Fixes issue #12233: Avoid log(0) in KL, ruff and build checks

machine_learning/ridge_regression/model.py

@@ -3,33 +3,34 @@
 
 
 class RidgeRegression:
-    def __init__(self, 
-                 alpha:float=0.001, 
-                 regularization_param:float=0.1, 
-                 num_iterations:int=1000) -> None:
-        self.alpha:float = alpha
-        self.regularization_param:float = regularization_param
-        self.num_iterations:int = num_iterations
-        self.theta:np.ndarray = None
-
-
-    def feature_scaling(self, x:np.ndarray)-> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        mean = np.mean(x, axis=0)
-        std = np.std(x, axis=0)
+    def __init__(self,
+            alpha: float = 0.001,
+            regularization_param: float = 0.1,
+            num_iterations: int = 1000,
+            ) -> None:
+        self.alpha: float = alpha
+        self.regularization_param: float = regularization_param
+        self.num_iterations: int = num_iterations
+        self.theta: np.ndarray = None
+
+    def feature_scaling(
+        self, X: np.ndarray
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        mean = np.mean(X, axis=0)
+        std = np.std(X, axis=0)
 
         # avoid division by zero for constant features (std = 0)
         std[std == 0] = 1  # set std=1 for constant features to avoid NaN
 
         x_scaled = (x - mean) / std
         return x_scaled, mean, std
 
-
-    def fit(self, x:np.ndarray, y:np.ndarray) -> None:
+    def fit(self, x: np.ndarray, y: np.ndarray) -> None:
         x_scaled, mean, std = self.feature_scaling(x)
         m, n = x_scaled.shape
         self.theta = np.zeros(n)  # initializing weights to zeros
 
         for i in range(self.num_iterations):
             predictions = x_scaled.dot(self.theta)
             error = predictions - y
 
@@ -39,13 +40,11 @@
             ) / m
             self.theta -= self.alpha * gradient  # updating weights
 
+    def predict(self, X: np.ndarray) -> np.ndarray:
+        X_scaled, _, _ = self.feature_scaling(X)
+        return X_scaled.dot(self.theta)
 
-    def predict(self, x:np.ndarray) -> np.ndarray:
-        x_scaled, _, _ = self.feature_scaling(x)
-        return x_scaled.dot(self.theta)
-
-
-    def compute_cost(self, x:np.ndarray, y:np.ndarray) -> float:
+    def compute_cost(self, x: np.ndarray, y: np.ndarray) -> float:
         x_scaled, _, _ = self.feature_scaling(x)
         m = len(y)
 
@@ -56,20 +55,19 @@
                 ) * np.sum(self.theta**2)
         return cost
 
-
-    def mean_absolute_error(self, y_true:np.ndarray, y_pred:np.ndarray) -> float:
+    def mean_absolute_error(self, y_true: np.ndarray, y_pred: np.ndarray) -> float:
         return np.mean(np.abs(y_true - y_pred))
 
 
 # Example usage
 if __name__ == "__main__":
     df = pd.read_csv("ADRvsRating.csv")
     x = df[["Rating"]].values
     y = df["ADR"].values
     y = (y - np.mean(y)) / np.std(y)
 
     # added bias term to the feature matrix
-    x = np.c_[np.ones(x.shape[0]), x] 
+    x = np.c_[np.ones(x.shape[0]), x]
 
     # initialize and train the ridge regression model
     model = RidgeRegression(alpha=0.01, regularization_param=0.1, num_iterations=1000)