from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression, SGDRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import Ridge

import joblib


def mylinear():
    """
    线性回归直接预测房子的价格
    :return: None
    """
    #获取数据
    lb = load_boston()

    #分割数据集到训练集和测试集
    x_train, x_test, y_train, y_test = train_test_split(lb.data, lb.target, test_size=0.25)
    print(y_train, y_test)

    #进行标准化处理   目标值是否需要进行标准化处理？
    #特征值和目标值都需要进行标准化处理  实例化两个标准化API
    std_x = StandardScaler()
    x_train = std_x.fit_transform(x_train)
    x_test = std_x.transform(x_test)

    #目标值 标准化
    std_y = StandardScaler()
    y_train = std_y.fit_transform(y_train.reshape(-1, 1))
    y_test = std_y.transform(y_test.reshape(-1, 1))

    #预测房价结果
    model = joblib.load("./tmp/test.pkl")
    y_predict = std_y.inverse_transform(model.predict(x_test))
    print("保存的模型预测的结果：", y_predict)

    # #estimator预测
    # #正规方程求解方程预测结果(线性回归)
    # lr = LinearRegression()

    # lr.fit(x_train, y_train)

    # print(lr.coef_)   #打印权重参数--回归系数

    # #保存训练好的模型
    # joblib.dump(lr, "./tmp/test.pkl")

    # #预测测试集的房子价格
    # y_lr_predict = std_y.inverse_transform(lr.predict(x_test))

    # print("正规方程测试集每个房子的预测价格：", y_lr_predict)
    # print("正规方程的均方误差", mean_squared_error(std_y.inverse_transform(y_test), y_lr_predict))

    # #梯度下降进行房价预测
    # sgd = SGDRegressor()
    #
    # sgd.fit(x_train, y_train)
    #
    # print(sgd.coef_)  # 打印权重参数--回归系数
    #
    # # 预测测试集的房子价格
    # y_sgd_predict = std_y.inverse_transform(sgd.predict(x_test))
    #
    # print("梯度下降测试集每个房子的预测价格：", y_sgd_predict)
    # print("梯度下降的均方误差", mean_squared_error(std_y.inverse_transform(y_test), y_sgd_predict))
    #
    # # 通过岭回归进行房价预测
    # rd = Ridge(alpha=1.0)
    #
    # rd.fit(x_train, y_train)
    #
    # print(rd.coef_)  # 打印权重参数--回归系数
    #
    # # 预测测试集的房子价格
    # y_rd_predict = std_y.inverse_transform(rd.predict(x_test))
    #
    # print("岭回归测试集每个房子的预测价格：", y_rd_predict)
    # print("岭回归下降的均方误差", mean_squared_error(std_y.inverse_transform(y_test), y_rd_predict))

    return None

if __name__ == "__main__":
    mylinear()