# -*- coding: utf-8 -*- ''' Created on 2018年1月18日 @author: Jason.F @summary: 特征抽取-PCA方法，無監督、線性 ''' import pandas as pd import numpy as np from sklearn.cross_validation import train_test_split from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap from sklearn.decomposition import PCA #定義繪制函數 def plot_decision_regions(X, y, classifier, resolution=0.02):# setup marker generator and color mapmarkers = ('s', 'x', 'o', '^', 'v')colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')cmap = ListedColormap(colors[:len(np.unique(y))])# plot the decision surfacex1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),np.arange(x2_min, x2_max, resolution))Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)Z = Z.reshape(xx1.shape)plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)plt.xlim(xx1.min(), xx1.max())plt.ylim(xx2.min(), xx2.max())# plot class samplesfor idx, cl in enumerate(np.unique(y)):plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],alpha=0.8, c=cmap(idx),marker=markers[idx], label=cl)#第一步:導入數據，對原始d維數據集做標準化處理 df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data',header=None) df_wine.columns=['Class label','Alcohol','Malic acid','Ash','Alcalinity of ash','Magnesium','Total phenols','Flavanoids','Nonflavanoid phenols','Proanthocyanins','Color intensity','Hue','OD280/OD315 of diluted wines','Proline'] print ('class labels:',np.unique(df_wine['Class label'])) #print (df_wine.head(5)) #分割訓練集合測試集 X,y=df_wine.iloc[:,1:].values,df_wine.iloc[:,0].values X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0) #特征值縮放-標準化 stdsc=StandardScaler() X_train_std=stdsc.fit_transform(X_train) X_test_std=stdsc.fit_transform(X_test) #第二步：PCA降維 pca=PCA(n_components=2)#參數設置選擇前2個方差最大的特征 lr=LogisticRegression() X_train_pca=pca.fit_transform(X_train_std) X_test_pca=pca.fit_transform(X_test_std) lr.fit(X_train_pca,y_train) plot_decision_regions(X_train_pca,y_train,classifier=lr) plt.xlabel('PC1') plt.ylabel('PC2') plt.legend(loc='lower left') plt.show() plot_decision_regions(X_test_pca,y_test,classifier=lr) plt.xlabel('PC1') plt.ylabel('PC2') plt.legend(loc='lower left') plt.show()#觀察不同主成分的防線貢獻率 pca_all=PCA(n_components=None)#None值保留所有成分 X_train_pca_all=pca_all.fit_transform(X_train_std) print (pca_all.explained_variance_ratio_)

結果：

('class labels:', array([1, 2, 3], dtype=int64)) [ 0.37329648 0.18818926 0.10896791 0.07724389 0.06478595 0.045920140.03986936 0.02521914 0.02258181 0.01830924 0.01635336 0.012842710.00642076]

總結

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