import numpy as npfrom .metrics import r2_scoreclass SimpleLinearRegression:    def __init__(self):        """初始化Simple Linear Regression模型"""        self.a_ = None        self.b_ = None    def fit(self, x_train, y_train):        """根据训练数据集x_train, y_train训练Simple Linear Regression模型"""        assert x_train.ndim == 1, \            "Simple Linear Regressor can only solve single feature training data."        assert len(x_train) == len(y_train), \            "the size of x_train must be equal to the size of y_train"        x_mean = np.mean(x_train)        y_mean = np.mean(y_train)        self.a_ = (x_train - x_mean).dot(y_train - y_mean) / (x_train - x_mean).dot(x_train - x_mean)#向量化 点乘运算        self.b_ = y_mean - self.a_ * x_mean        return self    def predict(self, x_predict):        """给定待预测数据集x_predict，返回表示x_predict的结果向量"""        assert x_predict.ndim == 1, \            "Simple Linear Regressor can only solve single feature training data."        assert self.a_ is not None and self.b_ is not None, \            "must fit before predict!"        return np.array([self._predict(x) for x in x_predict])    def _predict(self, x_single):        """给定单个待预测数据x，返回x的预测结果值"""        return self.a_ * x_single + self.b_    def score(self, x_test, y_test):        """根据测试数据集 x_test 和 y_test 确定当前模型的准确度"""        y_predict = self.predict(x_test)        return r2_score(y_test, y_predict)    def __repr__(self):        return "SimpleLinearRegression()"

 

衡量线性回归模型误差的三种方式

def mean_squared_error(y_true, y_predict):    """计算y_true和y_predict之间的MSE"""    assert len(y_true) == len(y_predict), \        "the size of y_true must be equal to the size of y_predict"    return np.sum((y_true - y_predict)**2) / len(y_true)def root_mean_squared_error(y_true, y_predict):    """计算y_true和y_predict之间的RMSE"""    return sqrt(mean_squared_error(y_true, y_predict))def mean_absolute_error(y_true, y_predict):    """计算y_true和y_predict之间的RMSE"""    assert len(y_true) == len(y_predict), \        "the size of y_true must be equal to the size of y_predict"    return np.sum(np.absolute(y_true - y_predict)) / len(y_true)

 

计算最终模型预测准确度R方

def r2_score(y_true, y_predict):    """计算y_true和y_predict之间的R Square"""    return 1 - mean_squared_error(y_true, y_predict)/np.var(y_true)