1143 lines
481 KiB
Plaintext
1143 lines
481 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0c601b14",
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"metadata": {},
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"source": [
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"# TP1 du module 6 : les algorithmes de classification\n",
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"\n",
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"Dans ce TP, nous allons mettre en pratique les principes de l'apprentissage supervisé. Objectifs :\n",
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"* Savoir mettre en place les principaux algorithmes de classification\n",
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"* Etudier l'impact de leurs paramètres sur leurs performances\n",
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"* Comparer les performances de différents algorithmes"
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]
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},
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{
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"cell_type": "code",
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"id": "8423b3aa",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-18T11:38:35.673605Z",
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"start_time": "2025-09-18T11:38:34.623332Z"
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}
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},
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"source": [
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"# Ajoutez ici les imports de librairies nécessaires\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import seaborn as sns\n",
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"import pandas as pd\n",
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"\n",
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"from sklearn.linear_model import Perceptron\n",
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"from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
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"from sklearn.model_selection import train_test_split, GridSearchCV\n",
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"from sklearn.neighbors import KNeighborsClassifier\n",
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"from sklearn.naive_bayes import GaussianNB\n",
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"from sklearn.neural_network import MLPClassifier\n",
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"from sklearn.svm import SVC"
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],
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"outputs": [],
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"execution_count": 1
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},
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{
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"cell_type": "markdown",
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"id": "82e63125",
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"metadata": {},
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"source": [
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"Commencez par charger à nouveau le jeu de données Titanic, à partir du csv généré dans le TP1 du module 4. Préparez les données d'entraînement et de test qui seront utilisées par la suite."
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]
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},
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{
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"cell_type": "code",
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"id": "79f23c2b",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-18T11:38:35.699577Z",
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"start_time": "2025-09-18T11:38:35.679773Z"
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}
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},
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"source": [
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"titanic = pd.read_csv(\"Titanic.csv\")\n",
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"\n",
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"X = titanic.drop(['Survived'], axis=1)\n",
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"y = titanic['Survived']\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
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],
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"outputs": [],
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"execution_count": 2
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},
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{
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"cell_type": "markdown",
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"id": "6f7717c4",
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"metadata": {},
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"source": [
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"## Partie 1 : découvrir Naive Bayes\n",
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"\n",
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"1. Commencez par créer un modèle basé sur Naive Bayes, sans changer les paramètres par défaut, en supposant que la répartition des données correspond à une Gaussienne (loi normale). Entraînez-le et testez-le. Quelle score (accuracy) obtenez-vous ? Que pouvez-vous dire de la précision et du rappel ? Comparez avec les scores obtenus sur les arbres de décision au module 5 : avez-vous des hypothèses pour expliquer cette différence ?"
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]
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},
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{
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"cell_type": "code",
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"id": "dd0f1d68",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-18T11:38:35.731457Z",
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"start_time": "2025-09-18T11:38:35.710071Z"
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}
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},
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"source": [
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"gnb = GaussianNB()\n",
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"\n",
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"#Entraînement\n",
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"gnb.fit(X_train, y_train)\n",
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"y_pred=gnb.predict(X_test)\n",
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"\n",
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"#Test\n",
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"print(\"Accuracy : \", gnb.score(X_test, y_test))\n",
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"print(\"Precision : \", precision_score(y_test, y_pred))\n",
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"print(\"Rappel : \", recall_score(y_test, y_pred))\n",
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"\n",
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"# sauvegarde des scores\n",
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"nb_best_accuracy = gnb.score(X_test, y_test)\n",
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"nb_best_pred = precision_score(y_test, y_pred)\n",
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"nb_best_recall = recall_score(y_test, y_pred)"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy : 0.6703910614525139\n",
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"Precision : 0.8235294117647058\n",
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"Rappel : 0.2\n"
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]
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}
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],
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"execution_count": 3
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},
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{
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"cell_type": "markdown",
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"id": "140c9a8c",
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"metadata": {},
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"source": [
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"**Observation :** le score d'accuracy est faible, d'autant plus en comparaison de performances de l'arbre de décision. Il est possible que l'hypothèse d'indépendance des variables ne soit pas adapté pour les données du Titanic.\n",
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"\n",
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"De plus, il est très intéressant de noter un bon rappel mais une mauvaise précision : cela signifie que si le modèle arrive à prédire correctement les dècés, il le fait en faisant énormément d'erreur sur la survie."
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]
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},
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{
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"cell_type": "markdown",
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"id": "1af14ab5",
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"metadata": {},
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"source": [
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"2. Affichez une matrice de corrélation des données du jeu d'entraînement, en y incluant un affichage textuel de la valeur de la corrélation. Voyez-vous des informations permettant d'expliquer les performance de l'algorithme Naive Bayes ?"
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]
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},
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{
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"cell_type": "code",
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"id": "3b3d1c81",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-18T11:38:36.142110Z",
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"start_time": "2025-09-18T11:38:35.757567Z"
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}
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},
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"source": [
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"sns.heatmap(titanic.corr(),annot=True)"
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],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Axes: >"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 640x480 with 2 Axes>"
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],
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
}
|
|
],
|
|
"execution_count": 4
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "6763d0ca",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Observation :** Il existe en effet une certaine corrélation entre plusieurs variable (par exemple entre Fare et Pclass), qui mettent à mal l'hypothèse d'indépendance des variables supposée par l'algorithme Naive Bayes."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "351eae75",
|
|
"metadata": {},
|
|
"source": [
|
|
"3. Proposez une représentation graphique des attributs continus, permettant de vérifier l'hypothèse que nous avons faite, selon laquelle ces données suivent une loi normale (Gaussienne)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "b6b5b059",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-09-18T11:38:36.455574Z",
|
|
"start_time": "2025-09-18T11:38:36.150489Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"fig, axes = plt.subplots(1, 2, figsize=(20, 5))\n",
|
|
"i=0\n",
|
|
"for c in ['Age', 'Fare']:\n",
|
|
" sns.histplot(X, x=c, kde=True, ax=axes[i])\n",
|
|
" i+=1"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 2000x500 with 2 Axes>"
|
|
],
|
|
"image/png": 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|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
}
|
|
],
|
|
"execution_count": 5
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b701994c",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Observation :** la répartition des données continues semble cohérente avec une loi Normale pour l'âge, mais moins adaptée pour le prix des billets. Cet élément peut également expliquer le faible score obtenu avec l'agorithme Naive Bayes."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "dab510d0",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Partie 2 : découvrir KNN"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "90779015",
|
|
"metadata": {},
|
|
"source": [
|
|
"1. Commencez par créer un modèle knn, en gardant le nombre de voisins par défaut (à regarder dans la documentation). Que pouvez-vous dire de l'accuracy, de la précision et du rappel ?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "8f8e1696",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-09-18T11:38:36.480242Z",
|
|
"start_time": "2025-09-18T11:38:36.463982Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"knn = KNeighborsClassifier()\n",
|
|
"\n",
|
|
"#Entraînement\n",
|
|
"knn.fit(X_train, y_train)\n",
|
|
"y_pred=knn.predict(X_test)\n",
|
|
"\n",
|
|
"#Test\n",
|
|
"print(\"Accuracy : \", knn.score(X_test, y_test))\n",
|
|
"print(\"Precision : \", precision_score(y_test, y_pred))\n",
|
|
"print(\"Rappel : \", recall_score(y_test, y_pred))"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Accuracy : 0.7094972067039106\n",
|
|
"Precision : 0.6551724137931034\n",
|
|
"Rappel : 0.5428571428571428\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 6
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5ddd7d42",
|
|
"metadata": {},
|
|
"source": "**Observation :** on obtient cette fois un meilleur score pour le rappel. L'écart avec la précision est bien moins important, indiquant que le modèle semble faire de bonnes prédictions de manière équilibrée entre les deux classes."
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f991f919",
|
|
"metadata": {},
|
|
"source": [
|
|
"2. Nous allons maintenant observer l'impact du nombre de voisins à prendre en considération. Faite varier k entre 1 et 20. Calculez à chaque fois accuracy, précision, et rappel. Tracez l'évolution de ces trois scores en fonction de k, sur un même graphique. Que constatez-vous ? Affichez la valeur de k pour laquelle l'accuracy est la plus élevée."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "b65bb998",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-09-18T11:38:36.755691Z",
|
|
"start_time": "2025-09-18T11:38:36.488005Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"accuracies = []\n",
|
|
"precisions = []\n",
|
|
"recalls = []\n",
|
|
"k_range = range(1,20)\n",
|
|
"\n",
|
|
"for k in k_range:\n",
|
|
" knn = KNeighborsClassifier(n_neighbors=k)\n",
|
|
" knn.fit(X_train, y_train)\n",
|
|
" y_pred = knn.predict(X_test)\n",
|
|
" accuracies.append(accuracy_score(y_test, y_pred))\n",
|
|
" precisions.append(precision_score(y_test, y_pred))\n",
|
|
" recalls.append(recall_score(y_test, y_pred))\n",
|
|
" \n",
|
|
"plt.plot(k_range, accuracies, label='Accuracy')\n",
|
|
"plt.plot(k_range, precisions, label='Precision')\n",
|
|
"plt.plot(k_range, recalls, label='Rappel')\n",
|
|
"plt.xticks(range(1, 20))\n",
|
|
"plt.xlabel('Nombre de voisins')\n",
|
|
"plt.ylabel('Score')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"k_max = np.argmax(accuracies) + 1\n",
|
|
"print(\"Score maximum pour k =\", k_max)"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
],
|
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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Score maximum pour k = 16\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 7
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "18ec66e2",
|
|
"metadata": {},
|
|
"source": [
|
|
"3. En prenant la valeur de k qui vous semble la plus pertinente, faite varier la dimension (p) utilisée pour calculer la distance de Minkowski entre deux données. Cette distance a-t'elle un fort impact sur les résultats d'accuracy obtenus ? Montrez-le en montrant l'évolution de ce score en fonction de p (faire varier entre 1 et 10). Ajoutez également la précision et le rappel."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "ce6b99d8",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-09-18T11:38:36.943793Z",
|
|
"start_time": "2025-09-18T11:38:36.764548Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"accuracies = []\n",
|
|
"precisions = []\n",
|
|
"recalls = []\n",
|
|
"\n",
|
|
"p_range = range(1,10)\n",
|
|
"\n",
|
|
"for dim in p_range:\n",
|
|
" knn = KNeighborsClassifier(n_neighbors=k_max, p=dim)\n",
|
|
" knn.fit(X_train, y_train)\n",
|
|
" y_pred = knn.predict(X_test)\n",
|
|
" accuracies.append(accuracy_score(y_test, y_pred))\n",
|
|
" precisions.append(precision_score(y_test, y_pred))\n",
|
|
" recalls.append(recall_score(y_test, y_pred))\n",
|
|
" \n",
|
|
"plt.plot(p_range, accuracies, label='Accuracy')\n",
|
|
"plt.plot(p_range, precisions, label='Precision')\n",
|
|
"plt.plot(p_range, recalls, label='Rappel')\n",
|
|
"plt.xticks(p_range)\n",
|
|
"plt.xlabel('Degré de la distance de Minkowski')\n",
|
|
"plt.ylabel('Scores')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"prof_max = np.argmax(accuracies) + 1\n",
|
|
"print(\"Score maximum pour p =\", prof_max)\n",
|
|
"\n",
|
|
"# sauvegarde des scores\n",
|
|
"knn_best_accuracy = accuracies[prof_max-1]\n",
|
|
"knn_best_pred = precisions[prof_max-1]\n",
|
|
"knn_best_recall = recalls[prof_max-1]"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
],
|
|
"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Score maximum pour p = 1\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 8
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "eb3a1544",
|
|
"metadata": {},
|
|
"source": "**Observation :** la différence n'est pas énorme mais elle existe. Ainsi, prendre cette prof_max semble légèrement améliorer les performances."
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0a7c6024",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Partie 3 : découvrir les SVM\n",
|
|
"\n",
|
|
"1. Créez un modèle de classification basée sur les machines à vecteur de support. Dans un premier temps, gardez les options par défaut. Que pouvez-vous dire des performances obtenues (accuracy, précision, rappel) ?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "3b136dbf",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-09-18T12:01:52.300932Z",
|
|
"start_time": "2025-09-18T12:01:52.259774Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"svm = SVC()\n",
|
|
"\n",
|
|
"#Entraînement\n",
|
|
"svm.fit(X_train, y_train)\n",
|
|
"y_pred=svm.predict(X_test)\n",
|
|
"\n",
|
|
"#Test\n",
|
|
"print(\"Accuracy : \", svm.score(X_test, y_test))\n",
|
|
"print(\"Precision : \", precision_score(y_test, y_pred))\n",
|
|
"print(\"Rappel : \", recall_score(y_test, y_pred))"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Accuracy : 0.6759776536312849\n",
|
|
"Precision : 0.6875\n",
|
|
"Rappel : 0.3142857142857143\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 19
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d02a1e2e",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Observation :** on obtient une accuracy similaire à KNN, mais on peut remarquer que le rappel est bien meilleur que la précision. Cela peut s'expliquer par le noyau utilisé, qui ne correspond peut-être pas à la distribution des données."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "905b31a7",
|
|
"metadata": {},
|
|
"source": [
|
|
"2. Testez les différents noyaux disponibles pour l'algorithme SVM (linéaire, polynomial, rbf et sigmoïde). Représentez graphiquement l'accuracy, la précision et le rappel, pour chaque noyau. Il y en a t'il un qui semble plus pertinent que les autres ? Affichez-le, ainsi que les scores obtenus pour ce noyau."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "e68429cd",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-09-18T11:38:41.054882Z",
|
|
"start_time": "2025-09-18T11:38:37.003015Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"noyaux = ['linear', 'poly', 'rbf', 'sigmoid']\n",
|
|
"\n",
|
|
"accuracies = []\n",
|
|
"precisions = []\n",
|
|
"recalls = []\n",
|
|
"\n",
|
|
"for n in noyaux:\n",
|
|
" svm = SVC(kernel=n)\n",
|
|
" svm.fit(X_train, y_train)\n",
|
|
" y_pred = svm.predict(X_test)\n",
|
|
" accuracies.append(accuracy_score(y_test, y_pred))\n",
|
|
" precisions.append(precision_score(y_test, y_pred))\n",
|
|
" recalls.append(recall_score(y_test, y_pred))\n",
|
|
" \n",
|
|
"plt.plot(noyaux, accuracies, label='Accuracy')\n",
|
|
"plt.plot(noyaux, precisions, label='Precision')\n",
|
|
"plt.plot(noyaux, recalls, label='Rappel')\n",
|
|
"plt.xticks(noyaux)\n",
|
|
"plt.xlabel('Noyau utilisé')\n",
|
|
"plt.ylabel('Score')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"pos_meilleur_noyau = np.argmax(accuracies)\n",
|
|
"meilleur_noyau = noyaux[pos_meilleur_noyau]\n",
|
|
"print(\"Noyau optimal : \", meilleur_noyau)\n",
|
|
"print(\"Accuracy du noyau\", meilleur_noyau, \": \", accuracies[pos_meilleur_noyau])\n",
|
|
"print(\"Précision du noyau\", meilleur_noyau, \": \", precisions[pos_meilleur_noyau])\n",
|
|
"print(\"Rappel du noyau\", meilleur_noyau, \": \", recalls[pos_meilleur_noyau])"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
],
|
|
"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Noyau optimal : linear\n",
|
|
"Accuracy du noyau linear : 0.776536312849162\n",
|
|
"Précision du noyau linear : 0.7586206896551724\n",
|
|
"Rappel du noyau linear : 0.6285714285714286\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 10
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "014b5138",
|
|
"metadata": {},
|
|
"source": [
|
|
"3. Nous allons essayer d'améliorer les performances obtenues avec le noyau polynomial. Utilisez ce noyau, et faites varier le degré du polynôme utilisé de 1 à 10. Représentez graphiquement l'accuracy, la précision et le rappel, en fonction du degré du polynôme. Il y en a t'il un qui semble plus pertinent que les autres ? Affichez-le, ainsi que les scores obtenus pour cette valeur. Comparez avec le meilleur score obtenu à la question précédente."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "544318b0",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-09-18T11:38:46.297848Z",
|
|
"start_time": "2025-09-18T11:38:41.513313Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"degrees = range(1,11)\n",
|
|
"\n",
|
|
"accuracies = []\n",
|
|
"precisions = []\n",
|
|
"recalls = []\n",
|
|
"\n",
|
|
"for d in degrees:\n",
|
|
" svm = SVC(kernel='poly', degree=d)\n",
|
|
" svm.fit(X_train, y_train)\n",
|
|
" y_pred = svm.predict(X_test)\n",
|
|
" accuracies.append(accuracy_score(y_test, y_pred))\n",
|
|
" precisions.append(precision_score(y_test, y_pred))\n",
|
|
" recalls.append(recall_score(y_test, y_pred))\n",
|
|
" \n",
|
|
"plt.plot(degrees, accuracies, label='Accuracy')\n",
|
|
"plt.plot(degrees, precisions, label='Precision')\n",
|
|
"plt.plot(degrees, recalls, label='Rappel')\n",
|
|
"plt.xticks(degrees)\n",
|
|
"plt.xlabel('Degré du polynôme')\n",
|
|
"plt.ylabel('Score')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"pos_meilleur_noyau = np.argmax(accuracies)\n",
|
|
"meilleur_degré = degrees[pos_meilleur_noyau]\n",
|
|
"print(\"Degré optimal : \", meilleur_degré)\n",
|
|
"print(\"Accuracy du degré\", meilleur_degré, \": \", accuracies[pos_meilleur_noyau])\n",
|
|
"print(\"Précision du degré\", meilleur_degré, \": \", precisions[pos_meilleur_noyau])\n",
|
|
"print(\"Rappel du degré\", meilleur_degré, \": \", recalls[pos_meilleur_noyau])\n",
|
|
"\n",
|
|
"# sauvegarde des scores\n",
|
|
"svm_best_accuracy = accuracies[pos_meilleur_noyau]\n",
|
|
"svm_best_pred = precisions[pos_meilleur_noyau]\n",
|
|
"svm_best_recall = recalls[pos_meilleur_noyau]"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
],
|
|
"image/png": 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7nu+//wbbtm3FlCkfY9as+UhKSsKHH07JcTzHjh3F4MHPoGnTGzF79gI88cTT6ozcq1f/leWxvkWdOnUxb9436NDhNrz77jgkJCQU6udAdD18di9A4G9PqjCTdkNHxPZcxDBTioNNXNcZiOrzh5reLXUoPod+QcjXtyPg92fhFZkx9dnVZN2jgD9fQNCv/VSYsQZWQUzPRUjo8E6pCzOCPTS5sdsR/EMvGM9tLtGnTS9/E2J6/VCgqXSTJ4/H1KmT1OXU1FSYTD6qILhz5zvxyy8/ok2bdmjUqIm6XXplpIj4s8/mQK/X44YbquK5517EuHFv4bHH+uOXX35S95XLom/fx5CSkpwjYJw9exYmkwnly5dHYGAQXnttNGJjY3Mc2+LFP6J27ToYMOA5tS/PJyHnyy/noUOHW9V1NWvWxsMP91OX+/cfgEWLvsLRo4edx0xU7Ox2+G6cBL8tH6nd5Lr/Q0LHCZytRM5g45Wtx+YXtZ6N6rGR4uHQ2i45Nu/DSxGw+jW1DpKc7DO5yVNIvPmVUr0UAANNXjQyP//JJweong3h7e2t6mkcw0eiXLnLS1kfP34UcXGx6NLl8piqzWZTQSg2NgYnThxHnTrZF5l66qlncjznPff0wp9//o577umCZs2aqx6du+7KWa9z7Ngx1K/fINt1jRo1xs8/f+/cr1SpsvOyn5+/s6eJqERY0xDw16vw2f+d2pWzNCfd9LJm/v6phIPNpb3w2yzBZunlYFPrnoyhqBIKNrLqtP/fr6s6H2EJqYX42ybDUq45SjsGmtzodBk9JZbkfN3dYNDDYinYCqG5P5C5wP9IZfgoayi4koQcB6vVqnpJJkx4L8f9JEwYDPn7dahevQa++24x1q9fi/Xr12DGjGlq6Orjj2fm+dyXj8GmNgejMeenYDvnxFIJkHMpBf72NLxPrYFd56V6ZVLqP+jqwyI3Jie4jOv6WfZgc/Bndc6oYg82djtMB3+C/5pR0KdEq9/ZpBufQ9JNLwBepuJ5To1hoMmLBAtjPmfsGPSArggCTTGrXLkKzp8/h+DgEDU7SmzatAFLly7B66+/hcqVK2PfvuxjwwMHPoH77vtftut++22JCiudOnXGbbfdjt27d2HgwMcRHR2V7X433FAF27dvzXbdnj071fVErqRPPIegxY/CELkXdoMv4rpOR1qVjJ5OogIFm01TYTryW7EGG33CWfivHqkWeBSWsPqI7zQFljINi+w5PAGLgkuRm29uhXLlymHMmDdw+PAh7NixDZMmjYOPj48apurV6z7s3LkNX3+9AKdOnVTFwlLPIkW9WSUmJuCDD95TNTlnzpzGH3/8hrJlI9Qspax69bofBw8ewIwZH6vhLAlCP/ywCL1731/CLSe6zCvqAIK/u0eFGVnlN6bXdwwzdP3B5s6ZiPrfcqRWvzOjePjgzwj5qhMClj8Hr6iDhXsCux0+e79Wj6dWq9YbkdhyKKLv/5VhJhfsoSlFJLRMmDAF77//Lp5+up+a9XTrrbfj+edfULfL0NXbb0/C9Okf4bPPPkHVqtUxceJUhIeXyfY4vXs/gAsXLmDs2FGIj49TdTcyjJW1dkdIeJo0aSo++eQDFZIiIsrh+edfwt1331Oi7SZyMJ7ZgMClT0KfGgtLcHXEdl8AW+ANrj4s8pBgk3ePjRQP1yrQY+rjTiJg1TB4n/xb7aeXbYL426a45bRxd6Gzl7KChUu5LFuenp6GyMizCAsrD6MxZ91HidXQuJgW2nGt10pGCsPDA3J9nbXGU9riLu0wHVysprjqbGlIL9dCrf5r9wnRXDsKi+0ofl4X98BvswSbZWpfzgOWWqtHxqyokJpXb4fdBp/d8+C/fhx0liR12g3plUlu0h/QG0rl66HLfOxrcd+fDhFRETFv/wz+68aoy6nVuyLujo8yivCJioG1TAPE3TkrW7DxOfgTTAd/zjPYCK+YI/BfORTeZzeq/bTyLZFw27uwBld3QSu0h4GGiDyXzQq/dWPgu/NztZvc6DEktHsL0GcfHiUqzmBjuLgbvjIUdfT3HMHGFlpT/Z6at82A74ZJ0FlTVaF6QpuRSGn4KKBjqWt+MdAQkWeypCDwz8Fqaq1IaP0akpsN5BozVOKkgDfurs9zBptDGTU2SDwJv9Nb1H3TKrVX52CyBea9HAfljoGGiDyOLAkftPRJGM/+q2aGxHeaitTaPV19WFTK5RpsDvykbrN5B6izYqfU68PQfZ0YaIjIo8jskKAlfWGIPgSbd6CafZJeqa2rD4so92Cz7ROYfEyIaf4qrH7lXX1omsZAQ0QeQ94gApc8Cq+kC7D6l0dst3lq2Xoidw028V0+gSk8ALZL8TIdigqBgYaIPILxxCoELhsAfXoiLGF1VZix+V8+lxkReTYGGiLSPNN/3yJg1avQ2SxIq9hWDTPZTYGuPiwiKkEMNESkXXY7fDd/AL9/J6vdlNq9EH/be4BXwRfIJCJtY6DRsPvu645z584693U6Hfz9A9CkSVO89NKr6lQDrrJ162YMHjwQa9dudtkxkIezWdQJ+8x7v1S7cubhxFbDuG4HUSnFQKNxgwcPQadOd6jLNpsNx44dwbvvjsc774zGhx9Od/XhERWPtEQELn8GpuMrYdfpkdB+LFIa9XP1URGRCzHQaJy/vz/CwsKd+2XKlEX//gPVGbUTEhLU7USeRJd0EUG/PgbjhR3qPDdxnT9BWvUurj4sInIxBhoPZDQa1Ve9Xo+jR4/go4+mYNeunbBaLahbtz5effU1VK1aTQ0LyRmzH3roUXzxxUx1tuz77vsf+vV7Un2/9PL4+fnhzJkz2Lz5X1SpUgUvvzwMjRo1UbfHx8fj/fcnYc2av2E2m9Gx42149tnBMJl8XNp+8lxyrpugxX3hFXccNp8QxN49B5ZyzV19WETkBjjYnAc5CXmyJblEt6I48fnp06cwf/4ctGzZBj4+Phg27CWUL18Bc+Z8iU8/nQ2r1YpPP/3Qef+oqEgsW/Yrpk79GMOHv4Yvv5yHX3750Xn7Tz99j2rVquOLLxaiadPmGDr0BcTExKjbJkwYo3qBPv30c4wfPxn//bcXU6ZMKnQbiHJjOLcFwd/3UGHGGlgFMff+zDBDRE7socmFBIvBGwZiT/SuEn3ehiGN8UGrT1Vxb35NnjweU6dmhAgJKwaDEe3b36Jqa1JTU9Gz573o1et+1YMi7ryzmwotDvI9w4e/gVq1aqNBg/p44IGH8PPPP+Cee3qp2yXMPPPMIHV50KCXsHbt31ix4ne0atUWa9asxtKlK53DWsOGvY7HH38Igwa9XKQ/FyLvI78jcPmz6sR96WWbqJ4Zu28ZVx8WEbkRBpo86KCNc2k8+eQAdOhwG5KSEjF79mc4e/YsBgx4HkFBwer2nj3vUz0w+/btxYkTx7B//36EhoY6v99s9lVhxqFOnXr46qv5zn3H8JJjCKt27do4duwYypWroIqQe/W6M9vxyHWnTp0s5lZTaeKzay7817wBnd2G1CqdENflU8Do6+rDIiI3w0CTC+khkZ6SFGtKvu5vMOhhsdgK/bw+Xj4F6p0RISGhqFQp46ysY8dORP/+j2L48CH47LM5SEtLw1NPParCTbt2t+D227uoUPPVVwuc3y91M1cGEl2Waa8GgyHH7Xq9TvXsSM/MrFmXw49DmTJlsGfP7gK1gygHuw1+GybAd+snaje5/kNI6DAO0PPfFhHlxP8MeZBgYTZkDNPkK9Cg8IGmKIqBhw9/HQMGPI5vvlmIqlWr49Kli5g792tnMNm0aUO2Wp2EhHicPXtG1dkI6cmpWbOm8/aDBw84L0uIkf3WrdvhhhuqqPoZ+TlVrFhJ3X748CHMmjUdI0e+WYKtJo9kTUPAyiHwOZBRz5XYciiSmg/mWYiJyD2LgqXGY+TIkWjRogXatWuH2bNn53nfP/74A3feeSeaNWuGBx98EHv27CnRY9WKevUa4O67e2DOnM8REBCA5ORkrFmzSoWWxYt/wvfff4v09PRs3zNx4ts4cuQQVq5cge+++wa9ej3gvG3bti2qR0d6dj74YDJSUlJw6623q1lSUnj81luv47//9mD//n1qVlRycpJ6XqLrpUuNUzOZJMzY9QbE3TYFSS1eYJghIvftoZk0aRJ2796NuXPnqqnBw4YNQ4UKFdC1a9ds9zt48CCGDBmCMWPG4MYbb8ScOXMwYMAAFXIcxa502YABz2HVqhVqhtJjj/XHe+9NVMNPNWrUVNOuJ0wYi4sXLzjv36pVGzz7bH9VTzNgwLPo3Pnyz1+GqrZu3YSZMz9FnTp11GwoR2B5440xqiD5hReeVUNXLVu2xksvDXVJm8kz6BPOIGjJozBE7oPN6Ie4rjOQfkNHVx8WEWmAzl4Uc4WvQ1JSElq1aoWZM2eiZcuW6rpPPvkE//zzD+bPz16XIQHml19+wQ8//KD2ZaijefPm+O6779CoUaMCPe+lS/Fy+pds0tPTEBl5FmFh5WE0FvwcMEVVQ+Pq0xNc2Q7pcRGvvZbx1R1c67WSD/Hh4QG5vs5a4yltyW87vCL3IWhJX3glnIXVtyzius2DpUxDuIvS9nq4O7aj9LRDl/nYbttDs2/fPlgsFjWE5CAhZfr06ZmFp5dHw4KDg3Ho0CFs2bJF3V+CjRSk3nDDDQV+3tx6rdmTrT3yml3ttfSE19RT2pKfdhhPrUPA0qegT4uDJaQm4rrPhy2wslvNNSxNr4cWsB2lpx26fD6mywLNxYsXERISAm/vy5+yw8PDVV2NLNyWdWrxXXfdhZUrV+Khhx5SQxsSdmbMmIGgoKACP29YWM6UJ3UhUVF6eHnpVC/F9bje73MlLy99jmPPetkx48qd2maz6dTrHxLipxYOLMjrrFWe0pY827HrO+CXgYAtHbihNQx9vkSo7+W/f3fj8a+HxrAd7iXMhe1wWaCRYtWsYUY49qXeI6vo6GgVgEaNGoUmTZrgq6++wogRI/Djjz8iLCysQM8bGZn7kJP0Clmt9usaOtLqkFOTJjeq4SbHsV/ZDsdsJXdqm7xG8lpFRyfCaMxe3Cwkg8kfVG6vs9Z4SlvybIfdDvO26fBb/47aTa1xN+Lv+ABIMgJJ8XA3Hv96aAzbUXraoct8bLcNNCaTKUdwcexf+cl78uTJakG3hx9+WO2PHTtWzXj6/vvv8fTTTxfoeeUHfeUPW8u/RKVVbq9jQW7XEk9pS7Z22KzwX/smzLvmqN2kJv2R2HYUIGsguXlbPfL10DC2w73YXdgOl40lREREqJ4XqaNxkF4YCTOBgYHZ7itTtOvWrevclyEH2ZeZUUXJRfXRVAB8jTyAJRmBvw9whpmEtm8isd3ojDBDRHSdXPYfpF69emqxt+3btzuvk6JfmbWUtSBYlC1bFocPH8523dGjR1GpUsaCboXleD45GzW5t7S0VPXVy4trQmqRLiUawT/3genIMtj13ojr/CmSmz7l6sMiIg/gsncFWT+mZ8+eGD16NMaNG4cLFy6ohfXGjx/v7K2R9U6kx+aBBx7A8OHD0bBhQzXLadGiRap3plevjBMoFpZe7wWj0QcJCTGq6Djr0v/5LVSV2g6tc+d2SM+MhJmEhGiYzf45Qi+5P33cCQT+8ggMMUdgMwUh7q7PkV6hlasPi4g8hEs/5kphrwSafv36qWnYgwYNQufOndVtsnKwhJvevXurWU6JiYlqZtO5c+dU744sxlfQguC8yGyeoKBQREaeQ1TU+QJ/v7y5SqGq1mmhHRJmAgPddwYM5eHMNgQvug/65Euw+ldEbPf5sIZePikqEZFmF9Zzlast+iM/Cosl58yZa1VfyxRimXWj5Z+kFtohw0zX6pnxlEWqPKkt3idWImjZM0B6Iixh9RHbfR5sfuWgNZ7yerAd7oXt8ICF9dyR9NQUdKVg+UHLsJhMIdb6L6MntIPci8/er+C/ajhgtyKt8i3qVAZ2b89Yb4OI3AsDDREVPbsdvpumwG/T1Iz9Jg8irs07qhCYiKg4sLKSiIqWNR3+K19xhpmkFoOBnp8CXgwzRFR82ENDREUnLRFBvw+A94lVsOv0SLhlHFIbPQJfrZ+ohojcHgMNERUJXeIFBP3aD8aLu2A3+Kg1ZtKq3eFWJ5gkIs/FQEOANQ3m7Z8BFzfBu85DSK3aWfunfqUS5RV9GEGLH4FX/EnYzGGIvXsOLBHNXH1YRFSKMNCUcoZzWxHw11AYovar/cDDK5BetgkSb34F6Td0ZLChazKc3YSgXx+HPjUGlqCqiO02H7bgaq4+LCIqZVgUXFqlJcJvzZsI/r6HCjM2n1Cg+eOwG31hvLADwUv6IvjH3jCeXu/qIyU35n14qTqVgYSZ9IhmiLn3Z4YZInIJBppSyHj8L4R+dRt8d34OHexIqXMvoh9eBXR/H1F91yOpydOwe5lgPLsJwT89gKCf+8BwbourD5vcjM/O2QhcNgA6aypSq96BmB7fwm4umtW7iYgKikNOpYguORL+a0fD58CPat8aUBnxHScg/YYOzpElu284EtuNQnKzp+G75SP47PkS3qfWqi21SicktXwFljKNXNsQci27DX7r34Hv9hlqN7lBXyTcMhbQ898JEbkO/wOVBnY7TAd+UGFGnxKtptMmN+6PxJavAEbfXL9FlqZPuOUdJDV9Br6b34fPvkUwHV+httTqdyLx5iGwhtUt8aaQi1lTEbDiZfgc/FntJrQajuQbn2OtFRG5HAONh9PHnUTA6uHwPrFa7VvC6iH+1ndhiWiar++3BVZCwm2TkXTjc2qhNNOBH2E68hu8jyxDaq0eSLr5ZViDqxdzK8gd6FJjEbj0SXif2QC73oD42yYjtc59rj4sIiKFNTSeymaFeccsVSsjYUZqYuTTdPT9S/MdZrI9XHA1xN/xIaL7/InUGner2hufgz8h5Mtb4b9iiApO5Ln08WcQ/ENvFWZsRn81k4lhhojcCXtoPJDXpb1qKrbMVhJpFVoi4dZ3i6QnxRpWR51g0HBxN3z/nQzTsT9h3vcNfA78gJT6DyKp+SDY/MsXQSvInX6fgpb0hVfieVj9IlSYsYbXd/VhERFlw0DjSSwp8N38IXy3fQKdzQKbdyAS27ymggZ0RdsZZynTEHF3z1Gzn/w2Tob3qTUw754Hn/++QXLDvmqIyu5bpkifk0qe8eRaBP7WH/r0BFhCaiO2+3zYAiq6+rCIiHJgoPEQxjMb4P/XqzDEHFH7UrgrM0+kuLc4Wco1R2yPr2A8/Q/8Nr4L49l/4btjFsx7FiK58RNIajYQdp+QYj0GKh6m/d8jYOUr0NnSkVahFeLunAW7T7CrD4uIKFcMNBqnS42D3z/jYN6zQO1bfSNUkEmrcVeJHkd6xdaI6fU9jCf/ht/GSWq4y3frx/DZPQ/JTZ5CcpP+sJsCS/SY6DrZ7TBv/Rj+Gyao3ZSa9yD+9qmAl8nVR0ZElCcGGg3zPvIb/Fe/Dq+k82o/uf7DSGwzEnZTkGsOSKdTa9rEVL4F3sf+VD02hsi98Ns0Beads1VvjfTa5DVVnNyAzQr/NW+o4UOR1HSAGrYs6iFLIqKixkCjQfrE8/D/+3U1fVpYgqoh4dZJqpfELeh06izLaVU7qaXx/f6dDEP0IfWJX4ajkpo/j+QGjwAGH1cfKWWVnozA5c/BdGw57NAhsd1oJDd50tVHRUSULww0WmK3w+e/r+C37m3o0+LUWiBJzZ5BUosX3DMc6PRIq9kNadXvhOngj/D7dyq84o6rBf7M26YjqcVgpNTrA3h5u/pISz1ZRTro18dgPL9NTfGPu+NDpNW429WHRUSUbww0GuEVc0QV/co6IELOiC0L5Gli+qzeS61ZklqzB3z2fQvfzR/AK+EMAlaPhO/WT5F404tIrXMvl853EX3sMQQtfgSG2GOwmYIRe/cXsJS/ydWHRURUIHwHcXfWdJi3z1Cr9MpJAO0GMxJbvppRi6L3gqZ4GZHS4GGk1L1PnSNKzhXlFX8SgSuHwLJlGpJuHoLUWvewXqMEGc5vUz0z+uRIdW4vmZZtDanp6sMiIiowBho3ZriwAwErh6rCWpFWuQPiO46HLfAGaJqXCSmNH1fDTebdc+G79RMYYo8i8I/nYdnykTpPlAxT8fxAxUsKtwN/fwY6SzLSyzRC7N1zYfcr6+rDIiK6Lgw07ig9SS1WZ945Czq7TQ0DJLQfjdTa93rWm7zRjORmA5HS4BE1C0rqagxR+xG07Gn1Bpt08ytIq3KbZ7XZTfjsWQD/1SPV71faDR0Q12UG7N7+rj4sIqLrxkDjZownViNg1XA1FCNSavVEQrvRsPuGw1PJG6kUCCc3fBTmHTPVOaiMF3ch6Nd+SC/XXA2xpVdq6+rD9Ax2uzplhd/mD9Ruct3/IaHjBDUcSESkZQw0bkKXEg3/tW/BZ/93at/qXwEJHcarqc+lhaxCm9RyqKoPkmEo8645MJ7bguCf/4e0iq1VsGGxaiFY0xGw6lX47FukdhNveglJN73MHjAi8gisvnQ1ux2mAz8h9MuOKszI+h9JjZ9A9IMrS1WYycpuDkNi2zcQ1Xc9kho9DrveG96n/0HID70yZuNknnST8k+XFq96vCTM2HVeaoacFGEzzBCRp2APjQvp40/Df/UImI6vVPuW0DqIv3WSOj8SATa/CCTeMlbV2chUbznxpfeJVWpLrdZFFQ9rYtq6i+kTzyFwST8YL+2B3eCLuK7TM2qTiIg8CAONK9is8Nk9F/7/TIDOkqR6IKSGJOnGZ7nIXC7k7M6yErIsIui3+X2Y9v8A09Hf1SbnGUq6+WVONc6DV9QBBC3uC6+E07CZwxHbbS4sZZu4+rCIiIocA00J84rcj4C/hsJ4fqvaTy9/E+I7ToI1tJarD83t2YKrIf72D5B04/Pw3TQFPocWw+fQLzAdXqIW5kts8SLswVVcfZhuw3hmIwKXPgF9aiwswdUR220+bEH8+RCRZ2INTUmxpsJ342SEfNtVhRmb0R/xHcapM1QzzBSM/Lziu3yKqP8tR2rVzmrqsdSGhH7ZAX5/DQNiT6G08z60BEE/P6jCjMwUi+n9E8MMEXk09tCUAMPZTapXRk7QKORNOKHD27D5V3D1oWma1M/E3T1brXYrJ8D0PrEa5j0Lgf++RWD5Fkiv1B5pldrBUrZxqTqtgnn7TPitGwMd7Eit3hVxd3wEGMyuPiwiomJVev7Lu2hmid8/E9RquMJmLoP4W8ZmnPSPs0uKjCWiGWK7L1RDLH4bJ6mvMitKNtm3eQcgvWIbFW7SK7eHNbiGZ/787TYVZOSM5iK50WNIaPeW9k6RQUR0HRhoion30eVqJVavxHNqP7leHyS2eV2ttULFI71CS8T2/h7hugtI2PU7jCfXwHhqnRp2cRQRC6tfORVsVMCp1E7NptI8SwoC/nwRPoeXqN2E1q+p2WEeGdyIiHLBQFPEdEkX4b9mlCpYFdbAKmoqNle6LUFhNZDSsC+SG/RVM8oMl3arcON9ai2MZzepkOm1b5FzgTlLSG2kVZZw0x7pFVvB7h0ArS3KGLi0P7zPboRdb0R8p6lIrd3T1YdFRFSiGGiKcoG8/76F/7oxqkdAFi9Lbvo0EmUlViPrF1xG76WmKcuW3Px5wJIM49nN8D61BsaTa2G4uAuG6ANqw87Z6nWTISzH8FR6RDO3nkqvjzuFoCV9YYg+CJt3IOLunMnwTESlEgNNUYg6gsCfn4f3qXVqV06smHDru7CUaejqI6MrGcwZQaVye6B1Ru+G8fR6eJ9cC+OpNTDEHoPx3Ga1YfP7aiG6tIqtMgqMK7eDNbSu2wzjGC7uRuCSR+GVdAFW//KI7TYP1rB6rj4sIiKXYKApJO8DPwMrh8DbkgK7wQeJN7+C5Cb9S9WsGi2z+4SoIm1VqK16PE5mDE2dWqu+6pMj1UrOjtWcZXG6tEptMwNOe7Xon6tOYhq47Gno0xNhCaurwgxnzRFRacZ33ULykWnClhQ1RBHfcQJsQVVdfUhUCLbAykip/6DaZNaQV+Q+eKv6mzVq9pQ++RJ8Dv6sNmEJqna5wLhimxIp+jbtW6SWAdDZLEir2AZxd86C3RRY7M9LROTOGGgKKeGODxGK84jza6xOLEkeRKdXa90ky9ZsAGBNU4siOgqMDee3wxB7VG3m3fNg1+lhKdPI2XsjC9rB4FN0x2O3w3fLh/Db+K7aTanVE/Gdprh1jQ8RUUlhoCkkm385ILwWcCkesLv6aKhYeXkjvUIrtSW1HApdahyMZzZcDjjRB2G8sENtvlunwe5lQnr5m50zqCzhDa5/TRibBf6rX4N570K1m3Tjc0hsNUyFLiIiV7Db7YhOTsfpmBSciUtBrYppqBHoug9YDDRE10mGedKqdVZbotTfJJxV696o4alTa+GVeF5dlg0YD5spWM1ASsssMLYFVslfgXF6EgKXPQPT8RWqFyih/VikNOpXEk0kolIu1WLD2bgUnI5NwemY5MyvmfuxyUhOtznv66XXYcVzreHn7ZpowUBDVERs/uWRWvc+tcnwkFf0ITVzSs2gOr0e+tQYmA7/qjZhDaic2XvTDmkV28LuG57zQRMuIujH+1Wvj/T4xHX+BGnVu5R844jI43tZTsUmZwkrGQHmYkLaVQcf5CNZRIAJFYN90LFuBPxNBvn35xIMNETFQadTJ9GULaXxE2rIyHBhhyowlpBjPLcVXvEnYd77ldqEJay+qr1RBcYVWkKfdA749VEYo4/B5hOC2LvnwCJ1OUREBZBmsakhofz0suTG1+ilAkvFINnMzsuVgs0oF2CCt0GvOpvDwwNwScovXISBhqgk6A0qjKhActOLahhJnXNKpoefXAND5F7n5rt9hlrx1y7FvumJsAbegNjuC2ANru7qVhCRm/eynI5NwSlHaLmOXhZnaJGvmfvBZiN0brL+1tUw0BC5gtEX6VVuVZvU3+iSLsH79LqMAuOTa+CVcBo6WzpQoRlius5WJzYlotIrey9LRs9KUfSyVAzyQflAH9XLonUMNERuQOpnUmv1UJsMQOtlxeKofQhs1g32OCtn0BGVgl6WqKTC9bKUlV4WNRSk3V6WwmCgKUV/LIlpVvUHE5WYhqikNEQmpSM6SS6nq329lxeqh/igYflANCwfgEAfo6sPu3TS6WALroa0kGqAty8A141JExUXi9WmehWS061ISrciJd2q9h2Xk9Iy9tVldZtcf/l22Zf/WenpFmiZhJTEdBuORyayl6WQGGg0zGa3Iy7ZgkgVStIQlZiOqOTLgSUjqFzeT7Ne+2P+iiyXq4X6qmDTqEKg2qqH+ULv4QmfiLJ/EJJpu45AoQKIChpZ9p2X83l75uX0fPw/Km2y9rI4elcqZQkupaGXpTAYaNzwU4sUd2WEk8yQIr0piemIztzPCDDpiElKQ0H/J0jCD/UzItTXG6G+l7+G+XnD18+EjYcuYteZOJyMScHRqCS1Ld5zXn2vn7dXRsApnxFw2ItD5L69HRkhxJajt0PuA4MXIuOSc95ucVy+3CNS3LFD1i4xG/Xqf5OP0Ut9lX112TvjOrNB77zsuN3s7YWQIF/Exye7bJpwUZB8Ur5MAAJ1dpQLYC9LYTDQlAD5hxGVObwT6ewxyQgqzq+ZwSU2peDdp0E+BhVMQjIDSlhmYLlyX4KL/EPIjWPK3d21w9Q/BznWXWfjVbjZdTYOe87GqyGrjcdj1ObAXhyigvV2OEJH9t6MK3sy8nl7CfZ2+Bj0MGcJE3I5W8BQX6+4nHm/7Ldf3peQYvS6vjfwrNOEtR5oPKEd7oCBppASUi3YvPssjp6NcwaUyMzA4qhPkSBQEF46INjZg+III5n7meEkLDOwyHa9/xCuJsTXG7fUCFObsNjsOHwp0Rlw2ItDpaXnIyZZekXTEZOcBv3ZBFyITEBSlrCR0SOSPXAkpWX0dmRcvnx7sfd26KBChKO3Q4WGzN4OR8CQ/dAgM5BuzQwkl2/P+L6M3pCsgUWu4wcVcncMNIU0YvF/+OdY9DXvZ/TSZRveka8hjn356pexH+ZrRJDZ6Hb/PAx6HeqU9VfbfU0rqOsK0ovTqEJGyGnIXhxyMeklyegVzV4Yn/WDiKMn9Xp6TPPDJEMoV+ntyL3nw9HjkXtvh2zyf+ZaNRbsESBPxUBTSJ3rloFVp4OfQYcQs6MXJSOYOAOLn7fqufC0Yq6C9uL8spu9OFQyM/iyD+lmv76gPaZ66TE1Zw7j+ntDflN9HOHCMQyTGUpy6+1wDK2wt4OoeDHQFFL3huXweMda/LRThL04EnKqsRenVLtyBl+09KDkCCwZ+1JEL70uBZG1x/TycG7OfflgImFbClfZs0Hk3hhoqFixF4ey1qNkhJDim8F3rcL4kMwhXn+T5/WYEpV2DDTkVr04u6UX5xx7cTxtBl9MitwnvcCPH6hm8F2xzIBf7oXyMrRDRKUXAw15VC+OFFRT8dWjFHYGn6MeRerKQsyXh3kcs/YcwzyO3pTimMFHRJ6JgYY8rhenWZUQmHRSuHl59kduBZpXLuaVnxkiWlQS9SgSTjJm712ewedc/8jPiBoVg6FPTXfWoxARFTUGGvLIXpzrXcPDsTqpmkaby+qkly9nX6/DfJUptj4GryJ/E3euKH3FwoxZ9yMzw0v0ddSjyHE7hnUkmDhqUa7cz089CotpiagkMNCQR/Xi7D4Xj7NJFlyKScqyhLvtKueasTrPcSVfpNenoMMo+V13xFzAFVW9DTpY9F44eTFeFc46A8t1ro9y9XqU7PusRyEirWGgIY/rxSlob4D09qTkEniuegbgK855k1dochyCDOPIFpNcdO1lPQoR0WUMNFTqSW+Pv8mgtuI6d0/WUORcGt95Tp+MpfGvDE1pVhsigs3wkxqVXNZLCWI9ChGREwMNUTGRuhKpuZEt5Lq+n7UnRET5xT5oIiIi0jwGGiIiItI8BhoiIiLSPJcGmtTUVIwcORItWrRAu3btMHv27Dzvu3//fjz44INo3Lgxunfvjg0bNpTosRIREZH7cmmgmTRpEnbv3o25c+fizTffxLRp07Bs2bIc94uPj8cTTzyBmjVrYvHixbjjjjvw/PPPIzIy0iXHTURERO7FZYEmKSkJixYtwmuvvYYGDRqokNK/f38sXLgwx31//PFH+Pr6YvTo0ahSpQoGDx6svkoYIiIiInLZtO19+/bBYrGgWbNmzuuaN2+O6dOnw2azQa+/nLX+/fdfdOrUCV5el1cv/f7770v8mImIiMg9uSzQXLx4ESEhIfD29nZeFx4erupqYmJiEBoa6rz+5MmTqnbmjTfewMqVK1GxYkUMGzZMBaCCKupzDzoeT+vnNGQ73I+ntIXtcC9sh3thO64tv4/pskCTnJycLcwIx35aWlqO4anPPvsMjz76KGbOnIlff/0VTz75JH777TeUL1++QM8bFhZQBEdfco9b0tgO9+MpbWE73Avb4V7YjsJzWaAxmUw5gotj38fHJ9v1MtRUr149VTsj6tevj3Xr1uHnn3/GwIEDC/S8kZFFu+qqJEd5AYv6cUsa2+F+PKUtbId7YTvcC9uR/8d220ATERGB6OhoVUdjMBicw1ASZgIDA7Pdt0yZMqhevXq266pWrYqzZ88W+HnlB10cvzTF9bglje1wP57SFrbDvbAd7oXt0PAsJ+lxkSCzfft253VbtmxBo0aNshUEi6ZNm6p1aLI6cuSIqqUhIiIiclmgMZvN6Nmzp5qKvXPnTvz5559qYT2pk3H01qSkpKjLffr0UYHmo48+wvHjx/HBBx+oQuEePXq46vCJiIjIjbh0Yb0RI0aoNWj69euHt956C4MGDULnzp3VbbJy8NKlS9Vl6YmZNWsW/vrrL3Tr1k19lSJhGbYiIiIi0tntnjBql3+XLhV9UXB4eECRP25JYzvcj6e0he1wL2yHe2E78v/Y18KTUxIREZHmMdAQERGR5jHQEBERkeYx0BAREZHmMdAQERGR5jHQEBERkeYx0BAREZHmMdAQERGR5jHQEBERkeYx0BAREZHmMdAQERGR5jHQEBERkeYx0BAREZHmMdAQERGR5jHQEBERUekNNPHx8Vi4cCHefvttREVF4a+//sKJEyeK9uiIiIiIiivQHDhwAJ07d8b333+Pr7/+GomJiVi+fDl69OiBf//993oekoiIiKhkA430yjz44IP44YcfYDQa1XXjx4/HQw89hEmTJl3/0RARERGVVKDZtWsXevbsmeP6Pn364NChQ9fzkEREREQlG2hCQ0Nx9OjRHNdv3boVYWFh1380RERERNfBcD3f9NRTT+H111/HwIEDYbfbsWHDBvz444+YO3cuXnrppet5SCIiIqKSDTQytFS2bFl8/vnn8PHxUXUz1apVw9ixY3HXXXdd/9EQERERlVSgmTVrFrp166ambRMRERFpsoZm+vTpSE9PL/qjISIiIiqpQCO9M59++imOHTuGtLS063kIIiIiItcOOf399984c+aMKgTOzX///VfY4yIiIiIq3kAzYcKE6/k2IiIiIvcJNDfffLP6KkNOhw8fhs1mU7OcatasWdTHR0RERFQ8gSYuLg4jRozAihUrEBQUBKvVqs7ndNNNN+Hjjz9GQEDA9TwsERERUcmey+ncuXNYunQpNm7ciM2bN2Px4sVISkpS53QiIiIicvtAs3LlSowePRrVq1d3XifDTaNGjVK9NkRERERuH2hMJhP0+pzfqtPp1PATERERkdsHmttuuw1vvfUWTpw44bxOCoRlKKpDhw5FeXxERERExVMUPHToUDz33HPo3LmzKgoWsbGxuOWWW/DGG29cz0MSERERlWygCQwMxPz587F//341bVuGoGTadtaaGiIiIiK3DjRyuoP3338fFStWxMMPP6yu6927N9q0aYMXXngBRqOxqI+TiIiIqOinba9evRp169Z1Xvfss89i1apVmDhx4vU8JBEREVHJBprly5dj8uTJaN68ufO622+/Xa1BI2vTEBEREbl9oLHb7UhNTc31+vT09KI4LiIiIqLiDTRdunRRs5lkhWBZHVi2rVu3qsX27rjjjut5SCIiIqKSLQqW8zi99tpr6NevnzoxpfDy8kKPHj0wcuTI6z8aIiIiopIINJcuXUJISAimTJmiTlIpC+pt2rRJTd2WmU6+vr7XcxxERERExT/kJGfTHjhwINq3b69CjJDzNvXp0wcLFy5UW/fu3dVJK4mIiIjcMtB89NFHOH36NBYsWKAW0JO6GZm+3bhxY/z+++/47bff0K5dOzX7iYiIiMgtA41M1Za6GZmqLSehXLt2req16du3r3MhPRlykuuJiIiI3DLQXLx4ETfccINzf/369aoQWHplHMLDw5GcnFz0R0lERERUFIEmIiICJ0+edK43IysFN2nSxHlySrFt2zaUL18+vw9JREREVLKBRqZkv/POO6oQeNy4cTh79iweeugh5+379u1TM5+6du1aNEdGREREVNTTtp955hkkJCSodWakhmbw4MHo1q2buk3O3/TFF1+gY8eO6n5EREREbhloDAaDWlBPtiv17NlTTdmuX79+UR8fERERUfGsFHylOnXqFMXDEBEREZXcuZyIiIiI3AkDDREREWkeAw0RERFpHgMNERERaR4DDREREWkeAw0RERFpHgMNERERaR4DDREREWkeAw0RERFpHgMNERERaR4DDREREWkeAw0RERFpHgMNERERaR4DDREREWkeAw0RERFpHgMNERERaR4DDREREWmeSwNNamoqRo4ciRYtWqBdu3aYPXv2Nb/n1KlTaNasGTZu3Fgix0hERETuz+DKJ580aRJ2796NuXPn4syZMxg2bBgqVKiArl275vk9o0ePRlJSUokeJxEREbk3lwUaCSWLFi3CzJkz0aBBA7UdPHgQCxcuzDPQ/PLLL0hMTCzxYyUiIiL35rIhp3379sFisajhI4fmzZtjx44dsNlsOe4fHR2Nd999F2PGjCnhIyUiIiJ357IemosXLyIkJATe3t7O68LDw1VdTUxMDEJDQ7Pdf8KECejVqxdq1apVqOfV6Qr17Xk+XlE/bkljO9yPp7SF7XAvbId7YTuuLb+P6bJAk5ycnC3MCMd+WlpatuvXr1+PLVu2YMmSJYV+3rCwgEI/Rkk+bkljO9yPp7SF7XAvbId7YTsKz2WBxmQy5Qgujn0fHx/ndSkpKRg1ahTefPPNbNdfr8jIeNjtKNLkKC9gUT9uSWM73I+ntIXtcC9sh3thO/L/2G4baCIiIlRdjNTRGAwG5zCUhJbAwEDn/Xbu3ImTJ09i8ODB2b7/qaeeQs+ePQtcUyM/6OL4pSmuxy1pbIf78ZS2sB3uhe1wL2xH4bks0NSrV08Fme3bt6t1aIQMKzVq1Ah6/eVa5caNG2P58uXZvrdz5854++230bZt2xI/biIiInI/Lgs0ZrNZ9bDIujLjxo3DhQsX1MJ648ePd/bWBAQEqB6bKlWq5NrDExYW5oIjJyIiInfj0pWCR4wYodaf6devH9566y0MGjRI9b4IWTl46dKlrjw8IiIi0giXrhQsvTQTJ05U25X279+f5/dd7TYiIiIqfXhySiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPAYaIiIi0jwGGiIiItI8BhoiIiLSPJcGmtTUVIwcORItWrRAu3btMHv27Dzvu2rVKvTo0QPNmjVD9+7dsWLFihI9ViIiInJfLg00kyZNwu7duzF37ly8+eabmDZtGpYtW5bjfvv27cPzzz+Pe++9Fz/99BP69OmDF154QV1PREREZHDVEyclJWHRokWYOXMmGjRooLaDBw9i4cKF6Nq1a7b7LlmyBK1atcKjjz6q9qtUqYKVK1fit99+Q926dV3UAiIiIkJpDzTSu2KxWNQQkkPz5s0xffp02Gw26PWXO4969eqF9PT0HI8RHx9f4OfV6Qpx0Fd5vKJ+3JLGdrgfT2kL2+Fe2A73wnZcW34f02WB5uLFiwgJCYG3t7fzuvDwcFVXExMTg9DQUOf1NWrUyPa90pPzzz//qKGnggoLCyjkkZfs45Y0tsP9eEpb2A73wna4F7aj8FwWaJKTk7OFGeHYT0tLy/P7oqKiMGjQINx4443o1KlTgZ83MjIedjuKNDnKC1jUj1vS2A734yltYTvcC9vhXtiO/D+22wYak8mUI7g49n18fHL9nkuXLuHxxx+H3W7Hhx9+mG1YKr/kB10cvzTF9bglje1wP57SFrbDvbAd7oXt0PAsp4iICERHR6s6mqzDUBJmAgMDc9z//PnzePjhh1XomTdvXrYhKSIiIirdXBZo6tWrB4PBgO3btzuv27JlCxo1apSj50VmRPXv319dv2DBAhWGiIiIiFw+5GQ2m9GzZ0+MHj0a48aNw4ULF9TCeuPHj3f21gQEBKgemxkzZuDEiROYP3++8zYht8l9XGnmvun49uiXsHlAX2GwKQhP1Hoad1bqDp3WS+6JiKjYWG0WHIk/jD3Ru7E7eif2xuxGGb9wTG7xIYx6E0pVoBEjRoxQgaZfv37w9/dXxb6dO3dWt8nKwRJuevfujd9//x0pKSm4//77s32/TOeeMGECXCkqNRIW2+VhMy2LSonC5F0TsPbc3xjSaDjCfMJdfUhEROQGEtLjVXjZE7MLe6J34b+YvUixJme7j01nlRJelx2jzi4VtqXIpUtFXYFth84/TfMV6tDZ8U/Many07SOk29IRYAzACw1ewW0V7oCWSMdSeHhAMbzOJc9T2sJ2uBe2w724YzvsdjtOJZ50hhfZjiUczXE/P4Mf6gc3RIOQRmgY2hjta7REUqy1WGY5yc/IrXtoPIEMzYSbwwEfk9v8Ml7vL8zjDR9HQ78bMX77WByM24+3t7+Jtef/VsEmyDvI1YdIRETFINWaiv2x/znDy56Y3YhNi8lxv4q+lTLCS0gj9bWKfzXodXrne4iv0RdJKPiCt0WFgYayqRZQHR+3mYkFh+ZgweG5WHV2BXZGbceQhsPROqKtqw+PiIgK6VLKxWzh5WDsfljs2UsnjHpv1Amq6wwv0hMTYnLv2cUMNJSDQW/AY7X7o3XZtpiwcyyOJxzDa1uG4s5K3fBsvRfgZ/Rz9SESEVEBi3f3xOxSBbznk8/luF+oKSwjvARnBJiagbXh7ZV98Vt3x0BDeaoTXA/T236B2Qc+w3dHv8Zvp5Zgy6VNGNbkdTQLa+7qwyMiolyKd/fG7FHBJa/iXT30qB5YwxleZCtnLq/52a0MNHRVJi8Tnqk3CG0i2mHSjndwNvkMhmwchN5V70f/Os/Axyv3VZ2JiKj4i3dPJ51yhpc9+Sjela1uUH2P7GlnoKF8aRLaDLPaz8P0/6Zh8cmf8MOxRfj34kYMb/w66oc0dPXhERF5vIIW7zbIHEKqElAVXjoveDoGGso3s8EXLzV6FW3L3YJ3d47DqcQTGPzPQDxY4xE8WutJGPVGVx8iEVGpLN5toMJLRi+MuxfvFhcGGiqwm8u0wuxbFuCjPVPw55nlWHh4Hv65sB4jmryBGoG1XH14RESaLN49HHft4t0Q71A0DGmMBiEN1VctFu8WFwYaui4BxkCMbDoa7SI6YOqed3Ek/hCeWfck+tV6En2qPwwvPX+1iIiystltiEmLxoXk87iQcgEXk8/jYsoFHE8+gh0Xd+ZavFstoIYzvHhK8W5x4bsOFcot5W9Fw9AmmLp7ItadX4PPD8zAPxfWYljjN1DZ/wZXHx4RUYmQAt349HhcTDmPC8kXcCElI6xkhJfzuJh8AZdSL6qV2PMixbv1ghs4w4unFu8WFwYaKrRQUyjG3DgBf5xeho/2TlFTBp9e2w9P1X0GPavc51xJkohIq5ItSapXRQKKI6hkCywpF5BiTbnm4+igU2u+lDVHoIxPWZQ1l0W9iNqoaqyNG/xLR/FucWGgoSIhXaCdK92JpmE3qoLhLZGbMG3v++rUCa82fk11kxIRuaM0a5oKJCqgZPamOIaELmT2uCRY8rekf5B3cEZQ8SmLMuYI9bWsTwTKmDO+hvuUUYuXuvO5nLSKgYaKlHzqmHTz+/jlxA+Yse9jbI/civ5r+uK5ei+ia6W7OfZLRCVebBuZGpkloEhwyQgpGV/PIzotOl+PJUNCGb0qjt4VCSyXL8tXWbuLXIOBhoqchJYeVe5Fi/CW6tQJMuXw3V3jsOb8agxpOAxhPuGuPkQi8pC6lajUKFxIyiWoZA4HSZix2a3XfCxvvTfKmstl9KxcEVoyelwiWM/i5hhoqNhU9KuE91t9gkVHv8YXBz7Dhgvr8OSaR/Biw6HoWL6Tqw+PiNw8rMgwz+WelMtBJWNI6Bwuply9yNZB6lIklFwZUDKGgTKuCzQGsQdZ4xhoqFjJPxKZxt2yTCuM3zEWh+IOYMy2N7Dm3GoMbjAEQd5Brj5EInKBZEtythlBziLbLDUsV05jzleRbWbtSoQKLBk1LLLQHCcneD4GGioRspbCx21mYsGhOWohvr/O/okdUdvwSqPhaFW2rasPj4hKrMg2o6dFpjgXtMjWOSRkLosIcwTqVKgOfZIZXjq+lREDDZUgOTXC47WfQuuybTFhx1icSDyOkZuH4q5K3fFMvcEcnyYqtUW2l4NKfots1ewg/wBcSuHsIMrAQEMlrm5wfcxoNwezD8zAd0e/wdJTi9U072GNX1fTvonIlSvZxmQLKuevWHeFRbbkrhhoyCXkU5f0yrQp2x6Tdr6Ds8ln8PLG59G76gN4qs4znPpIVFxFtinnsSc5HocuHMuoX8lSuyInQ2SRLWkVAw25VJOwZpjZfi6m/zcNS07+jB+OfYtNFzdgeJNRqBdc39WHR6T9lWyLqMjWEVTKZBbZckVbcjcMNORyvgY/vNxoGNpG3ILJu8bjZOIJDFr/NB6s0ReP1npC1d4QlWZFXWRbwb88QoxhKONzxUq25giEm7KvZEukFfytJbfRsmxrfN5+AabtnYI/zyzHwsNzsfHiegxvPArVA2u4+vCItLeSbZYl9x3X+RhMXGqfPBIDDbmVQO9AjGw6WvXWTN39Lg7FHcTAdY/jsdr98b/qD7ObmzRfZFuolWyzBJQrgwqLbKm0Y6Aht9Sh/G1oFNoUU3ZNwPoLazFr/3SsP79G1dZU8qvs6sMjuupKto4zMBekyFZOWph1yjKLbIkKhoGG3FaoKRRjm0/E8tO/YdreqdgbswdPrXkUT9d9Dj2q9ObKn1RiK9lm1K+cR+z+KJyMOZW5um3BimwvBxUW2RIVBwYacmvyibRLpbvU+jTv7hyHrZGb8dHeKVh3/m8MbTwSEeZyrj5E0miR7aXUi5nFtRl1KtdbZCs9J2Wdw0BXFNn6RKiTsbKwnaj4MdCQJkhwmXTz+/jl+I+YsW+aCjb91/TFc/VfRJeKd7ErnpysdiuiUiKzB5VsRbYXEJ0Wla/H8jX4ZqtTqRpWGf42WYo/Y19Ci4+XT7G3iYiujYGGNEOGmHpWvRctytysTp2wN2a3WpRv7bnVatq3dOuT59etxKRF5x5UMs/CfCn1UiGKbGWxOAkwGV/9jf7Zl9rn7CAit8VAQ5ojRcEftP4U3x75EnMOzlJFw7v/3oWXGg5Fxwq3ufrwqBiKbB1DQxdVkW3adRTZZg8qElxkPRb27BF5DgYa0iR5w5KF91qWaYMJO8eo6d1vbXsda87fgTG3vCn9Oa4+RMqlyFb1rCRfuUCcY2XbC0i2JhWwyPZyQMlaaMsiW6LSh4GGNE0W3Pu4zSwsODQHCw/Pw8ozf2D7T1tQJ7Cec20OVf+QOawgn9pZoFn0ZGqymgnkCCiOYtvM1W2lpyUuLa5ARbaXg8rl15FFtkSUFwYa0jx5c3u89lNoVbYtJu4YixOJx/FPyro8P93Lp/fcTqbnmKUi08X56T73IlvHOYKyDgNlrGSb/yLbvIIKi2yJqDAYaMhjyMksZ7afg6OW/Thw/gjOJ+U8OZ/UX0SlRqptf+x/uT6OhJkwU/jlFViznKTPcTnYO8Qj6i8cRbbZgsoVJzjMb5GtURXZXg4q8nOKkLBijkDt8tVgTPGHn+FykS0RUVFioCGP4u1lQtuItqhjapxjJoq8ecemxeSYHXP+ijdv6ZHIGC45n/fz6L2dPQpZV3e9PMxVVr15uzL0SHsTLQmZi8BdcQbmzJVs81tkq5ci28yQ52hffots1eygEM4OIqLixUBDpYa82QabQtRWO6hO3sMrqVGXF1jL/Ho5AFxAdGoU0mxpOJ10Sm15MXv5ZlkZNpfgY44o1PBKijUlW0Bx9EZdvi7/RbYyDJctqGQpsuUwHBFpAQMNURbypl3Gp4za6l+lAFbO0ZNtlk7mkFZGmDiPuPQ4FSaOJxxVW14CjYGZPR1XBB9zWYSaQnDMkoqD549lPG7yBZzPfC7Hc+SHPEdGiMr5HCyUJiJPwUBDVEDy5l/et4Larn4eoCvqUa4IPhJ4JJTIdjj+4HUdS3H3AhERaQUDDVExMBvMuMG/itoKXN+SGXykWDfMHIYw73DnUvvuVqdDROQuGGiIXEBCiL8xQG2ylk7u9+FS+0RE+cXlVImIiEjzGGiIiIhI8xhoiIiISPMYaIiIiEjzGGiIiIhI8xhoiIiISPMYaIiIiEjzGGiIiIhI8xhoiIiISPMYaIiIiEjzGGiIiIhI8xhoiIiISPMYaIiIiEjzGGiIiIhI8wwoZXS64nm8on7cksZ2uB9PaQvb4V7YDvfCdlxbfh9TZ7fb7fm7KxEREZF74pATERERaR4DDREREWkeAw0RERFpHgMNERERaR4DDREREWkeAw0RERFpHgMNERERaR4DDREREWkeAw0RERFpHgNNEUhLS0O3bt2wceNGaNH58+cxePBg3HzzzWjfvj3Gjx+P1NRUaM3x48fx5JNPolmzZujYsSNmzZoFrXv66acxfPhwaNEff/yBOnXqZNvk90yLf99vvfUWbrrpJrRp0wZTpkyBFhdY/+GHH3K8HrLVrVsXWnP27FkMGDAAN954I2677TbMmTMHWhQZGan+Jlq0aIE77rhDvUZaf+87efIkHnvsMTRt2hR33XUX1q5dW2LHU+rO5VTU5I1/yJAhOHjwILRI/jHLH1RgYCAWLlyI2NhYjBw5Enq9HsOGDYNW2Gw29ebfqFEj/PjjjyrcvPzyy4iIiED37t2hRb/++itWr16NXr16QYsOHTqEW2+9FWPHjnVeZzKZoDVvv/22+of9+eefIzExES+99BIqVKiAPn36QEvkzUU+sDhYLBb069dPhX+tefHFF9VrIAFAfs9eeeUVVKxYUYUCLf3vfe6559T/rnnz5qkPlvI/19/fH507d4YW3/vsmW2qXbs2vv/+e/z55594/vnnsXTpUvV6FTf20BSC/CE98MADOHHiBLTqyJEj2L59u+qVqVWrlvqkIAFnyZIl0JJLly6hXr16GD16NKpWrYoOHTqgdevW2LJlC7QoJiYGkyZNUgFNqw4fPqz+sZUpU8a5SXDW2usg/5gllDVu3Fj9Tj3xxBPYsWMHtMbHxyfba/HLL7+oNyAJA1oiH7rkf9Yzzzyj/tZvv/12FdT++ecfaMnu3buxbds2vPfee6hfv74K//3791fB2d3l9d63YcMG1UMzZswY1KhRQ/WiSU+N/A2VBAaaQvj333/RsmVLfPPNN9Aq+ccmQzPh4eHZrk9ISICWlC1bFu+//776dCP/pCXIbNq0SQ2jadHEiRPRo0cP1KxZE1oONPKGo2XyeyS/U1l/j6QnUD4AaJkEtZkzZ6pP2N7e3tBaMDObzap3Jj09XX0o27p1q/pAoyXyxh8aGorKlSs7r5MhQAk60i4tvvft2LFDhTNfX1/ndc2bN1cBtCRwyKkQHnroIWidfGLO2g0t3Z8LFixAq1atoFUypn7mzBn1iadLly7QGvmkuXnzZixevFj1OGmRhMqjR4+q8fMZM2bAarWia9euqvdPS2+g8qYjQxk//fQTpk+frt5oevfurXoHZFhWq7766iv1IUBeE62RYctRo0apXjMZqpHfLXlN7r//fmiJfIiMj49HcnKyCmji3LlzaihQrpewo7X3vosXL6rfq6zCwsJUu0qCdv8iqVi8++672Lt3r6oT0KoPP/xQvfn8999/mvskLePSb775pvqHLZ9EtUoCpfyjlvAiPWdSGyABTYbRtCQpKUnVY3399dfqd0naMX/+fM0WoTrC5qJFi/DII49Ay71/8oFFegjkdVm2bJkaQtOSJk2aqDd/CWaO37MvvvhC3ebuPTR5cfzNZyX7UjxcEthDQ9nCzNy5czF16lRV+6BVjroTCQdSH/Dqq69qpldg2rRpaNiwYbZeMy2SXg0ppA0KCoJOp1PDAdL7N3ToUIwYMQJeXl7QAoPBoIZfpc5B2uQIa9LDIbU0WrRr1y5VgHr33XdDi6QH87vvvlMF8xL65e9d2vPpp5/innvugZZ6miTsS4GzDMtIT4bU0EhAk2FOLTKZTGo4MysJMyX14YyBhhT5lCD/pCXUaHGYRoqCZZxWCgQdpP5EPunIG5I7d99eObNJ2iJTz4Xjk83vv/+uCgi1JDg4ONu+FAlKyJSiTq28HlJjJv+kHWFGVKtWTU0b1qo1a9ao4n8Jm1okNSZVqlTJ9iYpdRvSK6s1Umi+cuVKNVQTEhKCdevWqa9+fn7QooiICFUwnJX8P7tyGKq4cMiJVK+AdKnL+hpa/dR26tQpNT1QPqll/ccnb5xaefMUMpwhQzNSsyGb1APJJpe19qYpRYPSBe0gQ4AScrT0esiwgIQwqQdykCLUrAFHa3bu3KnWb9EqeXOU4ZmswxjymlSqVAlaIj0ZDz74IKKjo1Vwlt7AVatWaXYig+PvZc+ePUhJSUHWwnq5viQw0JRyMhb9ySef4KmnnlLdnvJJwbFpiXQ7N2jQQK2hI58QpDtaepsGDhwILZE3Svn06djkk5pscllLpIdJejZef/119WYjr4fUz0iXupZUr15drdMiw2T79u1TQe2zzz5Tb0RaJeuGaHn2nAR8o9GofrckaEoPh/TO9O3bF1oi4V5qZ+T/lBSfS12TTG/W2t9IVhLGypcvr/5e5PdM/lYkQN93330oCRxyKuVWrFihZgnI+LNsWe3fvx9aITUZEsxk6Ox///ufmjUg/+AeffRRVx9aqSQ1ALKexrhx43DvvfeqUCYL0Wnxn/XkyZPV75WEGPm9evjhhzX35nnlEIDW1gPKKiAgQBVlv/POO+qNUnr8ZNaZ/N1rjdQryiQAWfxTepg++OADNQylVV6Z/4dfe+01NfNMPoh9/PHHJbKontDZtbiGNxEREVEWHHIiIiIizWOgISIiIs1joCEiIiLNY6AhIiIizWOgISIiIs1joCEiIiLNY6AhIiIizWOgISIiIs1joCHyULJEfJ06ddRWt25ddToCWa1Xlu8vCXIOpFatWqnVTwti+PDhaitpH330UZGuADxkyBD1c5dzcxFR8WOgIfJgcm6rtWvXqnMpffPNN+qkhAMGDMD69euL/bl//vlndWZqOWloaSNnR//777/x3nvv4a233sLp06ddfUhEHo/nciLyYHLeGzmTr4iIiMCrr76qTjw6fvz4Yu85kPPT9OzZU53fpbSRswsvWbJE/cwbNmyozmVFRMWLPTREpYycxO/AgQM4fvy42o+Li8PQoUNV7027du3UiRhTUlKc99+9ezceeOABddI8GbKSISTH0IwM0zz77LPqhI1ypt1///0XaWlpePvtt9VZqtu3b49XXnkFMTExeR7P5s2bVfCRx3/hhReQnJx81WEgGUr74Ycfcn0suU1OXChhqmnTpnj66aeznTlezi7/5JNPqrbKsU2bNg02my3H43Tu3BlffPFFtuvkMeWMyPLcckwffvghWrZsiRYtWqiAmPW0eNID9thjj6k2vfTSS+rMw1mP8bvvvlMn7ZTbn3jiCdWDM2jQIBWEevToke3+8vORE/3JfeUYpPeHiHJioCEqZWrUqKG+Hjp0SH2VM+PGx8fjq6++UmfK3bVrF8aMGaNuk+vlDNkNGjTATz/9hG7duuGzzz7LccZ2uX7u3LnqTXfKlCkqBM2cORPz5s1DQkKCCiq5iYqKUkNgbdq0UY9fs2ZNLFu2rFDtkxAkxyxDbBKOJCg4nuuhhx5C2bJlVTCRsxwvWLBAHeOV7r777mzBQYLQ0aNHVdAR27ZtU/vyM3vjjTfUYziG8STwSCiUdkmbpG0SrM6fP+98vPfff1/V2Hz55ZfYu3cvevXqpe4nQUfO6C0/QyFhTB5HAo30qEm7pL5IQg4RZcchJ6JSOAwlEhMTceLECfz555+qZ8VxvbwZS4/JiBEjsHTpUvj6+uL1119XQ0fVq1fH1q1bs/V6hIeH48EHH1SXJUBISPj+++9VMbKYNGmS6snYv3+/8zqH3377DaGhoaqHSKfTqfAh9T6FIT0f0sshxo0bh9tvv131SG3YsEGFBWmfwWBQwU7a8fHHH6velKwkoH366ac4d+4cypUrp45Teq+CgoLU7VarVT2Ov7+/+plIr5AEwbZt22L+/PmqB0d+hkJ6qDZt2qR+LhJihAQUCTBCCqflOBw/w3vuuUeFQ7Fw4UJ1v0ceeUTtV6lSBf/995+6XXqGiOgyBhqiUkZ6TIS8GUvPgwy53HLLLdnuI9fJkJSEEOmdyVoHI0M5f/zxh3O/YsWKzssnT55Eenq6Gpq68vGOHTuWI9BIL5HMBJIw49CoUaNsw04FJcNJDpUrV0ZwcLBqp2zSFgkzDjLzS8KEDLtlJWFHjlV6iyTsSKCRnhKHsLAw9fNzkMsWi0Vdlud57rnnsj2e/Mzk+qzH5eDj45PtZyj78jMUR44cwV9//aWO00Fuk2JrIsqOgYaolJGQImrVqqUuS8+M9KhcSQpaJchkrQ0RV+6bTCbnZem5EDKUIj07WUkIyM2Vj2c0Gp2BJmvQcXAEh7xkDSyOY9Lr9dmO08FRP+M47iuHnZYvX65qbU6dOoVOnTo5b/P29s6zHbk9jzx+1lqdKwul5fhyI22VupmBAwdetY1ExBoaolJHwov0VEgvgXzSlzoZCQ4ynCGbFATLMJEU90rokSGOrG/Ge/bsyfOx5THlzVqKgB2PJ70XUjQbGRmZ4/7y+FJDkjVQyPNlDTcyNOYgl6UW5mr27dvnvCy9TNI+6W2RtsqxO3o/HLUwMuQlvThXkmGnHTt2qDqYDh065HumkjyPfF9Wsn89vSryPdIGx89SNqlZ4to2RDkx0BB5MHkzlyGVCxcuqN6Yd955R9XFOBauk6EVx0yknTt3qjd8qZ1JSkpCYGCg6qWQISoJJFIE++2336rvz4uEl/vvvx+jR4/Gxo0b1ZCSTBWXN+VKlSrluL88vvTGyHHJ8MqsWbOwZcuWbMNPElBkyEeef9SoUXn2ZjhIga686cv3yTo8UtdStWpV1dMhIU0eQ4Z/pHZICoildiW3nqAKFSqoImepV5HjzC8ZopJ6GQlCcsyTJ09Wx3LfffehoKSIWQqsp06dqobsJMhIwbAcGxFlx35LIg8mRbGyyRu29ETUr19fFbBmLSiV3hiZZi1vxDKUIQFHioCF9EpMnz5dLQ4nM3okYEgwkICUFwlLEydOxODBg1VvyE033aRmRuW2Ho0U2UqIkQAkhbxyX/nqGL5p3bq1Oi5HkHn88cev+txCZgzJm/6ZM2dUz4ocuyNsyXNJeJKCXfl59OvXL1ttzJXuuusuFQRlCnp+yfdcunRJTeuWMFmvXj3Mnj3bObusIKS2Rn7+Eoo+//xzNQwoP18pHCai7HT2KwewiYiyFPnKdOOsAUgCgvSqTJgwAe5G1niRlYllFlFRkJ4RmekkAY2I3BuHnIgoTzLcJL0iMttHFn+TIlk5pUHXrl3hyWSI6Mcff1TFzTKERkTuj0NORJQnGS6R4R4Zwjl79qyq3ZAam4IMwWiR1K3IMJzUsHC9FyJt4JATERERaR6HnIiIiEjzGGiIiIhI8xhoiIiISPMYaIiIiEjzGGiIiIhI8xhoiIiISPMYaIiIiEjzGGiIiIgIWvd/675Rl/1iHDIAAAAASUVORK5CYII="
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Degré optimal : 10\n",
|
|
"Accuracy du degré 10 : 0.6759776536312849\n",
|
|
"Précision du degré 10 : 0.9285714285714286\n",
|
|
"Rappel du degré 10 : 0.18571428571428572\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 11
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "25f11b31",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Partie 4 : découvrir les réseaux de neurones"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "77448c8c",
|
|
"metadata": {},
|
|
"source": [
|
|
"1. Commençons par étudier le réseau le plus simple : un perceptron. A l'aide de la classe `sklearn.linear_model.Perceptron`, créez un perceptron, en gardant les options par défaut. Affichez accuracy, précision et rappel : que pensez-vous de ces performances ?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "0d2620da",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-09-18T11:38:46.418668Z",
|
|
"start_time": "2025-09-18T11:38:46.403116Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"perceptron = Perceptron()\n",
|
|
"#Entraînement\n",
|
|
"perceptron.fit(X_train, y_train)\n",
|
|
"y_pred=svm.predict(X_test)\n",
|
|
"\n",
|
|
"#Test\n",
|
|
"print(\"Accuracy : \", perceptron.score(X_test, y_test))\n",
|
|
"print(\"Precision : \", precision_score(y_test, y_pred))\n",
|
|
"print(\"Rappel : \", recall_score(y_test, y_pred))"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Accuracy : 0.7094972067039106\n",
|
|
"Precision : 0.9285714285714286\n",
|
|
"Rappel : 0.18571428571428572\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 12
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "bb4bf1a7",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Observation :** on observe à nouveau une accuracy assez basse, avec un énorme déséquilibre entre précision et rappel : les bonnes prédictions des décès semblent se faire en se trompant énormément sur les survies."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "bda13ed8",
|
|
"metadata": {},
|
|
"source": [
|
|
"2. Regardez la documentation pour créer un réseau de neurones (`sklearn.neural_network.MLPClassifier`) : quelle est la structure d'un réseau de neurones par défaut avec scikit-learn ? Combien de couches cachées ? Combien de neurones par couche ?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "25fe018c",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Réponse :**\n",
|
|
"Lien vers la documentation : https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier\n",
|
|
"\n",
|
|
"Par défaut, il n'y qu'une seule couche cachée, composée de 100 neurones."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f4ae8d40",
|
|
"metadata": {},
|
|
"source": [
|
|
"2. Créer un réseau de neurones, en gardant ces options par défaut. Affichez accuracy, précision et rappel : que pensez-vous de ces performances, notamment en comparant par rapport au perceptron ? Avez-vous un message d'alerte ?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "7268b9a1",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-09-18T11:38:46.779472Z",
|
|
"start_time": "2025-09-18T11:38:46.432196Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"ann = MLPClassifier()\n",
|
|
"#Entraînement\n",
|
|
"ann.fit(X_train, y_train)\n",
|
|
"y_pred=ann.predict(X_test)\n",
|
|
"\n",
|
|
"#Test\n",
|
|
"print(\"Accuracy : \", ann.score(X_test, y_test))\n",
|
|
"print(\"Precision : \", precision_score(y_test, y_pred))\n",
|
|
"print(\"Rappel : \", recall_score(y_test, y_pred))"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Accuracy : 0.770949720670391\n",
|
|
"Precision : 0.7735849056603774\n",
|
|
"Rappel : 0.5857142857142857\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"C:\\Users\\ffotre\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:781: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
|
|
" warnings.warn(\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 13
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "ff623621",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Observation :** on obtient un bien meilleur score qu'avec un perceptron. On peut notamment voir que la précision s'est considérablement améliorée."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3f9b9f82",
|
|
"metadata": {},
|
|
"source": [
|
|
"3. Si vous avez observé un message d'alerte sur la question précédent, que signifie-t'il selon vous ? Que pouvez-vous faire pour y remédier ? Proposez un code permettant d'obtenir des résultats, sans message d'alerte. Qu'observez-vous sur l'évolution des scores ?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "478df749",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-09-18T11:38:47.181881Z",
|
|
"start_time": "2025-09-18T11:38:46.789079Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"ann = MLPClassifier(max_iter=300)\n",
|
|
"#Entraînement\n",
|
|
"ann.fit(X_train, y_train)\n",
|
|
"y_pred=ann.predict(X_test)\n",
|
|
"\n",
|
|
"#Test\n",
|
|
"print(\"Accuracy : \", ann.score(X_test, y_test))\n",
|
|
"print(\"Precision : \", precision_score(y_test, y_pred))\n",
|
|
"print(\"Rappel : \", recall_score(y_test, y_pred))"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Accuracy : 0.7932960893854749\n",
|
|
"Precision : 0.7894736842105263\n",
|
|
"Rappel : 0.6428571428571429\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 14
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "132b2069",
|
|
"metadata": {},
|
|
"source": [
|
|
"4. Nous allons à présent comparer différentes architectures du réseau de neurones :\n",
|
|
"- Trois couches de 50 neurones chacune\n",
|
|
"- Cinq couches de 50 neurones chacune\n",
|
|
"- Trois couches : première avec 50, deuxième avec 100, troisième avec 50 neurones\n",
|
|
"- Cinq couches : première avec 50, deuxième avec 100, troisième avec 50 neurones, quatrième avec 100, cinquième avec 50 neurones\n",
|
|
"\n",
|
|
"Représentez graphiquement l'accuracy, la précision et le rappel, pour chaque architecture. Il y en a t'il une qui semble plus pertinente que les autres ? Affichez-la, ainsi que les scores obtenus pour cette architecture. Comparez avec le score obtenu par l'architecture par défaut. Votre code ne doit générer aucun message d'alerte."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "1027a554",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-09-18T11:38:49.204732Z",
|
|
"start_time": "2025-09-18T11:38:47.195634Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"architectures = [(50,50,50),(50,50,50,50,50),(50,100,50),(50,100,50,100,50),]\n",
|
|
"labels = ['(50,50,50)','(50,50,50,50,50)','(50,100,50)','(50,100,50,100,50)']\n",
|
|
"\n",
|
|
"accuracies = []\n",
|
|
"precisions = []\n",
|
|
"recalls = []\n",
|
|
"\n",
|
|
"for archi in architectures:\n",
|
|
" ann = MLPClassifier(max_iter=500, hidden_layer_sizes=archi)\n",
|
|
" ann.fit(X_train, y_train)\n",
|
|
" y_pred = ann.predict(X_test)\n",
|
|
" accuracies.append(accuracy_score(y_test, y_pred))\n",
|
|
" precisions.append(precision_score(y_test, y_pred))\n",
|
|
" recalls.append(recall_score(y_test, y_pred))\n",
|
|
" \n",
|
|
"plt.plot(labels, accuracies, label='Accuracy')\n",
|
|
"plt.plot(labels, precisions, label='Precision')\n",
|
|
"plt.plot(labels, recalls, label='Rappel')\n",
|
|
"plt.xticks(labels)\n",
|
|
"plt.xlabel('Architecture du réseau de neurones')\n",
|
|
"plt.ylabel('Score')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"pos_meilleure_archi = np.argmax(accuracies)\n",
|
|
"meilleure_archi = architectures[pos_meilleure_archi]\n",
|
|
"print(\"Architecture optimale : \", meilleure_archi)\n",
|
|
"print(\"Accuracy de l'architecture\", meilleure_archi, \": \", accuracies[pos_meilleure_archi])\n",
|
|
"print(\"Précision de l'architecture\", meilleure_archi, \": \", precisions[pos_meilleure_archi])\n",
|
|
"print(\"Rappel de l'architecture\", meilleure_archi, \": \", recalls[pos_meilleure_archi])"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
],
|
|
"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Architecture optimale : (50, 50, 50)\n",
|
|
"Accuracy de l'architecture (50, 50, 50) : 0.7932960893854749\n",
|
|
"Précision de l'architecture (50, 50, 50) : 0.8666666666666667\n",
|
|
"Rappel de l'architecture (50, 50, 50) : 0.5571428571428572\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 15
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e437de59",
|
|
"metadata": {},
|
|
"source": [
|
|
"5. En utilisant l'architecture qui vous donnait les meilleures performances, étudier l'impact de la fonction d'activation utilisée sur les performances. Représentez sur un graphiques les scores (accuracy, précision et rappel) obtenus pour les quatres fonctions d'activation proposées par scikit-learn. Affichez la fonction qui vous parait la plus pertinente, ainsi que les scores associés."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "9ad2a684",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-09-18T11:38:52.542120Z",
|
|
"start_time": "2025-09-18T11:38:49.324242Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"activations = ['identity', 'logistic', 'tanh', 'relu']\n",
|
|
"\n",
|
|
"accuracies = []\n",
|
|
"precisions = []\n",
|
|
"recalls = []\n",
|
|
"\n",
|
|
"for f in activations:\n",
|
|
" ann = MLPClassifier(hidden_layer_sizes=meilleure_archi,max_iter=500, activation=f)\n",
|
|
" ann.fit(X_train, y_train)\n",
|
|
" y_pred = ann.predict(X_test)\n",
|
|
" accuracies.append(accuracy_score(y_test, y_pred))\n",
|
|
" precisions.append(precision_score(y_test, y_pred))\n",
|
|
" recalls.append(recall_score(y_test, y_pred))\n",
|
|
" \n",
|
|
"plt.plot(activations, accuracies, label='Accuracy')\n",
|
|
"plt.plot(activations, precisions, label='Precision')\n",
|
|
"plt.plot(activations, recalls, label='Rappel')\n",
|
|
"plt.xticks(activations)\n",
|
|
"plt.xlabel('Fonction d\\'activation des neurones')\n",
|
|
"plt.ylabel('Score')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"pos_meilleure_fun = np.argmax(accuracies)\n",
|
|
"meilleure_fun = activations[pos_meilleure_fun]\n",
|
|
"print(\"Fonction d'activation optimale : \", meilleure_fun)\n",
|
|
"print(\"Accuracy de la fonction\", meilleure_fun, \": \", accuracies[pos_meilleure_fun])\n",
|
|
"print(\"Précision de la fonction\", meilleure_fun, \": \", precisions[pos_meilleure_fun])\n",
|
|
"print(\"Rappel de la fonction\", meilleure_fun, \": \", recalls[pos_meilleure_fun])\n",
|
|
"\n",
|
|
"# sauvegarde des scores\n",
|
|
"ann_best_accuracy = accuracies[pos_meilleure_fun]\n",
|
|
"ann_best_pred = precisions[pos_meilleure_fun]\n",
|
|
"ann_best_recall = recalls[pos_meilleure_fun]"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
],
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fonction d'activation optimale : logistic\n",
|
|
"Accuracy de la fonction logistic : 0.7988826815642458\n",
|
|
"Précision de la fonction logistic : 0.8541666666666666\n",
|
|
"Rappel de la fonction logistic : 0.5857142857142857\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 16
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "60141a50",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Partie 5 : comparer les performances des différents algorithmes\n",
|
|
"\n",
|
|
"Nous allons à présent résumer les différentes performances des algorithmes que vous avez testé dans ce TP : récupérez les meilleurs scores (accuracy) obtenu pour chaque algorithme. Représentez-les sur un diagramme en barres, en regroupant par algorithme, et en représentant chaque score par une couleur. Un algorithme semble-t'il obtenir de meilleures performances que les autres ?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "2318f1a5",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-09-18T11:38:52.845128Z",
|
|
"start_time": "2025-09-18T11:38:52.676660Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"algos = ['Naive Bayes', 'KNN', 'SVM', 'ANN']\n",
|
|
"\n",
|
|
"scores = {\n",
|
|
" 'Accuracy': (nb_best_accuracy, knn_best_accuracy, svm_best_accuracy, ann_best_accuracy),\n",
|
|
" 'Precision': (nb_best_pred, knn_best_pred, svm_best_pred, ann_best_pred),\n",
|
|
" 'Rappel': (nb_best_recall, knn_best_recall, svm_best_recall, ann_best_recall)\n",
|
|
"}\n",
|
|
"\n",
|
|
"x = np.arange(len(algos)) # the label locations\n",
|
|
"width = 0.25 # the width of the bars\n",
|
|
"multiplier = 0\n",
|
|
"\n",
|
|
"fig, ax = plt.subplots(layout='constrained')\n",
|
|
"\n",
|
|
"for attribute, value in scores.items():\n",
|
|
" offset = width * multiplier\n",
|
|
" rects = ax.bar(x + offset, value, width, label=attribute)\n",
|
|
" ax.bar_label(rects, padding=3)\n",
|
|
" multiplier += 1\n",
|
|
"\n",
|
|
"# Add some text for labels, title and custom x-axis tick labels, etc.\n",
|
|
"ax.set_ylabel('Scores')\n",
|
|
"ax.set_title('Performances comparée des différents algorithmes de classification')\n",
|
|
"ax.set_xticks(x + width, algos)\n",
|
|
"ax.legend(loc='upper left', ncols=3)\n",
|
|
"ax.set_ylim(0, 1.1)\n",
|
|
"\n",
|
|
"plt.show()"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
],
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
}
|
|
],
|
|
"execution_count": 17
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "fe7a5b28",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Partie 6 : optimiser la recherche des paramètres optimaux\n",
|
|
"\n",
|
|
"Dans ce TP, nous avons souvent cherché à identifier la meilleur combinaison de paramètres. Nous avons procédé par itération, en cherchant à fixer un paramètre avant de faire évoluer les autres. Cette méthode est couteuse, et pour faire une recherche exhaustive, nécessite, de répéter très souvent le même code. Scikit-learn propose une classe, `sklearn.model_selection.GridSearchCV`, qui va permettre d'optimiser cette recherche de paramétrage optimal.\n",
|
|
"\n",
|
|
"Lien vers la documentation : https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html\n",
|
|
"\n",
|
|
"Le principe est de définir un dictionnaire, où la clé correspond à un paramètre, et la valeur à la liste de valeurs possibles à tester pour le paramètre considéré. \n",
|
|
"\n",
|
|
"**Consigne :** Appliquez ce principe pour déterminer la meilleure combinaison possible pour le réseau de neurones, en repartant des différentes configurations testées dans les parties précédentes."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "7f6eeac1",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-09-18T12:23:53.803671Z",
|
|
"start_time": "2025-09-18T12:22:40.984760Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"parameters = {\n",
|
|
" \"hidden_layer_sizes\": [(50,50,50),(50,50,50,50,50),(50,100,50),(50,100,50,100,50),],\n",
|
|
" \"activation\": ['identity', 'logistic', 'tanh', 'relu']\n",
|
|
"}\n",
|
|
"\n",
|
|
"ann = MLPClassifier(max_iter=700)\n",
|
|
"\n",
|
|
"ann = GridSearchCV(\n",
|
|
" ann, \n",
|
|
" parameters, \n",
|
|
" cv=5,\n",
|
|
" scoring='accuracy',\n",
|
|
")\n",
|
|
"\n",
|
|
"ann.fit(X_train, y_train)\n",
|
|
"\n",
|
|
"print('-----')\n",
|
|
"print(f'Meilleurs paramètres {ann.best_params_}')\n",
|
|
"print(\n",
|
|
" f'Score moyen de validation croisée pour la meilleure combinaison de paramètres: ' + \n",
|
|
" f'{ann.best_score_:.3f}'\n",
|
|
")\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"-----\n",
|
|
"Meilleurs paramètres {'activation': 'logistic', 'hidden_layer_sizes': (50, 100, 50)}\n",
|
|
"Score moyen de validation croisée pour la meilleure combinaison de paramètres: 0.813\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 20
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "d6221ddb",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-09-18T11:40:11.923701Z",
|
|
"start_time": "2025-09-18T11:40:11.922279Z"
|
|
}
|
|
},
|
|
"source": [],
|
|
"outputs": [],
|
|
"execution_count": null
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.9.16"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|