10.2 adaboost regression.py 文件源码

def test_AdaBoostRegressor_base_regr(*data):
    '''
    test the regression with different number of model and regression method
    :param data:  train_data, test_data, train_value, test_value
    :return: None
    '''
    from sklearn.svm import  LinearSVR
    X_train,X_test,y_train,y_test=data
    fig=plt.figure()
    regrs=[ensemble.AdaBoostRegressor(),
        ensemble.AdaBoostRegressor(base_estimator=LinearSVR(epsilon=0.01,C=100))]
    labels=["Decision Tree Regressor","Linear SVM Regressor"]
    for i ,regr in enumerate(regrs):
        ax=fig.add_subplot(2,1,i+1)
        regr.fit(X_train,y_train)
        ## graph
        estimators_num=len(regr.estimators_)
        X=range(1,estimators_num+1)
        ax.plot(list(X),list(regr.staged_score(X_train,y_train)),label="Traing score")
        ax.plot(list(X),list(regr.staged_score(X_test,y_test)),label="Testing score")
        ax.set_xlabel("estimator num")
        ax.set_ylabel("score")
        ax.legend(loc="lower right")
        ax.set_ylim(-1,1)
        ax.set_title("Base_Estimator:%s"%labels[i])
    plt.suptitle("AdaBoostRegressor")
    plt.show()