10.1 Adaboost classifer.py 文件源码

def test_AdaBoostClassifier_algorithm(*data):
    '''
    test performance with different algorithm
    :param data: train_data, test_data, train_value, test_value
    :return:  None
    '''
    X_train,X_test,y_train,y_test=data
    algorithms=['SAMME.R','SAMME']
    fig=plt.figure()
    learning_rates=[0.05,0.1,0.5,0.9]
    for i,learning_rate in enumerate(learning_rates):
        ax=fig.add_subplot(2,2,i+1)
        for i ,algorithm in enumerate(algorithms):
            clf=ensemble.AdaBoostClassifier(learning_rate=learning_rate,
                algorithm=algorithm)
            clf.fit(X_train,y_train)
            ## ??
            estimators_num=len(clf.estimators_)
            X=range(1,estimators_num+1)
            ax.plot(list(X),list(clf.staged_score(X_train,y_train)),
                label="%s:Traing score"%algorithms[i])
            ax.plot(list(X),list(clf.staged_score(X_test,y_test)),
                label="%s:Testing score"%algorithms[i])
        ax.set_xlabel("estimator num")
        ax.set_ylabel("score")
        ax.legend(loc="lower right")
        ax.set_title("learing rate:%f"%learning_rate)
    fig.suptitle("AdaBoostClassifier")
    plt.show()