TP_Apprentissage/eval.py

import matplotlib.pyplot as plt
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.metrics import precision_score
from sklearn.metrics import confusion_matrix
import random
import time
from sklearn.datasets import fetch_openml 

#mnist = fetch_openml('mnist_784') 

indices = [i for i in range(len(mnist.data))]
random.shuffle(indices)
indices = indices[:15000]

data = [mnist.data.values[i] for i in indices]
target = [mnist.target[i] for i in indices]

classifiers = [KNeighborsClassifier(3), MLPClassifier(hidden_layer_sizes = (100,)), SVC()]
xtrain,  xtest,  ytrain,  ytest  =  train_test_split(data, target, train_size=0.7)


for clf in classifiers:
    start = time.time()
    clf.fit(xtrain, ytrain)
    score = clf.score(xtest, ytest)
    end = time.time()
    print(f"training + evaluating time : {end - start}, score = {score}")
    print(confusion_matrix(ytest, clf.predict(xtest)))