7.3 liner_SVR.py 文件源码

def test_LinearSVR_epsilon(*data):
    '''
    test the performance with different epsilon
    :param data:  train_data,test_data, train_target, test_target
    :return: None
    '''
    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()