《Python机器学习及实践》----无监督学习之特征降维

Posted 2023-02-20 wangshuang1631

本片博客是根据《Python机器学习及实践》一书中的实例，所有代码均在本地编译通过。数据为从该书指定的百度网盘上下载的，或者是sklearn自带数据下载到本地使用的。
代码片段：

# coding: utf-8
import numpy as np
M = np.array([[1, 2], [2, 4]])
np.linalg.matrix_rank(M, tol=None)

import pandas as pd
digits_train = pd.read_csv('D:\\Source Code\\machinelearn\\optdigits.tra', header=None)
digits_test = pd.read_csv('D:\\Source Code\\machinelearn\\optdigits.tes', header=None)
X_digits = digits_train[np.arange(64)]
y_digits = digits_train[64]
from sklearn.decomposition import PCA
estimator = PCA(n_components=2)
X_pca = estimator.fit_transform(X_digits)
from matplotlib import pyplot as plt

def plot_pca_scatter():
    colors = ['black', 'blue', 'purple', 'yellow', 'white', 'red', 'lime', 'cyan', 'orange', 'gray']
    for i in xrange(len(colors)):
        px = X_pca[:, 0][y_digits.as_matrix() == i]
        py = X_pca[:, 1][y_digits.as_matrix()== i]
        plt.scatter(px, py, c=colors[i])
    plt.legend(np.arange(0,10).astype(str))
    plt.xlabel('First Principal Component')
    plt.ylabel('Second Principal Component')
    plt.show()
plot_pca_scatter()
X_train = digits_train[np.arange(64)]
y_train = digits_train[64]
X_test = digits_test[np.arange(64)]
y_test = digits_test[64]
from sklearn.svm import LinearSVC
svc = LinearSVC()
svc.fit(X_train, y_train)
y_predict = svc.predict(X_test)
estimator = PCA(n_components=20)
pca_X_train = estimator.fit_transform(X_train)
pca_X_test = estimator.transform(X_test)
pca_svc = LinearSVC()
pca_svc.fit(pca_X_train, y_train)
pca_y_predict = pca_svc.predict(pca_X_test)
from sklearn.metrics import classification_report
print svc.score(X_test, y_test)
print classification_report(y_test, y_predict, target_names=np.arange(10).astype(str))
print pca_svc.score(pca_X_test, y_test)
print classification_report(y_test, pca_y_predict, target_names=np.arange(10).astype(str))