NtD/hJGpJQ8wmQysW7dOj7++GNiY2MBePzxx7lx48YDniliYKEbFhZGYmIiderUsWyrV68e8+bNIzk5GVtbW8v2woULEx4ezo4dO6hTpw7Lly+nQIEClC9f3ojoIiLWR81kYoDo6GiGDBnCTz/9BICdnR2jR49m6NCh2NnZGZxOcgPDCt3z58/j5uaGo6OjZVuxYsWIj48nOjqaIkWKWLa/8MIL/PLLL7z66qvY2dlha2tLUFAQhQoVyvBx4+LiMiW/5Gwp51nn2zrofGcBkwmb6IPYRfyIXcRP2J3bnmaXZJcyJFVqT1KldiSX8r7VTJYIJF7Lsmg639Zh69atDB06lNOnTwNQoUIF5syZQ506dYiPjzc4nWQVk8mUqdc+NqzQjYuLS1XkApbPExISUm2/dOkS58+fZ/z48dSuXZslS5YwZswYvv/+e4oWLZqh4x47duyRckvuovNtXXS+H5EpGZeYfRSO2kzhqM04xZ1Is8s1lypEF2tGdLFmxBWoZm4muwJcCc/2uDrfedeFCxfo0aOHZXlCmzZtGDRoEE5OThw4cMDgdJLV7qwPH4VhhW6+fPnSFLQpnzs5OaXaPmPGDKpWrUq3bt0AmDRpEs8//zzfffcd/fv3z9BxK1asqAY2KxAXF8exY8d0vq2EzvcjSIrHNnIzdsd+wj7iJ2zizqV62IQNyaWeIqlSO5IqtoNClSkMFDYi600639ZhxIgRzJs3j8mTJ1OtWjWdbytx+PDhTH09wwrdkiVLcunSJRITE7G3N8c4f/48Tk5OFCxYMNW+//77Lz169LB8bmtri4eHB6dOncrwcZ2dncmfP/+jhZdcQ+fbuuh8p1O6mslaQ5VO2Li3wy5/CXLiakid77zDZDKxb9++VA3mI0aMoFevXri6unLgwAGdbyuR2bdsNqzQrV69Ovb29uzatYv69esDsGPHDry8vFI1ogGUKFGCI0eOpNoWERGhKy6IiKTXg5rJnNxuNZNVaK1mMsk2UVFRDBkyhA0bNrB27VpLk7qdnR0lS5bk2rWsW+8teZ9hha6zszOdOnXC39+fKVOmcO7cORYsWEBAQABgnt11dXXFycmJl19+mdGjR1OzZk3q1KnDsmXLOHXqFJ07dzYqvohIzpaeO5O5lrt184YyTXRnMsl2GzZswM/Pj3PnzEtmZs6cyRdffGFwKslLDL1hxJgxY/D396dXr14UKFCAN954g9atWwPg7e1NQEAAXbp04YUXXiA2NpagoCDOnDlD9erVCQkJyXAjmohInmZKhlN/3rx5wwq4dJe1bsW8brszWR3dmUwMcf36dfz9/QkODrZs69u3L/7+/saFkjzJ0ELX2dmZadOmMW3atDSPHTx4MNXnPj4++Pj4ZFc0EZHcIfE6nLjtzmTXzt6xgw2U8b5Z3HaEwu4GhBS5Zf/+/fTr189y9YTixYsTGBhomegSyUyGFroiIvIQrkebm8jCV0DEz/dtJsO9ne5MJjnGokWLGDVqlOU6uK1atSIwMJASJfR3VLKGCl0Rkdzgyn9wZOVtzWSJqR9XM5nkAiVKlCA+Ph4nJycmTZpE7969M73LXuR2KnRFRHIikwkuHritmezvtPuomUxymTZt2uDv70/r1q3x8PAwOo5YARW6IiI5hZrJJA+JjY1l7NixPPXUU6l6bIYMGWJgKrE2KnRFRIz0oGYyG1so/bSaySRX2blzJ76+voSHh7N8+XIaNmxI+fLljY4lVkiFrohIdntQM5m9k3mdrXtHNZNJrpKUlMTs2bMJCAggMdG8jrxZs2YUKKA142IMFboiItnhSuQddya7WzNZe/PMbcXW4OBiREqRhxYZGcmAAQP4/fffAXBxcSEgIIBu3bqp4UwMo0JXRCQrpKuZrPxtzWTeaiaTXGv58uUMHz6cmJgYAOrWrUtQUBDu7lpqI8ZSoSsiklmSk+B0SjPZyrs3kxWvBe6dbjaTPaFmMsn1Tp06hZ+fH9evX8fW1pZhw4YxcuRIHBz0i5sYT4WuiMijSLwOJzbe1kx2LvXjNra37kzm3hEKVzYipUiWKV26NBMmTGDOnDkEBQXRqFEjoyOJWKjQFRHJqOuX7mgmi039eEozWZVO5ps45C9uREqRLJGYmMiWLVto1qyZZVvfvn3p2rUrrq6uxgUTuQsVuiIi6XElEsJv3pkscrOaycQqRURE4Ovry44dO/jhhx94+umnAbCxsVGRKzmSCl0RkbsxmeDC/lvNZGe3p93n9maysk3AVv+kSt5kMplYsmQJo0eP5upV8+XwPv/8c0uhK5JT6V9lEZEUtzeTha+A6PC0+6iZTKxMdHQ0w4YNY+XKlQDY2dkxevRohg4damwwkXRQoSsi1k3NZCL3FBoaysCBAzl16hQAlStXJigoiHr16hmcTCR9VOiKiPVRM5nIA82ePZsJEyZgMpkA6N69O1OmTNFdziRXUaErItbhgc1kRcC9vXlZQsVWaiYTq1enTh0A3NzcmDVrFu3btzc4kUjGqdAVkbwpPc1kBSukvjOZmsnEiqXM3KbcrrdJkyb873//o0WLFpQuXdrIaCIPTf+qi0jekZwE/219QDNZ7VvFbfHaaiYTAaKiohgyZAitW7fmtddes2zv3r27caFEMoEKXRHJ3RKvY3t8DeUPLsZ521aIO5/6cRtbKNPkZnHbEQpVMiSmSE61YcMG/Pz8OHfuHL/++itPP/00jz/+uNGxRDKFCl0RyX3uaCZzuhGL0+2P2ztBheduayYrZkxOkRzs+vXr+Pv7ExwcbNnWvXt3ypYta2AqkcylQldEcoeYk3AkpZns1zTNZIn2hcC9HfbVXlIzmcgD7N+/n379+nHgwAEAihcvzpw5c2jVqpXByUQylwpdEcmZTCa48O9tzWQ70u5zs5nsetk2/BvtRnVPL+zz58/upCK5RnJyMkFBQUyYMIGEhAQAWrVqRWBgICVKlDA4nUjmU6ErIjlHchKc+uPmzRtWQPSRtPvcpZks+do1iDmQvVlFcqEzZ84QEBBAQkICTk5OTJo0id69e1uutCCS16jQFRFjJV6H4xtu3ZlMzWQiWaZ06dJ88MEHfPzxxwQHB+Ph4WF0JJEspUJXRLLf9UtwdJW5uD225i53JnO+485kaiYTeRixsbGsWbOGF1980bLt//7v/3jxxRdxcHAwMJlI9lChKyLZ4/ZmspObwZSU+vFUdyZrDQ5aayvyKHbu3Imvry/h4eG4urrSunVrwHxDCBW5Yi1U6IpI1kh3M1ln85IE3ZlMJFMkJSUxe/ZsAgICSEw0X51kzZo1lkJXxJrofxURyTzpaiZ74rZmslq6M5lIJoqMjGTAgAH8/vvvALi4uBAQEEC3bt0MTiZiDBW6IvJobsTBiY33byYr+4y5sHXvCIUqGhBSJO9bvnw5w4cPJyYmBoC6desSFBSEu7u7wclEjKNCV0QyLj3NZBVv3pmsUls1k4lkscmTJ/Phhx8CYGtry7Bhwxg5cqTW4orVU6ErIunzwGayouZmsiqdoEIrNZOJZKM2bdrwv//9j9KlSxMUFESjRo2MjiSSI6jQFZG7M5kgat+t4vauzWQVb623LfO0mslEskliYiK2trbY2toCUL9+fUJCQvD29qZgwYIGpxPJOfS/kojckpwEp36/daWEy0fT7qNmMhFDRURE4OvrS7t27RgyZIhl+wsvvGBgKpGcSYWuiLW7EQcnUu5M9qOayURyKJPJxJIlSxg9ejRXr15l9+7dtG7dWnc3E7kPFboi1ijuIkTcbCaLWAOJ11I/rmYykRzl0qVLDB8+nJUrVwJgb2/PqFGjePzxxw1OJpKzqdAVsRYxJyB8pfn6tid/VTOZSC4RGhrKgAEDOH36NADu7u4EBQVRt25dg5OJ5HwqdEXyqpRmspT1tuf+SbuPmslEcqyEhASmTJlCYGAgJpMJgB49evD+++9ToEABg9OJ5A76X00kL0lPM1mJOreK22JeaiYTyaEuXrzIF198gclkws3NjVmzZtG+fXujY4nkKip0RXK7VM1kP0BcVOrHbexuaybroGYykVziscceY9asWXz22WfMnTuX0qVLGx1JJNdRoSuSG6WrmayNubit3BacixqRUkQyICoqiu+//55+/fpZtrVr1462bdtio3deRB6KCl2R3CJdzWQdbjaTPatmMpFcZMOGDfj5+XHu3DmKFi1Kly5dLI+pyBV5eCp0RXKq9DaTPd7ZXNyWfkrNZCK5zPXr1/H39yc4ONiy7d9//01V6IrIw9P/iiI5iZrJRKzG/v376devHwcOHACgePHizJkzh1atWhmcTCTvUKErYrQbcXB8vbmwPfrj/ZvJqnSEghWMSCkimSQ5OZng4GAmTJhAfHw8AK1atSIwMJASJUoYnE4kb1GhK2KEuItw9CdzcXtsrZrJRKzIO++8Y1mq4OTkxKRJk+jdu7fW4opkARW6Itkl5ri5mSx8BUT+pmYyESvVs2dPQkJCqFq1KkFBQXh4eBgdSSTPUqErklVMJojae1sz2c60+xSqdGu9rZrJRPKk2NhYHB0dcXBwAKBGjRosX76cunXrki9fPoPTieRt+l9VJDMlJ8Gprbc1k0Wk3UfNZCJWY+fOnfj6+tKxY0feffddy/bGjRsbmErEeqjQFXlUaiYTkTskJSURGBjIlClTSExM5KOPPqJLly5Ur17d6GgiVkWFrsjDiLsAR1fdp5ksP1RqA+4d1UwmYmUiIyMZOHAgW7duBcDFxYWAgACtxRUxgApdkfR6UDOZc7FbzWTlnwUHZyNSioiBli9fzvDhw4mJiQGgbt26BAUF4e7ubnAyEeukQlfkXtLdTHb7ncnssjmkiOQEMTExjB49mq+//hoAW1tbhg0bxsiRIy1NaCKS/R660E1ISCAyMpLy5ctjMpn0gyx5Q3LiHXcmu1szWd3bmslqqplMRLhx4wabN28GoFy5cgQFBdGoUSNjQ4lIxgtdk8nEzJkzWbx4MTdu3GDt2rV89NFHODs74+/vr4JXcp/0NJOVawrunaBKBzWTiUgaRYsWZe7cuXz99ddMnz6dggULGh1JRHiIQnfx4sWsXLmS9957j4kTJwLw7LPPMmHCBIoVK8awYcMyPaRIpou7cPPOZCvv30xWpRNUagvORQyJKSI5U0REBEuWLGHMmDGWO5o1b96c5s2bG5xMRG6X4UJ36dKljB8/nlatWjFp0iQAXnjhBRwcHAgICFChKznX5WNwJKWZLFTNZCKSYSaTiSVLljB69GiuXr1K2bJl6dmzp9GxROQeMlzoRkZG3vU6gB4eHpw/fz5TQolkCpMJm6g9ELnWXNye35V2n0KV77gzmZrJROTuoqOjGTZsGCtXrgTA3t7ecnUFEcmZMlzolilThr1791K2bNlU23/77TfKlSuXacFEHkpyIvy3FYewZdQ89D35rp9Ku4+ayUQkg0JDQxk4cCCnTpn/TalcuTJBQUHUq1fP4GQicj8ZLnT79OnDhAkTOH/+PCaTiT/++IOlS5eyePFiRo8enRUZRe7vxrVbzWRHfoTrF0jVEpmqmawjFCxvTE4RyXUSEhKYMmUKgYGBmEwmALp3786UKVMoUKCAwelE5EEyXOi++OKLJCYm8sknn3D9+nXGjx9PkSJFGDp0KK+88kpWZBRJy9JMtuJmM1lcqodN9vmJLtyI/LVeIZ9HFzWTichDefvtt1m8eDEAbm5uzJo1i/bt2xucSkTSK8OF7qlTp/Dx8eH//u//uHjxIiaTiaJFi5KYmMiePXuoVatWVuQUSUczWXFLM1lc8cYcPXyM6tWqg3N+I9KKSB7w5ptvsnz5curXr8/cuXMpXbq00ZFEJAMyXOi2bNmSrVu3UqRIEYoUuTVLFhkZSY8ePdi9e3emBhQhcgv88sZ9mslS7kzW+FYz2bVrafcVEXmAqKgoChQogJOTE2Bei7t27Vo8PDywtbU1OJ2IZFS6Ct0vv/ySBQsWAOZLq7z44otpfuBjYmL0m65kPpMJNg6EqH23tpWsd6uZrKinmslEJFNs2LABPz8/OnfuTEBAgGV7jRo1DEwlIo8iXYVuly5duHTpEiaTiblz59KmTRtcXFxS7ePi4kLr1q2zJKRYsdN/3ipy674J9YarmUxEMlVcXBwTJkwgODgYgKCgIF577TWqVatmcDIReVTpKnSdnZ3x8/MDwMbGhj59+uDsrIvpSzbYY/6PB4cC8PRkcFSXs4hknv3799OvXz8OHDgAQPHixQkMDFSRK5JHZHiNrp+fH4mJiZw9e5akJHMzkMlkIiEhgb1799KhQ4dMDylW6no0HFxqHlfvpiJXRDJNcnIyQUFBTJgwgYSEBABatWpFYGAgJUqUMDidiGSWDBe6W7ZsYdSoUVy8eDHNY05OTip0JfMc+PLWZcNq9Tc2i4jkGVFRUfj6+rJp0ybA/H/XpEmT6N27NzZa8y+Sp2S4hfTDDz+kRo0aBAUF4eTkxJw5c3jnnXcoUKAA06dPz4qMYo1MJth7c9lCibpQsq6xeUQkz3B0dOTo0aMA1KxZk19++YU+ffqoyBXJgzI8oxseHs6UKVPw8PCgevXq5M+fnx49epA/f34+++wznn322azIKdbmzN9wfo95rNlcEclEBQsWJCgoiB9//JGxY8eSL18+oyOJSBbJ8IyunZ0drq6uAFSoUIFDhw4B0KhRI44cOZK56cR6WZrQXMBDd9wTkYe3a9cuhg0bRnJysmVbgwYNmDRpkopckTwuw4Xu448/zi+//AKYL6S9Y8cOAM6cOZO5ycR6xcdA2BLz2OMVyFfQ2DwikislJSUxa9YsWrduTUhICJ988onRkUQkm2V46UL//v0ZMmQIDg4OtGvXjsDAQPr378/Bgwdp1KhRVmQUaxP2FSTevLOZli2IyEOIjIxk4MCBbN26FTBf693Nzc3gVCKS3TI8o/vss8+ybNkynnjiCUqVKsWnn36KnZ0dLVu2ZOLEiVmRUaxNyrKF4rWhZH1js4hIrrN8+XK8vb0tRW7dunXZvHkzr776qsHJRCS7ZXhGF8DT09MybtCgAQ0aNADg33//pXDhwpkSTKzU2R1wbqd5XKu/bu8rIukWExPDqFGjWLrUfP1tW1tbhg0bxsiRI3FwcDA4nYgYId2F7p49e/j555+xt7enbdu2eHh4WB6Lj49n1qxZLF68mH379mVJULESKbO59s7mm0SIiKTTmDFjLEVuuXLlCAoK0pI6ESuXrqULq1evpmvXrixZsoQlS5bw0ksv8ffffwOwc+dO2rVrx+eff57hm0XEx8fzzjvvUL9+fby9vVmwYME99z148CCvvPIKtWrVon379vz5558ZOpbkAglX4MBX5nG1rpCvkLF5RCRXeeeddyhcuDA+Pj6EhoaqyBWR9BW68+fP59lnn+Wvv/7izz//pGvXrsyaNYuNGzfSo0cPTCYTn3/+OVOmTMnQwT/44AP27dtHSEgI7733HnPmzGHNmjVp9rty5Qq9e/emSpUq/Pjjj7Rq1Qo/Pz8uXLiQoeNJDhf2Ndy4ah6rCU1EHuD48eNcuXLF8nmZMmUIDQ0lKCiIggV1tRYRSWehe+zYMQYOHIijoyP29vYMGTKE3bt3M3bsWDp06MAPP/xA48aNM3Tga9eusWzZMt599108PT1p1aoVffv25csvv0yz7/fff0/+/Pnx9/enQoUKDBkyhAoVKmiZRF6TsmyhWE0o1dDYLCKSY5lMJtatW0fr1q155513Uj1WpkwZg1KJSE6UrkI3Li6O4sWLWz4vWLCgZa3ulClTyJ8/f4YPHBYWRmJiInXq1LFsq1evHrt37051UW+Av/76i5YtW2JnZ2fZ9t1339G0adMMH1dyqLM74ex289hLTWgicnfR0dEMHDiQ6dOnExsby9KlSwkPDzc6lojkUOluRrvzHuA2Njb83//930Mf+Pz587i5ueHo6GjZVqxYMeLj44mOjqZIkSKW7SdPnqRWrVqMGzeOX375hTJlyjBq1Cjq1auX4ePGxcU9dGbJOg47P8EBMNk5EVepC1y79kivl3Kedb6tg863ddi6dStDhw7l9OnTgPnunHPmzKF06dJce8R/MyTn0s+3dTGZTGlqzkfxUJcXS+Hk5PTQz42Li0tV5AKWzxMSElJtv3btGsHBwfTs2ZP58+ezatUq+vTpw88//0ypUqUydNxjx449dGbJGrZJcdQKMzehXSzWkmNHzwCZc6c9nW/rovOdN924cYOQkBC++eYbTCYTAG3atGHQoEE4OTlx4MABgxNKdtDPt/W4sz58FOkudHfu3EmhQre64E0mE3v27Elz698nn3wyXa+XL1++NAVtyud3FtB2dnZUr16dIUOGAFCjRg22bt3KypUrGTBgQHq/BAAqVqyIs7Nzhp4jWcvuQAh2SbEAuDQeRvVS1R/5NePi4jh27JjOt5XQ+c67Tp06RZ8+fdi7dy8AhQsXZvLkyVSrVk3n20ro59u6HD58OFNfL92F7htvvGH5TTrFiBEjUn1uY2OT7t+sS5YsyaVLl0hMTMTe3hzj/PnzODk5pemWLV68OJUrV061rWLFipa3rzLC2dn5odYUSxY6GGL+s0h1nCq3yNT1uTrf1kXnO+8pU6aMZRKkadOmzJ07l8KFC3PgwAGdbyuj820dMnPZAqSz0N24cWOmHhSgevXq2Nvbs2vXLurXN9/mdceOHXh5eWFrm7pH7oknnrBctzfF0aNHadeuXabnkmx2fg+c3mYe605oInIHZ2dn5s+fz+bNmxk0aBC2trZajysi6ZauQjcrLtfi7OxMp06d8Pf3Z8qUKZw7d44FCxYQEBAAmGd3XV1dcXJyomvXrnzxxRcEBgbSoUMHVqxYwcmTJ+nYsWOm55Jstme++U+7fFCjh7FZRMRwGzZsYNGiRSxYsMDybl/NmjWpWbOmwclEJDdK1+XFssqYMWPw9PSkV69eTJgwgTfeeIPWrVsD4O3tzerVqwFzof3pp5+yadMm2rVrx6ZNmwgODqZkyZJGxpdHdeMaHFhsHld9CZyLGptHRAwTFxfH6NGjefnll/npp5+YOXOm0ZFEJA94pKsuPCpnZ2emTZvGtGnT0jx28ODBVJ/Xq1eP5cuXZ1c0yQ6HlkH8ZfNYd0ITsVr//vsv/fr1IywsDDD3Zdx+jXURkYdl6IyuWLmUO6G5VYMyTYzNIiLZLjk5mY8//piWLVtaitxWrVoRGhpqeXdPRORRGDqjK1Ys6l849bt5XKufmtBErMzp06cZPHgwmzdvBsyXlZw0aRK9e/fO9K5rEbFeD1Xonjt3jm+++YajR4/y7rvv8vfff1O1atU0lwATuae9KU1ojlCjl7FZRCTbTZo0yVLk1qxZk+DgYDw8PIwNJSJ5ToaXLhw/fpz27dvz/fffs27dOq5du8bq1at58cUX2b17d1ZklLzmRhzsX2QeV+kC+YsZm0dEst3EiRN57LHH8PPzY/369SpyRSRLZLjQnTp1Ks8++ywbNmzAwcEBgA8//JAWLVowY8aMTA8oedDh7+D6JfNYTWgiVmHXrl1cunTJ8nmxYsX4888/mThxIvny5TMwmYjkZRkudP/55x9ef/31VGuo7O3tGTRoEPv378/UcJJHpTShFa4C5ZoZGkVEslZSUhIfffQRrVu3ZujQoanusHnnXTBFRDJbhtfoJicnk5ycnGZ7bGwsdnZ2mRJK8rALYfBfqHnspSY0kbwsMjKSAQMG8Pvv5sbTX375haNHj+Lu7m5wMhGxFhme0fX29iYoKChVsRsdHc306dNp1KhRpoaTPCilCc3WAWq+ZmgUEck6y5cvx9vb21Lk1q1bl82bN6vIFZFsleFCd/To0ezbtw9vb2/i4+MZOHAgzZs3JzIyklGjRmVFRskrEq/DvyHmcZVOkL+EoXFEJPPFxMQwcOBA+vbtS0xMDLa2towYMYKff/5ZRa6IZLsML10oWbIkK1as4KeffuLAgQMkJyfzyiuv0LFjRwoUKJAVGSWvOPw9XL9gHnv1MzaLiGS6Y8eO0blzZ44fPw5AuXLlCAoK0rt9ImKYDBe6//vf/+jSpQs+Pj5ZkUfysr03m9AKVYIKLY3NIiKZrnTp0hQpUoTjx4/j4+PD9OnT1XAmIobKcKH7448/Mm/ePOrWrUuXLl1o06YNLi4uWZFN8pKLh+DkZvPYqx/Y6O7TInmByWSyXIXH0dGRoKAgdu3axUsvvWRwMhGRh1iju2HDBr788kuqVq3KjBkz8Pb2ZuTIkfzxxx9ZkU/yir2fmv+0tYearxubRUQemclk4quvvqJt27bEx8dbtlepUkVFrojkGA81rVa3bl3ee+89QkND+eijjzCZTAwePJgWLVpkdj7JCxLj4d/PzWP3DuDymLF5ROSRREdH07t3b/z8/Pjzzz+ZPHmy0ZFERO4qw0sXbnfx4kUiIiI4efIk8fHxVKhQIbNySV5yZCXERZnHakITydVCQ0MZOHAgp06dAqBy5cp06tTJ2FAiIveQ4UL36tWrrF27lh9//JG///6b0qVL07lzZz766CNKlSqVFRklt0u5E1rBClChlbFZROShJCQkEBAQwOzZsy13N+vevTtTpkzRFXdEJMfKcKH71FNP4eDgQOvWrQkJCaF+/fpZkUvyiugjcGKjeezVF2x19zyR3ObQoUP4+vqye/duANzc3Jg1axbt27c3OJmIyP1luNCdMGECbdq0wdnZOSvySF6T0oRmYweeakITyY0CAwMtRW7Tpk2ZO3cupUuXNjiViMiDpavQ/fvvv6lTpw729vaULVuWffv23XPfJ598MtPCSS6XlAD7FpjHlduBaxlj84jIQ3n//ffZtm0bPXv2ZNCgQdja6vKAIpI7pKvQ7dGjB1u3bqVo0aL06NEDGxsbyxqt29nY2HDgwIFMDym51JEf4do587iWmtBEcouNGzfi6enJY4+Zr5BSsGBBtmzZgqOjo8HJREQyJl2F7saNG3Fzc7OMRdIlpQmtQFmo2MbYLCLyQNevX8ff35/g4GBatGjBN998Y5m9VZErIrlRut5/KlOmjOUfuzlz5lCoUCHKlCmT6sPFxYX3338/S8NKLnI5Ao6vN4/VhCaS4+3fv5+WLVsSHGz+BXXv3r2cPHnS4FQiIo8mXTO6O3bssPyDt2LFCjw9PdNcTubIkSO6O5rcsvczwGS+1W/N3kanEZF7SE5OJigoiAkTJpCQkABAq1atCAwMpESJEganExF5NOkqdG1sbBg9erRlfLe74OTPn58+ffpkbjrJnZJu3GpCq/QCFCxnbB4RuavTp08zePBgNm/eDICTkxOTJk2id+/e2NjYGBtORCQTpKvQrVu3LmFhYQB4eHiwZcsWihUrlqXBJBc7ugpiT5vHuhOaSI50+PBhnn/+eS5evAhAzZo1CQ4OxsPDw+BkIiKZJ8PXiAkLC1ORK/e3N6UJrTRUfsHYLCJyV5UrV7YUtX5+fqxfv15FrojkOema0e3Zsydz5syhYMGC9OzZ8777Llq0KFOCSS4VcwIi1pjHNfuAbYbvSSIiWSQhIcFy9QQ7OzvmzZtHeHg4zZo1MzaYiEgWSVcVcvtVF0qXLq21W3JvKU1o2ICX1myL5ARJSUnMnj2bZcuWsX79elxcXAAoW7YsZcuWNTidiEjWSVehGxAQYBlPnTo1y8JILpecCPs+M48rtYGCFYzNIyJERkYyYMAAfv/9d8B8G/cPPvjA4FQiItnjoe7j+M8//1gaGFasWIGvry9BQUF3vVuaWJGIn+Hqf+axmtBEDLd8+XK8vb0tRW7dunXx9fU1OJWISPbJcKH79ddf061bNw4ePEhYWBhjxozhxo0bLFy4kLlz52ZFRsktUu6E5vIYVG5nbBYRKxYTE8PAgQPp27cvMTEx2NraMmLECH7++Wfc3d2Njicikm0yXOiGhIQwduxYGjduzOrVq3n88cdZsGABH3zwAcuXL8+KjJIbXImEiNXmcc3eYOdgbB4RK7Vt2zaaNm3K0qVLAShXrhw//fQT7777Lg4O+rkUEeuS4UI3MjKSFi1aALB161aeeeYZANzd3YmKisrcdJJ77FsApmTz2KuvsVlErNiyZcs4fvw4AD4+PoSGhtKoUSODU4mIGCPDhW7RokU5d+4c58+f58CBAzz99NOArq9r1ZKTYO+n5nGF1lCokrF5RKzYxIkTqVevHsHBwQQFBVGwYEGjI4mIGCbDFzlt27Ytb731Fs7Ozjz22GM0aNCA1atXM2nSJF566aWsyCg53bG1cOWkeVxLTWgi2cVkMvH111/z9NNPU758ecB8O/Z169bpMpAiIjxEoTtixAgee+wxTp48Sbdu3bCzs+PChQt07dqVN954IysySk6X0oSWvwS4dzA2i4iViI6OZtiwYaxcuZKGDRvy448/Ym9v/iddRa6IiFmGC11bW1t69OiRatudn4sVuXoKjv5kHnu+DnaOxuYRsQKhoaEMHDiQU6dOAXD+/HnOnDmjmz+IiNzhoa6ju3HjRl5++WWeeOIJ6tevT9euXVm/fn1mZ5PcYN/nYEoyj9WEJpKlEhIS8Pf3p1OnTpYit0ePHmzevFlFrojIXWR4RnfdunW8+eabtGzZkrZt22Iymfj777958803CQwMpGXLllmRU3IiUzLsnW8el28JblWMzSOShx06dAhfX192794NgJubG7NmzaJ9+/YGJxMRybkyXOh+/PHHDB48GD8/P8u21157jTlz5jBv3jwVutbk+HqIMV/GSHdCE8k6YWFhtGzZkri4OACaNm3K3LlzKV26tMHJRERytgwvXTh69OhdZxDatWvHoUOHMiWU5BIpTWjOxaBKJ0OjiORl1apVo0mTJjg4ODBx4kS+++47FbkiIumQ4RndEiVKcPz4cSpUqJBq+/Hjx3F1dc20YJLDxZ6BIz+Yx56vgX0+Q+OI5DUxMTGWa+Da2NgQGBjImTNn8PLyMjiZiEjukeEZ3Xbt2uHv78+vv/7K1atXuXr1Kr/++isTJkzghRdeyIqMkhPtWwjJieaxli2IZJrr168zevRovL29iY6OtmwvXry4ilwRkQzK8IzuwIEDLU0RKddqNJlMNGvWjOHDh2d6QMmBbm9CK9cMilQ1NI5IXrF//3769evHgQMHAJgwYQIfffSRwalERHKvDBe6+fLl4+OPP+bIkSMcOnQIk8lEtWrVcHd3z4p8khOd+AUuHzWPNZsr8siSk5MJCgpiwoQJJCQkANCqVSvGjBljcDIRkdwt3YXumTNnWL9+PY6OjjRt2hR3d3cVt9YqpQnNqQg83sXYLCK53OnTpxk8eDCbN28GwMnJiUmTJtG7d2/d4UxE5BGlq9Ddvn07ffv25fr164D5XuqzZ8/G29s7S8NJDnTtHISvMI89e4G9k6FxRHKz1atXM2TIEC5evAiAl5cXQUFBeHh4GJxMRCRvSFcz2v/+9z8aN27Mb7/9xtatW2nSpAlTp07N6mySE/0bAsk3zGMtWxB5JH/99ZelyPXz82PdunUqckVEMlG6ZnT379/P0qVLKVGiBADvvPMOzZo14+rVqxQoUCBLA0oOYjLdWrZQpgkUrW5sHpFc7p133rE09zZt2tToOCIieU66ZnSvXbtG4cKFLZ+XLFkSBwcHLl++nFW5JCc6uRmiw83jWprNFcmIpKQk/ve//3Hw4EHLNkdHR7766isVuSIiWSRdha7JZErTFGFnZ0dycnKWhJIcKmU2N19hePwlQ6OI5CaRkZF07NiRCRMm4Ovra7mygoiIZK0M3zBCrNS1KAhfbh7X6AkOzsbmEcklli9fjre3N7///jsA9vb2lnW5IiKStdJ9ebEFCxbg7HyruElMTGTRokUUKlQo1X5+fn6Zl05yjv2LIOnmLJSWLYg8UExMDKNHj+brr78GwNbWlmHDhjFy5EgcHBwMTiciYh3SVeiWLl2an3/+OdW24sWLs3HjxlTbbGxsVOjmRbc3oZV+CorVNDaPSA63bds2BgwYwPHjxwEoV64c8+bNo3HjxgYnExGxLukqdH/55ZesziE52X+hcOlmA40uKSZyX//++y9t27a19DD4+Pgwffp0ChYsaHAyERHrozW68mCWJrRCUO1lY7OI5HA1atSgU6dOuLq6EhwcTFBQkIpcERGDpHuNrlipuItw6FvzuHp3cMhvbB6RHMZkMnH69GlKly4NmJdwzZw5k8uXL1O+fHmD04mIWDfN6Mr9HVgMSfHmsZYtiKQSHR1N7969ad68OefOnbNsL1SokIpcEZEcQIWu3NvtTWilGkKJ2sbmEclBQkND8fb2ZuXKlZw/f163RRcRyYEeqdDVRc/zuFO/w4X95rFmc0UA8797/v7+dOrUiVOnTgHQvXt3Jk6caHAyERG500Ot0V2yZAnz58/nzJkzrF27lk8//ZSSJUsyaNCgzM4nRkqZzXV0hWr/Z2wWkRzg0KFD+Pr6snv3bgDc3NyYNWsW7du3NziZiIjcTYZndH/88UdmzpxJ586dLRc9d3d3Z968eSxYsCDTA4pBrl+CQ9+Yx9W7gWMBY/OIGCwkJITmzZtbitymTZsSGhqqIldEJAfLcKG7YMEC3n33Xd544w1sbc1P79mzJ+PHj2fp0qWZHlAMcuBLSLxuHnv1NzaLSA5w/vx54uLicHBwYOLEiXz33XeWKy2IiEjOlOFCNyIigvr166fZ3rBhQ06fPp0pocRgtzehlawPJesYm0ckBxg2bBjdunVjw4YN+Pn5WX7RFxGRnCvD/1IXK1aMiIiINNt37txJiRIlMiWUGOz0Nojaax7XUhOaWJ/r16/zzjvvWJYpANjZ2REYGIiXl5eByUREJCMyXOj+3//9HxMnTmTjxo0AHD16lCVLlvD+++/TpUuXTA8oBkiZzXVwAY9XjM0iks32799Py5YtmTdvHv379+fatWtGRxIRkYeU4asu9OvXjytXrjB8+HDi4+Px9fXF3t6erl27MmDAgKzIKNkp/jIcvLnW2uNV8xUXRKxAcnIyQUFBTJgwwXLpxIoVK3L9+nXy59cdAUVEcqOHurzY8OHDGThwIOHh4ZhMJipXrkyBAurKzxMOfAWJN2ewaqkJTazDmTNnGDx4MJs2bQLAycmJiRMn0qdPH2xsbAxOJyIiDyvDhW7KBdIBihYtCkBMTAwxMTEA6kLOzUwm2BNkHpeoAyXrGZtHJBusXr2aIUOGcPHiRQBq1qxJcHAwHh4eBicTEZFHleFCt0WLFved4Thw4MAjBRIDnd0O528233j1A81kSR63d+9eunfvbvncz8+Pd999l3z58hmYSkREMkuGC91Fixal+jwpKYmIiAgWLlzI6NGjMy2YGCClCc0+P1R/1dgsItnAy8uLnj17sn79eubOnUuzZs2MjiQiIpkow4VugwYN0mxr3Lgx5cqVIzAwkBYtWmRKMMlmCVcgbIl57NEV8hUyNo9IFkhKSuLw4cOpliW8//77jB8/niJFihiYTEREskKmXfG8YsWKhIWFZdbLSXYLWwI3Ys1jNaFJHhQZGUnHjh1p06YNkZGRlu0uLi4qckVE8qhHakZLcfXqVYKCgihbtmymhBIDpCxbKF4LHks7ay+Smy1fvpzhw4dbmmYDAwOZNm2awalERCSrZUozmslkIn/+/EyfPj3Tgkk2OrvD/AFqQpM8JSYmhlGjRrF0qfna0La2tgwfPpy3337b4GQiIpIdHrkZDcDBwYGqVavi4uKSKaEkm+2Zb/7T3gmqd7//viK5xLZt2xgwYADHjx8HoFy5cgQFBdGoUSODk4mISHbJ8BrdRYsWUbRoURo0aGD5qFOnzkMVufHx8bzzzjvUr18fb29vFixY8MDnREZGUqdOHbZt25bh48ldJFyFA1+ax9X+D5wKGxpHJDN89NFHtG3b1lLk+vj4EBoaqiJXRMTKZHhG988//8y0a0x+8MEH7Nu3j5CQEE6dOsWoUaMoXbo0bdq0uedz/P39de/5zHRwKdy4ah57qQlN8ob8+fOTnJyMq6srM2bMwMfHx+hIIiJigAwXup07d2bGjBkMHjyYChUq4Ojo+FAHvnbtGsuWLWP+/Pl4enri6enJ4cOH+fLLL+9Z6P7www/ExsY+1PHkHlKa0Ip6QunGxmYRyST9+/fn7NmzvPbaa5QvX97oOCIiYpAMF7q//vorJ06cYO3atXd9PL13RgsLCyMxMZE6depYttWrV4958+aRnJyMrW3qVRWXLl1i+vTpLFiwgHbt2mU0ttzNuV1w5i/zuJaa0CR3unTpEsOGDaNZs2ZUr14dABsbG8aPH29wMhERMVqGC92BAwdmyoHPnz+Pm5tbqhnhYsWKER8fT3R0dJrrWk6dOpXOnTvz+OOPP9Jx4+LiHun5eYnDP5/gAJjs8hFX8UXIQ0tCUs6zznfetnXrVt58803OnDnDtm3baNasGSVKlDA6lmQx/XxbF51v62IymdJc3etRpKvQrV69Olu2bKFo0aJ07tw5Uw4cFxeXZtlDyucJCQmptv/+++/s2LGDn3766ZGPe+zYsUd+jbzANimOWmHmJrSLxVpwLOIscNbYUFlA5ztvunHjBgsXLmTZsmWYTCYA6tSpQ2RkJBcuXDA4nWQX/XxbF51v6/Gwy2LvJl2Fbsp/JJkpX758aQralM+dnJws265fv8748eN57733Um1/WBUrVsTZ2fmRXye3swtbjF2Seb2zS6NhVC9d3eBEmSsuLo5jx47pfOdB4eHhDB8+nL179wJQuHBhJk+eTLVq1XS+rYR+vq2Lzrd1OXz4cKa+XoaXLmSWkiVLcunSJRITE7G3N8c4f/48Tk5OFCxY0LLfnj17OHnyJEOGDEn1/H79+tGpUycmTpyYoeM6OzuTP3/+R/8CcruwEPOfRTxwcn82z67P1fnOO0wmEwsXLmTs2LGWtzCbNm3K3LlzKVy4MAcOHND5tjI639ZF59s6ZOayBchAofvzzz9ToECBB+7XqVOndL1e9erVsbe3Z9euXdSvXx+AHTt24OXllaoRrVatWqxbty7Vc1u3bs3kyZN5+umn0xtfbnd+L5z+wzzWndAkl9izZw8jRowAzG9rjRs3joEDB2Jra6tLDoqIyF2lu9CdPHnyA/exsbFJd6Hr7OxMp06d8Pf3Z8qUKZw7d44FCxYQEBAAmGd3XV1dcXJyokKFCmmeX7JkSYoWLZre+HK7vTfvhGbnCDV6GptFJJ1q167NoEGD2LhxI/Pnz6dmzZpGRxIRkRwu3YXu1q1bM72wHDNmDP7+/vTq1YsCBQrwxhtv0Lp1awC8vb0JCAigS5cumXpMq3cjDvYvNo8ffxHyFzM2j8g9xMXFsWfPHho2bGjZNm7cON59912t0xMRkXRJV6Gb2eslUjg7OzNt2jSmTZuW5rGDBw/e83n3e0we4PC3EB9tHtfSndAkZ/r333/p168fJ06cYPPmzVSpUgUg0+7KKCIi1sH2wbtkzVUXxCApd0JzexzKNjU2i8gdkpOT+fjjj2nZsiVhYWFcu3aNxYsXGx1LRERyqXTN6Hbu3FkzKXnBhf3w3xbzWE1oksOcPn2awYMHs3nzZsB8mcFJkybRu3dvY4OJiEiula5CN6VBTHK5PTeb0GwdwLOXsVlEbrNq1SrefPNNLl68CEDNmjUJDg7Gw8PD4GQiIpKbpWvpguQBiddh/yLzuEpnyK/bpErOMHbsWHr06GEpcv38/Fi/fr2KXBEReWSG3TBCstnh5XDdXEioCU1ykurVzXflK1WqFHPnzqVZs2bGBhIRkTxDha61SGlCK1QZyjc3NotYNZPJlOpKLq+++iqXL1+ma9euFClSxMBkIiKS12jpgjW4eBAifzWPvfqBjU67GCMyMpKOHTuyYcMGyzYbGxsGDRqkIldERDKdZnStgaUJzR5qvmZoFLFey5cvZ/jw4cTExHDo0CG2bNlCsWK6YYmIiGQdFbp5XWI8/LvQPHbvCC6PGRpHrE9MTAyjR4/m66+/BsDW1pYePXpQqFAhg5OJiEhep0I3rwtfAdcvmMdqQpNstm3bNgYMGMDx48cBKFeuHEFBQTRq1MjgZCIiYg20WDOv23uzCa1gRajwrKFRxHokJiYydepU2rZtaylyfXx8CA0NVZErIiLZRjO6edmlw3DiF/PYq6+a0CTb7N+/n5kzZ5KcnIyrqyszZ87kpZdeMjqWiIhYGRW6edneT81/2thBzdeNzSJWpVatWrz99tts3ryZefPmUb58eaMjiYiIFdIUX16VlAD7PjeP3dtDgdLG5pE8LTo6mrVr16baNnz4cH788UcVuSIiYhgVunnVkR8g7rx5rCY0yUKhoaF4e3vTq1cv9u7da9lub2+PnZ2dgclERMTaqdDNq1LuhOZaHiq0NjaL5EkJCQn4+/vTqVMnTp06RUJCAmvWrDE6loiIiIXW6OZF0Ufh+Hrz2KsP2GpWTTLXoUOH8PX1Zffu3QAULlyYWbNm0aFDB4OTiYiI3KIZ3bzI0oRmCzV7G5tF8hSTycTnn39O8+bNLUVu06ZN2bJli4pcERHJcTSjm9ck3YB9C8zjSm3BtayxeSRP8fPzY8mSJQA4OjoyduxYBg0ahK2tfmcWEZGcR/875TVHf4JrZ81jNaFJJmvd2rzeu2rVqqxfvx4/Pz8VuSIikmNpRjevSWlCK1AGKrUxNovkejdu3MDBwcHyeceOHQkKCqJdu3Y4OzsbmExEROTBNBWTl1w+BsduXsu0Zh+w1e8x8vD2799Ps2bNWLlyZartPj4+KnJFRCRXUKGbl+z7DDABNuarLYg8hOTkZD755BNatGjBgQMHGDp0KOfOnTM6loiISIZpyi+vSE68rQnteSiou1FJxp0+fZrBgwezefNmAJycnBg7dizFixc3NpiIiMhDUKGbVxxdDVdPmcdqQpOHsGrVKt58800uXrwIQM2aNQkODsbDw8PgZCIiIg9HSxfyir03m9BcSkHltsZmkVwlNjaWYcOG0aNHD0uR6+fnx/r161XkiohIrqYZ3bwg5gRE/Gwe1+ytJjTJkOPHj1uujVuqVCnmzp1Ls2bNjA0lIiKSCVQR5QX7FoApGTWhycOoUaMG48aNY9u2bcyaNYsiRYoYHUlERCRTaOlCbpecBHs/M48rtoZClYzNIzleZGQkS5cuTbVt0KBBhISEqMgVEZE8RTO6ud2xNXA10jxWE5o8wPLlyxk+fDhXr16lYsWKNGzYEAAbGxuDk4mIiGQ+zejmdil3QstfEiq3NzaL5FgxMTEMHDiQvn37EhMTA8CuXbuMDSUiIpLFNKObm135D47+ZB7XfB3sHO6/v1ilbdu2MWDAAI4fPw5AuXLlCAoKolGjRgYnExERyVqa0c3NLE1ogFdfY7NIjpOYmMjUqVNp27atpcj18fEhNDRURa6IiFgFzejmVslJsPdT87j8s1DY3dg8kuP07t2bn34yz/i7uroyc+ZMXnrpJYNTiYiIZB/N6OZWx9fDlRPmsZrQ5C569eoFQKNGjQgNDVWRKyIiVkczurlVShOac3Go0tHYLJIjXLlyhQIFCliuoNCyZUu+/fZbmjZtip2dncHpREREsp9mdHOjq6fhyA/msedrYOdoaBwxXmhoKI0bN+bLL79Mtb1FixYqckVExGqp0M2N/v0cTEnmsZrQrFpCQgL+/v506tSJU6dOMWbMGM6fP290LBERkRxBSxdyG1PyrSa0cs2hSFVj84hhDh06hK+vL7t37wbAzc2NWbNmUbx4cYOTiYiI5Aya0c1tjm+EyxHmsZrQrJLJZOLzzz+nefPmliK3adOmhIaG0r69bhoiIiKSQjO6uc3em01oTkWhSmdjs0i2i4qKYsiQIaxZswYAR0dHxo4dy6BBg7C11e+tIiIit1Ohm5vEnoXwFeaxZy+wz2doHMl+0dHR/PbbbwBUrVqV+fPn4+XlZXAqERGRnEmFbm7y70JITjSPvfoZGkWMUaVKFaZMmcK+ffuYMGECzs7ORkcSERHJsVTo5hamZNg73zwu+wwU9TA2j2SL/fv38+uvvzJw4EDLtp49exqYSEREJPdQoZtbnNwM0UfMYzWh5XnJyckEBQUxYcIEEhISqF69Os2aNTM6loiISK6iQje3SLkTmpMbPP6isVkkS505c4bBgwezadMmAJycnDhz5ozBqURERHIfFbq5wbXzcHi5eVyjJ9g7GZtHsszq1asZMmQIFy9eBMDLy4ugoCA8PLRURUREJKN0PaLc4N8QSL5hHqsJLU+KjY1l2LBhdO/e3VLk+vn5sW7dOhW5IiIiD0kzujmdyXSrCa3001DM09g8kiV69erFL7/8AkCpUqWYO3eu1uSKiIg8Is3o5nSRv8GlQ+axmtDyrLfeegtbW1vat29PaGioilwREZFMoBndnC6lCS1fIaj6krFZJNOcOXOGEiVKWO5m1qhRIzZu3EitWrWwsbExOJ2IiEjeoBndnCzuAhz+1jyu3gMc8hubRzLF8uXLadSoEcHBwam2165dW0WuiIhIJlKhm5PtXwRJCeaxli3kejExMQwcOJC+ffsSExPD5MmTuXDhgtGxRERE8iwVujmVyXRr2UKpRlDcy9g88ki2bdtG06ZNWbp0KQDlypVj2bJlFC1a1OBkIiIieZcK3Zzqv61wMcw81mxurpWYmEhAQABt27bl+PHjAPj4+BAaGkrjxo0NTiciIpK3qRktp9p7czbXsSBUe9nYLPJQTpw4Qd++fdm+fTsArq6uzJgxAx8fH4OTiYiIWAcVujlR3EU4+I15XL0bOLgYm0ceiq2tLYcPHwbMV1WYN28e5cuXNziViIiI9VChmxMd+AKS4s1jLVvItcqWLctHH33EkSNHGDp0KHZ2dkZHEhERsSoqdHOa25vQHnsSSjxhaBxJvy1btvDrr7/y7rvvWrZ16tTJuEAiIiJWToVuTnP6T7jwr3nspdnc3CAhIYGAgABmz56NyWSidu3atGvXzuhYIiIiVk+Fbk6TMpvrUAA8uhqbRR7o0KFD+Pr6snv3bgAKFy6sJQoiIiI5hC4vlpNcj4aD5uusUv1VcCxgaBy5N5PJxOeff07z5s0tRW7Tpk3ZsmULzz//vMHpREREBDSjm7Mc+BIS48xjNaHlWFFRUQwZMoQ1a9YA4OjoyNixYxk0aBC2tvrdUUREJKdQoZtTmEy3rp1boi6UrGdsHrmnPn36EBoaCkDVqlWZP38+Xl66c52IiEhOo+mnnOLM33B+j3ms2dwcbdKkSTg6OtKvXz82bdqkIldERCSH0oxuTpHShGafHzxeMTaLpHLo0CEqV66Mvb35x6VWrVr8/ffflCtXzuBkIiIicj+a0c0J4mMgbIl57PEK5CtobB4BIDk5mU8++YRnnnmGWbNmpXpMRa6IiEjOp0I3Jwj7ChKvmcdatpAjnD59mpdeeol3332XhIQEZs+ezaVLl4yOJSIiIhmgQjcnSFm2ULy2+W5oYqhVq1bRpEkTNm/eDEDNmjVZt24dbm5uxgYTERGRDFGha7SzO+DcTvO4Vn+wsTE2jxWLjY1l2LBh9OjRg4sXLwLg5+fH+vXr8fDwMDidiIiIZJSa0YxmaUJzhurdjM1ixcLCwujZsyfh4eEAlCpVirlz59KsWTNjg4mIiMhD04yukRKuwIGvzONq/wf5Chmbx4oVLVqUy5cvA9CuXTtCQ0NV5IqIiORymtE1UtjXcOOqeawmNEMVL16cOXPmcPbsWbp164aNlpCIiIjkeip0jZSybKFYTSjVyNgsVmb58uWEhoby4YcfWoraVq1aGZxKREREMpMKXaOc3Qlnt5vHXmpCyy4xMTGMGjWKpUuXAtCwYUO6du1qcCoRERHJCip0jbJ3vvlPeyeo0d3YLFZi27ZtDBgwgOPHjwPmmz5UrFjR2FAiIiKSZQxtRouPj+edd96hfv36eHt7s2DBgnvuu3nzZjp27EidOnVo3749GzduzMakmexGLBz4wjyu6gNOuj5rVkpMTCQgIIC2bdtailwfHx9CQ0Np1EhLRkRERPIqQ2d0P/jgA/bt20dISAinTp1i1KhRlC5dmjZt2qTaLywsDD8/P0aOHEnTpk3ZsmULb775Jt9++23uvL5p2FLzFRfAvGxBssyxY8d488032bFjBwCurq7MmDEDHx8fg5OJiIhIVjOs0L127RrLli1j/vz5eHp64unpyeHDh/nyyy/TFLo//fQTjRo1omfPngBUqFCBX375hZ9//jl3Frp7bzahFakOZZ42Nkse99Zbb1mK3EaNGjFv3jzKly9vcCoRERHJDoYtXQgLCyMxMZE6depYttWrV4/du3eTnJycat/OnTvz1ltvpXmNK1euZHnOTHd+D5zeZh7rTmhZburUqbi6uvLuu+/y448/qsgVERGxIobN6J4/fx43NzccHR0t24oVK0Z8fDzR0dEUKVLEst3d3T3Vcw8fPswff/zxUN3ycXFxDx86Ezj88wkOgMnWkbhKL8K1a4bmyWu2b9+Ol5eX5ZelMmXK8Mcff1C4cGHi4+MNTidZJeXn2uifb8keOt/WRefbuphMpky9lr1hhW5cXFyqIhewfJ6QkHDP5128eJE33niDunXr0rJlywwf99ixYxl+TmaxSbpOrZtNaBeLteBYxDngnGF58pIbN26wcOFCli1bho+PD/369QNune/Tp08bmE6yi5E/35L9dL6ti8639bizPnwUhhW6+fLlS1PQpnzu5OR01+dERUXx+uuvYzKZmD17Nra2GV95UbFiRZydnTMeOBPYhX2BfZL5TmgujYdSvXR1Q3LkNeHh4QwfPpy9e/cCsH79ekaOHMmlS5cMPd+SfeLi4jh27JjOt5XQ+bYuOt/W5fDhw5n6eoYVuiVLluTSpUskJiZib2+Ocf78eZycnChYsGCa/c+ePWtpRlu0aFGqpQ0Z4ezsTP78+R8++KM4GGL+060qTu6ttT73EZlMJhYuXMjYsWMtb2k1bdqUuXPnUrhwYS5dumTs+ZZsp/NtXXS+rYvOt3XIzGULYGAzWvXq1bG3t2fXrl2WbTt27MDLyyvNTO21a9fo27cvtra2fPHFF5QsWTKb02aCqH/h1O/msZrQHllUVBTdunVjxIgRlmUwEydO5LvvvqN06dJGxxMREZEcwLAZXWdnZzp16oS/vz9Tpkzh3LlzLFiwgICAAMA8u+vq6oqTkxNBQUGcOHGCxYsXWx4D8xIHV1dXo76EjEm5E5qtA9ToaWyWXG7Xrl107dqVc+fM65urVq3K/Pnz8fLyMjiZiIiI5CSG3hltzJgxeHp60qtXLyZMmMAbb7xB69atAfD29mb16tUArF27luvXr+Pj44O3t7fl4/333zcyfvrdiIP9i8zjx7tA/uLG5snlKlWqZFmo3q9fPzZt2qQiV0RERNIw9M5ozs7OTJs2jWnTpqV57ODBg5bxmjVrsjNW5jv8HVy/ZB7X0p3QHsbtlxspVKgQwcHBXLlyhVatWhmcTERERHIqQwtdq7Hn5p3QCleBcs0MjZLbJCcnExQUxB9//EFISIil2G3UqJHByURERCSnU6Gb1S6EwX+h5rFXX7AxdLVIrnL69GkGDx7M5s2bAfjss8/o27evsaFEREQk11DVldUsTWj24PmaoVFyk1WrVtGkSRNLkevl5YW3t7exoURERCRXUaGblRKvw783r51bpRO45MLLomWz2NhYhg0bRo8ePbh48SIAfn5+rFu3Dg8PD4PTiYiISG6ipQtZ6fD3cP2CeeylJrQH2blzJ76+voSHhwNQqlQp5s6dS7NmzYwNJiIiIrmSZnSz0t6bTWiFKkGFlsZmyQUmTJhgKXLbtWtHaGioilwRERF5aCp0s8rFQ3Bys3msJrR0CQwMpFSpUsyePZuQkJCHvs2ziIiICGjpQtbZ+6n5Txs78Hzd2Cw51OrVq2natCkuLi4AlCtXjn/++Yd8+fIZnExERETyAk0zZoXEePj3c/PYvQMUKGVsnhwmJiaGQYMG0b17d8aPH5/qMRW5IiIiklk0o5sVjqyEuCjzWHdCS2Xbtm0MGDCA48ePA7BhwwYuX75MoUKFDE4mIiIieY1mdLNCyp3QClaACrpFLUBiYiJTp06lbdu2liLXx8eH0NBQFbkiIiKSJTSjm9mij8CJjeZxzT5ga2dsnhwgIiICX19ftm/fDoCrqyszZ87kpZdeMjiZiIiI5GUqdDObpQnNFmr2NjZLDvDnn3/y8ssvc/XqVQAaNWrEvHnzKF++vMHJREREJK/T0oXMlJQA+xaYx5XbgWsZY/PkAF5eXjz22GPY29szduxYfvzxRxW5IiIiki00o5uZjvwI186Zx1bchJaYmIi9vfmvlouLC59++ik3btygXr16BicTERERa6JCNzOlNKEVKAsV2xibxQAJCQlMmTKFf/75h++//x47O/P65Fq1ahmcTERERKyRli5klssRcHydeexlfU1ohw4d4rnnnmP27Nls2bKFuXPnGh1JRERErJwK3cyy9zPzn1bWhGYymfj8889p3rw5u3fvBuCZZ57hxRdfNDiZiIiIWDstXcgMSTduNaFVeh4KWkezVVRUFEOGDGHNmjUAODg4MG7cOAYNGoStrX6HEhEREWOp0M0MR1dB7Gnz2Ms6mtA2bNiAn58f586Zm++qVq3K/Pnz8fLyMjiZiIiIiJmm3TLD3pQmtNJQ+QVjs2QDk8lEcHCwpcjt27cvv/zyi4pcERERyVFU6D6qmOMQYX7rnpq9wTbvT5Lb2NgQGBhI9erV+frrr/nggw/Inz+/0bFEREREUlGh+6j2LgBMgI35lr95UHJyMgsXLuTy5cuWbSVLliQ0NJTWrVsbmExERETk3lToPorkRNh382oLFZ+DQhUNjZMVzpw5g4+PD8OHD+ftt99O9ZgazkRERCQnU6XyKCJ+hqv/mcd58E5oq1evxtvbm02bNgEQFhZGTEyMwalERERE0keF7qNIuROay2NQuZ2xWTJRbGwsw4YNo3v37ly8eBEAPz8/1q9fT8GCBQ1OJyIiIpI+eb9zKqtciYSI1eax5+tg52Bsnkyyc+dOfH19CQ8PB6BUqVLMnTuXZs2aGRtMREREJINU6D6sfQvAlGwee/U1Nksm+e2333jppZdITEwEoF27dsyaNYsiRYoYnExEREQk47R04WEkJ8HeT83jCq2gcGVj82SShg0b4uHhgYuLC7NnzyYkJERFroiIiORamtF9GMfWwpWT5nEub0K7evUqBQoUACBfvnx89tln2Nra4u7ubnAyERERkUejGd2HkdKElr8EuHcwNstDiomJYeDAgXTo0IGEhATL9scff1xFroiIiOQJKnQz6uopOPqTeez5Gtg5GhrnYWzbto2mTZuydOlSdu3axdy5c42OJCIiIpLpVOhm1L7PwZRkHueyJrTExEQCAgJo27Ytx48fB8DHx4c+ffLmHd1ERETEummNbkaYkmHvfPO4fAtwe9zYPBkQERGBr68v27dvB8DV1ZWZM2fy0ksvGZxMREREJGtoRjcjjq+HGPNMKF65ownNZDKxZMkSmjZtailyGzVqRGhoqIpcERERydNU6GZEShOaczGo0snQKBmxdu1arl69ir29PWPHjuXHH3+kfPnyRscSERERyVJaupBesWfgyA/mcY1eYJ/P2DzpZGNjw0cffcSlS5cYP3489erVMzqSiIiISLbQjG567VsIyeY7hlGrn6FR7ichIYFp06YRFRVl2ebm5sbKlStV5IqIiIhV0YxuetzehFa2KRSpZmyeezh06BC+vr7s3r2b3bt38+WXX2JjY2N0LBERERFDaEY3PU78ApePmsc58E5oJpOJzz//nObNm7N7924A4uLiiI2NNTiZiIiIiHE0o5seKU1oTkXg8S7GZrlDVFQUQ4YMYc2aNQA4Ojoybtw4Bg4ciK2tfo8RERER66VC90GunYPwFeZxjZ5g72RonNtt2LABPz8/zp07B0DVqlWZP38+Xl5eBicTERERMZ6m/B7k3xBIvmEe56AmtA0bNvDyyy9bitx+/fqxadMmFbkiIiIiN2lG935MplvLFsp4Q9Eaxua5TbNmzWjQoAEREREEBgbSunVroyOJiIiI5CgqdO/n5GaIDjePDW5CS05OJioqihIlSgBgb2/Pp59+iqOjo2WbiIiIiNyipQv3kzKbm68wPG7c7XJPnz7NSy+9RIcOHbh27Zple9myZVXkioiIiNyDCt17uRYF4cvN4xo9wMHZkBirVq2iSZMmbN68mUOHDjFv3jxDcoiIiIjkNlq6cC/7F0FSgnnslf1NaLGxsYwdO5aQkBDLNj8/PwYPHpztWURERERyIxW6d3N7E1qpxlA8e69ksGvXLvr37094uHl9cKlSpZg7dy7NmjXL1hwiIiIiuZmWLtzNf6Fw6aB5nI1NaCaTif/973+0bt3aUuS2a9eO0NBQFbkiIiIiGaQZ3buxNKEVgmovZ9thbWxsOHLkCImJibi4uBAQEEC3bt2wsbHJtgwiIiIieYUK3TvFXYRD35rHHt3AIX+2Hn7KlClcu3aNMWPG4O7unq3HFhEREclLtHThTgcWQ1K8eZzFyxZiYmIYMWIEkZGRlm0FChTg008/VZErIiIi8og0o3u725vQHmsAJWpn2aG2bdvGgAEDOH78OIcPH+b777/Hzs4uy44nIiIiYm00o3u7U7/Dhf3mcRbN5iYmJhIQEEDbtm05fvw4YL6qQnx8fJYcT0RERMRaqdC9XcpsrqMrVPu/TH/5iIgIXnjhBaZPn05ycjKurq4EBwcTFBRE/vzZuxZYRCSnGz16NKNHjzY6Rq4WGRlJtWrVUn14enri7e3NpEmTSEhISLX/sWPHGD58OA0bNuSJJ56gS5cufPvtt3d97X/++QdfX18aNmzIk08+yeuvv87OnTuz48vKEh9++CHLli1LtW3btm1Uq1aNWbNmpdk/MDCQHj163PW1qlWrxrZt21JtW7FiBT4+PtSpUwdvb29GjRrF6dOnMy0/wMKFC2nSpAl16tThnXfeIS4u7q77BQYGpvl7Ua1aNVq2bGnZ57vvvqNNmzbUqVMHHx8fduzYYXnsxo0bTJ8+HW9vbxo1asS0adNITEwEICEhgc6dO3PhwoVM/doelgrdFNcvwaFvzGOPV8GxQKa9tMlk4quvvqJp06Zs374dgEaNGhEaGspLLxl3a2ERkZzs3Xff5d133zU6Rp6wbNkytmzZwpYtW1i7di3Dhw/nm2++ITg42LLPgQMH8PHxAWD+/Pn88MMPvPLKK0yfPp3x48ener21a9fSq1cvPDw8WLRoEV9//TVVq1alZ8+eqQqi3OLo0aOsX7+ezp07p9q+atUqypcvzw8//IDJZHro1w8ICCAgIICXX36ZFStWMHfuXM6fP0/37t25ePHio8YHzOdkzpw5TJw4kZCQEHbv3s306dPvum/v3r0tfx+2bNnC6tWrKVy4MD179gTgt99+Y+LEiQwaNIgVK1bw9NNP079/f86ePQvA7NmzWbFiBe+//z6fffYZf/zxB1OnTgXA0dGR7t273/PY2c5kJfbs2WPavn27KTY29u47/BNoMs3A/HFmR6Ye++effza5ubmZ3NzcTMWKFTPNmDHDlJiYmKnHkNRiY2Pvf74lT9H5ti463+l38uRJU9WqVU0nT55M89i7775r6tixo+XzDh06mEaMGJFmvz179pg8PDxMmzZtMplMJtOVK1dMDRo0MM2dOzfNvn5+fqb/+7//y7T8JlP2nO/Ro0ebgoKCUm1LSEgwNWjQwPTdd9+ZPDw8TH/++Weqx2fPnm3q3r37XV+vatWqlv3//vtvU7Vq1Ux///13qn2uXbtmeuaZZ0wffvhhpnwNr776qmn27NmWz//++29TrVq1TNeuXXvgc8eNG2d69dVXTcnJySaTyWQaOnSoafz48an2ad26tWnp0qWm5ORkU506dUzffvut5bFdu3aZPD09TVevXjWZTCZTfHy86cknnzRFRkZm+OvYvXu3ac+ePRl+3r1oRhdSN6GVrAcl62bqy7du3ZqmTZtSuXJl1qxZw4gRI9R4JiKZL/4ynN6WfR/xlzMUL+Vt9M2bN9OiRQvq1KnD5MmTOXToEF26dOGJJ57A19eXq1evAmmXLqxcuZI2bdrQuHFj3nvvPcLCwlLt16FDBxo3bsyxY8e4fPky48aN46mnnqJevXq8/fbbXL5877wJCQkEBATQpEkTPD09adGiBUuXLgVgyZIltGjRItX+S5cupXXr1pbnTp48mYYNG9KwYUPeeustoqOjU33Nc+fO5cknn2TixImYTCbmzZtHixYtqFmzJt7e3syZM8fy2snJycyYMcPyeh9//DGtWrWyvBUeExPD22+/Td26dS1LEK5fv56hcwHmmbeU/4v27NlDWFgYAwYMSLOfl5cXTZs25ZtvzO96/vLLL1y9etUy+3e7UaNGMXny5HseM+Uc1q5dm65du7J/v7kv5m7LVG5/+3/IkCH873//w9vbm06dOuHj48Ps2bNT7d+1a1c+/vhjAA4dOkSPHj2oVasWzz33HF9++eU9M8XExLB69epUb9sDbN26lStXrtCyZUtq167NihUr7vka97NixQpq1apF/fr1U213dnbmk08+oVu3bnd9XosWLe66vOBuy3mSkpLYu3dvqmM88cQT3Lhxw/Jzci8REREsX76cUaNGWa7b37dvX15//fU0+165coWLFy8SGxtL7dq3GvarVavGjRs32LdvH2D+u/XUU09ZfoaMpKsugPkf7Ki95nEmNKElJCTw33//UalSJQBsbW0JDg7G2dmZAgUyb0mEiIhF/GWYXxHio7PvmPkKQ79j5pvrZEBwcDAff/wx4eHhjBgxgt9++4333nsPJycnBg0axLfffstrr72W6jmhoaGWpQx16tTh448/ZsiQIfzyyy+AuYCaO3cuxYoVo2LFivTo0YO4uDjmzZsHgL+/P6NHj+aTTz65Z6bNmzcTGBhI0aJF+f7775k0aRItW7bkueeeY/Lkyezbt4+aNWsCsG7dOp5//nnAvLZz3759zJ8/n3z58vHRRx/x5ptvEhISYnn9f/75h++++47k5GRWrFhBSEgIH374IeXKlSM0NBR/f3+aN2+Op6cnQUFBrFixgpkzZ1KkSBH8/f05efKk5bXeffddbty4wZIlS4iPj2fy5MlMnDiRKVOmpOv7bzKZ+Ouvv/jxxx/p168fAPv27SN//vz3vLRl3bp1WbRoEQBhYWFUrlz5rv+flS1b9p7Hvf0cPvXUUyxevBhfX182btyYrtyrV6/ms88+w2Qy8eeff/Ldd98xZMgQAM6ePcuuXbuYNm0a169fp1+/fnTu3JlJkyZx9OhRxo0bh4uLC506dUrzun/99ReFCxdO87WvWrWKunXrUqhQIVq2bMnHH3/MuHHjMtxTExYWlqoovF2NGjXu+bxvv/2WpKSkNNudnJzSbIuJiSE+Pp4SJUpYttnb21O4cGHOnDlz33yfffYZjRo1olatWpZtnp6eqfb57bffOHbsGI0aNaJQoUI4ODhw9uxZqlSpAmBZa3zp0iXLc55++mm++uorhg8fft/jZzUVunBrNtfBBTxeeaSXOnToEL6+vkRHR/Prr79SsGBBAIoXL/6oKUVE8oRBgwbh4eGBh4cHU6ZMoW3btjz99NMANG7cmKNHj6Z5ztKlS2nXrh2vvPIK165do1u3bpQoUcIyS+vl5WWZdQ0LC+Ovv/5izZo1lgmH6dOn88ILL3D06FEqV66c5vU9PDxo1KgRTzzxBAADBgxg7ty5HDt2jPr169OoUSPWrVtHzZo1uXz5Mtu2bWPkyJHExcXxxRdf8N1331GtWjUAPvjgAxo2bMjBgwdxcXEBoFevXpQvXx6AM2fOEBAQQOPGjQF45ZVXmDt3LocPH8bT05OvvvqKoUOH4u3tDcDUqVMtRfWJEyfYsGEDf/31F66urgBMmjSJTp06MWbMGMu2O7Vr184yW5eQkECRIkXo2bMnffr0AeDy5cu4urre806chQoVssxSX7ly5aEmbW4/hwAjR47EwcHhvjPtt3vhhRcs32M3NzemTZvGsWPHqFixIuvWraNGjRpUqFCBZcuWUbRoUYYOHQpAxYoV+e+//1i0aNFdC939+/enKXKvX7/Oxo0bLYV069atmTFjBuvWrbvra9zPw36/ihQpku59U2b0HR0dU213dHRM03B4u6tXr7Jq1aq7NtulOHHiBGPGjKF9+/aWArhVq1Z8+OGHuLu74+LiwrRp07C3t+fGjRuW57m7uxMWFkZSUpKh72Kr0I2/DAe/No89XjFfceEhmEwmFi5cyNixYy1djgsXLrT8kIiIZKl8hcyzqxfv/zZlpirikeHZXIBy5cpZxk5OTpQpUybV53f7jzkiIoKuXbtaPre3t2f48OGW2bXbX+Po0aMULFjQUuSC+T/dQoUKcfToURYuXMiPP/5oeWzVqlU8++yzbN26lalTp3L06FHLW+opM2pt27YlODiY4cOHs3HjRipUqEC1atU4dOgQN27cSJUNzMsPjh07ZikMbs/XqFEjdu/ezcyZMzly5AgHDhzg/PnzJCcnc/HiRc6dO4eXl5dl/8qVK1OokPn7fOTIEZKTk3nmmWfSHO/48eOWGec7BQcHU7JkSU6dOsXEiRPx8PBgwIABlgKkUKFCXLx4keTkZGxt065qPHfuHIULFwagcOHCxMTE3PU493PnOXR0dGTUqFHpfn7p0qUt45IlS1K/fn3WrVtH//79WbduHS+88AJgPv9hYWHUqVPHsv/9iq2LFy/i5uaWatumTZuIjY21LGeoUKECVatWZcWKFZZC197enuTk5DSvl7LN3t5cYj3s96tt27acOnUqzfb27dszceLEVNvy5csHkOZnJyEhAWdn53seIzQ0FCcnJ5o0aXLXxyMiInj99dcpV65cqiUpY8eOZdiwYTRt2pT8+fMzcOBA9uzZk6qgL1y4MMnJyURHR1O0aNEHf8FZRIXuga8g8eblNx5y2UJUVBRDhgxhzZo1gPmHd9y4cQwcODCzUoqIPFi+QlCqodEpHujOguNuhdWdUoqGe0n5jx7SzmqlSEpKIikpiTfffNMykwlQokQJPvroI5YtW0aXLl3o1KkT7733Xqp1ua1ateK9997j8OHDqZYtpBTCX331VZq3tIsWLWqZBb0937Jly5gyZQo+Pj60bt2aUaNGWda7pnydpjs6/FM+T0pKwtXVle+++y7N11eyZMl7fn9Kly5N2bJlqVChAkFBQXTs2JFp06YxduxYAGrXrs2NGzc4dOgQHh4eaZ6/b98+S/Ht6enJggULuHr1apqZyu3bt7Nw4UKmT5+epsC63zm0sbFJ9TWnXKrqdrd/D8E8w/vtt9/y4osv8s8//1i6/hMTE2ncuHGaK0Xc79h3LhFYtWoVAM8995xlW3JyMuHh4Zw+fZpSpUpRsGBBrly5kub1UoralHd0PT09LWtX7xQSEkJUVBQjRoxI81hwcPBdvw93mx0uXLgw+fLlIyoqyjI7nZiYSHR09H3fUQ4NDaV58+Z3/Rk8fPgwr732GuXKlePTTz9NtWSiaNGiLFq0iOjoaPLly4fJZGLmzJmpfqFLOZ/3epcgu1h3M5rJBHuCzOPiT0DJ+vfd/W42bNiAt7e3pcitWrUq69evZ/Dgwen6x1tERB6sQoUKqZpqkpOTadu27V0vZVWpUiViYmJSLYEIDw/n6tWrVKpUiaJFi1KhQgXLh729PV9//TXjxo3jrbfe4oUXXrC8M5fyn7WrqytNmjTh559/5vfff6dt27aAeXbazs6O6Ohoy+sVKFCAgICAe15HdMmSJQwePJh33nmHTp064ebmxoULFzCZTBQsWJASJUrw77//WvY/efKkpXiqVKkSV65cwcbGxnK869ev88EHH9z3LerblS9fnjfeeIMvvviC3bt3A+ZirGbNmmkavMDcqPbrr79aLj3WpEkTXF1d+eKLL9LsGxISwpkzZ+46i3jnOUxKSqJFixbs2LEDBwcHYmNjU33ND/Lcc89x8OBBli1bhpeXl6XIqlSpEhEREZbCvkKFCuzatYvFixff9XVu/4UEzG/n//bbb/Tv358VK1ZYPlLWKK9cuRIwN2AdPXo0zdKL3bt34+zsTMWKFQHzDOyePXvS/F2NjY0lJCTkrutwwfwuwO1/T1M+7jY7amtri5eXV6pj7Nq1C3t7+7v+4pJiz5491K2btgH/3Llz9O7dmwoVKvDZZ5+lKa7ffvtttmzZQuHChXF2dubXX3+laNGiljW7YF6va29vn2a2PLtZdyV2djucN/+QU6s/ZOC3DpPJxJgxY3j55Zc5d+4cAP369WPTpk2p3nISEZFH16NHD3744Qe+//57Tpw4weLFi0lOTk7TNAPmZQrPPPMMo0aNYs+ePezZs4dRo0bx5JNPUrVq1bu+fuHChdm0aRMnT55k+/btjBw5Ekj9VnDbtm35/PPPqVy5smVZRIECBfDx8cHf359t27YRHh7OyJEjOX78+D0bs9zc3Pjjjz+IiIhg3759DBs2jBs3bliO1aNHD2bPns0ff/xBWFgYY8aMAcwzY+7u7jRp0oS33nqLPXv28O+//zJmzBiuXbtmmUFMj549e+Lu7s7EiRMtb7UHBATwzz//MGLECPbu3UtkZCTff/89AwYMwMfHxzLD7eLiwjvvvENgYCCzZs2yLL8YN24cmzdvtswS3+n2c3j8+HECAgIwmUx4enri5eXF1q1b+eOPPzh06BATJ07EwcHhvl9DkSJFaNiwIUFBQZYZdoAOHTpw/fp1xo8fz5EjR/j11195//337/n2eY0aNTh06JDl8/Xr15OUlETPnj2pWrWq5aNBgwY0adKE77//HjA36FWtWpU333yT3bt3c/LkSdatW8eECRPo3r27JX/KDRdSGi1PnDjBX3/9Rb9+/bC1tbU0BD6qV199lc8++4wNGzawZ88e/P39efnlly2/dERHR6eagU5MTCQiIiJVcZpi2rRpJCcn8/7773Pt2jXOnz///+3deVQUV/bA8W83qE1UJG6YwX1BURAQGeMocYsRcEE0KqLjoBhJDCqJ+4a4rzPGBMcgLqNHMokLIhijjnqcODFK4obIIhhF1CSDROPSQIPdvz8Y+mcLKi1IS3M/5/QJ/ep11a1cCi+PV6/IysrS/zJiY2PD2rVruXz5MqdPn2bx4sVMmDDBYIAvNTUVBwcHk4/oVu2pC0U3oVm+Bg7+Rn1UoVDok9egQQPCw8Pp27dveUcohBACcHd3Z8GCBfqF9ps3b86nn35a4h3oUPgP9ZIlSwgICMDCwoI+ffroC8aSLFu2jLCwMPr374+trS3Dhg3DwsKC5ORk/XzYXr16odPp9HNBi8yaNYuVK1cyefJk8vPzcXd3Z+PGjU+dEzpnzhzmzJmDj48P9erVw8vLCysrK5KTk4HCxfz/+9//MmnSJCwsLJgwYQI//vijvnBatWqV/twsLS3x8PB4anH5NJaWlsybN4+AgAD27NnDsGHDsLe3Z9euXYSHh/PBBx/w4MEDWrZsyUcffaQfzS0yaNAgrK2tiYyMJCoqCoVCgZOTE1FRUQZ37z/uyRw6Ojry+eefo1Kp8PHx4ezZs0ycOJHatWszZcoUMjIynnse/fv35+TJkwaFbq1atYiMjGTZsmUMHjwYGxsbRo0aRVBQUIn76NKlC/fu3ePq1au0aNGC/fv389Zbb5X4J/+RI0fy/vvvc/78eVxcXNi0aROrV6/mww8/5Pfff6dRo0aMGDGiWPG6cOFCWrVqxbZt21iyZAnW1tZ069aNtWvXltuIZ//+/bl58yahoaFoNBreeecdpk+frt8+adIk7Ozs9FM87t69S0FBQbFfkHQ6HUeOHCE3NxdPT0+DbcHBwUyaNImQkBAWLlyIv78/r732GgEBAcVWSjlz5kyxueSmoNA9ORHITF28eBGNRoODg0PhPCrNffj8Dch/CB3GgucWo/eZm5tLWFgYU6dOlVUVXjFqtZrk5OT/z7cwa5LvqsXc8/3tt9/i6Oiov+v+t99+o2vXrhw9evSZy3eZq4rI96xZs2jcuDHBwcEvZf9VjVqt5q233iImJsbo79mEhAT9L07loepOXUj5Z2GRC6W6Ce2XX35hzJgxXLlyRd+mUqlYsWKFFLlCCCHKzVdffcWcOXNIT0/nypUrhIWF4eTkVCWL3Ioyfvx4YmNjDZbHEi8uLi6Onj17vhLfs1W30C2atlDf6bl3KX/99dd0796d/fv3ExQUJBeCEEKIlyY0NBSlUomfnx/Dhw9Hq9Wyfv16U4dl1lq3bk3fvn2Jjo42dSiVnkajISoqyqil416mqjlH99czhS945k1oDx8+ZN68eQZPt+natWuxZV+EEEKI8mJra6t/lK2oOI/PZxUvrnr16sTGxpo6DL2qWegmRBb+11IFDqNL7HLu3DmCgoJIT08H4I033mD9+vX07NmzgoIUQgghhBBlUfWmLuQ/gOSowq/th4PKxmDzo0eP+OSTT+jXr5++yB0wYAAnTpyQIlcIIYQQohKpciO6Ful7CotdKPEmtEOHDukfrVezZk2WL1/OqFGjTL4OnBBCCCGEME6VK3Qtk/63jFi99vCHPxXb7uXlxYABA7h16xYRERH6R+kJIYQQQojKpUoVugqtBov//lj45n83od27d4+MjAz9em0KhYLw8HCsrKye+1QWIYQQQgjx6qpSc3SVBerCLyxqgMOfOX36ND169GDEiBEGzyS3traWIlcIIYQQopIzaaGbl5fHnDlz6Ny5M927d2fLlqc/nSwpKYlhw4bh7OzM0KFDSUxMNPp4Fo8KC92CVkNYsW4j/fv3JyMjg19++YXdu3e/8HkIIYQQQohXj0kL3VWrVpGYmMi2bdtYsGAB4eHhHDx4sFg/tVrNhAkT6Ny5M9HR0bi6uhIUFIRarTbyiFqu3lHi/derrFq1Cq1WS+3atdm4ceNTn4EthBBCCCEqJ5MVumq1ml27djF37lw6dOhA3759GT9+PFFRUcX6HjhwgBo1ajBjxgxatWrF3LlzqVmzZolF8bM80Ch4a7MNPyakAvDmm29y4sQJ3n333XI5JyGEEEII8eowWaGbkpJCQUEBrq6u+jY3NzcuXLiAVqs16HvhwgXc3Nz0S3wpFAo6derE+fPnjTpmtlrJwzwdlpaWzJs3j7i4OJo2bVrmcxFCCCGEEK8ek626kJWVxeuvv0716tX1bfXr1ycvL4+7d+9St25dg76tW7c2+Hy9evVIS0sr9fHy8/Np0KABX3wRRb169alRowaXLl0q+4mIV1LRY5rT0tJkDeQqQPJdtUi+qxbJd9WSn59frnk2WaGbk5NjUOQC+vcajaZUfZ/s9ywKhYJq1aphZ9f4BSMWlYlCoSj2PSPMl+S7apF8Vy2S76pFoVCYR6Fbo0aNYoVq0XuVSlWqvk/2e5bHp0gIIYQQQgjzZ7I5ura2tty5c4eCggJ9W1ZWFiqVCmtr62J9b9++bdB2+/ZtGjZsWCGxCiGEEEKIysdkha6DgwOWlpYGN5SdOXMGJycnlErDsJydnTl37px+no5Op+Ps2bM4OztXZMhCCCGEEKISMVmha2VlxeDBgwkLCyMhIYEjR46wZcsWxowZAxSO7ubm5gLg6enJvXv3WLp0Kenp6SxdupScnBy8vLxMFb4QQgghhHjFKXRFw6QmkJOTQ1hYGIcPH6ZWrVoEBgYSEBAAQNu2bVm+fDlDhgwBICEhgQULFnDlyhXatm3LwoULad++valCF0IIIYQQrziTFrpCCCGEEEK8LCZ9BLAQQgghhBAvixS6QgghhBDCLEmhK4QQQgghzJJZFbp5eXnMmTOHzp070717d7Zs2fLUvklJSQwbNgxnZ2eGDh1KYmJiBUYqyoMx+T5+/Dg+Pj64uroycOBAjh49WoGRivJgTL6L3LhxA1dXV06fPl0BEYryZEy+U1NTGTlyJB07dmTgwIGcOnWqAiMV5cGYfP/rX//Cy8sLV1dXRo4cyaVLlyowUlGeNBoNAwYMeObP6LLWa2ZV6K5atYrExES2bdvGggULCA8P5+DBg8X6qdVqJkyYQOfOnYmOjsbV1ZWgoCDUarUJohYvqrT5TklJITg4mKFDhxITE4Ofnx9TpkwhJSXFBFGLF1XafD8uLCxMrutKqrT5vn//PuPGjaN169bExcXRt29fgoODyc7ONkHU4kWVNt9paWlMnTqVoKAg9u3bh4ODA0FBQeTk5JggalEWeXl5fPzxx6SlpT21T7nUazoz8fDhQ52Tk5Pu1KlT+rb169frRo8eXazvrl27dL1799ZptVqdTqfTabVaXd++fXV79uypsHhF2RiT79WrV+sCAwMN2saNG6f729/+9tLjFOXDmHwX2bdvn87Pz09nb29v8Dnx6jMm39u2bdO9/fbbuoKCAn3bkCFDdMePH6+QWEXZGZPvrVu36nx9ffXv79+/r7O3t9clJCRUSKyifKSlpekGDRqkGzhw4DN/RpdHvWY2I7opKSkUFBTg6uqqb3Nzc+PChQtotVqDvhcuXMDNzQ2FQgGAQqGgU6dOBk9pE682Y/Lt6+vLtGnTiu3j/v37Lz1OUT6MyTfAnTt3WL16NYsWLarIMEU5MSbf8fHx9OnTBwsLC33bnj176NGjR4XFK8rGmHzb2NiQnp7OmTNn0Gq1REdHU6tWLZo2bVrRYYsyiI+Pp0uXLnz11VfP7Fce9ZplWQJ9lWRlZfH6669TvXp1fVv9+vXJy8vj7t271K1b16Bv69atDT5fr169Zw6fi1eLMflu1aqVwWfT0tL4/vvv8fPzq7B4RdkYk2+AFStW4OvrS5s2bSo6VFEOjMl3ZmYmHTt2ZP78+Rw7dgw7OztmzpyJm5ubKUIXL8CYfHt7e3Ps2DH8/f2xsLBAqVQSERFBnTp1TBG6eEH+/v6l6lce9ZrZjOjm5OQYXCSA/r1GoylV3yf7iVeXMfl+3G+//cakSZPo1KkTffr0eakxivJjTL5PnjzJmTNnmDhxYoXFJ8qXMflWq9Vs3LiRBg0aEBkZibu7O4GBgfz8888VFq8oG2PyfefOHbKysggNDWXnzp34+Pgwe/ZsmZNtpsqjXjObQrdGjRrFTrzovUqlKlXfJ/uJV5cx+S5y+/Zt/vKXv6DT6fj0009RKs3m29/slTbfubm5hIaGsmDBArmeKzFjrm8LCwscHByYPHky7du3Z/r06TRv3px9+/ZVWLyibIzJ95o1a7C3t2fUqFE4OjqyePFirKys2LNnT4XFKypOedRrZvMvva2tLXfu3KGgoEDflpWVhUqlwtrauljf27dvG7Tdvn2bhg0bVkisouyMyTfAr7/+yqhRo9BoNGzfvr3Yn7rFq620+U5ISCAzM5PJkyfj6uqqn/P33nvvERoaWuFxixdjzPXdoEEDWrZsadDWvHlzGdGtRIzJ96VLl2jXrp3+vVKppF27dty6davC4hUVpzzqNbMpdB0cHLC0tDSYoHzmzBmcnJyKjdw5Oztz7tw5dDodADqdjrNnz+Ls7FyRIYsyMCbfarWa8ePHo1Qq2bFjB7a2thUcrSir0ua7Y8eOHD58mJiYGP0LYMmSJUyZMqWCoxYvypjr28XFhdTUVIO2n376CTs7u4oIVZQDY/LdsGFDrly5YtB29epVGjduXBGhigpWHvWa2RS6VlZWDB48mLCwMBISEjhy5AhbtmxhzJgxQOFvh7m5uQB4enpy7949li5dSnp6OkuXLiUnJwcvLy9TnoIwgjH5joiI4Pr166xcuVK/LSsrS1ZdqERKm2+VSkWzZs0MXlA4KlCvXj1TnoIwgjHXt5+fH6mpqXz22WdkZGSwbt06MjMz8fHxMeUpCCMYk+/hw4ezc+dOYmJiyMjIYM2aNdy6dQtfX19TnoIoR+Ver5V1LbRXiVqt1s2YMUPn4uKi6969u27r1q36bfb29gbrrl24cEE3ePBgnZOTk+7dd9/VXbp0yQQRi7Iobb779euns7e3L/aaOXOmiSIXL8KY6/txso5u5WRMvn/88Uedr6+vztHRUefj46OLj483QcSiLIzJ986dO3Wenp46FxcX3ciRI3WJiYkmiFiUlyd/Rpd3vabQ6f43HiyEEEIIIYQZMZupC0IIIYQQQjxOCl0hhBBCCGGWpNAVQgghhBBmSQpdIYQQQghhlqTQFUIIIYQQZkkKXSGEEEIIYZak0BVCCCGEEGZJCl0hhBBCCGGWpNAVQlRaf/7zn2nbtm2Jr6JHPj/P6dOnadu2LTdu3HgpMd64caNYbO3bt6dr166EhIRw69atcjtW7969+eyzz4DCZ8Lv3buX7OxsAKKjo2nbtm25HetJRft//OXg4IC7uztjx44lKSnJqP3dunWLr7/++iVFK4SoKixNHYAQQpSFl5cXc+fOLdZuZWVlgmie7rPPPsPV1RUArVZLZmYmc+fOJSgoiNjYWBQKRZmPsXv3bmrUqAHADz/8wKxZszh69CgA3t7eeHh4lPkYz/Of//xH//WjR4+4evUqy5YtIzAwkCNHjlCzZs1S7WfmzJnY2dnRv3//lxWqEKIKkEJXCFGpqVQqGjRoYOownqtOnToGcdra2hIcHMy0adNITU2lXbt2ZT5G3bp19V8/+XR3lUqFSqUq8zGe58lcNGrUiNDQUEaPHs2pU6fo06fPS49BCCGKyNQFIYRZ+/3335k3bx4eHh506NCBrl27Mm/ePHJyckrsf+3aNQIDA3Fzc8PV1ZXAwEBSU1P12+/fv8/8+fN58803cXNzY8yYMVy8ePGFYrOwsACgWrVqAPz8889MmzaNbt264eLiQmBgICkpKfr+2dnZTJ48mS5dutCxY0f8/PyIj4/Xby+aunD69GnGjBkDQJ8+fYiOjjaYujBr1iyGDRtmEMvNmzdp164dJ0+eBODs2bOMGjWKjh070rNnTxYuXMiDBw9e6DyLRpktLQvHVrRaLREREfTr1w9HR0c6derE+PHjuX79OlA4JSU+Pp69e/fSu3dvADQaDatXr8bDwwNXV1eGDx9uMHoshBAlkUJXCGHWZs2aRVJSEuHh4Rw6dIjZs2cTExPDV199VWL/jz/+GFtbW/bs2cOuXbtQKpUEBwcDhaOk7733HpmZmURERLBz505cXFwYOXKkUXNQtVotycnJbNiwgXbt2tGiRQsePHjAyJEj+fXXX9mwYQNffvklKpWK0aNHc/PmTQDCwsLIy8tjx44dxMXF0aJFCyZOnIharTbYv6urq36u7q5du/D29jbYPmTIEBISEvSFJUBcXByNGjXizTffJCUlhbFjx+Lh4UFsbCxr1qzh0qVLjBs3rthI8fNkZmayevVq/vCHP+Du7g7A9u3b2bx5M7NmzeLQoUOsX7+ea9eusWLFCuD/p3l4eXmxe/duAGbPns13333HmjVr2Lt3L15eXrz//vscP37cqHiEEFWLTF0QQlRqcXFxHDp0yKDNzc2NTZs2AdCtWzfc3d31o5mNGzdmx44dXL58ucT9Xb9+nT/96U/Y2dlRrVo1li1bxk8//YRWq+X06dOcP3+eU6dOYWNjAxQWxmfPnmX79u36Qq0k7733nn4EV6PRoNPp6Ny5M4sXL0apVBIbG8udO3eIjo7WT0H461//yttvv01UVBQzZszg+vXr2Nvb06RJE1QqFXPnzmXgwIH6/RapXr06derUAQqnMzw5ZcHd3Z0mTZoQGxurL+Lj4uLw8fFBqVSyefNmunXrxvvvvw9A8+bN9bHEx8fTpUuXp55n0TxkgPz8fKpVq0b37t1Zvnw5r732GgBNmzZl5cqV9OrVCwA7Ozs8PT05ePAgADY2NlSrVg2VSkXdunXJyMhg//79xMTE4ODgAMDYsWNJSUlh8+bN9OzZ86nxCCGqNil0hRCVWu/evZk2bZpB2+OFnb+/P8eOHWPv3r1cu3aN9PR0bty4QcuWLUvc30cffcSyZcv44osv+OMf/4iHhwcDBgxAqVRy6dIldDqdvkArotFoyMvLe2acS5YswdnZGSj8E369evUM4rx8+TLNmzc3mGerUqno2LGjvigPDg5m+vTpHDp0CDc3N7p3786AAQP0UwNKS6FQMHjwYOLi4ggODiYpKYn09HT+/ve/A5CUlERGRoZB0VrkypUrzyx0Y2JigMJpFp988gnZ2dmEhITQuHFjfZ/evXtz4cIF1q1bx9WrV7l69Srp6enY2tqWuM+i0XJ/f3+D9vz8fKytrY06dyFE1SKFrhCiUqtZsybNmjUrcZtWqyUoKIi0tDQGDBiAt7c3HTp0YP78+U/d36hRo/D09OTf//4333//PZ9++ikbNmwgJiYGrVZLrVq1iI6OLva56tWrPzNOW1vbp8YJxW8ee/wciua29u3blxMnTnDixAlOnjzJ1q1bCQ8PZ+fOnbRp0+aZx3+Sr68v4eHhXLx4kQMHDtCpUyd9fFqtloEDB+pHdB/3eCFekqJ9NGvWjIiICIYNG0ZgYCB79+7l9ddfB2Djxo2sX78eX19funbtSkBAAEePHn3qcmJF/2+ioqKKrdqgVMoMPCHE08lPCCGE2UpOTubbb79l3bp1TJs2jUGDBtG0aVOuX79eYmGZnZ3NokWLyM/PZ8iQIaxevZrY2FiysrKIj4/H3t6eBw8ekJ+fT7NmzfSvyMhI/TJeL6pt27Zcu3ZNv+4tQF5eHomJibRu3RqNRsPy5cvJzMzE29ubJUuWcOTIEZRKZYnzVJ+3XJmdnR1dunTh0KFDfPPNNwwZMkS/rU2bNqSnpxucY0FBAcuXL+fnn38u9TlZWVmxZs0abt++zaJFi/Ttn3/+OR9++CFhYWGMGDECFxcXrl279tRiv6iIz8rKMoip6CY7IYR4Gil0hRBmq379+lhaWvLNN9+QmZnJxYsXCQkJISsrC41GU6x/nTp1OH78OPPmzSM5OZnMzEy+/PJLqlWrhqOjIx4eHjg4OPDRRx9x6tQpMjIyWL58OdHR0bRq1apMsQ4cOBAbGxtCQkJISEggJSWFadOmoVarGTFiBNWrV+fixYvMnz+f8+fPc+PGDaKjo1Gr1SVOMSiaD5uSksLDhw9LPKavry9ffPEFd+/excvLS98+btw4kpKSWLhwIVeuXOHcuXNMnTqVa9eu0bx5c6POq127dowfP54DBw5w7NgxAN544w2+++470tPT+emnn1i7di2HDx82yEnNmjW5efMmv/zyC23atKFXr14sWLCAY8eOkZmZSWRkJBERETRt2tSoeIQQVYsUukIIs2Vra8uKFSs4duwY3t7eTJkyBVtbWwICAkhMTCzW39LSksjISJRKJQEBAfTv35+TJ0+yceNGmjZtioWFBVu2bMHR0ZGQkBAGDRrEDz/8QHh4OF27di1TrLVr12bHjh1YW1sTEBCAv78/ubm5/POf/6RJkyYArF27liZNmvDBBx/g6enJl19+yZo1a+jcuXOx/dnb29OjRw9CQkKeusJEv379AHj77bepVauWvt3FxYVNmzaRnJyMr68vH3zwAS1atOAf//jHc6dolGTixIm0bNlSv0TZqlWryM3NZejQoYwePZrLly+zcOFCsrOz9U+K8/Pz4/LlywwaNIhHjx6xdu1a3nnnHUJDQ/H29iYmJoalS5fi6+trdDxCiKpDoTN2rRghhBBCCCEqARnRFUIIIYQQZkkKXSGEEEIIYZak0BVCCCGEEGZJCl0hhBBCCGGWpNAVQgghhBBmSQpdIYQQQghhlqTQFUIIIYQQZkkKXSGEEEIIYZak0BVCCCGEEGZJCl0hhBBCCGGWpNAVQgghhBBm6f8AC19Yn4sbZ5oAAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 800x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ "*** Logistic Regression (Lasso) ***\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x700 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "*** Classification Metrics ***\n",
+ "F1 Score = 0.6438240872172648\n",
+ "******************************\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "AUC scores for each class: {0: 0.8281442473674488, 1: 0.6823395052163733, 2: 0.6777494903376493}\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ "*** Logistic Regression (Elastic Net) ***\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x700 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "*** Classification Metrics ***\n",
+ "F1 Score = 0.6437964101699942\n",
+ "******************************\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "AUC scores for each class: {0: 0.8281442473674488, 1: 0.6823109258263989, 2: 0.677713766661707}\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Evaluate all models\n",
+ "for name, model in dict_models_pen.items():\n",
+ " print('\\n')\n",
+ " print(f\"*** {name} ***\")\n",
+ " model.fit(X_train_prep, y_train)\n",
+ " y_test_pred = model.predict(X_test_prep)\n",
+ " evaluate_model_multiclass(y_test, y_test_pred, model.classes_)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Courbes de validation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_validation_curve(model, X_train, y_train, param_name, param_range, cv=5, scoring='f1_weighted', title=None):\n",
+ " \"\"\"\n",
+ " Plot validation curve for a logistic regression model.\n",
+ "\n",
+ " Args:\n",
+ " model: The logistic regression model to evaluate.\n",
+ " X_train: Training data.\n",
+ " y_train: Training labels.\n",
+ " param_name: Name of the parameter to vary.\n",
+ " param_range: Range of parameter values to evaluate.\n",
+ " cv: Number of cross-validation folds.\n",
+ " scoring: Scoring method to use.\n",
+ " title: Title of the plot.\n",
+ " \"\"\"\n",
+ " train_scores, val_scores = validation_curve(\n",
+ " model, \n",
+ " X_train, \n",
+ " y_train, \n",
+ " param_name=param_name, \n",
+ " param_range=param_range, \n",
+ " cv=cv, \n",
+ " scoring=scoring\n",
+ " )\n",
+ "\n",
+ " plt.figure(figsize=(12, 4))\n",
+ " plt.plot(param_range, train_scores.mean(axis=1), label='train')\n",
+ " plt.plot(param_range, val_scores.mean(axis=1), label='validation')\n",
+ " plt.legend()\n",
+ "\n",
+ " print(\"train scores:\", train_scores.mean(axis=1))\n",
+ " print(\"val scores:\", val_scores.mean(axis=1))\n",
+ "\n",
+ " # Find the best C (maximum validation score)\n",
+ " best_C_idx = np.argmax(val_scores.mean(axis=1))\n",
+ " best_C = param_range[best_C_idx]\n",
+ "\n",
+ " if title is not None:\n",
+ " plt.title(f'{title} (Best C: {best_C:.5f})')\n",
+ " else:\n",
+ " # plt.title(f'Validation Curve for {model.__class__.__name__}')\n",
+ " plt.title(f'Validation Curve for Ridge Logistic Regression (Best C: {best_C:.5f})')\n",
+ " plt.ylabel('Score')\n",
+ " plt.xlabel(f'{param_name} (Regularization parameter)')\n",
+ " plt.show()\n",
+ "\n",
+ " return best_C"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "train scores: [0.63933963 0.64488755 0.64498086 0.64502132 0.64501938 0.64502546\n",
+ " 0.64502591 0.64502683 0.64502497 0.64503112 0.64503453 0.64503222\n",
+ " 0.64503104 0.64502986 0.64502988 0.64502819 0.64502974 0.64502976\n",
+ " 0.6450331 0.64503252 0.64503485 0.64503264 0.6450332 0.64503549\n",
+ " 0.64503946 0.64504289 0.64504396 0.64504282 0.64504512 0.64504396]\n",
+ "val scores: [0.63939036 0.64486432 0.64497586 0.64498028 0.64503305 0.64503985\n",
+ " 0.64504906 0.6450416 0.64505077 0.64504148 0.64504617 0.64504609\n",
+ " 0.6450507 0.64504381 0.64504149 0.64502309 0.64502991 0.6450253\n",
+ " 0.64502759 0.64502528 0.64502065 0.64502068 0.64502297 0.64501797\n",
+ " 0.6450065 0.64501108 0.64501108 0.64501108 0.64501571 0.64500879]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1200x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "list_hyperparams = np.linspace(0.0001, 0.06, 30)\n",
+ "\n",
+ "best_C = plot_validation_curve(\n",
+ " logregRidge,\n",
+ " X_train_prep, \n",
+ " y_train, \n",
+ " param_name='C', \n",
+ " param_range=list_hyperparams, \n",
+ " cv=5, \n",
+ " scoring='f1_weighted',\n",
+ " title=\"Courbe de validation pour la régression logistique Ridge\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x700 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "*** Classification Metrics ***\n",
+ "F1 Score = 0.6437688009729335\n",
+ "******************************\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "AUC scores for each class: {0: 0.8281442473674488, 1: 0.6822609118428947, 2: 0.6776994772730072}\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Entrainer un modèle de régression logistique Ridge avec le meilleur hyperparamètre\n",
+ "logregRidge = LogisticRegression(C=best_C, penalty='l2', solver='lbfgs', tol=10e-6, random_state=42)\n",
+ "logregRidge.fit(X_train_prep, y_train)\n",
+ "\n",
+ "# Prédire les données test et évaluer le modèle\n",
+ "y_test_pred = logregRidge.predict(X_test_prep)\n",
+ "evaluate_model_multiclass(y_test, y_test_pred, logregRidge.classes_)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Optimisation des hyper paramètres avec Optuna"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Définir une fonction objectif pour l'optimisation des hyperparamètres\n",
+ "def objective(trial):\n",
+ " \"\"\"\n",
+ " Objective function for hyperparameter optimization.\n",
+ " \"\"\"\n",
+ " # Définir l'espace de recherche pour les hyperparamètres\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ " penalty = trial.suggest_categorical('penalty', ['l1', 'l2', 'elasticnet'])\n",
+ " solver = trial.suggest_categorical('solver', ['lbfgs', 'saga'])\n",
+ " l1_ratio = trial.suggest_float('l1_ratio', 0.0, 1.0) if penalty == 'elasticnet' else None\n",
+ "\n",
+ " # Vérifier les combinaisons valides de solveurs et de pénalités\n",
+ " if solver == 'lbfgs' and penalty not in ['l2', 'none']:\n",
+ " raise optuna.exceptions.TrialPruned()\n",
+ " if solver != 'saga' and penalty == 'elasticnet':\n",
+ " raise optuna.exceptions.TrialPruned()\n",
+ " \n",
+ " # Initialiser le modèle\n",
+ " hyperparameters = {\n",
+ " 'C': C,\n",
+ " 'penalty': penalty,\n",
+ " 'solver': solver,\n",
+ " 'tol': 1e-6,\n",
+ " 'max_iter': 1000,\n",
+ " 'random_state': 42\n",
+ " }\n",
+ " if l1_ratio is not None:\n",
+ " hyperparameters['l1_ratio'] = l1_ratio\n",
+ "\n",
+ " model = LogisticRegression(**hyperparameters)\n",
+ "\n",
+ " # Entraîner le modèle\n",
+ " model.fit(X_train_prep, y_train)\n",
+ "\n",
+ " # Prédire les résultats sur l'ensemble de test\n",
+ " y_test_pred = model.predict(X_test_prep)\n",
+ "\n",
+ " # Calculer le score F1\n",
+ " f1 = f1_score(y_test, y_test_pred, average='weighted')\n",
+ "\n",
+ " return f1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[I 2024-06-11 22:09:09,562] A new study created in memory with name: no-name-b775647b-cded-4406-be4b-b6756835ccfc\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "[I 2024-06-11 22:15:55,059] Trial 0 finished with value: 0.6448187550323489 and parameters: {'C': 0.006948915389640227, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.5458422666537074}. Best is trial 0 with value: 0.6448187550323489.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "[I 2024-06-11 22:25:44,284] Trial 1 finished with value: 0.6448743212512922 and parameters: {'C': 0.0072731227197688035, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.4197342049955981}. Best is trial 1 with value: 0.6448743212512922.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "[I 2024-06-11 22:36:36,761] Trial 2 finished with value: 0.6448649669478943 and parameters: {'C': 0.008206584501895596, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.5268123996067613}. Best is trial 1 with value: 0.6448743212512922.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:37:20,578] Trial 3 finished with value: 0.6446128131080148 and parameters: {'C': 0.000709654802605142, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 1 with value: 0.6448743212512922.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:38:07,080] Trial 4 finished with value: 0.6449052224010018 and parameters: {'C': 0.004829572359853787, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 4 with value: 0.6449052224010018.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:39:39,127] Trial 5 finished with value: 0.6428340133017482 and parameters: {'C': 0.00032566607245162327, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 4 with value: 0.6449052224010018.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:39:39,147] Trial 6 pruned. \n",
+ "[I 2024-06-11 22:41:43,080] Trial 7 finished with value: 0.6445820467838419 and parameters: {'C': 0.004192322884841636, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 4 with value: 0.6449052224010018.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:42:35,083] Trial 8 finished with value: 0.6437134601033372 and parameters: {'C': 0.000496980153964605, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.7584949306590276}. Best is trial 4 with value: 0.6449052224010018.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:43:00,309] Trial 9 finished with value: 0.6449688800248122 and parameters: {'C': 0.0452576710096763, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 9 with value: 0.6449688800248122.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:43:02,794] Trial 10 finished with value: 0.6449688800248122 and parameters: {'C': 0.08722612410869846, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 9 with value: 0.6449688800248122.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:43:05,205] Trial 11 finished with value: 0.6449688800248122 and parameters: {'C': 0.09664021018046742, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 9 with value: 0.6449688800248122.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:43:07,903] Trial 12 finished with value: 0.6449688800248122 and parameters: {'C': 0.09957384384912533, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 9 with value: 0.6449688800248122.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:43:10,817] Trial 13 finished with value: 0.6449505606808399 and parameters: {'C': 0.027068220769192398, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 9 with value: 0.6449688800248122.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:43:13,614] Trial 14 finished with value: 0.6449505606808399 and parameters: {'C': 0.025342113735310232, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 9 with value: 0.6449688800248122.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:43:16,462] Trial 15 finished with value: 0.6449504589514621 and parameters: {'C': 0.03186000680142746, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 9 with value: 0.6449688800248122.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:43:19,243] Trial 16 finished with value: 0.6447674745921143 and parameters: {'C': 0.00163386731965747, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 9 with value: 0.6449688800248122.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:43:19,265] Trial 17 pruned. \n",
+ "[I 2024-06-11 22:43:52,529] Trial 18 finished with value: 0.6449688800248122 and parameters: {'C': 0.045519055483579755, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 9 with value: 0.6449688800248122.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:43:55,734] Trial 19 finished with value: 0.6449597204051692 and parameters: {'C': 0.05632941490480927, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 9 with value: 0.6449688800248122.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:50:36,936] Trial 20 finished with value: 0.6448751530720159 and parameters: {'C': 0.014031278042010346, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 9 with value: 0.6449688800248122.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:50:39,358] Trial 21 finished with value: 0.6449597204051692 and parameters: {'C': 0.08555109395812602, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 9 with value: 0.6449688800248122.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:50:41,912] Trial 22 finished with value: 0.6449597204051692 and parameters: {'C': 0.05758124071254177, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 9 with value: 0.6449688800248122.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:50:44,392] Trial 23 finished with value: 0.6449597204051692 and parameters: {'C': 0.08093299031902558, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 9 with value: 0.6449688800248122.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:50:46,929] Trial 24 finished with value: 0.6449785969299281 and parameters: {'C': 0.014268894304764574, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 24 with value: 0.6449785969299281.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:50:49,457] Trial 25 finished with value: 0.6449602769671248 and parameters: {'C': 0.013292756381352282, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 24 with value: 0.6449785969299281.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:50:51,819] Trial 26 finished with value: 0.6449504589514621 and parameters: {'C': 0.03681025417382874, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 24 with value: 0.6449785969299281.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 22:50:54,548] Trial 27 finished with value: 0.6447575978727199 and parameters: {'C': 0.0018535723823544172, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 24 with value: 0.6449785969299281.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "[I 2024-06-11 22:59:45,053] Trial 28 finished with value: 0.6449217403378886 and parameters: {'C': 0.021078660612877963, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 24 with value: 0.6449785969299281.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 23:00:29,756] Trial 29 finished with value: 0.644987756754254 and parameters: {'C': 0.01082215109209323, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 29 with value: 0.644987756754254.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 23:06:52,635] Trial 30 finished with value: 0.6449598360992362 and parameters: {'C': 0.00935558671471969, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.054128625086766635}. Best is trial 29 with value: 0.644987756754254.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 23:07:18,263] Trial 31 finished with value: 0.6449785969299281 and parameters: {'C': 0.011034452267902523, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 29 with value: 0.644987756754254.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 23:07:45,952] Trial 32 finished with value: 0.6449785969299281 and parameters: {'C': 0.010863236385057245, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 29 with value: 0.644987756754254.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 23:08:18,340] Trial 33 finished with value: 0.6448197365654076 and parameters: {'C': 0.0024525543336909403, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 29 with value: 0.644987756754254.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 23:08:53,472] Trial 34 finished with value: 0.6449325951449033 and parameters: {'C': 0.006870878309316885, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 29 with value: 0.644987756754254.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 23:13:59,757] Trial 35 finished with value: 0.6449969164738766 and parameters: {'C': 0.01018582682710467, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.013060193273211174}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 23:17:34,127] Trial 36 finished with value: 0.644924744114177 and parameters: {'C': 0.005854251748338102, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.014950651288679591}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 23:24:17,806] Trial 37 finished with value: 0.6447375744840922 and parameters: {'C': 0.004150833431019003, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.24070598272728472}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "[I 2024-06-11 23:31:54,133] Trial 38 finished with value: 0.6449217333219018 and parameters: {'C': 0.00836741798847916, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.30548137983055423}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "[I 2024-06-11 23:40:15,323] Trial 39 finished with value: 0.6449781484503655 and parameters: {'C': 0.01866509703513016, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.1865548392773937}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 23:45:44,270] Trial 40 finished with value: 0.6445983505938611 and parameters: {'C': 0.0036165937816056543, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.7215176424338046}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-11 23:46:08,157] Trial 41 finished with value: 0.644987756754254 and parameters: {'C': 0.010108808468318457, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "[I 2024-06-12 00:00:55,518] Trial 42 finished with value: 0.6448017372014565 and parameters: {'C': 0.005992341173532938, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.3829753317364203}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:01:24,153] Trial 43 finished with value: 0.644987756754254 and parameters: {'C': 0.010334012749578445, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:04:49,958] Trial 44 finished with value: 0.6445876448797052 and parameters: {'C': 0.002865520733136802, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:05:14,250] Trial 45 finished with value: 0.644667810917689 and parameters: {'C': 0.001043307013749095, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:05:44,464] Trial 46 finished with value: 0.6449248522460665 and parameters: {'C': 0.005269676489237214, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "[I 2024-06-12 00:12:06,498] Trial 47 finished with value: 0.6449028334898432 and parameters: {'C': 0.0146607378088183, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.9498913190037415}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:12:26,666] Trial 48 finished with value: 0.642670937164173 and parameters: {'C': 0.00014927923238424004, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:12:54,834] Trial 49 finished with value: 0.6449966640499977 and parameters: {'C': 0.024004688861790698, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "[I 2024-06-12 00:19:49,365] Trial 50 finished with value: 0.6448940655658251 and parameters: {'C': 0.024935873424703787, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:20:16,823] Trial 51 finished with value: 0.644951016072896 and parameters: {'C': 0.008096268532297522, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:20:42,566] Trial 52 finished with value: 0.6449601759995448 and parameters: {'C': 0.01144181322295035, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:21:08,621] Trial 53 finished with value: 0.6449689817493237 and parameters: {'C': 0.019328013481902218, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:21:34,534] Trial 54 finished with value: 0.6449781414760827 and parameters: {'C': 0.02833248562205267, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:22:01,509] Trial 55 finished with value: 0.6449781414760827 and parameters: {'C': 0.035823476266374714, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:22:28,218] Trial 56 finished with value: 0.6449692270179778 and parameters: {'C': 0.01605932157083894, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:22:53,660] Trial 57 finished with value: 0.6449785969299281 and parameters: {'C': 0.0099182898665263, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:23:19,253] Trial 58 finished with value: 0.6449782429839364 and parameters: {'C': 0.02214696497792427, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:23:19,286] Trial 59 pruned. \n",
+ "[I 2024-06-12 00:23:45,745] Trial 60 finished with value: 0.6449052224010018 and parameters: {'C': 0.004810830127364858, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:24:12,321] Trial 61 finished with value: 0.6449601759995448 and parameters: {'C': 0.01243342969591729, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:24:38,546] Trial 62 finished with value: 0.6449692270179778 and parameters: {'C': 0.016227500362517598, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 35 with value: 0.6449969164738766.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:25:04,276] Trial 63 finished with value: 0.6449970178603115 and parameters: {'C': 0.010606096740994778, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:25:30,552] Trial 64 finished with value: 0.6449694370008885 and parameters: {'C': 0.009253334361180813, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:25:33,068] Trial 65 finished with value: 0.6449688800248122 and parameters: {'C': 0.04874430786545269, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:25:58,538] Trial 66 finished with value: 0.6449689817493237 and parameters: {'C': 0.032875723698929965, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:29:41,636] Trial 67 finished with value: 0.6447279731646841 and parameters: {'C': 0.007237436884435483, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:29:44,662] Trial 68 finished with value: 0.6449693285855566 and parameters: {'C': 0.014818116131411749, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:30:13,601] Trial 69 finished with value: 0.6449782429839364 and parameters: {'C': 0.022716427642417193, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:30:46,571] Trial 70 finished with value: 0.6449780395397792 and parameters: {'C': 0.06563942531781455, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:31:16,876] Trial 71 finished with value: 0.6449785969299281 and parameters: {'C': 0.010853138248845953, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:31:52,843] Trial 72 finished with value: 0.6449601759995448 and parameters: {'C': 0.012929090681640028, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:32:21,449] Trial 73 finished with value: 0.644951016072896 and parameters: {'C': 0.008333935223156619, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:32:47,991] Trial 74 finished with value: 0.6449511168286262 and parameters: {'C': 0.006593686890742458, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:32:48,022] Trial 75 pruned. \n",
+ "[I 2024-06-12 00:33:16,098] Trial 76 finished with value: 0.6449785969299281 and parameters: {'C': 0.01104938703300965, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:33:41,836] Trial 77 finished with value: 0.6448775714086449 and parameters: {'C': 0.004148279616374861, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:38:50,604] Trial 78 finished with value: 0.6449399611700664 and parameters: {'C': 0.005130429478687986, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.0996117788171192}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:38:50,634] Trial 79 pruned. \n",
+ "[I 2024-06-12 00:39:26,059] Trial 80 finished with value: 0.6449688800248122 and parameters: {'C': 0.03950881847834572, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:39:55,368] Trial 81 finished with value: 0.6449785969299281 and parameters: {'C': 0.009860343647606287, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:40:29,236] Trial 82 finished with value: 0.6449601759995448 and parameters: {'C': 0.012886215735225788, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:41:02,735] Trial 83 finished with value: 0.6449781414760827 and parameters: {'C': 0.01954619615263038, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:41:31,108] Trial 84 finished with value: 0.644951016072896 and parameters: {'C': 0.008540825063587464, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:42:00,288] Trial 85 finished with value: 0.6449695377518584 and parameters: {'C': 0.00631410156032945, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:42:36,994] Trial 86 finished with value: 0.6449784883241221 and parameters: {'C': 0.014649610358127809, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:47:39,528] Trial 87 finished with value: 0.6446836001375492 and parameters: {'C': 0.0030372245627677125, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.3220486859663902}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:48:05,929] Trial 88 finished with value: 0.6449785969299281 and parameters: {'C': 0.011093859006082164, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:48:09,078] Trial 89 finished with value: 0.6449414008518135 and parameters: {'C': 0.023401653365454658, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:48:36,202] Trial 90 finished with value: 0.6449418560415159 and parameters: {'C': 0.007585200994769566, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:49:03,176] Trial 91 finished with value: 0.6449694370008885 and parameters: {'C': 0.009187017054963859, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:49:41,652] Trial 92 finished with value: 0.644987756754254 and parameters: {'C': 0.010186384740935563, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:50:19,819] Trial 93 finished with value: 0.6449781484503655 and parameters: {'C': 0.017485741879168404, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:50:49,697] Trial 94 finished with value: 0.6449694370008885 and parameters: {'C': 0.012179581860429365, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 00:51:17,532] Trial 95 finished with value: 0.6449693285855566 and parameters: {'C': 0.014259037145667387, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "[I 2024-06-12 00:57:30,984] Trial 96 finished with value: 0.6448014462428581 and parameters: {'C': 0.005390066409150243, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.4411251307154843}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 01:01:25,548] Trial 97 finished with value: 0.644874974887042 and parameters: {'C': 0.010094352950924026, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 01:01:49,876] Trial 98 finished with value: 0.6449692270179778 and parameters: {'C': 0.016206508052956755, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 63 with value: 0.6449970178603115.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_29468\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 01:01:51,974] Trial 99 finished with value: 0.6443371580247199 and parameters: {'C': 0.0002719974066880147, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 63 with value: 0.6449970178603115.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of finished trials: 100\n",
+ "Best trial: {'C': 0.010606096740994778, 'penalty': 'l2', 'solver': 'saga'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "import optuna\n",
+ "\n",
+ "# Optimiser les hyperparamètres de la régression logistique Ridge\n",
+ "\n",
+ "study = optuna.create_study(direction='maximize')\n",
+ "study.optimize(objective, n_trials=100)\n",
+ "\n",
+ "print(\"Number of finished trials: \", len(study.trials))\n",
+ "print(\"Best trial:\", study.best_trial.params)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x700 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "*** Classification Metrics ***\n",
+ "F1 Score = 0.6437964674035427\n",
+ "******************************\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "AUC scores for each class: {0: 0.8281442473674488, 1: 0.6822823464364245, 2: 0.6777423458474918}\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Entraîner un modèle de régression logistique Ridge avec les meilleurs hyperparamètres\n",
+ "logregRidge = LogisticRegression(C=0.010606096740994778, penalty='l2', solver='saga', tol=10e-6, random_state=42)\n",
+ "logregRidge.fit(X_train_prep, y_train)\n",
+ "\n",
+ "# Prédire les données test et évaluer le modèle\n",
+ "y_test_pred = logregRidge.predict(X_test_prep)\n",
+ "evaluate_model_multiclass(y_test, y_test_pred, logregRidge.classes_)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<font color='red'>**Commentaire**</font> \n",
+ "\n",
+ "En étudiant la matrice de confusion, nous remarquons que la plus grande faiblesse du modèle se trouve à deux niveaux : \n",
+ "- prédire la classe 3 (fumeur) alors que l'étiquette est 2 (ancien fumeur)\n",
+ "- prédire la classe 2 (ancien fumeur) alors que l'étiquette est 3 (fumeur)\n",
+ "\n",
+ "Rappelons que nous n'avons pas d'information supplémentaire sur les anciens fumeurs. Ils pourraient avoir arrêté de fumer il y a des dizaines d'années comme très récemment avant l'étude. Ainsi, il est plus ou moins compliqué de cerner la différence avec un fumeur. Nous pensons que cela n'est pas une faiblesse du modèle, mais simplement des valeurs features très difficiles à discriminer.\n",
+ "\n",
+ "Nous allons fusionner ces deux classes et observer les résultats du modèle de régression logistique en conséquence."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Fusion des classes 3 (fumeur) et 2 (ancien fumeur)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(data_path)\n",
+ "\n",
+ "# Fusionner les labels 2 et 3 en un seul label 2\n",
+ "df['SMK_stat_type_cd'] = df['SMK_stat_type_cd'].replace({3: 2})\n",
+ "\n",
+ "# Sauvegarder le nouveau dataset\n",
+ "df.to_csv('../data/smoking_driking_dataset_Ver01_modified.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "SMK_stat_type_cd\n",
+ "1.0 602441\n",
+ "2.0 388905\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['SMK_stat_type_cd'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Distribution des classes dans le dataset équilibré :\n",
+ "SMK_stat_type_cd\n",
+ "1.0 388905\n",
+ "2.0 388905\n",
+ "Name: count, dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Équilibrer le dataset\n",
+ "df_balanced = data_loader.balance_dataset('../data/smoking_driking_dataset_Ver01_modified.csv', 'SMK_stat_type_cd', '../data/balanced_smoking_drinking_dataset_modified.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def reduce_dataset(data_path, target_column, output_path, train_size=100000, random_state=42):\n",
+ " \"\"\"\n",
+ " Réduit le dataset à un nombre spécifié de lignes tout en conservant les proportions des classes.\n",
+ " \n",
+ " Parameters:\n",
+ " - data_path (str): Le chemin du fichier CSV à charger.\n",
+ " - target_column (str): Le nom de la colonne cible pour le stratified sampling.\n",
+ " - output_path (str): Le chemin du fichier CSV où sauvegarder le dataset réduit.\n",
+ " - train_size (int): Le nombre de lignes du dataset réduit (par défaut 100 000).\n",
+ " - random_state (int): La graine aléatoire pour la reproductibilité (par défaut 42).\n",
+ " \"\"\"\n",
+ " # Charger les données\n",
+ " df = pd.read_csv(data_path)\n",
+ " \n",
+ " # Réduire le dataset à la taille spécifiée en utilisant un échantillonnage stratifié\n",
+ " df_reduced, _ = train_test_split(df, stratify=df[target_column], train_size=train_size, random_state=random_state)\n",
+ " \n",
+ " # Sauvegarder le nouveau dataset\n",
+ " df_reduced.to_csv(output_path, index=False)\n",
+ " \n",
+ " # Vérifier la distribution des classes dans le nouvel ensemble de données\n",
+ " distribution = df_reduced[target_column].value_counts()\n",
+ " print(\"Distribution des classes dans le dataset réduit :\")\n",
+ " print(distribution)\n",
+ " \n",
+ " return df_reduced"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Distribution des classes dans le dataset réduit :\n",
+ "SMK_stat_type_cd\n",
+ "1.0 50000\n",
+ "2.0 50000\n",
+ "Name: count, dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_reduced = reduce_dataset('../data/balanced_smoking_drinking_dataset_modified.csv', 'SMK_stat_type_cd', '../data/reduced_smoking_drinking_dataset.csv', train_size=100000)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Selected feature names: ['pipeline-1__height', 'pipeline-1__weight', 'pipeline-1__waistline', 'pipeline-1__SBP', 'pipeline-1__DBP', 'pipeline-1__HDL_chole', 'pipeline-1__triglyceride', 'pipeline-1__hemoglobin', 'pipeline-1__serum_creatinine', 'pipeline-1__SGOT_ALT', 'pipeline-1__gamma_GTP', 'pipeline-2__sex']\n"
+ ]
+ }
+ ],
+ "source": [
+ "cleaning.convertType(df_reduced, intToFloat, floatToInt)\n",
+ "\n",
+ "cont_features = df_reduced.select_dtypes(include=['float64']).columns\n",
+ "cat_features = df_reduced.select_dtypes(include=['object', 'int64']).columns\n",
+ "cat_features = cat_features.drop(['SMK_stat_type_cd', 'DRK_YN'])\n",
+ "\n",
+ "cont_features, cat_features\n",
+ "\n",
+ "X = df_reduced.drop(columns=['SMK_stat_type_cd', 'DRK_YN'])\n",
+ "y = df_reduced['SMK_stat_type_cd']\n",
+ "\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
+ "\n",
+ "y_train.value_counts(), y_test.value_counts()\n",
+ "\n",
+ "X_train_prep, X_test_prep, selected_feature_names = preprocess_data(X_train, X_test, cont_features, cat_features, y_train, k)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def evaluate_model_binary(y_true, y_pred, class_labels):\n",
+ " \"\"\"\n",
+ " Evaluate a multi-class classification model using confusion matrix, classification metrics, \n",
+ " and ROC AUC.\n",
+ "\n",
+ " Args:\n",
+ " y_true (array): True class labels.\n",
+ " y_pred (array): Predicted class labels.\n",
+ " class_labels (array): List of class labels.\n",
+ " \"\"\"\n",
+ " # Plot confusion matrix\n",
+ " plot_confusion_matrix(y_true, y_pred, classes=class_labels)\n",
+ "\n",
+ " # Calculate classification metrics\n",
+ " # precision = precision_score(y_true, y_pred, average='weighted')\n",
+ " # recall = recall_score(y_true, y_pred, average='weighted')\n",
+ " f1 = f1_score(y_true, y_pred, average='weighted')\n",
+ "\n",
+ " # Display classification metrics\n",
+ " print(\"*** Classification Metrics ***\")\n",
+ " # print(\"Precision =\", precision)\n",
+ " # print(\"Recall =\", recall)\n",
+ " print(\"F1 Score =\", f1)\n",
+ " print(\"******************************\")\n",
+ "\n",
+ " # Binarize the output\n",
+ " y_onehot_test = label_binarize(y_true, classes=class_labels)\n",
+ " y_score = label_binarize(y_pred, classes=class_labels)\n",
+ "\n",
+ " # ROC AUC\n",
+ "\n",
+ " fpr, tpr, _ = roc_curve(y_onehot_test[:, 0], y_score[:, 0])\n",
+ " roc_auc = auc(fpr, tpr)\n",
+ "\n",
+ " # Plot ROC curve\n",
+ " plt.figure(figsize=(8, 6))\n",
+ " plt.plot(fpr, tpr, label='ROC curve (AUC=%0.3f)' % roc_auc)\n",
+ " plt.plot([0, 1], [0, 1], 'k--')\n",
+ " plt.xlim([0.0, 1.0])\n",
+ " plt.ylim([0.0, 1.05])\n",
+ " plt.title('ROC Curve')\n",
+ " plt.xlabel('False Positive Rate')\n",
+ " plt.ylabel('True Positive Rate')\n",
+ " plt.legend(loc=\"lower right\")\n",
+ " plt.show()\n",
+ "\n",
+ " # Print AUC score\n",
+ " print(\"AUC score:\", roc_auc)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "*** Test Set ***\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x700 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "*** Classification Metrics ***\n",
+ "F1 Score = 0.8272309370253356\n",
+ "******************************\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "AUC score: 0.8289500000000001\n"
+ ]
+ }
+ ],
+ "source": [
+ "logreg.fit(X_train_prep, y_train)\n",
+ "\n",
+ "y_train_pred = logreg.predict(X_train_prep)\n",
+ "y_test_pred = logreg.predict(X_test_prep)\n",
+ "print(\"*** Test Set ***\")\n",
+ "evaluate_model_binary(y_test, y_test_pred, logreg.classes_)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[I 2024-06-12 10:51:24,297] A new study created in memory with name: no-name-c7863ca5-68d7-40d4-93cf-c9f91dd2b715\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:51:29,606] Trial 0 finished with value: 0.825797896351141 and parameters: {'C': 0.0003775860476507921, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 0 with value: 0.825797896351141.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:51:37,498] Trial 1 finished with value: 0.8271717171717171 and parameters: {'C': 0.00605502973460608, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.7103454110332732}. Best is trial 1 with value: 0.8271717171717171.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:51:37,498] Trial 2 pruned. \n",
+ "[I 2024-06-12 10:51:45,573] Trial 3 finished with value: 0.8272901291006285 and parameters: {'C': 0.0008545974764029181, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 3 with value: 0.8272901291006285.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:51:49,644] Trial 4 finished with value: 0.7931523702916183 and parameters: {'C': 0.00014326403359069383, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 3 with value: 0.8272901291006285.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:51:53,636] Trial 5 finished with value: 0.8174999737571396 and parameters: {'C': 0.0004691628182326267, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.12409372211973235}. Best is trial 3 with value: 0.8272901291006285.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:52:00,213] Trial 6 finished with value: 0.8271786928681142 and parameters: {'C': 0.06293888956861317, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 3 with value: 0.8272901291006285.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:52:07,569] Trial 7 finished with value: 0.8271264467847894 and parameters: {'C': 0.022683389484118164, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.5513251134381651}. Best is trial 3 with value: 0.8272901291006285.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:52:14,048] Trial 8 finished with value: 0.8271786928681142 and parameters: {'C': 0.07736412808963646, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 3 with value: 0.8272901291006285.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:52:20,874] Trial 9 finished with value: 0.8270811712798269 and parameters: {'C': 0.017848830420956266, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.17840447887531385}. Best is trial 3 with value: 0.8272901291006285.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:52:20,897] Trial 10 pruned. \n",
+ "[I 2024-06-12 10:52:21,023] Trial 11 finished with value: 0.82481725323135 and parameters: {'C': 0.0019279264176849582, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 3 with value: 0.8272901291006285.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:52:27,434] Trial 12 finished with value: 0.8271786928681142 and parameters: {'C': 0.07981959795983082, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 3 with value: 0.8272901291006285.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:52:32,929] Trial 13 finished with value: 0.8267920850870804 and parameters: {'C': 0.0066818524402364994, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 3 with value: 0.8272901291006285.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:52:32,929] Trial 14 pruned. \n",
+ "[I 2024-06-12 10:52:34,079] Trial 15 finished with value: 0.8053740807531007 and parameters: {'C': 0.00013026817685421127, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 3 with value: 0.8272901291006285.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:52:41,276] Trial 16 finished with value: 0.8272727272727272 and parameters: {'C': 0.018100508334965584, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 3 with value: 0.8272901291006285.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:52:48,331] Trial 17 finished with value: 0.8272727272727272 and parameters: {'C': 0.01632703798463364, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 3 with value: 0.8272901291006285.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:52:48,331] Trial 18 pruned. \n",
+ "[I 2024-06-12 10:52:55,436] Trial 19 finished with value: 0.827175206835975 and parameters: {'C': 0.030509984874863685, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 3 with value: 0.8272901291006285.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:53:03,279] Trial 20 finished with value: 0.8273807049161934 and parameters: {'C': 0.0011410862721328435, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:53:13,244] Trial 21 finished with value: 0.8273423637485381 and parameters: {'C': 0.0008034970061477133, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:53:22,951] Trial 22 finished with value: 0.8273354195648681 and parameters: {'C': 0.0009251793640053003, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:53:30,036] Trial 23 finished with value: 0.8245470743224643 and parameters: {'C': 0.0002985834252135132, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:53:38,638] Trial 24 finished with value: 0.8273807049161934 and parameters: {'C': 0.001096469295698242, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:53:49,230] Trial 25 finished with value: 0.8273772229589021 and parameters: {'C': 0.003591177437043604, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:53:49,248] Trial 26 pruned. \n",
+ "[I 2024-06-12 10:53:58,691] Trial 27 finished with value: 0.8273284608697621 and parameters: {'C': 0.002894141493532029, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:54:05,490] Trial 28 finished with value: 0.8231188700011227 and parameters: {'C': 0.0002545772695121779, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:54:14,560] Trial 29 finished with value: 0.8273807049161934 and parameters: {'C': 0.0015082557058602442, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:54:22,112] Trial 30 finished with value: 0.8261609743925726 and parameters: {'C': 0.00043527133747078703, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:54:31,965] Trial 31 finished with value: 0.8273807049161934 and parameters: {'C': 0.0012382121181659005, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:54:41,524] Trial 32 finished with value: 0.8273807049161934 and parameters: {'C': 0.0013592053735815561, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:54:49,835] Trial 33 finished with value: 0.8273807049161934 and parameters: {'C': 0.001396220753982783, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:54:54,748] Trial 34 finished with value: 0.8271960685620957 and parameters: {'C': 0.0007286643541552383, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:54:58,643] Trial 35 finished with value: 0.8207959144890541 and parameters: {'C': 0.0002264728365431336, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.9789650388295288}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:55:03,921] Trial 36 finished with value: 0.8273807049161934 and parameters: {'C': 0.0011145451848627648, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:55:03,946] Trial 37 pruned. \n",
+ "[I 2024-06-12 10:55:08,583] Trial 38 finished with value: 0.8257592160551384 and parameters: {'C': 0.0018839710770214148, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.316307770561513}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:55:13,279] Trial 39 finished with value: 0.8270044708184919 and parameters: {'C': 0.0006099370627219979, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:55:19,812] Trial 40 finished with value: 0.8272692360175886 and parameters: {'C': 0.009565642321036872, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:55:25,135] Trial 41 finished with value: 0.8273807049161934 and parameters: {'C': 0.0013172316505497225, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:55:32,266] Trial 42 finished with value: 0.8273284608697621 and parameters: {'C': 0.0025855437315299742, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:55:37,773] Trial 43 finished with value: 0.8273807049161934 and parameters: {'C': 0.0016908513952892605, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:55:41,453] Trial 44 finished with value: 0.821778962761067 and parameters: {'C': 0.0010647739083110058, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.01029473400235914}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:55:47,417] Trial 45 finished with value: 0.8273214876597782 and parameters: {'C': 0.00482613842421319, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:55:52,076] Trial 46 finished with value: 0.8270079282175608 and parameters: {'C': 0.0005849532070673123, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:55:52,193] Trial 47 finished with value: 0.8254056426348767 and parameters: {'C': 0.002244352267997279, 'penalty': 'l2', 'solver': 'lbfgs'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:55:57,576] Trial 48 finished with value: 0.8273807049161934 and parameters: {'C': 0.0015268319272492593, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:56:03,068] Trial 49 finished with value: 0.8273423637485381 and parameters: {'C': 0.000945366113706863, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.9746196631986771}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:56:08,985] Trial 50 finished with value: 0.8259374738791081 and parameters: {'C': 0.00039541509493767164, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:56:15,217] Trial 51 finished with value: 0.8273807049161934 and parameters: {'C': 0.0013170587373492076, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:56:21,889] Trial 52 finished with value: 0.8272448335177626 and parameters: {'C': 0.0007518461513920017, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:56:35,870] Trial 53 finished with value: 0.8273807049161934 and parameters: {'C': 0.0017262343288467405, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:56:41,385] Trial 54 finished with value: 0.8255741492150186 and parameters: {'C': 0.002494407017706838, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:56:51,389] Trial 55 finished with value: 0.8273807049161934 and parameters: {'C': 0.0011917330600230952, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:56:51,404] Trial 56 pruned. \n",
+ "[I 2024-06-12 10:56:58,451] Trial 57 finished with value: 0.8270044708184919 and parameters: {'C': 0.000631084644795922, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:57:06,540] Trial 58 finished with value: 0.8273354195648681 and parameters: {'C': 0.0009177925276162502, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:57:13,381] Trial 59 finished with value: 0.8269836501266403 and parameters: {'C': 0.00837718068255895, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:57:21,792] Trial 60 finished with value: 0.8273284608697621 and parameters: {'C': 0.003223251997881236, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 20 with value: 0.8273807049161934.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:57:27,240] Trial 61 finished with value: 0.8274329470375831 and parameters: {'C': 0.0010463186742213418, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:57:33,539] Trial 62 finished with value: 0.8273807049161934 and parameters: {'C': 0.0020608600608734737, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:57:39,110] Trial 63 finished with value: 0.8273807049161934 and parameters: {'C': 0.0015275360011763911, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:57:44,275] Trial 64 finished with value: 0.8274329470375831 and parameters: {'C': 0.001064590339081206, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:57:49,050] Trial 65 finished with value: 0.8267778760107489 and parameters: {'C': 0.0005068577529216292, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:57:49,050] Trial 66 pruned. \n",
+ "[I 2024-06-12 10:57:54,893] Trial 67 finished with value: 0.8245926592441328 and parameters: {'C': 0.0003018624056075777, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:58:00,093] Trial 68 finished with value: 0.8272935985796627 and parameters: {'C': 0.0007890881288903895, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:58:02,272] Trial 69 finished with value: 0.8121505216997456 and parameters: {'C': 0.00018648396891563743, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.41205073741933806}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:58:08,448] Trial 70 finished with value: 0.8273807049161934 and parameters: {'C': 0.0011793931522234102, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:58:15,890] Trial 71 finished with value: 0.8273807049161934 and parameters: {'C': 0.0015347251389956137, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:58:22,652] Trial 72 finished with value: 0.8271960685620957 and parameters: {'C': 0.0006957038073701831, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:58:29,686] Trial 73 finished with value: 0.8273284608697621 and parameters: {'C': 0.002116542315671713, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:58:36,080] Trial 74 finished with value: 0.8273807049161934 and parameters: {'C': 0.0012378810516960861, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:58:42,871] Trial 75 finished with value: 0.8272901291006285 and parameters: {'C': 0.0008506300354374984, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:58:57,590] Trial 76 finished with value: 0.8273284608697621 and parameters: {'C': 0.002679191808545383, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:58:57,611] Trial 77 pruned. \n",
+ "[I 2024-06-12 10:59:02,331] Trial 78 finished with value: 0.8175845548556081 and parameters: {'C': 0.0005367945954135291, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:59:03,133] Trial 79 finished with value: 0.7798096148675789 and parameters: {'C': 0.0001073832303414248, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:59:10,082] Trial 80 finished with value: 0.8274329470375831 and parameters: {'C': 0.001049459166656213, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:59:18,267] Trial 81 finished with value: 0.8274329470375831 and parameters: {'C': 0.001051425936430309, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:59:26,076] Trial 82 finished with value: 0.8274329470375831 and parameters: {'C': 0.0010306074956844809, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:59:36,166] Trial 83 finished with value: 0.8274329470375831 and parameters: {'C': 0.00107543402395972, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:59:45,258] Trial 84 finished with value: 0.8274329470375831 and parameters: {'C': 0.0010434560553839259, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:59:51,349] Trial 85 finished with value: 0.8271960685620957 and parameters: {'C': 0.0006791839236100288, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 10:59:56,899] Trial 86 finished with value: 0.8254444728576448 and parameters: {'C': 0.0003477505741257891, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 11:00:04,715] Trial 87 finished with value: 0.8274329470375831 and parameters: {'C': 0.0009809049536804507, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 11:00:11,566] Trial 88 finished with value: 0.825627219509321 and parameters: {'C': 0.0008731010256260409, 'penalty': 'elasticnet', 'solver': 'saga', 'l1_ratio': 0.6450944830892233}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 11:00:19,429] Trial 89 finished with value: 0.8274329470375831 and parameters: {'C': 0.0010388819279696104, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 11:00:27,488] Trial 90 finished with value: 0.8261609743925726 and parameters: {'C': 0.0004348817949590216, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 11:00:33,795] Trial 91 finished with value: 0.8274329470375831 and parameters: {'C': 0.000996676657731192, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 11:00:40,779] Trial 92 finished with value: 0.8274329470375831 and parameters: {'C': 0.0010080796258668265, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 11:00:47,650] Trial 93 finished with value: 0.8272448335177626 and parameters: {'C': 0.0007515771021603744, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 11:00:52,795] Trial 94 finished with value: 0.8270079282175608 and parameters: {'C': 0.0005954482945530993, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 11:00:59,024] Trial 95 finished with value: 0.8274329470375831 and parameters: {'C': 0.0010444488568128192, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 11:01:07,057] Trial 96 finished with value: 0.827175206835975 and parameters: {'C': 0.05067069754174184, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 11:01:13,879] Trial 97 finished with value: 0.8272901291006285 and parameters: {'C': 0.0008705758950074678, 'penalty': 'l1', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n",
+ "C:\\Users\\33667\\AppData\\Local\\Temp\\ipykernel_1460\\4054425019.py:7: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
+ " C = trial.suggest_loguniform('C', 0.0001, 0.1)\n",
+ "[I 2024-06-12 11:01:13,911] Trial 98 pruned. \n",
+ "[I 2024-06-12 11:01:18,338] Trial 99 finished with value: 0.8217970869493579 and parameters: {'C': 0.0010261045777301898, 'penalty': 'l2', 'solver': 'saga'}. Best is trial 61 with value: 0.8274329470375831.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of finished trials: 100\n",
+ "Best trial: {'C': 0.0010463186742213418, 'penalty': 'l1', 'solver': 'saga'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Find the best model using Optuna\n",
+ "import optuna\n",
+ "\n",
+ "study = optuna.create_study(direction='maximize')\n",
+ "study.optimize(objective, n_trials=100)\n",
+ "\n",
+ "print(\"Number of finished trials: \", len(study.trials))\n",
+ "print(\"Best trial:\", study.best_trial.params)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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/VrB/uSK/RnFx2MbX3d1dKZfTNWTd2ZIuBaVMbX93zfprsKZ9cUFHL2SXdDkoRdY+266kS0BplJMp1ysnletbWXLjL0f4jculn+XuVlpbX+nchWt6OG5tkV9n9dSeqlW1bPz/Run9aQIAAACFyGETXwAAAKdnkGHag+8WAAAAnAKJLwAAgKMyuCPTHiS+AAAAcAokvgAAAI6KOb524bsFAAAAp0DiCwAA4KiY42sXEl8AAAA4BRJfAAAAR2QYxTPHtwylyiS+AAAAcAokvgAAAI6qDKWxxYHEFwAAAIXm/PnzGj58uKKionTPPfdo/fr11rHk5GQNGDBA4eHh6tq1q7Zv325z7DfffKPu3bvLZDKpf//+Sk5Othl/55131LZtW0VERGj06NHKyMiwqzYaXwAAAEdluBT9yw4Wi0VDhw7VmTNntGzZMo0ePVrTpk3T//3f/1nHAgMDtW7dOj3wwAMaNmyYTp8+LUk6ffq0hg4dqpiYGK1du1b+/v566qmnZLFYJEmffPKJ5s2bp4kTJ2rp0qVKTEzUjBkz7KqPqQ4AAAAoFPv27dP333+vzz77TNWrV1fjxo31xBNPaPHixSpfvrySk5P13nvvycfHR3Xq1NGOHTu0bt06Pf3001qzZo2aNm2qgQMHSpKmTp2qNm3aaPfu3YqOjtayZcv0+OOP6+6775YkTZgwQYMGDdLzzz8vb2/vAtVH4gsAAOCoDKPoX3ZITk6Wv7+/qlevbt3WoEED7du3T/Hx8WrcuLF8fHysY5GRkUpISJAkJSYmKioqyjrm7e2tJk2aKCEhQbm5udq7d6/NeHh4uLKzs3XgwIEC10fjCwAAgEIRGBioK1eu2My9PXPmjHJycpSamqrg4GCb/QMCAnTmzBlJ+sPxy5cvKysry2bczc1Nfn5+1uMLgsYXAADAIRnFNMe34KmvyWRScHCwJk2apPT0dB0/flxLliyRJJnNZnl4eNjs7+HhIbPZLEnKyMi46XhmZqb1/c2OLwgaXwAAABQKT09PzZ49Wzt37lRkZKQeffRR9e7dW5JkGEa+JtVsNsvLy8t67I3Gvb295enpaX1/o/GCovEFAABwVKVsjq8khYWF6YsvvtC2bdv01Vdf6Y477lClSpVUo0YNpaWl2eyblpZmnb4QEhJyw/GgoCD5+fnJ09PTZjwnJ0cXL15UUFBQgWuj8QUAAEChuHjxovr06aNffvlFQUFBcnNz01dffaWWLVvKZDLphx9+sE5bkKT4+HiZTCZJ16dJxMfHW8cyMjK0f/9+mUwmubi4qFmzZjbjCQkJcnNzU8OGDQtcH40vAAAACoWfn5/S09M1Y8YMJScna82aNVq3bp2eeOIJtWzZUlWqVFFcXJwOHTqkRYsWKSkpST179pQk9ejRQ3v27NGiRYt06NAhxcXFqVq1aoqOjpYk9e3bV4sXL9Znn32mpKQkjR8/Xg8//DBTHQAAAJxCKXuAhSS9+uqrSk5O1v3336+lS5fqtddeU1hYmFxdXbVgwQKlpqYqJiZGH374oebPn6/Q0FBJUrVq1TR37lytW7dOPXv21MWLFzV//nwZ/51u0a1bNw0ePFhjx47VwIEDFRYWpueff96u2niABQAAAApN7dq19e67795wrGbNmlq+fPlNj23Xrp3atWt30/HY2FjFxsbecm00vgAAAI7qFm4+c2ZMdQAAAIBTIPEFAABwRIZuaQ7uLV2njCDxBQAAgFMg8QUAAHBIRvEkvmUo8iXxBQAAgFMg8QUAAHBULmUnjS0OJL4AAABwCiS+AAAAjqpY5viWHXy3AAAA4BRIfAEAABwVT26zC4kvAAAAnAKJLwAAgKNijq9d+G4BAADAKZD4AgAAOCrm+NqFxBcAAABOgcYXAAAAToGpDgAAAI7IMIrn5rYyNJ2CxBcAAABOgcQXAADAUZWhNLY4kPgCAADAKZD4AgAAOCoeYGEXvlsAAABwCiS+AAAAjoo5vnYh8QUAAIBTIPEFAABwSMW0jq/KTqpM4gsAAACnQOILAADgqJjjaxcSXwAAADgFEl8AAABHxTq+duG7BQAAAKdA4gsAAOCIDBVP4luGphGT+AIAAMApkPgCAAA4KlZ1sAuJLwAAAJwCjS8AAACcAlMdAAAAHBKPLLYXiS8AAACcAokvAACAo+LmNruQ+AIAAMApkPgCAAA4Kh5ZbBe+WwAAAHAKJL4AAACOijm+diHxBQAAgFMg8QUAAHBQBomvXUh8AQAA4BRIfAEAABwUia99SHwBAADgFEh8AQAAHJHx31dxXKeMIPEFAACAUyDxBQAAcEhGMc3xLTuRL4kvAAAACk1KSooGDx6s5s2bq0OHDnrnnXesY/v371evXr1kMpnUo0cP7du3z+bYTZs2qVOnTjKZTBo6dKguXLhgHbNYLJo5c6ZatWqlli1bavr06crLy7OrNhpfAAAAB2UYRpG/7PXMM8/Ix8dH69ev1+jRozV79mx9+umnSk9PV2xsrKKiorR+/XpFRERo8ODBSk9PlyQlJSVpzJgxGjZsmFatWqXLly8rLi7Oet4lS5Zo06ZNmjdvnubMmaONGzdqyZIldtVG4wsAAIBCcenSJSUkJGjIkCGqVauWOnXqpLZt22rHjh3avHmzPD099cILL6hOnToaM2aMypUrpy1btkiSli9fri5duujBBx9Uw4YNNX36dG3dulXJycmSpGXLlmn48OGKiopSq1atNHLkSK1YscKu+mh8AQAAHFRpS3y9vLzk7e2t9evXKzs7W0ePHtWePXvUqFEjJSYmKjIy0npOwzDUvHlzJSQkSJISExMVFRVlPVeVKlUUGhqqxMREnT17VikpKWrRooV1PDIyUqdOndK5c+cKXB+NLwAAAAqFp6enxo4dq1WrVslkMqlLly76y1/+ol69eik1NVXBwcE2+wcEBOjMmTOSpHPnzt10PDU1VZJsxgMDAyXJenxBsKoDAAAACs2RI0d09913629/+5sOHTqkSZMmqXXr1srIyJCHh4fNvh4eHjKbzZKkzMzMm45nZmZa3/9+TJL1+IKg8QUAAHBQpe2RxTt27NDatWu1detWeXl5qVmzZjp79qxef/11Va9ePV+Tajab5eXlJel6WnyjcW9vb5sm19PT0/q1JHl7exe4PqY6AAAAoFDs27dPNWvWtDazktS4cWOdPn1aISEhSktLs9k/LS3NOn3hZuNBQUEKCQmRJOuUh99/HRQUVOD6aHwBAAAclVEMLzsEBwfr+PHjNsnt0aNHVa1aNZlMJn3//feyWCySrq/Lu2fPHplMJkmSyWRSfHy89biUlBSlpKTIZDIpJCREoaGhNuPx8fEKDQ3NNy/4j9D4AgAAoFB06NBB7u7u+te//qVjx47piy++0BtvvKF+/fqpc+fOunz5siZPnqzDhw9r8uTJysjIUJcuXSRJffr00QcffKA1a9bowIEDeuGFF9S+fXtVr17dOj5z5kzt2rVLu3bt0qxZs9S/f3+76mOOLwAAgIMqbXN8y5cvr3feeUeTJ09Wz5495e/vryFDhuiRRx6RYRhauHChxo0bp9WrV6tBgwZatGiRfHx8JEkRERGaOHGi5syZo0uXLqlNmzaaNGmS9dyDBg3S+fPnNWzYMLm6uqpnz54aMGCAXfXR+AIAAKDQ1K1b96ZPVAsLC9OGDRtuemxMTIxiYmJuOObq6qq4uDibp7nZi8YXAADAARlG8SS+pSxUvi3M8QUAAIBTIPEFAABwUKVtjm9pR+ILAAAAp0DiCwAA4KBIfO1D4gsAAACnQOILAADgqAh87ULiCwAAAKdA4gsAAOCQjGKa41t2YmUSXwAAADgFEl8AAAAHxaoO9iHxBQAAgFOg8QUAAIBTYKoDAACAg2Kqg31IfAEAAOAUSHwBAAAcFYGvXUh8AQAA4BRIfAEAAByRUUxzfMtQqkziCwAAAKdA4gsAAOCADBVP4luGAl8SXwAAADgHEl8AAAAHxTq+9iHxBQAAgFMg8QUAAHBQJL72IfEFAACAUyDxBQAAcFQEvnYh8QUAAIBTIPEFAABwSEYxzfEtO7EyiS8AAACcAokvAACAg2JVB/uQ+AIAAMApkPgCAAA4IqOYEt8yFCqT+AIAAMAp0PgCAADAKTDVAQAAwFGVoWkIxYHEFwAAAE6BxBcAAMBBsZyZfUh8AQAA4BRIfAEAABwUia99SHwBAADgFEh8Uapkntyr1Pf/ddPxCi37qGLL3so8maTLu9+T+fzPMlzd5Vm5kfzaPC63ilWs+15LPaHUjxcr69ReGS5u8qzWTBVb95e7X+gNz23Jy9W5tf+U4eap4JjJhf7ZABSNvLw8vf/eO1q3/C2dSj6uSgGB+kunrnryH3HyLV9B0XX8bnps8+i79PqytZKk9GvXNO+Vf+nLLR8qI/2awlvcqWf/NUU1a9ezOWbT2hX69+L5Sv75iAKCgtW956P629Dn5erqWpQfE8jHUPEkvmUpU6bxRaniEVxHwT2n59t+aedymc8dlk+9tspK+VGpH4yT9x3RCrhnhCw5Wbr87SqdXTdKlfvMlRSgU6dOKWHJSFk8yqnSXwbLxcdP1/b/n86tfUEhD8+SW4WQfNe4HL9O5nOH5BnatBg+KYDC8u6i17TwlZf06JPD1eLOdjpx7LAWvjpZRw/+qDlLN+ittZ/mO+arTzZq+ZtzFNN3oHXb2OeGaF/iHg375wSVK19eb815WU89er9WbtmpChX9JElr3n1Tsya8oEefeFojXpyqvd9/q7fmviyz2aynRo4tro8M4BaVisbXbDYrJiZGL774oqKjo0u6HJQgFw8feVZuYLMt49guZZ1MUkDnF+ReqapSNy2Ru391BXR5QYZxfbaOZ+WGOr30CV378XOp6sNauXKlcrOzVLnXLLlVrCxJ8qoRrnNrXtClncsVcO9zNtcwpx3Tlfg1cvGpVDwfFEChyMvL07sLZ+vBPn/T0OfHSZJatmmvin7++tc/BurA3gQ1i2hhc8zZ0yf1waql6tnvSd3TPUbKyVRSUpL+8+X/6dXFa3Rn+3skSeFRd+qh9iatW/6W/jZ0pDLSr2nBzIl67MnhGvbPCZKkqDvb6fKli/r2668kGl+UAOb42qfE5/hmZWVpxIgROnToUEmXglIoLydLv2x7U141o+RTt40kybNyffma/mpteiXJ1TdALh4+yrl8RpJ07NgxlQuqYW16JckwXORZtYkyfo63uYYlN1sXPp0t37DucverWgyfCkBhuXb1sro8+Ijuu7+nzfaada5PTzh54li+Y16b+i95enlryHMvWrft2LFD3j4+im7bwbqtUkCgIlq20TdfXU+Md/3nC6VfvaJe/WNtzveP0S9pyYYvCu0zASg6Jdr4Hj58WA8//LBOnDhRkmWgFLuauFG5V8/Lr+0T1m0Voh6Wb+NONvtlntqnvKyrcvevIUny8/OT+eoFWXJzbPbLuXRGFvM15WZesW67/O0qWfJyVDG6bxF+EgBFoXwFPz03brpMUa1stm/79CNJUu16DW227/3+W32++X0Nee5F+ZavYN3+888/q2q1mvnm6VareYeOH7sezBz8ca98y1fQhbRUDe7dRW0aBqlLdH29PW+GLBZLUXw84M8ZxfAqQ0p0qsPu3bsVHR2tZ599VuHh4XYf72pItf3dC78wlAp5udnatXeTgpu2U4PaNW66X3b6JX2/db48ygeoSet7VbWim+68/3598sknyto2R3d06C83z3I6m/SFsk58L0mqXi5XXhXddfnUQZ38/n2FD5iuCkE+uuJ+/f/D+XNVRuVklnQFKAb7Evdo2Ruz1fbue1WnTm2bn/vyha+qStXq6tz9gd+255p19epV+fiWy/dnpJy3t65dvSLlZOpi2lnl5ubo2UE91fvxWD057Dnt+nqr3nxtqjLTr+ipEaOL82OiOPAXmjKnRBvfvn1vL2ELLOeqWX8NLqRqUNps2bJF/7n6i2bHPan69W/8c05LS9OwYS/KyLyoRQsWqGnTWv8daaVJkyZp1qxZ2j13kCSpZcuW6jRogBYuXKjJ91eVt7e3HnvsNfV/rK+eHtJWkhS76XrDy5+rMurKyZKuAEUsISFBzz77rEJDq2jcmBfk+ruf+dmzZ7Xt8y169tln5Zlxxua4vLw8GbnZNvtLkmG+LBfDkOuVk8pJv6iM9HQNjo3VY31jJEnRTWrpatoprVzyhgb27aFy5coV/YdEMSvdq3Uwx9c+peLmtluVdi1X0764UNJloIj8sHqzfIJqauEBP+nAuXzjV88e0773xis3K0NNe0/QkqPB0tFzqlrRTSPa+etHn2iFDXtXGb+ckau7pzwrBOrjL9+VDBdN/CpDRz9boPOXs3WkygMa8X6KJOlImlmSrr83XPgXShkz/dHmJV0CitCnm9/XpFHPqHqt2nrtrZUqHxyi3N+Nf75hiwzDUMeYAcotH/TbQK5Zvr6+On/xsnLLV7M559VsQ+XKV1Bu+Wry9ru+Gkybzr1s9mvV8X6tX79eh89eU1OT7c25cGwuV1NKugQUModufHMt0tEL2SVdBoqAJTdH5w/Hq3zzmBv+jDNPJilt81S5ePgo6KGpOu9bQ+d/t9+xY8cUv22n0qv/RTKCpRxJF7KVeuKQ3ANq6tjFPKX8sF25V85p+7SYfOff9tL98u84XOUadSzKj4ni5uZV0hWgiCx/c67mvTxWzaPv0vQ3lsu3fMV8+2zf+rnCW9ypgMrV843VrFlTO3buUp6Lh1xcfrv9JfnECd1Rt4Hk5qXqtetLksx5svmzlJN3/S/InuUq8GesrCnt4YdhFE9AU9q/D3Yo8VUdgBvJPn9clpwseVZplG/MnHpUaZtekqtvoEJ6Tpd7QP75v0eOHNFPH8xS9i+//doy+8IJZSZ/L+/a12+CCew2RiG9Ztq83IPqyD2ojkJ6zZRXrRb5zgug9Fn/7yWaO+1Fdez6kF5bsu6GTa/FYtH+xHiFRd54ycxWrVop/dpV7dz2uXXbL+fTlPDtN4q+6/pKD63/0kmGYej/Nq6zOXbb55tVsZK/7qhD2guUdg6d+KLsyj5/XJLk7p8/mbnwxVxZ8nJVsWUf5VxNU87VNOuYq3cFyb+G7rrrLnlVqqLz//eKKkb3lcWcrovfvCO3CiEqH/5XSZJHYK1853Zx974+FlIv3xiA0ud86lnNnjxaVarVUK/+T+rAD4k249Vq3KFKAYE6czpZV69c1h11G97wPM2bN1fzlndq3IgnNeyfE1XRr5LenDNNvhUqKubR6/cJVK1RS736Panli16Tm5ubIlq00fYvt2jL+6s1ctx0ublzUyyKXxkKY4sFjS9KpdyMi5IkF09fm+05l84oO/WoJOn8lpfzHefTsIN0x0h5eXkp7NFJStr4hs7/3ysy3NzlVaO5/Fr3k4uHT5HXD6B4fP3V/ykrM0MpJ09o8CNd8o2/+PJ8de/5qC6kpUqS9QlsN/LyvLc1++WJmjvtReXlWRQWGa0pc9+xOebZF6cpuEo1vb9yiZYtnK2q1Wtp9JQ5euCR/oX90QCHtH79esXFxeXbbhiGDhw4oP3792vcuHE6ePCg6tatqwkTJqhp09+emLpp0ybNnj1bqampuuuuuzRp0iT5+/tLuv6bm1mzZmnt2rXKy8tTz549NXLkSJvpSX/GsJSSxQcbNGigZcuWFfjJbXv37tWJ8+kasu5sEVcGR1Pb312z/hqs5z48xxxw2Fj7bLuSLgGlUU6mXK+cvH7DGnN08Tsul36Wp5uLmjVrVtKl5LN3714lX8jQ8I3ni/xac+4PUHV/7wJ9HzIzM3Xlym9r5efk5Ojxxx9X+/bt9cwzz+jee+/V/fffr549e2rlypX6+OOP9emnn8rHx0dJSUnq16+fJkyYoIYNG2ry5Mny8fHRwoULJUlvv/22li1bppkzZyonJ0fPP/+8BgwYoEGDBhX4s5SaOb4//fQTjysGAACwg/HfG9yK8mUPLy8vBQUFWV8ffvihLBaLRo4cqc2bN8vT01MvvPCC6tSpozFjxqhcuXLasmWLJGn58uXq0qWLHnzwQTVs2FDTp0/X1q1blZycLElatmyZhg8frqioKLVq1UojR47UihUr7Kqv1DS+AAAAKDsuXryoN998U88995w8PDyUmJioyMhIazNtGIaaN2+uhIQESVJiYqKioqKsx1epUkWhoaFKTEzU2bNnlZKSohYtfrvxPDIyUqdOndK5c/mXPL0ZGl8AAAAHZRhF/7pVK1euVHBwsDp37ixJSk1NVXCw7QOiAgICdObM9QfKnDt37qbjqanX5+n/fjwwMFCSrMcXBI0vAAAACpXFYtGaNWv02GOPWbdlZGTIw8PDZj8PDw+ZzdcfHpWZmXnT8czMTOv7349Jsh5fEKzqAAAA4IAMFc8ji2/lCnv37tXZs2fVrVs36zZPT898TarZbJaXl9cfjnt7e9s0uZ6entavJcnb27vAdZH4AgAAoFD95z//UVRUlCpW/O2BMiEhIUpLS7PZLy0tzTp94WbjQUFBCgm5/sjwX6c8/P7roKAgFRSNLwAAgIMqrXN8k5KS1Lx5c5ttJpNJ33//vX5dSddisWjPnj0ymUzW8fj4eOv+KSkpSklJkclkUkhIiEJDQ23G4+PjFRoamm9e8B+h8QUAAEChOnTokOrWrWuzrXPnzrp8+bImT56sw4cPa/LkycrIyFCXLtcfPtOnTx998MEHWrNmjQ4cOKAXXnhB7du3V/Xq1a3jM2fO1K5du7Rr1y7NmjVL/fvb9/AY5vgCAAA4IkNycSmGZxbfwiXS0tJUoUIFm22+vr5auHChxo0bp9WrV6tBgwZatGiRfHyuP1E1IiJCEydO1Jw5c3Tp0iW1adNGkyZNsh4/aNAgnT9/XsOGDZOrq6t69uypAQMG2FUXjS8AAAAKVVJS0g23h4WFacOGDTc9LiYmRjExMTccc3V1VVxc3A0fiVxQNL4AAAAOqhgWdShTmOMLAAAAp0DiCwAA4KCKYx3fsoTEFwAAAE6BxBcAAMBBEfjah8QXAAAAToHEFwAAwEExx9c+JL4AAABwCiS+AAAADshQ8SS+ZSlTJvEFAACAU6DxBQAAgFNgqgMAAICD4t42+5D4AgAAwCmQ+AIAADgko5iWMys7sTKJLwAAAJwCiS8AAIAjMoppjm/ZCXxJfAEAAOAcSHwBAAAcFI8stg+JLwAAAJwCiS8AAICDIvC1D4kvAAAAnAKJLwAAgINijq99SHwBAADgFEh8AQAAHJCh4pnjW5YyZRJfAAAAOAUSXwAAAAfFHF/7kPgCAADAKZD4AgAAOCgCX/uQ+AIAAMAp0PgCAADAKTDVAQAAwEFxc5t9SHwBAADgFEh8AQAAHJFRTDe3laFQmcQXAAAAToHEFwAAwEExx9c+JL4AAABwCiS+AAAADorA1z4kvgAAAHAKJL4AAAAOijm+9iHxBQAAgFMg8QUAAHBAhoon8S1LmTKJLwAAAJwCiS8AAICDYoqvfUh8AQAA4BRIfAEAABySUUyrOpSdWJnEFwAAAE6BxBcAAMARGcU0x7fsBL4kvgAAAHAOJL4AAAAOiie32YfEFwAAAE6BxhcAAABOgcYXAADAQRlG0b/sZTabNWHCBLVo0UJ33nmnXnnlFVksFknS/v371atXL5lMJvXo0UP79u2zOXbTpk3q1KmTTCaThg4dqgsXLljHLBaLZs6cqVatWqlly5aaPn268vLy7KqNxhcAAACF5qWXXtI333yjxYsXa9asWVq9erVWrVql9PR0xcbGKioqSuvXr1dERIQGDx6s9PR0SVJSUpLGjBmjYcOGadWqVbp8+bLi4uKs512yZIk2bdqkefPmac6cOdq4caOWLFliV23c3AYAAOCgXErZzW0XL17UunXrtGTJEoWFhUmSBg4cqMTERLm5ucnT01MvvPCCDMPQmDFjtG3bNm3ZskUxMTFavny5unTpogcffFCSNH36dN19991KTk5W9erVtWzZMg0fPlxRUVGSpJEjR+q1117ToEGDClwfiS8AAAAKRXx8vHx9fdWyZUvrttjYWE2dOlWJiYmKjIy0rkRhGIaaN2+uhIQESVJiYqK1qZWkKlWqKDQ0VImJiTp79qxSUlLUokUL63hkZKROnTqlc+fOFbg+Gl8AAAAHZKh45vjakyknJyeratWqev/999W5c2d17NhR8+fPV15enlJTUxUcHGyzf0BAgM6cOSNJOnfu3E3HU1NTJclmPDAwUJKsxxcEUx0AAABQKNLT03X8+HG99957mjp1qlJTUzV27Fh5e3srIyNDHh4eNvt7eHjIbDZLkjIzM286npmZaX3/+zFJ1uMLgsYXAADAQZW2B1i4ubnp6tWrmjVrlqpWrSpJOn36tFauXKmaNWvma1LNZrO8vLwkSZ6enjcc9/b2tmlyPT09rV9Lkre3d4HrY6oDAAAACkVQUJA8PT2tTa8k3XHHHUpJSVFISIjS0tJs9k9LS7NOX7jZeFBQkEJCQiTJOuXh918HBQUVuD4aXwAAAAflYhT9yx4mk0lZWVk6duyYddvRo0dVtWpVmUwmff/999Y1fS0Wi/bs2SOTyWQ9Nj4+3npcSkqKUlJSZDKZFBISotDQUJvx+Ph4hYaG5psX/IffL/s+DgAAAHBjtWvXVvv27RUXF6cDBw7oP//5jxYtWqQ+ffqoc+fOunz5siZPnqzDhw9r8uTJysjIUJcuXSRJffr00QcffKA1a9bowIEDeuGFF9S+fXtVr17dOj5z5kzt2rVLu3bt0qxZs9S/f3+76mOOLwAAgIMqbXN8JWnmzJmaNGmS+vTpI29vbz366KPq16+fDMPQwoULNW7cOK1evVoNGjTQokWL5OPjI0mKiIjQxIkTNWfOHF26dElt2rTRpEmTrOcdNGiQzp8/r2HDhsnV1VU9e/bUgAED7KqNxhcAAACFpnz58po+ffoNx8LCwrRhw4abHhsTE6OYmJgbjrm6uiouLs7maW72ovEFAABwRP9dZ7c4rlNWMMcXAAAAToHEFwAAwEEZZSmOLQYkvgAAAHAKJL4AAAAOyJD96+ze6nXKChJfAAAAOAUaXwAAADgFpjoAAAA4qNL4AIvSjMQXAAAAToHEFwAAwEER+NqHxBcAAABOgcQXAADAIRnFspxZWULiCwAAAKdA4gsAAOCgmONrHxJfAAAAOAUSXwAAAAdVPOv4WorhGsWDxBcAAABO4ZYa38zMTJnNZknSkSNHtHjxYu3Zs6dQCwMAAMDNGUbxvcoKuxvfb7/9Vn/5y18UHx+vc+fOqVevXnr99dfVr18/ffzxx0VRIwAAAHDb7G58X3nlFXXs2FHNmjXTpk2b5Ovrq6+//lpjxozRwoULi6JGAAAA3ICLYRT5qyyxu/Hdv3+/nnrqKfn6+mr79u1q3769PD091a5dOx09erQoagQAAABum92Nr7e3t8xms7KyshQfH6/WrVtLktLS0lS+fPlCLxAAAAA3ZhTDqyyxezmz6OhozZgxQxUrVpSLi4vatm2rH3/8US+99JKio6OLokYAAADgttmd+I4bN07u7u766aefNGPGDPn6+uqDDz6Qh4eH4uLiiqJGAAAA3IBhGEX+KkvsTnz9/f01d+5cm20jRoyQh4dHoRUFAAAAFLYCNb7ffvttgU/YokWLWy4GAAAABedStgLZIlegxrdfv34yDEMWyx8/ss4wDP3444+FUhgAAABQmArU+H7++edFXQcAAABQpArU+FatWjXfNrPZrJMnT6pGjRqyWCxyd3cv9OIAAABwY4ZULDeflaXZFHav6mCxWDRz5ky1aNFC3bt3V0pKiv75z39qzJgxys7OLooaAQAAgNtmd+P77rvv6oMPPtC4ceOsKzl06tRJn332mebNm1foBQIAAODGDKPoX2WJ3Y3vqlWrNHbsWMXExFjj9a5du+qll17Sxo0bC71AAAAAoDDYvY7vyZMn1ahRo3zbGzZsqNTU1EIpCgAAAH+urD1goqjZnfhWrVpVe/fuzbd927Ztql69eqEUBQAAABQ2uxPfQYMGacKECUpNTZXFYtGOHTu0atUqvfvuuxo1alRR1AgAAIAb4AEW9rG78e3Ro4dycnL0+uuvKzMzU2PHjpW/v7+eeeYZ9enTpyhqBAAAAG6b3Y2vJD3yyCN65JFHdOHCBVksFgUEBBR2XQAAAPgjRjHN8S1DqfItNb6pqan697//rUOHDsnDw0P169dX3759VaFChcKuDwAAACgUdt/ctmvXLt1zzz364IMPZBiGMjMz9e9//1v33nuvDhw4UBQ1AgAA4AaMYniVJXYnvtOnT9f999+v8ePHy9XVVdL1xxePGjVKL730kpYvX17oRQIAAAC3y+7E9+DBgxo4cKC16ZUkDw8PPfXUU0pKSirU4gAAAHBjhiQXwyjyV1lKfe1ufO+44w4dPHgw3/bjx4+ratWqhVIUAAAAUNgKNNXh22+/tX7drVs3jR07VmlpaWrevLlcXFz0ww8/aNasWXr66aeLrFAAAADY4sFt9ilQ49uvXz8ZhiGLxWLdNmnSpHz7TZgwQb179y686gAAAIBCUqDG9/PPPy/qOgAAAGAXo3jW8S1Ds3wL1PgWdO5uVlbWbRUDAAAAFBW7lzP75Zdf9MYbb+jgwYPKzc2VJFksFmVnZ+vw4cP67rvvCr1IAAAA4HbZvarDhAkT9P7776tSpUr67rvvFBISomvXrikhIUGxsbFFUSMAAABuwDCK/lWW2J347tixQy+//LLat2+vn376SYMGDVLDhg314osv6vDhw0VRIwAAAHDb7E58r127pgYNGkiSateubX1M8WOPPaZdu3YVbnUAAAC4qeJ4gEVZYnfjGxISolOnTkmSatWqpZ9++kmS5O3trUuXLhVudQAAAHAon376qRo0aGDzGj58uCRp//796tWrl0wmk3r06KF9+/bZHLtp0yZ16tRJJpNJQ4cO1YULF6xjFotFM2fOVKtWrdSyZUtNnz5deXl5dtVmd+N77733Ki4uTvHx8brzzju1YcMGbdmyRXPmzFHNmjXtPR0AAABuUWmc43v48GHdfffd2r59u/X10ksvKT09XbGxsYqKitL69esVERGhwYMHKz09XZKUlJSkMWPGaNiwYVq1apUuX76suLg463mXLFmiTZs2ad68eZozZ442btyoJUuW2FWb3Y3vs88+q/bt2+v06dNq3bq17r33Xj3zzDPaunWr/vnPf9p7OgAAAJQhR44cUf369RUUFGR9VahQQZs3b5anp6deeOEF1alTR2PGjFG5cuW0ZcsWSdLy5cvVpUsXPfjgg2rYsKGmT5+urVu3Kjk5WZK0bNkyDR8+XFFRUWrVqpVGjhypFStW2FWb3Y2vh4eHxowZo/vvv1+SNHHiRO3cuVM7duzQnXfeae/pAAAAcAuuJ7JGMbzsq+vIkSOqVatWvu2JiYmKjIy0PnTDMAw1b95cCQkJ1vGoqCjr/lWqVFFoaKgSExN19uxZpaSkqEWLFtbxyMhInTp1SufOnStwbXav6nAjfn5++vbbbzVq1Khifcpb9QAfHXz1r8V2PTiGjPR0HT30ozY8107ePj4lXQ5KkUothpV0CSiF6tcI0Ntj/6onn1+ggyfOl3Q5KEVWT+2pWlUDSroMh2KxWHTs2DFt375dCxcuVG5urjp37qzhw4crNTVVdevWtdk/ICBAhw4dkiSdO3dOwcHB+cbPnDmj1NRUSbIZDwwMlCSdOXMm33E3UyiNryRlZmbq9OnThXU6AAAA/Am7f3VfxE6fPq2MjAx5eHho9uzZOnnypF566SVlZmZat/+eh4eHzGazpOu95M3GMzMzre9/PybJenxBFFrjCwAAAOdWtWpV7dq1SxUrVpRhGGrUqJHy8vL0/PPPq2XLlvmaVLPZLC8vL0mSp6fnDce9vb1tmlxPT0/r19L1lcUKqrT9RQEAAAAFVBxzfO3l5+dnc1ydOnWUlZWloKAgpaWl2eyblpZmnaYQEhJyw/GgoCCFhIRIknXKw++/DgoKKnBtNL4AAAAoFP/5z38UHR2tjIwM67Yff/xRfn5+ioyM1Pfffy+LxSLp+nzgPXv2yGQySZJMJpPi4+Otx6WkpCglJUUmk0khISEKDQ21GY+Pj1doaGiB5/dKBZzqMG/evD/d5/jx4wW+KAAAAG6fSyl7sFpERIQ8PT31r3/9S0OHDlVycrKmT5+uJ554Qp07d9asWbM0efJk9e7dW++9954yMjLUpUsXSVKfPn3Ur18/hYeHq1mzZpo8ebLat2+v6tWrW8dnzpypypUrS5JmzZqlgQMH2lVfgRrf9evXF+hkVapUseviAAAAKDt8fX21ePFiTZkyRT169FC5cuXUu3dvPfHEEzIMQwsXLtS4ceO0evVqNWjQQIsWLZLPf1dgioiI0MSJEzVnzhxdunRJbdq00aRJk6znHjRokM6fP69hw4bJ1dVVPXv21IABA+yqr0CN7xdffGHXSQEAAFD0SlviK0n16tW76RPVwsLCtGHDhpseGxMTo5iYmBuOubq6Ki4uzuZpbvZiji8AAACcAsuZAQAAOCBDuqVVF27lOmUFiS8AAACcAokvAACAgyqNc3xLs9tKfO15RBwAAABQkm6p8V25cqU6dOig8PBwJScna9y4cVqwYEFh1wYAAAAUGrsb340bN2rWrFl66KGH5O7uLun6o+jeeOMNvf3224VeIAAAAG7MMIr+VZbY3fi+/fbbGjNmjJ5++mm5uFw/vH///ho7dqxWrVpV6AUCAAAAhcHuxvfYsWOKiorKtz06OlopKSmFUhQAAAD+nIthFPmrLLG78Q0MDNSxY8fybf/+++8VHBxcKEUBAAAAhc3uxveRRx7RxIkT9fnnn0uSjh49qpUrV2ry5Mk3fcQcAAAACpeh641cUb/KUuZr9zq+Tz75pK5cuaIRI0YoKytLgwcPlpubm3r37q2///3vRVEjAAAAcNtu6QEWI0aM0JAhQ3T48GFZLBbVrl1bvr6+hV0bAAAAbqa4Vl0oQ5Gv3Y3v6dOnrV8HBARIki5fvqzLly9LkkJDQwupNAAAAKDw2N34dujQQcYf/PXixx9/vK2CAAAAUDBlbdWFomZ347ts2TKb97m5uTp27JjeeecdjRo1qtAKAwAAAAqT3Y1vy5Yt821r3bq1qlevrrlz56pDhw6FUhgAAAD+GIGvfexezuxmatWqpQMHDhTW6QAAAIBCdVs3t/3q6tWrWrhwoapVq1YoRQEAAODPuZD42qVQbm6zWCzy8fHRjBkzCq0wAAAAoDDd9s1tkuTu7q769eurXLlyhVIUAAAA/pih4lnVoSyFyrfU+D777LOqU6dOUdQDAAAAFAm7G9+dO3fK09OzKGoBAACAHVjVwT52r+rw0EMPaebMmTp06JDMZnNR1AQAAAAUOrsT361bt+rEiRP65JNPbjjOk9sAAABQGtnd+A4ZMqQo6gAAAICdWM7MPgVqfBs1aqTt27crICBADz30UFHXBAAAABS6AjW+FoulqOsAAACAXQwZxbLYWNmJlQvtkcUAAABAaVbgOb4ff/yxfH19/3S/Bx988HbqAQAAQEEYxTTHt+wEvgVvfF966aU/3ccwDBpfAAAAlEoFbny//vprBQQEFGUtAAAAKKDrjywunuuUFQWa42vwWBAAAAA4OFZ1AAAAcFCEk/YpUOL70EMPydPTs6hrAQAAAIpMgRLfqVOnFnUdAAAAsBNPbrMP6/gCAADAKRR4VQcAAACULkzxtQ+JLwAAAJwCiS8AAICDciHytQuJLwAAAJwCiS8AAIAD4slt9iPxBQAAgFMg8QUAAHBQTPG1D4kvAAAAnAKNLwAAAJwCUx0AAAAclEuZuvWs6JH4AgAAwCmQ+AIAADgio5hubitDoTKJLwAAAJwCiS8AAICDKo4HWJQlJL4AAAAoErGxsRo1apT1/f79+9WrVy+ZTCb16NFD+/bts9l/06ZN6tSpk0wmk4YOHaoLFy5YxywWi2bOnKlWrVqpZcuWmj59uvLy8uyqh8YXAADAAV1/ZLFR5K9bDZU/+ugjbd261fo+PT1dsbGxioqK0vr16xUREaHBgwcrPT1dkpSUlKQxY8Zo2LBhWrVqlS5fvqy4uDjr8UuWLNGmTZs0b948zZkzRxs3btSSJUvsqonGFwAAAIXq4sWLmj59upo1a2bdtnnzZnl6euqFF15QnTp1NGbMGJUrV05btmyRJC1fvlxdunTRgw8+qIYNG2r69OnaunWrkpOTJUnLli3T8OHDFRUVpVatWmnkyJFasWKFXXXR+AIAADgowyj61614+eWX9cADD6hu3brWbYmJiYqMjJTx35MahqHmzZsrISHBOh4VFWXdv0qVKgoNDVViYqLOnj2rlJQUtWjRwjoeGRmpU6dO6dy5cwWui8YXAAAAhWbHjh367rvv9NRTT9lsT01NVXBwsM22gIAAnTlzRpJ07ty5m46npqZKks14YGCgJFmPLwhWdQAAAHBQLsWykG/BZWVlady4cRo7dqy8vLxsxjIyMuTh4WGzzcPDQ2azWZKUmZl50/HMzEzr+9+PSbIeXxAkvgAAACgU8+bNU9OmTdW2bdt8Y56envmaVLPZbG2Qbzbu7e19wyb316+9vb0LXB+JLwAAgIMqZYGvPvroI6WlpSkiIkLSb83pJ598ou7duystLc1m/7S0NOv0hZCQkBuOBwUFKSQkRNL16RLVqlWzfi1JQUFBBa6PxBcAAACF4t1339XGjRv1/vvv6/3331eHDh3UoUMHvf/++zKZTPr+++9lsVgkXV+Xd8+ePTKZTJIkk8mk+Ph467lSUlKUkpIik8mkkJAQhYaG2ozHx8crNDQ037zgP0LiCwAA4IAMFU+CaU+oXLVqVZv35cqVkyTVrFlTAQEBmjVrliZPnqzevXvrvffeU0ZGhrp06SJJ6tOnj/r166fw8HA1a9ZMkydPVvv27VW9enXr+MyZM1W5cmVJ0qxZszRw4EC7PguNLwAAAIqcr6+vFi5cqHHjxmn16tVq0KCBFi1aJB8fH0lSRESEJk6cqDlz5ujSpUtq06aNJk2aZD1+0KBBOn/+vIYNGyZXV1f17NlTAwYMsKsGGl8AAAAHZZS2Sb7/Y9q0aTbvw8LCtGHDhpvuHxMTo5iYmBuOubq6Ki4uzuZpbvZiji8AAACcAo0vAAAAnAJTHQAAABxU6Z7oUPqQ+AIAAMApkPgCAAA4IsOQS7FkvmUnVybxBQAAgFMg8QUAAHBQZSeLLR4kvgAAAHAKJL4AAAAOqlieX2EphmsUExJfAAAAOAUSXwAAAAdVLI8sJvEFAAAAHAuJLwAAgAMyVDwJZllaOYLEFwAAAE6BxBcAAMBBFcsc3zKExBcAAABOgcQXAADAQZH32ofEFwAAAE6BxBcAAMBBMcfXPiS+AAAAcAokvgAAAA6KBNM+fL8AAADgFGh8AQAA4BSY6gAAAOCADBXPzW1l6fY5El8AAAA4BRJfAAAAB1WW0tjiQOILAAAAp0DiCwAA4KB4foV9SHwBAADgFEh8AQAAHJQLs3ztQuILAAAAp0DiCwAA4KCY42sfEl8AAAA4BRJfAAAAB2Uwx9cuJL4AAABwCiS+AAAAjsgopjm+ZShUJvEFAACAUyDxBQAAcECGimcd3zIU+JL4AgAAwDmQ+AIAADgo1vG1D4kvAAAAnAKNLwAAAJwCUx0AAAAcFFMd7EPiCwAAAKdA4gsAAOCQjGJ6ZHHZiZVJfAEAAOAUSHwBAAAclEvZCWOLBYkvAAAAnAKJLwAAgAMypGKZ41uWQmUSXwAAADgFEl8AAAAHxTq+9iHxBQAAQKE5fvy4Bg0apIiICLVv315vvfWWdSw5OVkDBgxQeHi4unbtqu3bt9sc+80336h79+4ymUzq37+/kpOTbcbfeecdtW3bVhERERo9erQyMjLsqo3GFwAAwEEZxfA/e+Tl5Sk2NlaVKlXShg0bNGHCBL3++uvauHGjLBaLhg4dqsDAQK1bt04PPPCAhg0bptOnT0uSTp8+raFDhyomJkZr166Vv7+/nnrqKVksFknSJ598onnz5mnixIlaunSpEhMTNWPGDLvqo/EFAABAoUhLS1OjRo00fvx41apVS+3atVPr1q0VHx+vnTt3Kjk5WRMnTlSdOnU0ePBghYeHa926dZKkNWvWqGnTpho4cKDq1aunqVOn6tSpU9q9e7ckadmyZXr88cd19913KywsTBMmTNC6devsSn1pfAEAAByUi1H0L3sEBwdr9uzZ8vX1lcViUXx8vL799lu1bNlSiYmJaty4sXx8fKz7R0ZGKiEhQZKUmJioqKgo65i3t7eaNGmihIQE5ebmau/evTbj4eHhys7O1oEDBwr+/bLv4wAAAAB/rkOHDurbt68iIiJ03333KTU1VcHBwTb7BAQE6MyZM5L0h+OXL19WVlaWzbibm5v8/PysxxcEqzoAAAA4qOJYx/dWzZkzR2lpaRo/frymTp2qjIwMeXh42Ozj4eEhs9ksSX84npmZaX1/s+MLgsYXAAAAha5Zs2aSpKysLI0cOVI9evTINx/XbDbLy8tLkuTp6ZmviTWbzapQoYI8PT2t7/933Nvbu8A1MdUBpd62rV/J29246WvypAk2++fk5GjAgAGaNnXyDc+Xk5Ojv7RppZcmji/64gEUOYvFopxz3ytr/7vKTHxDWT/+WzmpSTb75KWnynxkozL3Llbm3rdkPvKh8tJTbfbJyUpX9okvlfnDO8pMelPmwx/k28eSl6vs0zuU+cPS69f6aZVyfzlU5J8RuBnDKPqXPdLS0vTZZ5/ZbKtbt66ys7MVFBSktLS0fPv/On0hJCTkhuNBQUHy8/OTp6enzXhOTo4uXryooKCgAtdH44tSLzyiub76z458r7s7dFSFChX08CN9rPtmZmbqyUF/0759+254rszMTD3er6++3b2ruMoHUMRyTn+tnNPfyKV8dbnf0VWuQWHKOfOtsk9dXx80L+uizIc3yJKXI/caHeReo6MsebkyH1qvvMxfJF1vno9/u1q5l47KrXJLude6V5JF5sMblJd12Xqt7OOfKPdcglwr1Zd77W5y9aun7OQv8jXagLM6efKkhg0bprNnz1q37du3T/7+/oqMjNQPP/xgnbYgSfHx8TKZTJIkk8mk+Ph461hGRob2798vk8kkFxcXNWvWzGY8ISFBbm5uatiwYYHrK9HG9+zZsxo+fLhatmyptm3baurUqcrKyirJklAKVahQQdGtWtm8UlPP6csvPtfrixarXv36kqTt2/+jv9wZrW1bv7rheX4d/+qLz4uxegBFyZKTodzUJLn6N5Z79fZyrVBDboFN5V6jo3JTk5SX+YtyU5MkFzd51O4u14p3yLXiHfKo3V1ycVNu2l5J0okTJ3Ttwgm5hbaWW0BjuVaoKfc7ukqWXOX9cv2O8bz0VOVdOia3yi3kHtparuWryy2kudyqtFZOyg5ZcvjvF4qfUQwvezRr1kxNmjTR6NGjdfjwYW3dulUzZszQ3//+d7Vs2VJVqlRRXFycDh06pEWLFikpKUk9e/aUJPXo0UN79uzRokWLdOjQIcXFxalatWqKjo6WJPXt21eLFy/WZ599pqSkJI0fP14PP/ywY0x1sFgsGj58uDIyMrRixQq9+uqr+vLLLzV79uySKgkOIiMjQyOeeVpdunZTTI+e1u29Hvqrqteooa+2fX3D434d/2b3nuIqFUARs2RdlGSRS8VaNttdfKtKsijv8gkZXv5yCw6X4epuHTdc3WV4+MqSdUnSb/MGDdff3Tjj4i4ZbrLkZP73WtfT4RteKy9HeVdPFeZHAxySq6urFixYIG9vbz3yyCMaM2aM+vXrp/79+1vHUlNTFRMTow8//FDz589XaGioJKlatWqaO3eu1q1bp549e+rixYuaP3++jP/Ot+jWrZsGDx6ssWPHauDAgQoLC9Pzzz9vV30ldnPb0aNHlZCQoK+//lqBgYGSpOHDh+vll1/WP//5z5IqCw5g3tzXdPrUKX38iW1y++kX29S0WTNlpKff8LhfxwGUIa7Xkx6L+YrNZov5kvWf7tX+ku+wvKyLsmRckEtQNUlSvXr15BtYS9fOfCvDs5IM93LKOfudlJcj10r1/nstr9+u5R34u2tdtvkn4OxCQkI0b968G47VrFlTy5cvv+mx7dq1U7t27W46Hhsbq9jY2FuurcQa36CgIL311lvWpvdXV69eLaGK4AjMZrMWzH1NvR7urTp169qM/VlTS9MLlD0uXn4yylVRzpndMtx95VK+qixZl5Wd/JVkuEp5OfmOseTlKPvE55KLq9yCfvv3QtWwbjq4/V2Zf3rPus29Rke5lKty/Vq+VWV4VFD2yf9ILm5y8QmRJSNNOad3XN85L7soPyqQjyHJxd67z27xOmVFiTW+FSpUUNu2ba3v8/LytHz5crVq1arA57BYLDdN91A2rV2zSmfOnNFTQ4fd9GeflXl9qZTcnOw//PORnf3H4yh76tcIKOkSUASyg/voVNJHuvzzx5IkV3cvhTbupLM/bVXFir6q+rufe25Olo7vXqWs9HOqGdVLFavcoRqVK+rYsWM6+vXb8vSuqJCw++Ti7qlLp/frwokvVSW4kvxCG0uSsgL762TCh7p25ENJkpunr6o366wT361VkH9FBfFnrExxd2MNgLKm1KzjO2PGDO3fv19r164t8DE52dk6eujHIqwKpc3KFe+qdu3a8vFy/9Of/eVLF/9wn1/Op/Lnx8m8PfavJV0CikxfXblyRampqapWrZpcXFx0552bFdMpXIMHX/+5nzlzRs8++6zMl09r+svT1KFDB+vRkydPloebiz5Yt1x+fn7W7aNGjVJ8/Oda9/o/rfMMpQG6cOGCLl26pOrVq+vMmTN68MG1erJna/31r/wZQ/EqS2lscSgVje+MGTO0dOlSvfrqq6r/3zv0C8LN3V01atX98x1RJmRnZ2vXrl0a/swI1a7X6Kb7/Zr4Vqjo94f7VQoI+sNxlD3t+71c0iWgCFw8tU+evkHyrhjy3y37lH7xtPLy8vR/CZe1a+KHyrh8Vsd2rJAlL0c1W/bR8u1XtXz79dS2RuWKSktJkbtPgEbM2WZz7rSzbvrll1/Uf8xKubp76tLpH1XOv7o8ylX67x57dfH0fknSyq1n9H7Ch8X0qVEcXn66o6qGePz5jnAYJd74Tpo0SStXrtSMGTN033332XWsYRjy9vEpospQ2hzYs0fp6en6S7v2Bfq5u7q5/+F+7u5/PI6y5+CJ8yVdAopA1oGvZHgFyKPWvdZt5uNbJRcPncksrzOHf1bWwTWS4SKPOg/p9DVf6Zrtn4XGtWpp97d79NPRUzLcvH47T/IRycVDR89mSMpQ1t7Ncg1sKvfQ1pIkiyVP5sPfyPCooBMX3WRc4s9YWZKdk1fSJfyxW1lv7FavU0aUaOM7b948vffee3rllVfUuXPnkiwFDuCHfdfX22zYqHEJVwKgNHENDFPOya+Uc8ZfRrnKyrt4SHm/HJJbtXYyXD1lPvG5lJMht2rtpFyz8q6d+d3BHpIC9Oijj2rNug9kPvKB3EIiJRcP5V06qryLh+QW2kaG4fLfazVVbmqSDHdfGV5+yk3bK8u1FLnf0fV3UyEAlFYl1vgeOXJECxYsUGxsrCIjI5Wa+ttjIe159Bycx9lz158CU6lSpT/ZE4AzcQtsIllylJuaJMu5eBmefnKveY9cK9WXJS9XeZeOS5JyTm7Nd6xRLlSqX09VqlRRnbZ/0+H4Lco+8aUkiwwvf7nX6ixXvzq/XatKS8kwlHNuj5SbKcM7SO61u8u1Qo3i+riADaMsxbHFoMQa388//1y5ubl6/fXX9frrr9uM/fTTTyVUFUqz50a+oOdGvlCgfb/77rs/nL+bkW0prLIAlAJuQSa5BZnybTdcXOUVPqRA5/DyDZTHHV3+cB/DcJV7lVZyr1LwFYgAlB4l1vje7gLEAAAAzo4ZNvZhgToAAAA4hRJf1QEAAAC3hsDXPiS+AAAAcAokvgAAAI6KyNcuJL4AAABwCiS+AAAADop1fO1D4gsAAACnQOILAADggAzr/0FBkfgCAADAKdD4AgAAwCkw1QEAAMBBFcdMB0sxXKO4kPgCAADAKZD4AgAAOCpubrMLiS8AAACcAokvAACAQzKK5QEWljIUK5P4AgAAwCmQ+AIAADgoo+yEscWCxBcAAABOgcQXAADAQRH42ofEFwAAAE6BxBcAAMBREfnahcQXAAAAToHEFwAAwEEVxzq+ZQmJLwAAAJwCiS8AAIAjMoppHd8yFCqT+AIAAMApkPgCAAA4qDIUxhYLEl8AAAA4BRJfAAAAR0XkaxcSXwAAADgFGl8AAAA4BaY6AAAAOCBDxfMAi7I0m4LEFwAAAE6BxBcAAMBBFcsDLMoQEl8AAAA4BRJfAAAAB0Xgax8SXwAAADgFEl8AAABHReRrFxJfAAAAOAUSXwAAAAdVHOv4liUkvgAAAHAKJL4AAAAOinV87UPiCwAAgEJz9uxZDR8+XC1btlTbtm01depUZWVlSZKSk5M1YMAAhYeHq2vXrtq+fbvNsd988426d+8uk8mk/v37Kzk52Wb8nXfeUdu2bRUREaHRo0crIyPDrtpofAEAAByUUQwve1gsFg0fPlwZGRlasWKFXn31VX355ZeaPXu2LBaLhg4dqsDAQK1bt04PPPCAhg0bptOnT0uSTp8+raFDhyomJkZr166Vv7+/nnrqKVksFknSJ598onnz5mnixIlaunSpEhMTNWPGDLvqo/EFAABAoTh69KgSEhI0depU1atXT1FRURo+fLg2bdqknTt3Kjk5WRMnTlSdOnU0ePBghYeHa926dZKkNWvWqGnTpho4cKDq1aunqVOn6tSpU9q9e7ckadmyZXr88cd19913KywsTBMmTNC6devsSn1pfAEAABxVKYt8g4KC9NZbbykwMNBm+9WrV5WYmKjGjRvLx8fHuj0yMlIJCQmSpMTEREVFRVnHvL291aRJEyUkJCg3N1d79+61GQ8PD1d2drYOHDhQ4PpofAEAAFAoKlSooLZt21rf5+Xlafny5WrVqpVSU1MVHBxss39AQIDOnDkjSX84fvnyZWVlZdmMu7m5yc/Pz3p8QdD4AgAAOCijGP53O2bMmKH9+/fr2WefVUZGhjw8PGzGPTw8ZDabJekPxzMzM63vb3Z8QdD4AgAAoNDNmDFDS5cu1YwZM1S/fn15enrma1LNZrO8vLwk6abj3t7e8vT0tL6/0XhB0fgCAACgUE2aNElLlizRjBkzdN9990mSQkJClJaWZrNfWlqadfrCzcaDgoLk5+cnT09Pm/GcnBxdvHhRQUFBBa6LxhcAAMBBGUbRv+w1b948vffee3rllVfUrVs363aTyaQffvjBOm1BkuLj42Uymazj8fHx1rGMjAzt379fJpNJLi4uatasmc14QkKC3Nzc1LBhwwLXRuMLAACAQnHkyBEtWLBATz75pCIjI5Wammp9tWzZUlWqVFFcXJwOHTqkRYsWKSkpST179pQk9ejRQ3v27NGiRYt06NAhxcXFqVq1aoqOjpYk9e3bV4sXL9Znn32mpKQkjR8/Xg8//LBdUx14ZDEAAIADupUHTNzqdQrq888/V25url5//XW9/vrrNmM//fSTFixYoDFjxigmJkY1a9bU/PnzFRoaKkmqVq2a5s6dqylTpmj+/PmKiIjQ/PnzZfw3du7WrZtOnTqlsWPHymw2695779Xzzz9v12eh8QUAAEChiI2NVWxs7E3Ha9asqeXLl990vF27dmrXrt0tn//P0PgCAAA4quKIfMsQ5vgCAADAKZD4AgAAOKjbfcCEsyHxBQAAgFMg8QUAAHBEt7jO7q1cp6wg8QUAAIBTIPEFAABwUGUojC0WJL4AAABwCiS+AAAAjorI1y4kvgAAAHAKJL4AAAAOinV87UPiCwAAAKdA4gsAAOCgimUd3zKExBcAAABOgcQXAADAQRH42ofEFwAAAE6BxhcAAABOgakOAAAADshQ8dzcVpamU5D4AgAAwCmQ+AIAADisspTHFj0SXwAAADgFEl8AAAAHxQMs7EPiCwAAAKdA4gsAAOCgCHztQ+ILAAAAp0DiCwAA4KCY42sfEl8AAAA4BRJfAAAAh2TIKJZZvmUnVibxBQAAgFMg8QUAAHBUZSeMLRYkvgAAAHAKJL4AAAAOisDXPiS+AAAAcAokvgAAAI7IKKZ1fMtQrEziCwAAAKdA4wsAAACnwFQHAAAAB1U8D7AoO0h8AQAA4BRIfAEAABwVga9dSHwBAADgFEh8AQAAHJCh4gl8y1KoTOILAAAAp0DiCwAA4KCK5QEWZQiJLwAAAJwCiS8AAICDYh1f+5D4AgAAwCmQ+AIAADgo5vjah8QXAAAAToHGFwAAAE6BxhcAAABOgTm+AAAADoo5vvYh8QUAAEChM5vN6t69u3bt2mXdlpycrAEDBig8PFxdu3bV9u3bbY755ptv1L17d5lMJvXv31/Jyck24++8847atm2riIgIjR49WhkZGXbVROMLAADgoIxi+N+tyMrK0ogRI3To0CHrNovFoqFDhyowMFDr1q3TAw88oGHDhun06dOSpNOnT2vo0KGKiYnR2rVr5e/vr6eeekoWi0WS9Mknn2jevHmaOHGili5dqsTERM2YMcOuumh8AQAAUGgOHz6shx9+WCdOnLDZvnPnTiUnJ2vixImqU6eOBg8erPDwcK1bt06StGbNGjVt2lQDBw5UvXr1NHXqVJ06dUq7d++WJC1btkyPP/647r77boWFhWnChAlat26dXakvjS8AAICDMoyif9lr9+7dio6O1qpVq2y2JyYmqnHjxvLx8bFui4yMVEJCgnU8KirKOubt7a0mTZooISFBubm52rt3r814eHi4srOzdeDAgQLXxs1tAAAAKDR9+/a94fbU1FQFBwfbbAsICNCZM2f+dPzy5cvKysqyGXdzc5Ofn5/1+IIg8QUAAECRy8jIkIeHh802Dw8Pmc3mPx3PzMy0vr/Z8QVB4wsAAOCAjGJ8FQZPT898TarZbJaXl9cfjnt7e8vT09P6/kbjBUXjCwAAgCIXEhKitLQ0m21paWnW6Qs3Gw8KCpKfn588PT1txnNycnTx4kUFBQUVuAYaXwAAAEflKHGvJJPJpB9++ME6bUGS4uPjZTKZrOPx8fHWsYyMDO3fv18mk0kuLi5q1qyZzXhCQoLc3NzUsGHDAtdA4wsAAIAi17JlS1WpUkVxcXE6dOiQFi1apKSkJPXs2VOS1KNHD+3Zs0eLFi3SoUOHFBcXp2rVqik6OlrS9ZvmFi9erM8++0xJSUkaP368Hn74YbumOrCqAwAAgIO61QdMlARXV1ctWLBAY8aMUUxMjGrWrKn58+crNDRUklStWjXNnTtXU6ZM0fz58xUREaH58+fL+O+aat26ddOpU6c0duxYmc1m3XvvvXr++eftqoHGFwAAAEXip59+snlfs2ZNLV++/Kb7t2vXTu3atbvpeGxsrGJjY2+5HhpfAAAAB3UrD5hwZszxBQAAgFMg8QUAAHBQBL72IfEFAACAUyDxBQAAcESFvM7uH16njCDxBQAAgFMg8QUAAHBQjrSOb2lA4gsAAACnQOILAADgoFjH1z6GxWKxlHQRt2LPnj2yWCxy9/Ao6VJQyljyLMrJyZabm7sMF/6NgN8cP3W+pEtAKeTu5qKgSuWU+ss1ZefklXQ5KEWC/cvJ3c1VzZs3L+lS8tm7d6/MZnOx9EHZZrM8PDzUrFmzIr9WUXPYxPfX5zbT1uB/GS6GPPgLEW6gVtWAki4BpVjVEP69AVvZ2dnWfqO0Kc7/znl4eJSZ/646bOILAAAA2IOb2wAAAOAUaHwBAADgFGh8AQAA4BRofAEAAOAUaHwBAADgFGh8Adjo0KGDGjRoYH01bNhQzZs312OPPaZvv/220K+3a9cuNWjQQCdPnpQk9evXT6NGjSrQsenp6VqxYsVtXf/kyZNq0KCBdu3aVaD6bsXcuXPVoUOHWz6+sM4BAM7OYdfxBVB0Bg4cqIEDB0qSLBaLLl68qFdeeUVPPPGEPv74Y4WGhhbZtefOnStXV9cC7fv2229r/fr1evTRR4usHgBA2UHiCyAfHx8fBQUFKSgoSMHBwapfv74mTJigzMxMffrpp0V6bT8/P5UvX75A+7IMOQDAHjS+AArEze36L4h+fXpPhw4d9PLLL6tr166Kjo7W7t27ZbFY9Oabb6pjx44ymUx64IEH9OGHH9qc57vvvlOvXr0UFhamv/71rzpw4IDN+P9OdUhKStKAAQMUERGhO++8U+PGjVNGRobmzp2refPm6dSpUzZTEdatW6cuXbooLCxMXbp00dKlS5WX99tjaA8ePKj+/fsrPDxc99xzj3bs2HHb35uDBw9q8ODBatGihZo2baqOHTvq7bffzrff/PnzFR0drebNm2vkyJG6ePGidezKlSt68cUX1apVK0VGRqp///7au3fvTa+5detWxcTEyGQyqXXr1ho1apQuXbp0258FAMoyGl8Af+rs2bOaOHGifHx81K5dO+v25cuX61//+pfeeusthYeH69VXX9XKlSv14osvauPGjerfv7/Gjx9vnYebnJysgQMHqlGjRtqwYYOGDh2qBQsW3PS6ycnJevzxxxUcHKxVq1Zp7ty5+vrrrzVhwgTrdIzKlStr+/btqlKlilatWqXp06dr2LBh+uijj/TMM8/ozTff1MyZMyVdby4HDBig8uXLa82aNRo/frxef/312/reZGRkaODAgfLz89N7772nTZs2qXPnznr55Zf1448/Wvc7deqUdu7cqSVLluiNN97Q3r17FRcXJ+l6cv3kk08qOTlZCxcu1OrVqxUeHq4+ffpo//79+a554cIFDRs2TD169NDmzZs1b948ffvtt5o+ffptfRYAKOuY4wsgn4ULF1oTy5ycHJnNZtWpU0ezZ8+2md/brl073XnnnZKu32j2zjvv6JVXXlH79u0lSTVq1NCpU6e0ePFiPfroo1q9erUCAwM1btw4ubq6qk6dOkpJSdHUqVNvWMfq1avl5+enKVOmWBPnl156Sd9//73KlSsnHx8fubq6KigoSJK0YMECDRkyRN26dZMkVa9eXVevXtWECRP0j3/8Qx999JEyMjI0bdo0lS9fXvXq1dPo0aM1dOjQW/5eZWRkqH///nr00UdVrlw5SdLw4cP11ltv6aefflKjRo0kSZ6ennr11VcVGBgoSRo7dqwGDhyo48eP6/Tp00pISNDOnTvl5+cnSRoxYoT27NmjZcuWadq0aTbXPHv2rMxms0JDQ1W1alVVrVpVb7zxhnJzc2/5cwCAM6DxBZBP79691a9fP0mSi4vLTefd1qxZ0/r14cOHlZWVpeeee04uLr/9MunXxjkzM1MHDx5U48aNbW5ea968+U3rOHjwoJo0aWJteiWpVatWatWqVb59L1y4oDNnzuiVV17Ra6+9Zt2el5enrKwsnTx5UgcPHlStWrVsPktERMSffTv+kL+/v/r27atNmzZp//79OnHihHX6xu+nWNSsWdPa9EqSyWSSJB06dEg///yzLBaL7r77bptzm81mZWVl5btmo0aN1L17d/39739XUFCQ2rRpo/bt2+uee+65rc8CAGUdjS+AfCpWrGjT1N6Ml5eX9etfbzSbPXu2ateunW9fDw8PGYZh0wxKsmlq/9cfjf2vX88bFxdnTaF/r0qVKnZfvyBSU1P1yCOPyN/fXx06dNBdd92lZs2a2UwJkZRvpYpf01l3d3fl5eXJ19dX69evz3f+X+dU/69Zs2Zp6NCh2rZtm7755hs9//zzioyM1NKlS2/r8wBAWcYcXwCFonbt2nJzc9Pp06dVs2ZN62vr1q1avHixXFxc1LBhQ+3bt09ms9l63L59+256zrp162r//v02v8L/9NNP1aFDB2VlZckwDOv2gIAA+fv7Kzk52eb6P/zwg2bPni1JatiwoX7++WdduHChQNcviE2bNunixYtauXKlnnrqKd1zzz3Wm8x+v+rEzz//rKtXr1rfx8fHyzAM1a1bV/Xr19fVq1eVnZ1tU/ubb76pzz//PN81ExMTNWXKFNWuXVsDBgzQokWLNGXKFO3cuVPnz5+/rc8DAGUZjS+AQlG+fHn17t1br732mj744AMlJydr7dq1mjFjhoKDgyVJffr0UUZGhkaPHq0jR47oyy+/1Ny5c296zr59++qXX37RuHHjdOTIEesNXK1atZKnp6d8fHx06dIlHTt2TDk5OXryySf17rvvavny5Tpx4oQ+/fRTjR8/Xl5eXvLw8FC3bt0UEBCg5557TgcOHNDu3bs1efLkAn2+b7/9Vtu2bbN5HT9+XJUrV1ZGRoa2bNmi06dPa/v27RoxYoQk2TT4WVlZeuaZZ7R//359/fXXmjRpkh588EFVrVpVbdu2VaNGjfTss89q586dOn78uKZOnar169erTp06+Wrx9fXVv//9b82YMUPHjx/XwYMHtXnzZtWqVUuVKlWy58cGAE6FqQ4ACk1cXJwqVaqk1157TefOnVOVKlU0fPhwPfHEE5KkkJAQLV26VFOmTNFDDz2kKlWqaMiQIZowYcINzxcSEqK3335bM2bM0IMPPqiKFSuqa9eu1sby3nvv1erVq/XXv/5Vy5cv18CBA+Xp6al3331X06ZNU2BgoB5++GENHz5c0vX1iZcuXapJkyapT58+qlixooYPH25dXeGP3OhpcsOGDdOwYcP0ww8/aNq0abp69aqqVq2qXr166fPPP9fevXvVp08fSVLTpk3VqFEj9e/fX4ZhqGvXrtZzurq6Wj/nM888o4yMDNWpU0fz5s1T69at8123Tp061uXc/v3vf8vFxUWtWrXSm2++aTO/GgBgy7CwAjwAAACcANEAAAAAnAKNLwAAAJwCjS8AAACcAo0vAAAAnAKNLwAAAJwCjS8AAACcAo0vAAAAnAKNLwAAAJwCjS8AAACcAo0vAAAAnAKNLwAAAJzC/wMWfUfpt7sssQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 1000x700 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "*** Classification Metrics ***\n",
+ "F1 Score = 0.8274329470375831\n",
+ "******************************\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "AUC score: 0.82915\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Train a logistic regression model with the best hyperparameters\n",
+ "logreg = LogisticRegression(C=0.0010463186742213418, penalty='l1', solver='saga', tol=10e-6, random_state=42)\n",
+ "logreg.fit(X_train_prep, y_train)\n",
+ "\n",
+ "# Predict the test data and evaluate the model\n",
+ "y_test_pred = logreg.predict(X_test_prep)\n",
+ "evaluate_model_binary(y_test, y_test_pred, logreg.classes_)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Tracer les coefficients du modèle de régression logistique\n",
+ "def plot_coefficients(model, feature_names):\n",
+ " \"\"\"\n",
+ " Plot the coefficients for a logistic regression model.\n",
+ "\n",
+ " Args:\n",
+ " model: Trained logistic regression model.\n",
+ " feature_names: List of feature names.\n",
+ " \"\"\"\n",
+ " coef = model.coef_.ravel()\n",
+ " \n",
+ " # Plot the coefficients\n",
+ " plt.figure(figsize=(10, 6))\n",
+ " plt.bar(np.arange(len(coef)), coef)\n",
+ " plt.xticks(np.arange(len(coef)), feature_names, rotation=90)\n",
+ " plt.xlabel('Feature')\n",
+ " plt.ylabel('Coefficient')\n",
+ " plt.title('Logistic Regression Coefficients')\n",
+ " plt.grid(True)\n",
+ " plt.show()\n",
+ "\n",
+ "# Plot the coefficients\n",
+ "plot_coefficients(logreg, selected_feature_names)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<font color='red'>**Commentaire:**</font> \n",
+ "\n",
+ "Nous remarquons qu'une très grande importance est donnée au sexe dans la modélisation. Nous l'avions remarqué lors de l'AED : très peu de femmes sont fumeuses/ex-fumeuses. \n",
+ "\n",
+ "Nous avions également cité une possible différence de distribution pour les variables height, weight, hemoglobin, gamma_GTP et SGOT_ALT. Cela se reflète dans le coefficients du modèle pour les variables hemoglobin, weight et gamma_GTP."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# function to plot learning curve\n",
+ "def plot_learning_curve(model, X, y, cv=5, train_sizes=np.linspace(0.1, 1.0, 5)):\n",
+ " \"\"\"\n",
+ " Plot the learning curve for a model.\n",
+ "\n",
+ " Args:\n",
+ " model: Trained model.\n",
+ " X: Features.\n",
+ " y: Target.\n",
+ " cv: Number of cross-validation folds.\n",
+ " train_sizes: Array of training set sizes.\n",
+ " \"\"\"\n",
+ " # Create the learning curve visualizer\n",
+ " sizes, train_scores, test_scores, fit_times, _ = learning_curve(\n",
+ " model, X, y, cv=cv, train_sizes=train_sizes, scoring='f1_weighted', return_times=True\n",
+ " )\n",
+ "\n",
+ " # Calculate mean and standard deviation for training set scores\n",
+ " train_scores_mean = np.mean(train_scores, axis=1)\n",
+ " train_scores_std = np.std(train_scores, axis=1)\n",
+ "\n",
+ " # Calculate mean and standard deviation for test set scores\n",
+ " test_scores_mean = np.mean(test_scores, axis=1)\n",
+ " test_scores_std = np.std(test_scores, axis=1)\n",
+ "\n",
+ " # Plot the learning curve\n",
+ " plt.figure(figsize=(10, 6))\n",
+ " plt.fill_between(sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color='r')\n",
+ " plt.fill_between(sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color='g')\n",
+ " plt.plot(sizes, train_scores_mean, 'o-', color='r', label='Training score')\n",
+ " plt.plot(sizes, test_scores_mean, 'o-', color='g', label='Cross-validation score')\n",
+ " plt.title('Learning Curve')\n",
+ " plt.xlabel('Training Examples')\n",
+ " plt.ylabel('Score')\n",
+ " plt.grid(True)\n",
+ " plt.legend(loc='best')\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "c:\\Users\\33667\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot learning curve for the logistic regression model\n",
+ "plot_learning_curve(logreg, X_train_prep, y_train, cv=5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<font color='red'>**Commentaire:**</font> \n",
+ "\n",
+ "La learning curve semble se stabiliser à partir de 35 000 observations. Les performances de notre modèle peuvent largement atteindre leur maximum avec les données disponibles."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<font color='red'>**Conclusion**</font> \n",
+ "\n",
+ "Nous concluons que la faiblesse du modèle de régression logistique multiclasse initial était bien dû à une frontière trop fine entre les valeurs des features pour les fumeurs et les anciens fumeurs. L'augmentation du nombre d'observations ne peut avoir aucun impact sur les résultats. \n",
+ "\n",
+ "En regroupant ces deux classes et après optimisation, le modèle a une performance de 0,82 (score F1). Les valeurs des coefficients sont également cohérentes avec les hypothèses formulées lors de l'AED."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "base",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.5"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}