python SVM 线性分类模型

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 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_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)