from sklearn.datasets import load_boston

 from sklearn.cross_validation import train_test_split

 from sklearn.preprocessing import StandardScaler

 from sklearn.neighbors import KNeighborsRegressor

 from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error

 import numpy as np

 # 1 准备数据

 # 读取波士顿地区房价信息

 boston = load_boston()

 # 查看数据描述

 # print(boston.DESCR)   # 共506条波士顿地区房价信息，每条13项数值特征描述和目标房价

 # 查看数据的差异情况

 # print("最大房价：", np.max(boston.target))   # 50

 # print("最小房价：",np.min(boston.target))    # 5

 # print("平均房价：", np.mean(boston.target))   # 22.532806324110677

 x = boston.data

 y = boston.target

 # 2 分割训练数据和测试数据

 # 随机采样25%作为测试 75%作为训练

 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=33)

 # 3 训练数据和测试数据进行标准化处理

 ss_x = StandardScaler()

 x_train = ss_x.fit_transform(x_train)

 x_test = ss_x.transform(x_test)

 ss_y = StandardScaler()

 y_train = ss_y.fit_transform(y_train.reshape(-1, 1))

 y_test = ss_y.transform(y_test.reshape(-1, 1))

 # 4 两种k近邻回归行学习和预测

 # 初始化k近邻回归模型 使用平均回归进行预测

 uni_knr = KNeighborsRegressor(weights="uniform")

 # 训练

 uni_knr.fit(x_train, y_train)

 # 预测 保存预测结果

 uni_knr_y_predict = uni_knr.predict(x_test)

 # 多初始化k近邻回归模型 使用距离加权回归

 dis_knr = KNeighborsRegressor(weights="distance")

 # 训练

 dis_knr.fit(x_train, y_train)

 # 预测 保存预测结果

 dis_knr_y_predict = dis_knr.predict(x_test)

 # 5 模型评估

 # 平均k近邻回归 模型评估

 print("平均k近邻回归的默认评估值为：", uni_knr.score(x_test, y_test))

 print("平均k近邻回归的R_squared值为：", r2_score(y_test, uni_knr_y_predict))

 print("平均k近邻回归的均方误差为:", mean_squared_error(ss_y.inverse_transform(y_test),

                                               ss_y.inverse_transform(uni_knr_y_predict)))

 print("平均k近邻回归 的平均绝对误差为:", mean_absolute_error(ss_y.inverse_transform(y_test),

                                                  ss_y.inverse_transform(uni_knr_y_predict)))

 # 距离加权k近邻回归 模型评估

 print("距离加权k近邻回归的默认评估值为：", dis_knr.score(x_test, y_test))

 print("距离加权k近邻回归的R_squared值为：", r2_score(y_test, dis_knr_y_predict))

 print("距离加权k近邻回归的均方误差为:", mean_squared_error(ss_y.inverse_transform(y_test),

                                            ss_y.inverse_transform(dis_knr_y_predict)))

 print("距离加权k近邻回归的平均绝对误差为:", mean_absolute_error(ss_y.inverse_transform(y_test),

                                               ss_y.inverse_transform(dis_knr_y_predict)))

 '''

 平均k近邻回归的默认评估值为： 0.6903454564606561

 平均k近邻回归的R_squared值为： 0.6903454564606561

 平均k近邻回归的均方误差为: 24.01101417322835

 平均k近邻回归 的平均绝对误差为: 2.9680314960629928

 距离加权k近邻回归的默认评估值为： 0.7197589970156353

 距离加权k近邻回归的R_squared值为： 0.7197589970156353

 距离加权k近邻回归的均方误差为: 21.730250160926044

 距离加权k近邻回归的平均绝对误差为: 2.8050568785108005

 '''