Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Projet MGL869 - Prédiction d'obésité"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jeu de données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contexte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Jeu de données : Estimation des niveaux d'obésité en fonction des habitudes alimentaires et de la condition physique**\n",
    "\n",
    "Ce jeu de données comprend des données pour l'estimation des niveaux d'obésité chez des individus des pays du Mexique, du Pérou et de la Colombie, en se basant sur leurs habitudes alimentaires et leur condition physique.\n",
    "\n",
    "Les données contiennent 17 attributs et 2111 enregistrements, ces enregistrements sont étiquetés avec la variable de classe NObesity (Niveau d'obésité), ce qui permet de classifier les données en utilisant les valeurs suivantes : Insuffisance pondérale, Poids normal, Surpoids Niveau I, Surpoids Niveau II, Obésité Type I, Obésité Type II et Obésité Type III. 77% des données ont été générées synthétiquement à l'aide de l'outil Weka et du filtre SMOTE, 23% des données ont été collectées directement auprès des utilisateurs via une plateforme web.\n",
    "\n",
    "[Jeu de donnée disponible à cet addresse](https://archive.ics.uci.edu/dataset/544/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition) (2019)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 - Importation des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>family_history_with_overweight</th>\n",
       "      <th>FAVC</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CAEC</th>\n",
       "      <th>SMOKE</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>SCC</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
       "      <th>CALC</th>\n",
       "      <th>MTRANS</th>\n",
       "      <th>NObeyesdad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.62</td>\n",
       "      <td>64.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>no</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.52</td>\n",
       "      <td>56.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>77.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>87.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Walking</td>\n",
       "      <td>Overweight_Level_I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>89.8</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Overweight_Level_II</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender   Age  Height  Weight family_history_with_overweight FAVC  FCVC  \\\n",
       "0  Female  21.0    1.62    64.0                            yes   no   2.0   \n",
       "1  Female  21.0    1.52    56.0                            yes   no   3.0   \n",
       "2    Male  23.0    1.80    77.0                            yes   no   2.0   \n",
       "3    Male  27.0    1.80    87.0                             no   no   3.0   \n",
       "4    Male  22.0    1.78    89.8                             no   no   2.0   \n",
       "\n",
       "   NCP       CAEC SMOKE  CH2O  SCC  FAF  TUE        CALC  \\\n",
       "0  3.0  Sometimes    no   2.0   no  0.0  1.0          no   \n",
       "1  3.0  Sometimes   yes   3.0  yes  3.0  0.0   Sometimes   \n",
       "2  3.0  Sometimes    no   2.0   no  2.0  1.0  Frequently   \n",
       "3  3.0  Sometimes    no   2.0   no  2.0  0.0  Frequently   \n",
       "4  1.0  Sometimes    no   2.0   no  0.0  0.0   Sometimes   \n",
       "\n",
       "                  MTRANS           NObeyesdad  \n",
       "0  Public_Transportation        Normal_Weight  \n",
       "1  Public_Transportation        Normal_Weight  \n",
       "2  Public_Transportation        Normal_Weight  \n",
       "3                Walking   Overweight_Level_I  \n",
       "4  Public_Transportation  Overweight_Level_II  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np # algèbre linéaire\n",
    "import pandas as pd # traitement des données, I/O de fichier CSV (par ex. pd.read_csv)\n",
    "import seaborn as sns # bibliothèque de tracé qui utilise matplot\n",
    "import matplotlib.pyplot as plt # pour tracer certains paramètres dans seaborn\n",
    "from sklearn.preprocessing import StandardScaler, OrdinalEncoder, OneHotEncoder\n",
    "\n",
    "\n",
    "df = pd.read_csv(\"data/ObesityDataSet_raw_and_data_sinthetic.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 - Adaptation des types de données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On transforme les données pour que les types correspondent à ceux données sur le site ou est proposé le dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>family_history_with_overweight</th>\n",
       "      <th>FAVC</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CAEC</th>\n",
       "      <th>SMOKE</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>SCC</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
       "      <th>CALC</th>\n",
       "      <th>MTRANS</th>\n",
       "      <th>NObeyesdad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.62</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.52</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>77.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>87.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Walking</td>\n",
       "      <td>Overweight_Level_I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>89.8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Overweight_Level_II</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender   Age  Height  Weight family_history_with_overweight   FAVC  FCVC  \\\n",
       "0  Female  21.0    1.62    64.0                              1  False     2   \n",
       "1  Female  21.0    1.52    56.0                              1  False     3   \n",
       "2    Male  23.0    1.80    77.0                              1  False     2   \n",
       "3    Male  27.0    1.80    87.0                          False  False     3   \n",
       "4    Male  22.0    1.78    89.8                          False  False     2   \n",
       "\n",
       "   NCP       CAEC  SMOKE  CH2O    SCC  FAF  TUE        CALC  \\\n",
       "0  3.0  Sometimes  False   2.0  False  0.0    1          no   \n",
       "1  3.0  Sometimes   True   3.0   True  3.0    0   Sometimes   \n",
       "2  3.0  Sometimes  False   2.0  False  2.0    1  Frequently   \n",
       "3  3.0  Sometimes  False   2.0  False  2.0    0  Frequently   \n",
       "4  1.0  Sometimes  False   2.0  False  0.0    0   Sometimes   \n",
       "\n",
       "                  MTRANS           NObeyesdad  \n",
       "0  Public_Transportation        Normal_Weight  \n",
       "1  Public_Transportation        Normal_Weight  \n",
       "2  Public_Transportation        Normal_Weight  \n",
       "3                Walking   Overweight_Level_I  \n",
       "4  Public_Transportation  Overweight_Level_II  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy();\n",
    "# Object to category\n",
    "df2[[\"Gender\", \"CAEC\", \"CALC\", \"MTRANS\", \"NObeyesdad\"]] = df2[[\"Gender\", \"CAEC\", \"CALC\", \"MTRANS\", \"NObeyesdad\"]].astype(\"category\")\n",
    "\n",
    "# Object to binary\n",
    "df2[\"family_history_with_overweight\"] = df[\"family_history_with_overweight\"].map({\"yes\": 1, \"no\": False})\n",
    "df2[\"FAVC\"] = df[\"FAVC\"].map({\"yes\": 1, \"no\": False})\n",
    "df2[\"SMOKE\"] = df[\"SMOKE\"].map({\"yes\": True, \"no\": False})\n",
    "df2[\"SCC\"] = df[\"SCC\"].map({\"yes\": True, \"no\": False})\n",
    "\n",
    "# Float to int\n",
    "df2[[\"FCVC\", \"TUE\"]] = df2[[\"FCVC\", \"TUE\"]].astype(\"int\")\n",
    "\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 - Intuitions vis à vis du jeu de données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'on remarque la présence des données \"height\" and \"weight\" dans notre dataset. En examinant l'article associé, l'on remarque que le niveau d'obésité est dérivé de l'IMC, un calcul ayant pour paramètre la taille et le poids. Si nous conservons ces données dans notre modèle, alors celui-ci ne présente plus d'intéret, car un simple calcul serait plus fiable.\n",
    "\n",
    "Ainsi, une direction plus appropriée pour notre modèle serait de détermiiner si une personne est sujet à l'obésité à l'aide de caractéristiques autres que la taille et le poids, plus en rapport au mode de vie. Nous supprimons donc ces colonnes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df3.drop(columns=[\"Height\", \"Weight\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De plus, un certains nombre de propriétés sont déclarer comme des flotants. Or, quand on regarde les questions posées dans le papier pour l'obtention des informations, les réponses sont données sous forme de catégories avec une relation ordinal. Ce qui fait qu'avoir des nombres à virgules ne fait pas beacoup de sens dans certains cas.\n",
    "\n",
    "Par exmple, la donnée CH2O représente la quantité d'eau bu par jour :\n",
    "- 0 : Moins de 1L\n",
    "- 1 : Entre 1 et 2L\n",
    "- 2 : Plus de 2L\n",
    "\n",
    "Dans ce contexte, une valeur de 0.5 ne fait pas de sens.\n",
    "\n",
    "Pour remédier à ce problème, nous convertissons les données sujet à ce soucis en int."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>family_history_with_overweight</th>\n",
       "      <th>FAVC</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CAEC</th>\n",
       "      <th>SMOKE</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>SCC</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
       "      <th>CALC</th>\n",
       "      <th>MTRANS</th>\n",
       "      <th>NObeyesdad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Walking</td>\n",
       "      <td>Overweight_Level_I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Overweight_Level_II</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender   Age family_history_with_overweight   FAVC  FCVC  NCP       CAEC  \\\n",
       "0  Female  21.0                              1  False     2    3  Sometimes   \n",
       "1  Female  21.0                              1  False     3    3  Sometimes   \n",
       "2    Male  23.0                              1  False     2    3  Sometimes   \n",
       "3    Male  27.0                          False  False     3    3  Sometimes   \n",
       "4    Male  22.0                          False  False     2    1  Sometimes   \n",
       "\n",
       "   SMOKE  CH2O    SCC  FAF  TUE        CALC                 MTRANS  \\\n",
       "0  False     2  False    0    1          no  Public_Transportation   \n",
       "1   True     3   True    3    0   Sometimes  Public_Transportation   \n",
       "2  False     2  False    2    1  Frequently  Public_Transportation   \n",
       "3  False     2  False    2    0  Frequently                Walking   \n",
       "4  False     2  False    0    0   Sometimes  Public_Transportation   \n",
       "\n",
       "            NObeyesdad  \n",
       "0        Normal_Weight  \n",
       "1        Normal_Weight  \n",
       "2        Normal_Weight  \n",
       "3   Overweight_Level_I  \n",
       "4  Overweight_Level_II  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = [\"NCP\", \"CH2O\", \"FAF\"]\n",
    "\n",
    "for col in columns:\n",
    "    #round to nearest whole number\n",
    "    df3[col] = round(df3[col]).astype('int')\n",
    "\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4 - Analyse des données\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Infos et description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2111 entries, 0 to 2110\n",
      "Data columns (total 15 columns):\n",
      " #   Column                          Non-Null Count  Dtype   \n",
      "---  ------                          --------------  -----   \n",
      " 0   Gender                          2111 non-null   category\n",
      " 1   Age                             2111 non-null   float64 \n",
      " 2   family_history_with_overweight  2111 non-null   object  \n",
      " 3   FAVC                            2111 non-null   object  \n",
      " 4   FCVC                            2111 non-null   int32   \n",
      " 5   NCP                             2111 non-null   int32   \n",
      " 6   CAEC                            2111 non-null   category\n",
      " 7   SMOKE                           2111 non-null   bool    \n",
      " 8   CH2O                            2111 non-null   int32   \n",
      " 9   SCC                             2111 non-null   bool    \n",
      " 10  FAF                             2111 non-null   int32   \n",
      " 11  TUE                             2111 non-null   int32   \n",
      " 12  CALC                            2111 non-null   category\n",
      " 13  MTRANS                          2111 non-null   category\n",
      " 14  NObeyesdad                      2111 non-null   category\n",
      "dtypes: bool(2), category(5), float64(1), int32(5), object(2)\n",
      "memory usage: 106.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df3.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On remarque qu'il n'y a pas de valeur NaN. Nous n'avons donc pas d'étape de supression des NaNs à effectuer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>Age</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>24.312600</td>\n",
       "      <td>2.213169</td>\n",
       "      <td>2.687826</td>\n",
       "      <td>2.014685</td>\n",
       "      <td>1.006632</td>\n",
       "      <td>0.381336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.345968</td>\n",
       "      <td>0.599397</td>\n",
       "      <td>0.809680</td>\n",
       "      <td>0.688616</td>\n",
       "      <td>0.895462</td>\n",
       "      <td>0.582536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.947192</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.777890</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age         FCVC          NCP         CH2O          FAF  \\\n",
       "count  2111.000000  2111.000000  2111.000000  2111.000000  2111.000000   \n",
       "mean     24.312600     2.213169     2.687826     2.014685     1.006632   \n",
       "std       6.345968     0.599397     0.809680     0.688616     0.895462   \n",
       "min      14.000000     1.000000     1.000000     1.000000     0.000000   \n",
       "25%      19.947192     2.000000     3.000000     2.000000     0.000000   \n",
       "50%      22.777890     2.000000     3.000000     2.000000     1.000000   \n",
       "75%      26.000000     3.000000     3.000000     2.000000     2.000000   \n",
       "max      61.000000     3.000000     4.000000     3.000000     3.000000   \n",
       "\n",
       "               TUE  \n",
       "count  2111.000000  \n",
       "mean      0.381336  \n",
       "std       0.582536  \n",
       "min       0.000000  \n",
       "25%       0.000000  \n",
       "50%       0.000000  \n",
       "75%       1.000000  \n",
       "max       2.000000  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On remarque que la portée des données diffères beacoup entre l'âge et les valeurs ordinals. Nous aurons donc besoin d'une normalisation ou d'une standardisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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>Gender</th>\n",
       "      <th>CAEC</th>\n",
       "      <th>CALC</th>\n",
       "      <th>MTRANS</th>\n",
       "      <th>NObeyesdad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2111</td>\n",
       "      <td>2111</td>\n",
       "      <td>2111</td>\n",
       "      <td>2111</td>\n",
       "      <td>2111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Male</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Obesity_Type_I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1068</td>\n",
       "      <td>1765</td>\n",
       "      <td>1401</td>\n",
       "      <td>1580</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gender       CAEC       CALC                 MTRANS      NObeyesdad\n",
       "count    2111       2111       2111                   2111            2111\n",
       "unique      2          4          4                      5               7\n",
       "top      Male  Sometimes  Sometimes  Public_Transportation  Obesity_Type_I\n",
       "freq     1068       1765       1401                   1580             351"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.describe(include=[\"category\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La présence de données catégoriels nous indique la nécéssité de mettre en place du one hot encoding pour ces valeurs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Donnée numérique floatants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.9.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "from importlib.metadata import version\n",
    "print(matplotlib.__version__)\n",
    "\n",
    "fig, axes = plt.subplots(1,2, figsize=(10, 5))\n",
    "axes = axes.flatten()\n",
    "\n",
    "sns.histplot(df3[\"Age\"], kde=True, ax=axes[0])\n",
    "sns.boxplot(df3[\"Age\"], ax=axes[1])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Age**\n",
    "  - Type de valeur : Numérique continue\n",
    "  - Distribution : Non normal\n",
    "  - Problèmes : Données non normalisée\n",
    "    - *Solution 1* : Appliquer une transformation pour normaliser la valeur\n",
    "      - Exemple en dessous\n",
    "    - *Solution 2* : Transformer la valeur en valeur categoriel\n",
    "    - *Solution 3* : Garder les données telle quelles\n",
    "  - Box plot : Indication de outliers\n",
    "    - Cependant, données vérifiées car il s'agit d'un âge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PowerTransformer\n",
    "\n",
    "example = PowerTransformer().fit_transform(pd.DataFrame(df3[\"Age\"]))\n",
    "\n",
    "fig, axes = plt.subplots(1,2, figsize=(10, 5))\n",
    "axes = axes.flatten()\n",
    "\n",
    "sns.histplot(example, kde=True, ax=axes[0])\n",
    "sns.boxplot(example, ax=axes[1])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Donnée numériques int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,3, figsize=(10, 5))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for ax, col in zip(axes, [\"FCVC\", \"NCP\", \"CH2O\", \"FAF\", \"TUE\"]) :\n",
    "    sns.histplot(df3[col], kde=True, ax=ax)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Données**\n",
    "\n",
    "- FCVC (Légumes dans les repas)\n",
    "- NCP (Nombre de repas par jour)\n",
    "- CH2O (Quantité d'eau bu par jour)\n",
    "- FAF (Fréquence activité physique)\n",
    "- TUE (Temps passé sur les écrans)\n",
    "\n",
    "**Problèmes**  \n",
    "Données non équilibrés\n",
    "\n",
    "**Solutions**  \n",
    "Downsampling et Upweighting. Il s'agit de deux techniques de construction de set de données permetant de mitiger les problèmes de données non équilibrés. Cependant, le déséquilibre n'etant pas extrème, nous n'implementerons pas cette solution.\n",
    "\n",
    "**Remarques**  \n",
    "De part leur nature, l'on pourrait penser les convertir en catégorie. Cependant, ces variables présentent des relations ordinales qu'il convient de conserver pour améliorer la justesse du modèle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Données catégoriels et booléenes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ll182NpuNqfM3wAMJgdxFvC98iPf5h3jvPMU53jMSHU7n4nLl23D9+vVq2LCh3nrrLT377LN6/fXXVaVKFUmSh4eH/vWvf6lSpUoaPXq0JOmpp57S5cuXNXz4cAUFBaly5crq06cP/yFEoTBkyBB5eHiocePG+sc//qG3335bL7zwgo4fP6727dvr1KlT6tWrl6pUqaJly5apTZs2mjNnjk6fPq1Dhw7po48+UosWLeTu7u7srgDIZQcPHlR8fLykKyObrpaWlqbhw4dr5syZ+u9//6vLly/rzjvv1JdffmnV9fHxUXp6ur755htlZ2frww8/VEBAgDZu3KgRI0ZY5/r1118VHh6usmXLqlGjRvnWv+Jk48aNuu222zR//nz973//0wMPPKBZs2apXLly6tu3rypWrKglS5ZY9WvXrq3u3bvru+++02uvvSZJuu2229S+fXu5urpe8/2Aa109EjDna3d3d+3evVs9evTQjz/+KEl68803VbJkSZUvX96q36RJEw0fPlzPPfecLl68KEkqU6aMunbtqrFjx6pMmTIyxuRjbwqXnL/pAfw1xPuig3if/4j3zlOs472zs/jAnj17TK9evcyoUaPMypUrzeXLl83OnTtNpUqVzLhx46x62dnZ5q233jIlS5a0Hlpw7tw5s2vXLv5TiELpnXfeMW3atDHr1q0ze/fuNWFhYebee+81Hh4eZuvWrcYYY1atWmWaNGlirTl29UN2ARQ9v/32m2nUqJFp3769dezChQtm586d5rfffjPGGDNhwgTj4eFh+vfvbyZOnGgyMzONv7+/efvtt40xxpw/f9785z//MW5ubqZOnTrGw8PDrFy50jpfzsidy5cvWw+1wq3746ic9PR0ExISYp577jnrWM7nsnnzZmOMMWPGjDF33XWX+fzzz606v/zyi5kyZYrZv39/vrS7qPnll19M//79zfbt240xV54hcuLECXP69Gljs9nMvHnzTHp6ujHGmIEDB5o6deqYc+fOWa//4YcfTJ06dczgwYOd0n4AxQPxvvAi3hcMxHs4A0l05Jvs7OzrTk366aefjM1mM+XKlTMHDhywjs+ZM8eUL1/efPvtt9axCxcumLvuuss0btw4X9oM5KWMjAzzyCOPmC5dupjk5GTz888/mwEDBhibzWZWrVpl1XvsscfMsmXLnNhSAHkl5wb36imp69evN+7u7mbLli3GmCs3YlWrVjXz5s0zxhizYcMG06tXLzN37lzz0EMPmaFDh5pnn33W9OzZ0+7cUVFR5u2337YeLIa8l5mZad1cb9iwwdSpU8cYc+Wf/v379zfly5c3I0eOtG7iDhw4YLp06WIeffRRpm/nkm+++cZ069bNdOrUyTz88MPGZrOZDz/80BhjzOjRo81tt91mvv76a2OMMWfPnjWenp7mhRdesF6fnZ1t5syZY7y8vExSUpJT+gCg6CHeFy3Ee+cj3sMZSKIjT/zxv7NX71+8eNHs2rXLnD9/3lr/a8qUKcbd3d1az82YKwnGxo0bm/DwcLvX79ixw8yfPz+PewDkjz179pg2bdqYF1980RhzJZifOHHC+toYY/0HHUDxkJaWZnr16mXq169vjLnyu2DixImmSZMm5t133zXffvut6dWrl4mOjjZHjhwxFStWNC1btjQdO3Y033///XXPmbNWJPJOZGSkufvuu60RZfv37zd16tQxEydONBUrVjQhISHm4MGDVv2ckYaTJ082TZs2NXv37rU739WJFvy5P75Xffv2NTabzTRr1sxu1Jkxxnh7e5sRI0ZY7//MmTNNxYoVzfHjx606aWlpPHMEQJ4j3hdOxHvnId7D2YrxQjbIC+b/rxuVs0bS8uXLlZaWZu1Pnz5d1atXV0REhNq3b69JkyZJkiZMmKDy5ctr1apVSk1NlSSVLFlSM2fO1OrVq7V582brGm3bttXQoUPzsVdA3mndurXuvPNObdmyRbGxsbLZbKpVq5ays7Ottd3c3Nyc3EoAeeXkyZN68MEHtWDBAutYmTJl9Mwzz+j06dOaO3eubDabhg4dqn/+858aMGCAEhMT9e233+r48eNq0KCBFi5cqJSUFG3dulUpKSnXXMMYoxIlSuRnt4q87Ozsa441adJEBw8e1Pr165Wenq5SpUrJ29tbc+fO1dtvv62NGzeqWbNmkqRFixZZa6MOGTJEn332mVq3bm13vqvX+sT1ZWdnKysry+69SkxMVNWqVRUcHCx3d3eVKVNGknTp0iVJ0iuvvKKFCxfqwIEDkqQRI0bI3d1dzzzzjPV3bJkyZXjmCIBcRbwvnIj3BQPxHgWGU1P4KFKuHi2+Zs0ac9ttt5lKlSpZ65UvWrTI1K9f37z//vsmOTnZrFmzxthsNvPqq68aY4x56623jLu7u7UWdI57773XDB8+nP/Qosg6deqUueeee0zfvn2d3RQA+WzBggXGz8/PeHh4mAULFpi4uDhjzJXnHzz33HPGy8vLGkFjjDF9+vQx//jHP0zz5s1NcHCwdXzdunVm8eLF+d38Yu3kyZNm7969Jjs72/ob5aWXXjJVqlQxX375pcnKyjKjRo0y9evXN1988YX1us8++8zccccdZsKECXYzjf44iw9/7uq/C48fP27mzp1rtmzZYs1y3Lp1q2nevLl59tlnjTHGbvp8s2bNzEMPPWQSExONMcZs2bLFrFu3Lh9bD6C4Id4XXsR75yLeoyAhiY5cFRcXZ+6++27j6elpZs6caR1PT083rVu3NgsWLDDGGPP999+bzp07m7Jly5pFixZZ9Vq2bGl69OhhEhISrGOpqan51wHASV555RUzZ84c/lkEFDOvvfaaGT9+vPn4449N7969Tfv27c3FixeNMcb8/PPPplq1aiYiIsKqn5SUZP79738bHx8f4+PjYz1oG3nrer+b27dvb+rWrWt+/vlnu+O333676devn0lPTzffffed6devn3FzczMdOnQwwcHBpnTp0nZrcuLmZWVlmZEjR5py5cqZDh06mKpVq5qOHTuaffv2mcuXL5tnn33W1K9f31oTNWfKdnR0tLHZbOb99993ZvMBFCPE+8KBeF8wEe9RUJBER645f/68ady4salSpYr1X8EcSUlJpk+fPuaDDz4wo0ePNuXKlTP9+vUzp06dMsYY6yEoe/fuNTabzbzzzjv53n7AmUieA8VLzs/8rl27TMWKFc2lS5fMr7/+alq3bm0eeeQRa5TMf//7X+Pi4mK++eYb67Xx8fFmxIgR5v777zfnz593SvuLi8uXL9v9ft64caP55JNPjDFXBgS4ubmZ+fPn261B+/777xs3Nzfz0UcfWcdWrlxp5s6dayZPnmx++eUX6zij0W7NRx99ZJo2bWpiYmKMMVf+nvT09DQ9e/Y0qampZu/eveb+++834eHhxpgr73fOjMdFixaZ5ORkp7UdQPFAvC8ciPcFG/EeBQVJdOSqF154wbRv395uSZY333zT9O3b1zRq1Mi4u7ubu+++2+zZs8cqP3PmjHnxxRfNr7/+aoy5MtWNpyMDAIqDM2fOmPbt25uPP/7YOta7d2/j7e1tPv/8c3PmzBnTrVs3u6ncxthPVUXeuPpm+sCBA+aDDz4wNWrUMI8++qg5e/asMcaYYcOGmapVq9olPYwxpmrVqiYsLMwcOXLkuuf+4806HMvOzr7m+z3n/Xv00UfN1KlTjTFXlji44447TPXq1c2nn35qvXbRokWmQoUKplevXsbLy8t069aNWY4A8h3xvuAi3hcMxHsUBjxYFLkqIiJC7u7uevfdd7VlyxY1a9ZMkyZN0qBBgzRy5EhlZ2dr7NixuuOOOyRdeUDE22+/rZiYGCUkJEiSnnzySXl6ejqzGwAA5AubzabU1FQ1atRI0pU4umbNGlWtWlXDhw/XggULNGrUKEVFRWn9+vXW61xdXSVJWVlZTml3cWCz2ZSSkqKwsDDdd999Wr9+vTIyMrRp0yZt3LhRkjRv3jwlJydryZIl+u233yRJBw8eVLly5fTRRx9p79691sOrchhj5OrqyoPE/iKbzSZXV1f98ssvOnDggLKzs6337/z584qLi9Njjz2mRx99VCEhIfrqq6/UvXt3Xbx4UZL06KOPasmSJXJ1ddXs2bO1du1alS1b1sm9AlDcEO8LLuJ9wUC8R6Hg3Bw+iqLVq1ebmjVrmhIlSpiXXnrJbhrTvffea5o3b2569epl3njjDXPHHXeYwMBAa6oUAADFRc7IpODgYHP77bcbb29v07JlS/P5558bY66sn+rn52dCQ0PNwoULzYULF5zX2GLgeiPFVqxYYWrWrGm+//578/vvv5uzZ8+ahg0bmgceeMBan3bRokXG19fXjBgxwuzfv9/07dvXvPPOOzy46hbkjETL+Uyef/55U7ZsWVOjRg3zj3/8w3z33XfGGGPmzZtnSpYsabp06WI9NMyYK+sLP//889baqADgTMT7goV4X3AQ71HYMBIdue7BBx9Uy5Yt1bZtWz355JOqVKmS0tPTJUmrVq1SRESE0tLS9Mknn6hdu3Y6deqUHnjgASe3GgCA/JUzMqlt27b6/fffNXXqVEVHR6t9+/aSpOHDh+v999/X5MmTNXjwYHl5eV0zygm3Ljs7W9nZ2XYjxbKyspSdna0DBw6oWrVq8vPzk5ubm/z8/DR16lQdO3bMGik4cOBAPf744/riiy/UuXNnnT17VsHBweratask8Zn9TTkjz6QrPyM//fSTYmNjtWHDBr3++uvav3+/Xn31VZ07d05t27ZVixYtVLZsWVWuXFmXL1/WxYsX9cYbb2jDhg3W6DQAcCbifcFAvC9YiPcojGyGn3Tkgb1792rEiBHq1q2bnn32WRljrpnGdOnSJZUqVcpJLQQAoGB46aWX9NFHH2n//v3XjZfS/92YMSU4d139fh88eFB79+5VixYtVL9+fZUrV05DhgzRwYMHtXv3bmVlZVk3e23btlVqaqrmzp2rtm3bKjMzU6mpqfr1119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      "text/plain": [
       "<Figure size 1500x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3,3, figsize=(15, 10))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for ax, col in zip(axes, [\"Gender\", \"CAEC\", \"CALC\", \"MTRANS\", \"NObeyesdad\", \"family_history_with_overweight\", \"FAVC\", \"SMOKE\", \"SCC\"]) :\n",
    "    sns.countplot(x=df3[col], ax=ax)\n",
    "    ax.tick_params(axis='x', labelrotation=30)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Données**\n",
    "\n",
    "- Gender\n",
    "- CAEC (Nourriture entre les repas)\n",
    "- CALC (Fréquence alcool)\n",
    "- MTRANS (Moyen de transport)\n",
    "- Nobeyesdad (Catégorie d'obésité) (variable cible)\n",
    "- family_history_with_overweight\n",
    "- FAVC (Consomation fréquente de nourriture à haute calories)\n",
    "- SMOKE\n",
    "- SCC (Surveillance des calories)\n",
    "\n",
    "**Problèmes**  \n",
    "Données non équilibrés\n",
    "\n",
    "**Solutions**  \n",
    "Downsampling et Upweighting. Il s'agit de deux techniques de construction de set de données permetant de mitiger les problèmes de données non équilibrés. Cependant, le déséquilibre n'etant pas extrème, nous n'implementerons pas cette solution.\n",
    "\n",
    "**Remarques**  \n",
    "Certaines de ces catégories (CAEC, CALC, Nobeyesdad) ont des relation que l'on pourrait considérer ordinal. Conserver cette relation pourrait être intéréssant, nous pouvons donc appliquer un encoder oridinal sur ces valeurs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Matrice de corrélation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  background-color: #5d7ce6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_16306_row8_col2, #T_16306_row9_col1 {\n",
       "  background-color: #516ddb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_16306_row8_col4 {\n",
       "  background-color: #6f92f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_16306_row8_col7 {\n",
       "  background-color: #81a4fb;\n",
       "  color: #f1f1f1;\n",
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       "</style>\n",
       "<table id=\"T_16306\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_16306_level0_col0\" class=\"col_heading level0 col0\" >Age</th>\n",
       "      <th id=\"T_16306_level0_col1\" class=\"col_heading level0 col1\" >family_history_with_overweight</th>\n",
       "      <th id=\"T_16306_level0_col2\" class=\"col_heading level0 col2\" >FAVC</th>\n",
       "      <th id=\"T_16306_level0_col3\" class=\"col_heading level0 col3\" >FCVC</th>\n",
       "      <th id=\"T_16306_level0_col4\" class=\"col_heading level0 col4\" >NCP</th>\n",
       "      <th id=\"T_16306_level0_col5\" class=\"col_heading level0 col5\" >SMOKE</th>\n",
       "      <th id=\"T_16306_level0_col6\" class=\"col_heading level0 col6\" >CH2O</th>\n",
       "      <th id=\"T_16306_level0_col7\" class=\"col_heading level0 col7\" >SCC</th>\n",
       "      <th id=\"T_16306_level0_col8\" class=\"col_heading level0 col8\" >FAF</th>\n",
       "      <th id=\"T_16306_level0_col9\" class=\"col_heading level0 col9\" >TUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_16306_level0_row0\" class=\"row_heading level0 row0\" >Age</th>\n",
       "      <td id=\"T_16306_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
       "      <td id=\"T_16306_row0_col1\" class=\"data row0 col1\" >0.21</td>\n",
       "      <td id=\"T_16306_row0_col2\" class=\"data row0 col2\" >0.06</td>\n",
       "      <td id=\"T_16306_row0_col3\" class=\"data row0 col3\" >-0.02</td>\n",
       "      <td id=\"T_16306_row0_col4\" class=\"data row0 col4\" >-0.04</td>\n",
       "      <td id=\"T_16306_row0_col5\" class=\"data row0 col5\" >0.09</td>\n",
       "      <td id=\"T_16306_row0_col6\" class=\"data row0 col6\" >-0.02</td>\n",
       "      <td id=\"T_16306_row0_col7\" class=\"data row0 col7\" >-0.12</td>\n",
       "      <td id=\"T_16306_row0_col8\" class=\"data row0 col8\" >-0.13</td>\n",
       "      <td id=\"T_16306_row0_col9\" class=\"data row0 col9\" >-0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16306_level0_row1\" class=\"row_heading level0 row1\" >family_history_with_overweight</th>\n",
       "      <td id=\"T_16306_row1_col0\" class=\"data row1 col0\" >0.21</td>\n",
       "      <td id=\"T_16306_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
       "      <td id=\"T_16306_row1_col2\" class=\"data row1 col2\" >0.21</td>\n",
       "      <td id=\"T_16306_row1_col3\" class=\"data row1 col3\" >0.01</td>\n",
       "      <td id=\"T_16306_row1_col4\" class=\"data row1 col4\" >0.06</td>\n",
       "      <td id=\"T_16306_row1_col5\" class=\"data row1 col5\" >0.02</td>\n",
       "      <td id=\"T_16306_row1_col6\" class=\"data row1 col6\" >0.14</td>\n",
       "      <td id=\"T_16306_row1_col7\" class=\"data row1 col7\" >-0.19</td>\n",
       "      <td id=\"T_16306_row1_col8\" class=\"data row1 col8\" >-0.05</td>\n",
       "      <td id=\"T_16306_row1_col9\" class=\"data row1 col9\" >-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16306_level0_row2\" class=\"row_heading level0 row2\" >FAVC</th>\n",
       "      <td id=\"T_16306_row2_col0\" class=\"data row2 col0\" >0.06</td>\n",
       "      <td id=\"T_16306_row2_col1\" class=\"data row2 col1\" >0.21</td>\n",
       "      <td id=\"T_16306_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
       "      <td id=\"T_16306_row2_col3\" class=\"data row2 col3\" >-0.07</td>\n",
       "      <td id=\"T_16306_row2_col4\" class=\"data row2 col4\" >-0.01</td>\n",
       "      <td id=\"T_16306_row2_col5\" class=\"data row2 col5\" >-0.05</td>\n",
       "      <td id=\"T_16306_row2_col6\" class=\"data row2 col6\" >0.01</td>\n",
       "      <td id=\"T_16306_row2_col7\" class=\"data row2 col7\" >-0.19</td>\n",
       "      <td id=\"T_16306_row2_col8\" class=\"data row2 col8\" >-0.10</td>\n",
       "      <td id=\"T_16306_row2_col9\" class=\"data row2 col9\" >-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16306_level0_row3\" class=\"row_heading level0 row3\" >FCVC</th>\n",
       "      <td id=\"T_16306_row3_col0\" class=\"data row3 col0\" >-0.02</td>\n",
       "      <td id=\"T_16306_row3_col1\" class=\"data row3 col1\" >0.01</td>\n",
       "      <td id=\"T_16306_row3_col2\" class=\"data row3 col2\" >-0.07</td>\n",
       "      <td id=\"T_16306_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
       "      <td id=\"T_16306_row3_col4\" class=\"data row3 col4\" >0.07</td>\n",
       "      <td id=\"T_16306_row3_col5\" class=\"data row3 col5\" >0.03</td>\n",
       "      <td id=\"T_16306_row3_col6\" class=\"data row3 col6\" >0.12</td>\n",
       "      <td id=\"T_16306_row3_col7\" class=\"data row3 col7\" >0.07</td>\n",
       "      <td id=\"T_16306_row3_col8\" class=\"data row3 col8\" >-0.02</td>\n",
       "      <td id=\"T_16306_row3_col9\" class=\"data row3 col9\" >-0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16306_level0_row4\" class=\"row_heading level0 row4\" >NCP</th>\n",
       "      <td id=\"T_16306_row4_col0\" class=\"data row4 col0\" >-0.04</td>\n",
       "      <td id=\"T_16306_row4_col1\" class=\"data row4 col1\" >0.06</td>\n",
       "      <td id=\"T_16306_row4_col2\" class=\"data row4 col2\" >-0.01</td>\n",
       "      <td id=\"T_16306_row4_col3\" class=\"data row4 col3\" >0.07</td>\n",
       "      <td id=\"T_16306_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
       "      <td id=\"T_16306_row4_col5\" class=\"data row4 col5\" >0.01</td>\n",
       "      <td id=\"T_16306_row4_col6\" class=\"data row4 col6\" >0.06</td>\n",
       "      <td id=\"T_16306_row4_col7\" class=\"data row4 col7\" >-0.01</td>\n",
       "      <td id=\"T_16306_row4_col8\" class=\"data row4 col8\" >0.13</td>\n",
       "      <td id=\"T_16306_row4_col9\" class=\"data row4 col9\" >-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16306_level0_row5\" class=\"row_heading level0 row5\" >SMOKE</th>\n",
       "      <td id=\"T_16306_row5_col0\" class=\"data row5 col0\" >0.09</td>\n",
       "      <td id=\"T_16306_row5_col1\" class=\"data row5 col1\" >0.02</td>\n",
       "      <td id=\"T_16306_row5_col2\" class=\"data row5 col2\" >-0.05</td>\n",
       "      <td id=\"T_16306_row5_col3\" class=\"data row5 col3\" >0.03</td>\n",
       "      <td id=\"T_16306_row5_col4\" class=\"data row5 col4\" >0.01</td>\n",
       "      <td id=\"T_16306_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
       "      <td id=\"T_16306_row5_col6\" class=\"data row5 col6\" >-0.04</td>\n",
       "      <td id=\"T_16306_row5_col7\" class=\"data row5 col7\" >0.05</td>\n",
       "      <td id=\"T_16306_row5_col8\" class=\"data row5 col8\" >0.01</td>\n",
       "      <td id=\"T_16306_row5_col9\" class=\"data row5 col9\" >0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16306_level0_row6\" class=\"row_heading level0 row6\" >CH2O</th>\n",
       "      <td id=\"T_16306_row6_col0\" class=\"data row6 col0\" >-0.02</td>\n",
       "      <td id=\"T_16306_row6_col1\" class=\"data row6 col1\" >0.14</td>\n",
       "      <td id=\"T_16306_row6_col2\" class=\"data row6 col2\" >0.01</td>\n",
       "      <td id=\"T_16306_row6_col3\" class=\"data row6 col3\" >0.12</td>\n",
       "      <td id=\"T_16306_row6_col4\" class=\"data row6 col4\" >0.06</td>\n",
       "      <td id=\"T_16306_row6_col5\" class=\"data row6 col5\" >-0.04</td>\n",
       "      <td id=\"T_16306_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
       "      <td id=\"T_16306_row6_col7\" class=\"data row6 col7\" >-0.00</td>\n",
       "      <td id=\"T_16306_row6_col8\" class=\"data row6 col8\" >0.12</td>\n",
       "      <td id=\"T_16306_row6_col9\" class=\"data row6 col9\" >-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16306_level0_row7\" class=\"row_heading level0 row7\" >SCC</th>\n",
       "      <td id=\"T_16306_row7_col0\" class=\"data row7 col0\" >-0.12</td>\n",
       "      <td id=\"T_16306_row7_col1\" class=\"data row7 col1\" >-0.19</td>\n",
       "      <td id=\"T_16306_row7_col2\" class=\"data row7 col2\" >-0.19</td>\n",
       "      <td id=\"T_16306_row7_col3\" class=\"data row7 col3\" >0.07</td>\n",
       "      <td id=\"T_16306_row7_col4\" class=\"data row7 col4\" >-0.01</td>\n",
       "      <td id=\"T_16306_row7_col5\" class=\"data row7 col5\" >0.05</td>\n",
       "      <td id=\"T_16306_row7_col6\" class=\"data row7 col6\" >-0.00</td>\n",
       "      <td id=\"T_16306_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
       "      <td id=\"T_16306_row7_col8\" class=\"data row7 col8\" >0.07</td>\n",
       "      <td id=\"T_16306_row7_col9\" class=\"data row7 col9\" >0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16306_level0_row8\" class=\"row_heading level0 row8\" >FAF</th>\n",
       "      <td id=\"T_16306_row8_col0\" class=\"data row8 col0\" >-0.13</td>\n",
       "      <td id=\"T_16306_row8_col1\" class=\"data row8 col1\" >-0.05</td>\n",
       "      <td id=\"T_16306_row8_col2\" class=\"data row8 col2\" >-0.10</td>\n",
       "      <td id=\"T_16306_row8_col3\" class=\"data row8 col3\" >-0.02</td>\n",
       "      <td id=\"T_16306_row8_col4\" class=\"data row8 col4\" >0.13</td>\n",
       "      <td id=\"T_16306_row8_col5\" class=\"data row8 col5\" >0.01</td>\n",
       "      <td id=\"T_16306_row8_col6\" class=\"data row8 col6\" >0.12</td>\n",
       "      <td id=\"T_16306_row8_col7\" class=\"data row8 col7\" >0.07</td>\n",
       "      <td id=\"T_16306_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
       "      <td id=\"T_16306_row8_col9\" class=\"data row8 col9\" >0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16306_level0_row9\" class=\"row_heading level0 row9\" >TUE</th>\n",
       "      <td id=\"T_16306_row9_col0\" class=\"data row9 col0\" >-0.24</td>\n",
       "      <td id=\"T_16306_row9_col1\" class=\"data row9 col1\" >-0.10</td>\n",
       "      <td id=\"T_16306_row9_col2\" class=\"data row9 col2\" >-0.05</td>\n",
       "      <td id=\"T_16306_row9_col3\" class=\"data row9 col3\" >-0.15</td>\n",
       "      <td id=\"T_16306_row9_col4\" class=\"data row9 col4\" >-0.00</td>\n",
       "      <td id=\"T_16306_row9_col5\" class=\"data row9 col5\" >0.06</td>\n",
       "      <td id=\"T_16306_row9_col6\" class=\"data row9 col6\" >-0.04</td>\n",
       "      <td id=\"T_16306_row9_col7\" class=\"data row9 col7\" >0.03</td>\n",
       "      <td id=\"T_16306_row9_col8\" class=\"data row9 col8\" >0.08</td>\n",
       "      <td id=\"T_16306_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1862b4c4e00>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = df3.select_dtypes(exclude=['category']).corr()\n",
    "corr_matrix.style.background_gradient(cmap='coolwarm').format(precision=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'on conclut qu'aucune valeurs ne sont manifestement corrélées entre elles.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 - Nettoyage des données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "À l'aide de nos explorations, nous avons déterminer que le nottoyage des données nécéssaire était :\n",
    "- Suppresion des colonnes \"Height\" et \"Weight\"\n",
    "- Transformation des colonnes restantes aux types spécifiées\n",
    "- Conversions des données flotantes en int (sauf pour âge)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>family_history_with_overweight</th>\n",
       "      <th>FAVC</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CAEC</th>\n",
       "      <th>SMOKE</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>SCC</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
       "      <th>CALC</th>\n",
       "      <th>MTRANS</th>\n",
       "      <th>NObeyesdad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Walking</td>\n",
       "      <td>Overweight_Level_I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Overweight_Level_II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Motorbike</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender   Age  family_history_with_overweight   FAVC  FCVC  NCP       CAEC  \\\n",
       "0      0  21.0                            True  False     2    3  Sometimes   \n",
       "1      0  21.0                            True  False     3    3  Sometimes   \n",
       "2      1  23.0                            True  False     2    3  Sometimes   \n",
       "3      1  27.0                           False  False     3    3  Sometimes   \n",
       "4      1  22.0                           False  False     2    1  Sometimes   \n",
       "5      1  29.0                           False   True     2    3  Sometimes   \n",
       "6      0  23.0                            True   True     3    3  Sometimes   \n",
       "7      1  22.0                           False  False     2    3  Sometimes   \n",
       "8      1  24.0                            True   True     3    3  Sometimes   \n",
       "9      1  22.0                            True   True     2    3  Sometimes   \n",
       "\n",
       "   SMOKE  CH2O    SCC  FAF  TUE        CALC                 MTRANS  \\\n",
       "0  False     2  False    0    1          no  Public_Transportation   \n",
       "1   True     3   True    3    0   Sometimes  Public_Transportation   \n",
       "2  False     2  False    2    1  Frequently  Public_Transportation   \n",
       "3  False     2  False    2    0  Frequently                Walking   \n",
       "4  False     2  False    0    0   Sometimes  Public_Transportation   \n",
       "5  False     2  False    0    0   Sometimes             Automobile   \n",
       "6  False     2  False    1    0   Sometimes              Motorbike   \n",
       "7  False     2  False    3    0   Sometimes  Public_Transportation   \n",
       "8  False     2  False    1    1  Frequently  Public_Transportation   \n",
       "9  False     2  False    1    1          no  Public_Transportation   \n",
       "\n",
       "            NObeyesdad  \n",
       "0        Normal_Weight  \n",
       "1        Normal_Weight  \n",
       "2        Normal_Weight  \n",
       "3   Overweight_Level_I  \n",
       "4  Overweight_Level_II  \n",
       "5        Normal_Weight  \n",
       "6        Normal_Weight  \n",
       "7        Normal_Weight  \n",
       "8        Normal_Weight  \n",
       "9        Normal_Weight  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def import_clean_data(path):\n",
    "\n",
    "    df = pd.read_csv(path, dtype={\n",
    "      'Gender':'category',\n",
    "      'CAEC':'category',\n",
    "      'CALC':'category',\n",
    "      'MTRANS':'category',\n",
    "      'NObeyesdad':'category',\n",
    "    })\n",
    "\n",
    "    # No inplace possible ?\n",
    "    df[[\"NCP\", \"CH2O\", \"FAF\",\"FCVC\", \"TUE\"]] = \\\n",
    "        df[[\"NCP\", \"CH2O\", \"FAF\",\"FCVC\", \"TUE\"]].apply(np.rint, axis=1).astype(\"int64\")\n",
    "    df[[\"family_history_with_overweight\", \"FAVC\", \"SMOKE\", \"SCC\"]] = \\\n",
    "        df[[\"family_history_with_overweight\", \"FAVC\", \"SMOKE\", \"SCC\"]].apply(lambda x: x.map({\"yes\": True, \"no\": False}), axis=0)\n",
    "    df[[\"Gender\"]] = \\\n",
    "        df[[\"Gender\"]].apply(lambda x: x.map({\"Male\": 1, \"Female\": 0}), axis=0)\n",
    "\n",
    "    return df\n",
    "\n",
    "def drop_age_and_weight(df) :\n",
    "    return df.drop(['Height', 'Weight'], axis=1)\n",
    "\n",
    "\n",
    "df_clean = import_clean_data(\"data/ObesityDataSet_raw_and_data_sinthetic.csv\")\n",
    "df_clean = drop_age_and_weight(df_clean)\n",
    "df_clean.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6 - Validation des données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour la validation des données, nous avons tenté d'utiliser les mécanismes de validation inclus dans la bibliothèque Tensorflow. Cependant, du à des diffcultés d'import et à des icompatibilités de versions, nous avons choisi de réaliser notre propre classe de validation, nommée DataValidator.\n",
    "\n",
    "Celle-ci permet de valider les données à l'aide d'un schéma sous la forme :\n",
    "- Nom de la colonne\n",
    "- Type attendu de la colonne\n",
    "- Fonction lambda permettant d'ajouter des critères supplémentaires paramétrables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data is validated !\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mickael\\AppData\\Local\\Temp\\ipykernel_19364\\2476852676.py:15: UserWarning: Data validation failed : Columns don't match\n",
      "  warnings.warn(\"Data validation failed : Columns don't match\")\n",
      "C:\\Users\\Mickael\\AppData\\Local\\Temp\\ipykernel_19364\\2476852676.py:30: UserWarning: Data validation failed : column 'B' has type float64, but should be int64\n",
      "  warnings.warn(f\"Data validation failed : column '{col}' has type {df_type}, but should be {schema_type}\")\n",
      "C:\\Users\\Mickael\\AppData\\Local\\Temp\\ipykernel_19364\\2476852676.py:36: UserWarning: Data validation failed : 'A' validator returned false on value 3\n",
      "  warnings.warn(f\"Data validation failed : '{col}' validator returned false on value {value}\")\n",
      "C:\\Users\\Mickael\\AppData\\Local\\Temp\\ipykernel_19364\\2476852676.py:23: UserWarning: Data validation failed : NaN values found\n",
      "  warnings.warn(\"Data validation failed : NaN values found\")\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "class DataValidator:\n",
    "    def __init__(self, schema : list[tuple[str, str, callable]]) -> None:\n",
    "        self.schema = schema\n",
    "\n",
    "    def isValid(self, df):\n",
    "        # Check if the columns are the same\n",
    "        df_cols = df.columns.tolist()\n",
    "        df_cols.sort()\n",
    "        schema_cols = [i for i, _, _ in self.schema]\n",
    "        schema_cols.sort()\n",
    "\n",
    "        if df_cols != schema_cols:\n",
    "            warnings.warn(\"Data validation failed : Columns don't match\")\n",
    "            return False\n",
    "\n",
    "        # Validate data\n",
    "        for (col, datatype, validator) in self.schema:\n",
    "            \n",
    "            # Check for NaNs\n",
    "            if df.isnull().sum().sum() > 0:\n",
    "                warnings.warn(\"Data validation failed : NaN values found\")\n",
    "                return False\n",
    "\n",
    "            # Validate type\n",
    "            df_type = str(df[col].dtype)\n",
    "            schema_type = datatype\n",
    "            if df_type != schema_type:\n",
    "                warnings.warn(f\"Data validation failed : column '{col}' has type {df_type}, but should be {schema_type}\")\n",
    "                return False\n",
    "\n",
    "            # Run validator\n",
    "            for _, value in df[col].items():\n",
    "                if validator and not validator(value):\n",
    "                    warnings.warn(f\"Data validation failed : '{col}' validator returned false on value {value}\")\n",
    "                    return False\n",
    "\n",
    "        print(\"Data is validated !\")\n",
    "        return True\n",
    "\n",
    "# Helper functions to generate lambda expressions\n",
    "\n",
    "def lambda_range(min, max):\n",
    "    return lambda x : x >= min and x <= max\n",
    "\n",
    "def lambda_in(possible):\n",
    "    return lambda x : x in possible\n",
    "\n",
    "\n",
    "# Unit test\n",
    "\n",
    "def testValidator():\n",
    "    good_data = {\n",
    "        'A': [1, 2, 3],\n",
    "        'B': [4.0, 5.5, 6.2],\n",
    "        'C': ['x', 'y', 'z'],\n",
    "        'D': [True, True, False]\n",
    "    }\n",
    "    \n",
    "    bad_data_nan = {\n",
    "        'A': [1, 2, 3, 2],\n",
    "        'B': [4.0, 5.5, 6.2, np.nan],\n",
    "        'C': ['x', 'y', 'z', 'z'],\n",
    "        'D': [True, True, False, False]\n",
    "    }\n",
    "\n",
    "    \n",
    "    good_schema = [\n",
    "        (\"A\", \"int64\", lambda_range(0, 4)),\n",
    "        (\"B\", \"float64\", lambda x : x >= 4),\n",
    "        (\"C\", \"category\", None),\n",
    "        (\"D\", \"bool\", None)\n",
    "    ]\n",
    "    \n",
    "    bad_schema_miss_col = [\n",
    "        (\"A\", \"int64\", lambda_range(0, 4)),\n",
    "        (\"B\", \"float64\", lambda x : x >= 4),\n",
    "        (\"C\", \"category\", None),\n",
    "    ]\n",
    "    \n",
    "    bad_schema_type = [\n",
    "        (\"A\", \"int64\", lambda_range(0, 4)),\n",
    "        (\"B\", \"int64\", lambda x : x >= 4),\n",
    "        (\"C\", \"category\", None),\n",
    "        (\"D\", \"bool\", None)\n",
    "    ]\n",
    "    \n",
    "    bad_schema_validator = [\n",
    "        (\"A\", \"int64\", lambda_range(0, 2)),\n",
    "        (\"B\", \"float64\", lambda x : x >= 4),\n",
    "        (\"C\", \"category\", None),\n",
    "        (\"D\", \"bool\", None)\n",
    "    ]\n",
    "    \n",
    "    # Working case\n",
    "    df_test = pd.DataFrame(good_data)\n",
    "    df_test['C'] = df_test['C'].astype(\"category\")\n",
    "    data_validator = DataValidator(good_schema)\n",
    "    assert data_validator.isValid(df_test) == True\n",
    "    \n",
    "    # Schema error cases\n",
    "    for i in [bad_schema_miss_col, bad_schema_type, bad_schema_validator]:\n",
    "        data_validator = DataValidator(i)\n",
    "        assert data_validator.isValid(df_test) == False\n",
    "    \n",
    "    # NaN case\n",
    "    df_test = pd.DataFrame(bad_data_nan)\n",
    "    df_test['C'] = df_test['C'].astype(\"category\")\n",
    "    data_validator = DataValidator(good_schema)\n",
    "    assert data_validator.isValid(df_test) == False\n",
    "    \n",
    "    \n",
    "testValidator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On réalise ensuite un schéma pour notre data frame nettoyé."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data is validated !\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_schema = [\n",
    "    (\"Age\", \"float64\", lambda_range(14,99)),\n",
    "    (\"family_history_with_overweight\", \"bool\", None),\n",
    "    (\"FAVC\", \"bool\", None),\n",
    "    (\"SMOKE\", \"bool\", None),\n",
    "    (\"SCC\", \"bool\", None),\n",
    "    (\"FCVC\", \"int64\", lambda_in ([1, 2, 3])),\n",
    "    (\"NCP\", \"int64\", lambda_in ([1, 2, 3, 4])),\n",
    "    (\"CH2O\", \"int64\", lambda_in ([1, 2, 3])),\n",
    "    (\"FAF\", \"int64\", lambda_in ([0, 1, 2, 3])),\n",
    "    (\"TUE\", \"int64\", lambda_in ([0, 1, 2])),\n",
    "    (\"Gender\", \"category\", lambda_in ([0,1])),\n",
    "    (\"CAEC\", \"category\", lambda_in ([\"Always\", \"Frequently\", \"Sometimes\", \"no\"])),\n",
    "    (\"CALC\", \"category\", lambda_in ([\"Always\", \"Frequently\", \"Sometimes\", \"no\"])),\n",
    "    (\"MTRANS\", \"category\", lambda_in ([\"Automobile\", \"Bike\", \"Motorbike\",\n",
    "                                            \"Public_Transportation\", \"Walking\"])),\n",
    "    (\"NObeyesdad\", \"category\", lambda_in ([\n",
    "        \"Insufficient_Weight\", \"Normal_Weight\",\n",
    "        \"Obesity_Type_I\", \"Obesity_Type_II\",\n",
    "        \"Obesity_Type_III\", \"Overweight_Level_I\",\n",
    "        \"Overweight_Level_II\"]))\n",
    "]\n",
    "\n",
    "data_validator = DataValidator(df_schema)\n",
    "\n",
    "data_validator.isValid(drop_age_and_weight(import_clean_data(\"data/ObesityDataSet_raw_and_data_sinthetic.csv\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7 - Pipeline de traitement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avec notre analyse, nous pouvons imaginer la pipelines suivantes:\n",
    "- Pour la colonne age, on peut:\n",
    "  - Appliquer le Power Encoding sur Age pour rendre normale la distribution\n",
    "  - Catégoriser la variable\n",
    "- Appliquer le Ordinal Encoding sur CAEC et CALC\n",
    "- Appliquer le OneHot Encoding sur Gender, MTRANS, familiy_history_with_overweight, FAVC\n",
    "- Standardiser l'ensemble des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data is validated !\n"
     ]
    }
   ],
   "source": [
    "# https://scikit-learn.org/stable/tutorial/statistical_inference/putting_together.html\n",
    "# https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.feature_selection import SelectPercentile, chi2\n",
    "from sklearn.preprocessing import OrdinalEncoder, PowerTransformer, OneHotEncoder, StandardScaler, Normalizer, KBinsDiscretizer\n",
    "\n",
    "# Dataset import\n",
    "df = import_clean_data(\"data/ObesityDataSet_raw_and_data_sinthetic.csv\")\n",
    "df = drop_age_and_weight(df)\n",
    "\n",
    "# Data valdation\n",
    "data_validator.isValid(df)\n",
    "\n",
    "\n",
    "# Dataset split\n",
    "Y = df['NObeyesdad']\n",
    "X = df.drop(['NObeyesdad'], axis=1) # On enlève le target\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Public_Transportation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender   Age  family_history_with_overweight   FAVC  FCVC  NCP       CAEC  \\\n",
       "0      0  21.0                            True  False     2    3  Sometimes   \n",
       "1      0  21.0                            True  False     3    3  Sometimes   \n",
       "2      1  23.0                            True  False     2    3  Sometimes   \n",
       "3      1  27.0                           False  False     3    3  Sometimes   \n",
       "4      1  22.0                           False  False     2    1  Sometimes   \n",
       "\n",
       "   SMOKE  CH2O    SCC  FAF  TUE        CALC                 MTRANS  \n",
       "0  False     2  False    0    1          no  Public_Transportation  \n",
       "1   True     3   True    3    0   Sometimes  Public_Transportation  \n",
       "2  False     2  False    2    1  Frequently  Public_Transportation  \n",
       "3  False     2  False    2    0  Frequently                Walking  \n",
       "4  False     2  False    0    0   Sometimes  Public_Transportation  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>-0.986295</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   power_scaler__Age  scaler__FCVC  scaler__NCP  scaler__CH2O  scaler__FAF  \\\n",
       "0          -0.414452     -0.725454     0.385644     -0.021330    -1.124415   \n",
       "1          -0.414452      0.987559     0.385644      1.431202     2.226606   \n",
       "2           0.055560     -0.725454     0.385644     -0.021330     1.109599   \n",
       "3           0.739738      0.987559     0.385644     -0.021330     1.109599   \n",
       "4          -0.165942     -0.725454    -2.085053     -0.021330    -1.124415   \n",
       "\n",
       "   scaler__TUE  oneHotEncoder__Gender_0  oneHotEncoder__Gender_1  \\\n",
       "0     0.497717                      1.0                      0.0   \n",
       "1    -0.986295                      1.0                      0.0   \n",
       "2     0.497717                      0.0                      1.0   \n",
       "3    -0.986295                      0.0                      1.0   \n",
       "4    -0.986295                      0.0                      1.0   \n",
       "\n",
       "   oneHotEncoder__MTRANS_Automobile  oneHotEncoder__MTRANS_Bike  \\\n",
       "0                               0.0                         0.0   \n",
       "1                               0.0                         0.0   \n",
       "2                               0.0                         0.0   \n",
       "3                               0.0                         0.0   \n",
       "4                               0.0                         0.0   \n",
       "\n",
       "   oneHotEncoder__MTRANS_Motorbike  \\\n",
       "0                              0.0   \n",
       "1                              0.0   \n",
       "2                              0.0   \n",
       "3                              0.0   \n",
       "4                              0.0   \n",
       "\n",
       "   oneHotEncoder__MTRANS_Public_Transportation  oneHotEncoder__MTRANS_Walking  \\\n",
       "0                                          1.0                            0.0   \n",
       "1                                          1.0                            0.0   \n",
       "2                                          1.0                            0.0   \n",
       "3                                          0.0                            1.0   \n",
       "4                                          1.0                            0.0   \n",
       "\n",
       "   oneHotEncoder__family_history_with_overweight_False  \\\n",
       "0                                                0.0     \n",
       "1                                                0.0     \n",
       "2                                                0.0     \n",
       "3                                                1.0     \n",
       "4                                                1.0     \n",
       "\n",
       "   oneHotEncoder__family_history_with_overweight_True  \\\n",
       "0                                                1.0    \n",
       "1                                                1.0    \n",
       "2                                                1.0    \n",
       "3                                                0.0    \n",
       "4                                                0.0    \n",
       "\n",
       "   oneHotEncoder__FAVC_False  oneHotEncoder__FAVC_True  \\\n",
       "0                        1.0                       0.0   \n",
       "1                        1.0                       0.0   \n",
       "2                        1.0                       0.0   \n",
       "3                        1.0                       0.0   \n",
       "4                        1.0                       0.0   \n",
       "\n",
       "   oneHotEncoder__SMOKE_False  oneHotEncoder__SMOKE_True  \\\n",
       "0                         1.0                        0.0   \n",
       "1                         0.0                        1.0   \n",
       "2                         1.0                        0.0   \n",
       "3                         1.0                        0.0   \n",
       "4                         1.0                        0.0   \n",
       "\n",
       "   oneHotEncoder__SCC_False  oneHotEncoder__SCC_True  ordinalEncoder__CAEC  \\\n",
       "0                       1.0                      0.0                   1.0   \n",
       "1                       0.0                      1.0                   1.0   \n",
       "2                       1.0                      0.0                   1.0   \n",
       "3                       1.0                      0.0                   1.0   \n",
       "4                       1.0                      0.0                   1.0   \n",
       "\n",
       "   ordinalEncoder__CALC  \n",
       "0                   0.0  \n",
       "1                   1.0  \n",
       "2                   2.0  \n",
       "3                   2.0  \n",
       "4                   1.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Power Transformer\n",
    "power_features = [\"Age\"]\n",
    "\n",
    "# Standardization\n",
    "numeric_features = X.select_dtypes(include=[\"float64\", \"int64\"]).drop([\"Age\"], axis=1).columns\n",
    "\n",
    "# One hot encoding\n",
    "categorical_features_onehot = [\"Gender\", \"MTRANS\",\n",
    "                               \"family_history_with_overweight\", \"FAVC\",\n",
    "                               \"SMOKE\", \"SCC\"]\n",
    "\n",
    "# Ordinal encoding\n",
    "categorical_features_ordinal = [\"CAEC\", \"CALC\"]\n",
    "\n",
    "\n",
    "# On créer la pipeline de preprocessing\n",
    "preprocessor = ColumnTransformer([\n",
    "    # (\"power\", KBinsDiscretizer(encode='ordinal', strategy='uniform'), power_features), # Version catégorisation\n",
    "    (\"power_scaler\", Pipeline([\n",
    "        (\"power\", PowerTransformer()),\n",
    "        (\"scaler\", StandardScaler())\n",
    "    ]), power_features), # Version Normalisation\n",
    "    (\"scaler\", StandardScaler(), numeric_features),\n",
    "    (\"oneHotEncoder\", OneHotEncoder(handle_unknown=\"ignore\"), categorical_features_onehot),\n",
    "    (\"ordinalEncoder\", OrdinalEncoder(categories=[[\"no\", \"Sometimes\", \"Frequently\", \"Always\"],[\"no\", \"Sometimes\", \"Frequently\", \"Always\"]]), categorical_features_ordinal),\n",
    "])\n",
    "\n",
    "with pd.option_context('display.max_columns', None):\n",
    "    display(X.head(5))\n",
    "    display(pd.DataFrame(preprocessor.fit_transform(X), columns=preprocessor.get_feature_names_out()).head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8 - Mise en oeuvre des modèles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les modèles que nous avons choisis sont : \n",
    "- RandomForest\n",
    "- DecisionTreeClassifier\n",
    "- LinearSVC\n",
    "\n",
    "Pour les choisir, nous avons utiliser l'aide mémoire de sklearn :\n",
    "\n",
    "![sckit_map](figures/map.png)\n",
    "\n",
    "Les méthodes que nous avons choisis d'utiliser pour l'optimisation des hyperparamètres sont :\n",
    "- RandomSearch\n",
    "- GridSearch\n",
    "\n",
    "Nous avons de plus utiliser la cross-validation sur le DecisionTree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **RandomForest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On rajoute le model\n",
    "pipeline = Pipeline(\n",
    "    steps=[\n",
    "        (\"preprocessor\", preprocessor),\n",
    "        (\"rforest\", RandomForestClassifier())\n",
    "    ]\n",
    ")\n",
    "\n",
    "param_grid = {\n",
    "    'rforest__n_estimators': [300, 500, 700],\n",
    "    'rforest__max_depth': [10, 15, 20],\n",
    "    'rforest__min_samples_split': [2, 5, 8],\n",
    "    'rforest__bootstrap': [True, False],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Optimisation et mesures avec GridSearch**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Params: {'rforest__bootstrap': False, 'rforest__max_depth': 15, 'rforest__min_samples_split': 2, 'rforest__n_estimators': 300} \\n F1 Score: 0.7873855506723474\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1ms\n",
    "grid_search = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=3, n_jobs=-1, scoring=\"f1_macro\")\n",
    "grid_search.fit(X_train, Y_train)\n",
    "display(f'Params: {grid_search.best_params_} \\n F1 Score: {grid_search.best_score_}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7966903073286052\n",
      "Recall : 0.7933561827229394\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "import timeit\n",
    "# 0s\n",
    "Y_pred = grid_search.best_estimator_.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(Y_test, Y_pred)\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "recall_val = recall_score(Y_test, Y_pred, average='macro')\n",
    "print(f\"Recall : {recall_val}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Optimisation et mesures avec RandomSearch**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Params: {'rforest__bootstrap': False, 'rforest__max_depth': 15, 'rforest__min_samples_split': 2, 'rforest__n_estimators': 300} \\nScore: 0.7873855506723474\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint as sp_randint\n",
    "\n",
    "param_distribs = {\n",
    "    \"rforest__n_estimators\": sp_randint(100, 700),\n",
    "    \"rforest__max_depth\": sp_randint(10, 100),\n",
    "    \"rforest__min_samples_split\": sp_randint(2, 20),\n",
    "    \"rforest__min_samples_leaf\": sp_randint(1, 10),\n",
    "    \"rforest__bootstrap\": [True, False]\n",
    "}\n",
    "\n",
    "# Effectuer la recherche aléatoire\n",
    "random_search = RandomizedSearchCV(\n",
    "    estimator=pipeline,\n",
    "    param_distributions=param_distribs,\n",
    "    scoring=\"f1_macro\",\n",
    "    cv=3,\n",
    "    n_iter=100,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "# Entraîner le pipeline sur les données d'entraînement\n",
    "# 1ms\n",
    "random_search.fit(X_train, Y_train)\n",
    "display(f'Params: {grid_search.best_params_} \\nScore: {grid_search.best_score_}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8108747044917257\n",
      "Recall : 0.8067887716393993\n"
     ]
    }
   ],
   "source": [
    "# 0s\n",
    "Y_pred = random_search.predict(X_test)\n",
    "accuracy = accuracy_score(Y_test, Y_pred)\n",
    "\n",
    "print(f'Accuracy: {accuracy}')\n",
    "# Calculer le rappel\n",
    "recall_val = recall_score(Y_test, Y_pred, average='macro')\n",
    "print(f\"Recall : {recall_val}\")\n",
    "bestRforest = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **DecisionTreeClassifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "pipeline = Pipeline(\n",
    "    steps=[\n",
    "        (\"preprocessor\", preprocessor),\n",
    "        (\"dTree\", DecisionTreeClassifier())\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**Entraînement**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;power_scaler&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;power&#x27;,\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Age&#x27;]),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler(),\n",
       "                                                  Index([&#x27;FCVC&#x27;, &#x27;NCP&#x27;, &#x27;CH2O&#x27;, &#x27;FAF&#x27;, &#x27;TUE&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                                 (&#x27;oneHotEncoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;Gender&#x27;, &#x27;MTRANS&#x27;,\n",
       "                                                   &#x27;family_history_with_overweight&#x27;,\n",
       "                                                   &#x27;FAVC&#x27;, &#x27;SMOKE&#x27;, &#x27;SCC&#x27;]),\n",
       "                                                 (&#x27;ordinalEncoder&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;no&#x27;,\n",
       "                                                                              &#x27;Sometimes&#x27;,\n",
       "                                                                              &#x27;Frequently&#x27;,\n",
       "                                                                              &#x27;Always&#x27;],\n",
       "                                                                             [&#x27;no&#x27;,\n",
       "                                                                              &#x27;Sometimes&#x27;,\n",
       "                                                                              &#x27;Frequently&#x27;,\n",
       "                                                                              &#x27;Always&#x27;]]),\n",
       "                                                  [&#x27;CAEC&#x27;, &#x27;CALC&#x27;])])),\n",
       "                (&#x27;dTree&#x27;, DecisionTreeClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;power_scaler&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;power&#x27;,\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Age&#x27;]),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler(),\n",
       "                                                  Index([&#x27;FCVC&#x27;, &#x27;NCP&#x27;, &#x27;CH2O&#x27;, &#x27;FAF&#x27;, &#x27;TUE&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                                 (&#x27;oneHotEncoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;Gender&#x27;, &#x27;MTRANS&#x27;,\n",
       "                                                   &#x27;family_history_with_overweight&#x27;,\n",
       "                                                   &#x27;FAVC&#x27;, &#x27;SMOKE&#x27;, &#x27;SCC&#x27;]),\n",
       "                                                 (&#x27;ordinalEncoder&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;no&#x27;,\n",
       "                                                                              &#x27;Sometimes&#x27;,\n",
       "                                                                              &#x27;Frequently&#x27;,\n",
       "                                                                              &#x27;Always&#x27;],\n",
       "                                                                             [&#x27;no&#x27;,\n",
       "                                                                              &#x27;Sometimes&#x27;,\n",
       "                                                                              &#x27;Frequently&#x27;,\n",
       "                                                                              &#x27;Always&#x27;]]),\n",
       "                                                  [&#x27;CAEC&#x27;, &#x27;CALC&#x27;])])),\n",
       "                (&#x27;dTree&#x27;, DecisionTreeClassifier())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;power_scaler&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;power&#x27;, PowerTransformer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;Age&#x27;]),\n",
       "                                (&#x27;scaler&#x27;, StandardScaler(),\n",
       "                                 Index([&#x27;FCVC&#x27;, &#x27;NCP&#x27;, &#x27;CH2O&#x27;, &#x27;FAF&#x27;, &#x27;TUE&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                (&#x27;oneHotEncoder&#x27;,\n",
       "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                 [&#x27;Gender&#x27;, &#x27;MTRANS&#x27;,\n",
       "                                  &#x27;family_history_with_overweight&#x27;, &#x27;FAVC&#x27;,\n",
       "                                  &#x27;SMOKE&#x27;, &#x27;SCC&#x27;]),\n",
       "                                (&#x27;ordinalEncoder&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;no&#x27;, &#x27;Sometimes&#x27;,\n",
       "                                                             &#x27;Frequently&#x27;,\n",
       "                                                             &#x27;Always&#x27;],\n",
       "                                                            [&#x27;no&#x27;, &#x27;Sometimes&#x27;,\n",
       "                                                             &#x27;Frequently&#x27;,\n",
       "                                                             &#x27;Always&#x27;]]),\n",
       "                                 [&#x27;CAEC&#x27;, &#x27;CALC&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">power_scaler</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Age&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PowerTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.PowerTransformer.html\">?<span>Documentation for PowerTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PowerTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">scaler</label><div class=\"sk-toggleable__content fitted\"><pre>Index([&#x27;FCVC&#x27;, &#x27;NCP&#x27;, &#x27;CH2O&#x27;, &#x27;FAF&#x27;, &#x27;TUE&#x27;], dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">oneHotEncoder</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Gender&#x27;, &#x27;MTRANS&#x27;, &#x27;family_history_with_overweight&#x27;, &#x27;FAVC&#x27;, &#x27;SMOKE&#x27;, &#x27;SCC&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">ordinalEncoder</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;CAEC&#x27;, &#x27;CALC&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder(categories=[[&#x27;no&#x27;, &#x27;Sometimes&#x27;, &#x27;Frequently&#x27;, &#x27;Always&#x27;],\n",
       "                           [&#x27;no&#x27;, &#x27;Sometimes&#x27;, &#x27;Frequently&#x27;, &#x27;Always&#x27;]])</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('power_scaler',\n",
       "                                                  Pipeline(steps=[('power',\n",
       "                                                                   PowerTransformer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['Age']),\n",
       "                                                 ('scaler', StandardScaler(),\n",
       "                                                  Index(['FCVC', 'NCP', 'CH2O', 'FAF', 'TUE'], dtype='object')),\n",
       "                                                 ('oneHotEncoder',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
       "                                                  ['Gender', 'MTRANS',\n",
       "                                                   'family_history_with_overweight',\n",
       "                                                   'FAVC', 'SMOKE', 'SCC']),\n",
       "                                                 ('ordinalEncoder',\n",
       "                                                  OrdinalEncoder(categories=[['no',\n",
       "                                                                              'Sometimes',\n",
       "                                                                              'Frequently',\n",
       "                                                                              'Always'],\n",
       "                                                                             ['no',\n",
       "                                                                              'Sometimes',\n",
       "                                                                              'Frequently',\n",
       "                                                                              'Always']]),\n",
       "                                                  ['CAEC', 'CALC'])])),\n",
       "                ('dTree', DecisionTreeClassifier())])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Résultat de la Cross-validation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.71005917 0.74556213 0.73964497 0.75147929 0.73372781 0.79289941\n",
      " 0.73372781 0.73372781 0.73809524 0.74404762]\n",
      "0.7422971259509721\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cv_dt = cross_val_score(pipeline, X_train, Y_train, cv=10)\n",
    "\n",
    "print(cv_dt)\n",
    "print(cv_dt.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mesures sans optimisations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy sur les données de validation : 0.7163120567375887\n",
      "Rappel sur les données de validation : 0.7122110007689224\n",
      "F1 score sur les données de validation : 0.7123232388481491\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score, f1_score\n",
    "pipeline.fit(X_train, Y_train)\n",
    "\n",
    "# 0s\n",
    "y_pred_val = pipeline.predict(X_test)\n",
    "\n",
    "acc_val = accuracy_score(Y_test, y_pred_val)\n",
    "print(f\"Accuracy sur les données de validation : {acc_val}\")\n",
    "\n",
    "# Calculer le rappel\n",
    "recall_val = recall_score(Y_test, y_pred_val, average='macro')\n",
    "print(f\"Rappel sur les données de validation : {recall_val}\")\n",
    "\n",
    "# Calculer le F1 score\n",
    "f1_val = f1_score(Y_test, y_pred_val, average='macro')\n",
    "print(f\"F1 score sur les données de validation : {f1_val}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Optimisation et mesures avec GridSearch**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Params: {'dTree__criterion': 'entropy', 'dTree__max_depth': 20, 'dTree__min_samples_leaf': 1, 'dTree__min_samples_split': 2} \\nScore: 0.714523058861972\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {\n",
    "    \"dTree__criterion\": [\"gini\", \"entropy\", 'log_loss'],\n",
    "    \"dTree__max_depth\": [None, 10, 20, 30],\n",
    "    \"dTree__min_samples_split\": [2, 5, 10],\n",
    "    \"dTree__min_samples_leaf\": [1, 2, 4]\n",
    "}\n",
    "\n",
    "# 3s\n",
    "grid_search = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=3, n_jobs=-1, scoring='f1_macro')\n",
    "grid_search.fit(X_train, Y_train)\n",
    "display(f'Params: {grid_search.best_params_} \\nScore: {grid_search.best_score_}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7163120567375887\n",
      "Recall : 0.7069045814612317\n"
     ]
    }
   ],
   "source": [
    "# Évaluer le modèle DecisionTreeClassifier optimisé sur les données de validation\n",
    "# 0s\n",
    "Y_pred = grid_search.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(Y_test, Y_pred)\n",
    "print(f'Accuracy: {accuracy}')\n",
    "recall_val = recall_score(Y_test, Y_pred, average='macro')\n",
    "print(f\"Recall : {recall_val}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Optimisation et mesures avec RandomSearch**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Params: {'dTree__criterion': 'entropy', 'dTree__max_depth': 20, 'dTree__min_samples_leaf': 1, 'dTree__min_samples_split': 2} \\nScore: 0.714523058861972\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint as sp_randint\n",
    "\n",
    "# Définir le dictionnaire de grille\n",
    "param_distribs = {\n",
    "    \"dTree__criterion\": [\"gini\", \"entropy\"],\n",
    "    \"dTree__max_depth\": sp_randint(10, 100),\n",
    "    \"dTree__min_samples_split\": sp_randint(2, 20),\n",
    "    \"dTree__min_samples_leaf\": sp_randint(1, 10)\n",
    "}\n",
    "\n",
    "# Effectuer la recherche aléatoire\n",
    "random_search = RandomizedSearchCV(\n",
    "    estimator=pipeline,\n",
    "    param_distributions=param_distribs,\n",
    "    scoring=\"f1_macro\",\n",
    "    cv=3,\n",
    "    n_iter=100,\n",
    "    n_jobs=-1,\n",
    ")\n",
    "\n",
    "# Entraîner le pipeline sur les données d'entraînement\n",
    "# 3s\n",
    "random_search.fit(X_train, Y_train)\n",
    "display(f'Params: {grid_search.best_params_} \\nScore: {grid_search.best_score_}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.723404255319149\n",
      "Recall : 0.7149041961269834\n"
     ]
    }
   ],
   "source": [
    "# 0s\n",
    "Y_pred = random_search.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(Y_test, Y_pred)\n",
    "print(f'Accuracy: {accuracy}')\n",
    "# Calculer le rappel\n",
    "recall_val = recall_score(Y_test, Y_pred, average='macro')\n",
    "print(f\"Recall : {recall_val}\")\n",
    "bestDTree = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **SVC Linéaire**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy sur les données de validation : 0.5910165484633569\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "\n",
    "pipeline = Pipeline(\n",
    "    steps=[\n",
    "        (\"preprocessor\", preprocessor),\n",
    "        (\"svcl\", LinearSVC())\n",
    "        # (\"voting\", model)\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Entraîner le modèle LinearSVC sur les données d'entraînement\n",
    "pipeline.fit(X_train, Y_train)\n",
    "\n",
    "# Évaluer le modèle LinearSVC sur les données de validation\n",
    "# 0s\n",
    "y_pred_val = pipeline.predict(X_test)\n",
    "acc_val = accuracy_score(Y_test, y_pred_val)\n",
    "print(f\"Accuracy sur les données de validation : {acc_val}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Optimisation et mesures avec GridSearch**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Mickael\\.virtualenvs\\PS-407efJxD\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"Params: {'svcl__C': 10, 'svcl__max_iter': 1000, 'svcl__penalty': 'l1'} \\nScore: 0.5308952967721933\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_grid = {\n",
    "    \"svcl__C\": [0.01, 0.1, 1, 10, 100],\n",
    "    \"svcl__penalty\": [\"l1\", \"l2\"],\n",
    "    \"svcl__max_iter\":[1000, 3000]\n",
    "}\n",
    "\n",
    "# 5s\n",
    "grid_search = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=3, n_jobs=-1, scoring='f1_macro')\n",
    "grid_search.fit(X_train, Y_train)\n",
    "display(f'Params: {grid_search.best_params_} \\nScore: {grid_search.best_score_}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5933806146572104\n",
      "Rappel sur les données de validation : 0.5855278176984836\n"
     ]
    }
   ],
   "source": [
    "# Évaluer le modèle DecisionTreeClassifier optimisé sur les données de validation\n",
    "# 0s\n",
    "Y_pred = grid_search.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(Y_test, Y_pred)\n",
    "print(f'Accuracy: {accuracy}')\n",
    "# Calculer le rappel\n",
    "recall_val = recall_score(Y_test, Y_pred, average='macro')\n",
    "print(f\"Rappel sur les données de validation : {recall_val}\")\n",
    "bestSvcl = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On ne test pas la recherche aléatoire sur ce modèle puisque de toute façons les résultats sont beaucoup moins bon que pour les 2 précédents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9 - Comparaison des modèles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"RandomForest Accuracy: 0.7966903073286052, Rappel : 0.7933561827229394, Precision : 0.7896558831428949, f1 : 0.78996896078442, Prediction Time : (0.01645135299989488, 'seconds')\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\"DecisionTree Accuracy: 0.7163120567375887, Rappel : 0.7069045814612317, Precision : 0.7103020148998179, f1 : 0.7081393465391412, Prediction Time : (0.006635948200011626, 'seconds')\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\"LinearSVC Accuracy: 0.5933806146572104, Rappel : 0.5855278176984836, Precision : 0.5755115339756129, f1 : 0.5621789677754875, Prediction Time : (0.006685978800058365, 'seconds')\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score, precision_score\n",
    "\n",
    "\n",
    "for model, name in [(bestRforest, \"RandomForest\"), (bestDTree, \"DecisionTree\"), (bestSvcl, \"LinearSVC\")]:\n",
    "    Y_pred = model.predict(X_test)\n",
    "    accuracy_val = accuracy_score(Y_test, Y_pred)\n",
    "    recall_val = recall_score(Y_test, Y_pred, average='macro')\n",
    "    precision_val = precision_score(Y_test, Y_pred, average='macro')\n",
    "    f1_val = f1_score(Y_test, Y_pred, average='macro')\n",
    "\n",
    "    onerow = X_test.head(1)\n",
    "    timeToPredict = timeit.timeit(lambda: model.predict(onerow), number=500)/500, \"seconds\"\n",
    "\n",
    "    display(f'{name} Accuracy: {accuracy_val}, Rappel : {recall_val}, Precision : {precision_val}, f1 : {f1_val}, Prediction Time : {timeToPredict}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On observe que parmis nos modèles le RandomForest est le meilleur. Cependant, le DecisionTree a un temps d'entrainement beaucoup plus court ce qui implique aussi de plus faible émission de co2 dans notre cas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1sMTGxuLq1avw9PT8XwADA3h6euL8+fNaxYiOjkZcXByyZs0KAAgLC0NERIRGTFtbW1SoUCHdmDExMfjw4YPGxhhjjLGfl6iE5c2bN0hISICDg4PGfgcHB0RERGgVY8SIEXB0dBQSFPXjxMScPn06bG1thc3Z2VnMP4MxxhhjP5jvOktoxowZ2Lp1K/bs2QMzMzPJcUaNGoWoqChhe/LkiR5byRhjjDGlMRJz5+zZs8PQ0BAvX77U2P/y5UvkzJkzw8fOmTMHM2bMwLFjx1CiRAlhv/pxL1++RK5cuTRienh4pBnL1NQUpqamYprOGGOMsR+YqB4WExMTlClTBgEBAcK+xMREBAQEoFKlSuk+btasWZg8eTL8/f1RtmxZjWP58uVDzpw5NWJ++PABFy9ezDAmY4wxxn4donpYAGDw4MHo1KkTypYti/Lly2PBggX4/PkzunTpAgDo2LEjnJycMH36dADAzJkzMW7cOGzZsgUuLi7CuBQrKytYWVlBpVJh4MCBmDJlCgoWLIh8+fJh7NixcHR0hLe3t/7+pYwxxhj7YYlOWNq0aYPXr19j3LhxiIiIgIeHB/z9/YVBs+Hh4TAw+F/HzbJlyxAbG4uWLVtqxBk/fjwmTJgAABg+fDg+f/6Mnj17IjIyElWrVoW/v79O41wYY4wx9vMQXYdFibgOC2OMMfbjka0OC2OMMcZYZuCEhTHGGGOKxwkLY4wxxhSPExbGGGOMKR4nLIwxxhhTPE5YGGOMMaZ4nLAwxhhjTPE4YWGMMcaY4nHCwhhjjDHF44SFMcYYY4rHCQtjjDHGFI8TFsYYY4wpHicsjDHGGFM8TlgYY4wxpnicsDDGGGNM8ThhYYwxxpjiccLCGGOMMcXjhIUxxhhjiscJC2OMMcYUjxMWxhhjjCkeJyyMMcYYUzxOWBhjjDGmeJywMMYYY0zxOGFhjDHGmOJxwsIYY4wxxeOEhTHGGGOKxwkLY4wxxhSPExbGGGOMKR4nLIwxxhhTPE5YGGOMMaZ4nLAwxhhjTPE4YWGMMcaY4nHCwhhjjDHF44SFMcYYY4rHCQtjjDHGFI8TFsYYY4wpHicsjDHGGFM8TlgYY4wxpnicsDDGGGNM8ThhYYwxxpjiccLCGGOMMcXjhIUxxhhjiscJC2OMMcYUjxMWxhhjjCkeJyyMMcYYUzxOWBhjjDGmeJywMMYYY0zxOGFhjDHGmOJxwsIYY4wxxeOEhTHGGGOKxwkLY4wxxhSPExbGGGOMKR4nLIwxxhhTPE5YGGOMMaZ4nLAwxhhjTPE4YWGMMcaY4nHCwhhjjDHF44SFMcYYY4rHCQtjjDHGFI8TFsYYY4wpHicsjDHGGFM8TlgYY4wxpnicsDDGGGNM8ThhYYwxxpjiccLCGGOMMcXjhIUxxhhjiscJC2OMMcYUT1LCsnTpUri4uMDMzAwVKlTApUuX0r3v7du30aJFC7i4uEClUmHBggWp7jNhwgSoVCqNrUiRIlKaxhhjjLGfkOiEZdu2bRg8eDDGjx+Pa9euoWTJkvDy8sKrV6/SvH90dDTy58+PGTNmIGfOnOnGLVq0KF68eCFsZ86cEds0xhhjjP2kRCcs8+bNQ48ePdClSxe4u7tj+fLlsLCwwJo1a9K8f7ly5TB79mz8/vvvMDU1TTeukZERcubMKWzZs2cX2zTGGGOM/aREJSyxsbG4evUqPD09/xfAwACenp44f/68Tg0JDg6Go6Mj8ufPDx8fH4SHh6d735iYGHz48EFjY4wxxtjPS1TC8ubNGyQkJMDBwUFjv4ODAyIiIiQ3okKFCli3bh38/f2xbNkyhIWFoVq1avj48WOa958+fTpsbW2FzdnZWfJzM8YYY0z5FDFLqEGDBmjVqhVKlCgBLy8v/Pvvv4iMjMT27dvTvP+oUaMQFRUlbE+ePPnOLWaMMcbY92Qk5s7Zs2eHoaEhXr58qbH/5cuXGQ6oFcvOzg6FChVCSEhImsdNTU0zHA/DGGOMsZ+LqB4WExMTlClTBgEBAcK+xMREBAQEoFKlSnpr1KdPn/Dw4UPkypVLbzEZY4wx9uMS1cMCAIMHD0anTp1QtmxZlC9fHgsWLMDnz5/RpUsXAEDHjh3h5OSE6dOnA0gaqHvnzh3h/589e4agoCBYWVnB1dUVADB06FA0adIEefPmxfPnzzF+/HgYGhqibdu2+vp3MsYYY+wHJjphadOmDV6/fo1x48YhIiICHh4e8Pf3FwbihoeHw8Dgfx03z58/R6lSpYTbc+bMwZw5c1CjRg0EBgYCAJ4+fYq2bdvi7du3sLe3R9WqVXHhwgXY29vr+M9jjDHG2M9ARUSU2Y3Q1YcPH2Bra4uoqCjY2NhoHHMZeVDrOI9mNNJ30xhjjDGWjox+v1NSxCwhxhhjjLGMcMLCGGOMMcXjhIUxxhhjiscJC2OMMcYUjxMWxhhjjCkeJyyMMcYYUzxOWBhjjDGmeJywMMYYY0zxOGFhjDHGmOJxwsIYY4wxxeOEhTHGGGOKxwkLY4wxxhSPExbGGGOMKR4nLIwxxhhTPE5YGGOMMaZ4nLAwxhhjTPE4YWGMMcaY4nHCwhhjjDHF44SFMcYYY4rHCQtjjDHGFI8TFsYYY4wpHicsjDHGGFM8TlgYY4wxpnicsDDGGGNM8ThhYYwxxpjiccLCGGOMMcXjhIUxxhhjiscJC2OMMcYUjxMWxhhjjCkeJyyMMcYYUzxOWBhjjDGmeJywMMYYY0zxOGFhjDHGmOJxwsIYY4wxxeOEhTHGGGOKxwkLY4wxxhSPExbGGGOMKR4nLIwxxhhTPE5YGGOMMaZ4nLAwxhhjTPE4YWGMMcaY4nHCwhhjjDHF44SFMcYYY4rHCQtjjDHGFI8TFsYYY4wpHicsjDHGGFM8TlgYY4wxpnicsDDGGGNM8ThhYYwxxpjiccLCGGOMMcXjhIUxxhhjiscJC2OMMcYUjxMWxhhjjCkeJyyMMcYYUzxOWBhjjDGmeJywMMYYY0zxOGFhjDHGmOJxwsIYY4wxxeOEhTHGGGOKxwkLY4wxxhSPExbGGGOMKR4nLIwxxhhTPE5YGGOMMaZ4nLAwxhhjTPE4YWGMMcaY4nHCwhhjjDHF44SFMcYYY4onKWFZunQpXFxcYGZmhgoVKuDSpUvp3vf27dto0aIFXFxcoFKpsGDBAp1jMsYYY+zXIjph2bZtGwYPHozx48fj2rVrKFmyJLy8vPDq1as07x8dHY38+fNjxowZyJkzp15iMsYYY+zXIjphmTdvHnr06IEuXbrA3d0dy5cvh4WFBdasWZPm/cuVK4fZs2fj999/h6mpqV5iMsYYY+zXIiphiY2NxdWrV+Hp6fm/AAYG8PT0xPnz5yU1QErMmJgYfPjwQWNjjDHG2M9LVMLy5s0bJCQkwMHBQWO/g4MDIiIiJDVASszp06fD1tZW2JydnSU9N2OMMcZ+DD/kLKFRo0YhKipK2J48eZLZTWKMMcaYjIzE3Dl79uwwNDTEy5cvNfa/fPky3QG1csQ0NTVNdzwMY4wxxn4+onpYTExMUKZMGQQEBAj7EhMTERAQgEqVKklqgBwxGWOMMfZzEdXDAgCDBw9Gp06dULZsWZQvXx4LFizA58+f0aVLFwBAx44d4eTkhOnTpwNIGlR7584d4f+fPXuGoKAgWFlZwdXVVauYjDHGGPu1iU5Y2rRpg9evX2PcuHGIiIiAh4cH/P39hUGz4eHhMDD4X8fN8+fPUapUKeH2nDlzMGfOHNSoUQOBgYFaxWSMMcbYr01FRJTZjdDVhw8fYGtri6ioKNjY2Ggccxl5UOs4j2Y00nfTGGOMMZaOjH6/U/ohZwkxxhhj7NfCCQtjjDHGFI8TFsYYY4wpHicsjDHGGFM8TlgYY4wxpnicsDDGGGNM8ThhYYwxxpjiccLCGGOMMcXjhIUxxhhjiscJC2OMMcYUjxMWxhhjjCkeJyyMMcYYUzxOWBhjjDGmeJywMMYYY0zxOGFhjDHGmOJxwsIYY4wxxeOEhTHGGGOKxwkLY4wxxhSPExbGGGOMKR4nLIwxxhhTPE5YGGOMMaZ4nLAwxhhjTPE4YWGMMcaY4nHCwhhjjDHF44SFMcYYY4rHCQtjjDHGFM8osxvwI3IZeVDr+z6a0UjGljDGGGO/Bu5hYYwxxpjicQ+LgnDPDWOMMZY2Tlh+AdomQpwEMcYYUyq+JMQYY4wxxeOEhTHGGGOKxwkLY4wxxhSPExbGGGOMKR4nLIwxxhhTPE5YGGOMMaZ4nLAwxhhjTPE4YWGMMcaY4nHCwhhjjDHF44SFMcYYY4rHCQtjjDHGFI8TFsYYY4wpHicsjDHGGFM8Xq2ZSaLtCtAArwLNGGNMd9zDwhhjjDHF44SFMcYYY4rHCQtjjDHGFI8TFsYYY4wpHicsjDHGGFM8TlgYY4wxpnicsDDGGGNM8ThhYYwxxpjiceE4pijaFqTjYnSMMfZr4R4WxhhjjCkeJyyMMcYYUzxOWBhjjDGmeJywMMYYY0zxOGFhjDHGmOJxwsIYY4wxxeOEhTHGGGOKxwkLY4wxxhSPC8exn562xegALkjHGGNKxQkLYxJxIsQYY98PXxJijDHGmOJxwsIYY4wxxeOEhTHGGGOKxwkLY4wxxhRPUsKydOlSuLi4wMzMDBUqVMClS5cyvP+OHTtQpEgRmJmZoXjx4vj33381jnfu3BkqlUpjq1+/vpSmMcYYY+wnJDph2bZtGwYPHozx48fj2rVrKFmyJLy8vPDq1as073/u3Dm0bdsW3bp1w/Xr1+Ht7Q1vb2/8999/GverX78+Xrx4IWx+fn7S/kWMMcYY++mITljmzZuHHj16oEuXLnB3d8fy5cthYWGBNWvWpHn/hQsXon79+hg2bBjc3NwwefJklC5dGkuWLNG4n6mpKXLmzClsWbJkkfYvYowxxthPR1TCEhsbi6tXr8LT0/N/AQwM4OnpifPnz6f5mPPnz2vcHwC8vLxS3T8wMBA5cuRA4cKF0bt3b7x9+zbddsTExODDhw8aG2OMMcZ+XqISljdv3iAhIQEODg4a+x0cHBAREZHmYyIiIr55//r162PDhg0ICAjAzJkzcfLkSTRo0AAJCQlpxpw+fTpsbW2FzdnZWcw/gzHGGGM/GEVUuv3999+F/y9evDhKlCiBAgUKIDAwEHXq1El1/1GjRmHw4MHC7Q8fPnDSwhhjjP3ERPWwZM+eHYaGhnj58qXG/pcvXyJnzpxpPiZnzpyi7g8A+fPnR/bs2RESEpLmcVNTU9jY2GhsjDHGGPt5iUpYTExMUKZMGQQEBAj7EhMTERAQgEqVKqX5mEqVKmncHwCOHj2a7v0B4OnTp3j79i1y5colpnmMMcYY+0mJniU0ePBgrFq1CuvXr8fdu3fRu3dvfP78GV26dAEAdOzYEaNGjRLuP2DAAPj7+2Pu3Lm4d+8eJkyYgCtXrqBv374AgE+fPmHYsGG4cOECHj16hICAADRr1gyurq7w8vLS0z+TMcYYYz8y0WNY2rRpg9evX2PcuHGIiIiAh4cH/P39hYG14eHhMDD4Xx5UuXJlbNmyBWPGjMFff/2FggULYu/evShWrBgAwNDQEDdv3sT69esRGRkJR0dH1KtXD5MnT4apqame/pmMMcYY+5FJGnTbt29foYckpcDAwFT7WrVqhVatWqV5f3Nzcxw+fFhKMxhjjDH2i+C1hBhjjDGmeJywMMYYY0zxOGFhjDHGmOIponAcYyyJy8iDWt/30YxGMraEMcaUhXtYGGOMMaZ43MPC2C+Ae24YYz867mFhjDHGmOJxwsIYY4wxxeOEhTHGGGOKxwkLY4wxxhSPExbGGGOMKR4nLIwxxhhTPE5YGGOMMaZ4nLAwxhhjTPE4YWGMMcaY4nHCwhhjjDHF44SFMcYYY4rHawkxxiTTdo0iMesT8bpHjLG0cA8LY4wxxhSPExbGGGOMKR4nLIwxxhhTPB7Dwhj7Jcgx3oYx9v1wDwtjjDHGFI8TFsYYY4wpHicsjDHGGFM8TlgYY4wxpnicsDDGGGNM8ThhYYwxxpjiccLCGGOMMcXjOiyMMSYRr3vE2PfDPSyMMcYYUzxOWBhjjDGmeJywMMYYY0zxOGFhjDHGmOJxwsIYY4wxxeOEhTHGGGOKxwkLY4wxxhSPExbGGGOMKR4nLIwxxhhTPK50yxhjCsMVdBlLjRMWxhj7BXASxH50fEmIMcYYY4rHCQtjjDHGFI8TFsYYY4wpHicsjDHGGFM8TlgYY4wxpnicsDDGGGNM8ThhYYwxxpjiccLCGGOMMcXjhIUxxhhjiscJC2OMMcYUjxMWxhhjjCkeJyyMMcYYUzxOWBhjjDGmeJywMMYYY0zxjDK7AYwxxn5cLiMPan3fRzMaydgS9rPjhIUxxpiicBLE0sKXhBhjjDGmeJywMMYYY0zx+JIQY4yxXwJfavqxcQ8LY4wxxhSPExbGGGOMKR4nLIwxxhhTPE5YGGOMMaZ4nLAwxhhjTPF4lhBjjDGmA21nH4mZecQzmlLjHhbGGGOMKZ6khGXp0qVwcXGBmZkZKlSogEuXLmV4/x07dqBIkSIwMzND8eLF8e+//2ocJyKMGzcOuXLlgrm5OTw9PREcHCylaYwxxhj7CYlOWLZt24bBgwdj/PjxuHbtGkqWLAkvLy+8evUqzfufO3cObdu2Rbdu3XD9+nV4e3vD29sb//33n3CfWbNmYdGiRVi+fDkuXrwIS0tLeHl54evXr9L/ZYwxxhj7aYgewzJv3jz06NEDXbp0AQAsX74cBw8exJo1azBy5MhU91+4cCHq16+PYcOGAQAmT56Mo0ePYsmSJVi+fDmICAsWLMCYMWPQrFkzAMCGDRvg4OCAvXv34vfff9fl38cYY4yx//cjj40RlbDExsbi6tWrGDVqlLDPwMAAnp6eOH/+fJqPOX/+PAYPHqyxz8vLC3v37gUAhIWFISIiAp6ensJxW1tbVKhQAefPn+eEhTHGGFOw75UEiUpY3rx5g4SEBDg4OGjsd3BwwL1799J8TERERJr3j4iIEI6r96V3n5RiYmIQExMj3I6KigIAfPjwIdV9E2OiM/onaUjr8WmRI6YS4nJbMz/uj9RWMXG5rZkfl9ua+XF/pLaKiatLTPVtIvr2g0mEZ8+eEQA6d+6cxv5hw4ZR+fLl03yMsbExbdmyRWPf0qVLKUeOHEREdPbsWQJAz58/17hPq1atqHXr1mnGHD9+PAHgjTfeeOONN95+gu3JkyffzEFE9bBkz54dhoaGePnypcb+ly9fImfOnGk+JmfOnBneX/3fly9fIleuXBr38fDwSDPmqFGjNC4zJSYm4t27d8iWLRtUKlWG/4YPHz7A2dkZT548gY2NTYb3FUOOuD9SW+WKy23ltv5IbZUrLreV2ypX3MxuKxHh48ePcHR0/GZMUQmLiYkJypQpg4CAAHh7ewNIShYCAgLQt2/fNB9TqVIlBAQEYODAgcK+o0ePolKlSgCAfPnyIWfOnAgICBASlA8fPuDixYvo3bt3mjFNTU1hamqqsc/Ozk7MPwU2NjZ6fXPkjPsjtVWuuNxWbuuP1Fa54nJbua1yxc3Mttra2moVS/QsocGDB6NTp04oW7YsypcvjwULFuDz58/CrKGOHTvCyckJ06dPBwAMGDAANWrUwNy5c9GoUSNs3boVV65cwcqVKwEAKpUKAwcOxJQpU1CwYEHky5cPY8eOhaOjo5AUMcYYY+zXJjphadOmDV6/fo1x48YhIiICHh4e8Pf3FwbNhoeHw8Dgf+VdKleujC1btmDMmDH466+/ULBgQezduxfFihUT7jN8+HB8/vwZPXv2RGRkJKpWrQp/f3+YmZnp4Z/IGGOMsR+dpLWE+vbtm+4loMDAwFT7WrVqhVatWqUbT6VSYdKkSZg0aZKU5ohiamqK8ePHp7qkpMS4P1Jb5YrLbeW2/khtlSsut5XbKlfcH6mtKiJt5hIxxhhjjGUeXvyQMcYYY4rHCQtjjDHGFI8TFsYYY4wpHicsjDHGGFM8TlgkevToEVatWoWlS5fiv//+y+zmMJbpQkJCcPjwYXz58gUAtFsbhGWaH+n9+lHaGh8fj2PHjmHFihX4+PEjAOD58+f49OmTzrG/fv2qc4zvSZb2frN4P0vl+PHjZGFhQSqVilQqFRkbG9PGjRt1ivngwQP6/fffKSoqKtWxyMhIatu2LT18+FARcZ2dnenNmzfC7cWLF6cZXwltdXNzo7dv3wq3e/fuTa9fvxZuv3z5kszNzSW3OTo6mvbt20ezZ8+m2bNn0759+yg6OlpyvG8JDw+nLl26iH7c69evaebMmeTt7U0VK1akihUrkre3N82aNYtevXqlU5vevHlDderUIZVKRQYGBsJ71KVLFxo8eLDkuBs2bKDKlStTrly56NGjR0RENH/+fNq7d6/kmPHx8bR69Wpq27Yt1alTh2rVqqWxSXXq1Cny8fGhihUr0tOnT4X2nz59WnJMIqKQkBAaPXo0/f777/Ty5UsiIvr333/pv//+kxzzR3q/5Girh4cHlSpVKtVWunRpqly5MnXs2JGOHz8uOu6jR4+oSJEiZGFhQYaGhkJb+/fvT7169ZLU1oSEBJo0aRI5OjpqxBwzZgytXr1aUky19+/f06pVq2jkyJHCd+TVq1eFz6/S2ktE9Ev1sDx8+BBjxoxB27Zt8erVKwDAoUOHcPv2bVFxxo4di7p16+LZs2d4+/YtevTogeHDh+vUttmzZ8PZ2TnNEsa2trZwdnbG7NmzFRH36dOnSEhIEG7/9ddfePPmjei2fY+23rt3D/Hx8cLtTZs2aawWSkSSzwT++ecf5M2bF97e3hg+fDiGDx8Ob29v5M2bF/v375cU81vevXuH9evXi3rM5cuXUahQISxatAi2traoXr06qlevDltbWyxatAhFihTBlStXJLdp0KBBMDIyQnh4OCwsLIT9bdq0gb+/v6SYy5Ytw+DBg9GwYUNERkYKnzc7OzssWLBAclsHDBiAAQMGICEhAcWKFUPJkiU1Nil27doFLy8vmJub4/r168JK8lFRUZg2bZrktp48eRLFixfHxYsXsXv3buEs/caNGxg/frzkuD/S+yVHW+vXr4/Q0FBYWlqiVq1aqFWrFqysrPDw4UOUK1cOL168gKenJ/bt2ycq7oABA1C2bFm8f/8e5ubmwv7mzZsjICBAUlunTJmCdevWYdasWTAxMRH2FytWDKtXr5YUEwBu3ryJQoUKYebMmZgzZw4iIyMBALt378aoUaMkx5WrvQKdU54fRGBgIJmbm5OnpyeZmJgImd/06dOpRYsWomLZ2trS7du3hdufP38mQ0NDjV4HsQoVKkSXLl1K9/iVK1eoUKFCioirUqmEsz0iIisrK0m9PyllRlsjIiLIwMBAdFvPnj1LxsbG1KJFCzp37hy9f/+e3r9/T2fPnqXffvuNTExM6Pz586Lj7tu3L8Nt/vz5ottboUIF6tmzJyUmJqY6lpiYSD179qSKFSuKbquag4MDBQUFEZHm6/vw4UOytLSUFNPNzY327NmTKuatW7coW7ZsktuaLVs2OnjwoOTHp8XDw4PWr19PRJptvXbtGjk4OEiOW7FiRZo7d26quBcvXiQnJyfJcX+k90uOtnbv3p0mTZqUav/kyZOpe/fuREQ0btw4KlOmjKi4WbNmpXv37qVqa1hYmORe3AIFCtCxY8dSxbx79y7Z2dlJiklEVKdOHRo2bFiquGfPnqW8efNKjitXe9V+mYRFn3/8KX8EU8aUwszMTOhGTcujR48kfejliCtXwpIZbZWasDRo0IB69uyZ7vGePXtSgwYNRMdVd32rLzemtYltr5mZGd29ezfd43fv3iUzMzPRbVWzsrKiBw8eCP+vfn0vX75MWbNmlRQz+WchecwHDx7o1NZcuXLR/fv3JT8+Lebm5hQWFkZEqX9UTU1NJce1tLSk0NDQVHHDwsJ0ivsjvV9ytNXGxoaCg4NT7Q8ODiYbGxsiSvqbsLKyEhXXzs5OOJFN3tbTp09Tjhw5JLU1vdf19u3bkhM2oqTXICQkJFXcR48e6fTZkqu9ar/MJaFbt26hefPmqfbnyJFD0uWMw4cP459//hE29arVyfeJYWtri4cPH6Z7PCQkRNJKmnLFXb16NRYtWoRFixYhPj4e69atE26rNyW0VaVSQaVSpdqnqwsXLqS7PAUA/Pnnnzh//rzouLly5cLu3buRmJiY5nbt2jXRMXPmzIlLly6le/zSpUvCWmBSVKtWDRs2bBBuq1QqJCYmYtasWahVq5akmPny5UNQUFCq/f7+/nBzc5PaVAwZMgQLFy7U66DNnDlzIiQkJNX+M2fOIH/+/JLj2tnZ4cWLF6n2X79+HU5OTpLj/kjvlxxtNTMzw7lz51LtP3funLB+XWJioui17OrVq6dx+UulUuHTp08YP348GjZsKKmt7u7uOH36dKr9O3fuRKlSpSTFBJLK5ie/NK724MED2NvbS44rV3sFOqc8PwgnJyc6e/YsEWlmfrt376b8+fOLipXR2a/Us+BWrVqRt7d3usebNm1KLVu2FBVTrrh58+YlFxeXDLd8+fIpoq0qlYqKFy8uDKwzNDSkokWLCreLFy8uqYdFm94gKWeWTZo0obFjx6Z7PCgoiFQqlaiYS5YsIVNTU+rfvz/t27ePLly4QBcuXKB9+/ZR//79ydzcnJYuXSq6rWq3bt2iHDlyUP369cnExIRatmxJbm5u5ODgIJzFibVq1SpycnKirVu3kqWlJfn5+dGUKVOE/5fK29ubbG1tKV++fNS4cWNq3ry5xibFtGnTyN3dnS5cuEDW1tZ0+vRp2rRpE9nb29OiRYskt3XIkCFUtWpVevHiBVlbW1NwcDCdOXOG8ufPTxMmTJAc90d6v+Ro6+TJk8nc3Jz69+9PGzdupI0bN1L//v3JwsKCpkyZQkRE8+bNI09PT1Fxnzx5Qu7u7uTm5kZGRkZUsWJFypYtGxUuXDhVj7y29u7dS7a2tjRjxgyysLCg2bNnU/fu3cnExISOHDkiKSYRUbdu3cjb25tiY2PJysqKQkND6fHjx1SqVCkaMGCA5LhytVftl0lY5Prj15dr166RqakptWjRgi5evEiRkZEUGRlJFy5coN9++41MTU3p6tWriokrBznaOmHCBK02sYoXL05r1qxJ97ivry8VL15cdNxTp07RoUOH0j3+6dMnCgwMFB1369atVKFCBTIyMhKSaiMjI6pQoQJt27ZNdLyUIiMjacqUKdSqVStq0KABjR49mp4/f65TzE2bNpGrq6vQXicnJ51nGnTu3DnDTYrExEThx1ndVjMzMxozZoxObY2JiaHu3bsL75mxsTEZGBhQ+/btKT4+XqfYP8r7JWdbK1asSFmyZKEsWbJQxYoVafPmzcLx6Oho+vLli+i4cXFxtGnTJho2bBj17t2bVq1apfOswVOnTpGnpyfZ29uTubk5ValShQ4fPqxTzMjISPL09CQ7OzsyNDQkZ2dnMjY2purVq9OnT58U1161XyZhkfOPX1/2799P9vb2ZGBgoLHZ29vTvn37FBc3Pe/fv6fFixdLeuz3bqtU8+bNo6xZs6Y5gPPAgQOULVs2YcyUnJ48eUIJCQla3z82NpaeP39Oz58/p9jYWL3ElNvnz58ln6F+TzExMXT79m26ePEiffz4UW9xw8PD6eDBg7Rt2zZhPIeS/SjvFyM6c+YMLV26lGbOnElHjx4lIkpzcL62njx5ku4xKZMQUvrlVmsODw/Hf//9h0+fPqFUqVIoWLCg6BhXr17F0KFDsW/fvlRjKqKiouDt7Y0FCxZImib55csX+Pv7IyQkBESEQoUKoV69ehpT+qSQK25yAQEB8PX1xZ49e2BhYYG3b98qtq26SkxMRJs2bbBr1y4ULlwYbm5uICLcvXsXwcHB8Pb2xo4dO2BgIO8wMRsbGwQFBek0VkLXmP7+/rCyskLVqlUBAEuXLsWqVavg7u6OpUuXIkuWLHprm768fv0a9+/fBwAULlxYp+v230tCQgJu3bqFvHnz6vSanjp1KsPj1atXlxxb3+T8bMXGxuLVq1dITEzU2J8nTx5J8davX4/s2bOjUaNGAIDhw4dj5cqVcHd3h5+fH/LmzSu5rfo2e/ZsDBs2LNX+hIQEtG/fHn5+fpLiuru748yZM8iaNavG/rNnz6JRo0bC9GnJdE55fkFt27ZNc1qc2tSpU8nHx0evz/nkyRPq0aOH6MctXryYIiMj9dqW5MLDw2nixInk4uJCBgYG1K5dOzp06FC6Z+/fm52dndDtm3xzcXGhevXq6XxddevWrdSsWTNyc3MjNzc3atasmU7X7MXS1wwtXWIWK1ZM6Gm6efMmmZiY0KhRo6hixYqSL7Oox0Glt0n16dMn6tKlCxkaGmpcGuvatSt9/vxZ6zgpx75ktEk1YMAA4ZJKfHw8ValShVQqFVlaWtKJEyckx01vzJ16k0Ku90uOz9aDBw+oatWqqXpxpYw9TK5QoUIUEBBARETnzp0jc3NzWrFiBTVp0kTy5yDle5Nyk8re3j7V5br4+Hhq2bIlFSlSRHLcLl26UJkyZejDhw/CvpMnT5KNjQ3NmzdPclw1I93SnR/H4MGD09yvUqlgZmYGV1dXNGvWLFVmmJaLFy9i5MiR6R5v0qSJforkJPP27Vv4+vpi5cqVoh43evRooaBZ9+7dUbt2bZ3bEhcXh71792L16tU4ffo06tevj9mzZ6Nt27YYPXo03N3dJcVt2LAh/Pz8YGtrCwCYMWMG/vjjD9jZ2QFIeg2qVauGO3fuaB0zvaJVkZGRuHr1Kho3boydO3eiSZMmktrcpk0btGnTRtJjfxZhYWHCe75r1y40adIE06ZNw7Vr1yTPjhg4cKDG7bi4OFy/fh3+/v5pnhlqa/DgwTh58iT279+PKlWqAEiazdO/f38MGTIEy5Yt0yqO+jMqp507d6J9+/YAgP379yM0NBT37t3Dxo0bMXr0aJw9e1ZS3Pfv32vcVr+2Y8eOxdSpUyXFlOv9kuOz1blzZxgZGeHAgQPIlSuXXmYNAsCTJ0/g6uoKANi7dy9atmyJnj17okqVKqhZs6akmHv27NG4rX5d169fj4kTJ0pu68GDB1GvXj3Y2tqiZcuWiI+PR+vWrXHv3j2cOHFCctzVq1ejZcuWaNKkCQ4fPoxz586hadOmmDJlCgYMGCA5rkDnlOcHUbNmTbKxsSFLS0sqXbo0lS5dmqysrMjW1pYqVKggnIknLwiXHlNTU6E+QlpCQ0N1qj2QlqCgIEkZdXR0NK1fv55q1qxJBgYG5OLiQpMmTaLw8HDJbbG3t6dq1arRihUr6N27d8J+IyMjrV6/9BgYGGhc+7a2ttZLzZSMzJ07lypVqqTXmN+TEnpYkv/dVKlShVasWEFEuhXMSs+SJUskn1kTJRWOS6t34vjx45Q9e3YdWqZ/pqamwpiAHj16CLM3QkNDydraWu/PFxgYSKVLl9ZrTF3fLzk+WxYWFhnWJZLK3t6erl27RkRJxQQ3bNhAREnLK+ijBklymzdvpqZNm+oUIyAggKytrWnfvn3UtGlTcnd3p4iICJ3bFhMTQ56enlS5cmWysrKSPJ4xLb9MHZZmzZrB09MTz58/x9WrV3H16lU8ffoUdevWRdu2bfHs2TNUr14dgwYN+mYse3t74fp3Wu7du4fs2bPrs/mSmZubo2PHjjhx4gSCg4PRoUMH+Pr6Il++fKhfvz527NiBuLg4UTHj4+OF+iaGhoZ6ayulGE6V8rYcGjdujHv37ol+XFxcHIYPHw5XV1eUL18ea9as0Tj+8uVLvb42Sla1alUMHjwYkydPxqVLl4Rr+A8ePEDu3Ln1+lwNGjTArl27JD8+Ojo6zZozOXLkQHR0tC5N0zsHBwfcuXMHCQkJ8Pf3R926dQEk/Rvk+Gw5ODhk+L0mha7vlxyfLXd3d70sJZJS3bp10b17d3Tv3h0PHjwQeoBu374NFxcXvT5XxYoVJZf7V6tduzY2bNiAFi1aICwsDCdPnpRUj+nmzZsa27179zBhwgQ8efIE7du3R/Xq1YVjOtNb6qNwjo6OaZ79//fff+To6EhESQs/aVNGunPnzlS1atU0jyUmJlKVKlV0OqtIi9QelrQkJibSkSNHqF27dmRhYUH29vaiHv/lyxfatGkT1apVi8zNzem3336j3bt3k7GxsU49LHJVpc3IzZs3JZVPHz9+PDk4ONDs2bNp9OjRZGtrq1H5NiIiQnS9FClS9kJlRszHjx9To0aNqESJEhrXxQcOHEj9+vXTa9tmzpypU+nw2rVrU6tWrTSmrEZHR1OrVq2oTp06WscpVaqU0LuY3mJ66k2q8ePHk62tLRUpUoTy5MlDX79+JaKkKfO6LKVw48YNjS0oKIgOHTpENWrUoCpVqkiOmxZd3y85PlsBAQFUqVIlOnHiBL1584aioqI0Nqnev39Pf/75JzVt2lSjNMG4ceOE+i76EB0dTQMGDBC9TEl6Y6xy5cpF1apVkzzuKq3q3Mlv62N8kNovM4YlKioKr169SjW+4vXr10LFPzs7O8TGxn4z1pgxY1CmTBlUqFABQ4YMQeHChQEk9azMnTsXDx48wLp160S177fffsvwuM6jq5NRqVQwMjKCSqUCEYnuYTEzM4OPjw98fHzw8OFDrF27Fv3790d8fDymTp2Kzp07o3bt2qLPAuWqSpsRX19feHh4iH7c5s2bsXr1ajRu3BhA0nXxBg0aoEuXLkJvi9xtB+TphRIbM0+ePDhw4ECq/fPnz5fchlKlSmm8fkSEiIgIvH79Gn///bfkuAsXLoSXlxdy584tzOK7ceMGzMzMcPjwYa3jNGvWDKampgAAb29vye3JyIQJE1CsWDE8efIErVq1Ep7P0NAwwzF03+Lh4SH87SdXsWLFVD2F2pLr/ZLjs+Xp6QkAqFOnjsZ+IoJKpdJY2FUMOzs7LFmyJNV+XcaaZMmSJdXr+vHjR1hYWGDTpk2iYqU37srLy0ty+4CkcUbfyy8zrdnHxwfnz5/H3LlzUa5cOQBJq9gOHToUlStXxsaNG7F161bMmTNHq5Vrr1y5gs6dO+POnTvCB4qI4O7ujrVr1wrPoa0uXbpodb+1a9eKipvckydPsHbtWqxbtw7h4eGoXr06unXrhhYtWoguQ51SYmIiDh8+DF9fX+zfvx/W1taiu10NDAzQoEED4Yt5//79qF27NiwtLQEAMTEx8Pf3F/WFkt5g66ioKFy7dg0PHjzAqVOnUKZMGVFttbCwwJ07dzS6ep89e4batWujXLlymDVrFpydnSV9+cXFxcHc3BxBQUEoVqxYhvd98uQJHB0dv5kc6jtmWmW90yNl6YeUX/IGBgawt7dHzZo1UaRIEdHxkouOjsbmzZuFS4Fubm7w8fHRWGH3Z/b48WON2+rXVpfvAH2+X3J/tk6ePJnh8Ro1amgdS8xljhIlSmh9X7WUK7OrX9cKFSooslyA3H6ZhOXTp08YNGgQNmzYgPj4eACAkZEROnXqhHnz5sHKykpYC0PMGXdQUBCCg4OFeiFSztblFBsbi927d2PNmjU4fvw4cuXKhU6dOqFr166Sa3d07NgRS5cuhbW1NYCkM1R3d3cYGxsDSOq12rhxY7rJQnrkSNpq1qyZZk+HjY0NChcujN69eyNfvnxax1PLnz8/Vq1aleos7fnz56hVqxby5s2LgIAAyWdr+fPnx549eyTV8vkeMQ0MDL7ZgyT1jDU+Ph5btmyBl5eXTmscfW9XrlzB3bt3ASSNkxCbBAMQtQZX//79RcePi4tD/fr1sXz5ckk1qNKi7/dLzs+Wvqnbmt7PqPqY1L+DadOmoWvXrnofC6ZP//zzDxo0aABjY+NvrqHXtGlTnZ7rl0lY1D59+oTQ0FAASV/gVlZWmdyibyMi+Pv7w9fXFzt37hT12KxZs+Lz589o0qQJunXrBi8vL52LmRkaGuLFixfIkSMHAHmKlyld9+7dQUTw9fVNdezZs2eoWbMmQkNDJX+h+vr6Yvfu3di4caNWU+2/d8xvnaUmJ+aMVc3CwgJ3797VS7Etub9Qnz59irZt2+Ls2bPCFPzIyEhUrlwZW7duFfVjo23yrFKphO8xsezt7XHu3Dm9JSyAft8vOT5bN2/eRLFixWBgYPDNXhExPSEpe6syIuW1sba2xq1bt/QyaLd06dIICAhAlixZUl3CS0nMIqsGBgaIiIhAjhw5Mvxt0UeC+cuMYVGzsrISPpBEhEOHDolOBCZNmqTV/caNGyepjWphYWFYs2YN1q1bh9evXwvXXsUYM2YMOnTooNdKnilz3O+R80pN2lImV/oyduzYdGcXOTk54eTJkzh69Kjk+EuWLEFISAgcHR2RN29e4bKYmpRVm/UZU0oSIkb58uVx/fp1vfwAent7C1+oGY03kfqF2r17d8TFxeHu3bvCeLb79++jS5cu6N69O/z9/bWO9T3GA7Rv3x6+vr6YMWOG3mLq8/2S47Pl4eEhfAbSG8MDiP8MyF29tnbt2jh58qReEha5xl0lrxScsmqwvv1yCQugeyKQsphPciqVCvfv38fXr18lJSwxMTHYuXMnfH19cebMGSQkJGDOnDno1q2bpOu1Xbp0waZNm9CpU6c0lxHYsGFDmseUQtf3Sq5kKm/evBl+WTk6OqJTp06S48sxkFOuwaFq0dHRCA8PTzVwXcq1+z59+mDIkCF4+vQpypQpkyq5EhNT7i/UkydP4ty5c0KyAiSV+1+8eDGqVaum9+fTVXx8PNasWYNjx46l+drOmzdPdEx9vl9p0fWzFRYWJpy0yZ0U3rlzJ822Sum9a9CgAUaOHIlbt26l+bqKiTl+/Pg0//9H8stcEtJ3IpCWoKAgjBw5EsePH0fXrl2xfPlyrR979epV+Pr6ws/PD66urujQoQPatGmD3LlzC2NEpJg8eTJu3ryJHTt2pHm8devWKFmyJEaPHq11TAMDAxw/fly4rFC5cmVs3749Vde31C8pfb5Xybsr9elblxbUdL1m+yN4/fo1unTpgkOHDqV5XMwZa9euXbFgwQLh0kpyuowHUNuwYQPatGkjnGmqxcbGYuvWrejYsaPomIUKFcKmTZtQvnx5jf2XLl1Cu3btEBISIqmtQNLlpn/++SfNH0CxiYW6tzGjyswqlQrHjx/XOqbc75c+P1tqHz58SPd7JCQkRKhWK1ZoaCiaN2+OW7duafTgqC+9iGlr7dq1sWvXLmTLli3d++jjEos+xl2pffz4EQ8ePEDhwoVhZWWFa9euYcGCBfjy5Qu8vb3h4+OjU1sB/Px1WK5cuUK9e/cmOzs7Klu2LC1cuJAiIiJ0rsqaXGhoKPn4+JCRkRG1bt1a0oqqhoaGNHDgQLp3757Gfl3bWbJkSTp27Fi6x48dO0YeHh6iYqY1714fc+7leK9UKhVNnTqVFi5cmOEmJW56dQeS79PF+/fvadWqVTRy5Eh6+/YtESXVCnr69KmiYrZr146qVKlCly9fJktLSzpy5Aht3LiRChcuTAcOHBAVS13t+NGjRxluUqWspqz25s0bye/X3r17qXz58nT58mVh3+XLl6lixYq0Z88eqU2lY8eOkYWFBRUrVoyMjIzIw8OD7OzsyNbWlmrVqiU6Xso6R/og9/ulz8+WWtWqVTXq8Kjdu3ePnJycJLe1cePG1KxZM3r9+jVZWVnRnTt36PTp01S+fHk6deqUqFhyvFfJPXnyhKpWrUoqlUpYX02lUlGVKlUyXHE5PSdPniRra2tSqVSUNWtWOnz4MFlbW1ORIkWoaNGiZGBgQCtXrtS53T99wiJXIkBE9Pr1a+rbty+ZmJhQ7dq16dKlS5Jj1atXj6ytrYXFA9VLfOvaTisrK3r8+HG6xx8/fiy6zPe3vpykfknJ8V6pVCpydnYmFxeXdDddFmdT03eJ/Bs3bpC9vT25urqSkZGREHv06NHUoUMHxcQkIsqZMyddvHiRiJKKzt2/f5+IiPbt2ye6EJncX9QqlYpevXqVan9QUBBlyZJF6zgpF9U0MTEhAwMDMjEx0fh/MTFTKleuHI0bN46I/vf5+vjxIzVt2pT+/vtv0fHkeG3lfr/0+dlSq1+/PjVo0IDi4uKEfXfu3KGcOXNS//79Jbc1W7ZsdOPGDSIisrGxEb7HAgICJJ0Uyvm6enl5UYUKFTS+a+/du0eVKlUiLy8v0fGqVatGXbt2padPn9KkSZPIzs6ORo0aJRyfPHkylSxZUud2//RjWOrUqQNfX1+8evUKHTp0gJeXl84FvT5//ow5c+Zg3rx5cHV1xf79+1GvXj2dYh4+fFiok9K7d298+fJF6LrVpb2GhoZ4/vx5ukumP3/+XPSsIbEDzfr06YNJkyZ9c7kCOd4rIKnbU9+XhOQ2ePBgdO7cGbNmzRKmjwNJC0S2a9dOMTGBpL8H9eubJUsWvH79GoUKFULx4sUlDQ7++PHjN2uCiL00qJ4VoVKpUKdOHRgZ/e+rLyEhAWFhYahfv77W8dJbVFOf7t69Cz8/PwBJJRi+fPkCKysrTJo0Cc2aNUPv3r1Fx1y9evU3Z0aKnS4tx/ulpu/PFgDs3r0bnp6e8PHxwdatW3H79m3UqVMHPj4+ksbvqCUkJAh/V9mzZ8fz589RuHBh5M2bV9KSB3fu3EFERESG95F62V3f465u3ryJlStXwsnJCSNGjMCECRM0Lj3+/vvvmDlzpqS2JvfTJyxyJAIFChTAx48f0a9fP7Rt2xYqlSrNqXJiP0zOzs4YN24cxo0bh6NHj2Lt2rUwMjJCs2bN0LJlS7Rs2RKlS5cWFbNUqVLYu3cvKlasmObxPXv2oFSpUqJiirVp0yYMHTr0mwmLHO/V96g2K4fLly9jxYoVqfY7OTl980vse8YEkr7o7t+/DxcXF5QsWRIrVqyAi4sLli9fjly5comOV6hQoXSPkcQxEeoBx0FBQfDy8tL40TYxMYGLiwtatGihdTxdBlRry9LSUhi3kitXLjx8+BBFixYFAMlr4SxfvjzDgoAqlUp0wiLH+6Wm788WkLS+2sGDB1GzZk20bt0ap06dQseOHTF79mxJ8dSKFSuGGzduIF++fKhQoQJmzZoFExMTrFy5UlLJhzp16qQ7k0nX19XZ2TnNCucJCQlwdHQUHe/Dhw/CmEYTExNYWFhonBRZW1vrZ60unftofjBHjhyhtm3bkpmZGRUsWJBGjRpFV69eFRUjo7EL+lw3gYjo3bt3tGjRIvLw8JAUc+fOnWRkZESLFy+m+Ph4YX98fDwtWrSIjI2NaceOHXppa3qkXi7R13slZ9eqmr4vCSVf+TV57CNHjlDu3LkVE5OIaOPGjbR27VoiShqHlD17djIwMCAzMzPaunWrqFgqlYp2795NgYGBGW5SrVu3Ls3xC7qKj4+nnTt30uTJk2ny5Mm0e/dujb83KZo1ayZc9x8yZAi5urrSlClTqHTp0qLWPVKT65KQnO+Xvj5bKdcLioqKonv37pGzszP17t1bL2sJ+fv7065du4iIKDg4mAoXLkwqlYqyZ89OAQEBomKpVCq6fPmybGOD9D3uysDAQONSq7W1NYWGhgq39bUO3C+XsKjpkgjINYbjW8T+WKv99ddfpFKpyMbGhjw8PMjDw4NsbGzIwMCARowYoedWpqbrj7ku79WECRPo8+fPWt+/d+/e9Pr1a7FNTPUHqqtu3bqRt7c3xcbGkpWVFYWGhtLjx4+pVKlSNGDAAEXEDAgI0BgHoPb582e6evWqpNfxeyWY+hQcHEwFCxYkCwsLYcFDCwsLKly4MIWEhEiO+/DhQ2FMxKdPn6hXr15UvHhx+u233yR9v6Q34FgXcr1f+v5sqU8iU276ONH09fUVFqZM6e3bt8J4RLHt1ffrKue4K5VKRcWLFxc+/4aGhlS0aFHhdvHixfWSsPwy05ozcu3aNeFSi7bjLcTQJmZwcDDGjRuHFStWpFkvpXfv3pgyZYrkarKXLl3C5s2bERISIiwj0K5du1RTMeVgbW2NGzdu6KUSbvL3Sg7aVu1NuShZZGQkbGxsUo0HevfunaR2REVFoWXLlrhy5Qo+fvwIR0dHREREoFKlSvj3339T1WPIjJgpi/JVrFgRu3btgpOTk+i2qck1DV0tISEB8+fPx/bt29OcKizl/WrYsCGICJs3bxa6xd++fYv27dvDwMAABw8eFBXvW0tfSCXHayvX+6Xvz5aclZlTttXR0RHnzp3TqdibHK9rynWJMiL2kqe2CzzqXP9F55TnJ2Ntba3Xrn1tY/bo0YOGDRuW7vHhw4fTH3/8odd2fS/a9rA8ePCAfv/99zS7ZSMjI6lt27Z6f29S0rat69at02rT1enTp2np0qU0c+ZMOnr0qM7x9Bkz5VmgPi6Lubi40Js3b7S+/5kzZ9I9u03L2LFjKVeuXDRnzhwyMzOjyZMnU7du3ShbtmySprcTEVlYWNDNmzdT7Q8KCiJLS0vR8VL2hOjrO0mO3ka53i85PltykaOtNWvWpPfv32t9/y1bttCnT5+0um9cXBytX7+eIiIiJLZOd2L/btU4YUlBjj8MbWIWKlQow2nRV65coUKFCol+7g4dOtCHDx+E20FBQRQbGys6ji60fU2VkLQp+YtRaZTwoyL2xzx//vxC/Q4rKyvhks3ChQupbdu2ktqQJUsWOnv2bKr9Z86ckdy9ntmvK1HmnbwR6f81uHHjhtabWEp4v8S+V+bm5rIMW9CW1M/WTz9L6EcRHh6eYfdf9uzZ8eTJE9FxN2/ejDlz5gjdy9WqVfvuCxW2b99eq2mNJ0+exKZNm9I93rp1a52m3+rT+/fv9b7kgRwr9cq5+q96mnB6t78HEnlFOyIiAsWLFweQtK5YVFQUAKBx48YYO3aspDY0btwYPXv2hK+vr3CJ9eLFi/jjjz9+6ErHYl9bfcbU92cro/WDUj6v2Jk3P+LfgT7XfpJC6meLExaFsLW1xcOHD9P9AIWEhEiqZZDygyHHl9C3bN68GcOGDfvmuCC5kjY5LFmyBDdv3kS/fv1SHbO1tcXp06fx4cMHUUsezJ8/X+P269evER0drbECsIWFBXLkyKF1ciFHTDUi0qhpEh0djSZNmsDExETjflLrZcghd+7cePHiBfLkyYMCBQrgyJEjKF26NC5fvpyqXL+2Fi1ahE6dOqFSpUrCOJP4+Hg0bdoUCxculBQzeQ0OIsK9e/fw6dMnjfvouj6Pkun7syXn+kH0/2MC1UnKp0+fUKpUKb2NZ5OD3Gs/yYUTFoWoXr06Fi9ejNq1a6d5fNGiRYpcSE0b2iZJciVtcti1axfmzp2b7vFevXph6NChohKW5F+qW7Zswd9//w1fX1+NFYB79OiBXr16ZWpMtZQD6Jo1ayY6xvfWvHlzBAQEoEKFCujXr5+wcnF4eDgGDRokKaadnR327duH4OBgYQVvNzc3yWvSAKlrcDRu3BiAfmpw/Aj0/dmSsydh7dq1ssWWy++//w5As1f1R/hs8SyhFPQ5o0VMzOvXr6NSpUpo3Lgxhg8fLvyg3Lt3D7NmzcLBgwdx7tw50TNk5F6oUBvavqatW7dGXFxcuqthN2vWDCYmJuku5KgPvXv3xuTJk7/ZG2RtbY3bt2+nW0E4PDwcxYoVw4cPHyS1o0CBAti5c2eqon5Xr15Fy5YtJZ0xyhFTjLNnz6Js2bKSezLSouvf64ULF3Du3DkULFgQTZo00Vu7dPH48WOt7id3d35mfRdKIeWzpc9VlcXw8/ND06ZNJc30S4/Y1/VbnzHFfrZ0Hj3zk/njjz8k1Y/QR8z9+/eTvb19qloB9vb2tG/fPknPLddChWJoOwjt2rVrZGpqSi1atKCLFy9SZGQkRUZG0oULF+i3334jU1NTybVotKXtYDBbW1s6f/58usfPnz9Ptra2ktthbm6e5iDsixcvkrm5uWJiipGZgzjVTp48mWZ9j7i4ODp58qSkNiQmJtL27dupd+/e1KJFC2revLnGJjeptYO+RY7Bo3J8BsTGffjwIZUoUSLVd6P6+1ZucrwGP9pkAamvgbhFZH4BmzdvlnxWrGvMxo0b4/Hjx9i5cydmzJiB6dOnY9euXXj06JHkrD8sLAyhoaEICwtLtan3h4aGSoqtb6VKlcLOnTtx6tQpVKpUCVmzZkXWrFlRuXJlnD59Gtu3b5e1Bgug/eUr9ZIH6dF1yYM6deqgV69eGtfor169it69e8PT01MxMcXQ9rWVM2atWrXSHEsQFRWFWrVqSWrDwIED0aFDB4SFhcHKygq2trYam9w2bdqk9+8sQPvB8mLI8RkQG3fAgAHIly8fXr16BQsLC9y+fRunTp1C2bJlERgYKEv7kpPjNcibN6/oOj0PHz5Ev3794OnpCU9PT/Tv3x8PHz7Ue9vSIvk10GfW9DPIrGnNYhUrVozCw8P1GpNInrM1sf/+6Oho2r17N82aNYtmzpxJe/bsEVU/QhfatlXuJQ9evXpFDRo0IJVKpVGJskGDBpIrYMoRUwwxn4NatWqlWYciKiqKatWqJbkN6a3WfP/+fdGrlqtlyZKFDh48KLlNusrMOkfR0dG0b98+jdIJalFRUbRv3z5J9TbEEvPZ0ueqylJo09aLFy9muLTD169fadu2bZLb4O/vTyYmJlS+fHkaNGgQDRo0iMqXL0+mpqZ05MgRyXFTCgwMpIMHD9K7d+/0Eo8TlhR+lIRFri5Abbvq4uLiaOLEifTkyZNv3leOy2xE8iRtYl7X77Hkwf3792nfvn20b98+un//vmJjakPMa5teafKXL1+SkZGR6OdWX54xMDCghg0balyyadq0Kbm4uJCXl5fouERJxdPu3r0r6bH6kJl1jhYsWEC1a9dO93idOnVoyZIlomJKIeazZWdnJyyjkT9/fjp+/DgREYWEhHyXS6PatPVbxQN1XZvHw8Mjze+oESNGUKlSpUTHmzFjBo0ZM0a4nZiYSF5eXsLlNgcHB/rvv/8kt1eNZwkxDaRlV52RkRFmz56Njh07fvO+y5Yt07VZaXr06FGaK45+L1OnTkWzZs00ljyoUaOGXpc8KFSoUIar4Solpr4kX/U8+dReIKmsvr+/v6Ty7OpLM0QEa2trmJubC8dMTExQsWJF9OjRQ1KbJ0yYgIkTJ2LNmjUacZVGjjpHmzdvzrB+zcCBAzFp0iT8+eefouLKSd+rKssh5fdwWt/L2n5Xp+Xu3bvYvn17qv1du3bFggULRMfbtm0bRowYIdxWX9o/ffo03Nzc0LFjR0ycODHN5xSDExYmWe3atXHy5Emd1sz40ZUvX16r5ETsGlUJCQlYt24dAgIC8OrVKyQmJmocP378uOi2yhFTDG2KaakLfKlUqjSn+Jubm2Px4sWin3vt2rXCF/zixYthZWUlOkZ6WrduDT8/P+TIkQMuLi6pxhIopQ6NHHWOgoODUbJkyXSPlyhRAsHBwaJiSiGmUNuYMWPw+fNnAMCkSZPQuHFjVKtWDdmyZcO2bdvkaqLe6VKczt7eHkFBQShYsKDG/qCgIEnrF4WFhWnMNP3333/RsmVLVKlSBUDSa96qVSvJ7VXjhIVJ1qBBA4wcORK3bt1Ks/iQUqp8xsfHY9q0aejatWuq6dwpyTHQEEgaGDl06FCtE5YBAwZg3bp1aNSoEYoVK6aXyplyxBRDmzPCsLAwEBHy58+PS5cuwd7eXjhmYmKCHDlywNDQUPLzb968GX/99VeqL2pddOrUCVevXkX79u3h4ODw3V9XbclR5yg+Ph6vX79Od3r/69evER8fL7qtYmnz2Tpx4gSqVKkCLy8vYZ+rqyvu3buHd+/epVrQVC5SBsjqW48ePdCzZ0+EhoaicuXKAJKmhs+cORODBw8WHS8+Pl5jSvn58+cxcOBA4bajoyPevHmjc7t/iTEscoy3yOwxHHKNYRE7ziC97XtMDxTTVisrKwoLC5O3Qd94fjHvV7Zs2fQ+kFOOmETyDZCVg7u7e4bT0aWwsLCg06dP6zWmGNp+v7Rq1Yq8vb3TPd60aVNq2bKlqOeuUKECzZgxI93j06ZNowoVKoiKSSTPYF6VSkXm5uZUq1YtmjRpEp0+fTrNKe5iyTFAVqVS0YkTJ4T1jSwtLengwYPC7YCAAJ2+YxMTE2nevHnk5OQkfGc7OTnRggULKDExUXS8kiVL0tq1a4mI6PHjx6RSqej27dvC8bNnz5KTk5Pk9qr9EgkLkTw/WJn5I6iEhCWziWlr06ZN9bJ6slRiX9dcuXLpfUCsHDGJ9D9AVu3Bgwe0YsUKmjx5Mk2cOFFjk+qff/6hqlWr0q1btyTHSKlw4cKSFs1Lj1yrlstR52jFihVkaWlJ+/fvT3Xsn3/+IUtLS1qxYoXotsoxmPfRo0e0Zs0a6tSpE+XNm5dUKhVZWlpSvXr1aPr06XThwgVKSEgQ3VY5Bsh+z/pZHz58SDMxFGPlypVkaWlJXbt2JXd3d6pcubLG8cmTJ1Pjxo11eg6iXyhhkeMHKzN/BPPlyyfLtGapvUFfvnzRe1u+RUwSsGzZMsqZMycNGTKEtmzZIsySUW9yE5uwzJkzh/r06SPpbOd7xVSf7aU8G7xx4wZdu3aNpk2bRnnz5pUUe+XKlWRoaEgODg5UsmRJYRaWh4eHpFkManZ2dsJ0bjMzM8qSJYvGJsWBAwfIy8tLbycvcq5aLkdxSh8fH1KpVOTm5kbe3t7k7e1NRYoUIQMDA/r9998lxSxXrhz9888/Gf47ypUrJym22sOHD8nX15c6dOhAefLkIQMDA0nFHr+1WnNERASpVCpRMR89eqTVJpUcvaK+vr7k7e1Nf/zxB7148ULjWO/evWn37t2S4ib3y5TmX758OSZOnAgfHx+9jbeQI6a25CpxbWNjo/VqzgkJCZg2bRqWL1+Oly9f4sGDB8ifPz/Gjh0LFxcXdOvWTa9tSyl//vw4efIknJ2dv3nflAuRJfc91s4Q+341b94cJ06cQNasWVG0aNFU17x3794tug36jmlgYCBc80/ra0Q9QLZr166i25o3b1706dNHY+aBPqxfvz7D4506dRIdM0uWLIiOjkZ8fDwsLCxSva5iF70rXLgwNm3ahHLlyqV5/OrVq2jXrh3u378vuq0A8OXLF/j7+wsz2woVKoR69erBwsJCUjwA2L59O7Zs2YLg4GAhZrt27dC6dWtJ8bJkyYIbN25kuPRFyZIl8f79e8ltBpJK1B8/fhyBgYHYu3cv4uLiEB0dLSqGgYEBIiIihMGqKf/WX758CUdHR1HfMc+fP4ejo2OG99m6dauwJpBYKdus9urVKzg5OWXq7MuM/DKDbvv06QMAmDdvXqpjUn+w5IiZ2cTkr1OnTsX69esxa9YsjSmhxYoVw4IFC2RPWF6/fq31H1bKGTFKZ2dnh+bNmys6ppwDZN+/f6+XWQUpSUlIvkXKNNCMyLVq+ZIlS9ChQwfY2trq/bPVunVryclJWuQazBseHo7AwECcOHECgYGBePPmDSpXroxq1arhwIEDqFChgq5N14t69erhzJkzwqrqKW3duhUdO3YUnbDIVTbgu9G5j4ZlCiWMYSlQoAAdO3Ys1ePu3r1LdnZ2em9bSlJfg8y4fCVX8byfVdeuXWnZsmWyxI6Pj6edO3fS5MmTafLkybR79+4MB01+bw4ODhQQEJDu8WPHjpGDg4PouDY2NmRubk5t27bNML4Yz549oyFDhqQ73mbo0KEUEREhOq4cg3nz5ctHdnZ21KhRI5o5cyadP39eL4Nu5RggW7NmTapYsWKaFb63bdtGRkZGNGvWLEltVV8GTGt8jIWFBfn6+uoUN73N0NBQdNyUfpkeluS+fv0KMzMzxcdUumfPnsHV1TXV/sTERMV1KWb25avNmzdj2LBhWk9rBpLOMgMDA/Hw4UO0a9cO1tbWeP78OWxsbCTXEZEjJpBUj+PEiRNp1ncZN26cVjEWLVok/L+rqyvGjh2LCxcuoHjx4qkus/Tv319SO0NCQtCwYUM8e/ZMWBF9+vTpcHZ2xsGDB1GgQAFJcRMSErB3717cvXsXAFC0aFE0bdpUUg9T9erVsXjx4jTr0ABJr1O1atVEx42IiMCOHTuwdu1a1K1bF3ny5EHXrl3RuXNnrS6rpmXevHn48OFDmtOhbW1t8fHjR8ybNw8zZ84UFbdr164YPHgwihYtisaNG2sc279/P6ZOnZpmz3ZGvnz5AiDpcoiRkRGMjY0l9wCmVKdOHY3eaXWbVSoViEj0dOn9+/ejZs2a8Pb2xsGDB4XP/44dO9ChQwdMmTIFw4YNE91OuXpF9+zZk+6x8+fPY9GiRfrp5dY55flBxMfH06RJk8jR0ZEMDQ2FM/MxY8bQ6tWrFRNTW0roYSldujRt3Lgx1eMmTpxIVatW1XvbUhLT1okTJ1L+/Plp06ZNZG5uLjxu69atVLFiRTmbSUTi369Hjx5RkSJFyMLCQuOz1b9/f+rVq5ekNsgRk0h/A2RdXFy02vLlyye5rQ0aNKD69evT27dvhX1v3ryh+vXrU8OGDSXFDA4OpoIFC5KFhQWVKlWKSpUqRRYWFlS4cGEKCQkRHe97rFr+8OFDGjt2LOXNm5cMDQ3Jy8uLtm/fTrGxsaLiFC1aNMMp3WfPniV3d3dJbZRjMO/du3dp2bJl1Lp1a3JwcCBbW1tq1KgRzZ49my5duiRplpBcA2RfvXpFRYoUoZYtW1JiYiLt2LGDjI2NaerUqaJjZYZ79+6Rt7c3GRoaUseOHXUaJKz2yyQscvxgZeaPoBISlr1795KtrS3NmDGDLCwsaPbs2dS9e3cyMTHR6wJa6fmZL181a9aM2rdvTzExMRqPPXHiBLm6ukpqgxwxiYjy5MmTYfe9klhYWNDNmzdT7Q8KCiJLS0tJMeVIguSYzZOWxMREOnLkCLVr144sLCzI3t5e1OMtLCzo8ePH6R5//PgxWVhYSG7ftm3bqFmzZuTu7k5ubm7UrFkznRb9S+nOnTu0dOlSatWqFdna2kqaJfTs2bNv3sfPz09C64jCw8MpT548VKdOHTIxMaHJkydLipOWkJAQ6tu3L9WpU4fq1KlD/fr1k5Rgp/Ts2TPq3r07GRsbU+PGjfVaQuCXSVjk+MHSd8zY2FiqXbs2PXjw4Jv33bx5M3369EmruHIWuTt16hR5enqSvb09mZubU5UqVejw4cNaP14XYpIAMzMzIcNP/rjbt29L/qESQ2zCkjVrVmEV2eSPDQsLk7xAmxwxibRfMFMJsmTJQmfPnk21/8yZM5KnNcuRBBF9v1XLjx8/Tj4+PmRubi76eytbtmx08uTJdI+fPHmSsmXLpmsTZREREUFbt26lXr16UaFChUilUpGZmZnoOEWLFk1zirCan58fGRsbi4qZvETAtm3byNTUlFq3bq2xX5faP3Ks1hwZGUnDhw8nc3NzqlSpEp06dUpy+9LzyyQscvxgyREze/bsWiUsYmV2pVdtyZW0/UiXr4iS6oWoK0Umf+zp06cpR44cktogR0wieQbIqr9EU26DBw+mv/76i9asWaPRo6GtDh06UNGiRenChQuUmJhIiYmJdP78eSpWrBh16tRJUlv1nQQtXryYIiMjJbVFW+Hh4TRx4kTKly8fGRoaUq1atWjTpk2iB6Q3bNiQunfvnu7xbt26UYMGDUS3T47BvC9fvqRt27ZR7969hUtLpqamVK1aNRo3bhydOHFCdPVcInkGyKYsEJeyaJyuheP0vVrzzJkzKWvWrOTu7k579+6V3K5v+WUSFjl+sOSIOXDgwDQ/SLrK7EqvYsiRtP1Il6+IiFq3bk09evQQHhsaGkofP36k2rVrU+fOnSW1QZ8xFy5cKGzTpk2j7NmzU6dOnWjOnDkaxxYuXCiprTVr1iQbGxuytLSk0qVLU+nSpcnKyopsbW2pQoUKZGdnR1myZNEo/62N9+/fU9OmTUmlUpGJiYlQRM7b21tykqDvJEiO2TxERDExMeTn50d169YlQ0NDyp07N40ePVqn3rHjx4+ToaEhDRkyRCOBiIiIoMGDB5OhoaGkf8OQIUOEz2paevXqRcOHDxcVU/2eV65cmUaPHk3Hjh2j6Oho0W1L6ePHj1SmTBmqW7euxhig7du3k4mJiaTLpXIXjjM1NU3zO/b+/ftkamoqOp56hlHTpk2pefPm6W66+mUSFjl+sOSI2bdvX7KxsaEyZcpQz549U51hSqWvSq/qHwptNqnkStr0fflKzkttT548Ea7bGxkZUcWKFSlbtmxUuHDhNMvgf++Ycg+QnT9/Pv32228aZ9iRkZHUsmVLWrBgAX3+/JmaNWtG9erVkxT/wYMHtG/fPvrnn38oODhYUgy1jJKgjC4VpCc6OprWr19PNWvWJAMDA3JxcaFJkybpXNk6S5YswmDef//9V9IA07QsX76cTE1NycDAQPh+UPde/P3335JiyjGY19/fX+seWaKkHjJte1x+tAGyuXPnpu3bt6fav23bNnJ2dhYdr1OnTtS5c+dvbrr6ZSrdAsDp06cxadIk3LhxA58+fULp0qUxbtw41KtXTzExa9Wqle4xlUqF48ePS4qrr0qv36oUmpzUIl39+vXDhg0bULBgwTQrCIudzigna2tr3Lp1Cy4uLnqPHR8fj61bt+LmzZvCZ8vHxwfm5uaKiikHJycnHD16FO7u7hr7b9++jXr16uHZs2e4du0a6tWrJ3kVWPVXn75W6A0JCRGmNbu5uaU55V+s0NBQrFu3Dhs2bMDTp0/h6emJbt26wdvbW/SKv/PmzUOHDh00prKqffjwAZs3b4avry+uXLkiup3Pnj3D9u3bNarntmzZ8puro6fH0tISd+/ezbDSrZubGz5//iwpvjbEVP0GgCdPnqBq1aooWLAgTp8+jbFjx2LMmDE6teHy5cvw8/PDgwcPAECoIFy2bFmd4k6aNAnz58/HyJEj01yteezYsTrFl43OKQ9jelazZs10N6Wt/vsjXWr7kVhaWtKJEydS7T9x4gRZWVkRUdLUXGtra9GxV69eTUWLFhV6Q4oWLUqrVq2S3NaJEyemOX4hOjpap4Uak9N1Nk96jh8/Tu3btycLCwvKlSsX9enTRy9xdaWEwbzaXsaVa4DssGHDSKVSkbW1NZUsWZJKlixJVlZWZGBgIPpyWEr6Xq35W8/177//UosWLXSO9Uv1sPxIQkJC8PDhQ1SvXh3m5uaSig+lR67CebGxsRr70iom9T1lyZJF69dM7HovavpeTyoxMRG3b99G8eLFhfjJX1dDQ0P07t07wx6z7xEzucGDB6e5X6VSwczMDK6urmjWrBmyZs2qdUwfHx+cP38ec+fOFdbUuXz5MoYOHYrKlStj48aN2Lp1K+bMmSOqR2DcuHGYN28e+vXrh0qVKgFIKmy1ZMkSDBo0CJMmTdI6lpqhoSFevHiRqpz+27dvkSNHDr0t0XHixAn4+vpi9+7dMDU1lbyOzrNnz7Bu3TqsXbsWkZGReP/+PbZs2YLWrVuL/o558OABIiMjUb58eWFfQEAApkyZgs+fP8Pb2xt//fWX6DY2atQIjo6OWLVqVZrHu3fvjufPn+Pff/8VHVtb2q7/pV5TS/0dTcl67pL/v5jPwfr16/HHH39g9uzZ6NWrl9CbFhcXh2XLlmHEiBFYsWIFOnbsKPFf9z8fP34EkPTv1aewsDCsWbMG69atw+vXr+Hp6YkDBw7oFlTnlEfB5BhvIfcYjjdv3lDt2rWFUeDqDL9Lly40ePBgSTGJ5Cly9+nTJ/rzzz/TrBehj6XPg4ODyd/fXxgYJzbzX7dundabVGmVt9ZlFP/mzZupWrVqwm0rKyvKnTu3MCbEyspK9PslR8zk5Bgg+/HjR2E8mPrzZGJiQj169BDGIVy/fp2uX78uqq3Zs2enLVu2pNq/ZcsWyWfsKpWKXr16lWp/QEAAZc+eXVJMNX3N5iEi2rlzJzVo0IAsLS2pZcuWtHfvXoqJiSEjIyPRg5fVvL29aezYscLt0NBQMjc3p3r16lH//v3JysqK5s+fLzquXIN5xdC2h0WOAbLlypWjefPmpXt87ty5Oq9WLYevX7/Spk2bqFatWmRsbEwGBgY0b968NGd7SfFTl+bX96JkcsVMbtCgQTA2Nhau0aq1adMGgwcPxty5cyXFlWOhwuHDh+PEiRNYtmwZOnTogKVLl+LZs2dYsWIFZsyYIamdQNKZaevWrXHixAmoVCoEBwcjf/786NatG7JkyaL1ayDHQncp6XtRxbVr1+LPP//U2Hfy5EnhLG/58uXYtGmTqPdLjpjJqXtP1q5dK/SqRUVFoXv37qhatSp69OiBdu3aYdCgQTh8+LBWMa2srLBq1SrMnz8foaGhAJJW506+fICHh4fotsbFxaV5/b9MmTKiF9NT9+CpVCoUKlRIo3ciISEBnz59wh9//CG6jbGxsdi9ezfWrFmD48ePI1euXOjUqRO6du2q0+rsbdq0wYgRI7Bt2za9nU1fuXIFw4cPF25v3rwZhQoVEt7nEiVKYPHixRg4cKCouLVq1cLSpUsxYMAAzJ8/HzY2NlCpVIiKioKxsXGGSxd8b3nz5tV7zNu3b6NZs2bpHvf29pY0zkTb10zsWMmrV6/C19cXfn5+cHV1RYcOHeDn54fcuXPDy8tLf73tekl7mN44ODhQUFAQEWlm+A8fPtSpCJUchfOcnZ2FcQbW1tbCbIsNGzZIqr2g1qFDB/Ly8qInT55otNXf319yme/kvnz5QlFRURqbPuhjUcXcuXNrVJtMeZZ3584d0b13csRMztHRMc0z9P/++48cHR2JiOjq1auKKCDWt2/fNGfbDRkyRPT4jXXr1tHatWtJpVLRwoULNXrstmzZQufOnZPURrlm8/Ts2ZNsbW2pcuXKtGzZMnr37h0RkU49LGZmZhqzl2rXrk1jxowRboeEhEiqHqv29OlTmjdvHvXp04d69+5N8+fP12pWnj6ILYh46dIlGjRoEDVq1IgaNWpEgwYNosuXL0t+7rt376Z7/N69e5LGb6lUKnJxcaE///yTBg4cmO4mlqGhIQ0cOFAoTKmmy2crLb9kwiLHD5a+YlpZWQnz45P/sFy+fJmyZs0quX1yFLmztLQUynI7OTnRxYsXiSipW1iX5EqOpE2uy1f6vtRmamqqkVy8evVK4wcrODiYTExMMj1mcvoaINu8eXPh7yajWg661HNQlw0oWrQodevWjbp160bFihUjGxsbIZkRW0IgMDBQ9Bo8GZk7d26al5iIiKKioujvv/+mMmXKSIodHR1N69ato+rVq5OpqSk1bdqUDA0NJZdPd3R0FP7uExISyMbGhg4cOCAcv3PnDtnY2EiKndnE1E7S9wDZGjVqaCR+KY0ePZpq1KghOu6sWbPIzc2NcuTIQYMGDdJb2fx69eqRtbU1tWvXjg4dOiRcvtd3wiJtlN0P6PPnz+jbty9y5MgBS0tLZMmSRWNTSsxq1aphw4YNwm2VSoXExETMmjUrwynP3+Lu7o7Tp0+n2r9z506UKlVKUsz8+fMjLCwMAFCkSBFs374dQNJKo3Z2dpLb+vnzZ1hYWKTa/+7dO5iamkqKOXz4cBw/fhzLli2DqakpVq9ejYkTJ8LR0VHj9RZr6tSpWLduHWbNmgUTExNhf7FixbB69WrR8RwcHHD//n3htr29vcZg2Lt37yJnzpyZHjO5Zs2aoWvXrtizZw+ePn2Kp0+fYs+ePcL0WwC4dOkSChUqlGEcW1tb4bKKra1thptU//33H0qXLg17e3s8fPgQDx8+RPbs2VG6dGn8999/uH79Oq5fv46goCCtY+bLlw8vXrxAeHh4mptYgwcPTjX1+MSJE+jQoQNy5cqFyZMno0KFCqLjAoC5uTk6deqEkydP4tatWyhatCgcHBxQpUoVtGvXDrt37xYVr2bNmpg8eTKePHmCBQsWIDExETVr1hSO37lzR9KU/wcPHuDSpUsa+wICAlCrVi2UL18e06ZNEx0zPSdPnsS///6bahDzx48ftboEt379eixevBiLFi3C27dvERQUhKCgILx79w7z58/HokWLRH/HDB06FNOnT8fw4cPx8uVLYX9ERASGDRuGmTNnYujQoaJiAsCwYcNw584d7N27Fx8/fkSVKlVQvnx5LF++HB8+fBAdT+3w4cO4ffs2ChcujN69eyNXrlwYMGAAAP2VDQDw61wS6tOnD7m5udHOnTvJ3Nyc1qxZQ5MnT6bcuXPTpk2bFBPz1q1blCNHDqpfvz6ZmJhQy5Ytyc3NjRwcHHRamEqOInfz5s0TKpkePXqUzMzMhAJSCxYskNzWBg0aCGcX6oqsCQkJ1KpVK8lT4+S6fKXvS21dunShypUrp3ksMTGRKlWqRF26dMn0mMnJNUD2R6EeYJ3eJtXTp09pypQpVKBAAcqWLRsZGBjQ1q1bJU87Te/SUkJCAv3zzz/UrFkz0T1tYWFh5OrqSiqVioyMjFIVimvWrJmkSwxyDOadMWOGRq9FYmIieXl5CYPkHRwc6L///hPdVrkGyC5atEj4m1JP5FD/beny/Zrc58+fad26dVSuXDmytLTU2+XxI0eOUNu2bcnMzIwKFixIo0aNoitXrugc95dJWOT4wZLrRzAyMpKmTJlCrVq1ogYNGtDo0aPp+fPnkuOpyb1Q4aNHj2jXrl06LcpFJE/SJtflK31fagsJCSEbGxsqX748bd++nYKCgigoKIi2bdtG5cqVIxsbG9GVWeWImZaPHz8KNSc+fvyoc7y4uDg6evQoLV++nD58+EBESWvM6CO2WlRUFO3ZsyfD8QLfon491dvly5dp5cqVVKRIEdq1a5foeHLM5iEiMjAw0KhoPHTo0FTrMUmpohwXF0dBQUFprlocFBREb968ER0zd+7cGmOAJk+eTCVLlhRur169WuO2NkqVKkVbt24Vbm/fvp3Mzc3pzJkz9PbtW2rUqBG1atVKdFstLCwyvHT08OFDyStWP3nyhObNm0e9e/cWxvDoWvE4udOnT1OXLl3IysqKKlSooJelCpJ79+4dLVq0iDw8PPQyc/SXSVjk+MGS60eQ6T9pK168OAUGBhIRUZ06dWjIkCFElLQmjpOTk+S4cqwndfHiRXJzc9M4c1epVOTm5kYXLlxQTEw5PXr0iIoUKUIWFhYaY4P69+9PvXr1khy3VatWtHjxYiJKGs9RsGBBMjY2JiMjI9q5c6de2q524MABSeMMDA0N6a+//hKSNDVdExaVSqWRkGTGKtvaPqccg3nt7Ozozp07wu3OnTtThw4dhNvnz5+n3Llzi4pJJN8AWbk8e/aMpk6dSgULFiQHBwcaMmSIXseZpGfmzJk6x/ippzUnpx5vkSdPHmG8Rfny5XUab6GvmDdv3tT6viVKlJDQUnn0798frq6u6N+/v8b+JUuWICQkRKcp4La2thg9erSOLfyfLl264MaNG6hRowZGjhyJJk2aYMmSJYiLi9Op1P+4cePQqVMnPHv2DImJidi9ezfu37+PDRs2SC6SVL58edy5cwdBQUFCSe6CBQtKHmskR8zffvsN69atg42NDX777bcM7yt2XAQADBgwAGXLlsWNGzeQLVs2YX/z5s01puWLderUKeFztWfPHhARIiMjsX79ekyZMgUtWrSQHDulwoUL4/Lly6If161bNyxduhSBgYHo0KED2rRpI3lMXEYoE2qGavucWbNmxYsXL+Ds7IzExERcuXJFo0BhbGys6PbHx8drjIE7f/68xnRrR0dHScs8lC5dGps3b8bkyZPTPL5x40aULl1aVMxTp05pdb/q1auLituwYUOcOHEC9erVw+zZs9GoUSMYGeknDYiPj8e9e/dgYmKiMV5t3759GD9+PO7evasxBV4SnVOeH4Qc4y30FTPlUuLJz4BT7hND7iJ3jo6OaV6XvHr1quhei5RlrDPa9EFfl6+I5L/UlhE5zpK1idm5c2ehB0COBc+yZs0qTJFM3nMVFhZG5ubmkmISaZ65d+jQQVhk8/Hjx5J7RVPODoyMjKS7d+9SmzZtRF+2UNP3bB6i1D0sYlcQ1wdtn7Ndu3bUuHFjCg8Pp7lz55KVlZXGwoU7d+6kEiVKiHrukiVL0tq1a4ko6f1WqVQaPQtnz56V1Nu6f/9+MjQ0pGHDhmkUuXvx4gUNHTqUjIyMaP/+/aJipuwJ1VdhSpVKRY6OjuTh4UGlSpVKdxPr1q1blDdvXqHNzZs3p4iICKpevTplzZqVRowYoZfp6L9MD8ugQYOE//f09MS9e/dw9epVuLq6Su610FdM9UwbALh+/TqGDh2KYcOGaZQOnzt3LmbNmiWqfXIXuXv79m2aMzZsbGxEn6l4eHholLdWozQWqNNHqfO8efPqreBTtWrVcPToUb3EEotkOEvWJubatWvT/H99SUxMTPN9fvr0qU5Fz5ydnXH+/HlkzZoV/v7+2Lp1KwDg/fv3kpersLOzSzUTgojg7OwsxBdLPZunU6dOCA4Oxtq1a3HlyhVUqVIFjRo1QsuWLb/Zs5WWcePGCTPwYmNjMXXq1FR/w0pYXHTq1KmoW7cu8ubNC0NDQyxatEhj2YuNGzeKLhz3559/om/fvjh9+jQuXLiASpUqaSyuefz4cUk9jo0bN8b8+fMxdOhQzJ07V3g9o6KiYGRkhDlz5qBx48aiYmbJkgXW1tbo3LkzOnTogOzZs4tuV1rGjx+vlzgpjRgxAq6urliyZAn8/Pzg5+eHu3fvolu3bvD399ff4qo6pzxMr8qVK0cHDx5Mtf/gwYNUunTpTGhR+ooWLSqMB0hu0aJF5ObmJipW8hLWe/bsoQIFCtDy5cuFXpXly5dTwYIFac+ePZLa2q9fP6E3LLnFixfTgAEDJMVUAjnOkqXE1PcA2datW1OPHj2E9oSGhtLHjx+pdu3aOi1Tv3TpUjIyMiI7OzsqWbKkMHNm0aJFVLNmTUkxAwMDNbZTp07R3bt3KS4uTlI8OWbzECXV9shoYdHvsbiomM+WHIN5fX19ydvbm/744w968eKFxrHevXvT7t27RcdU0+cA2ZiYGNq6dSvVq1ePzM3NhSKC+l6Y8FvOnDlDX79+/eb97O3thVmAkZGRpFKpaMOGDXpvzy+TsMjxgyVHTDMzM42BYWp37twhMzMzSTFT0leRO19fXzI3N6dx48YJX9Zjx44lc3NzWrlypeT2yZG06fPyldyX2sRQQsIixwDZJ0+ekLu7O7m5uZGRkRFVrFiRsmXLRoULF5Y0kyW5y5cv0+7duzWSqQMHDtCZM2d0iptSQkKC6EsBRPLN5lECuS5DZcYA4u/l8ePHNHHiRMqfPz85OTnRX3/9JTkZFkvb1zWty43qAqj69Mus1uzk5IR//vkHZcqU0dh/7do1NG3aFE+fPlVEzNKlSwtFx9SFyGJjY9G9e3f8999/uHbtmuiYQFIxthEjRmD79u14+/ZtquNSL7MsW7YMU6dOxfPnzwEkFdEaP368TquImpub49q1axprKQFJBc5Kly6NL1++iI5pZmaG//77D66urhr7Q0JCUKxYMXz9+lXrWOvXr9f6vnKvZ6TtirJyxvT29oa1tTV8fX2RLVs24bGBgYHo0aMHgoODJbUjPj4eW7duxc2bN/Hp0yeULl0aPj4++utelklISIjGKrVxcXGiHm9gYICIiAhh9WcbGxsEBQXp9T3WhhzPK8fnVc642pBrgGxKYWFh6NatG06ePInXr1+LWv1cKm1fV0NDQzx48AD29vbC5dAzZ86kKhqo65pCv8wYFn2Ot5Az5vLly9GkSRPkzp1bGAdz8+ZNqFQq7N+/X1JMQJ6FCr98+YJOnTqhd+/eeP36NV6+fImjR4/CwcFBcjsBwM3NDdOnT0+VtE2fPj1VEqMtV1dX+Pv7o2/fvhr7Dx06JPpL7nssqvgjOX36NM6dO6dR6RcAXFxc8OzZM8lxjYyM0L59e12bpyEhIQHr1q1DQEAAXr16lWrxSrGLvql9+fIFO3bswOrVq3H27FlUq1YN48aNQ/PmzXVuc2adU8rxvF++fJF0wqEPBgYG36y6qlKpRC+CWbNmTSFueq+ZSqWSdFIYExODXbt2Yc2aNTh//jwaNWqEgwcPfpdkRQwi0pgZREQa44Ho/8cm6jr+8JdJWPT5gyVnzPLlyyM0NBSbN2/GvXv3ACStstquXTuNQWdi7d+/Hxs2bEDNmjXRpUsXVKtWDa6ursibNy82b94MHx8f0TGbNWuG3377DX/88QeMjY3h6ekJY2NjvHnzBvPmzUPv3r0ltVWOpG3w4MHo27cvXr9+LQzWCwgIwJw5c7Bw4UJJMVP6+vUrYmNjNfbpbZVSBZNjgGyePHlQs2ZN1KhRA7Vq1dLbmfOAAQOwbt06NGrUCMWKFdO5bPjly5exevVqbN26FQUKFICPjw/OnTuHv//+W2NAJ0tibm6eaT1ke/bsSffY+fPnsWjRIkmrr8sxQPbSpUtYu3Yttm7dChcXF3Tp0gXbt29XXKKiduLEie/zRHq/yKRQ6Y23sLCwkDzeQq4xHHKQo8hdtmzZhFLWq1atohIlSlBCQgJt376dihQpolN7P336RCtWrBAWolu5cqXGtEYp/v77b3JychKmBebPn5/Wr1+vczvlWFRRW4aGhpLKieszphwDZDdu3Eg9evSgggULkkqloty5c5OPjw+tXLlSp2vj2bJlS3N8lBTFixenvHnz0qhRozReL30UeOvVq5fw2TcxMaGuXbtqLMwoZnFGqZQwPkruuPfu3SNvb28yNDSkjh07ClWrxZBjgKxKpaK8efPSuHHjaN++feluctP2dV2/fr1Wg3N19cskLESpf7Dy5cun8w+WHD+CISEh1LdvX6pTpw7VqVOH+vfvr9M6QkTyVHo1NzcXkqBWrVrRhAkTiIgoPDxcp1oZcoiOjqbPnz8TUdJqxbdu3aJ58+aRv7+/TnHlWE9KDCX8qMg5QJaI6Pnz5+Tn50c+Pj5kZGSkUyKYK1cuun//vs5tIiIyMTGhDh060JEjRzR+nHRNWJQwm4dIGZ8tueI+e/aMunfvTsbGxtS4cWO9rVqsrwGy6dVe0bUOi1jaDrpNOVBcLr9UwqL26tUrvaxHIsePoL+/P5mYmFD58uWFM6ny5cuTqamp5EUKieQpnFe8eHFauHAhhYeHk42NjbD2x5UrV8jBwUFyW4n0n7TVrVuXli1bRkRE79+/JwcHB8qdOzeZmZmlWrBNDLnWk9KWUn5U4uLiaOPGjTRs2DDq3bs3rVq1Sud1ST5//kyHDx+mUaNGUcWKFcnU1JQ8PDwkLaanNmfOHOrTp49epocmX5zQ0dGRhgwZQteuXSNjY+PvUupcbkr5bOkzbmRkJA0fPpzMzc2pUqVKdOrUKb23hSip57pWrVpkYGCQaobXj0Tb1zXlLCG5/DIJS/LkgihpKub8+fN1qkgqx4+gh4eHUH0zuREjRkiqQJgefVR63bFjBxkbG5OBgQHVrVtX2D9t2jSqX7++5LhyJG1yXb7K7PWkfqQfFTEqVapEZmZmVKpUKRo0aBDt3buX3r17p3Ncb29vsrW1pXz58lHjxo2pefPmGptUAQEB5OPjQ+bm5qRSqWjYsGF668n5Frmm9MrxOZCrrdpcxpw5cyZlzZqV3N3dae/evXpvw9evX2nz5s1Up04dsrCwoFatWtGhQ4f0/jxyCAwMpIMHD0r+G1OpVPTq1Ss9tyq1X2bQbfIBopGRkShfvjxMTEx0GiB67do1zJ8/HwCwc+dOODg44Pr169i1axfGjRsnKebdu3exffv2VPu7du2q18q1+qj02rJlS1StWhUvXrxAyZIlhf116tTRaXbEyJEjMWjQoFSzl0aOHIkRI0agbt26omNGR0cLA0CPHDmC3377DQYGBqhYsSIeP34sua1yrFH1o5FjgOy9e/dgaWmJIkWKoEiRInBzc9PLejp2dnZ6mbmTUu3atVG7dm1ERUVh8+bNWLNmDebMmYNixYqJWitMCpJpFpEcM3rkaqs2g3lHjhwJc3NzuLq6Yv369emWJxC79pUcA2QfPHgg/E6pBQQEYMqUKfj8+TO8vb3x119/iY47c+ZMfPr0SVj3iIjQoEEDHDlyBACQI0cOBAQEoGjRoqJj16lT55vrEkkty6H2yyQsKZOLnDlz6pxcyPEjaG9vj6CgIBQsWFBjf1BQkFCXQQq5FirMmTMncubMqbEv+R+ZFHIkba6urti7dy+aN2+Ow4cPC8sqvHr1SqeZPHItqqgtXWe56CPmtGnTcOrUKcycORM9evSAk5MTatSogRo1aqBmzZqpPsvaePv2LW7duoXAwEAcPnwYo0ePhomJiZAUSV0AUY5lBJKztbVFnz590KdPHwQFBWHNmjXCsbNnz6Js2bIaC/ApmbYzeuLi4lCkSBEcOHDgm2UHDh06BCcnJ301UZSOHTvK8vdSsWJF5MmTB/379xdqcp05cybV/Zo2bap1zBEjRqB48eLCd2lYWBiaNGmCatWqoUSJEpg+fTosLCw0Fm/UxrZt2zBixAjh9s6dO3Hq1CmcPn0abm5u6NixIyZOnJjm9++3eHl5wcrKSvTjRJG9D0ch5BggKscYjokTJ5KdnR3NmDGDTp06RadOnaLp06eTnZ0dTZo0SVJMIv1WepVb7ty5afv27an2b9u2jZydnSXFlOvyVUr6XFRRG0q7JKTPAbJqiYmJdPnyZerUqZPeYmaGH+nSjdi4jo6OaVbo/l4y8zKmHANkc+fOLfyeEBFNnjxZYyHN1atXS1pY087OTuN96ty5M3Xo0EG4ff78ecqdO7fouDyGRc/kSC7k+BFMTEykefPmacw8cnJyogULFug0UNDU1FQYEJpccHAwmZqaSo4rB7mSthcvXtC1a9c01mq5ePEi3b17Vx/N1pvY2FjKnz+/Vj8Ap0+f1mo6oRwxk9P3ANmrV6/S3LlzqUmTJpQlSxYyMjLSGM+iix07dlCrVq2oQoUKOq9SK4YSEgu54k6dOpU6der03UrGp6Tra5CYmEj//vsvtWjRQo+tki75quJERLVr16YxY8YIt0NCQsjW1lZ03JSvU+HChYVxmERJs5ykLAHDs4T0TK4zbDl/BD98+CAsJKcrfS5UKDe5kjY5yLWoohxnrHKdBcsxQNbQ0JDKli1LQ4YMoX/++YciIyP10taFCxeSlZUV9e3bl0xMTKhXr17k6elJtra29Ndff+nlOdKjhMRCrrje3t5kbW1NuXLlonr16ultMLO2pPZehYaG0pgxYyh37txkampKjRo1kqF14jk6OgoD+BMSEsjGxoYOHDggHL9z5w7Z2NiIjluyZElau3YtESUlJyqVSmNG29mzZyX1uHMPiwy0SS6ePHmS7mqp30NoaGiahbEePHhAYWFhkuP+SEXuktNn0iYHuS61yXHGKtdZcJYsWShbtmzUtm1bWrFihc6zY+Lj42nfvn16mRWUUuHChWnLli1EpPmDPHbsWPrzzz/1/nzJKSGxEENMEtC5c+cMN7mJeQ2+fv1KmzZtolq1agknsfPmzZO8COz9+/eF5ELt2LFjVLNmTSpXrhxNnTpVdMx27dpR48aNKTw8nObOnUtWVlYahTN37txJJUqUEB135cqVZGlpSV27diV3d3eqXLmyxvHJkydT48aNRcd99OjRdzmZ/GUG3QLaDRB1d3fPlIXG1Dp37oyuXbumGqh48eJFrF69GoGBgZLidu3aFTExMZg6daowQjxfvnxYvny5TgsVyiEsLAzx8fEoWLCgRmn34OBgGBsbp1pQKzPJsZ4UkFTyPSAgAEeOHEHx4sVTLcsgdiaDXDEB/Q+QNTQ0ROvWrXH37l29zAxKLjw8HJUrVwaQNKj048ePAIAOHTqgYsWKWLJkiV6f73uQa30eEjGjR47BzPoezHv16lX4+vrCz88Prq6u6NChA/z8/JA7d254eXlJHnwvxwDZqVOnom7dusibNy8MDQ2xaNEijb/XjRs3CkuMiNGjRw8YGhpi//79qF69OsaPH69x/Pnz5+jatavouNouCDtu3DjRsZP7pRIWbYj5I5XD9evXUaVKlVT7K1asmGrNIjHkWqhQDnIlbXKQYz0pIGn6bYsWLXRtnuwxgaRZRSVKlECJEiXQr18/XL16FUuWLMHmzZuxbds2STN6ihUrhtDQUOTLl0+vbc2ZMyfevXuHvHnzIk+ePLhw4QJKliyJsLAw2f/25ZihAohbn0fOGT3x8fEIDAzEw4cP0a5dO1hbW+P58+ewsbGRNHvE2NhY61XUq1at+s37VKhQAf369cOFCxdQuHBh0e1Jz5UrVzB8+HDh9ubNm1GoUCEcPnwYAFCiRAksXrxYVMLi4uKCu3fv4vbt27C3t4ejo6PG8YkTJyJ37tyS2tu1a9d0k5K///5bUswJEybA0dEROXLkyHABSF0Tll/qkpA2Mrtolo2NDV27di3V/itXrpCVlZXkuHJVepVD8oqxyQUHB0saaCanH/VSmz7JMUD20KFD5OHhQfv376fnz59TVFSUxiZVt27dhBmCS5YsIXNzc/L09CQ7Ozvq2rWr5LjaUMolITnGMj169IiKFClCFhYWZGhoKLSnf//+1KtXL8lx9XkZs169emRtbU3t2rWjQ4cOCZcwdF1KQa4BsmLINQNNWw0bNiQzMzNq1qwZ7du3T7ZhFZywpJDZCUvjxo2pVatWFB8fL+yLj4+nFi1a6DQ4WM6FCvVNrqRNLnKsJ0WUVO7+6NGjtHz5cmEcz7Nnz3RaVkKOmHIMkE05JVS96bqGSkJCgsaPn5+fH/Xr148WLVpEMTExOrebSPeqoWKJ/c6SYyxTs2bNqH379hQTE6PRnhMnTpCrq6vkuPoezBseHk4TJ04kFxcXcnBwoP79+5ORkZFOCZxcA2TFEFNCP61FWpNvhoaGktrw7NkzmjZtGhUqVIhy5sxJw4cPp3v37kmKlW77iTL5GojCWFtb48aNG5k2huXOnTuoXr067OzsUK1aNQDA6dOn8eHDBxw/fhzFihWTFNfCwgL37t1Dnjx50Lp1axQtWhTjx4/HkydPULhwYURHR+vzn6GTJk2awNzcHH5+fjA0NAQAJCQkoE2bNvj8+TMOHTqUyS38ny9fvoCIYGFhoXGpzd3dHV5eXpLjPn78GPXr10d4eDhiYmLw4MED5M+fHwMGDEBMTAyWL1+uiJgJCQk4ePAgqlWrptfxJidPnszweI0aNUTHjI+Px7Rp09C1a1fJ3enJyVk1VAyx31nNmzdHQEAArKys9DaWKVu2bDh37hwKFy6s0Z5Hjx7B3d1d8vdLly5dMjyuy9iZo0ePYu3atdizZw+cnZ3RsmVLtGjRQij+pi0fHx98+PABf//9N3bs2IHx48cjIiJCeF137dqFSZMm4caNG5Lb+i3afgb27duX7rHz589j0aJFSExM1PpSXHpOnTqFtWvXYteuXShevDiOHTum9WXLDOk1/fkJZHbXGlFSpjpq1Chq2LAhtWjRgiZOnKjzAlpyLlSob7dv36Zs2bJRgQIFhFkGBQoUIHt7e72tqqovcl1qk+OMVa6zYFNTUwoNDZX8+O/J0tJSp9l2yZUqVYq2bt0q3N6+fTuZm5vTmTNn6O3bt9SoUSNq1aqVXp4rI2K/s+SY0WNnZydcVkn+2Tp9+jTlyJFDUszv5d27d7Ro0SLy8PCQ1HsXFhZGrq6upFKpyMjIKNXffbNmzXRasFMbulwZuHfvHnl7e5OhoSF17NiRHj16pHN7oqOjaf369VS+fHkyNzfX6TJucpywpJDZl4Tk8r0qveqLHEmbHOS61JY1a1ahOzX5ZzIsLExyZWY5YhIRlSlTho4dOyb58ek5deoU+fj4UKVKlejp06dElLQK9unTpyXHbNq0Ka1bt04v7ZOraqhYSvjOat26NfXo0UNoT2hoKH38+JFq166t87RmOS5jpmfmzJmSHhcXF0dBQUH07NmzVMeCgoLozZs3ujYtQ1I+A8+ePaPu3buTsbExNW7cWC8ng+fOnaPu3buTjY0NlS1blpYuXUrv37/XOa7aL5GwxMbGkqGhoVZvSHh4uMb4kczw/v17Onz4MG3cuJHWr1+vseniR6n0+iORY8kHInnOWOU6C5ZjgOzOnTvJ3NycunfvTqampkJbFy9eTA0aNJDc1mXLllHOnDlpyJAhtGXLFtq3b5/GJoZcVUOJ5K9MrO8k4MmTJ+Tu7k5ubm5kZGREFStWpGzZslHhwoV1Kiim78G8cXFxdOvWrVS1gvbu3UslS5YkExMTyW3Vlhy9+GISlsjISBo+fDiZm5tTpUqV6NSpUzo//8yZM8nNzY3s7e1p4MCBsi1N8kskLERE+fLlo6CgoMxuxjf9888/ZG1tTSqVimxtbcnOzk7YsmTJktnN+27kStr0Ta5LbXKcscp1FizHAFkPDw/hvU7+ZXzt2jVJr2utWrXo3bt3el3vRa6qoWpyVSaWa0ZPXFwcbdy4kYYNG0a9e/emVatWUXR0tE5t1edlzFu3blHevHmFz2fz5s0pIiKCqlevTlmzZqURI0bQkydPdGqvNjJz/a+ZM2dS1qxZyd3dXeclLpJTqVSUN29e+vPPP2nQoEHpbjo/D9GvMejW19cXu3fvxsaNG3Va9ltuhQoVQsOGDTFt2jRYWFhkdnMyxf79++Hj44NPnz7BxsZGo36FSqXCu3fvMrF1mnbu3Il27dohISEBderUEQZcTp8+HadOnZI8QPjp06fw8vICESE4OBhly5ZFcHAwsmfPjlOnTklauVuOmIA8A2QtLCxw584duLi4aAwoDA0Nhbu7u+hBgQYGBoiIiNBpxfOUVq1ahUGDBqFNmza4cOEC7OzscPbsWeH4lClTcPHiRezfv19S/GnTpuHBgwdYvXo1jIz0VzLL29sb1tbW8PX1RbZs2YTXNjAwED169EBwcLDenktX+hzM26hRI8TExGDgwIHw8/ODn58fChcujG7duuHPP//Uz6BQLcgxscPIyAg3btz45gBvAwMDmJubw9PTU5jQkBaxA69r1qz5zTpDKpUKx48fFxU3pV+mcNySJUsQEhICR0dH5M2bN9XI+GvXrmVSyzQ9e/YM/fv3/2WTFQAYMmQIunbt+kMkbS1btkTVqlXx4sULlCxZUthfp04dNG/eXHLc3Llz48aNG9i6dStu3ryJT58+oVu3bvDx8ZH8xSpHTEBaQvItOXPmREhISKqqxmfOnMm0GXwpyVU1VE2uysSnT5/GuXPnYGJiorHfxcUFz5490zrOP//8o/V9mzZtqvV9k0tMTERCQkKq/U+fPtWogq2Ny5cv48iRI/Dw8EC1atXg5+eHv/76Cx06dJDUNiXRtnhgx44dZSlg+L2Kef4yCYu3t3dmN0ErXl5euHLlimK+lDPDj5a0abPkgxRGRkZo3769znHkjgkk/QiuWLECoaGh2LFjB5ycnLBx40bky5dPqyqkKfXo0QMDBgzAmjVroFKp8Pz5c5w/fx5Dhw7F2LFjJbXxzp07iIiIyPA+JUqUEBVTjqqhanJVJtZXEpDyO1WlUqWqcqr+cUzr+bRRr149LFiwACtXrhTiffr0CePHj0fDhg1FxXrz5o1QMdbW1haWlpaoWLGipHb9qNatW5fZTQCQtHSJlCVwfpmEJeXZj1I1atQIw4YNw507d1C8eHEYGxtrHJd6pvIj+VWTNjnOWL/HWfCuXbvQoUMH+Pj44Nq1a4iJiQEAREVFYdq0afj3339Fxxw5ciQSExNRp04dREdHo3r16jA1NcXQoUPRr18/Se2sU6dOmmXD1T+0KpVK8g+rHORYnwfQXxKQmJgo/P+xY8cwYsQITJs2DZUqVQKQVNdjzJgxmDZtmuS2zp07F15eXsJlwHbt2gmXMf38/ETFUqlU+PjxI8zMzIT3+8uXL/jw4YPG/aSuKfSjIyL4+/vD19cXO3fulP25pPhlxrAAQGRkJHbu3ImHDx9i2LBhyJo1K65duwYHBwdRa2bIycDAIN1jSvtClYuvry8mTZqELl26/FJJW8r3Xh9nrHLETKlUqVIYNGgQOnbsqHF9/vr162jQoME3ezUyEhsbi5CQEHz69Anu7u6S1qQBkl6HS5cuwd7ePsP75c2bV1RMba7bx8fHax0zJX2vzwPIM5apWLFiWL58earetNOnT6Nnz564e/eupLYCSa9B8suYpUuXlnQZM+X7pU5aUt6W+ztWjjEsusQMCwvDmjVrsG7dOrx+/Rqenp44cOCA3tqWFqnt/WV6WG7evAlPT0/Y2tri0aNH6NGjB7JmzYrdu3cjPDwcGzZsyOwmAtA8a/lVqRfLmzRpUqpjP3PSJscZ6/c4C75//z6qV6+ear+trS0iIyMlxdy0aRN+++03WFhYwN3dXXLbksuTJ49eB93u2bMn3WPJq4ZKlbIycd26dWFtbY2ZM2dKrkwMyDOW6eHDh7Czs0u1X/19qwt9XcY8ceKEzjH0QY7VtcWOS4mJicHOnTvh6+uLM2fOICEhAXPmzEG3bt2U3cOk8zyjH0SdOnVo2LBhRKQ5Bezs2bOUN2/eTGwZY6kVLVo0zQJpp06dklyQTo6YREklA44ePUpEmn9b69evJzc3N0kxs2fPTpaWltS2bVs6ePCgzrWRVCqVTvVAtKXPqqFyVSaWQ7Vq1ahu3boUEREh7IuIiKB69epR9erVRcVKWRsno02M9evXi65XI4fMnNZ85coV6t27N9nZ2VHZsmVp4cKFFBERofMCkGJJfQ1+mR6Wy5cvY8WKFan2Ozk56dRlrS8NGzaEn58fbG1tAQAzZszAH3/8IZy1vH37FtWqVcOdO3cysZXse5HjjFWus2A5Bsi+ePEC/v7+8PPzQ+vWrWFhYYFWrVrBx8cHlStXFh2vRo0aqWbFZMTPzw9NmzZNNTMnPc+fP8f48eOxfv16eHl5ISgoSPK6X2r6ms0DyD+Wac2aNWjevDny5MkDZ2dnAMCTJ09QsGBB7N27V1QsuQbzdunSBfXr19drL5uc4uLiUKRIERw4cABubm4Z3vfQoUNaDWuoUKEC+vXrhwsXLqBw4cL6aur3o//cSZns7e2FFYCTZ3dHjhz5LuWzv8XAwEDjDDBlNcSIiAidVqn9ETRo0EBjpd/p06drlHV+8+aN5DP2H40+z1jljElElJiYSFOmTCFLS0uhCJuZmRmNGTNGcszkPn/+TJs2baKGDRuSiYkJ5c+fXy9xM6JtNVI5qoaq6bMycVpF8tLap8t3TGJiIh0+fJgWLlxICxcupCNHjlBiYqLkeERER48epdKlS5O/v79QOdnf35/Kli1LR44cERXre/WyfYuY3gV9Fw+sV68eWVtbU7t27ejQoUPC+/Oj9LD8MglLt27dyNvbm2JjY4Uqn48fP6ZSpUrRgAEDMrt5qf6YUr6hv0LCwknb/wQHB1OxYsXIxMSEChQoQAUKFCATExMqWrQoBQcHKyZmcjExMXT79m26ePGi3td5ef36NS1evJiKFi36XT4D2nyhylU1VE2uysT6TALkps/LmCqVil69eqWvpkkm5sd66tSp1KlTJ4qLi9Pb84eHh9PEiRPJxcWFHBwcqH///mRkZCRLVeX0GBoaCmuwifHLzBKKiopCy5YtceXKFXz8+BGOjo6IiIhApUqV8O+//2rd9SuXlJU4U46ifvnyJRwdHX/aAacAvwYpERGOHj2Ke/fuAQDc3Nzg6empU+EnOWImHyCrT9HR0dizZw82b96MgIAAODs7o23btvDx8UGRIkX0+lwpaTOLQa6qoWpyVSaWa0bPyZMnMWfOHOHx7u7uGDZsGKpVqyYpHpBUEO3y5cupLq/dvHkTFSpUEDV41cDAAMWKFftm1WC5i4iKqUHSvHlzBAQEwMrKSq/FA9WOHj2KtWvXYs+ePXB2dkbLli3RokULlClTRqe438KzhL7B1tYWR48exZkzZzSmx3l6emZ20wAkXZNN+aMhR0VC9uNQqVSoV68e6tWrp+iYgwYNwh9//IGmTZuiffv28PLyyvAHXBu///47Dhw4AAsLC7Ru3Rpjx44VZjYphVxVQ9Xkqkwsx1imTZs2oUuXLvjtt9/Qv39/AElVievUqYN169ahXbt2kuKWK1cOgwcPxsaNG+Hg4PB/7d15XM15/z/+x2k/pcVSllASKoqamLGGkJmxNQYXZiylbDNZY1xfzGiITCUz13VNGKMwY7mMZXSNZEvJFqMykiKUUGOmhihtr98f/c75dDrhvLc6Tc/77dbt5rydnr2iznm+X6/n6/kCUH3jEhgYyKs5o5eXF+/t4GLhMkcgVfNAheHDh2P48OEoLCzE7t278f333yMkJER7bwpFnefRYiUlJQ09hNeSyWTsvffeY97e3szb25vp6emxESNGKB+/9957f/vlEB0dHZUpW8U0uEJTWhJijLH4+Hg2atQo5fLN6NGjBddISBGzvLycHT16lE2ZMoWZmJgwS0tLNm/ePJaUlMQ75pQpU0TZHcSXFDs5tIUUtUwODg4sPDxc7XpYWJigHWhiLmNKWcMi9ena9SkkJETyr8H396vJLAkZGRmhT58+8PDwwJAhQ9C3b996O+xKEzNnztToeVJ1v9QGOjo6ePfdd2FoaAig+hDEoUOHKqdBX758idjYWO3N/kVU8461f//+AKrvWA8fPsz7jlWKmLUplnF+/PFHnDx5Eu3bt8edO3cEx61vQpt7MZ5dQ+ujM/Ht27fh7e2NzMzMOnf02Nvbc45paGiIGzduqH3u7du30aNHD86HVdbERFrG1NXVxaNHjyTbJWRtbY2TJ0++cUcPV2I3D6yoqEBGRgYMDAzQtWtX5fUjR47g888/x82bN5XdqqXC9/erySQs586dQ0JCAuLj43H+/HlUVFTA3d0dHh4eGDx4MIYPH97QQ2zyKGn7P46OjvD398eiRYtUroeHh2Pbtm286gykiFmXJ0+eYO/evYiMjMTNmzd5J5hS1ERoqkePHjh27JjyDV1TQruG1kdnYkD8WiZ7e3sEBgZi9uzZKtcjIyMRFhamFSdAS3Fid01SnK5du3lgZmYm7OzssGDBAl7NA3/77TeMGjUKubm5AICxY8fi22+/xcSJE/Hbb7/Bz88Pn3zyCdq3by/K+F+F71lCTWZJqKby8nJ2/vx5Nn36dKanp9eklhlI42BgYFDnlHdWVhYzNDTUmpgKiq3H7777rnL6fuXKlezmzZu84u3atYvp6emxiRMnKrfJTpw4kenr67MffviB9zgvX77MLl68qHb94sWLLDk5mVfM0tJStnv3bjZkyBCmr6/PdHR0WHh4OPvrr794j5OxxrWb5z//+Q8zMDBgc+bMYTt37mQ7d+5ks2fPZoaGhiwyMlJQbLGWMe/duyd4m/XrjBs3jpmamrK2bduqLOcrPvgQu3nge++9xzw9PZVLuDKZjDk4OLCvvvqKvXjxgtcY+aDGcRrIzMxEfHy88uPly5cYNWoUBg8e3KDj+uCDDzR+rtCqcNI4dOjQAadOnVKbYj958iTnu34pYwLSFMiuW7cOGzduVJkNCggIQHh4OL788kvey1fz58/HsmXL8Pbbb6tcz8vLQ0hICC5duqRxrKtXr2L79u3Ys2cP7O3t8fHHH2PPnj1o3749vLy8BLc4X7hwodpuHi8vLxgbGws+n0fs2au5c+eiTZs2CAsLw/79+wFUz9rs27cPY8eO5T1OMYt5o6OjNXre6tWreY1VigJZMZsHAtUNVOPi4tCrVy8MHDgQe/bswT//+U98/PHHgscqRaO72ppMwmJtbY2SkhIMHjwYgwcPxvLly+Hi4qIVO3EU3W2bMkraVC1ZsgQBAQFISUlRdnZNSkpCVFQUNm/erDUxgeragP3794uyO0ghOzsbo0ePVrs+ZswY/POf/+QdNz09HW5ubmrXXV1dOXeRlrprqFSdiaXa0ePt7Q1vb2/e46qLmInrF198gXbt2sHKyuqVO3VkMhnvhEWKpeqqqqo6l/4ePHgAU1NTzvGePHmCdu3aAaj+OTIxMcE777wjeJwAoK+vr3GtUu0t9ZpqMgmLpaUlMjIy8PjxYzx+/Bj5+fkoKSkRvXcEH02hJuNNKGlTJcUdq1R3wT/88APvz30VqWaDDA0NkZ+fr7Z2/ujRI851B56enti+fTsKCgrw8ccfw8vLS9QbILG39CpIMXuVnJyMqqoqtZmrS5cuQVdXF+7u7rzGKmbi+u677+L06dNwd3eHj48PRo0apVYzJJTYBbIjRoxAREQEtm7dCqA6oSouLsbnn3+O9957j3M8mUyGZ8+ewcjISHk6dUlJCZ4+faryPL6zg/Pnz0dISIiodTwqRF+c0mKFhYXsyJEjbPHixeytt95SttP+5z//2dBDI6RRE3u7tFQ1Ef/4xz+Yh4eHyhEQhYWFzMPDg02YMIFzPCm7hkrVmViKWqbevXuz//73v2rXf/rpJ9anTx9eMRljrHPnznX+f3/77be8ajjy8vJYcHAw69q1K2vTpg1btmwZy8jI4D2+mu7du8ccHByYsbEx09XVVdZoBAQEsNmzZ/OKmZuby5ycnJijoyPT09Nj77zzDmvZsiXr1q0bry3aNY9gUBzPUNdjvqSo41EZP2NNY5dQTX/88Qfi4+Nx5MgR7Nmz55XTbg3lwIED2L9/P3JyclBWVqbyd1J3YSTaQYo7VqnuguvaLp2UlIRDhw4JWmI4dOgQwsLClHUWjo6OCAwMFDQblJeXh0GDBuGPP/6Aq6srACAlJQWtW7fGiRMnBM3eSNE1lEnQmViKHT3NmjVDWlqa2szV3bt34eLigmfPnvEa67fffouFCxfCx8enzmXM2t8DFwkJCdixYwd++uknODs74+TJk4JaXYwbNw6mpqbYvn07WrZsqdy2Gx8fDz8/P947pSoqKlSaB7q5ufFuHnj27FmNnufh4cE5NvDmnZ6CVxMEpzyNxE8//cQ+/fRT5uzszHR1dZmlpSXz9vZmmzdvZikpKQ09PKXNmzezZs2asU8++YQZGBiw2bNns2HDhjFzc/MmNRP03//+l02YMIG9/fbbzNXVVeWjKZDijlWqu2CpmoZp4scff2TFxcWcPqe4uJht2bKFzZs3jy1ZsoRFR0ezsrIy0cb0559/sq+//pr16tVLK3cgSjF71aJFC3b+/Hm160lJSczCwkLQeA8ePMj69+/PWrRowVq0aMH69+8vytlNL168YNHR0axPnz5MLpcL3tXVokUL5WxNzV0wd+/eZXK5XPB4xRAdHa3VTevepMkkLJaWlmz8+PHsm2++YWlpaQ09nFfq1q0b+/HHHxljqj/0q1atYvPnz2/IodUbStoYMzExqXPbX3Z2NmvWrJnWxGRM2u3Sb6LpqcoNRWjXUCk6EzMmfhIg9lKblM6fP89mzZrFzMzMmLu7O/v3v/+tcio8X2Kdrn3kyBGNP7iqfcCsFMrLy9mJEydYZGQke/r0KWOseilOjANRm0zC0ljI5XJ27949xlh1kqWY/cnMzGQtWrRoyKHVG0rapLljleouWOw6Ay406edw5MgR5QyK2G8A5eXl7Pr16+zWrVsq1w8fPsx69uzJDAwMOMdUqKsXzYQJEwT3opHCgwcPmJ2dHTM3N2eDBw9mgwcPZhYWFqxbt24sJyeHd1wx++aEhIQwR0dHZmlpyRYuXMhSU1N5j6suYp2uLZPJVD4UtSW1r/GZvZPyeALGpKnjqalJJSwVFRXswIED7Msvv2Rffvkl++mnnxrsrJJX6dSpE/v1118ZY4y99dZbyjeC48ePs+bNmzfk0OoNJW3S3LFKdRcsZdOwN9EkYan5Il37hb/2mwAX169fZzY2Nso3D29vb/b48WM2aNAg1qJFC7Z8+XKWm5vL+3uTaqlNiuZ5jEmz1CbmMqZMJmM2NjZs/vz5bNGiRa/84EvsAlnGxG8eKJPJVM5rE5vYje5qazIJS1ZWFuvSpQszNjZW1kIYGxuzbt26sdu3bzf08JR8fX3ZF198wRhj7F//+heTy+Vs2LBhzMLCgvn4+DTw6OoHJW3S3LFKdRfMmHR1Bm/SkIcUSt01VKqlNqlqmaQg5jKmh4eH8uf+VR9DhgwRNN7y8nK2a9cuFhgYyObOncu2bdsm6Gehe/fuLDExUe16QkICr6RVJpMxZ2dntbpAseoEpa7jaTJ9WAICAtC5c2dcvHgRLVq0AFC9W+ijjz5CQEAA/ve//zXwCKtt3boVVVVVAKr3tLds2RLnz5/HmDFjBFXENyZDhw7Fzz//DFdXV8ycOROLFi3CgQMHcOXKFU4N5hoza2trpKWl4YcffkBqairkcjlmzpyJyZMnQ19fX2tiKmjSNGzPnj0YM2aM8jDLxk7KrqGAdL1oxGyeV1NWVhbOnDmDgoIC5WuYAt9mbGL2zYmPj+c1Bi709PTw0UcfiRZPiuaBXl5evHrCaELsRne1NZltzSYmJrh48SKcnZ1VrqempqJ///4oLi5uoJGR2qqqqlBVVaV8Qdq7dy/Onz+PLl26YPbs2WptqknjwPvAs9fQ5NTXr7/+WuN4is6vmqh9mJ6pqSl+/fVXdOnSReMYryPVlt6WLVsiJiZG7fiE8+fP4/3330dhYSHnmNu2bcPcuXPRqlUrtGnTRmXbtUwm492OYfLkyXj06BGOHDmibC5ZVFSEcePGwcrKStkAUQqa/LxKfbr2oEGDYGRkpNY8cNq0aSgtLdV4m7KC1AdATpo0Cebm5ti6dStMTU2RlpYGS0tLjB07Fh07dhS8rbnJJCwtWrRATEyM8hdfISkpCaNHj8aff/7ZQCNTV1RUhMuXL9d5pzJt2rQGGhWpb1LcsUoRU1N8j5R/HU1OVe7UqZNGsWQyGbKzszX+2rq6usjMzISlpSUYY+jQoQPOnTsHW1tblecJOVNIil40UiQBNjY2mDdvHpYvX857XHWRsm/Om2jy8yr16dq3b9+Gt7c3MjMzld9rbm4uunTpgsOHD6vNvr2Jrq4uHj16JFnC8uDBA3h5eYExhqysLLi7uyMrKwutWrVCQkKC4K/bZBKWadOm4ddff8X27duVba0vXboEPz8/vPXWW4iKimrYAf7/jh49iqlTp6K4uBhmZmZqdyralFhJqaknbVLcsUp1F6wpPglLWVlZnT8DHTt2FHt4nOno6Kj8G7L/v9V57cfa1JQSkCYJkGL2TOH58+cqy5guLi6iLGO+Cdef15MnT2L58uUIDg5Wzl5duHABK1euRHBwMIYPH85rHEzE5oFSz7AA4ja6q63JJCxFRUWYPn06jh49qvxBLy8vx9ixYxEVFaU1Z9l07doV7733HoKDg7XinKOGQEmbNHesUt0Fa4rLG0BWVhZ8fHxw/vx5letiJgGKlz6+XWOl7hoqVWdiQPwkwNfXF71798acOXN4j0nbcE1YevTooXa6NlB94rLQ07XFcv/+fXTs2FErDv3lo8kU3VpYWODIkSO4ffu2srDMycmJ85Sa1PLy8hAQENBkkxWg+lRhHx+fJp20FRYWYsKECVofUyozZsyAnp4eYmJi0LZtW1FfYLdv345NmzYpW6V36dIFCxcuxKxZszjFuX//PiZNmgRDQ0PRxlbT/PnzsWzZMrWEJS8vDyEhIbh06RLv2CYmJvD39xc6RCV7e3usWrVKWSdYO/HhUhtUW0MuY3Ih1enaZ8+eRWhoqDLhcXJyQmBgIAYOHMg5VnR0tEbP4/LvKnUdT01NZoYFEO+FSkoffPAB/vGPf2DixIkNPZQGY2JiguvXr0syvdxYSHHH2tB3wVzuWE1MTHD16lU4ODiIOobVq1cjPDwcn376qcq0/b/+9S8sWrQIQUFBGseSuh5AqvN5APGTgNfVCXGtDaqpIZcxuc6wiF0gC9R9Tte5c+dw+PBhXud06ejooF27drCyslKrtVHg+u8qdR1PTU1mhuVVL1SLFi1CTk4OpxcqKb3//vsIDAxEenp6nXcqQjPUxsDLywtXrlxp0gmLFHesUt4Fa8LGxkbjJQcnJyc8efJE9DF8++232LZtGyZPnqy8NmbMGLi4uODTTz/l9Dog9b2emFt6a3pTEsAnYbl79y7v8bzO2rVrsW7dugZbxuTi+++/h7e3Nzp27FhngSwf69atw8aNG7Fo0SLltYCAAISHh+PLL7/knLC8++67OH36NNzd3eHj44NRo0apJRxc1Ux431THI1STmWGxtLTE119/rfJCBVT3hvj0008leXHk43U/PNpYwCeF7du3IygoCDNnzmyySZsUd6xS3QUrCC2Qffr0qfLPV65cUb7I1fUzwHfnjYWFBZKTk9W2HmdmZqJPnz4oKirSOJaOjg7y8/NhaWnJayxvItWW3oauZeJCymLeN9HT00Nqaiq6d++u8eeIWSALVCetN27cUCtduH37Nnr06IHS0lLOMR8+fIjo6GhERUXh6dOnmDZtGnx8fNCtWzdeY6xJ8joewa3nGglzc3OWmZmpdv3WrVvM3Ny8/gdEXknM9ulEepmZmWzAgAHKFvWKD67/XzXPSKn5+UJi1vbJJ5/U2X59yZIlbN68eZxiSd01VKrOxGIeGOno6Mj++OMP5eO5c+ey33//Xfk4Pz9fUIdTHx8f9u233woaI18N2UVZQepzus6ePctmzJjBTE1NWb9+/QR3aDYyMmLXr19Xu56amsqMjIwExWasCXW6/fjjj/Htt98iPDxc5frWrVsxderUBhoVqUvtO3Si3cQqkD1z5ozII6u2ePFi5Z9lMhm+++47xMXF4Z133gFQvesmJyeH13Z5KbuGStWZeMKECYiLixOllikjIwMVFRXKx7t378bSpUvRqlUrANUzDnxmARQaehmTKzELZIHqDQgBAQFISUmps3mgUL1798a9e/eQnp6Oa9euoby8XND24969e2Px4sVqdTyBgYHKdiJCNJkloU8//RQ7d+5Ehw4d6nyhqvmLUDupkdrXX38Nf39/GBkZvbErp7b9ghJxOTk54dy5c8rjI+bNm4egoCDlG0BBQQFsbW3x4sWLBo1ZkxQFsjk5OejQoYNa8sMYQ25uLqc+LEOGDNHoeTKZDKdPn9Y4bn30tJDC+vXrER4ejvfff19wElBXt9+ahar5+flo164d76VsqZcxX4dr0a3YBbIKUjQPvHDhAr7//nvs378fXbt2xcyZMzFlypQ6dzlxIXaju9qaTMIi1YuWGDp16oQrV66gZcuWDfoL2pAoaatW+w2g9hp+fn4+2rZty2kWSoqYNfXu3RubNm1SW7cW4lU7cP744w9YWVlpRS2X1LuEAGm29Ir5GiN1wtKQuCYsjo6O8Pf3VymQBapvgLdt26YVfVg2btyIqKgoPHnyBFOnTsXMmTPh4uIi6tdgItfx1NRkEhai3ShpqybFG4AUMaUukH1VQev9+/fh5OSE58+fc44pNqlnWBq6M7EmdHV18fjxY+X/k+L8GMXvcGNOWLgW/EpRICt280AdHR107NgRo0aNeu2ZbPW9yqCpJlPDQrRbzW2RUm2RJOKxsLBQa0Pv6emp8hzGoyutot5EJpNh1apVKo0DKysrcenSJfTq1Yv3uIcMGfLaOz0us6t3796VbIcQ0Di29Cr+3xXbrEtKSjB69Gjlm2HN+hYupF7G1ATXe3kpTtcWu3ngoEGDIJPJcOPGjVc+R+hMiNh1PDVRwqIFahYFvom2Zr5EHDKZTO0FQ+gLiBQxpSqQvXbtGoDqN4vr16+r3AUaGBigZ8+eWLp0Ke/4tZOd8vJypKSk4LfffsP06dM5xZKia2hNYncmliIJ+Pzzz1Ue11VXMX78eM5jlaqYt7y8HA4ODoiJiYGjo+Nrn3vs2DFYW1trHFuKAtn09HS4ubmpXXd1dVV2bOciPj6e1zg0VbOOR7F0f+7cOXh6egqq41GgJSEtULu+5tdff0VFRYVyX3xmZiZ0dXXx1ltv1Xt9TX2hpK2ajo4OevToobxjTUtLg4ODg8od640bNzgvCYkdsyYxC2QVZs6cic2bNws66ZiLL774AsXFxQgNDdX4c6ToGlqT2J2Jpa5lEpOUtTHW1tY4efLkGxMWPsQukG3ZsiViYmKUTdgUzp8/j/fffx+FhYWCx/w6XJfFpK7joYRFy4SHhyM+Ph7R0dFo3rw5gOo7rZkzZ2LgwIFYsmRJA49QGpS0VVuzZo1Gz6t9Z1vfMWtqDAWyb3L79m306dOH08Ga77//Pk6fPg0vLy/RuobWJOZuHkDaJGDo0KE4ePCg2i6Tp0+fYty4cZx/Z6Uca3BwMDIzM/Hdd98J6hhcH6RqHqgproXHUtTx1KTd/1tNUFhYGOLi4pTJCgA0b94ca9euxYgRI/62CUvNJYbw8HCYmpq+Mmn7O+ObNNR3zJoUtSq1FRcXw8jISOM4H3zwAaKiomBmZoYPPvjgtc89ePAg53G+zoULFziNFQD+97//KbuGBgYGYvbs2aJ2Dd26dSuaNWuGs2fPqp1DI5PJtGq3XHx8PMrKytSul5aWIjExkXM8KZYxFZKTk3Hq1CnExcXB2dkZJiYmKn/P92dLitO1Q0NDMWjQINjY2MDV1RUAkJKSgtatW2PXrl28xiklKep4aqKERcs8ffoUv//+u9r133//XdBhZ41JU03aahL7jlWKmGIXyJqbmyvflBR3k2KrnQgxxvDo0SNcuXIFq1at4hyvXbt2WLFiBVasWIGEhATs2LEDvXv3hrOzM06ePCmoCZfYxedSJAFpaWnKP6enp+Px48fKx5WVlYiNjeVUB6IgVTEvUF0wzqeu5k2kOF1bquaBUpG60R0lLFrG29sbM2fORFhYmLIz4KVLlxAYGPjGu86/C0raxL9jlSKm2AWyO3bsqPPPYjIzM1N5k9bR0UG3bt0QFBSEESNGCIotdtdQsUmRBPTq1UuZCA0dOlTt7+VyOb755hvOcaUq5gWk+9kSu0BWwcTEBP7+/kKGVm/mzp2LNm3aICwsTLlc5ejoiH379glqdKdACYuWiYyMxNKlSzFlyhSUl5cDqD6Ey9fXF1999VUDj65+NOWkTYo7VqnughXLeFIUyH7//fcYMmTIa3vy8BEVFSVqPODVXUP5/ntItaVXiiTg7t27YIzBzs4Oly9fVtnmbWBgACsrK+jq6nKKWddYxVZRUYH4+HjcuXMHU6ZMgampKR4+fAgzMzPeRy1Idbq2FM0DpeTt7Q1vb29pggs+jYiIpqKigp09e5b9+eefrLi4mKWmprLU1FRWXFzc0EOrV8+fP2dz585lhoaGykPvDAwM2Ny5c//2/xY1D/yr6/BHY2Njtn379gaPKTV7e3umo6PDOnTowD766CO2bds2lpWVxTtecXExmzNnDmvXrh1r1aoVmzRpEisoKBA0xpCQEObo6MgsLS3ZwoULWWpqqqB4CjKZjOXn5ysf1z6s8PHjx0wmk4nytbTdkCFDWGFhodr1v/76iw0ZMoRXzHv37jEHBwdmbGzMdHV1lf+2AQEBbPbs2bzH+o9//IN5eHiwoqIi5bXCwkLm4eHBJkyYwCvm1q1bma6uLmvdujXr2bMn69Wrl/JDyMGamuJ6AOTly5fZxYsX1a5fvHiRJScnCx4P7RLSMkZGRrh586bod5aNRWVlJZKSkuDs7AwDAwPcuXMHANC5c2e14ri/o/v374t+xypFzPookM3Ly0N8fDwSEhJw9uxZZGVloW3bthg8eDB2797NKdbixYuVB50aGRlhz5496N+/Pw4dOsRrbIB0XUOlbncvRX0UANy5cwcREREqDcMWLFiAzp0784oHvLqbcEFBAaytrZWz0FyMGzcOpqam2L59O1q2bKn8t42Pj4efnx+ysrJ4jTUvLw+DBg3CH3/8oVYge+LECV5FpzY2Npg3b16DNQ/U09NDamoqunfvrtHz+/Tpg2XLluHDDz9UuX7w4EHedTwq4xH02UR0PXr0QHZ2dpNNWHR1dTFixAhl0ib2ORfazsbGBoC4J1ZLEbM+CmStra0xdepUeHt7IzExEXv27MEPP/yAvXv3ck5YDh06hB07digbsU2bNg3vvPMOKioqeE/X10fXUClIUR91/PhxjBkzBr169VIe/JeUlITu3bvj6NGjGD58OKd4Ui1jAkBiYiLOnz+vlmTa2toiLy+PV0xAmgJZsZsHciWXyznVYUlVx6NACYuWWbt2LZYuXYovv/wSb731ltqsQn010mpITT1pU5DijlWsmFIXyMbFxSE+Ph7x8fG4du0aHB0d4eHhgQMHDmDQoEGc4z148ED5RgoAb731FvT19fHw4UNeje0A6bqGSrWlV8ok4LPPPsOiRYuwYcMGtevLly/nnLBIVcwLVCfudc1OPXjwAKamprxiKohdIDthwgTExcWJ1jxQalLV8SjQkpCWqdl4qvZZLVzPZWmsYmNjsWLFiiadtL3qjjU1NZXXHatUMQFpCmR1dHRgaWmJJUuWwN/fX/Cx97UP6QOqf45SU1PrLTHWtGuoVJ2JdXR0lK8pdb3sK5IAHx8fTnGB6qXs69evo0uXLirXMzMz4eLiwrlhmBTLmAqTJk2Cubk5tm7dqjys0dLSEmPHjkXHjh0FJeBiF8iK3TyQK66N46RudEcJi5ap3SCqNg8Pj3oaScOhpK16CtXLy6vOO9a4uDhe7d6liAkAXbp0QXZ2NqytreHh4QEPDw8MHjxYrXkUFxEREUhISEBCQgIMDQ2VMQcPHoyuXbtyjlc7CQDUEwEAkp6ArOmLv1SdiaVMAjp06IDw8HC15Yv9+/dj6dKlyMnJ4RVXCg8ePICXlxcYY8jKyoK7uzuysrLQqlUrJCQk8D59W4rTtRv65HquCYsUdTw1UcKihYqKirB9+3aVaXtfX1/JagW0DSVt4t+xShVTQcwC2dquX7+Os2fP4vTp04iJiYGVlRUePHjAKYbUxxNoguuLf2MSFBSETZs24bPPPlNpGBYSEoLFixfzasqnIMXSaEVFBfbu3Yu0tDQUFxfDzc0NU6dOFdQ3p6ELZKXA9SwhAHj+/LlKHY+Li4t4je4E7zMiokpOTmYtW7Zk1tbWzNvbm3l7e7P27duzli1bsqtXrzb08OpNYWEhCw0NZb6+vszX15eFhYWpbBf8u2vfvj3bv3+/2vV9+/axDh06aE3M2p4/f85iY2PZ9OnTmZ6eHtPV1eUdq6qqil29epWFhYWxUaNGMQsLC6arq8t69eolylhf59y5c6y0tFTUmFy3iEqxpVfh9u3b7JNPPmGenp7M09OTffrpp+z27du841VVVbHw8HBmbW2t3C5vbW3NIiIiWFVVFe+4sbGxzMDAgPXp04ctWrSILVq0iPXp04cZGhqyuLg43nGlUHv7+d8B159ZqdEMi5YZOHAg7O3tsW3bNuX0dUVFBWbNmoXs7GwkJCQ08Aild+XKFYwcORJGRkbKxnHJyckoKSlBXFxcnVXofzdS3LFKdRf8qgLZwYMHY9CgQSpHLGhq9OjRSEpKwtOnT9GzZ08MHjwYHh4eGDRokOB6Fk3wubN8E64zLFJs6QWkq2VSUHSjFlrACoi3jPnzzz9r/DXHjBnDaYwKYp6uLVXzQAAoLy+Hg4MDYmJi3nhi9blz59C7d28YGhpqHF/SRncNnTERVUZGRuzmzZtq12/cuMHkcnkDjKj+DRgwgM2YMYOVl5crr5WXl7Pp06ezgQMHNuDI6o8Ud6xS3QXLZDJmZWXFQkJC6pwR4GPp0qXs6NGjDTarJsWdpaYxFQ0jZTIZO3PmjPJxamoq+/XXX1lwcDCzsbHhPY5evXqx5cuXq11fvnx5vTQj48LQ0JBlZmaqXb916xYzNDTUOE7tZol1NVFUNFfkKzg4mLVq1YpNnz6dhYaGss2bN6t8cCF188B27dqx9PR03p//KlI3uqOERctYWVmx48ePq12PjY1lVlZWDTCi+kdJm6qnT5+yp0+fam3MTZs2MW9vb9ayZUvWrl07NnnyZLZlyxZ269YtUeK/To8ePVhOTo7ocRsyYZG6M7FYSQBjTPlG9KYPvqRYxjxx4gRzc3NjsbGx7K+//mJ//fUXi42NZe7u7oKWmWxtbV/50alTJ06xaicstX92Hj9+LCi5WrduHZs+fbrKTaEYOnbsyDZs2CBqzJqoD4uWmTRpEnx9fREaGqoybR8YGIjJkyc38Ojqh5mZGXJycuDg4KByPTc3V5Rp5sZGiu9ZzJgLFy7EwoULAfxfgWxsbCw++eQTXgWyXNy7d4/30kh9KykpQUlJyRufJ9X5PAqWlpZISUlRK75OSUnhvENm3Lhxyj8zxrB+/XrMmTNHuZQhlJ+fH/z9/ZGdnV3nMiYfCxcuRGRkJAYMGKC85uXlBWNjY/j7+yuLe7kS+3RtKSUnJ+PUqVOIi4uDs7OzWusIvt2ppW50RwmLlgkNDYVMJsO0adOUp6fq6+tj7ty5auu4f1dNOWlzdXXVqEkYly2SUsSsjTGGa9euIT4+HmfOnMG5c+dQVVWl8mbb1GnaNVSKzsQ1iZkE1N5VFRYWhgULFohW+7Nq1SqYmpoiLCwMK1asAAC0a9cOX3zxBe8eJHfu3KmzDsrc3Bz37t0TMFrxSNU8UMHCwoL3adevI3WjO0pYtIyBgQE2b96M9evXq5yjY2xs3MAjqz9NOWmT4o5V6rvgugpk/fz86q1AVgra0lJfii29UiQBUpHJZFi0aBEWLVokWjFv7969sXjxYuzatQutW7cGUH0+U2BgoLLInwspCmQZY/D09FRuvCgpKcHo0aNVmgcKIUV3agCwt7fHqlWrcPHiRUka3dEuIaK1Xrx40WSTNgUpeneIHTMwMBAeHh4YOHBgvfcKkqq3iTb8u0u9mwcQd0ePIo6295q5ffs2vL29kZmZqWxklpubiy5duuDw4cOcGx7W3s1Ve4dZfn4+2rZty2nGrD76BlVUVCA+Ph537tzBlClTYGpqiocPH8LMzAzNmjXjFVPqRnc0w0K0lrGxMZydnRt6GOQNvvrqK42e5+zsjF9++UVwt0sxnT17Fs+fP0ffvn1Vtl8r3sgbktjn89RFW2vCpFzGtLe3R1paGk6cOIGMjAwAgKOjI4YNGybKzFpdcwBc40rZwBCo7no8cuRI5OTk4OXLlxg+fDhMTU0REhKCly9fIjIykldcqet4KGEhhNSLhiyQDQkJQXFxMb788ksA1W8q7777LuLi4gAAVlZWOHXqFLp37y7ZGLi+ad28ebPOs1d8fHwQERHB+etLkQR8/fXXKo8rKioQFRWlXA5R4LoUIPUypkwmw4gRIzBixAhR4kll6NChOHjwoNrS6tOnTzFu3DicPn2aV9wFCxbA3d0dqampaNmypfK6t7c3/Pz8hAxZUpSwEEK0TnZ2tkbLClu2bFHWIbzOvn37VFqmHzhwAAkJCUhMTISjoyOmTZuGNWvWCD6c7XW4rr6LuZsHkCYJ2LRpk8rjNm3aYNeuXSrXZDIZ54RF6mLes2fPIjQ0VKU2KDAwEAMHDuQcS8oC2fj4eJSVlaldLy0tRWJiIu+4iYmJOH/+vMo5WgBga2uLvLw8zvGkbHRXEyUshGgRKe5YpboLlpK9vT08PDzg6+uLDz/8EEZGRnU+b8qUKRrFu3v3LlxcXJSPf/nlF3z44YfK2pCVK1fy2o7JpWvosWPHYG1trXFssbf0SpEENKatvAq7d+/GzJkz8cEHHyh/5s+dOwdPT09ERUVp/DOlIEWBbFpamvLP6enpePz4sfJxZWUlYmNjOf0s1VZVVVXnIbIPHjzgtUyYkZGh8n3u3r0bS5cuVb7GMMYEnVWmQEW3hGiR1xWtKXAtXpMiJh9cCjJTUlKwY8cO7NmzB2VlZcqt7nx2cdT1tR0cHLBw4ULl9sucnBx069ZNoz4ptVlbW+PkyZNvTFi4YowhIiICYWFhePjwIYDq3TyBgYEICAgQfBcvVYFsaWnpKxNMvsQcq6OjI/z9/bFo0SKV6+Hh4di2bRvnPixSFMjq6Ogo/3/reouWy+X45ptv4OPjo3HMmiZNmgRzc3Ns3boVpqamSEtLg6WlJcaOHYuOHTty3kVUu/C49v9Xfn4+2rVrV2eSxIlkLekIIaQGPt1jy8vL2U8//cRGjx7N9PX1Wffu3VlYWBgrKCjgFKdnz55sx44djDHG7t+/z2QyGbtx44by75OSkpi1tTWnmApSdQ2tSYpux2J2862oqGBBQUGsXbt2TFdXVxl35cqV7LvvvhMcX8yxGhgYsKysLLXrWVlZnDv9SuXevXvs7t27TCaTseTkZHbv3j3lx8OHD1lFRYWg+Lm5uczJyYk5OjoyPT099s4777CWLVuybt26qXTY1ZTUnXkVaEmIEC0nxR2rFDGloKenhw8++ADvv/8+/vOf/2DFihVYunQp/vnPf2LixIkICQlB27Zt3xhn/vz5+OSTT5CYmIiLFy+ib9++cHJyUv796dOn4erqymuMUnUNrUlbd/MorFu3DtHR0di4caNK0WaPHj0QEREBX19fTvGkXMbs0KEDTp06pbZ9+eTJk4J2sIlZICt188D27dsjNTUVe/fuRVpaGoqLi+Hr64upU6dq1NywNqkb3SlQwkKIFqqsrERwcDAiIyORn5+PzMxM2NnZYdWqVbC1teX8BiBVTED8Atmarly5gu+//x579+6FiYkJli5dCl9fXzx48ABr1qzB2LFjcfny5TfG8fPzg66uLo4ePYpBgwapTc8/fPiQ9/S62F1DpdrSK2USsHPnTmzduhWenp4qXU579uyp3DrMhVTFvACwZMkSBAQEICUlRaU2KCoqCps3b+YcT0GqAlkpmgcC1TcDH330kaAYCkziRncKVMNCiBYKCgpCdHQ0goKC4Ofnh99++w12dnbYt28fIiIicOHCBa2ICVSvX2tSIMtFeHg4duzYgVu3buG9997DrFmz8N5770FHR0f5nAcPHsDW1la0F0NtUbMmgr1mNw/XXh1S1jLJ5XJkZGTAxsZGpX4hPT0dffr0QXFxMeeYUjp06BDCwsKUSYCjoyMCAwMxduxYzrEUBbK9evXC6dOnVf6fFAWyW7Zs4dX2X8zmgT///LPGzx0zZgyncdZHozsAVMNCiDbq3LkzO3nyJGNMdT345s2bzMLCQmtiMsbYtWvXWEBAALO0tGTm5ubM39+fXbp0iXc8xhizt7dnwcHB7OHDh698zsuXL1lUVJSgryOW8vJyduLECRYZGamsNcnLy2PPnj0THFuKk6PF5ubmxnbt2sUYUx3vmjVr2IABA0T5GiUlJaLEEZuUp2v36tWLLV++XO368uXLOZ+CXXtcdY1X8X1oK0pYCNFCRkZG7N69e4wx1TeAGzduMBMTE62JWZNYBbLl5eXs888/Z7m5uYLHpFDzxfhVH7q6urxi37t3jzk4ODBjY2OVgtOAgAA2e/ZswWOXMmERKwk4fPgwMzc3Zxs2bGDGxsbsq6++YrNmzWIGBgYsLi6Od1wpinkvX77MLl68qHb94sWLLDk5mXM8KQtkDQ0NWWZmptr1W7duCSoQPnHiBHNzc2OxsbHsr7/+Yn/99ReLjY1l7u7ugv6/hgwZwgoLC9Wu//XXX2zIkCG84ypQwkKIFpLijrU+7oIZY6y0tJSFh4czQ0NDJpPJmKGhIfv4449fO1tSW7Nmzdjdu3dFG9Phw4df+bF8+XIml8t5vwGMHTuWffTRR+zly5cq/65nzpxh9vb2gscudsIi1Y6ehIQENmzYMGZpacnkcjnr378/O378uKCxrlmzhtnZ2bHdu3czuVyuHOvevXvZO++8wytm79692X//+1+16z/99BPr06ePoPGKrX379mz//v1q1/ft28c6dOjAO2737t1ZYmKi2vWEhATm4ODAO27t3UIK+fn5TE9Pj3dcBUpYCNFCUtyxSnUXrJCcnMzmzp3Lmjdvztq3b8/+3//7fyw7O5slJCQwT09P1rt3b41jjRkzRvLlnoyMDDZu3Dimq6vLpk2bppx94qpFixYsIyODMaaaXNy9e5fJ5XLB4xQ7YZEiCZCKFMuYJiYmdf57Zmdns2bNmvEfLGPs9u3b7JNPPmGenp7M09OTffrpp+z27du8461Zs4ZZWFiwDRs2sISEBJaQkMDWr1/PLCwsWFBQEO+4RkZG7Pr162rXU1NTmZGREed4qampLDU1lclkMnbmzBnl49TUVPbrr7+y4OBgZmNjw3u8CpSwEKKlpLhjlSJmWFgY69GjB9PX12djx45lR48eZZWVlSrPyc3N5bTk8u2337I2bdqwJUuWsB9//JEdOXJE5UOIvLw8NmvWLKavr89GjRpV5ws3FxYWFsqeLjXfVBMTE5mVlRXneJs3b1b5MDIyYqtWrVK7zpdUtUyMVdcV5ebmsvv376t88CXFMmaLFi3Y+fPn1a4nJSUJ+v5jY2OZgYEB69OnD1u0aBFbtGgR69OnDzM0NOR9Q1BVVcXCw8OZtbW1ss7E2tqaRUREsKqqKt5jHThwIBs+fDh7/Pix8trjx4/ZiBEj2KBBgzjHk7KOpyZKWAghgkhRIFvXi17NwkA+ioqK2LJly5hcLmd9+/ZlCQkJvOLUNnHiRObn58cYq35Tzc7OZs+ePWNDhw5lM2bM4BzP1tb2jR+dOnXiPV4pkoDMzEw2YMAAtbogIf9fjEmzjPmPf/yDeXh4sKKiIuW1wsJC5uHhwSZMmMB7rGIWyNZFzOaBWVlZrEePHszAwIB17tyZde7cmRkYGLDu3bvX2VTvTaRudKdAfVgI0WJlZWUoKChQayDVsWNHrYhZUVGBqVOn4uOPP35tAzcDAwNMnz5d47hiN8zauHEjQkJC0KZNG+zZs4fX9tVXCQsLg5eXF5ycnFBaWoopU6YgKysLrVq1wp49ezjHk/p8HicnJyQmJiqbkykcOHCAd/O8GTNmQE9PDzExMWjbtq1oTcNWr16N6dOnIy8vD1VVVTh48CBu3bqFnTt3IiYmhlfM0NBQDBo0CDY2NsrvNyUlBa1bt1br9cKF2Kdr1yZm80B7e3ukpaXhxIkTyj45jo6OGDZsGK//O6kb3SlQHxZCtFBWVhZ8fHxw/vx5leuMMchkMl5nckgRE6h+Ib1+/TpsbW15fX590NHRgVwux7Bhw6Crq/vK5/HtSltRUaHSNdTNzY1319C6iNmZ+MiRI5g+fTpWrFiBoKAgrFmzRiUJ4NLbQ8HExARXr16Fg4ODKGOsKTExEUFBQUhNTVX+265evRojRozgHfP58+f44YcfkJqaCrlcDhcXF0yePBn6+vq8Y3bo0AHh4eFqh2ju378fS5cuRU5OjsaxpGoeWB+kanQHUKdbQrSSFHesUt0FDx06FGfPnhU9YTl79ixCQ0NVXvgCAwMxcOBAzrGmTZsmSatwBTG7hipI1Zl47NixOHr0KIKCgmBiYoLVq1fDzc2NcyOympycnPDkyRNen/smAwcOxIkTJ0SNaWJiAn9/f1Fjinm69rhx45R/Zq9pHiiEmL9fCq9qdNe9e3dBP18KNMNCiBaS4o5VqrvgyMhIrFmzBlOnTsVbb72ldpYO166ZQPXx9DNnzsQHH3yg8sJ36NAhREVFYcqUKaKMnS8pu4YqSNWZWAqnT5/GypUrERwcDGdnZ7WZCjMzM0HxxV4azcrKwpkzZ+qMuXr1al4xmYSna4t9snZdv1/nzp3D4cOHBf1+ubq6wsvLCxs2bFC5/tlnnyEuLk74jJAolTCEEFG5u7vX2SdB22IyJk2BrIODAwsPD1e7HhYWJqhPRF2qqqrYL7/8wsaPH6/x59RH11Apd/MwJu6Onrq+bzGKbqUo5t26dSvT1dVlrVu3Zj179mS9evVSfohRHMuY+Kdri721XarfL6ka3SnQkhAhWigkJATLli0T9Y5VipiANIV22dnZGD16tNr1MWPG4J///KcoX+Pu3bv4/vvvERUVhd9//x3Dhg3T+HNrfs8nT57E8uXLERwcjL59+wIALly4oJxx4CsvL0/tRGHF1y4vL+cdV4papjNnzvAez+tIsYy5du1arFu3DsuXLxdhhHXT9tO1pfr9srS0REpKCrp06aJyPSUlBVZWVrzjKlDCQogWUrx5Dh06VOVFWsibihQxpdKhQwecOnVK7Q375MmT6NChA++4L1++xIEDB7B9+3acO3cOlZWVCA0Nha+vL++EbeHChYiMjMSAAQOU17y8vGBsbAx/f39ljQBXUuzmAaRJAjw8PFBUVITt27er1ET4+vrC3Nycd9yUlBTRlzELCwvVCmOFaIwFslL9folZx1MXSlgI0UJS3LFKdRcMiF/At2TJEgQEBCAlJUXlhS8qKgqbN2/mHO/q1avYvn079uzZA3t7e3z88cfYs2cP2rdvDy8vL0E1Fnfu3IGFhYXadXNzc14n9CpIsaUXkCYJuHLlCkaOHAkjIyP06dMHALBp0yYEBwcjLi4Obm5uvOJKUcw7YcIExMXFYc6cOaLEk6JA9uuvv1Z5XFFRgaioKLRq1UrlekBAAK/4Yv9+KaxatQqmpqYICwvDihUrAFTX8XzxxRe8x1oTFd0SoqWkuGOVIqZUBbKHDh1CWFiYcqyOjo4IDAzk1UNFT08Pn376KebMmYNu3bopr+vr6yM1NRVOTk68xggAgwYNgpGREXbt2oXWrVsDAPLz8zFt2jSUlpbi7NmzvGNLsaW3d+/e2LRpk8qMkFADBw6Evb09tm3bBj296vvgiooKzJo1C9nZ2UhISOAVV4pi3vXr1yM8PBzvv/9+nTGFvrGKUSDbqVOnNz5HJpMhOzub99cQ8/erLs+ePQMg7vIYJSyEaKG67liTk5NRUlLC+45ViphA9Qudv78/Fi1apHI9PDwc27Zt470kIiYvLy9cuHABo0ePxscffwwvLy/IZDJREpbbt2/D29sbmZmZyun03NxcdOnSBYcPH66zDqUhSZEEyOVyXLt2TW3WJj09He7u7njx4gWvsero6Cj/LNYy5uuSAaFJACD+jh7yfyhhIUQLSXHHKtVdsKGhIW7cuKH2xnz79m306NEDpaWlnGPa2dkhOTkZLVu2VLleVFQENzc3Xm8qubm52LFjB3bs2IGSkhJMmjQJ//nPf5CWlgZHR0fO8WpijInWNbQ2sbf0SpEEKLrE1p75OX78OKZNm4b8/HxeY33T7JSHhwevuFKSKmERs3lgcnIyqqqq8Pbbb6tcv3TpEnR1deHu7s4pXn3V8VANCyFa6MqVKyqJBVC9rLFs2TLOLyZSxgSkKeC7d+9enW+cL1++RF5eHu9xrl69GqtXr8aJEyewY8cO6OnpYezYsfjwww8xfvx4vPXWW7xiy2QyjBgxQtBSTW1SdSaWopZp0qRJ8PX1RWhoqEpNRGBgICZPnsw7rlTFvI2FVM0D58+fj2XLlqklLHl5eQgJCcGlS5c4xauPRncAJSyEaCUzMzPk5OSoTbHn5ubyXhOWIiYgbgFfzYZsx48fV3lTqqysxKlTp0TpqDt8+HAMHz4chYWF2L17N77//nuEhITwTgKk6BoqVWdiKZKA0NBQyGQyTJs2DRUVFQCq64Pmzp2r1kSMCzGLeZ2cnHDu3Dnlm+i8efMQFBSkLGQtKCiAra0t5+UrKQtk161bh+joaGzcuBF+fn7K6z169EBERATvhCU9Pb3OfztXV1ekp6dzjvf555+rPA4LC8OCBQvEXxYT3MmFECK6Tz/9lLVv357t3buX5eTksJycHLZnzx7Wvn17tmDBAq2JqXDw4EHWv39/1qJFC9aiRQvWv39/dvjwYc5xXteIzcDAgHXt2pUdPXpU0FhfJSQkhNfn7dq1i+np6bGJEyeyzZs3s82bN7MJEyYwfX199sMPP/Aej7GxMbt58ybvz3+V5ORk1rJlS2Ztbc28vb2Zt7c3a9++PWvZsiW7evWqoNjPnz9naWlpLC0tjT1//lzwWAcMGMBmzJjBysvLldfKy8vZ9OnT2cCBAznFkslkLD8/X/nY1NRUpRnb48ePmUwm4zxGKU/Xlqp5YIsWLdj58+fVriclJYnSlFDsRncKlLAQooVevnzJAgICmIGBgbK7p6GhIVu4cCErLS3VmphSsbW1Zb///ruoMcvLy9n169fZrVu3VK4fPnyY9ezZkxkYGPCKK1XXUKk6E4uZBEjNyMiozqTtxo0bTC6Xc4pVO2Gp/ab6+PFjQV15pWBkZMTu3bvHGFMd740bN5iJiQnvuP/4xz+Yh4cHKyoqUl4rLCxkHh4ebMKECcIGzShhIaRJEvuOVYqYnTp1Yk+ePFG7XlhYyPvOsi6FhYW8P/f69evMxsZGmah5e3uzx48fs0GDBrEWLVqw5cuXs9zcXF6xDQwMWFZWltr1rKwsQe3IT506xfr27cvOnDnDnjx5wv766y+VD77ETAKkZmVlxY4fP652PTY2lllZWXGKVZ8JS0lJiShx3Nzc2K5duxhjquNds2YNGzBgAO+4Dx48YHZ2dszc3JwNHjyYDR48mFlYWLBu3bqxnJwcweOWKmGhGhZCtJixsTGcnZ21OqYUBbIhISGwtbXFpEmTAFQ3+/rpp5/Qtm1b/PLLL+jZsyeneMuXL4e9vT3+9a9/Yc+ePdizZw9u3rwJX19fxMbGQi6X8xonIF3XUKk6E0tVyyQFMYt5ZTKZWh2QmCd4S1EgK1XzQGtra6SlpeGHH35Aamoq5HI5Zs6cicmTJ6ttc9eE1I3uFGhbMyGEF0WB7Lhx4xAdHV1ngeyJEydw69YtzrE7deqEH374Af369cOJEycwceJE7Nu3D/v370dOTg7i4uI4xbOyskJcXBx69eqFv/76C82bN0d0dDQ+/vhjzmOr7dtvv8XChQvh4+NTZ9Hx7NmzecWVaktvQEAADh06VGcSMH78eERERPCKK4WysjIEBgYiMjKyzmJeQ0NDjWPp6OigR48eyl1yaWlpcHBwgIGBAYDqN9kbN27wTgSlOl1biuaBYquPRncAJSyEEJ4U/TxkMhlqv4zo6+vD1tYWYWFhGDVqFOfYcrlc2YhtwYIFKC0txZYtW5CZmYm3334bhYWFnMf6+PFj5QFspqam+PXXX9UOaeNLqq6hUmzpFTMJqC8vXrzAnTt3AACdO3eGsbEx5xhr1qzR6Hm1d7xoyt7eHlu2bIGnp6dKL5aMjAz07duX88+s1LKysnDmzJk6e/ysXr26gUb1epSwEEIE6dSpE5KTk9Wmf4Vo164dDhw4gH79+qFbt25Yu3YtJkyYgFu3bqF37954+vQpp3i6urrIzMyEpaUlGGPo0KEDzp07p7ZFWsiZQmKTqjOxghhJAPk/crkcGRkZsLGxUUlY0tPT0adPHxQXF/OOLXbzwG3btmHu3Llo1aoV2rRpo7I0JpPJRDmoUcxGd0qiV8UQQpo8IQWyjDE2f/58ZmNjw4YNG8ZatmzJnj17xhhjbM+ePczV1ZVzPMVWacXHqx7zcfnyZXbx4kW16xcvXmTJycm8YjLWuHbzNCZDhgyp8+fzr7/+YkOGDOEdV4oC2czMTDZgwACVn1WhP6+MMdaxY0e2YcMG3p//KhUVFSwoKIi1a9eO6erqKv8NVq5cyb777jvB8anolhAiiNgFskB1czBbW1vk5uZi48aNaNasGQDg0aNHmDdvHud4Up5ULXbXUAWpOhM3dfHx8SgrK1O7XlpaisTERN5xpSiQlap5YGFhISZMmCBKrJqkanSnJDjlIYQ0aba2tiwpKYkxxlhcXByzsLBgx48fZ76+vmz48OENPLpq0dHRkvWaMTExqXMLZ3Z2NmvWrBnvuGJu6SWMpaamstTUVCaTydiZM2eUj1NTU9mvv/7KgoODmY2NjaCvkZCQwIYNG8YsLS2ZXC5n/fv3r/P/UFNSNQ/08fFh3377rehxpWp0p0AzLIQQQR4/fqzcvhsTE4OJEydixIgRsLW1VZt1eJ2ff/4Z7777LvT19VVa9NdlzJgxnMY4c+ZMjBw5Ull0KyZDQ0Pk5+ertSF/9OiRyuwIV1Kdz9NU9erVS7m1eejQoWp/L5fL8c033wj6GgMHDsSJEycExajJyckJT548ES2egr29PVatWoWLFy/WeWI33+3HeXl5dZ5OXlVVhfLycl4xa6KEhRAiSPPmzZGbm4sOHTogNjYWa9euBVDdL4TLFtFx48Ypd/LUPEytNj49SJiEewtGjBiBFStW4MiRI8rdO0VFRfjnP/+J4cOH844r1fk8TdXdu3fBGIOdnR0uX74MS0tL5d8ZGBjAysoKurq6gr+OmAWyISEhWLZsGYKDg+tMLPgWiW/duhXNmjXD2bNn1bbPy2Qy3gmLk5MTEhMTYWNjo3L9wIEDcHV15RVTZWxMyt9kQsjf3ieffIKYmBh06dIF165dw71799CsWTPs3bsXGzduFGXHgVA6OjrIz89XeZMSS15eHgYNGoQ//vhD+aKckpKC1q1b48SJE4KaxwG0m6exkOJ0bUXrAACiNg+UypEjRzB9+nSsWLECQUFBWLNmjUodj5AEHqCEhRAiUHl5OTZv3ozc3FzMmDFD+aa9adMmmJqaYtasWQ08QvWmYa/CN7l6/vy5StdQFxcX3l1DifTu3LmDiIgIlf42CxYsQOfOnXnH7N+/P/T09PDZZ5/VWSDLp/hcquaBUpKy0R0lLIQQrXTq1CmcOnWqzun177//nlMsHR0dLFmyRLnb6FX4Ng0jjcfx48cxZswY9OrVC/379wdQXRuUmpqKo0eP8p4FMDExwdWrV9WOPBBKzOaBTk5OOHfuHFq0aAEAmDdvHoKCgpQ9lAoKCmBra4sXL16I9w2IiBIWQghnUhbIAtVdSYOCguDu7l7n3eqhQ4c4xavd6VZsjbFraFPl6uoKLy8vtTqgzz77DHFxcbxn2Xr37o1NmzZhwIABYgwTgPjNA2v/HpiZmSElJUVZMJ6fn4+2bduq/QxzJXajOwVKWAghnNV84au5zl4b33X2tm3bYuPGjaKc9QNUd7p99OiRJAlLfXQNJeIxMjLC9evX1Y5lyMzMhIuLC0pLS3nFPX36NFauXClqgezAgQNhb2+v0o+noqICs2bNQnZ2NhISEjjFq+uICkVHXqA6YWnXrh3v2hgp6nhqol1ChBDOat45Cb0bq0tZWZlyK68YpLwvW7t2LdatW4fly5dL9jWIeCwtLZGSkqKWsKSkpAhKaKU4XbuxNQ+UqtGdAiUshBCtM2vWLPz4449YtWqVKPHu3r0ryQ4hQLquoUQafn5+8Pf3R3Z2tkp/m5CQECxevJh3XCm6KZuZmSEnJ0etLiY3Nxempqac4yn60NS+JpaUlBRJ6ngUKGEhhAgmRoFszTeLqqoqbN26FSdPnoSLi4va9Hp4eDin8UVHR2v0PD71JhMmTEBcXBzmzJnD+XNJ/Vu1ahVMTU0RFhaGFStWAKg+bPOLL77g3X8EqN6xI/bp2mI3D2SMwdPTUzljU1JSgtGjR8PAwAAAlP1++JKq0Z0C1bAQQgQRq0B2yJAhGj1PJpPh9OnTnMaoo6ODdu3awcrK6pXLQ3zrTdavX4/w8HC8//77onYNJdJ79uwZAPCarahNitO1y8rKEBgYiMjIyDqbBxoaGnKKt2bNGo2ex3e3nBR1PDVRwkIIEUTsAlkuHjx4gHbt2r228BcA3n//fZw+fRpeXl7w8fHBqFGj3vg5murUqdMr/04mkyE7O1uUr0O0m9gFsjU1luaBUje6o4SFECJIy5YtcfnyZUFNt/iqvS3zdR4+fIjo6GhERUXh6dOnmDZtGnx8fNCtW7d6GClpaK6urhrVa/Dd1SWXy3Ht2jW1+o309HS4u7trVW+ToUOH4uDBg7CwsFC5/vTpU4wbN47zDKaC1I3uqIaFECKI2AWyXHC532rXrh1WrFiBFStWICEhATt27EDv3r3h7OyMkydPQi6XSzhS0tBqnk/FGMP69esxZ84cZRM1ocQukJVSfHw8ysrK1K6XlpYiMTGRd1wp6nhqohkWQghntQtko6Oj4eLiIkqBLBe1+0hoqqSkBP/973/x73//G9evX8fjx485r6839q6hTR3fn51XCQgIwKFDh+oskB0/fjwiIiJE+TpCpKWlAag+ufr06dMqyVplZSViY2OxZcsW3Lt3j1d8Kep4aqKEhRDCmZQFslxwfdO5cOECvv/+e+zfvx9du3bFzJkzMWXKFLWpcU3UV9dQIg2xExaxC2SloKOjo1wWq+utXy6X45tvvoGPjw+v+FLW8QCUsBBC6ommBbJcaPqms3HjRkRFReHJkyeYOnUqZs6cCRcXF0FfW+quoURaYicsCtpcIHv//n0wxmBnZ4fLly+r9CYyMDCAlZUVdHV1eceXuo6HalgIIfXCyclJ4wJZTWna9Oqzzz5Dx44dMXHiRMhkMkRFRdX5PCmXr0jTYGxsDGdn54YeRp1sbGwASNOdGpC+jocSFkJIvZBiMlfTmIMGDYJMJsONGzde+RyuHT+l7hpKxPX111+rPK6oqEBUVJSy5kihqfTNuXPnDiIiIlSKYxcsWCBot5/Yje5qoyUhQki94DIFv2PHDkyaNOmN0+m5ublo166doGlsvnR0dNCjRw/lWn1aWhocHBxUuobeuHGDloS0xOv65Sg0lb45x48fx5gxY9CrVy/0798fQHVikZqaiqNHj2L48OG84kpdx0MJCyGkXnBJWFq3bo2SkhJMmDABvr6+oh6EqAlN+rtI3TWUEKm4urrCy8sLGzZsULn+2WefIS4uTvAJ41LV8VDCQgipF1wSloqKChw9ehRRUVE4duwY7OzsMHPmTEyfPh1t2rTRqrGSxqu0tBRGRkYNPYx6Z2RkhOvXr6udWJ2ZmQkXFxeUlpY20MheT7xyfUIIeQ0u9R16enrw9vbGkSNHkJubCz8/P/zwww/o2LEjxowZgyNHjmjNduGhQ4eiqKhI7frTp08xdOjQ+h8Qea3Kykp8+eWXsLa2RrNmzZRLQKtWrcL27dsbeHT1w9LSEikpKWrXU1JSlLvetBElLISQesF3Mrd169YYMGAA+vbtCx0dHVy/fh3Tp09H586dER8fL+4geZCqayiRxrp16xAVFYWNGzcq640AoEePHvjuu+8acGT1x8/PD/7+/ggJCUFiYiISExOxYcMGzJ49G35+fg09vFeiXUKEEEE0LZBNT09Hu3btNI6bn5+PXbt2YceOHcjOzsa4ceMQExODYcOG4fnz5wgKCsL06dNx//59od8CL4quoUD19/b48WPlY0XXUGtr64YYGnmNnTt3YuvWrfD09MScOXOU13v27ImMjIwGHFn9WbVqFUxNTREWFoYVK1YAqD664osvvtDqXVJUw0IIEUSKAtnRo0fj+PHj6Nq1K2bNmoVp06apnflSUFCANm3aSLI0pEkNi9RdQ4k05HI5MjIyYGNjo/L/nJ6ejj59+qC4uLihh1ivnj17BgBad95RXWiGhRAiSF5enrJAdvDgwaIUyFpZWeHs2bPo27fvK59jaWmJu3fv8h22YHfv3pW0ayiRhpOTExITE5VN1BQOHDgAV1fXBhpVw2kMiYoCzbAQQkSTn5+P3bt3Izo6GhkZGRg5ciR8fX0xevRoUVvyS01PTw+pqano3r17Qw+FiOzIkSOYPn06VqxYgaCgIKxZswa3bt3Czp07ERMTw7sHSWPg6uqqUfG70G3NUqEZFkKIaBQFspmZmcjMzFQWyDZv3hw7duzA4MGDX/m5tTuRvo7U6+xyuRxyuVzj50vRNZRIY+zYsTh69CiCgoJgYmKC1atXw83NTVDDtMZi3Lhxyj8zxrB+/XrMmTNHbblVW9EMCyFEsLoKZH19fVUKZPfu3fvaAllNOpEC9dONlEsfFqm6hhIitcbWb4gSFkKIIA1dICsFLi/kUncNJdIoKytDQUGB2s9kx44dG2hE9a+xJSy0JEQIEaQxFMhK6ebNm9i/f7/adR8fH0RERNT/gMhrZWVlwcfHB+fPn1e5zhiDTCajs5+0GCUshBBBNOkOKpPJ1HZl1LZ48WJ8+eWXMDExweLFi1/73PDwcE5j5IpLV15F19Dabc61vWtoUzVjxgzo6ekhJiYGbdu2pRO2GxFKWAghnElRIHvt2jWUl5cr//wq9fEGw2WlXNE1NDs7W9mDJikpCSEhIW9MvEj9S0lJwdWrV+Hg4NDQQ6l3tX9vKyoqEBUVhVatWqlc19bmcVTDQgjhTJsKZDVVXl4OBwcHxMTEwNHR8bXPPXfuHHr37g1DQ8M3xmWMISIiAmFhYXj48CGA6q6hgYGBCAgIoDt4LdO7d29s2rQJAwYMaOih1DtNfm+16Xe2NkpYCCFNhrW1NU6ePPnGhIWvxtQ1tKk6ffo0Vq5cieDgYDg7O0NfX1/l783MzBpoZORNKGEhhGid0tJSfPPNNzhz5kydOzn47rwJDg5GZmYmvvvuO+jp0Yp4U1SzgWHN2a+mWnRbWloKIyOjhh6GRug3lhDCmdQFsr6+voiLi8OHH36IPn36iLaskpycjFOnTiEuLg7Ozs4wMTFR+fuDBw9qHKuxdw1tqs6cOdPQQ2hwlZWVCA4ORmRkJPLz85GZmQk7OzusWrUKtra28PX1begh1okSFkIIZ1IXyMbExOCXX35RNmITi4WFBcaPHy9KrMbeNbSp8vDwQFFREbZv367SmdjX1xfm5uYNPLr6sW7dOkRHR2Pjxo3w8/NTXu/RowciIiK0NmGhJSFCiNZxcnLC3r174eLi0tBD0Vhja8LVVF25cgUjR46EkZER+vTpA6B65q2kpARxcXFwc3Nr4BFKz97eHlu2bIGnp6fKz21GRgb69u2LwsLChh5inRrPaWSEkCYjLCwMy5cvf20rf74qKipw8uRJbNmyRVkk+/DhQxQXF4v+tYj2WbRoEUaPHo179+7h4MGDOHjwIO7evYtRo0Zh4cKFDT28epGXlwd7e3u161VVVcqZU21ES0KEEEGkKJB1d3dHaWkp7OzsYGxsrLaT488//+Q11vv372PkyJHIycnBy5cvMXz4cJiamiIkJAQvX75EZGQkr7ik8bhy5Qq2bdumUnStp6eHZcuWwd3dvQFHVn+cnJyQmJio1szxwIEDcHV1baBRvRklLIQQQaQokJ08eTLy8vIQHByM1q1bi1Z0u2DBAri7uyM1NRUtW7ZUXvf29lZZyyd/X2ZmZsjJyVFrHJebm9tktqOvXr0a06dPR15eHqqqqnDw4EHcunULO3fuRExMTEMP75UoYSGECCJFgez58+dx4cIF9OzZU7SYAJCYmIjz58/DwMBA5bqtrS3y8vI4xWrsXUObqkmTJsHX1xehoaEqnYkDAwMxefLkBh5d/Rg7diyOHj2KoKAgmJiYYPXq1XBzc9P608UpYSGECGJtbS36namDgwNKSkpEjQlUr9HX1WfjwYMHnL+HTZs2qTxu06YNdu3apXJNJpNRwqJlQkNDIZPJMG3aNFRUVAAA9PX1MXfuXLUTt//OBg4ciBMnTjT0MDihXUKEEEGOHTuGr7/+GpGRkW884FBTcXFxWLNmDdatWydqN9JJkybB3NwcW7duhampKdLS0mBpaYmxY8eiY8eO2LFjhxjDJ43AixcvcOfOHQBA586dYWxs3MAjqn9lZWV11p117NixgUb0epSwEEIE+f333zFx4kQkJCSIViCr6EZau3ZFaDfSBw8ewMvLC4wxZGVlwd3dHVlZWWjVqhUSEhIEn67cmLqGkqYrKysLPj4+OH/+vMp1be/2S0tChBBBpCiQlaobafv27ZGamoq9e/ciLS0NxcXF8PX1xdSpUyGXy3nFbKxdQ0nTNWPGDOjp6SEmJgZt27ZtNAd00gwLIUQQY2NjSQpkG4ugoCBER0cjKCgIfn5++O2332BnZ4d9+/YhIiICFy5caOghEqLCxMQEV69eVdsppe1ohoUQIohUBbJAdZ1BTk4OysrKVK5z6YD7888/a/zcMWPGaPxchZ07d2Lr1q3w9PTEnDlzlNd79uyJjIwMzvEIkZqTkxOePHnS0MPgjBIWQoggGzZswJIlS0QtkP39998xc+ZMHDt2rM6/57LGXvPMH6C6Lqb2xLJiSpzP2n1j7RpKmq6QkBAsW7YMwcHBoha1S41a8xNCBBk5ciQuXLgAT09PWFlZoXnz5mjevDksLCzQvHlzXjEXLlyIoqIiXLp0CXK5HLGxsYiOjkaXLl04zZgA1YmD4iMuLg69evXCsWPHUFRUhKKiIhw7dgxubm6IjY3lNVZF19DatL1rKGm6hg0bhosXL2Lo0KGi/c7WB5phIYQIIkWB7OnTp3HkyBG4u7tDR0cHNjY2GD58OMzMzLB+/Xq8//77vOIuXLgQkZGRGDBggPKal5cXjI2N4e/vrzy9l4vG2jWUNF1SFbVLjRIWQoggHh4eosd8/vy5cotx8+bN8fvvv6Nr165wdnbmdTaRwp07d2BhYaF23dzcHPfu3eMVs7F2DSVNl4eHB4qKirB9+3Zlku7k5ARfX1+Ym5s38OhejXYJEUJEIUaBrELv3r2xdu1aeHl5YcyYMbCwsMD69evx9ddf48CBA8qGX1wNGjQIRkZG2LVrF1q3bg0AyM/Px7Rp01BaWoqzZ8/yiktIY3LlyhWMHDkSRkZG6NOnDwAgOTkZJSUliIuLg5ubWwOPsG6UsBBCBBGzQFZh9+7dqKiowIwZM3D16lWMHDkSf/75JwwMDBAVFYVJkybxGuvt27fh7e2NzMxMdOjQAUD1oXddunTB4cOH6yye1VRj6xpKmq6BAwfC3t5e5dTqiooKzJo1C9nZ2UhISGjgEdaNEhZCiCBTp07F/fv3ERERgcGDB+PQoUPIz8/H2rVrERYWpnG9ydOnT1+5O+HFixfIyMhAx44d1Q4X5IoxhhMnTii3HDs6OmLYsGG8m2c11q6hpOmSy+W4du2aWh+W9PR0uLu748WLFw00stejGhZCiCBiFcg2b94cjx49gpWVFYYOHYqDBw8q602MjY1Fm6aWyWQYMWIERowYIUq8xto1lDRdZmZmyMnJUUtYcnNzRT/IVEyUsBBCBBGrQLZZs2b4448/YGVlhfj4eMl6mJw9exahoaEqxYaBgYEYOHAgr3gpKSmNsmsoabomTZoEX19fhIaGol+/fgCApKQkBAYGYvLkyQ08ulejhIUQIki3bt1w69Yt2NraomfPntiyZQtsbW0RGRmJtm3bahxn2LBhGDJkCBwdHQEA3t7eMDAwqPO5p0+f5jXW3bt3Y+bMmfjggw8QEBAAADh37hw8PT0RFRWFKVOmcI7ZWLuGkqYrNDQUMpkM06ZNQ0VFBQBAX18fc+fOxYYNGxp4dK9GNSyEEEHEKpAtKSlBdHQ07ty5g7CwMPj5+cHY2LjO527atInXWB0dHeHv749FixapXA8PD8e2bdt49WE5ffo0Vq5c2ei6hhLy4sUL5Y67zp07v/L3TVtQwkII4UzqAtkhQ4bg0KFDdfZMEcLQ0BA3btxQ2w10+/Zt9OjRA6WlpZxj6uj8X8PwmvUrVHRLiLhoSYgQwpnUBbKaduI0MzNDSkoK7OzsNHp+hw4dcOrUKbWE5eTJk8ptzlw11q6hhDQ2lLAQQjirrwLZN+E6QbxkyRIEBAQgJSVFpdgwKioKmzdv5jWGxto1lJDGhpaECCGcjR8/HklJSXB0dMTZs2fRr18/0QtkNWFqaorU1FSNZ1gA4NChQwgLC1MmF46OjggMDMTYsWN5jaGxdg0lpLGhhIUQwll9FMhqgk/CIrbG2jWUkMaGEhZCiCBSFchqgmvCkpycjKqqKrz99tsq1y9dugRdXV24u7tzHkNj7RpKSGOj8+anEELIq505c0ajZMXMzAzZ2dnSD+g15s+fj9zcXLXreXl5mD9/Pq+Yiq6htWl711BCGhtKWAgh9UIbJnPT09PrrClxdXVFeno6r5iKrqH79u1Dbm4ucnNzsXfvXsyaNUuru4YS0tjQLiFCSKNVUlKCkpISjZ9vaGiI/Px8tSWkR48eKetPuGqsXUMJaWyohoUQUi+kKJDlGnPy5Ml49OgRjhw5otxyXFRUhHHjxsHKygr79+/nPZbG1jWUkMaGZlgIIU1GaGgoBg0aBBsbG7i6ugKoPrywdevW2LVrl6DYxsbGcHZ2FmOYhJA6UMJCCGkyrK2tkZaWhh9++AGpqamQy+WYOXMmJk+erHYGECFEu1DCQghpUkxMTODv79/QwyCEcEQJCyGkXnAtkNVEzcMGNZWVlYUzZ86goKAAVVVVKn+3evVqsYZGCBEZJSyEkHohl8shl8tFjcl1z8C2bdswd+5ctGrVCm3atFFJeGQyGSUshGgxSlgIIVqlvLwcDg4OiImJgaOj42ufe+zYMVhbW2sce+3atVi3bh2WL18udJiEkHpGCQshRKvo6+ujtLRUo+cOGDCAU+zCwkJMmDCBz7AIIQ2MOt0SQrTO/PnzERISomzEJpYJEyYgLi5O1JiEkPpBMyyEkHrBpUA2OTkZp06dQlxcHJydnWFiYqLy9wcPHuQ1Bnt7e6xatQoXL16Es7Oz2lbmgIAAXnEJIdKjTreEkHrBpSvtzJkzX/v3O3bs4DWGTp06vfLvZDJZgx/OSAh5NUpYCCG8cSmQPXfuHHr37g1DQ8N6Gh0h5O+EalgIIbxxLZDlkqxUVFTg5MmT2LJlC549ewYAePjwIYqLi3mNlRDSuFHCQggRRIoC2fv378PZ2Rljx47F/Pnz8fvvvwMAQkJCsHTpUs7xnJyc8Oeffyofz5s3D0+ePFE+LigooMMKCdFytCRECBHE29sbp06dQrNmzUQrkB03bhxMTU2xfft2tGzZUln7Eh8fDz8/P2RlZXGKp6Ojg8ePH8PKygoAYGZmhpSUFGU9TX5+Ptq2bavW+ZYQoj1olxAhRBALCwuMHz9e1JiJiYk4f/48DAwMVK7b2toiLy9PcPy67tP4tPknhNQfSlgIIYLw3bHzOlVVVaisrFS7/uDBA5iamor+9Qgh2o9qWAghgoldIDtixAhEREQoH8tkMhQXF+Pzzz/He++9xzmeTCZTm0GhGRVCGheqYSGECHL//n2MHDkSOTk5ePnyJTIzM2FnZ4cFCxbg5cuXiIyM5BzzwYMH8PLyAmMMWVlZcHd3R1ZWFlq1aoWEhARlLYqmdHR00KNHD+jpVU8qp6WlwcHBQbnkVFFRgRs3btQ5q0MI0Q6UsBBCBBG7QFahoqICe/fuRVpaGoqLi+Hm5oapU6fyOvF5zZo1Gj3v888/5xybEFI/KGEhhAjSsmVLnD9/Ht26dVPpZnvv3j04OTnhxYsXDT1EQsjfABXdEkIEEatA9ueff9b4uWPGjNH4uTUNHToUBw8ehIWFhcr1p0+fYty4cTh9+jSvuIQQ6dEMCyFEkEmTJsHc3Bxbt26Fqakp0tLSYGlpibFjx6Jjx44a7yLS0VHdAyCTydS2HysKZfnWmtTux6JQUFAAa2trlJeX84pLCJEezbAQQgQJCwuDl5cXnJycUFpaiilTpigLZPfs2aNxnJpN206ePInly5cjODgYffv2BQBcuHABK1euRHBwMOcxpqWlKf+cnp6Ox48fKx9XVlYiNjYW1tbWnOMSQuoPzbAQQgQTs0AWAHr06IHIyEgMGDBA5XpiYiL8/f1x8+ZNTvF0dHSUszN1veTJ5XJ888038PHx4TVeQoj0aIaFECKYnp4ePvroI9Hi3blzR63OBADMzc1x7949zvHu3r0Lxhjs7Oxw+fJlWFpaKv/OwMAAVlZW0NXVFTBiQojUaIaFEMKZ1AWygwYNgpGREXbt2oXWrVsDqD7vZ9q0aSgtLcXZs2c5xySENG6UsBBCOJO6QPb27dvw9vZGZmYmOnToAADIzc1Fly5dcPjwYdjb2/McefXsTUREhHJZycnJCQsWLEDnzp15xySESI9a8xNCOKuqqlJ+xMXFoVevXjh27BiKiopQVFSEY8eOwc3NDbGxsbzi29vbIy0tDUePHkVAQAACAgIQExOD69evC0pWjh8/DicnJ1y+fBkuLi5wcXHBpUuX0L17d5w4cYJ3XEKI9GiGhRAiiNgFslJydXWFl5cXNmzYoHL9s88+Q1xcHH799dcGGhkh5E1ohoUQIojYBbIKZ8+exejRo2Fvbw97e3uMGTMGiYmJ/AcK4ObNm/D19VW77uPjg/T0dEGxCSHSooSFECJI7969sXjxYuTn5yuv5efnIzAwEH369OEVc/fu3Rg2bBiMjY2VS0JGRkbw9PTEjz/+yHuslpaWSElJUbuekpLC+UBFQkj9oiUhQoggUhTIOjo6wt/fH4sWLVK5Hh4ejm3btvFeZgoKCsKmTZvw2WefoV+/fgCApKQkhISEYPHixVi1ahWvuIQQ6VHCQggRjDGGEydOICMjA0B1wjFs2DDlTiGuDA0NcePGDbVk5/bt2+jRowdKS0t5jzMiIgJhYWF4+PAhAKBdu3YIDAxEQEAA7/ESQqRHCQshROvY29sjMDAQs2fPVrkeGRmJsLAwZGVlCf4az549AwBOBzQSQhoOdbolhAh29uxZhIaGqvQ2CQwMxMCBA3nFW7JkCQICApCSkqKydBMVFYXNmzeLMmZKVAhpXGiGhRAiyO7duzFz5kx88MEH6N+/PwDg3LlzOHz4MKKiojBlyhRecQ8dOoSwsDBlEuTo6IjAwECMHTuWcyxXV1eNlntoWzMh2osSFkKIIFIVyIppzZo1yj8zxrB+/XrMmTMHLVq0UHne559/Xt9DI4RoiBIWQoggUhTIJicno6qqCm+//bbK9UuXLkFXVxfu7u6CxmxqaorU1FTY2dkJikMIqT/Uh4UQIkiHDh1w6tQptesnT55UbnPmav78+cjNzVW7npeXh/nz5/OKSQhp3KjolhAiiBQFsunp6XBzc1O77urqSh1pCWmiKGEhhAgyd+5ctGnTBmFhYdi/fz+A6rqWffv28SqQBaqXmfLz89WWbB49egQ9PXrZIqQpohoWQojWmTx5Mh49eoQjR47A3NwcAFBUVIRx48bByspKmRhp6uuvv1Z5vHz5cgQGBqJVq1Yq1wMCAoQNnBAiGUpYCCGCSFEgm5eXh0GDBuGPP/6Aq6srgOrzflq3bo0TJ05wro3p1KnTG58jk8mQnZ3NeayEkPpBCQshRJA+ffpg2bJl+PDDD1WuHzx4ECEhIbh06RKvuM+fP8cPP/yA1NRUyOVyuLi4YPLkydDX1xdj2ISQRoYSFkKIIM2aNUNaWppavcndu3fh4uKibIGvbUpLS2FkZNTQwyCEaIiq1wghgkhVIJuVlYUzZ86goKAAVVVVKn+3evVqXjErKysRHByMyMhI5OfnIzMzE3Z2dli1ahVsbW3h6+vLe7yEEGnRDAshRBCxC2QBYNu2bZg7dy5atWqFNm3aqLTVl8lkvFvoBwUFITo6GkFBQfDz88Nvv/0GOzs77Nu3DxEREbhw4QKvuIQQ6VHCQggRROwCWQCwsbHBvHnzsHz5clHHam9vjy1btsDT01Ol221GRgb69u2LwsJCUb8eIUQ8tCRECBHE2toaaWlpKgWyM2fOFFQgW1hYiAkTJog80urkqvYRAgBQVVWF8vJy0b8eIUQ8lLAQQgQzMTGBv7+/aPEmTJiAuLg4zJkzR7SYAODk5ITExETY2NioXD9w4IBydogQop0oYSGECCZ2gay9vT1WrVqFixcvwtnZWW2mhm+Dt9WrV2P69OnIy8tDVVUVDh48iFu3bmHnzp2IiYnhFZMQUj+ohoUQIogUBbKva/QmtMFbYmIigoKCkJqaiuLiYri5uWH16tUYMWIE75iEEOlRwkIIEUSqAllCCKmJEhZCiCBmZmZISUlR68OizcrKyupcvurYsWMDjYgQ8iY6DT0AQkjjpiiQFYOTkxP+/PNP5eN58+bhyZMnyscFBQUwNjbmHT8rKwsDBw6EXC6HjY0NOnXqhE6dOsHW1laj84YIIQ2Him4JIYKIWSCbkZGBiooK5ePdu3dj6dKlylOVGWMoLS3lPdYZM2ZAT08PMTExaNu2rUq9DSFEu9GSECFEEDELZHV0dPD48WNYWVkBgEpzNwDIz89Hu3btUFlZyWusJiYmuHr1KhwcHHh9PiGk4dAMCyFEkLt37zb0EDTm5OSkssRECGk8qIaFEKI1ZDKZ2jKNmMs2ISEhWLZsGeLj4/HHH3/g6dOnKh+EEO1FS0KEEF6cnJxw7tw5tGjRAkB1gWxQUJCy3qSgoAC2trZ48eKFxjF1dHTQo0cP5SnPaWlpcHBwgIGBAQCgoqICN27c4L0kpKPzf/doNRMhxhhkMhnvuIQQ6VHCQgjhpXa9Se3tzfn5+Wjbtq3a1uHXWbNmjUbP+/zzz7kPGMDZs2df+/ceHh684hJCpEc1LIQQUdR178N1OYdvIqIpDw8PFBUVYfv27bh58yaA6pkiX19fmJubS/q1CSHCUA0LIUTrDB06FEVFRWrXnz59iqFDh/KOe+XKFdjb22PTpk34888/8eeff2LTpk3o3LkzryMECCH1h5aECCG86Orq4vHjx7C0tARQvQU5LS1Nuc1ZyBbk2stNCgUFBbC2tkZ5eTmvMQ8cOBD29vbYtm2bsk6moqICs2bNQnZ2NhISEnjFJYRIj5aECCG8MMbg6empfOMvKSnB6NGjVQpkuUpLS1P+OT09HY8fP1Y+rqysRGxsLKytrXmP+cqVKyrJCgDo6elh2bJlcHd35x2XECI9SlgIIbzUrjcZO3as2nPGjx/PKWavXr2UW5vrWvqRy+X45ptvuA20BjMzM+Tk5Kg1jsvNzYWpqSnvuIQQ6dGSECFEa9y/fx+MMdjZ2eHy5cvK5SYAMDAwgJWVFXR1dXnHDwgIwKFDhxAaGop+/foBAJKSkhAYGIjx48cjIiJC6LdACJEIJSyEEEGGDh2KgwcPwsLCQuX606dPMW7cOJw+fbphBlaHsrIyBAYGIjIyUrlkpa+vj7lz52LDhg0wNDRs4BESQl6FEhZCiCBSFcjeuXMHERERKtuPFyxYgM6dOwse84sXL3Dnzh0AQOfOnQWdAE0IqR9Uw0II4UXKAtnjx49jzJgx6NWrF/r37w+geumme/fuOHr0KIYPHy5o7MbGxnB2dhYUgxBSv2iGhRDCi46OjrIxXF0vI4oCWR8fH86xXV1d4eXlhQ0bNqhc/+yzzxAXF0c9UwhpgihhIYTwImWBrJGREa5fv44uXbqoXM/MzISLiwtKS0sFjZ0Q0vjQkhAhhBcbGxsA4HRWkKYsLS2RkpKilrCkpKSo1coQQpoGSlgIIYKJXSDr5+cHf39/ZGdnq2w/DgkJweLFi0UbNyGk8aAlIUKIIK8qkE1NTeVdIMsYQ0REBMLCwvDw4UMAQLt27RAYGIiAgADOhyoSQho/SlgIIYJIXSD77NkzAKBOtIQ0cZSwEEIEoQJZQkh9oBoWQoggYhbIurq6arTcQ9uaCWl6KGEhhAgiZoHsuHHjlH9mjGH9+vWYM2cOWrRoIeaQCSGNEC0JEUIEkbJA1tTUFKmpqbCzsxNruISQRooSFkKIaMQukKWEhRCiQEtChBDR0E4eQohUKGEhhPBCBbKEkPpECQshhBcpCmS//vprlccVFRWIiopCq1atVK4HBATw/hqEkMaJalgIIaIQo96kU6dOb3yOTCZDdnY2769BCGmcaIaFEKI17t6929BDIIRoKZ2GHgAhhLwOdcolhACUsBBCtFBlZSW+/PJLWFtbo1mzZsoloFWrVmH79u0NPDpCSEOgJSFCCC9SFsiuW7cO0dHR2LhxI/z8/JTXe/TogYiICPj6+vIbNCGk0aKiW0IIL1IWyNrb22PLli3w9PRUKebNyMhA3759UVhYyGfIhJBGjGZYCCG8SFkgm5eXB3t7e7XrVVVVKC8vl+zrEkK0F9WwEEJEI1aBrJOTExITE9WuHzhwAK6urqJ8DUJI40IzLIQQQSorKxEcHIzIyEjk5+cjMzMTdnZ2WLVqFWxtbXnVm6xevRrTp09HXl4eqqqqcPDgQdy6dQs7d+5ETEyMBN8FIUTb0QwLIUSQdevWISoqChs3boSBgYHyeo8ePfDdd9/xijl27FgcPXoUJ0+ehImJCVavXo2bN2/i6NGjGD58uFhDJ4Q0IlR0SwgRhApkCSH1gZaECCGCSFkgW1ZWhoKCAlRVValc79ixo6C4hJDGhxIWQoggigJZGxsbletCCmSzsrLg4+OD8+fPq1xnjEEmk6GyspL3eAkhjRMlLIQQQaQokJ0xYwb09PQQExODtm3bQiaTiTxqQkhjQzUshBDBEhMTERQUhNTUVBQXF8PNzQ2rV6/GiBEjeMUzMTHB1atX4eDgIPJICSGNFc2wEEIEGzhwIE6cOCFaPCcnJzx58kS0eISQxo9mWAghohCzQPb06dNYuXIlgoOD4ezsDH19fZW/NzMzEzRWQkjjQwkLIUQQKQpkdXT+r0VUzfoVKrolpOmiJSFCiCBSFMieOXNGhJERQv5OaIaFECKIVAWyRUVF2L59O27evAmguq7F19cX5ubmon4dQkjjQK35CSGCSFEge+XKFdjb22PTpk34888/8eeff2LTpk3o3Lkzfv31V1G/FiGkcaAZFkKIIFIUyA4cOBD29vbYtm0b9PSqV64rKiowa9YsZGdnIyEhQZSxE0IaD0pYCCGCSFEgK5fLce3aNbVlpvT0dLi7u+PFixf8B0wIaZSo6JYQIogUBbJmZmbIyclRS1hyc3Nhamoq+tcjhGg/SlgIIYJ4eHiIXiA7adIk+Pr6IjQ0FP369QMAJCUlITAwEJMnTxZt7ISQxoOWhAghgly5cgUjR46EkZER+vTpAwBITk5G5n2fmQAACK5JREFUSUkJ4uLi4ObmxjlmWVkZAgMDERkZiYqKCgCAvr4+5s6diw0bNsDQ0FDU74EQov0oYSGECCJlgeyLFy9w584dAEDnzp1hbGwsypgJIY0PJSyEEEGoQJYQUh+oDwshRBBFgWxtVCBLCBETJSyEEEEUBbL79u1Dbm4ucnNzsXfvXsyaNYsKZAkhoqFdQoQQQUJDQyGTyTBt2rQ6C2QJIUQMVMNCCBEFFcgSQqRECQshhBBCtB7VsBBCCCFE61HCQgghhBCtRwkLIYQQQrQeJSyEEEII0XqUsBDyN8YYg7+/P1q0aAGZTIaUlJSGHhIhhPBCu4QI+Rs7duwYxo4di/j4eNjZ2aFVq1bK8374mjFjBoqKinD48GFxBkkIIRqgxnGE/I3duXMHbdu2Rb9+/Rp6KGoqKyshk8mgo0MTvYSQN6NXCkL+pmbMmIFPP/0UOTk5kMlksLW1RVVVFdavX49OnTpBLpejZ8+eOHDggPJzKisr4evrq/z7bt26YfPmzcq//+KLLxAdHY0jR45AJpNBJpMhPj4e8fHxkMlkKCoqUj43JSUFMpkM9+7dAwBERUXBwsICP//8M5ycnGBoaIicnBy8fPkSS5cuhbW1NUxMTPD2228jPj5eo+9REfP48eNwdHREs2bNMHLkSDx69Ej5nOTkZAwfPhytWrWCubk5PDw88Ouvv6rEkclk2LJlC0aNGgVjY2M4OjriwoULuH37NgYPHgwTExP069dP2RhP4ciRI3Bzc4ORkRHs7OywZs0aZbdfxhi++OILdOzYEYaGhmjXrh0CAgI0+r4IIXVghJC/paKiIhYUFMTat2/PHj16xAoKCtjatWuZg4MDi42NZXfu3GE7duxghoaGLD4+njHGWFlZGVu9ejVLTk5m2dnZbPfu3czY2Jjt27ePMcbYs2fP2MSJE9nIkSPZo0eP2KNHj9jLly/ZmTNnGABWWFio/PrXrl1jANjdu3cZY4zt2LGD6evrs379+rGkpCSWkZHBnj9/zmbNmsX69evHEhIS2O3bt9lXX33FDA0NWWZm5hu/R0XMYcOGseTkZHb16lXm6OjIpkyZonzOqVOn2K5du9jNmzdZeno68/X1Za1bt2ZPnz5VPgcAs7a2Zvv27WO3bt1i48aNY7a2tmzo0KEsNjaWpaens3feeYeNHDlS+TkJCQnMzMyMRUVFsTt37rC4uDhma2vLvvjiC8YYY//973+ZmZkZ++WXX9j9+/fZpUuX2NatW3n/fxLS1FHCQsjf2KZNm5iNjQ1jjLHS0lJmbGzMzp8/r/IcX19fNnny5FfGmD9/Phs/frzy8fTp09nYsWNVnqNpwgKApaSkKJ9z//59pqury/Ly8lTieXp6shUrVrzx+1PEvH37tvLav//9b9a6detXfk5lZSUzNTVlR48eVV4DwFauXKl8fOHCBQaAbd++XXltz549zMjISGWMwcHBKrF37drF2rZtyxhjLCwsjHXt2pWVlZW98fsghLwZ1bAQ0kTcvn0bL168wPDhw1Wul5WVwdXVVfn43//+N77//nvk5OSgpKQEZWVl6NWrlyhjMDAwgIuLi/Lx9evXUVlZia5du6o87+XLl2jZsqVGMY2NjdG5c2fl47Zt26KgoED5OD8/HytXrkR8fDwKCgpQWVmJFy9eICcnRyVOzXG1bt0aAODs7KxyrbS0FE+fPoWZmRlSU1ORlJSEdevWKZ9TWVmJ0tJSvHjxAhMmTEBERATs7OwwcuRIvPfeexg9erTgomdCmir6zSGkiSguLgYA/O9//4O1tbXK3xkaGgIA9u7di6VLlyIsLAx9+/aFqakpvvrqK1y6dOm1sRWFs6zGpsPy8nK158nlcshkMpUx6erq4urVq9DV1VV5brNmzTT6vvT19VUey2QylXFMnz4df/zxBzZv3gwbGxsYGhqib9++KCsre2UcxRjrulZVVaUc+5o1a/DBBx+ojcnIyAgdOnTArVu3cPLkSZw4cQLz5s3DV199hbNnz6qNmRDyZpSwENJE1Cx09fDwqPM5SUlJ6NevH+bNm6e8VrvQ1MDAAJWVlSrXLC0tAQCPHj1C8+bNAUCjni+urq6orKxEQUEBBg4cyOXb0VhSUhL+85//4L333gMA5Obm4smTJ4Ljurm54datW7C3t3/lc+RyOUaPHo3Ro0dj/vz5cHBwwPXr1+Hm5ib46xPS1FDCQkgTYWpqiqVLl2LRokWoqqrCgAED8NdffyEpKQlmZmaYPn06unTpgp07d+L48ePo1KkTdu3aheTkZHTq1EkZx9bWFsePH8etW7fQsmVLmJubw97eHh06dMAXX3yBdevWITMzE2FhYW8cU9euXTF16lRMmzYNYWFhcHV1xe+//45Tp07BxcUF77//vuDvu0uXLti1axfc3d3x9OlTBAYGQi6XC467evVqjBo1Ch07dsSHH34IHR0dpKam4rfffsPatWsRFRWFyspKvP322zA2Nsbu3bshl8thY2Mj+GsT0hTRtmZCmpAvv/wSq1atwvr16+Ho6IiRI0fif//7nzIhmT17Nj744ANMmjQJb7/9Nv744w+V2RYA8PPzQ7du3eDu7g5LS0skJSVBX18fe/bsQUZGBlxcXBASEoK1a9dqNKYdO3Zg2rRpWLJkCbp164Zx48YhOTkZHTt2FOV73r59OwoLC+Hm5oaPP/4YAQEBsLKyEhzXy8sLMTExiIuLQ+/evfHOO+9g06ZNyoTEwsIC27ZtQ//+/eHi4oKTJ0/i6NGjGtfmEEJUUadbQgghhGg9mmEhhBBCiNajhIUQorXeffddNGvWrM6P4ODghh4eIaQe0ZIQIURr5eXloaSkpM6/a9GiBVq0aFHPIyKENBRKWAghhBCi9WhJiBBCCCFajxIWQgghhGg9SlgIIYQQovUoYSGEEEKI1qOEhRBCCCFajxIWQgghhGg9SlgIIYQQovUoYSGEEEKI1vv/ANiNmcUi9SDgAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = bestRforest.named_steps.popitem()[1]\n",
    "names = bestRforest[:-1].get_feature_names_out()\n",
    "feature_importance_df = pd.DataFrame \\\n",
    "    .from_dict({'feature_importance': model.feature_importances_, 'feature_names': names}) \\\n",
    "    .set_index('feature_names').sort_values('feature_importance', ascending = False).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sauvegarde des modèles pour un usage dans l'appli web."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "\n",
    "for estimator, name in [(bestRforest, \"RandomForest\"), (bestDTree, \"DecisionTree\"), (bestSvcl, \"LinearSVC\")]:\n",
    "    # We store the fit function's input's metadata for easier reshaping\n",
    "    joblib.dump({\n",
    "        'estimator': estimator,\n",
    "        'input_columns': X.columns,\n",
    "        'input_dtypes': X.dtypes,\n",
    "    }, f\"models/{name}.joblib\", compress=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10 - Essai avec Height et Weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comme expliqué précédemment, l'inclusion de la taille et du poids ne fait pas de sens dans notre cas. Cependant, nous avons essayé de les inclures pour voir l'amélioration qui pourrait ếtre produite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Walking</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>1.78</td>\n",
       "      <td>89.8</td>\n",
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       "  Gender   Age  Height  Weight  family_history_with_overweight   FAVC  FCVC  \\\n",
       "0      0  21.0    1.62    64.0                            True  False     2   \n",
       "1      0  21.0    1.52    56.0                            True  False     3   \n",
       "2      1  23.0    1.80    77.0                            True  False     2   \n",
       "3      1  27.0    1.80    87.0                           False  False     3   \n",
       "4      1  22.0    1.78    89.8                           False  False     2   \n",
       "\n",
       "   NCP       CAEC  SMOKE  CH2O    SCC  FAF  TUE        CALC  \\\n",
       "0    3  Sometimes  False     2  False    0    1          no   \n",
       "1    3  Sometimes   True     3   True    3    0   Sometimes   \n",
       "2    3  Sometimes  False     2  False    2    1  Frequently   \n",
       "3    3  Sometimes  False     2  False    2    0  Frequently   \n",
       "4    1  Sometimes  False     2  False    0    0   Sometimes   \n",
       "\n",
       "                  MTRANS  \n",
       "0  Public_Transportation  \n",
       "1  Public_Transportation  \n",
       "2  Public_Transportation  \n",
       "3                Walking  \n",
       "4  Public_Transportation  "
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       "   power__Age  scaler__Height  scaler__Weight  scaler__FCVC  scaler__NCP  \\\n",
       "0   -0.414452       -0.875589       -0.862558     -0.725454     0.385644   \n",
       "1   -0.414452       -1.947599       -1.168077      0.987559     0.385644   \n",
       "2    0.055560        1.054029       -0.366090     -0.725454     0.385644   \n",
       "3    0.739738        1.054029        0.015808      0.987559     0.385644   \n",
       "4   -0.165942        0.839627        0.122740     -0.725454    -2.085053   \n",
       "\n",
       "   scaler__CH2O  scaler__FAF  scaler__TUE  oneHotEncoder__Gender_0  \\\n",
       "0     -0.021330    -1.124415     0.497717                      1.0   \n",
       "1      1.431202     2.226606    -0.986295                      1.0   \n",
       "2     -0.021330     1.109599     0.497717                      0.0   \n",
       "3     -0.021330     1.109599    -0.986295                      0.0   \n",
       "4     -0.021330    -1.124415    -0.986295                      0.0   \n",
       "\n",
       "   oneHotEncoder__Gender_1  oneHotEncoder__MTRANS_Automobile  \\\n",
       "0                      0.0                               0.0   \n",
       "1                      0.0                               0.0   \n",
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       "3                      1.0                               0.0   \n",
       "4                      1.0                               0.0   \n",
       "\n",
       "   oneHotEncoder__MTRANS_Bike  oneHotEncoder__MTRANS_Motorbike  \\\n",
       "0                         0.0                              0.0   \n",
       "1                         0.0                              0.0   \n",
       "2                         0.0                              0.0   \n",
       "3                         0.0                              0.0   \n",
       "4                         0.0                              0.0   \n",
       "\n",
       "   oneHotEncoder__MTRANS_Public_Transportation  oneHotEncoder__MTRANS_Walking  \\\n",
       "0                                          1.0                            0.0   \n",
       "1                                          1.0                            0.0   \n",
       "2                                          1.0                            0.0   \n",
       "3                                          0.0                            1.0   \n",
       "4                                          1.0                            0.0   \n",
       "\n",
       "   oneHotEncoder__family_history_with_overweight_False  \\\n",
       "0                                                0.0     \n",
       "1                                                0.0     \n",
       "2                                                0.0     \n",
       "3                                                1.0     \n",
       "4                                                1.0     \n",
       "\n",
       "   oneHotEncoder__family_history_with_overweight_True  \\\n",
       "0                                                1.0    \n",
       "1                                                1.0    \n",
       "2                                                1.0    \n",
       "3                                                0.0    \n",
       "4                                                0.0    \n",
       "\n",
       "   oneHotEncoder__FAVC_False  oneHotEncoder__FAVC_True  \\\n",
       "0                        1.0                       0.0   \n",
       "1                        1.0                       0.0   \n",
       "2                        1.0                       0.0   \n",
       "3                        1.0                       0.0   \n",
       "4                        1.0                       0.0   \n",
       "\n",
       "   oneHotEncoder__SMOKE_False  oneHotEncoder__SMOKE_True  \\\n",
       "0                         1.0                        0.0   \n",
       "1                         0.0                        1.0   \n",
       "2                         1.0                        0.0   \n",
       "3                         1.0                        0.0   \n",
       "4                         1.0                        0.0   \n",
       "\n",
       "   oneHotEncoder__SCC_False  oneHotEncoder__SCC_True  ordinalEncoder__CAEC  \\\n",
       "0                       1.0                      0.0                   1.0   \n",
       "1                       0.0                      1.0                   1.0   \n",
       "2                       1.0                      0.0                   1.0   \n",
       "3                       1.0                      0.0                   1.0   \n",
       "4                       1.0                      0.0                   1.0   \n",
       "\n",
       "   ordinalEncoder__CALC  \n",
       "0                   0.0  \n",
       "1                   1.0  \n",
       "2                   2.0  \n",
       "3                   2.0  \n",
       "4                   1.0  "
      ]
     },
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    },
    {
     "data": {
      "text/plain": [
       "\"Params: {'dTree__criterion': 'entropy', 'dTree__max_depth': 10, 'dTree__min_samples_leaf': 1, 'dTree__min_samples_split': 2} \\nScore: 0.9379678624258049\""
      ]
     },
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    },
    {
     "data": {
      "text/plain": [
       "'Accuracy: 0.9574468085106383, Rappel : 0.957531986526107, Precision : 0.9578519084655847, f1 : 0.9574604295334693'"
      ]
     },
     "metadata": {},
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = import_clean_data(\"data/ObesityDataSet_raw_and_data_sinthetic.csv\")\n",
    "# df = drop_age_and_weight(df) (On garde height et weight)\n",
    "\n",
    "# Data valdation\n",
    "# data_validator.validate(df)\n",
    "\n",
    "# Dataset split\n",
    "Y = df['NObeyesdad']\n",
    "X = df.drop(['NObeyesdad'], axis=1)\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Power Transformer\n",
    "power_features = [\"Age\"]\n",
    "\n",
    "# Standardization\n",
    "numeric_features = X.select_dtypes(include=[\"float64\", \"int64\"]).columns.drop([\"Age\"])\n",
    "\n",
    "# One hot encoding\n",
    "categorical_features_onehot = [\"Gender\", \"MTRANS\",\n",
    "                               \"family_history_with_overweight\", \"FAVC\",\n",
    "                               \"SMOKE\", \"SCC\"]\n",
    "\n",
    "# Ordinal encoding\n",
    "categorical_features_ordinal = [\"CAEC\", \"CALC\"]\n",
    "\n",
    "# On créer la pipeline de preprocessing\n",
    "preprocessor = ColumnTransformer([\n",
    "    # (\"power\", KBinsDiscretizer(encode='ordinal', strategy='uniform'), power_features),\n",
    "    (\"power\", PowerTransformer(), power_features),\n",
    "    (\"scaler\", StandardScaler(), numeric_features),\n",
    "    (\"oneHotEncoder\", OneHotEncoder(handle_unknown=\"ignore\"), categorical_features_onehot),\n",
    "    (\"ordinalEncoder\", OrdinalEncoder(categories=[[\"no\", \"Sometimes\", \"Frequently\", \"Always\"],[\"no\", \"Sometimes\", \"Frequently\", \"Always\"]]), categorical_features_ordinal),\n",
    "])\n",
    "\n",
    "with pd.option_context('display.max_columns', None):\n",
    "    display(X.head(5))\n",
    "    display(pd.DataFrame(preprocessor.fit_transform(X), columns=preprocessor.get_feature_names_out()).head(5))\n",
    "\n",
    "pipeline = Pipeline(\n",
    "    steps=[\n",
    "        (\"preprocessor\", preprocessor),\n",
    "        (\"dTree\", DecisionTreeClassifier())\n",
    "    ]\n",
    ")\n",
    "\n",
    "param_grid = {\n",
    "    \"dTree__criterion\": [\"gini\", \"entropy\", 'log_loss'],\n",
    "    \"dTree__max_depth\": [None, 10, 20, 30],\n",
    "    \"dTree__min_samples_split\": [2, 5, 10],\n",
    "    \"dTree__min_samples_leaf\": [1, 2, 4]\n",
    "}\n",
    "\n",
    "# 3s\n",
    "grid_search = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=3, n_jobs=-1, scoring='f1_macro')\n",
    "grid_search.fit(X_train, Y_train)\n",
    "model = grid_search.best_estimator_\n",
    "display(f'Params: {grid_search.best_params_} \\nScore: {grid_search.best_score_}')\n",
    "\n",
    "# 0s\n",
    "Y_pred = model.predict(X_test)\n",
    "accuracy_val = accuracy_score(Y_test, Y_pred)\n",
    "recall_val = recall_score(Y_test, Y_pred, average='macro')\n",
    "precision_val = precision_score(Y_test, Y_pred, average='macro')\n",
    "f1_val = f1_score(Y_test, Y_pred, average='macro')\n",
    "display(f'Accuracy: {accuracy_val}, Rappel : {recall_val}, Precision : {precision_val}, f1 : {f1_val}')\n",
    "\n",
    "modelplt = grid_search.best_estimator_.named_steps.popitem()[1]\n",
    "namesplt = grid_search.best_estimator_[:-1].get_feature_names_out()\n",
    "feature_importance_df = pd.DataFrame \\\n",
    "    .from_dict({'feature_importance': modelplt.feature_importances_, 'feature_names': namesplt}) \\\n",
    "    .set_index('feature_names').sort_values('feature_importance', ascending = False).plot.bar()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On observe que le modèle donne une grande importance au poids et la taille.\n",
    "Il ignore presque le reste des variables à l'exception du genre."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PS-407efJxD",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}