from sklearn.datasets import load_boston

 from sklearn.cross_validation import train_test_split

 from sklearn.preprocessing import StandardScaler

 from sklearn.linear_model import LinearRegression, SGDRegressor

 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 使用两种线性回归模型进行训练和预测

 # 初始化LinearRegression线性回归模型

 lr = LinearRegression()

 # 训练

 lr.fit(x_train, y_train)

 # 预测 保存预测结果

 lr_y_predict = lr.predict(x_test)

 # 初始化SGDRRegressor随机梯度回归模型

 sgdr = SGDRegressor()

 # 训练

 sgdr.fit(x_train, y_train)

 # 预测 保存预测结果

 sgdr_y_predict = sgdr.predict(x_test)

 # 5 模型评估

 # 对Linear模型评估

 lr_score = lr.score(x_test, y_test)

 print("Linear的默认评估值为：", lr_score)

 lr_R_squared = r2_score(y_test, lr_y_predict)

 print("Linear的R_squared值为：", lr_R_squared)

 lr_mse = mean_squared_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(lr_y_predict))

 print("Linear的均方误差为:", lr_mse)

 lr_mae = mean_absolute_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(lr_y_predict))

 print("Linear的平均绝对误差为:", lr_mae)

 # 对SGD模型评估

 sgdr_score = sgdr.score(x_test, y_test)

 print("SGD的默认评估值为：", sgdr_score)

 sgdr_R_squared = r2_score(y_test, sgdr_y_predict)

 print("SGD的R_squared值为：", sgdr_R_squared)

 sgdr_mse = mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(sgdr_y_predict))

 print("SGD的均方误差为:", sgdr_mse)

 sgdr_mae = mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(sgdr_y_predict))

 print("SGD的平均绝对误差为:", sgdr_mae)

 '''

 Linear的默认评估值为： 0.6763403830998702

 Linear的R_squared值为： 0.6763403830998701

 Linear的均方误差为: 25.09698569206773

 Linear的平均绝对误差为: 3.5261239963985433

 SGD的默认评估值为： 0.659795654161198

 SGD的R_squared值为： 0.659795654161198

 SGD的均方误差为: 26.379885392159494

 SGD的平均绝对误差为: 3.5094445431026413

 '''