10.6 Gradient_regresion.py 文件源码

def test_GradientBoostingRegressor_learning(*data):
    '''
    test the performance with different learning rate
    :param data:   train_data, test_data, train_value, test_value
    :return:  None
    '''
    X_train,X_test,y_train,y_test=data
    learnings=np.linspace(0.01,1.0)
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    testing_scores=[]
    training_scores=[]
    for learning in learnings:
        regr=ensemble.GradientBoostingRegressor(learning_rate=learning)
        regr.fit(X_train,y_train)
        training_scores.append(regr.score(X_train,y_train))
        testing_scores.append(regr.score(X_test,y_test))
    ax.plot(learnings,training_scores,label="Training Score")
    ax.plot(learnings,testing_scores,label="Testing Score")
    ax.set_xlabel("learning_rate")
    ax.set_ylabel("score")
    ax.legend(loc="lower right")
    ax.set_ylim(-1,1.05)
    plt.suptitle("GradientBoostingRegressor")
    plt.show()