<span role="heading" aria-level="2">线性回归案例</span>

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


def linear_model1():
    """
    正规方程
    :return: None
    """
    # 1.获取数据
    boston = load_boston()

    # 2.数据基本处理
    # 2.1 数据集划分
    x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2)

    # 3.特征工程 --标准化
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.fit_transform(x_test)

    # 4.机器学习(线性回归)
    estimator = LinearRegression()
    estimator.fit(x_train, y_train)
    print("这个模型的偏置是:\n", estimator.intercept_)

    # 5.模型评估
    # 5.1 预测值和准确率
    y_pre = estimator.predict(x_test)
    print("预测值是:\n", y_pre)

    score = estimator.score(x_test, y_test)
    print("准确率是:\n", score)

    # 5.2 均方误差
    ret = mean_squared_error(y_test, y_pre)
    print("均方误差是:\n", ret)


def linear_model2():
    """
    梯度下降法
    :return: None
    """
    # 1.获取数据
    boston = load_boston()

    # 2.数据基本处理
    # 2.1 数据集划分
    x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2)

    # 3.特征工程 --标准化
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.fit_transform(x_test)

    # 4.机器学习(线性回归)
    estimator = SGDRegressor(max_iter=1000, learning_rate="constant", eta0=0.001)

    estimator.fit(x_train, y_train)
    print("这个模型的偏置是:\n", estimator.intercept_)

    # 5.模型评估
    # 5.1 预测值和准确率
    y_pre = estimator.predict(x_test)
    print("预测值是:\n", y_pre)

    score = estimator.score(x_test, y_test)
    print("准确率是:\n", score)

    # 5.2 均方误差
    ret = mean_squared_error(y_test, y_pre)
    print("均方误差是:\n", ret)


def linear_model3():
    """
    岭回归
    :return: None
    """
    # 1.获取数据
    boston = load_boston()

    # 2.数据基本处理
    # 2.1 数据集划分
    x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2)

    # 3.特征工程 --标准化
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.fit_transform(x_test)

    # 4.机器学习(线性回归)
    # estimator = Ridge()
    estimator = RidgeCV(alphas=(0.001, 0.1, 1, 10, 100))

    estimator.fit(x_train, y_train)
    print("这个模型的偏置是:\n", estimator.intercept_)

    # 5.模型评估
    # 5.1 预测值和准确率
    y_pre = estimator.predict(x_test)
    print("预测值是:\n", y_pre)

    score = estimator.score(x_test, y_test)
    print("准确率是:\n", score)

    # 5.2 均方误差
    ret = mean_squared_error(y_test, y_pre)
    print("均方误差是:\n", ret)


if __name__ == '__main__':
    linear_model1()
    linear_model2()
    linear_model3()