python SVM 非线性分类模型

运行环境：win10 64位 py 3.6 pycharm 2018.1.1

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.data,diabetes.target,test_size=0.25,random_state=0)

#线性回归SVR
def test_LinearSVR(*data):
    X_train,X_test,y_train,y_test = data
    cls = svm.LinearSVR()
    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_regression()
test_LinearSVR(X_train,X_test,y_train,y_test)

#考查损失函数的影响
def test_LinearSVR_loss(*data):
    X_train,X_test,y_train,y_test = data
    losses = ['epsilon_insensitive','squared_epsilon_insensitive']
    for loss in losses:
        regr = svm.LinearSVR(loss=loss)
        regr.fit(X_train,y_train)
        print('loss:%s'%loss)
        print('Coefficients:%s,intercept%s'%(regr.coef_,regr.intercept_))
        print('Score:%.2f'%regr.score(X_test,y_test))
X_train,X_test,y_train,y_test = load_data_regression()
test_LinearSVR_loss(X_train,X_test,y_train,y_test)

#考察预测范围的影响
def test_LinearSVR_epsilon(*data):
    X_train,X_test,y_train,y_test = data
    epsilons = np.logspace(-2,2)
    train_scores = []
    test_scores = []
    for epsilon in epsilons:
        regr = svm.LinearSVR(epsilon=epsilon,loss='squared_epsilon_insensitive')
        regr.fit(X_train,y_train)
        train_scores.append(regr.score(X_train,y_train))
        test_scores.append(regr.score(X_test,y_test))
    fig = plt.figure()
    ax = fig.add_subplot(1,1,1)
    ax.plot(epsilons,train_scores,label = 'Training score',marker='+')
    ax.plot(epsilons,test_scores,label = 'Testing score',marker='o')
    ax.set_xscale('log')
    ax.set_xlabel(r'$\epsilon$')
    ax.set_ylabel(r'score')
    ax.set_ylim(-1,1.05)
    ax.set_title('LinearSVR_epsilon')
    ax.legend(loc='best')
    plt.show()
X_train,X_test,y_train,y_test = load_data_regression()
test_LinearSVR_epsilon(X_train,X_test,y_train,y_test)

#考察罚项系数C的影响
def test_LinearSVR_C(*data):
    X_train,X_test,y_train,y_test = data
    Cs = np.logspace(-1,2)
    train_scores = []
    test_scores = []
    for C in Cs:
        regr = svm.LinearSVR(epsilon=0.1, loss='squared_epsilon_insensitive',C=C)
        regr.fit(X_train,y_train)
        train_scores.append(regr.score(X_train,y_train))
        test_scores.append(regr.score(X_test,y_test))
    fig = plt.figure()
    ax = fig.add_subplot(1,1,1)
    ax.plot(Cs,train_scores,label = 'Training score',marker='+')
    ax.plot(Cs,test_scores,label = 'Testing score',marker='o')
    ax.set_xlabel(r'C')
    ax.set_xscale('log')
    ax.set_ylabel(r'score')
    ax.set_ylim(-1, 1.05)
    ax.set_title('LinearSVC_C')
    ax.legend(loc='best')
    plt.show()
X_train,X_test,y_train,y_test = load_data_regression()
test_LinearSVR_C(X_train,X_test,y_train,y_test)

#非线性分类SVR
#线性核
def test_SVR_linear(*data):
    X_train, X_test, y_train, y_test = data
    regr = svm.SVR(kernel='linear')
    regr.fit(X_train,y_train)
    print('Coefficients:%s,intercept%s'%(regr.coef_,regr.intercept_))
    print('Score:%.2f'%regr.score(X_test,y_test))
X_train,X_test,y_train,y_test = load_data_regression()
test_SVR_linear(X_train,X_test,y_train,y_test)

#考察多项式核
def test_SVR_poly(*data):
    X_train, X_test, y_train, y_test = data
    fig = plt.figure()
    ###测试degree###
    degrees = range(1, 20)
    train_scores = []
    test_scores = []
    for degree in degrees:
        regr = svm.SVR(kernel='poly',degree=degree,coef0=1)
        regr.fit(X_train,y_train)
        train_scores.append(regr.score(X_train, y_train))
        test_scores.append(regr.score(X_test,y_test))
    ax = fig.add_subplot(1,3,1)
    ax.plot(degrees,train_scores,label = 'Training score',marker='+')
    ax.plot(degrees,test_scores,label = 'Testing score',marker='o')
    ax.set_xlabel(r'p')
    ax.set_ylabel(r'score')
    ax.set_ylim(-1,1.05)
    ax.set_title('SVR_poly_degree')
    ax.legend(loc='best')

    ###测试gamm###
    gamms = range(1, 40)
    train_scores = []
    test_scores = []
    for gamm in gamms:
        regr = svm.SVR(kernel='poly', gamma=gamm, degree=3, coef0=1)
        regr.fit(X_train, y_train)
        train_scores.append(regr.score(X_train, y_train))
        test_scores.append(regr.score(X_test, y_test))
    ax = fig.add_subplot(1, 3, 2)
    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(-1, 1.05)
    ax.set_title('SVR_poly_gamma')
    ax.legend(loc='best')

    ###测试r  ###
    rs = range(0, 20)
    train_scores = []
    test_scores = []
    for r in rs:
        cls = svm.SVR(kernel='poly', gamma=20, degree=3, 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, 3, 3)
    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(-1, 1.05)
    ax.set_title('SVC_poly_r')
    ax.legend(loc='best')
    plt.show()
X_train,X_test,y_train,y_test = load_data_regression()
test_SVR_poly(X_train,X_test,y_train,y_test)

#考察高斯核
def test_SVR_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:
        regr = svm.SVR(kernel='rbf', gamma=gamm)
        regr.fit(X_train, y_train)
        train_scores.append(regr.score(X_train, y_train))
        test_scores.append(regr.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(-1, 1.05)
    ax.set_title('SVR_rbf')
    ax.legend(loc='best')
    plt.show()
X_train,X_test,y_train,y_test = load_data_regression()
test_SVR_rbf(X_train,X_test,y_train,y_test)

#考察sigmoid核
def test_SVR_sigmod(*data):
    X_train, X_test, y_train, y_test = data
    fig = plt.figure()
    ###测试gamm###
    gamms = np.logspace(-1, 3)
    train_scores = []
    test_scores = []
    for gamm in gamms:
        regr = svm.SVR(kernel='sigmoid',gamma=gamm,coef0=0.01)
        regr.fit(X_train, y_train)
        train_scores.append(regr.score(X_train, y_train))
        test_scores.append(regr.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(-1, 1.05)
    ax.set_title('SVR_sigmoid_gamm')
    ax.legend(loc='best')

    #测试r
    rs = np.linspace(0,5)
    train_scores = []
    test_scores = []
    for r in rs:
        regr = svm.SVR(kernel='sigmoid', gamma=10, coef0=r)
        regr.fit(X_train, y_train)
        train_scores.append(regr.score(X_train, y_train))
        test_scores.append(regr.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(-1, 1.05)
    ax.set_title('SVR_sigmoid_r')
    ax.legend(loc='best')
    plt.show()
X_train,X_test,y_train,y_test = load_data_regression()
test_SVR_sigmod(X_train,X_test,y_train,y_test)