python SVM 线性分类模型的实现

 import matplotlib.pyplot as plt import numpy as np from sklearn import datasets,linear_model,cross_validation,svm def load_data_regression(): diabetes = datasets.load_diabetes() return cross_validation.train_test_split(diabetes,diabetes.target,test_size=0.25,random_state=0) def load_data_classfication(): iris = datasets.load_iris() X_train = iris.data y_train = iris.target return cross<strong style="color:transparent">来源gaodai#ma#com搞@@代~&码网</strong>_validation.train_test_split(X_train,y_train,test_size=0.25,random_state=0,stratify=y_train)

 #线性分类SVM def test_LinearSVC(*data): X_train,X_test,y_train,y_test = data cls = svm.LinearSVC() cls.fit(X_train,y_train) print('Coefficients:%s,intercept%s'%(cls.coef_,cls.intercept_)) print('Score:%.2f'%cls.score(X_test,y_test)) X_train,X_test,y_train,y_test = load_data_classfication() test_LinearSVC(X_train,X_test,y_train,y_test)

 def test_LinearSVC_loss(*data): X_train,X_test,y_train,y_test = data losses = ['hinge','squared_hinge'] for loss in losses: cls = svm.LinearSVC(loss=loss) cls.fit(X_train,y_train) print('loss:%s'%loss) print('Coefficients:%s,intercept%s'%(cls.coef_,cls.intercept_)) print('Score:%.2f'%cls.score(X_test,y_test)) X_train,X_test,y_train,y_test = load_data_classfication() test_LinearSVC_loss(X_train,X_test,y_train,y_test)

 #考察罚项形式的影响 def test_LinearSVC_L12(*data): X_train,X_test,y_train,y_test = data L12 = ['l1','l2'] for p in L12: cls = svm.LinearSVC(penalty=p,dual=False) cls.fit(X_train,y_train) print('penalty:%s'%p) print('Coefficients:%s,intercept%s'%(cls.coef_,cls.intercept_)) print('Score:%.2f'%cls.score(X_test,y_test)) X_train,X_test,y_train,y_test = load_data_classfication() test_LinearSVC_L12(X_train,X_test,y_train,y_test)

 #考察罚项系数C的影响 def test_LinearSVC_C(*data): X_train,X_test,y_train,y_test = data Cs = np.logspace(-2,1) train_scores = [] test_scores = [] for C in Cs: cls = svm.LinearSVC(C=C) cls.fit(X_train,y_train) train_scores.append(cls.score(X_train,y_train)) test_scores.append(cls.score(X_test,y_test)) fig = plt.figure() ax = fig.add_subplot(1,1,1) ax.plot(Cs,train_scores,label = 'Training score') ax.plot(Cs,test_scores,label = 'Testing score') ax.set_xlabel(r'C') ax.set_xscale('log') ax.set_ylabel(r'score') ax.set_title('LinearSVC') ax.legend(loc='best') plt.show() X_train,X_test,y_train,y_test = load_data_classfication() test_LinearSVC_C(X_train,X_test,y_train,y_test)

 #非线性分类SVM #线性核 def test_SVC_linear(*data): X_train, X_test, y_train, y_test = data cls = svm.SVC(kernel='linear') cls.fit(X_train,y_train) print('Coefficients:%s,intercept%s'%(cls.coef_,cls.intercept_)) print('Score:%.2f'%cls.score(X_test,y_test)) X_train,X_test,y_train,y_test = load_data_classfication() test_SVC_linear(X_train,X_test,y_train,y_test)

 #考察高斯核 def test_SVC_rbf(*data): X_train, X_test, y_train, y_test = data ###测试gamm### gamms = range(1, 20) train_scores = [] test_scores = [] for gamm in gamms: cls = svm.SVC(kernel='rbf', gamma=gamm) cls.fit(X_train, y_train) train_scores.append(cls.score(X_train, y_train)) test_scores.append(cls.score(X_test, y_test)) fig = plt.figure() ax = fig.add_subplot(1, 1, 1) ax.plot(gamms, train_scores, label='Training score', marker='+') ax.plot(gamms, test_scores, label='Testing score', marker='o') ax.set_xlabel(r'$\gamma$') ax.set_ylabel(r'score') ax.set_ylim(0, 1.05) ax.set_title('SVC_rbf') ax.legend(loc='best') plt.show() X_train,X_test,y_train,y_test = load_data_classfication() test_SVC_rbf(X_train,X_test,y_train,y_test)

 #考察sigmoid核 def test_SVC_sigmod(*data): X_train, X_test, y_train, y_test = data fig = plt.figure() ###测试gamm### gamms = np.logspace(-2, 1) train_scores = [] test_scores = [] for gamm in gamms: cls = svm.SVC(kernel='sigmoid',gamma=gamm,coef0=0) cls.fit(X_train, y_train) train_scores.append(cls.score(X_train, y_train)) test_scores.append(cls.score(X_test, y_test)) ax = fig.add_subplot(1, 2, 1) ax.plot(gamms, train_scores, label='Training score', marker='+') ax.plot(gamms, test_scores, label='Testing score', marker='o') ax.set_xlabel(r'$\gamma$') ax.set_ylabel(r'score') ax.set_xscale('log') ax.set_ylim(0, 1.05) ax.set_title('SVC_sigmoid_gamm') ax.legend(loc='best') #测试r rs = np.linspace(0,5) train_scores = [] test_scores = [] for r in rs: cls = svm.SVC(kernel='sigmoid', gamma=0.01, coef0=r) cls.fit(X_train, y_train) train_scores.append(cls.score(X_train, y_train)) test_scores.append(cls.score(X_test, y_test)) ax = fig.add_subplot(1, 2, 2) ax.plot(rs, train_scores, label='Training score', marker='+') ax.plot(rs, test_scores, label='Testing score', marker='o') ax.set_xlabel(r'r') ax.set_ylabel(r'score') ax.set_ylim(0, 1.05) ax.set_title('SVC_sigmoid_r') ax.legend(loc='best') plt.show() X_train,X_test,y_train,y_test = load_data_classfication() test_SVC_sigmod(X_train,X_test,y_train,y_test)