/
TP3_prog1.py
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TP3_prog1.py
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from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
import numpy as np
import time
from sklearn import metrics, model_selection
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.metrics import confusion_matrix
import pandas as pd
import seaborn as sn
def plot_fig(data):
plt.figure(figsize=(20, 13))
plt.subplot(3, 1, 1)
plt.plot(data[0], data[3], 'x-', color='red')
plt.xlabel('k')
plt.ylabel('Erreur sur le jeu de données')
plt.grid(True)
plt.subplot(3, 1, 2)
plt.plot(data[0], data[1], 'x-', color='green')
plt.xlabel('k')
plt.ylabel("Durée d'entraînement")
plt.grid(True)
plt.subplot(3, 1, 3)
plt.plot(data[0], data[2], 'x-', color='blue')
plt.xlabel('k')
plt.ylabel("Durée de prédiction")
plt.grid(True)
plt.show()
def main():
mnist = fetch_openml(name='mnist_784')
echantillon = np.random.randint(70000, size=5000)
data = mnist.data[echantillon]
target = mnist.target[echantillon]
xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.7)
classifier = svm.SVC(kernel='linear')
classifier.fit(xtrain, ytrain)
error = 1 - classifier.score(xtest, ytest)
print(f"Score SVM linéaire : {error}")
kernels = []
print("Modification du kernel : ")
for kernel in ['linear', 'poly', 'rbf', 'sigmoid']:
classifier = svm.SVC(kernel=kernel)
start_training = time.time()
classifier.fit(xtrain, ytrain)
final_training = time.time() - start_training
start_prediction = time.time()
ypred = classifier.predict(xtest)
final_prediction = time.time() - start_prediction
error = metrics.zero_one_loss(ytest, ypred)
kernels.append((kernel, final_training, final_prediction, error))
print(f"\t {kernels[-1]}")
kernels_liste = list(zip(*kernels))
plot_fig(kernels_liste)
tol = []
print("Evolution de la tolérance : ")
for tolerance in np.linspace(0.1, 1.0, num=5):
svm = svm.SVC(C=tolerance)
start_training = time.time()
svm.fit(xtrain, ytrain)
final_training = time.time() - start_training
start_prediction = time.time()
ypred = svm.predict(xtest)
final_prediction = time.time() - start_prediction
error = metrics.zero_one_loss(ytest, ypred)
error_training = svm.score(xtrain, ytrain)
tol.append((tolerance, final_training, final_prediction, error, error_training))
print(f"\t {tol[-1]}")
tol_list = list(zip(*tol))
plot_fig(tol_list)
plt.figure(figsize=(19, 9))
plt.plot(tol_list[0], tol_list[3], 'x-', color='blue') # erreur de test
plt.plot(tol_list[0], tol_list[-1], 'x-', color='orange') # erreur d'entrainement
plt.grid(True)
plt.show()
best_kernel = 'rbf'
best_tolerance = 1.0
best_svm = svm.SVC(kernel=best_kernel, C=best_tolerance)
start_training = time.time()
best_svm.fit(xtrain, ytrain)
best_final_entrainement = time.time() - start_training
start_prediction = time.time()
ypred = best_svm.predict(xtest)
best_final_prediction = time.time() - start_prediction
cross_val = model_selection.cross_val_score(best_svm, data, target, cv=10)
meilleure_erreur = 1 - np.mean(cross_val)
print(f"Durée de l'entraînement : {best_final_entrainement}")
print(f"Durée de la prédiction : {best_final_prediction}")
print(f"Erreur : {meilleure_erreur}")
cm = confusion_matrix(ytest, ypred)
df_cm = pd.DataFrame(cm, columns=np.unique(ytest), index=np.unique(ytest))
df_cm.index.name = 'Valeur réelle'
df_cm.columns.name = 'Valeur prédite'
plt.figure(figsize=(16, 9))
sn.heatmap(df_cm, cmap="Blues", annot=True)
plt.show()
if __name__ == "__main__":
main()