farina/TP_Apprentissage
1
0
Fork 0
Code Issues Pull requests Releases Wiki Activity

recovered tp3.py

Browse source
This commit is contained in:
Farina Louis 2021-12-14 20:33:53 +01:00
parent 277dafbfb7
commit 77f08cfbd5
1 changed files with 35 additions and 49 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

84
tp3.py
View file

@ -9,6 +9,8 @@ from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold from sklearn.model_selection import KFold
from sklearn.metrics import precision_score from sklearn.metrics import precision_score
from sklearn.metrics import confusion_matrix
from sklearn.svm import SVC
import random import random
import time import time
@ -18,7 +20,6 @@ mnist = fetch_openml('mnist_784')
""" """
""" """
---Ex 1---
images = mnist.data.values.reshape((-1, 28, 28)) images = mnist.data.values.reshape((-1, 28, 28))
plt.imshow(images[0],cmap=plt.cm.gray_r,interpolation="nearest") plt.imshow(images[0],cmap=plt.cm.gray_r,interpolation="nearest")
plt.show() plt.show()
@ -26,8 +27,6 @@ plt.show()
print(mnist.target[0]) print(mnist.target[0])
""" """
"""
---Ex 2---
""" """
indices = [i for i in range(len(mnist.data))] indices = [i for i in range(len(mnist.data))]
random.shuffle(indices) random.shuffle(indices)
@ -35,60 +34,47 @@ indices = indices[:15000]
data = [mnist.data.values[i] for i in indices] data = [mnist.data.values[i] for i in indices]
target = [mnist.target[i] for i in indices] target = [mnist.target[i] for i in indices]
"""
bestClf = None
bestScore = 0
scores = []
train_sizes = [0.05*i for i in range(1,20)]
xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.7) xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.7)
"""
network = MLPClassifier(hidden_layer_sizes = (50))
network.fit(xtrain, ytrain)
ytest_pred = clf.predict(xtest)
print("Average = macro:")
print(precision_score(ytest, ytest_pred, average = "macro"))
print("Average = None:")
print(precision_score(ytest, ytest_pred, average = None))
clf = SVC()
clf.fit(xtrain, ytrain)
ypred = clf.predict(xtest)
print(confusion_matrix(ytest, ypred))
"""
print(target[4]) print(target[4])
print(network.predict(data[4].reshape(1,-1))) print(clf.predict(data[4].reshape(1,-1)))
plt.imshow(data[4].reshape(28, 28), cmap=plt.cm.gray_r, interpolation="nearest") plt.imshow(data[4].reshape(28, 28), cmap=plt.cm.gray_r, interpolation="nearest")
plt.show() plt.show()
""" """
"""
n_layers = [i for i in range(1,51)]
for n in n_layers:
print(n)
network = MLPClassifier(hidden_layer_sizes = (50,)*n)
network.fit(xtrain, ytrain)
scores += [network.score(xtest, ytest)]
plt.plot(n_layers, scores)
"""
""" """
for _ in range(5): kernels = ["linear", "poly", "rbf", "sigmoid"]
n_layers = random.randint(1,3) for k in kernels:
layer_size = tuple([random.randint(10,300) for _ in range(n_layers)]) print(k + ":")
print(layer_size)
network = MLPClassifier(hidden_layer_sizes = layer_size, solver = "adam", activation = "relu")
network.fit(xtrain, ytrain)
print(f"training time = {time.time() - start_time}, score = {network.score(xtest, ytest)}")
"""
"""
solvers = ["lbfgs", "sgd", "adam"]
for s in solvers:
print(s + ":")
start_time = time.time() start_time = time.time()
network = MLPClassifier(hidden_layer_sizes = (50,)*5, solver = s) clf = SVC(kernel = k)
network.fit(xtrain, ytrain) clf.fit(xtrain, ytrain)
print(f"training time = {time.time() - start_time}, score = {network.score(xtest, ytest)}") print(f"training time = {time.time() - start_time}, score = {clf.score(xtest, ytest)}")
""" """
"""
scores = []
times = []
tolerance=[i*0.1 for i in range(1,10,2)]
for C in tolerance:
print(f"C = {C} :")
start_time = time.time()
clf = SVC(C = C)
clf.fit(xtrain, ytrain)
training_time = time.time() - start_time
score = clf.score(xtest, ytest)
times += [training_time]
scores += [score]
plt.plot(tolerance, scores)
plt.show()
plt.plot(tolerance, times)
"""