03--机器学习之SVM（暂定）

from __future__ import print_functionfrom time import time  #某些步骤需要计时，要用到这个模块import logging  #打印程序进展方面的信息的模块import matplotlib.pyplot as plt #在程序最后把识别出的人脸绘制出来，看一下预测与实际的是否符合，要用到这个木块from sklearn.cross_validation import train_test_splitfrom sklearn.datasets import fetch_lfw_peoplefrom sklearn.grid_search import GridSearchCVfrom sklearn.metrics import classification_reportfrom sklearn.metrics import confusion_matrixfrom sklearn.decomposition import RandomizedPCAfrom sklearn.svm import SVCprint(__doc__)#Display progress logs on stdoutlogging.basicConfig(level= logging.INFO,format='%(asctime)s%(message)s')#basicConfig程序进展的信息##################################################################################Download the data, if not already on disk and load it as numpy arrayslfw_people = fetch_lfw_people(min_faces_per_person=70,resize=0.4) #因为是人脸识别，所以用到这个下载数据集的函数（人脸的数据集）（类型字典结构）#introspect the images arrays to find the shapes(for plotting)n_samples,h,w = lfw_people.images.shape #数据集上有多少个实例，hw#for machine learning we use the 2 data directly(as relative pixel positions info is ignored by this model)X = lfw_people.data #提取特征向量（每一个行是一个实例，每一个列是一样featuren_features = X.shape[1]#返回特征向量的维度#the label to predict is the id of the persony = lfw_people.target #提取每个实例的label（类似字典结构）target_names = lfw_people.target_names#返回类别中有谁的名字n_classes = target_names.shape[0]#有多少个人需要区分print("Total dataset size:")print("n_samples:%d"%n_samples) #实例的个数print("n_features:%d"%n_features)#特征向量的维度print("n_classes:%d"%n_classes)#总共有多少类（多少人）#################################################################################Split into a training set and a test set using a stratified k fold#Split into a training and testing setX_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25)#把数据集分成训练集和测试集#X_train,X_test 是一个矩阵，而y_train，y_test是对应的一个向量#################################################################################Compute a PCA(eigenfaces)on the face dataset(treated as unlabeled dataset):#unsupervised feature extraction / dimensionality reductionn_components = 150  #组成元素的数量（参数）print("Extracting the top %d eigenfaces from %d faces"%(n_components,X_train.shape[0]))t0 = time()pca = RandomizedPCA(n_components=n_components,whiten=True).fit(X_train)#高维的向量降为低维的（对X_train中的特征向量进行降维）print("Done in %0.3fs"%(time() - t0))eigenfaces = pca.components_.reshape((n_components,h,w))#提取特征点print("Prohecting the input data on the eigenfaces orthonormal basis")t0 = time()X_train_pca = pca.transform(X_train)#将高维转化为低维X_test_pca = pca.transform(X_test)#将高维转化为低维print("done in %0.3fs"%(time()-t0))###############################################################################Train a SVM classification modelprint("Fitting the classifier to the training set")t0 = time()param_grid = {'C':[1e3,5e3,1e4,5e4,1e5],#选用的参数值，多个量的尝试              'gamma':[0.0001,0.0005,0.001,0.005,0.01,0.1],}clf = GridSearchCV(SVC(kernel='rbf',class_weight='auto'),param_grid)#对上面参数进行尝试，看哪组能产生更好的结果clf = clf.fit(X_test_pca,y_train)#建模print("done in %0.3fs"%(time()-t0))print("Best estimator found by grid search:")print(clf.best_estimator_)################################################################################Quantitative evaluation of the model quality on the test setprint("Predicting people's names on the test set")t0 = time()y_pred = clf.predict(X_test_pca)#对新来的测试集数据进行测试print("done in %0.3fs"%(time() - t0))print(classification_report(y_test,y_pred,target_names=target_names))#把预测的标签与真实的标签做比较print(confusion_matrix(y_test,y_pred,labels=range(n_classes)))#################################################################################Qualitative evaluation of the predictions using matplotlibdef plot_gallery(images,titles,h,w,n_row=3,n_col=4):    """Helper function to polt a gallery of portraits"""    plt.figure(figsize=(1.8 * n_col,2.4*n_row))    plt.subplots_adjust(bottom=0,left = .01,right = .99,top=.90,hspace=.35)    for i in range(n_row*n_col):        plt.subplot(n_row,n_col,i+1)        plt.imshow(images[i].reshape((h,w)),cmap=plt.cm.gray)        plt.title(titles[i],size = 12)        plt.xticks(())        plt.yticks(())#plot the result of the prediction on a portion of the test setdef title(y_pred,y_test,target_names,i):    pred_name = target_names[y_pred[i]].rsplit('',1)[-1]    true_name = target_names[y_test[i]].rsplit('',1)[-1]    return 'predicted:%s\ntrue:    %s'%(pred_name,true_name)prediction_titles = [title(y_pred,y_test,target_names,i)                     for i in range(y_pred.shape[0])]plot_gallery(X_test,prediction_titles,h,w)#plot the gallery of the most significative eigenfaceseigenfaces_titles = ["eigenface %d"%i for i in range(eigenfaces.shape[0])]plot_gallery(eigenfaces,eigenfaces_titles,h,w)plt.show()