première version des TP
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Farina Louis
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b9636a6ad0
коміт
277dafbfb7
3 змінених файлів з 307 додано та 0 видалено
94
tp1.py
Звичайний файл
94
tp1.py
Звичайний файл
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import matplotlib.pyplot as plt
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import KFold
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import random
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import time
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"""
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from sklearn.datasets import fetch_openml
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mnist = fetch_openml('mnist_784')
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"""
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"""
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---Ex 1---
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images = mnist.data.values.reshape((-1, 28, 28))
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plt.imshow(images[0],cmap=plt.cm.gray_r,interpolation="nearest")
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plt.show()
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print(mnist.target[0])
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"""
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"""
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---Ex 2---
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"""
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indices = [i for i in range(len(mnist.data))]
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random.shuffle(indices)
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indices = indices[:5000]
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data = [mnist.data.values[i] for i in indices]
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target = [mnist.target[i] for i in indices]
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bestClf = None
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bestScore = 0
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bestK = 0
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scores = []
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kvalues = [i for i in range(2,16)]
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train_sizes = [0.05*i for i in range(1,20)]
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pvalues = [i for i in range(1,11)]
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k = 3
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t = 0.9
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start = time.time()
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xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=t)
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clf = KNeighborsClassifier(3, n_jobs = 1)
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clf.fit(xtrain, ytrain)
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score = clf.score(xtest, ytest)
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end = time.time()
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print(f"n_jobs = 1, training + evaluating time : {end - start}")
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start = time.time()
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xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=t)
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clf = KNeighborsClassifier(3, n_jobs = -1)
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clf.fit(xtrain, ytrain)
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score = clf.score(xtest, ytest)
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end = time.time()
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print(f"n_jobs = -1, training + evaluating time : {end - start}")
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"""
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#for k in kvalues:
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#for t in train_sizes:
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for p in pvalues:
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print(p)
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xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=t)
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clf = KNeighborsClassifier(k, p = p)
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clf.fit(xtrain, ytrain)
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score = clf.score(xtest, ytest)
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scores += [score]
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if score > bestScore:
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bestClf = clf
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#bestK = k
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#bestSize = t
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bestP = p
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bestScore = score
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print(target[4])
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print(bestClf.predict(data[4].reshape(1,-1)))
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plt.imshow(data[4].reshape(28, 28), cmap=plt.cm.gray_r, interpolation="nearest")
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plt.show()
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#plt.plot(kvalues, scores)
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#plt.plot(train_sizes, scores)
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plt.plot(pvalues, scores)
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print(bestScore)
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#print(bestSize)
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"""
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119
tp2.py
Звичайний файл
119
tp2.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import matplotlib.pyplot as plt
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from sklearn.neural_network import MLPClassifier
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import KFold
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from sklearn.metrics import precision_score
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import random
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import time
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"""
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from sklearn.datasets import fetch_openml
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mnist = fetch_openml('mnist_784')
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"""
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"""
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images = mnist.data.values.reshape((-1, 28, 28))
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plt.imshow(images[0],cmap=plt.cm.gray_r,interpolation="nearest")
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plt.show()
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print(mnist.target[0])
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"""
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indices = [i for i in range(len(mnist.data))]
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random.shuffle(indices)
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indices = indices[:15000]
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data = [mnist.data.values[i] for i in indices]
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target = [mnist.target[i] for i in indices]
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bestClf = None
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bestScore = 0
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scores = []
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train_sizes = [0.05*i for i in range(1,20)]
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xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.7)
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"""
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network = MLPClassifier(hidden_layer_sizes = (50))
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network.fit(xtrain, ytrain)
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ytest_pred = clf.predict(xtest)
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print("Average = macro:")
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print(precision_score(ytest, ytest_pred, average = "macro"))
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print("Average = None:")
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print(precision_score(ytest, ytest_pred, average = None))
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print(target[4])
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print(network.predict(data[4].reshape(1,-1)))
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plt.imshow(data[4].reshape(28, 28), cmap=plt.cm.gray_r, interpolation="nearest")
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plt.show()
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"""
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"""
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n_layers = [i for i in range(1,51)]
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for n in n_layers:
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print(n)
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network = MLPClassifier(hidden_layer_sizes = (50,)*n)
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network.fit(xtrain, ytrain)
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scores += [network.score(xtest, ytest)]
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plt.plot(n_layers, scores)
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"""
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"""
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for _ in range(5):
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n_layers = random.randint(1,3)
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layer_size = tuple([random.randint(10,300) for _ in range(n_layers)])
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print(layer_size)
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network = MLPClassifier(hidden_layer_sizes = layer_size, solver = "adam", activation = "relu")
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network.fit(xtrain, ytrain)
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print(f"training time = {time.time() - start_time}, score = {network.score(xtest, ytest)}")
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"""
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"""
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solvers = ["lbfgs", "sgd", "adam"]
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for s in solvers:
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print(s + ":")
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start_time = time.time()
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network = MLPClassifier(hidden_layer_sizes = (50,)*5, solver = s)
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network.fit(xtrain, ytrain)
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print(f"training time = {time.time() - start_time}, score = {network.score(xtest, ytest)}")
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"""
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"""
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functions = ["identity", "logistic", "tanh", "relu"]
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for f in functions:
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print(f + ":")
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start_time = time.time()
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network = MLPClassifier(hidden_layer_sizes = (50,)*5, activation = f)
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network.fit(xtrain, ytrain)
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print(f"training time = {time.time() - start_time}, score = {network.score(xtest, ytest)}")
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"""
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times = []
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scores = []
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for i in range(-7,7):
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alpha = 10**i
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print(f"alpha = {alpha}:")
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start_time = time.time()
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network = MLPClassifier(hidden_layer_sizes = (50,)*5, alpha = alpha)
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network.fit(xtrain, ytrain)
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trainingTime = time.time() - start_time
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score = network.score(xtest, ytest)
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times +=[trainingTime]
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scores += [score]
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plt.plot([i for i in range(-7,7)], times)
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plt.show()
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plt.plot([i for i in range(-7,7)], scores)
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94
tp3.py
Звичайний файл
94
tp3.py
Звичайний файл
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@ -0,0 +1,94 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import matplotlib.pyplot as plt
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from sklearn.neural_network import MLPClassifier
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import KFold
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from sklearn.metrics import precision_score
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import random
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import time
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"""
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from sklearn.datasets import fetch_openml
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mnist = fetch_openml('mnist_784')
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"""
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"""
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---Ex 1---
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images = mnist.data.values.reshape((-1, 28, 28))
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plt.imshow(images[0],cmap=plt.cm.gray_r,interpolation="nearest")
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plt.show()
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print(mnist.target[0])
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"""
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"""
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---Ex 2---
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"""
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indices = [i for i in range(len(mnist.data))]
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random.shuffle(indices)
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indices = indices[:15000]
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data = [mnist.data.values[i] for i in indices]
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target = [mnist.target[i] for i in indices]
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bestClf = None
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bestScore = 0
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scores = []
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train_sizes = [0.05*i for i in range(1,20)]
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xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.7)
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"""
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network = MLPClassifier(hidden_layer_sizes = (50))
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network.fit(xtrain, ytrain)
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ytest_pred = clf.predict(xtest)
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print("Average = macro:")
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print(precision_score(ytest, ytest_pred, average = "macro"))
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print("Average = None:")
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print(precision_score(ytest, ytest_pred, average = None))
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print(target[4])
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print(network.predict(data[4].reshape(1,-1)))
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plt.imshow(data[4].reshape(28, 28), cmap=plt.cm.gray_r, interpolation="nearest")
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plt.show()
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"""
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"""
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n_layers = [i for i in range(1,51)]
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for n in n_layers:
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print(n)
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network = MLPClassifier(hidden_layer_sizes = (50,)*n)
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network.fit(xtrain, ytrain)
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scores += [network.score(xtest, ytest)]
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plt.plot(n_layers, scores)
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"""
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"""
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for _ in range(5):
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n_layers = random.randint(1,3)
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layer_size = tuple([random.randint(10,300) for _ in range(n_layers)])
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print(layer_size)
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network = MLPClassifier(hidden_layer_sizes = layer_size, solver = "adam", activation = "relu")
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network.fit(xtrain, ytrain)
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print(f"training time = {time.time() - start_time}, score = {network.score(xtest, ytest)}")
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"""
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"""
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solvers = ["lbfgs", "sgd", "adam"]
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for s in solvers:
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print(s + ":")
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start_time = time.time()
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network = MLPClassifier(hidden_layer_sizes = (50,)*5, solver = s)
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network.fit(xtrain, ytrain)
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print(f"training time = {time.time() - start_time}, score = {network.score(xtest, ytest)}")
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"""
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