110 lines
No EOL
3 KiB
Python
110 lines
No EOL
3 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Wed Dec 15 19:07:59 2021
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@author: chouiya
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"""
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from time import time
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import neighbors
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from sklearn.datasets import fetch_openml
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from sklearn.model_selection import train_test_split, KFold
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from sklearn.metrics import accuracy_score
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#**********Echantillons de données "data" avec une taille de 5000 échantillons **********
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mnist = fetch_openml('mnist_784', as_frame=False)
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index= np.random.randint(70000, size=5000)
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data = mnist.data[index]
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target = mnist.target[index]
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# *************utilisation de 80% de la base de données pour le training ***********
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xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.8)
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# **********classifieur k-nn avec k=10 ********
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xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.8, test_size=0.2)
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clf = neighbors.KNeighborsClassifier(10)
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clf.fit(xtrain,ytrain)
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prediction = clf.predict(xtest)
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score = clf.score(xtest, ytest)
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# **********Classe de l'image 4 et sa classe prédite ****************
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print("Prédiction : {}, Valeur : {}, Score : {}".format(prediction[4], ytest[4], score))
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#*********Taux d'erreur sur les données d'apprentissage *******
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xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.8, test_size=0.2)
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clf = neighbors.KNeighborsClassifier(10)
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clf.fit(xtrain,ytrain)
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prediction = clf.predict(xtrain)
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score = clf.score(xtrain, ytrain)
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print("score: ", score*100)
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# **********Variation du nombre de voisins k de 2 à 15 en utilisant une boucle*****
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xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.8, test_size=0.2)
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tab_scores=[]
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for i in range (2,16):
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clf = neighbors.KNeighborsClassifier(i)
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clf.fit(xtrain, ytrain)
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prediction = clf.predict(xtest)
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score = clf.score(xtest, ytest)
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tab_scores.append(score)
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print("K : {}, Score: {}".format(i, score*100))
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#plot score=f(k)
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range_tab=range(2,16)
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plt.plot(range_tab,tab_scores)
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plt.xlabel("valeurs de K pour KNN")
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plt.ylabel("score")
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# ******** Variation du nombre de voisins k de 2 à 15 en utilisant la fonction KFold******
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kf = KFold(14,shuffle=True)
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kf.get_n_splits(data)
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k = 2
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for train_index, test_index in kf.split(data):
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xtrain, xtest = data[train_index], data[test_index]
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ytrain, ytest = target[train_index], target[test_index]
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clf = neighbors.KNeighborsClassifier(k)
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clf.fit(xtrain,ytrain)
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prediction = clf.predict(xtest)
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score = clf.score(xtest, ytest)
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print("K : {}, Score : {}".format(k, score*100))
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k = k + 1
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# *********Variation du pourcentage des échantillons du training et test************
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change_percent = range (2,10)
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for s in change_percent:
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xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=(s/10))
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clasifier = neighbors.KNeighborsClassifier(5)
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clasifier.fit(xtrain,ytrain)
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prediction = clasifier.predict(xtest)
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print("Training size = {} %, Score = {} ".format((s/10), clasifier.score(xtest, ytest)))
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