Merge pull request #1 from ankana2113/main

fixes ruff check in loss_functions.py

machine_learning/frequent_pattern_growth.py

@@ -240,7 +240,7 @@ def ascend_tree(leaf_node: TreeNode, prefix_path: list[str]) -> None:
         ascend_tree(leaf_node.parent, prefix_path)
 
 
-def find_prefix_path(base_pat: frozenset, tree_node: TreeNode | None) -> dict:  # noqa: ARG001
+def find_prefix_path(_: frozenset, tree_node: TreeNode | None) -> dict:
     """
     Find the conditional pattern base for a given base pattern.
 

machine_learning/loss_functions.py

@@ -629,13 +629,15 @@ def smooth_l1_loss(y_true: np.ndarray, y_pred: np.ndarray, beta: float = 1.0) ->
     return np.mean(loss)
 
 
-def kullback_leibler_divergence(y_true: np.ndarray, y_pred: np.ndarray) -> float:
+def kullback_leibler_divergence(
+    y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-10
+) -> float:
     """
     Calculate the Kullback-Leibler divergence (KL divergence) loss between true labels
     and predicted probabilities.
 
-    KL divergence loss quantifies dissimilarity between true labels and predicted
+    KL divergence loss quantifies the dissimilarity between true labels and predicted
-    probabilities. It's often used in training generative models.
+    probabilities. It is often used in training generative models.
 
     KL = Σ(y_true * ln(y_true / y_pred))
 
@@ -649,6 +651,7 @@ def kullback_leibler_divergence(y_true: np.ndarray, y_pred: np.ndarray) -> float
     >>> predicted_probs = np.array([0.3, 0.3, 0.4])
     >>> float(kullback_leibler_divergence(true_labels, predicted_probs))
     0.030478754035472025
+
     >>> true_labels = np.array([0.2, 0.3, 0.5])
     >>> predicted_probs = np.array([0.3, 0.3, 0.4, 0.5])
     >>> kullback_leibler_divergence(true_labels, predicted_probs)
@@ -659,7 +662,13 @@ def kullback_leibler_divergence(y_true: np.ndarray, y_pred: np.ndarray) -> float
     if len(y_true) != len(y_pred):
         raise ValueError("Input arrays must have the same length.")
 
-    kl_loss = y_true * np.log(y_true / y_pred)
+    # negligible epsilon to avoid issues with log(0) or division by zero
+    epsilon = 1e-10
+    y_pred = np.clip(y_pred, epsilon, None)
+
+    # calculate KL divergence only where y_true is not zero
+    kl_loss = np.where(y_true != 0, y_true * np.log(y_true / y_pred), 0.0)
+
     return np.sum(kl_loss)
 
 

machine_learning/ridge_regression/ridge_regression.py

@@ -15,68 +15,68 @@ class RidgeRegression:
         self.theta: np.ndarray = None
 
     def feature_scaling(
-        self, x: np.ndarray
+        self, features: np.ndarray
     ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        mean = np.mean(x, axis=0)
+        mean = np.mean(features, axis=0)
-        std = np.std(x, axis=0)
+        std = np.std(features, 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
+        features_scaled = (features - mean) / std
-        return x_scaled, mean, std
+        return features_scaled, mean, std
 
-    def fit(self, x: np.ndarray, y: np.ndarray) -> None:
+    def fit(self, features: np.ndarray, target: np.ndarray) -> None:
-        x_scaled, mean, std = self.feature_scaling(x)
+        features_scaled, mean, std = self.feature_scaling(features)
-        m, n = x_scaled.shape
+        m, n = features_scaled.shape
         self.theta = np.zeros(n)  # initializing weights to zeros
 
         for _ in range(self.num_iterations):
-            predictions = x_scaled.dot(self.theta)
+            predictions = features_scaled.dot(self.theta)
-            error = predictions - y
+            error = predictions - target
 
             # computing gradient with L2 regularization
             gradient = (
-                x_scaled.T.dot(error) + self.regularization_param * self.theta
+                features_scaled.T.dot(error) + self.regularization_param * self.theta
             ) / m
             self.theta -= self.alpha * gradient  # updating weights
 
-    def predict(self, x: np.ndarray) -> np.ndarray:
+    def predict(self, features: np.ndarray) -> np.ndarray:
-        x_scaled, _, _ = self.feature_scaling(x)
+        features_scaled, _, _ = self.feature_scaling(features)
-        return x_scaled.dot(self.theta)
+        return features_scaled.dot(self.theta)
 
-    def compute_cost(self, x: np.ndarray, y: np.ndarray) -> float:
+    def compute_cost(self, features: np.ndarray, target: np.ndarray) -> float:
-        x_scaled, _, _ = self.feature_scaling(x)
+        features_scaled, _, _ = self.feature_scaling(features)
-        m = len(y)
+        m = len(target)
 
-        predictions = x_scaled.dot(self.theta)
+        predictions = features_scaled.dot(self.theta)
-        cost = (1 / (2 * m)) * np.sum((predictions - y) ** 2) + (
+        cost = (1 / (2 * m)) * np.sum((predictions - target) ** 2) + (
             self.regularization_param / (2 * m)
         ) * 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, target: np.ndarray, predictions: np.ndarray) -> float:
-        return np.mean(np.abs(y_true - y_pred))
+        return np.mean(np.abs(target - predictions))
 
 
 # Example usage
 if __name__ == "__main__":
     data = pd.read_csv("ADRvsRating.csv")
-    x = data[["Rating"]].to_numpy()
+    features_matrix = data[["Rating"]].to_numpy()
-    y = data["ADR"].to_numpy()
+    target = data["ADR"].to_numpy()
-    y = (y - np.mean(y)) / np.std(y)
+    target = (target - np.mean(target)) / np.std(target)
 
     # added bias term to the feature matrix
-    x = np.c_[np.ones(x.shape[0]), x]
+    x = np.c_[np.ones(features_matrix.shape[0]), features_matrix]
 
     # initialize and train the ridge regression model
     model = RidgeRegression(alpha=0.01, regularization_param=0.1, num_iterations=1000)
-    model.fit(x, y)
+    model.fit(features_matrix, target)
 
     # predictions
-    predictions = model.predict(x)
+    predictions = model.predict(features_matrix)
 
     # results
     print("Optimized Weights:", model.theta)
-    print("Cost:", model.compute_cost(x, y))
+    print("Cost:", model.compute_cost(features_matrix, target))
-    print("Mean Absolute Error:", model.mean_absolute_error(y, predictions))
+    print("Mean Absolute Error:", model.mean_absolute_error(target, predictions))

machine_learning/ridge_regression/test_ridge_regression.py

@@ -12,7 +12,10 @@ To run these tests, use the following command:
 """
 
 import numpy as np  # noqa: F401
-from ridge_regression import RidgeRegression  # noqa: F401
+
+from machine_learning.ridge_regression.ridge_regression import (
+    RidgeRegression,  # noqa: F401
+)
 
 
 def test_feature_scaling():
@@ -20,9 +23,9 @@ def test_feature_scaling():
        Tests the feature_scaling function of RidgeRegression.
     --------
        >>> model = RidgeRegression()
-       >>> X = np.array([[1, 2], [2, 3], [3, 4]])
+       >>> features = np.array([[1, 2], [2, 3], [3, 4]])
-       >>> X_scaled, mean, std = model.feature_scaling(X)
+       >>> features_scaled, mean, std = model.feature_scaling(features)
-       >>> np.round(X_scaled, 2)
+       >>> np.round(features_scaled, 2)
        array([[-1.22, -1.22],
               [ 0.  ,  0.  ],
               [ 1.22,  1.22]])
@@ -40,14 +43,14 @@ def test_fit():
     >>> model = RidgeRegression(alpha=0.01,
     ...                          regularization_param=0.1,
     ...                          num_iterations=1000)
-    >>> X = np.array([[1], [2], [3]])
+    >>> features = np.array([[1], [2], [3]])
-    >>> y = np.array([2, 3, 4])
+    >>> target = np.array([2, 3, 4])
 
     # Adding a bias term
-    >>> X = np.c_[np.ones(X.shape[0]), X]
+    >>> features = np.c_[np.ones(features.shape[0]), features]
 
     # Fit the model
-    >>> model.fit(X, y)
+    >>> model.fit(features, target)
 
     # Check if the weights have been updated
     >>> np.round(model.theta, decimals=2)
@@ -62,17 +65,17 @@ def test_predict():
     >>> model = RidgeRegression(alpha=0.01,
     ...                          regularization_param=0.1,
     ...                          num_iterations=1000)
-    >>> X = np.array([[1], [2], [3]])
+    >>> features = np.array([[1], [2], [3]])
-    >>> y = np.array([2, 3, 4])
+    >>> target = np.array([2, 3, 4])
 
     # Adding a bias term
-    >>> X = np.c_[np.ones(X.shape[0]), X]
+    >>> features = np.c_[np.ones(features.shape[0]), features]
 
     # Fit the model
-    >>> model.fit(X, y)
+    >>> model.fit(features, target)
 
     # Predict with the model
-    >>> predictions = model.predict(X)
+    >>> predictions = model.predict(features)
     >>> np.round(predictions, decimals=2)
     array([-0.97,  0.  ,  0.97])
     """
@@ -83,9 +86,9 @@ def test_mean_absolute_error():
     Tests the mean_absolute_error function of RidgeRegression
     --------
     >>> model = RidgeRegression()
-    >>> y_true = np.array([2, 3, 4])
+    >>> target = np.array([2, 3, 4])
-    >>> y_pred = np.array([2.1, 3.0, 3.9])
+    >>> predictions = np.array([2.1, 3.0, 3.9])
-    >>> mae = model.mean_absolute_error(y_true, y_pred)
+    >>> mae = model.mean_absolute_error(target, predictions)
     >>> float(np.round(mae, 2))
     0.07
     """