2461 lines
549 KiB
Text
2461 lines
549 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Introduction\n",
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"\n",
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"- Projet final présenté par: **François Pelletier**\n",
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"- Matricule: **908144032**\n",
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"- Dans le cadre du cours: **IFT-7025**\n",
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"\n",
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"# Librairies utilisées"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import load_datasets as ld\n",
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"import matplotlib.pyplot as plt\n",
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"import DecisionTree\n",
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"import NeuralNet\n",
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"import time"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Iris Dataset\n",
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"\n",
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"La présentation des résultats liés à ce jeu de données servira aussi à expliquer les concepts et répondre aux questions. Pour les autres jeux de données, seuls les résultats et la discussion seront détaillés.\n",
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"\n",
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"- Chargement du jeu de données"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"train1, train_labels1, test1, test_labels1 = (\n",
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" ld.load_iris_dataset(train_ratio = 0.7))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Entrainement de l'arbre de décision\n",
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"\n",
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"Dans cette section, on entraine un arbre de décision basé sur la mesure d'entropie."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"dt1 = DecisionTree.DecisionTree(attribute_type=\"continuous\")\n",
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"start_time = time.time()\n",
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"_ = dt1.train(train1, train_labels1,verbose=False)\n",
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"dt1_compute_time = time.time() - start_time"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Courbe d'apprentissage\n",
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"\n",
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"Je trace une courbe d'apprentissage qui effectue une validation croisée sur 10 sous-ensembles de l'ensemble d'entrainement. Je fais la moyenne de l'accuracy pour chaque sous-ensemble de test, puis j'itère en ajoutant un exemple à la fois à l'ensemble d'entrainement.\n",
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"\n",
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"- Bogue connu: J'ai débuté la courbe d'apprentissage avec 30 données, car j'ai de la difficulté à exécuter la validation croisée avec moins sans que ça plante vu que je crée les sous-groupes à l'avance."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"def courbe_apprentissage_dt(dt,train,train_labels,nb_cv_split=10):\n",
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" accuracy_cum = []\n",
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" range_lc = range(30,len(train_labels))\n",
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" for i in range_lc:\n",
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" range_lc_split_test = np.array_split(range(i),nb_cv_split)\n",
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" range_lc_split_train = (\n",
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" [np.setdiff1d(range(i),t) for t in range_lc_split_test])\n",
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" accuracy_cv = []\n",
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" for r_i in range(nb_cv_split):\n",
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" try:\n",
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" training = dt.train(train[range_lc_split_train[r_i]], \n",
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" train_labels[range_lc_split_train[r_i]],\n",
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" verbose=False)\n",
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" testres = dt.test(train[range_lc_split_test[r_i]], \n",
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" train_labels[range_lc_split_test[r_i]],\n",
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" verbose=False)\n",
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" accuracy_cv.append(testres[1])\n",
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" except:\n",
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" pass\n",
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" accuracy_cum.append(np.mean(accuracy_cv))\n",
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" return range_lc,accuracy_cum"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/francois/ift7025-projet/Code/metrics.py:40: RuntimeWarning: invalid value encountered in double_scalars\n",
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" myPrecision = cm[label_num,label_num] / sum(cm[:,label_num])\n"
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]
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}
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],
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"source": [
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"dt_range_lc1,dt_accuracy_cum1 = courbe_apprentissage_dt(dt1,train1,train_labels1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Voici le graphique de la courbe d'apprentissage"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7fcdac6442e8>]"
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]
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},
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"execution_count": 6,
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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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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.plot(dt_range_lc1,dt_accuracy_cum1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Test et évaluation de la performance de l'arbre de décision"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Matrice de confusion:\n",
|
||
"[[15. 0. 0.]\n",
|
||
" [ 0. 16. 1.]\n",
|
||
" [ 0. 0. 13.]]\n",
|
||
"\n",
|
||
"Exactitude:\n",
|
||
"0.9777777777777777\n",
|
||
"\n",
|
||
"Précision:\n",
|
||
"[1.0, 1.0, 0.9285714285714286]\n",
|
||
"\n",
|
||
"Rappel:\n",
|
||
"[1.0, 0.9411764705882353, 1.0]\n",
|
||
"\n",
|
||
"Calculé en:\n",
|
||
"0.0002942085266113281s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"dt1_testres = dt1.test(test1, test_labels1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Réseaux de neurones: Choix du nombre de neurones dans la couche cachée\n",
|
||
"\n",
|
||
"On doit identifier le nombre optimal de neurones dans la couche cachée dans l'intervalle $[4,50]$ pour chacun des 5 jeux de données. On itère sur chacun des jeux de validation croisée, pour chaque dimension, puis on calcule l'*accuracy* moyenne. La dimension qui a la meilleure *accuracy* moyenne est choisie pour construire le réseau de neurones."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"choix_n_neurones = range(4,51)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- Pour faire la séparation du jeu de données en $k_{cv}=5$ jeux de validation croisée, on génère une permutation sur les indices du jeu d'entrainement, puis, on sépare cet ensemble en 5 groupes."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"k_cv = 5\n",
|
||
"all_indices = range(len(train_labels1))\n",
|
||
"np.random.seed(12345)\n",
|
||
"indices_cv_test = (\n",
|
||
" np.sort(np.array_split(np.random.permutation(all_indices),\n",
|
||
" k_cv)))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"indices_cv_train = (\n",
|
||
" [np.setdiff1d(all_indices,indices_cv_test[i]) for i in range(k_cv)])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Ce jeu de données a trois classes possibles comme variable de sortie. On utilisera donc un réseau de neurones avec 3 neurones dans la couche de sortie, une pour chacune des valeurs possibles. Les valeurs de sortie du jeu de données sont transformées à l'aide d'un encodage binaire où la valeur de sortie est convertie en rang dans un vecteur (on commence à 0), prenant la valeur 1. Par exemple, la valeur 2 devient $[0,0,1]$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"accuracy_cum = []\n",
|
||
"for n_neurones in choix_n_neurones:\n",
|
||
" accuracy_cv=[]\n",
|
||
" for cv_set in range(k_cv):\n",
|
||
" nn1 = NeuralNet.NeuralNet(np.array([4,n_neurones,3]),range(3))\n",
|
||
" nn1.train(train1[indices_cv_train[cv_set]], \n",
|
||
" train_labels1[indices_cv_train[cv_set]], 0.1, 1, \n",
|
||
" verbose=False)\n",
|
||
" _,accuracy,_,_,_ = nn1.test(train1[indices_cv_test[cv_set]], \n",
|
||
" train_labels1[indices_cv_test[cv_set]], \n",
|
||
" verbose=False)\n",
|
||
" accuracy_cv.append(accuracy)\n",
|
||
" accuracy_cum.append(np.mean(np.array(accuracy_cv)))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[<matplotlib.lines.Line2D at 0x7fcdac57c630>]"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
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"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.plot(choix_n_neurones,accuracy_cum)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Le nombre de neurones qui maximise l'*accuracy* est de:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"49"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"n_neurones_optimal1 = (\n",
|
||
" choix_n_neurones[np.where(accuracy_cum==max(accuracy_cum))[0][0]])\n",
|
||
"n_neurones_optimal1"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Choix du nombre de couches cachées\n",
|
||
"\n",
|
||
"On choisit un nombre de 1 à 5 couches cachées. Le nombre de couches ayant l'*accuracy* maximale sera sélectionné pour la construction du réseau. On effectue 10 époques étant donné la taille des réseaux à entrainer."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"choix_n_couches = range(1,6)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"accuracy_cum = []\n",
|
||
"lc_cum = []\n",
|
||
"for n_couches in choix_n_couches:\n",
|
||
" accuracy_cv=[]\n",
|
||
" nn1 = NeuralNet.NeuralNet(\n",
|
||
" np.hstack((4,\n",
|
||
" np.repeat(n_neurones_optimal1,n_couches),\n",
|
||
" 3)),\n",
|
||
" range(3))\n",
|
||
" lc = nn1.train(train1, train_labels1, 0.1, 10, verbose=False)\n",
|
||
" lc_cum.append(lc)\n",
|
||
" _,accuracy,_,_,_ = nn1.test(train1, train_labels1, verbose=False)\n",
|
||
" accuracy_cv.append(accuracy)\n",
|
||
" accuracy_cum.append(np.mean(np.array(accuracy_cv)))\n",
|
||
"lc_cum = np.array(lc_cum)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"L'*accuracy* pour les différentes profondeur est de :"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[1. , 2. , 3. , 4. , 5. ],\n",
|
||
" [0.6952381 , 0.97142857, 0.88571429, 0.93333333, 0.79047619]])"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"np.vstack((choix_n_couches,accuracy_cum))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Le nombre de couches cachées qui maximise l'*accuracy* est de:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"2"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"n_couches_optimal1 = (\n",
|
||
" choix_n_couches[np.where(accuracy_cum==max(accuracy_cum))[0][0]])\n",
|
||
"n_couches_optimal1"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Un problème rencontré avec un nombre de couches élevées est appelé le *Vanishing gradient problem*. Dans ces situations, le nombre de multiplications nécessaires ainsi que le fait que les valeurs $\\Delta$ sont suffisamment petites pour que le gradient tende vers 0 et ainsi ne permette pas de mettre les poids à jour. Dans ces situations, l'entrainement va stagner."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Courbes d'apprentissage\n",
|
||
"\n",
|
||
"Ce graphique présente les courbes d'apprentissage pour chacun des niveaux de profondeur du réseau"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.subplot(111)\n",
|
||
"for i in choix_n_couches:\n",
|
||
" plt.plot(range(10),lc_cum[i-1], label=\"%d couches\"%(i,))\n",
|
||
"leg = plt.legend(loc='best', ncol=2, mode=\"expand\", shadow=True, fancybox=True)\n",
|
||
"leg.get_frame().set_alpha(0.5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Choix des poids initiaux\n",
|
||
"Les poids initiaux ont été choisis à partir d'une distribution uniforme sur $[-1,1]$. On compare ici les courbes d'apprentissage en initialisant les poids à 0 et en initialisant les poids aléatoirement, pour le réseau de dimension et de profondeur optimale sélectionnées précédemment.\n",
|
||
"\n",
|
||
"- Réseau initialisé avec les poids à 0 $RN_{0}$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"nn1_poidszero = NeuralNet.NeuralNet(\n",
|
||
" np.hstack((4,\n",
|
||
" np.repeat(n_neurones_optimal1,n_couches_optimal1),\n",
|
||
" 3)),\n",
|
||
" range(3),\n",
|
||
" input_weights=0)\n",
|
||
"lc_nn1_poidszero = (\n",
|
||
" nn1_poidszero.train(train1, train_labels1, 0.1, 10, verbose=False))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- Réseau initialisé avec les poids uniformes $RN_{\\neg{0}}$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"nn1_poidsunif = NeuralNet.NeuralNet(\n",
|
||
" np.hstack((4,\n",
|
||
" np.repeat(n_neurones_optimal1,n_couches_optimal1),\n",
|
||
" 3)),\n",
|
||
" range(3))\n",
|
||
"np.random.seed(12345)\n",
|
||
"start_time = time.time()\n",
|
||
"lc_nn1_poidsunif = (\n",
|
||
" nn1_poidsunif.train(train1, train_labels1, 0.1, 10, verbose=False))\n",
|
||
"nn1_compute_time = time.time() - start_time"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Graphique des courbes d'apprentissage"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.subplot(111)\n",
|
||
"plt.plot(range(10),lc_nn1_poidszero, label=\"RN_Zero\")\n",
|
||
"plt.plot(range(10),lc_nn1_poidsunif, label=\"RN_Non_Zero)\")\n",
|
||
"leg = plt.legend(loc='best', \n",
|
||
" ncol=2, \n",
|
||
" mode=\"expand\", \n",
|
||
" shadow=True, \n",
|
||
" fancybox=True)\n",
|
||
"leg.get_frame().set_alpha(0.5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"On remarque clairement que l'initialisation des poids à 0 ne permet pas de démarrer l'entrainement du réseau. Il est donc nécessaire d'initialiser les poids aléatoirement pour permettre l'apprentissage et éviter que les différentes fonctions pour calculer la rétropropagation des erreurs aient toutes des valeurs de 0."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Entrainement et tests\n",
|
||
"On reprend les résultats du dernier entrainement, puisqu'il utilise les poids aléatoires et les hyperparamètres optimaux."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Matrice de confusion:\n",
|
||
"[[15. 0. 0.]\n",
|
||
" [ 0. 16. 1.]\n",
|
||
" [ 0. 3. 10.]]\n",
|
||
"\n",
|
||
"Exactitude:\n",
|
||
"0.9111111111111111\n",
|
||
"\n",
|
||
"Précision:\n",
|
||
"[1.0, 0.8421052631578947, 0.9090909090909091]\n",
|
||
"\n",
|
||
"Rappel:\n",
|
||
"[1.0, 0.9411764705882353, 0.7692307692307693]\n",
|
||
"\n",
|
||
"Calculé en:\n",
|
||
"0.040042877197265625s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"res_test1 = nn1_poidsunif.test(test1, test_labels1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## MONKS Dataset\n",
|
||
"\n",
|
||
"- Chargement des jeux de données"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"train2, train_labels2, test2, test_labels2 = (\n",
|
||
" ld.load_monks_dataset(1))\n",
|
||
"train3, train_labels3, test3, test_labels3 = (\n",
|
||
" ld.load_monks_dataset(2))\n",
|
||
"train4, train_labels4, test4, test_labels4 = (\n",
|
||
" ld.load_monks_dataset(3))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Entrainement de l'arbre de décision\n",
|
||
"\n",
|
||
"Dans cette section, on entraine un arbre de décision basé sur la mesure d'entropie."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"dt2 = DecisionTree.DecisionTree(attribute_type=\"discrete\")\n",
|
||
"dt3 = DecisionTree.DecisionTree(attribute_type=\"discrete\")\n",
|
||
"dt4 = DecisionTree.DecisionTree(attribute_type=\"discrete\")\n",
|
||
"\n",
|
||
"start_time = time.time()\n",
|
||
"_ = dt2.train(train2, train_labels2,verbose=False)\n",
|
||
"dt2_compute_time = time.time() - start_time\n",
|
||
"start_time = time.time()\n",
|
||
"_ = dt3.train(train3, train_labels3,verbose=False)\n",
|
||
"dt3_compute_time = time.time() - start_time\n",
|
||
"start_time = time.time()\n",
|
||
"_ = dt4.train(train4, train_labels4,verbose=False)\n",
|
||
"dt4_compute_time = time.time() - start_time"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Courbe d'apprentissage\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"dt_range_lc2,dt_accuracy_cum2 = (\n",
|
||
" courbe_apprentissage_dt(dt2,train2,train_labels2))\n",
|
||
"dt_range_lc3,dt_accuracy_cum3 = (\n",
|
||
" courbe_apprentissage_dt(dt3,train3,train_labels3))\n",
|
||
"dt_range_lc4,dt_accuracy_cum4 = (\n",
|
||
" courbe_apprentissage_dt(dt4,train4,train_labels4))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Voici les graphiques de la courbe d'apprentissage pour les 3 jeux de données\n",
|
||
"- 1er jeu de données"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[<matplotlib.lines.Line2D at 0x7fcdac406710>]"
|
||
]
|
||
},
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.plot(dt_range_lc2,dt_accuracy_cum2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- 2e jeu de données"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[<matplotlib.lines.Line2D at 0x7fcdac3dadd8>]"
|
||
]
|
||
},
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.plot(dt_range_lc3,dt_accuracy_cum3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- 3e jeu de données"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[<matplotlib.lines.Line2D at 0x7fcdac33d898>]"
|
||
]
|
||
},
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.plot(dt_range_lc4,dt_accuracy_cum4)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"On peut facilement observer que dans les trois cas, il y a très peu d'apprentissage de réalisé par l'algorithme avec les exemples fournis."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Test et évaluation de la performance de l'arbre de décision"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Matrice de confusion:\n",
|
||
"[[144. 74.]\n",
|
||
" [131. 85.]]\n",
|
||
"\n",
|
||
"Exactitude:\n",
|
||
"0.5254629629629629\n",
|
||
"\n",
|
||
"Précision:\n",
|
||
"[0.5236363636363637, 0.5345911949685535]\n",
|
||
"\n",
|
||
"Rappel:\n",
|
||
"[0.6605504587155964, 0.39351851851851855]\n",
|
||
"\n",
|
||
"Calculé en:\n",
|
||
"0.0011060237884521484s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"dt2_testres = dt2.test(test2, test_labels2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Matrice de confusion:\n",
|
||
"[[219. 72.]\n",
|
||
" [ 88. 56.]]\n",
|
||
"\n",
|
||
"Exactitude:\n",
|
||
"0.6296296296296297\n",
|
||
"\n",
|
||
"Précision:\n",
|
||
"[0.7133550488599348, 0.4375]\n",
|
||
"\n",
|
||
"Rappel:\n",
|
||
"[0.7525773195876289, 0.3888888888888889]\n",
|
||
"\n",
|
||
"Calculé en:\n",
|
||
"0.0016949176788330078s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"dt3_testres = dt3.test(test3, test_labels3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Matrice de confusion:\n",
|
||
"[[ 30. 175.]\n",
|
||
" [ 25. 207.]]\n",
|
||
"\n",
|
||
"Exactitude:\n",
|
||
"0.5370370370370371\n",
|
||
"\n",
|
||
"Précision:\n",
|
||
"[0.5454545454545454, 0.5418848167539267]\n",
|
||
"\n",
|
||
"Rappel:\n",
|
||
"[0.14634146341463414, 0.8922413793103449]\n",
|
||
"\n",
|
||
"Calculé en:\n",
|
||
"0.001772165298461914s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"dt4_testres = dt4.test(test4, test_labels4)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Réseaux de neurones: choix du nombre de neurones dans la couche cachée"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- Pour faire la séparation du jeu de données en $k_{cv}=5$ jeux de validation croisée, on génère une permutation sur les indices du jeu d'entrainement, puis, on sépare cet ensemble en 5 groupes."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"all_indices2 = range(len(train_labels2))\n",
|
||
"all_indices3 = range(len(train_labels3))\n",
|
||
"all_indices4 = range(len(train_labels4))\n",
|
||
"np.random.seed(12345)\n",
|
||
"indices_cv_test2 = (\n",
|
||
" [np.sort(x) for x in np.array_split(np.random.permutation(all_indices2),k_cv)])\n",
|
||
"np.random.seed(12345)\n",
|
||
"indices_cv_test3 = (\n",
|
||
" [np.sort(x) for x in np.array_split(np.random.permutation(all_indices3),k_cv)])\n",
|
||
"np.random.seed(12345)\n",
|
||
"indices_cv_test4 = (\n",
|
||
" [np.sort(x) for x in np.array_split(np.random.permutation(all_indices4),k_cv)])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"indices_cv_train2 = (\n",
|
||
" [np.setdiff1d(all_indices,indices_cv_test2[i]) for i in range(k_cv)])\n",
|
||
"indices_cv_train3 = (\n",
|
||
" [np.setdiff1d(all_indices,indices_cv_test3[i]) for i in range(k_cv)])\n",
|
||
"indices_cv_train4 = (\n",
|
||
" [np.setdiff1d(all_indices,indices_cv_test4[i]) for i in range(k_cv)])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Ces jeux de données ont deux classes possibles comme variable de sortie. On utilisera donc un réseau de neurones avec deux neurone dans la couche de sortie, une pour chacune des valeurs possibles. Les valeurs de sortie du jeu de données sont transformées à l'aide d'un encodage binaire où la valeur de sortie est convertie en rang dans un vecteur (on commence à 0), prenant la valeur 1. Par exemple, la valeur 1 devient $[0,1]$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"accuracy_cum2 = []\n",
|
||
"for n_neurones in choix_n_neurones:\n",
|
||
" accuracy_cv=[]\n",
|
||
" for cv_set in range(k_cv):\n",
|
||
" nn2 = NeuralNet.NeuralNet(np.array([6,n_neurones,2]),range(2))\n",
|
||
" nn2.train(train2[indices_cv_train2[cv_set]], \n",
|
||
" train_labels2[indices_cv_train2[cv_set]], 0.1, 1, \n",
|
||
" verbose=False)\n",
|
||
" _,accuracy,_,_,_ = nn2.test(train2[indices_cv_test2[cv_set]], \n",
|
||
" train_labels2[indices_cv_test2[cv_set]], \n",
|
||
" verbose=False)\n",
|
||
" accuracy_cv.append(accuracy)\n",
|
||
" accuracy_cum2.append(np.mean(np.array(accuracy_cv)))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[<matplotlib.lines.Line2D at 0x7fcdac32d5c0>]"
|
||
]
|
||
},
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.plot(choix_n_neurones,accuracy_cum2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Le nombre de neurones qui maximise l'accuracy est de:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"17"
|
||
]
|
||
},
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"n_neurones_optimal2 = (\n",
|
||
" choix_n_neurones[np.where(accuracy_cum2==max(accuracy_cum2))[0][0]])\n",
|
||
"n_neurones_optimal2"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"accuracy_cum3 = []\n",
|
||
"for n_neurones in choix_n_neurones:\n",
|
||
" accuracy_cv=[]\n",
|
||
" for cv_set in range(k_cv):\n",
|
||
" nn3 = NeuralNet.NeuralNet(np.array([6,n_neurones,2]),range(2))\n",
|
||
" nn3.train(train3[indices_cv_train3[cv_set]], \n",
|
||
" train_labels3[indices_cv_train3[cv_set]], 0.1, 1, \n",
|
||
" verbose=False)\n",
|
||
" _,accuracy,_,_,_ = nn3.test(train3[indices_cv_test3[cv_set]], \n",
|
||
" train_labels3[indices_cv_test3[cv_set]], \n",
|
||
" verbose=False)\n",
|
||
" accuracy_cv.append(accuracy)\n",
|
||
" accuracy_cum3.append(np.mean(np.array(accuracy_cv)))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 38,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[<matplotlib.lines.Line2D at 0x7fcdac2962b0>]"
|
||
]
|
||
},
|
||
"execution_count": 38,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.plot(choix_n_neurones,accuracy_cum3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Le nombre de neurones qui maximise l'accuracy est de:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 39,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"15"
|
||
]
|
||
},
|
||
"execution_count": 39,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"n_neurones_optimal3 = (\n",
|
||
" choix_n_neurones[np.where(accuracy_cum3==max(accuracy_cum3))[0][0]])\n",
|
||
"n_neurones_optimal3"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"accuracy_cum4 = []\n",
|
||
"for n_neurones in choix_n_neurones:\n",
|
||
" accuracy_cv=[]\n",
|
||
" for cv_set in range(k_cv):\n",
|
||
" nn4 = NeuralNet.NeuralNet(np.array([6,n_neurones,2]),range(2))\n",
|
||
" nn4.train(train4[indices_cv_train4[cv_set]], \n",
|
||
" train_labels4[indices_cv_train4[cv_set]], 0.1, 1, \n",
|
||
" verbose=False)\n",
|
||
" _,accuracy,_,_,_ = nn4.test(train4[indices_cv_test4[cv_set]], \n",
|
||
" train_labels4[indices_cv_test4[cv_set]], \n",
|
||
" verbose=False)\n",
|
||
" accuracy_cv.append(accuracy)\n",
|
||
" accuracy_cum4.append(np.mean(np.array(accuracy_cv)))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[<matplotlib.lines.Line2D at 0x7fcdac274e80>]"
|
||
]
|
||
},
|
||
"execution_count": 41,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.plot(choix_n_neurones,accuracy_cum4)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Le nombre de neurones qui maximise l'*accuracy* est de:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 42,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"31"
|
||
]
|
||
},
|
||
"execution_count": 42,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"n_neurones_optimal4 = (\n",
|
||
" choix_n_neurones[np.where(accuracy_cum4==max(accuracy_cum4))[0][0]])\n",
|
||
"n_neurones_optimal4"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Choix du nombre de couches cachées"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 43,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def accuracy_couches(train,train_labels,n_neurones_optimal):\n",
|
||
" accuracy_cum = []\n",
|
||
" lc_cum = []\n",
|
||
" for n_couches in choix_n_couches:\n",
|
||
" accuracy_cv=[]\n",
|
||
" nn = NeuralNet.NeuralNet(\n",
|
||
" np.hstack((6,\n",
|
||
" np.repeat(n_neurones_optimal,n_couches),\n",
|
||
" 2)),\n",
|
||
" range(2))\n",
|
||
" lc = nn.train(train, train_labels, 0.1, 10, verbose=False)\n",
|
||
" lc_cum.append(lc)\n",
|
||
" _,accuracy,_,_,_ = nn.test(train, train_labels, verbose=False)\n",
|
||
" accuracy_cv.append(accuracy)\n",
|
||
" accuracy_cum.append(np.mean(np.array(accuracy_cv)))\n",
|
||
" lc_cum = np.array(lc_cum)\n",
|
||
" return accuracy_cum, lc_cum"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"accuracy_cum2, lc_cum2 = (\n",
|
||
" accuracy_couches(train2,train_labels2,n_neurones_optimal2))\n",
|
||
"accuracy_cum3, lc_cum3 = (\n",
|
||
" accuracy_couches(train3,train_labels3,n_neurones_optimal3))\n",
|
||
"accuracy_cum4, lc_cum4 = (\n",
|
||
" accuracy_couches(train4,train_labels4,n_neurones_optimal4))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"L'*accuracy* pour les différentes profondeur est respectivement (un jeu de données par ligne) de :"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 45,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[1. , 2. , 3. , 4. , 5. ],\n",
|
||
" [0.54032258, 0.5 , 0.48387097, 0.58064516, 0.5 ],\n",
|
||
" [0.62130178, 0.62130178, 0.62130178, 0.62130178, 0.62130178],\n",
|
||
" [0.62295082, 0.69672131, 0.87704918, 0.51639344, 0.50819672]])"
|
||
]
|
||
},
|
||
"execution_count": 45,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"np.vstack((choix_n_couches,accuracy_cum2,accuracy_cum3,accuracy_cum4))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Le nombre de couches cachées qui maximise l'*accuracy* est, pour chacun des 3 jeux de données, respectivement de:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 46,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(4, 1, 3)"
|
||
]
|
||
},
|
||
"execution_count": 46,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"n_couches_optimal2 = (\n",
|
||
" choix_n_couches[np.where(accuracy_cum2==max(accuracy_cum2))[0][0]])\n",
|
||
"n_couches_optimal3 = (\n",
|
||
" choix_n_couches[np.where(accuracy_cum3==max(accuracy_cum3))[0][0]])\n",
|
||
"n_couches_optimal4 = (\n",
|
||
" choix_n_couches[np.where(accuracy_cum4==max(accuracy_cum4))[0][0]])\n",
|
||
"(n_couches_optimal2,n_couches_optimal3,n_couches_optimal4)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Courbes d'apprentissage\n",
|
||
"\n",
|
||
"Ce graphique présente les courbes d'apprentissage pour chacun des niveaux de profondeur du réseau\n",
|
||
"\n",
|
||
"- MONKS1"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 47,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
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"data": {
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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.subplot(111)\n",
|
||
"for i in choix_n_couches:\n",
|
||
" plt.plot(range(10),lc_cum2[i-1], label=\"%d couches\"%(i,))\n",
|
||
"leg = plt.legend(loc='best', ncol=2, mode=\"expand\", shadow=True, fancybox=True)\n",
|
||
"leg.get_frame().set_alpha(0.5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- MONKS2"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 48,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.subplot(111)\n",
|
||
"for i in choix_n_couches:\n",
|
||
" plt.plot(range(10),lc_cum3[i-1], label=\"%d couches\"%(i,))\n",
|
||
"leg = plt.legend(loc='best', ncol=2, mode=\"expand\", shadow=True, fancybox=True)\n",
|
||
"leg.get_frame().set_alpha(0.5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- MONKS3"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 49,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.subplot(111)\n",
|
||
"for i in choix_n_couches:\n",
|
||
" plt.plot(range(10),lc_cum4[i-1], label=\"%d couches\"%(i,))\n",
|
||
"leg = plt.legend(loc='best', ncol=2, mode=\"expand\", shadow=True, fancybox=True)\n",
|
||
"leg.get_frame().set_alpha(0.5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Choix des poids initiaux\n",
|
||
"Les poids initiaux ont été choisis à partir d'une distribution uniforme sur $[-1,1]$. On compare ici les courbes d'apprentissage en initialisant les poids à 0 et en initialisant les poids aléatoirement, pour le réseau de dimension et de profondeur optimale sélectionnées précédemment.\n",
|
||
"\n",
|
||
"- Réseau initialisé avec les poids à 0 $RN_{0}$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 50,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"nn2_poidszero = NeuralNet.NeuralNet(\n",
|
||
" np.hstack((6,\n",
|
||
" np.repeat(n_neurones_optimal2,n_couches_optimal2),\n",
|
||
" 2)),\n",
|
||
" range(2),\n",
|
||
" input_weights=0)\n",
|
||
"lc_nn2_poidszero = (\n",
|
||
" nn2_poidszero.train(train2, train_labels2, 0.1, 10, verbose=False))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 51,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"nn3_poidszero = NeuralNet.NeuralNet(\n",
|
||
" np.hstack((6,\n",
|
||
" np.repeat(n_neurones_optimal3,n_couches_optimal3),\n",
|
||
" 2)),\n",
|
||
" range(2),\n",
|
||
" input_weights=0)\n",
|
||
"lc_nn3_poidszero = (\n",
|
||
" nn3_poidszero.train(train3, train_labels3, 0.1, 10, verbose=False))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 52,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"nn4_poidszero = NeuralNet.NeuralNet(\n",
|
||
" np.hstack((6,\n",
|
||
" np.repeat(n_neurones_optimal4,n_couches_optimal4),\n",
|
||
" 2)),\n",
|
||
" range(2),\n",
|
||
" input_weights=0)\n",
|
||
"lc_nn4_poidszero = (\n",
|
||
" nn4_poidszero.train(train4, train_labels4, 0.1, 10, verbose=False))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- Réseau initialisé avec les poids uniformes $RN_{\\neg{0}}$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 53,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"nn2_poidsunif = NeuralNet.NeuralNet(\n",
|
||
" np.hstack((6,\n",
|
||
" np.repeat(n_neurones_optimal2,n_couches_optimal2),\n",
|
||
" 2)),\n",
|
||
" range(2))\n",
|
||
"np.random.seed(12345)\n",
|
||
"start_time = time.time()\n",
|
||
"lc_nn2_poidsunif = (\n",
|
||
" nn2_poidsunif.train(train2, train_labels2, 0.1, 10, verbose=False))\n",
|
||
"nn2_compute_time = time.time() - start_time"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 54,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"nn3_poidsunif = NeuralNet.NeuralNet(\n",
|
||
" np.hstack((6,\n",
|
||
" np.repeat(n_neurones_optimal3,n_couches_optimal3),\n",
|
||
" 2)),\n",
|
||
" range(2))\n",
|
||
"np.random.seed(12345)\n",
|
||
"start_time = time.time()\n",
|
||
"lc_nn3_poidsunif = (\n",
|
||
" nn3_poidsunif.train(train3, train_labels3, 0.1, 10, verbose=False))\n",
|
||
"nn3_compute_time = time.time() - start_time"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 55,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"nn4_poidsunif = NeuralNet.NeuralNet(\n",
|
||
" np.hstack((6,\n",
|
||
" np.repeat(n_neurones_optimal4,n_couches_optimal4),\n",
|
||
" 2)),\n",
|
||
" range(2))\n",
|
||
"np.random.seed(12345)\n",
|
||
"start_time = time.time()\n",
|
||
"lc_nn4_poidsunif = (\n",
|
||
" nn4_poidsunif.train(train4, train_labels4, 0.1, 10, verbose=False))\n",
|
||
"nn4_compute_time = time.time() - start_time"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Graphique des courbes d'apprentissage"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- MONKS1"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 56,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.subplot(111)\n",
|
||
"plt.plot(range(10),lc_nn2_poidszero, label=\"MONKS1: RN_Zero\")\n",
|
||
"plt.plot(range(10),lc_nn2_poidsunif, label=\"MONKS1: RN_Non_Zero)\")\n",
|
||
"leg = plt.legend(loc='best', \n",
|
||
" ncol=2, \n",
|
||
" mode=\"expand\", \n",
|
||
" shadow=True, \n",
|
||
" fancybox=True)\n",
|
||
"leg.get_frame().set_alpha(0.5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- MONKS2"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 57,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
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"data": {
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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.subplot(111)\n",
|
||
"plt.plot(range(10),lc_nn3_poidszero, label=\"MONKS2: RN_Zero\")\n",
|
||
"plt.plot(range(10),lc_nn3_poidsunif, label=\"MONKS2: RN_Non_Zero)\")\n",
|
||
"leg = plt.legend(loc='best', \n",
|
||
" ncol=2, \n",
|
||
" mode=\"expand\", \n",
|
||
" shadow=True, \n",
|
||
" fancybox=True)\n",
|
||
"leg.get_frame().set_alpha(0.5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- MONKS3"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 58,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.subplot(111)\n",
|
||
"plt.plot(range(10),lc_nn4_poidszero, label=\"MONKS3: RN_Zero\")\n",
|
||
"plt.plot(range(10),lc_nn4_poidsunif, label=\"MONKS3: RN_Non_Zero)\")\n",
|
||
"leg = plt.legend(loc='best', \n",
|
||
" ncol=2, \n",
|
||
" mode=\"expand\", \n",
|
||
" shadow=True, \n",
|
||
" fancybox=True)\n",
|
||
"leg.get_frame().set_alpha(0.5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"On remarque ici aussi que l'initialisation des poids à 0 ne permet pas de démarrer l'entrainement du réseau. Dans le second jeu de données, cependant, l'initialisation aléatoire ne permet pas non plus de démarrer l'apprentissage."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Entrainement et tests\n",
|
||
"On reprend les résultats du dernier entrainement, puisqu'il utilise les poids aléatoires et les hyperparamètres optimaux."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 59,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Matrice de confusion:\n",
|
||
"[[ 0. 216.]\n",
|
||
" [ 1. 215.]]\n",
|
||
"\n",
|
||
"Exactitude:\n",
|
||
"0.4976851851851852\n",
|
||
"\n",
|
||
"Précision:\n",
|
||
"[0.0, 0.4988399071925754]\n",
|
||
"\n",
|
||
"Rappel:\n",
|
||
"[0.0, 0.9953703703703703]\n",
|
||
"\n",
|
||
"Calculé en:\n",
|
||
"0.20888018608093262s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"res_test2 = nn2_poidsunif.test(test2, test_labels2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 60,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Matrice de confusion:\n",
|
||
"[[255. 35.]\n",
|
||
" [126. 16.]]\n",
|
||
"\n",
|
||
"Exactitude:\n",
|
||
"0.6273148148148148\n",
|
||
"\n",
|
||
"Précision:\n",
|
||
"[0.6692913385826772, 0.3137254901960784]\n",
|
||
"\n",
|
||
"Rappel:\n",
|
||
"[0.8793103448275862, 0.11267605633802817]\n",
|
||
"\n",
|
||
"Calculé en:\n",
|
||
"0.049660444259643555s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"res_test3 = nn3_poidsunif.test(test3, test_labels3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 61,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Matrice de confusion:\n",
|
||
"[[203. 1.]\n",
|
||
" [199. 29.]]\n",
|
||
"\n",
|
||
"Exactitude:\n",
|
||
"0.5370370370370371\n",
|
||
"\n",
|
||
"Précision:\n",
|
||
"[0.5049751243781094, 0.9666666666666667]\n",
|
||
"\n",
|
||
"Rappel:\n",
|
||
"[0.9950980392156863, 0.12719298245614036]\n",
|
||
"\n",
|
||
"Calculé en:\n",
|
||
"0.30426526069641113s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"res_test4 = nn4_poidsunif.test(test4, test_labels4)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Congressionnal Dataset\n",
|
||
"\n",
|
||
"- Chargement du jeu de données"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 62,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"train5, train_labels5, test5, test_labels5 = (\n",
|
||
" ld.load_congressional_dataset(train_ratio = 0.7))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Entrainement de l'arbre de décision\n",
|
||
"\n",
|
||
"Dans cette section, on entraine un arbre de décision basé sur la mesure d'entropie."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 63,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"dt5 = DecisionTree.DecisionTree(attribute_type=\"discrete\")\n",
|
||
"start_time = time.time()\n",
|
||
"_ = dt5.train(train5, train_labels5, verbose=False)\n",
|
||
"dt5_compute_time = time.time() - start_time"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Courbe d'apprentissage"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 64,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"dt_range_lc5,dt_accuracy_cum5 = (\n",
|
||
" courbe_apprentissage_dt(dt5,train5,train_labels5))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Voici le graphique de la courbe d'apprentissage"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 65,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[<matplotlib.lines.Line2D at 0x7fcdac0306d8>]"
|
||
]
|
||
},
|
||
"execution_count": 65,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.plot(dt_range_lc5,dt_accuracy_cum5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Test et évaluation de la performance de l'arbre de décision"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 66,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Matrice de confusion:\n",
|
||
"[[26. 36.]\n",
|
||
" [ 8. 67.]]\n",
|
||
"\n",
|
||
"Exactitude:\n",
|
||
"0.6641221374045801\n",
|
||
"\n",
|
||
"Précision:\n",
|
||
"[0.7647058823529411, 0.6504854368932039]\n",
|
||
"\n",
|
||
"Rappel:\n",
|
||
"[0.41935483870967744, 0.8933333333333333]\n",
|
||
"\n",
|
||
"Calculé en:\n",
|
||
"0.0005347728729248047s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"dt5_testres = dt5.test(test5, test_labels5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Réseaux de neurones: Choix du nombre de neurones dans la couche cachée"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- Pour faire la séparation du jeu de données en $k_{cv}=5$ jeux de validation croisée, on génère une permutation sur les indices du jeu d'entrainement, puis, on sépare cet ensemble en 5 groupes."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 67,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"all_indices5 = range(len(train_labels5))\n",
|
||
"np.random.seed(12345)\n",
|
||
"indices_cv_test5 = (\n",
|
||
" [np.sort(x) for x in (np.array_split(np.random.permutation(all_indices5),\n",
|
||
" k_cv))])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 68,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"indices_cv_train5 = (\n",
|
||
" [np.setdiff1d(all_indices5,indices_cv_test5[i]) for i in range(k_cv)])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Ce jeux de données a trois classes possibles comme variable de sortie. On utilisera donc un réseau de neurones avec 3 neurones dans la couche de sortie, une pour chacune des valeurs possibles. Les valeurs de sortie du jeu de données sont transformées à l'aide d'un encodage binaire où la valeur de sortie est convertie en rang dans un vecteur (on commence à 0), prenant la valeur 1. Par exemple, la valeur 2 devient $[0,0,1]$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 69,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"accuracy_cum5 = []\n",
|
||
"for n_neurones in choix_n_neurones:\n",
|
||
" accuracy_cv=[]\n",
|
||
" for cv_set in range(k_cv):\n",
|
||
" accuracy=0\n",
|
||
" try:\n",
|
||
" nn5 = NeuralNet.NeuralNet(np.array([16,n_neurones,3]),range(3))\n",
|
||
" nn5.train(train5[indices_cv_train5[cv_set]], \n",
|
||
" train_labels5[indices_cv_train5[cv_set]], 0.1, 1, \n",
|
||
" verbose=False)\n",
|
||
" _,accuracy,_,_,_ = nn5.test(train5[indices_cv_test5[cv_set]], \n",
|
||
" train_labels5[indices_cv_test5[cv_set]], \n",
|
||
" verbose=False)\n",
|
||
" except:\n",
|
||
" pass\n",
|
||
" accuracy_cv.append(accuracy)\n",
|
||
" accuracy_cum5.append(np.mean(np.array(accuracy_cv)))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 70,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[<matplotlib.lines.Line2D at 0x7fcdabf9aeb8>]"
|
||
]
|
||
},
|
||
"execution_count": 70,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.plot(choix_n_neurones,accuracy_cum5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Le nombre de neurones qui maximise l'*accuracy* est de:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 71,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"25"
|
||
]
|
||
},
|
||
"execution_count": 71,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"n_neurones_optimal5 = (\n",
|
||
" choix_n_neurones[np.where(accuracy_cum5==max(accuracy_cum5))[0][0]])\n",
|
||
"n_neurones_optimal5"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Choix du nombre de couches cachées"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 72,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"accuracy_cum5 = []\n",
|
||
"lc_cum5 = []\n",
|
||
"for n_couches in choix_n_couches:\n",
|
||
" accuracy_cv=[]\n",
|
||
" nn5 = NeuralNet.NeuralNet(\n",
|
||
" np.hstack((16,\n",
|
||
" np.repeat(n_neurones_optimal5,n_couches),\n",
|
||
" 3)),\n",
|
||
" range(3))\n",
|
||
" lc = nn5.train(train5, train_labels5, 0.1, 10, verbose=False)\n",
|
||
" lc_cum5.append(lc)\n",
|
||
" _,accuracy,_,_,_ = nn5.test(train5, train_labels5, verbose=False)\n",
|
||
" accuracy_cv.append(accuracy)\n",
|
||
" accuracy_cum5.append(np.mean(np.array(accuracy_cv)))\n",
|
||
"lc_cum5 = np.array(lc_cum5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"L'*accuracy* pour les différentes profondeur est de :"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 73,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[1. , 2. , 3. , 4. , 5. ],\n",
|
||
" [0.94736842, 0.92105263, 0.92763158, 0.90460526, 0.8125 ]])"
|
||
]
|
||
},
|
||
"execution_count": 73,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"np.vstack((choix_n_couches,accuracy_cum5))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Le nombre de couches cachées qui maximise l'*accuracy* est de:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 74,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"1"
|
||
]
|
||
},
|
||
"execution_count": 74,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"n_couches_optimal5 = (\n",
|
||
" choix_n_couches[np.where(accuracy_cum5==max(accuracy_cum5))[0][0]])\n",
|
||
"n_couches_optimal5"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Courbes d'apprentissage\n",
|
||
"\n",
|
||
"Ce graphique présente les courbes d'apprentissage pour chacun des niveaux de profondeur du réseau"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 75,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.subplot(111)\n",
|
||
"for i in choix_n_couches:\n",
|
||
" plt.plot(range(10),lc_cum5[i-1], label=\"%d couches\"%(i,))\n",
|
||
"leg = plt.legend(loc='best', ncol=2, mode=\"expand\", shadow=True, fancybox=True)\n",
|
||
"leg.get_frame().set_alpha(0.5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Choix des poids initiaux\n",
|
||
"Les poids initiaux ont été choisis à partir d'une distribution uniforme sur $[-1,1]$. On compare ici les courbes d'apprentissage en initialisant les poids à 0 et en initialisant les poids aléatoirement, pour le réseau de dimension et de profondeur optimale sélectionnées précédemment.\n",
|
||
"\n",
|
||
"- Réseau initialisé avec les poids à 0 $RN_{0}$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 76,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"nn5_poidszero = NeuralNet.NeuralNet(\n",
|
||
" np.hstack((16,\n",
|
||
" np.repeat(n_neurones_optimal5,n_couches_optimal5),\n",
|
||
" 3)),\n",
|
||
" range(3),\n",
|
||
" input_weights=0)\n",
|
||
"lc_nn5_poidszero = (\n",
|
||
" nn5_poidszero.train(train5, train_labels5, 0.1, 10, verbose=False))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- Réseau initialisé avec les poids uniformes $RN_{\\neg{0}}$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 77,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"nn5_poidsunif = NeuralNet.NeuralNet(\n",
|
||
" np.hstack((16,\n",
|
||
" np.repeat(n_neurones_optimal5,n_couches_optimal5),\n",
|
||
" 3)),\n",
|
||
" range(3))\n",
|
||
"np.random.seed(12345)\n",
|
||
"start_time = time.time()\n",
|
||
"lc_nn5_poidsunif = (\n",
|
||
" nn5_poidsunif.train(train5, train_labels5, 0.1, 10, verbose=False))\n",
|
||
"nn5_compute_time = time.time() - start_time"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Graphique des courbes d'apprentissage"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 78,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.subplot(111)\n",
|
||
"plt.plot(range(10),lc_nn5_poidszero, label=\"RN_Zero\")\n",
|
||
"plt.plot(range(10),lc_nn5_poidsunif, label=\"RN_Non_Zero)\")\n",
|
||
"leg = plt.legend(loc='best', \n",
|
||
" ncol=2, \n",
|
||
" mode=\"expand\", \n",
|
||
" shadow=True, \n",
|
||
" fancybox=True)\n",
|
||
"leg.get_frame().set_alpha(0.5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"On remarque ici aussi que l'initialisation des poids à 0 ne permet pas de démarrer l'entrainement du réseau."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Entrainement et tests\n",
|
||
"On reprend les résultats du dernier entrainement, puisqu'il utilise les poids aléatoires et les hyperparamètres optimaux."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 79,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Matrice de confusion:\n",
|
||
"[[57. 2.]\n",
|
||
" [ 7. 65.]]\n",
|
||
"\n",
|
||
"Exactitude:\n",
|
||
"0.9312977099236641\n",
|
||
"\n",
|
||
"Précision:\n",
|
||
"[0.890625, 0.9701492537313433]\n",
|
||
"\n",
|
||
"Rappel:\n",
|
||
"[0.9661016949152542, 0.9027777777777778]\n",
|
||
"\n",
|
||
"Calculé en:\n",
|
||
"0.021606922149658203s\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"res_test5 = nn5_poidsunif.test(test5, test_labels5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Comparaison entre arbre de décision et réseau de neurones"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- Taux d'erreur sur l'ensemble de test. Sur la première ligne, les erreurs pour les arbres de décision, sur la seconde, pour les réseaux de neurones"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 80,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[0.97777778, 0.52546296, 0.62962963, 0.53703704, 0.66412214],\n",
|
||
" [0.91111111, 0.49768519, 0.62731481, 0.53703704, 0.93129771]])"
|
||
]
|
||
},
|
||
"execution_count": 80,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"np.array([[dt1_testres[1],\n",
|
||
" dt2_testres[1],\n",
|
||
" dt3_testres[1],\n",
|
||
" dt4_testres[1],\n",
|
||
" dt5_testres[1]],\n",
|
||
" [res_test1[1],\n",
|
||
" res_test2[1],\n",
|
||
" res_test3[1],\n",
|
||
" res_test4[1],\n",
|
||
" res_test5[1]]])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- Temps de prédiction d’un seul exemple. Sur la première ligne, les temps de prédiction moyens pour un exemple de l'ensemble de test pour les arbres de décision, sur la seconde, pour les réseaux de neurones"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 81,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[6.53796726e-06, 2.56024025e-06, 3.92342055e-06, 4.10223449e-06,\n",
|
||
" 4.08223567e-06],\n",
|
||
" [8.89841715e-04, 4.83518949e-04, 1.14954732e-04, 7.04317733e-04,\n",
|
||
" 1.64938337e-04]])"
|
||
]
|
||
},
|
||
"execution_count": 81,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"np.array([[dt1_testres[4]/len(test_labels1),\n",
|
||
" dt2_testres[4]/len(test_labels2),\n",
|
||
" dt3_testres[4]/len(test_labels3),\n",
|
||
" dt4_testres[4]/len(test_labels4),\n",
|
||
" dt5_testres[4]/len(test_labels5)],\n",
|
||
" [res_test1[4]/len(test_labels1),\n",
|
||
" res_test2[4]/len(test_labels2),\n",
|
||
" res_test3[4]/len(test_labels3),\n",
|
||
" res_test4[4]/len(test_labels4),\n",
|
||
" res_test5[4]/len(test_labels5)]])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- Temps d’apprentissage du modèle. Sur la première ligne, les temps d'entrainement pour les arbres de décision, sur la seconde, pour les réseaux de neurones"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 82,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[9.11688805e-03, 2.06351280e-03, 2.00963020e-03, 1.85656548e-03,\n",
|
||
" 1.11479759e-02],\n",
|
||
" [4.42183232e+00, 2.92573667e+00, 7.95306206e-01, 4.50003695e+00,\n",
|
||
" 2.64667988e+00]])"
|
||
]
|
||
},
|
||
"execution_count": 82,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"np.array([\n",
|
||
" [dt1_compute_time,\n",
|
||
" dt2_compute_time,\n",
|
||
" dt3_compute_time,\n",
|
||
" dt4_compute_time,\n",
|
||
" dt5_compute_time],\n",
|
||
" [nn1_compute_time,\n",
|
||
" nn2_compute_time,\n",
|
||
" nn3_compute_time,\n",
|
||
" nn4_compute_time,\n",
|
||
" nn5_compute_time]])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Conclusion"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Les arbres de décisions sont des algorithmes simples à mettre en oeuvre et démontrent une exactitude supérieure et comparable aux arbres de décisions lorsque le nombre d'attributs est limité. Cependant, lorsque le nombre d'attributs croît, les réseaux de neurones sont beaucoup plus performants. Ceci est démontré entre autres par la performance sur le jeu de données Congressionnal où le réseau de neurones est beaucoup plus performant. Pour les jeux de données plus difficiles tels que MONKS, on ne remarque pas une grande différence entre les deux algorithmes au niveau du taux d'erreur.\n",
|
||
"\n",
|
||
"Le temps de prédiction est deux ordres de magnitude plus élevé pour les réseaux de neurones que pour les arbres de décisions, car il faut effectuer toutes les multiplications des valeurs par les poids pour chacune des couches, ce qui est une opération $O(d^2)$ pour une dimension $d$. Pour l'arbre de décision, la prédiction est une recherche dans un arbre binaire, de complexité $O(log_2(d))$ pour une profondeur $d$.\n",
|
||
"\n",
|
||
"Les réseaux de neurones nécessitent beaucoup plus de temps d'entrainement que les arbres de décisions, et il faut aussi considérer que ce temps est multiplié lorsque l'on effectue une recherche des hyperparamètres optimaux. Ce besoin de capacités physique explique pourquoi il a fallu attendre l'apparition des processeurs graphiques très performants pour que la recherche avec les réseaux de neurones explose. La complexité est d'autant plus élevée, car le nombre de connexions croit de façon exponentielle avec l'ajout de nouvelles couches cachées."
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"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": "3.7.3"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|