Fixes issue #12108: Added Ridge Regression to Machine Learning

machine_learning/ridge_regression.py

@@ -0,0 +1,108 @@
+import numpy as np
+import pandas as pd
+
+
+class RidgeRegression:
+    def __init__(self, alpha=0.001, lambda_=0.1, iterations=1000):
+        """
+        Ridge Regression Constructor
+        :param alpha: Learning rate for gradient descent
+        :param lambda_: Regularization parameter (L2 regularization)
+        :param iterations: Number of iterations for gradient descent
+        """
+        self.alpha = alpha
+        self.lambda_ = lambda_
+        self.iterations = iterations
+        self.theta = None
+
+    def feature_scaling(self, X):
+        """
+        Normalize features to have mean 0 and standard deviation 1
+        :param X: Input features, shape (m, n)
+        :return: Scaled features, mean, and std for each feature
+        """
+        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, y):
+        """
+        Fit the Ridge Regression model to the training data
+        :param X: Input features, shape (m, n)
+        :param y: Target values, shape (m,)
+        """
+        X_scaled, mean, std = self.feature_scaling(X)  # Normalize features
+        m, n = X_scaled.shape
+        self.theta = np.zeros(n)  # Initialize weights to zeros
+
+        for i in range(self.iterations):
+            predictions = X_scaled.dot(self.theta)
+            error = predictions - y
+
+            # Compute gradient with L2 regularization
+            gradient = (X_scaled.T.dot(error) + self.lambda_ * self.theta) / m
+            self.theta -= self.alpha * gradient  # Update weights
+
+    def predict(self, X):
+        """
+        Predict values using the trained model
+        :param X: Input features, shape (m, n)
+        :return: Predicted values, shape (m,)
+        """
+        X_scaled, _, _ = self.feature_scaling(X)  # Scale features using training data
+        return X_scaled.dot(self.theta)
+
+    def compute_cost(self, X, y):
+        """
+        Compute the cost function with regularization
+        :param X: Input features, shape (m, n)
+        :param y: Target values, shape (m,)
+        :return: Computed cost
+        """
+        X_scaled, _, _ = self.feature_scaling(X)  # Scale features using training data
+        m = len(y)
+        predictions = X_scaled.dot(self.theta)
+        cost = (1 / (2 * m)) * np.sum((predictions - y) ** 2) + (
+            self.lambda_ / (2 * m)
+        ) * np.sum(self.theta**2)
+        return cost
+
+    def mean_absolute_error(self, y_true, y_pred):
+        """
+        Compute Mean Absolute Error (MAE) between true and predicted values
+        :param y_true: Actual target values, shape (m,)
+        :param y_pred: Predicted target values, shape (m,)
+        :return: MAE
+        """
+        return np.mean(np.abs(y_true - y_pred))
+
+
+# Example usage
+if __name__ == "__main__":
+    # Load dataset
+    df = pd.read_csv(
+        "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/master/Week1/ADRvsRating.csv"
+    )
+    X = df[["Rating"]].values  # Feature: Rating
+    y = df["ADR"].values  # Target: ADR
+    y = (y - np.mean(y)) / np.std(y)
+
+    # Add bias term (intercept) to the feature matrix
+    X = np.c_[np.ones(X.shape[0]), X]  # Add intercept term
+
+    # Initialize and train the Ridge Regression model
+    model = RidgeRegression(alpha=0.01, lambda_=0.1, iterations=1000)
+    model.fit(X, y)
+
+    # Predictions
+    predictions = model.predict(X)
+
+    # Results
+    print("Optimized Weights:", model.theta)
+    print("Cost:", model.compute_cost(X, y))
+    print("Mean Absolute Error:", model.mean_absolute_error(y, predictions))