python SVM 线性分类模型的实现

Python  /  管理员 发布于 7年前   278

运行环境：win10 64位 py 3.6 pycharm 2018.1.1

导入对应的包和数据

import matplotlib.pyplot as pltimport numpy as npfrom sklearn import datasets,linear_model,cross_validation,svmdef 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_validation.train_test_split(X_train,y_train,test_size=0.25,random_state=0,stratify=y_train)

#线性分类SVMdef 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)

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