import numpy as np

import matplotlib.pyplot as plt

from sklearn import datasets, linear_model,svm

from sklearn.model_selection import train_test_split

def load_data_regression():

    '''

    加载用于回归问题的数据集

    '''

    diabetes = datasets.load_diabetes() #使用 scikit-learn 自带的一个糖尿病病人的数据集

    # 拆分成训练集和测试集，测试集大小为原始数据集大小的 1/4

    return train_test_split(diabetes.data,diabetes.target,test_size=0.25,random_state=0)

#支持向量机线性回归SVR模型

def test_LinearSVR(*data):

    X_train,X_test,y_train,y_test=data

    regr=svm.LinearSVR()

    regr.fit(X_train,y_train)

    print('Coefficients:%s, intercept %s'%(regr.coef_,regr.intercept_))

    print('Score: %.2f' % regr.score(X_test, y_test))

# 生成用于回归问题的数据集

X_train,X_test,y_train,y_test=load_data_regression()

# 调用 test_LinearSVR

test_LinearSVR(X_train,X_test,y_train,y_test) 

def test_LinearSVR_loss(*data):

    '''

   测试 LinearSVR 的预测性能随不同损失函数的影响

    '''

    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))

# 调用 test_LinearSVR_loss

test_LinearSVR_loss(X_train,X_test,y_train,y_test) 

def test_LinearSVR_epsilon(*data):

    '''

    测试 LinearSVR 的预测性能随 epsilon 参数的影响

    '''

    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_title( "LinearSVR_epsilon ")

    ax.set_xscale("log")

    ax.set_xlabel(r"$\epsilon$")

    ax.set_ylabel("score")

    ax.set_ylim(-1,1.05)

    ax.legend(loc="best",framealpha=0.5)

    plt.show()

# 调用 test_LinearSVR_epsilon

test_LinearSVR_epsilon(X_train,X_test,y_train,y_test) 

def test_LinearSVR_C(*data):

    '''

    测试 LinearSVR 的预测性能随 C 参数的影响

    '''

    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_title( "LinearSVR_C ")

    ax.set_xscale("log")

    ax.set_xlabel(r"C")

    ax.set_ylabel("score")

    ax.set_ylim(-1,1.05)

    ax.legend(loc="best",framealpha=0.5)

    plt.show()

# 调用 test_LinearSVR_C

test_LinearSVR_C(X_train,X_test,y_train,y_test)