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added training precision and zero one loss in plot with varying tolerance

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Titouan Labourdette 2021-11-27 17:00:09 +01:00
parent 13bac40fa4
commit d286bedb4d
6 changed files with 413 additions and 251 deletions
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60
.ipynb_checkpoints/TP1_prog2.py-checkpoint.ipynb
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@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 1,
"id": "530f620c", "id": "530f620c",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -22,7 +22,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 2,
"id": "68b6a517", "id": "68b6a517",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -864,7 +864,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 26,
"id": "98107e41", "id": "98107e41",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -872,37 +872,59 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Matrice de confusion K-NN :\n", "Métriques pour K-NN :\n",
" [[51 0 0 0 0 1 0 0 0 0]\n", "Paramètres : (n_neighbors=3,p=2,n_jobs=1)\n",
" [ 0 56 0 0 0 0 0 0 0 0]\n", "Taille de l'échantillon : 10000\n",
" [ 3 1 45 1 0 0 1 1 0 0]\n", "Proportion des datasets : 90%\n",
" [ 0 1 1 35 0 1 0 1 1 1]\n", "Temps d'entraînement (secondes) : 0.01596\n",
" [ 0 3 0 0 48 0 0 0 0 2]\n", "Temps de prédiction (secondes) : 0.30718\n",
" [ 0 1 0 1 0 38 0 0 0 0]\n", "Précision pour chaque classe : [0.942, 0.891, 0.962, 0.959, 0.988, 0.944, 0.961, 0.97, 0.989, 0.918]\n",
" [ 0 0 0 0 0 2 44 0 0 0]\n", "Précision : 0.95\n",
" [ 0 2 0 0 3 0 0 47 0 0]\n", "Erreur : 0.05\n",
" [ 2 0 0 0 0 3 1 0 42 2]\n", "Matrice de confusion :\n",
" [ 0 0 0 0 4 1 0 1 2 50]]\n" " [[ 98 0 1 0 0 0 1 0 1 0]\n",
" [ 0 114 0 0 0 0 0 0 0 0]\n",
" [ 2 2 102 0 0 0 0 0 0 0]\n",
" [ 0 1 1 93 0 3 0 1 0 0]\n",
" [ 1 5 0 0 82 0 0 0 0 5]\n",
" [ 0 1 1 1 0 84 3 0 0 1]\n",
" [ 0 0 0 0 0 0 99 0 0 0]\n",
" [ 0 3 0 0 0 0 0 97 0 2]\n",
" [ 2 1 1 3 1 2 0 0 92 0]\n",
" [ 1 1 0 0 0 0 0 2 0 89]]\n"
] ]
} }
], ],
"source": [ "source": [
"### Create vector of 5000 random indexes\n", "### Create vector of 5000 random indexes\n",
"rand_indexes = np.random.randint(70000, size=5000)\n", "rand_indexes = np.random.randint(70000, size=10000)\n",
"### Load data with the previous vector\n", "### Load data with the previous vector\n",
"data = mnist.data[rand_indexes]\n", "data = mnist.data[rand_indexes]\n",
"# print(\"Dataset : \", data)\n",
"target = mnist.target[rand_indexes]\n", "target = mnist.target[rand_indexes]\n",
"\n",
"# Split the dataset\n", "# Split the dataset\n",
"xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n", "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
"\n", "\n",
"# Training on xtrain,ytrain\n",
"clf = neighbors.KNeighborsClassifier(n_neighbors=3,p=2,n_jobs=1)\n", "clf = neighbors.KNeighborsClassifier(n_neighbors=3,p=2,n_jobs=1)\n",
"# Training on xtrain,ytrain\n",
"t1 = time.time()\n",
"clf.fit(xtrain, ytrain)\n", "clf.fit(xtrain, ytrain)\n",
"t2 = time.time()\n",
"# Predicting on xtest\n", "# Predicting on xtest\n",
"pred = clf.predict(xtest)\n", "pred = clf.predict(xtest)\n",
"print(\"Matrice de confusion K-NN :\\n\", metrics.confusion_matrix(ytest, pred))" "t3 = time.time()\n",
"#Calcul de différentes metrics\n",
"precisions = [round(i,3) for i in metrics.precision_score(ytest, pred,average=None)]\n",
"\n",
"print(\"Métriques pour K-NN :\")\n",
"print(\"Paramètres : (n_neighbors=3,p=2,n_jobs=1)\")\n",
"print(\"Taille de l'échantillon :\", 10000)\n",
"print(\"Proportion des datasets :\", \"90%\")\n",
"print(\"Temps d'entraînement (secondes) :\", round(t2-t1,5))\n",
"print(\"Temps de prédiction (secondes) :\", round(t3-t2,5))\n",
"print(\"Précision pour chaque classe :\", precisions)\n",
"print(\"Précision :\", clf.score(xtest, ytest))\n",
"print(\"Erreur :\", round(metrics.zero_one_loss(ytest, pred),5))\n",
"print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
] ]
}, },
{ {

82
.ipynb_checkpoints/TP2_prog1.py-checkpoint.ipynb
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@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 1,
"id": "3eb7a65b", "id": "3eb7a65b",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -22,7 +22,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 2,
"id": "a8812842", "id": "a8812842",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -1233,7 +1233,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 9,
"id": "abb0fcf1", "id": "abb0fcf1",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -1241,23 +1241,33 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Matrice de confusion A-NN :\n", "Métriques pour A-NN\n",
" [[59 0 0 0 0 0 0 0 0 0]\n", "Paramètres : (random_state=1, max_iter=300, hidden_layer_sizes=((85,)*15),\n",
" [ 0 60 0 0 0 0 0 1 0 0]\n", "solver=adam, activation=relu, alpha= 0.0000001)\n",
" [ 0 0 42 0 0 1 2 1 2 0]\n", "Taille de l'échantillon : 10000\n",
" [ 0 0 1 44 0 1 0 0 0 0]\n", "Proportion des datasets : 90%\n",
" [ 0 0 0 0 46 0 1 0 0 4]\n", "Temps d'entraînement (secondes) : 27.9214\n",
" [ 0 0 0 0 0 31 0 0 1 0]\n", "Temps de prédiction (secondes) : 0.01396\n",
" [ 0 0 0 0 0 0 48 0 0 0]\n", "Précision pour chaque classe : [0.972, 0.974, 0.926, 0.97, 0.919, 0.939, 0.971, 0.967, 0.951, 0.927]\n",
" [ 1 0 0 1 0 0 0 49 0 0]\n", "Précision : 0.952\n",
" [ 0 1 1 5 0 1 0 0 48 0]\n", "Erreur : 0.048\n",
" [ 2 0 0 1 1 2 0 0 2 40]]\n" "Matrice de confusion :\n",
" [[103 0 1 0 0 0 1 0 0 0]\n",
" [ 0 114 0 0 1 0 0 0 0 0]\n",
" [ 0 0 100 0 2 1 1 1 1 0]\n",
" [ 0 1 3 97 0 1 0 0 1 1]\n",
" [ 0 0 0 0 79 0 0 0 0 4]\n",
" [ 2 0 0 3 0 93 1 0 1 0]\n",
" [ 0 0 0 0 0 3 99 0 1 0]\n",
" [ 1 1 1 0 1 0 0 89 0 1]\n",
" [ 0 1 3 0 0 1 0 0 77 2]\n",
" [ 0 0 0 0 3 0 0 2 0 101]]\n"
] ]
} }
], ],
"source": [ "source": [
"### Create vector of 5000 random indexes\n", "### Create vector of 5000 random indexes\n",
"rand_indexes = np.random.randint(70000, size=5000)\n", "rand_indexes = np.random.randint(70000, size=10000)\n",
"### Load data with the previous vector\n", "### Load data with the previous vector\n",
"data = mnist.data[rand_indexes]\n", "data = mnist.data[rand_indexes]\n",
"# print(\"Dataset : \", data)\n", "# print(\"Dataset : \", data)\n",
@ -1266,10 +1276,6 @@
"# Split the dataset\n", "# Split the dataset\n",
"xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n", "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
"\n", "\n",
"best_training_time = 0\n",
"best_precision_score = 0\n",
"best_zero_one_loss = 0\n",
"\n",
"r = 1\n", "r = 1\n",
"max_i = 300\n", "max_i = 300\n",
"nb_hl = 15\n", "nb_hl = 15\n",
@ -1281,31 +1287,27 @@
"\n", "\n",
"#Entraîne le classifier\n", "#Entraîne le classifier\n",
"clf = neural_network.MLPClassifier(random_state=r, max_iter=max_i, hidden_layer_sizes=hl, solver=sol, activation=act, alpha=a, verbose=False)\n", "clf = neural_network.MLPClassifier(random_state=r, max_iter=max_i, hidden_layer_sizes=hl, solver=sol, activation=act, alpha=a, verbose=False)\n",
"t1 = round(time.time(),5)\n", "t1 = time.time()\n",
"clf.fit(xtrain, ytrain)\n", "clf.fit(xtrain, ytrain)\n",
"t2 = round(time.time(),5)\n", "t2 = time.time()\n",
"#Prédiction sur le jeu de tests\n", "#Prédiction sur le jeu de tests\n",
"pred = clf.predict(xtest)\n", "pred = clf.predict(xtest)\n",
"# Probabilités des prédictions sur xtest\n", "t3 = time.time()\n",
"pred_proba = clf.predict_proba(xtest)\n",
"# On sauvegarde le temps de calcul, la précision et \n",
"# les taux d'erreurs par classe\n",
"best_training_time = t2-t1\n",
"best_precision_score = clf.score(xtest, ytest)\n",
"best_zero_one_loss = metrics.zero_one_loss(ytest, pred)\n",
"\n", "\n",
"# print(\"Paramètre :\\n\")\n", "#Calcul de différentes metrics\n",
"# print(\"random_state = \", r)\n", "precisions = [round(i,3) for i in metrics.precision_score(ytest, pred,average=None)]\n",
"# print(\"max_iter = \", max_i)\n", "\n",
"# print(\"nb_hidden_layer = \", nb_hl)\n", "print(\"Métriques pour A-NN\")\n",
"# print(\"hidden_layer_size = \", hl_size)\n", "print(\"Paramètres : (random_state=1, max_iter=300, hidden_layer_sizes=((85,)*15),\")\n",
"# print(\"solver = \", sol)\n", "print(\"solver=adam, activation=relu, alpha= 0.0000001)\")\n",
"# print(\"activation = \", act)\n", "print(\"Taille de l'échantillon :\", 10000)\n",
"# print(\"alpha = \", a)\n", "print(\"Proportion des datasets :\", \"90%\")\n",
"# print(\"Temps d'entraînement : \", best_training_time)\n", "print(\"Temps d'entraînement (secondes) :\", round(t2-t1,5))\n",
"# print(\"Score : \", best_precision_score)\n", "print(\"Temps de prédiction (secondes) :\", round(t3-t2,5))\n",
"# print(\"Zero-one loss : \", best_zero_one_loss)\n", "print(\"Précision pour chaque classe :\", precisions)\n",
"print(\"Matrice de confusion A-NN :\\n\", metrics.confusion_matrix(ytest, pred))" "print(\"Précision :\", clf.score(xtest, ytest))\n",
"print(\"Erreur :\", round(metrics.zero_one_loss(ytest, pred),5))\n",
"print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
] ]
}, },
{ {

213
.ipynb_checkpoints/TP3_prog1.py-checkpoint.ipynb
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60
TP1_prog2.py.ipynb
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@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 1,
"id": "530f620c", "id": "530f620c",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -22,7 +22,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 2,
"id": "68b6a517", "id": "68b6a517",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -864,7 +864,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 26,
"id": "98107e41", "id": "98107e41",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -872,37 +872,59 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Matrice de confusion K-NN :\n", "Métriques pour K-NN :\n",
" [[51 0 0 0 0 1 0 0 0 0]\n", "Paramètres : (n_neighbors=3,p=2,n_jobs=1)\n",
" [ 0 56 0 0 0 0 0 0 0 0]\n", "Taille de l'échantillon : 10000\n",
" [ 3 1 45 1 0 0 1 1 0 0]\n", "Proportion des datasets : 90%\n",
" [ 0 1 1 35 0 1 0 1 1 1]\n", "Temps d'entraînement (secondes) : 0.01596\n",
" [ 0 3 0 0 48 0 0 0 0 2]\n", "Temps de prédiction (secondes) : 0.30718\n",
" [ 0 1 0 1 0 38 0 0 0 0]\n", "Précision pour chaque classe : [0.942, 0.891, 0.962, 0.959, 0.988, 0.944, 0.961, 0.97, 0.989, 0.918]\n",
" [ 0 0 0 0 0 2 44 0 0 0]\n", "Précision : 0.95\n",
" [ 0 2 0 0 3 0 0 47 0 0]\n", "Erreur : 0.05\n",
" [ 2 0 0 0 0 3 1 0 42 2]\n", "Matrice de confusion :\n",
" [ 0 0 0 0 4 1 0 1 2 50]]\n" " [[ 98 0 1 0 0 0 1 0 1 0]\n",
" [ 0 114 0 0 0 0 0 0 0 0]\n",
" [ 2 2 102 0 0 0 0 0 0 0]\n",
" [ 0 1 1 93 0 3 0 1 0 0]\n",
" [ 1 5 0 0 82 0 0 0 0 5]\n",
" [ 0 1 1 1 0 84 3 0 0 1]\n",
" [ 0 0 0 0 0 0 99 0 0 0]\n",
" [ 0 3 0 0 0 0 0 97 0 2]\n",
" [ 2 1 1 3 1 2 0 0 92 0]\n",
" [ 1 1 0 0 0 0 0 2 0 89]]\n"
] ]
} }
], ],
"source": [ "source": [
"### Create vector of 5000 random indexes\n", "### Create vector of 5000 random indexes\n",
"rand_indexes = np.random.randint(70000, size=5000)\n", "rand_indexes = np.random.randint(70000, size=10000)\n",
"### Load data with the previous vector\n", "### Load data with the previous vector\n",
"data = mnist.data[rand_indexes]\n", "data = mnist.data[rand_indexes]\n",
"# print(\"Dataset : \", data)\n",
"target = mnist.target[rand_indexes]\n", "target = mnist.target[rand_indexes]\n",
"\n",
"# Split the dataset\n", "# Split the dataset\n",
"xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n", "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
"\n", "\n",
"# Training on xtrain,ytrain\n",
"clf = neighbors.KNeighborsClassifier(n_neighbors=3,p=2,n_jobs=1)\n", "clf = neighbors.KNeighborsClassifier(n_neighbors=3,p=2,n_jobs=1)\n",
"# Training on xtrain,ytrain\n",
"t1 = time.time()\n",
"clf.fit(xtrain, ytrain)\n", "clf.fit(xtrain, ytrain)\n",
"t2 = time.time()\n",
"# Predicting on xtest\n", "# Predicting on xtest\n",
"pred = clf.predict(xtest)\n", "pred = clf.predict(xtest)\n",
"print(\"Matrice de confusion K-NN :\\n\", metrics.confusion_matrix(ytest, pred))" "t3 = time.time()\n",
"#Calcul de différentes metrics\n",
"precisions = [round(i,3) for i in metrics.precision_score(ytest, pred,average=None)]\n",
"\n",
"print(\"Métriques pour K-NN :\")\n",
"print(\"Paramètres : (n_neighbors=3,p=2,n_jobs=1)\")\n",
"print(\"Taille de l'échantillon :\", 10000)\n",
"print(\"Proportion des datasets :\", \"90%\")\n",
"print(\"Temps d'entraînement (secondes) :\", round(t2-t1,5))\n",
"print(\"Temps de prédiction (secondes) :\", round(t3-t2,5))\n",
"print(\"Précision pour chaque classe :\", precisions)\n",
"print(\"Précision :\", clf.score(xtest, ytest))\n",
"print(\"Erreur :\", round(metrics.zero_one_loss(ytest, pred),5))\n",
"print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
] ]
}, },
{ {

82
TP2_prog1.py.ipynb
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@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 1,
"id": "3eb7a65b", "id": "3eb7a65b",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -22,7 +22,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 2,
"id": "a8812842", "id": "a8812842",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -1233,7 +1233,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 9,
"id": "abb0fcf1", "id": "abb0fcf1",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -1241,23 +1241,33 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Matrice de confusion A-NN :\n", "Métriques pour A-NN\n",
" [[59 0 0 0 0 0 0 0 0 0]\n", "Paramètres : (random_state=1, max_iter=300, hidden_layer_sizes=((85,)*15),\n",
" [ 0 60 0 0 0 0 0 1 0 0]\n", "solver=adam, activation=relu, alpha= 0.0000001)\n",
" [ 0 0 42 0 0 1 2 1 2 0]\n", "Taille de l'échantillon : 10000\n",
" [ 0 0 1 44 0 1 0 0 0 0]\n", "Proportion des datasets : 90%\n",
" [ 0 0 0 0 46 0 1 0 0 4]\n", "Temps d'entraînement (secondes) : 27.9214\n",
" [ 0 0 0 0 0 31 0 0 1 0]\n", "Temps de prédiction (secondes) : 0.01396\n",
" [ 0 0 0 0 0 0 48 0 0 0]\n", "Précision pour chaque classe : [0.972, 0.974, 0.926, 0.97, 0.919, 0.939, 0.971, 0.967, 0.951, 0.927]\n",
" [ 1 0 0 1 0 0 0 49 0 0]\n", "Précision : 0.952\n",
" [ 0 1 1 5 0 1 0 0 48 0]\n", "Erreur : 0.048\n",
" [ 2 0 0 1 1 2 0 0 2 40]]\n" "Matrice de confusion :\n",
" [[103 0 1 0 0 0 1 0 0 0]\n",
" [ 0 114 0 0 1 0 0 0 0 0]\n",
" [ 0 0 100 0 2 1 1 1 1 0]\n",
" [ 0 1 3 97 0 1 0 0 1 1]\n",
" [ 0 0 0 0 79 0 0 0 0 4]\n",
" [ 2 0 0 3 0 93 1 0 1 0]\n",
" [ 0 0 0 0 0 3 99 0 1 0]\n",
" [ 1 1 1 0 1 0 0 89 0 1]\n",
" [ 0 1 3 0 0 1 0 0 77 2]\n",
" [ 0 0 0 0 3 0 0 2 0 101]]\n"
] ]
} }
], ],
"source": [ "source": [
"### Create vector of 5000 random indexes\n", "### Create vector of 5000 random indexes\n",
"rand_indexes = np.random.randint(70000, size=5000)\n", "rand_indexes = np.random.randint(70000, size=10000)\n",
"### Load data with the previous vector\n", "### Load data with the previous vector\n",
"data = mnist.data[rand_indexes]\n", "data = mnist.data[rand_indexes]\n",
"# print(\"Dataset : \", data)\n", "# print(\"Dataset : \", data)\n",
@ -1266,10 +1276,6 @@
"# Split the dataset\n", "# Split the dataset\n",
"xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n", "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
"\n", "\n",
"best_training_time = 0\n",
"best_precision_score = 0\n",
"best_zero_one_loss = 0\n",
"\n",
"r = 1\n", "r = 1\n",
"max_i = 300\n", "max_i = 300\n",
"nb_hl = 15\n", "nb_hl = 15\n",
@ -1281,31 +1287,27 @@
"\n", "\n",
"#Entraîne le classifier\n", "#Entraîne le classifier\n",
"clf = neural_network.MLPClassifier(random_state=r, max_iter=max_i, hidden_layer_sizes=hl, solver=sol, activation=act, alpha=a, verbose=False)\n", "clf = neural_network.MLPClassifier(random_state=r, max_iter=max_i, hidden_layer_sizes=hl, solver=sol, activation=act, alpha=a, verbose=False)\n",
"t1 = round(time.time(),5)\n", "t1 = time.time()\n",
"clf.fit(xtrain, ytrain)\n", "clf.fit(xtrain, ytrain)\n",
"t2 = round(time.time(),5)\n", "t2 = time.time()\n",
"#Prédiction sur le jeu de tests\n", "#Prédiction sur le jeu de tests\n",
"pred = clf.predict(xtest)\n", "pred = clf.predict(xtest)\n",
"# Probabilités des prédictions sur xtest\n", "t3 = time.time()\n",
"pred_proba = clf.predict_proba(xtest)\n",
"# On sauvegarde le temps de calcul, la précision et \n",
"# les taux d'erreurs par classe\n",
"best_training_time = t2-t1\n",
"best_precision_score = clf.score(xtest, ytest)\n",
"best_zero_one_loss = metrics.zero_one_loss(ytest, pred)\n",
"\n", "\n",
"# print(\"Paramètre :\\n\")\n", "#Calcul de différentes metrics\n",
"# print(\"random_state = \", r)\n", "precisions = [round(i,3) for i in metrics.precision_score(ytest, pred,average=None)]\n",
"# print(\"max_iter = \", max_i)\n", "\n",
"# print(\"nb_hidden_layer = \", nb_hl)\n", "print(\"Métriques pour A-NN\")\n",
"# print(\"hidden_layer_size = \", hl_size)\n", "print(\"Paramètres : (random_state=1, max_iter=300, hidden_layer_sizes=((85,)*15),\")\n",
"# print(\"solver = \", sol)\n", "print(\"solver=adam, activation=relu, alpha= 0.0000001)\")\n",
"# print(\"activation = \", act)\n", "print(\"Taille de l'échantillon :\", 10000)\n",
"# print(\"alpha = \", a)\n", "print(\"Proportion des datasets :\", \"90%\")\n",
"# print(\"Temps d'entraînement : \", best_training_time)\n", "print(\"Temps d'entraînement (secondes) :\", round(t2-t1,5))\n",
"# print(\"Score : \", best_precision_score)\n", "print(\"Temps de prédiction (secondes) :\", round(t3-t2,5))\n",
"# print(\"Zero-one loss : \", best_zero_one_loss)\n", "print(\"Précision pour chaque classe :\", precisions)\n",
"print(\"Matrice de confusion A-NN :\\n\", metrics.confusion_matrix(ytest, pred))" "print(\"Précision :\", clf.score(xtest, ytest))\n",
"print(\"Erreur :\", round(metrics.zero_one_loss(ytest, pred),5))\n",
"print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
] ]
}, },
{ {

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