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ML_algorithms

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Chouiya Asma 2021-12-15 19:20:05 +01:00
parent 2f00d82aff
commit 1c56585b4e
2 changed files with 4 additions and 50 deletions
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54
TP1/knn_vrai.py → TP1/KNN.py
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@ -1,11 +1,13 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Dec 11 15:40:46 2021
Created on Wed Dec 15 19:07:59 2021
@author: chouiya
"""
from time import time
import numpy as np
@ -105,52 +107,4 @@ for s in change_percent:
#*******************Variation de la taille de l'echantillon training *****************
tab_sample=[5000,6000,8000,10000,20000,50000,70000]
for x in range(len(tab_sample)):
index= np.random.randint(70000, size=tab_sample[x])
data = mnist.data[index]
target = mnist.target[index]
clf = neighbors.KNeighborsClassifier(10)
xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.8)
clf.fit(xtrain,ytrain)
prediction = clf.predict(xtest)
score = clf.score(xtest, ytest)
print("sample size= {} , accuracy = {} ".format(tab_sample[x], score))
#*****************Variation du type de la distance p *******
xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.8, test_size=0.2)
for i in range(0,3):
tab_dist=["manhattan","euclidean", "minkowski"]
clf = neighbors.KNeighborsClassifier(10, p=(i+1))
clf.fit(xtrain,ytrain)
prediction = clf.predict(xtrain)
score = clf.score(xtrain, ytrain)
print("type de distance : {}, Score: {}".format(tab_dist[i], score))
# ************** fixer n_jobs à 1 puis à -1 **********
for i in [-1,1]:
clf = neighbors.KNeighborsClassifier(5,n_jobs=i)
clf.fit(xtrain, ytrain)
time_start = time()
prediction = clf.predict(xtest)
time_stop = time()
score = clf.score(xtest, ytest)
print("n_jobs : {}, Temps total : {}".format(i,time_stop-time_start))

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