4.2 KNN regressor.py 文件源码

def test_KNeighborsRegressor_k_w(*data):
    '''
    test the performance with different n_neighbors and weights
    :param data: train_data, test_data, train_value, test_value
    :return: None
    '''
    X_train,X_test,y_train,y_test=data
    Ks=np.linspace(1,y_train.size,num=100,endpoint=False,dtype='int')
    weights=['uniform','distance']

    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    ### graph
    for weight in weights:
        training_scores=[]
        testing_scores=[]
        for K in Ks:
            regr=neighbors.KNeighborsRegressor(weights=weight,n_neighbors=K)
            regr.fit(X_train,y_train)
            testing_scores.append(regr.score(X_test,y_test))
            training_scores.append(regr.score(X_train,y_train))
        ax.plot(Ks,testing_scores,label="testing score:weight={0}".format(weight))
        ax.plot(Ks,training_scores,label="training score:weight={0}".format(weight))
    ax.legend(loc='best')
    ax.set_xlabel("K")
    ax.set_ylabel("score")
    ax.set_ylim(0,1.05)
    ax.set_title("KNeighborsRegressor")
    plt.show()