吴裕雄 python 机器学习——KNN回归KNeighborsRegressor模型

import numpy as np

import matplotlib.pyplot as plt

from sklearn import neighbors, datasets

from sklearn.model_selection import train_test_split

def create_regression_data(n):

    '''

    创建回归模型使用的数据集

    '''

    X =5 * np.random.rand(n, 1)

    y = np.sin(X).ravel()

    # 每隔 5 个样本就在样本的值上添加噪音

    y[::5] += 1 * (0.5 - np.random.rand(int(n/5)))

    # 进行简单拆分，测试集大小占 1/4

    return train_test_split(X, y,test_size=0.25,random_state=0)

#KNN回归KNeighborsRegressor模型

def test_KNeighborsRegressor(*data):

    X_train,X_test,y_train,y_test=data

    regr=neighbors.KNeighborsRegressor()

    regr.fit(X_train,y_train)

    print("Training Score:%f"%regr.score(X_train,y_train))

    print("Testing Score:%f"%regr.score(X_test,y_test))

#获取回归模型的数据集

X_train,X_test,y_train,y_test=create_regression_data(1000)

# 调用 test_KNeighborsRegressor

test_KNeighborsRegressor(X_train,X_test,y_train,y_test)

def test_KNeighborsRegressor_k_w(*data):

    '''

    测试 KNeighborsRegressor 中 n_neighbors 和 weights 参数的影响

    '''

    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)

    ### 绘制不同 weights 下， 预测得分随 n_neighbors 的曲线

    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=%s"%weight)

        ax.plot(Ks,training_scores,label="training score:weight=%s"%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()

# 调用 test_KNeighborsRegressor_k_w

test_KNeighborsRegressor_k_w(X_train,X_test,y_train,y_test)

def test_KNeighborsRegressor_k_p(*data):

    '''

    测试 KNeighborsRegressor 中 n_neighbors 和 p 参数的影响

    '''

    X_train,X_test,y_train,y_test=data

    Ks=np.linspace(1,y_train.size,endpoint=False,dtype='int')

    Ps=[1,2,10]

    fig=plt.figure()

    ax=fig.add_subplot(1,1,1)

    ### 绘制不同 p 下， 预测得分随 n_neighbors 的曲线

    for P in Ps:

        training_scores=[]

        testing_scores=[]

        for K in Ks:

            regr=neighbors.KNeighborsRegressor(p=P,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:p=%d"%P)

        ax.plot(Ks,training_scores,label="training score:p=%d"%P)

    ax.legend(loc='best')

    ax.set_xlabel("K")

    ax.set_ylabel("score")

    ax.set_ylim(0,1.05)

    ax.set_title("KNeighborsRegressor")

    plt.show()

# 调用 test_KNeighborsRegressor_k_p

test_KNeighborsRegressor_k_p(X_train,X_test,y_train,y_test)