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  • 吴裕雄 python 机器学习——支持向量机线性回归SVR模型

    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) 

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  • 原文地址:https://www.cnblogs.com/tszr/p/10799246.html
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