added training precision and zero one loss in plot with varying tolerance
This commit is contained in:
Titouan Labourdette
parent
13bac40fa4
commit
d286bedb4d
6 changed files with 413 additions and 251 deletions
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 1,
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"id": "530f620c",
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"metadata": {},
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"outputs": [],
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{
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"cell_type": "code",
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"id": "68b6a517",
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"metadata": {},
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"outputs": [],
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@ -864,7 +864,7 @@
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 26,
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"id": "98107e41",
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"metadata": {},
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"outputs": [
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@ -872,37 +872,59 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Matrice de confusion K-NN :\n",
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" [[51 0 0 0 0 1 0 0 0 0]\n",
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" [ 0 56 0 0 0 0 0 0 0 0]\n",
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" [ 3 1 45 1 0 0 1 1 0 0]\n",
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" [ 0 1 1 35 0 1 0 1 1 1]\n",
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" [ 0 3 0 0 48 0 0 0 0 2]\n",
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" [ 0 1 0 1 0 38 0 0 0 0]\n",
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" [ 0 0 0 0 0 2 44 0 0 0]\n",
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" [ 0 2 0 0 3 0 0 47 0 0]\n",
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" [ 2 0 0 0 0 3 1 0 42 2]\n",
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" [ 0 0 0 0 4 1 0 1 2 50]]\n"
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"Métriques pour K-NN :\n",
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"Paramètres : (n_neighbors=3,p=2,n_jobs=1)\n",
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"Taille de l'échantillon : 10000\n",
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"Proportion des datasets : 90%\n",
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"Temps d'entraînement (secondes) : 0.01596\n",
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"Temps de prédiction (secondes) : 0.30718\n",
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"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",
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"Précision : 0.95\n",
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"Erreur : 0.05\n",
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"Matrice de confusion :\n",
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" [[ 98 0 1 0 0 0 1 0 1 0]\n",
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" [ 0 114 0 0 0 0 0 0 0 0]\n",
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" [ 2 2 102 0 0 0 0 0 0 0]\n",
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" [ 0 1 1 93 0 3 0 1 0 0]\n",
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" [ 1 5 0 0 82 0 0 0 0 5]\n",
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" [ 0 1 1 1 0 84 3 0 0 1]\n",
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" [ 0 0 0 0 0 0 99 0 0 0]\n",
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" [ 0 3 0 0 0 0 0 97 0 2]\n",
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" [ 2 1 1 3 1 2 0 0 92 0]\n",
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" [ 1 1 0 0 0 0 0 2 0 89]]\n"
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]
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}
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],
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"source": [
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"### Create vector of 5000 random indexes\n",
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"rand_indexes = np.random.randint(70000, size=5000)\n",
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"rand_indexes = np.random.randint(70000, size=10000)\n",
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"### Load data with the previous vector\n",
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"data = mnist.data[rand_indexes]\n",
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"# print(\"Dataset : \", data)\n",
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"target = mnist.target[rand_indexes]\n",
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"\n",
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"# Split the dataset\n",
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"xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
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"\n",
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"# Training on xtrain,ytrain\n",
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"clf = neighbors.KNeighborsClassifier(n_neighbors=3,p=2,n_jobs=1)\n",
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"# Training on xtrain,ytrain\n",
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"t1 = time.time()\n",
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"clf.fit(xtrain, ytrain)\n",
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"t2 = time.time()\n",
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"# Predicting on xtest\n",
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"pred = clf.predict(xtest)\n",
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"print(\"Matrice de confusion K-NN :\\n\", metrics.confusion_matrix(ytest, pred))"
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"t3 = time.time()\n",
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"#Calcul de différentes metrics\n",
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"precisions = [round(i,3) for i in metrics.precision_score(ytest, pred,average=None)]\n",
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"\n",
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"print(\"Métriques pour K-NN :\")\n",
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"print(\"Paramètres : (n_neighbors=3,p=2,n_jobs=1)\")\n",
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"print(\"Taille de l'échantillon :\", 10000)\n",
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"print(\"Proportion des datasets :\", \"90%\")\n",
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"print(\"Temps d'entraînement (secondes) :\", round(t2-t1,5))\n",
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"print(\"Temps de prédiction (secondes) :\", round(t3-t2,5))\n",
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"print(\"Précision pour chaque classe :\", precisions)\n",
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"print(\"Précision :\", clf.score(xtest, ytest))\n",
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"print(\"Erreur :\", round(metrics.zero_one_loss(ytest, pred),5))\n",
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"print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
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]
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},
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{
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 1,
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"id": "3eb7a65b",
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"metadata": {},
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"outputs": [],
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@ -22,7 +22,7 @@
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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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"execution_count": 2,
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"id": "a8812842",
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"metadata": {},
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"outputs": [],
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@ -1233,7 +1233,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 9,
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"id": "abb0fcf1",
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"metadata": {},
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"outputs": [
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@ -1241,23 +1241,33 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Matrice de confusion A-NN :\n",
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" [[59 0 0 0 0 0 0 0 0 0]\n",
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" [ 0 60 0 0 0 0 0 1 0 0]\n",
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" [ 0 0 42 0 0 1 2 1 2 0]\n",
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" [ 0 0 1 44 0 1 0 0 0 0]\n",
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" [ 0 0 0 0 46 0 1 0 0 4]\n",
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" [ 0 0 0 0 0 31 0 0 1 0]\n",
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" [ 0 0 0 0 0 0 48 0 0 0]\n",
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" [ 1 0 0 1 0 0 0 49 0 0]\n",
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" [ 0 1 1 5 0 1 0 0 48 0]\n",
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" [ 2 0 0 1 1 2 0 0 2 40]]\n"
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"Métriques pour A-NN\n",
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"Paramètres : (random_state=1, max_iter=300, hidden_layer_sizes=((85,)*15),\n",
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"solver=adam, activation=relu, alpha= 0.0000001)\n",
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"Taille de l'échantillon : 10000\n",
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"Proportion des datasets : 90%\n",
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"Temps d'entraînement (secondes) : 27.9214\n",
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"Temps de prédiction (secondes) : 0.01396\n",
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"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",
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"Précision : 0.952\n",
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"Erreur : 0.048\n",
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"Matrice de confusion :\n",
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" [[103 0 1 0 0 0 1 0 0 0]\n",
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" [ 0 114 0 0 1 0 0 0 0 0]\n",
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" [ 0 0 100 0 2 1 1 1 1 0]\n",
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" [ 0 1 3 97 0 1 0 0 1 1]\n",
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" [ 0 0 0 0 79 0 0 0 0 4]\n",
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" [ 2 0 0 3 0 93 1 0 1 0]\n",
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" [ 0 0 0 0 0 3 99 0 1 0]\n",
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" [ 1 1 1 0 1 0 0 89 0 1]\n",
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" [ 0 1 3 0 0 1 0 0 77 2]\n",
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" [ 0 0 0 0 3 0 0 2 0 101]]\n"
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]
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}
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],
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"source": [
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"### Create vector of 5000 random indexes\n",
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"rand_indexes = np.random.randint(70000, size=5000)\n",
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"rand_indexes = np.random.randint(70000, size=10000)\n",
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"### Load data with the previous vector\n",
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"data = mnist.data[rand_indexes]\n",
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"# print(\"Dataset : \", data)\n",
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@ -1266,10 +1276,6 @@
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"# Split the dataset\n",
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"xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
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"\n",
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"best_training_time = 0\n",
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"best_precision_score = 0\n",
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"best_zero_one_loss = 0\n",
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"\n",
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"r = 1\n",
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"max_i = 300\n",
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"nb_hl = 15\n",
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@ -1281,31 +1287,27 @@
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"\n",
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"#Entraîne le classifier\n",
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"clf = neural_network.MLPClassifier(random_state=r, max_iter=max_i, hidden_layer_sizes=hl, solver=sol, activation=act, alpha=a, verbose=False)\n",
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"t1 = round(time.time(),5)\n",
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"t1 = time.time()\n",
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"clf.fit(xtrain, ytrain)\n",
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"t2 = round(time.time(),5)\n",
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"t2 = time.time()\n",
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"#Prédiction sur le jeu de tests\n",
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"pred = clf.predict(xtest)\n",
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"# Probabilités des prédictions sur xtest\n",
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"pred_proba = clf.predict_proba(xtest)\n",
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"# On sauvegarde le temps de calcul, la précision et \n",
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"# les taux d'erreurs par classe\n",
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"best_training_time = t2-t1\n",
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"best_precision_score = clf.score(xtest, ytest)\n",
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"best_zero_one_loss = metrics.zero_one_loss(ytest, pred)\n",
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"t3 = time.time()\n",
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"\n",
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"# print(\"Paramètre :\\n\")\n",
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"# print(\"random_state = \", r)\n",
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"# print(\"max_iter = \", max_i)\n",
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"# print(\"nb_hidden_layer = \", nb_hl)\n",
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"# print(\"hidden_layer_size = \", hl_size)\n",
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"# print(\"solver = \", sol)\n",
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"# print(\"activation = \", act)\n",
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"# print(\"alpha = \", a)\n",
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"# print(\"Temps d'entraînement : \", best_training_time)\n",
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"# print(\"Score : \", best_precision_score)\n",
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"# print(\"Zero-one loss : \", best_zero_one_loss)\n",
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"print(\"Matrice de confusion A-NN :\\n\", metrics.confusion_matrix(ytest, pred))"
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"#Calcul de différentes metrics\n",
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"precisions = [round(i,3) for i in metrics.precision_score(ytest, pred,average=None)]\n",
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"\n",
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"print(\"Métriques pour A-NN\")\n",
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"print(\"Paramètres : (random_state=1, max_iter=300, hidden_layer_sizes=((85,)*15),\")\n",
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"print(\"solver=adam, activation=relu, alpha= 0.0000001)\")\n",
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"print(\"Taille de l'échantillon :\", 10000)\n",
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"print(\"Proportion des datasets :\", \"90%\")\n",
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"print(\"Temps d'entraînement (secondes) :\", round(t2-t1,5))\n",
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"print(\"Temps de prédiction (secondes) :\", round(t3-t2,5))\n",
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"print(\"Précision pour chaque classe :\", precisions)\n",
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"print(\"Précision :\", clf.score(xtest, ytest))\n",
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"print(\"Erreur :\", round(metrics.zero_one_loss(ytest, pred),5))\n",
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"print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
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]
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},
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 1,
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"id": "530f620c",
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"id": "68b6a517",
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"metadata": {},
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 26,
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"id": "98107e41",
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"metadata": {},
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"outputs": [
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@ -872,37 +872,59 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Matrice de confusion K-NN :\n",
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" [[51 0 0 0 0 1 0 0 0 0]\n",
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" [ 0 56 0 0 0 0 0 0 0 0]\n",
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" [ 3 1 45 1 0 0 1 1 0 0]\n",
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" [ 0 1 1 35 0 1 0 1 1 1]\n",
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" [ 0 3 0 0 48 0 0 0 0 2]\n",
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" [ 0 1 0 1 0 38 0 0 0 0]\n",
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" [ 0 0 0 0 0 2 44 0 0 0]\n",
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" [ 0 2 0 0 3 0 0 47 0 0]\n",
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" [ 2 0 0 0 0 3 1 0 42 2]\n",
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" [ 0 0 0 0 4 1 0 1 2 50]]\n"
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"Métriques pour K-NN :\n",
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"Paramètres : (n_neighbors=3,p=2,n_jobs=1)\n",
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"Taille de l'échantillon : 10000\n",
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"Proportion des datasets : 90%\n",
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"Temps d'entraînement (secondes) : 0.01596\n",
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"Temps de prédiction (secondes) : 0.30718\n",
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"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",
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"Précision : 0.95\n",
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"Erreur : 0.05\n",
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"Matrice de confusion :\n",
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" [[ 98 0 1 0 0 0 1 0 1 0]\n",
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" [ 0 114 0 0 0 0 0 0 0 0]\n",
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" [ 2 2 102 0 0 0 0 0 0 0]\n",
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" [ 0 1 1 93 0 3 0 1 0 0]\n",
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" [ 1 5 0 0 82 0 0 0 0 5]\n",
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" [ 0 1 1 1 0 84 3 0 0 1]\n",
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" [ 0 0 0 0 0 0 99 0 0 0]\n",
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" [ 0 3 0 0 0 0 0 97 0 2]\n",
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" [ 2 1 1 3 1 2 0 0 92 0]\n",
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" [ 1 1 0 0 0 0 0 2 0 89]]\n"
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]
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}
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],
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"source": [
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"### Create vector of 5000 random indexes\n",
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"rand_indexes = np.random.randint(70000, size=5000)\n",
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"rand_indexes = np.random.randint(70000, size=10000)\n",
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"### Load data with the previous vector\n",
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"data = mnist.data[rand_indexes]\n",
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"# print(\"Dataset : \", data)\n",
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"target = mnist.target[rand_indexes]\n",
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"\n",
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"# Split the dataset\n",
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"xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
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"\n",
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"# Training on xtrain,ytrain\n",
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"clf = neighbors.KNeighborsClassifier(n_neighbors=3,p=2,n_jobs=1)\n",
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"# Training on xtrain,ytrain\n",
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"t1 = time.time()\n",
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"clf.fit(xtrain, ytrain)\n",
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"t2 = time.time()\n",
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"# Predicting on xtest\n",
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"pred = clf.predict(xtest)\n",
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"print(\"Matrice de confusion K-NN :\\n\", metrics.confusion_matrix(ytest, pred))"
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"t3 = time.time()\n",
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"#Calcul de différentes metrics\n",
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"precisions = [round(i,3) for i in metrics.precision_score(ytest, pred,average=None)]\n",
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"\n",
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"print(\"Métriques pour K-NN :\")\n",
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"print(\"Paramètres : (n_neighbors=3,p=2,n_jobs=1)\")\n",
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"print(\"Taille de l'échantillon :\", 10000)\n",
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"print(\"Proportion des datasets :\", \"90%\")\n",
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"print(\"Temps d'entraînement (secondes) :\", round(t2-t1,5))\n",
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"print(\"Temps de prédiction (secondes) :\", round(t3-t2,5))\n",
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"print(\"Précision pour chaque classe :\", precisions)\n",
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"print(\"Précision :\", clf.score(xtest, ytest))\n",
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"print(\"Erreur :\", round(metrics.zero_one_loss(ytest, pred),5))\n",
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"print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
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]
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},
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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{
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"execution_count": 2,
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"id": "a8812842",
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "abb0fcf1",
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Matrice de confusion A-NN :\n",
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" [[59 0 0 0 0 0 0 0 0 0]\n",
|
||||
" [ 0 60 0 0 0 0 0 1 0 0]\n",
|
||||
" [ 0 0 42 0 0 1 2 1 2 0]\n",
|
||||
" [ 0 0 1 44 0 1 0 0 0 0]\n",
|
||||
" [ 0 0 0 0 46 0 1 0 0 4]\n",
|
||||
" [ 0 0 0 0 0 31 0 0 1 0]\n",
|
||||
" [ 0 0 0 0 0 0 48 0 0 0]\n",
|
||||
" [ 1 0 0 1 0 0 0 49 0 0]\n",
|
||||
" [ 0 1 1 5 0 1 0 0 48 0]\n",
|
||||
" [ 2 0 0 1 1 2 0 0 2 40]]\n"
|
||||
"Métriques pour A-NN\n",
|
||||
"Paramètres : (random_state=1, max_iter=300, hidden_layer_sizes=((85,)*15),\n",
|
||||
"solver=adam, activation=relu, alpha= 0.0000001)\n",
|
||||
"Taille de l'échantillon : 10000\n",
|
||||
"Proportion des datasets : 90%\n",
|
||||
"Temps d'entraînement (secondes) : 27.9214\n",
|
||||
"Temps de prédiction (secondes) : 0.01396\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",
|
||||
"Précision : 0.952\n",
|
||||
"Erreur : 0.048\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": [
|
||||
"### 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",
|
||||
"data = mnist.data[rand_indexes]\n",
|
||||
"# print(\"Dataset : \", data)\n",
|
||||
|
|
@ -1266,10 +1276,6 @@
|
|||
"# Split the dataset\n",
|
||||
"xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
|
||||
"\n",
|
||||
"best_training_time = 0\n",
|
||||
"best_precision_score = 0\n",
|
||||
"best_zero_one_loss = 0\n",
|
||||
"\n",
|
||||
"r = 1\n",
|
||||
"max_i = 300\n",
|
||||
"nb_hl = 15\n",
|
||||
|
|
@ -1281,31 +1287,27 @@
|
|||
"\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",
|
||||
"t1 = round(time.time(),5)\n",
|
||||
"t1 = time.time()\n",
|
||||
"clf.fit(xtrain, ytrain)\n",
|
||||
"t2 = round(time.time(),5)\n",
|
||||
"t2 = time.time()\n",
|
||||
"#Prédiction sur le jeu de tests\n",
|
||||
"pred = clf.predict(xtest)\n",
|
||||
"# Probabilités des prédictions sur xtest\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",
|
||||
"t3 = time.time()\n",
|
||||
"\n",
|
||||
"# print(\"Paramètre :\\n\")\n",
|
||||
"# print(\"random_state = \", r)\n",
|
||||
"# print(\"max_iter = \", max_i)\n",
|
||||
"# print(\"nb_hidden_layer = \", nb_hl)\n",
|
||||
"# print(\"hidden_layer_size = \", hl_size)\n",
|
||||
"# print(\"solver = \", sol)\n",
|
||||
"# print(\"activation = \", act)\n",
|
||||
"# print(\"alpha = \", a)\n",
|
||||
"# print(\"Temps d'entraînement : \", best_training_time)\n",
|
||||
"# print(\"Score : \", best_precision_score)\n",
|
||||
"# print(\"Zero-one loss : \", best_zero_one_loss)\n",
|
||||
"print(\"Matrice de confusion A-NN :\\n\", metrics.confusion_matrix(ytest, pred))"
|
||||
"#Calcul de différentes metrics\n",
|
||||
"precisions = [round(i,3) for i in metrics.precision_score(ytest, pred,average=None)]\n",
|
||||
"\n",
|
||||
"print(\"Métriques pour A-NN\")\n",
|
||||
"print(\"Paramètres : (random_state=1, max_iter=300, hidden_layer_sizes=((85,)*15),\")\n",
|
||||
"print(\"solver=adam, activation=relu, alpha= 0.0000001)\")\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))"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
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Loading…
Reference in a new issue