Fixes issue #12108: Added Ridge Regression to Machine Learning #12246
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,177 @@ | ||
| import numpy as np | ||
| import pandas as pd | ||
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| class RidgeRegression: | ||
| def __init__( | ||
| self, alpha: float = 0.001, lambda_: float = 0.1, iterations: int = 1000 | ||
| ) -> None: | ||
| """ | ||
| 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: np.ndarray | None = ( | ||
| None # Initialize as None, later will be ndarray | ||
| ) | ||
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| def feature_scaling( | ||
| self, features: np.ndarray | ||
| ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: | ||
| """ | ||
| Normalize features to have mean 0 and standard deviation 1. | ||
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| :param features: Input features, shape (m, n) | ||
| :return: Tuple containing: | ||
| - Scaled features | ||
| - Mean of each feature | ||
| - Standard deviation of each feature | ||
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| Example: | ||
| >>> rr = RidgeRegression() | ||
| >>> features = np.array([[1, 2], [2, 3], [4, 6]]) | ||
| >>> scaled_features, mean, std = rr.feature_scaling(features) | ||
| >>> np.allclose(scaled_features.mean(axis=0), 0) | ||
| True | ||
| >>> np.allclose(scaled_features.std(axis=0), 1) | ||
| True | ||
| """ | ||
| mean = np.mean(features, axis=0) | ||
| std = np.std(features, axis=0) | ||
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| # Avoid division by zero for constant features (std = 0) | ||
| std[std == 0] = 1 # Set std=1 for constant features to avoid NaN | ||
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| scaled_features = (features - mean) / std | ||
| return scaled_features, mean, std | ||
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| def fit(self, features: np.ndarray, target: np.ndarray) -> None: | ||
| """ | ||
| Fit the Ridge Regression model to the training data. | ||
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| :param features: Input features, shape (m, n) | ||
| :param target: Target values, shape (m,) | ||
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| Example: | ||
| >>> rr = RidgeRegression(alpha=0.01, lambda_=0.1, iterations=10) | ||
| >>> features = np.array([[1, 2], [2, 3], [4, 6]]) | ||
| >>> target = np.array([1, 2, 3]) | ||
| >>> rr.fit(features, target) | ||
| >>> rr.theta is not None | ||
| True | ||
| """ | ||
| features_scaled, mean, std = self.feature_scaling( | ||
|
Check failure on line 67 in machine_learning/ridge_regression.py
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| features | ||
| ) # Normalize features | ||
| m, n = features_scaled.shape | ||
| self.theta = np.zeros(n) # Initialize weights to zeros | ||
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| for _ in range(self.iterations): | ||
| predictions = features_scaled.dot(self.theta) | ||
| error = predictions - target | ||
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| # Compute gradient with L2 regularization | ||
| gradient = (features_scaled.T.dot(error) + self.lambda_ * self.theta) / m | ||
| self.theta -= self.alpha * gradient # Update weights | ||
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| def predict(self, features: np.ndarray) -> np.ndarray: | ||
| """ | ||
| Predict values using the trained model. | ||
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| :param features: Input features, shape (m, n) | ||
| :return: Predicted values, shape (m,) | ||
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| Example: | ||
| >>> rr = RidgeRegression(alpha=0.01, lambda_=0.1, iterations=10) | ||
| >>> features = np.array([[1, 2], [2, 3], [4, 6]]) | ||
| >>> target = np.array([1, 2, 3]) | ||
| >>> rr.fit(features, target) | ||
| >>> predictions = rr.predict(features) | ||
| >>> predictions.shape == target.shape | ||
| True | ||
| """ | ||
| if self.theta is None: | ||
| raise ValueError("Model is not trained yet. Call the `fit` method first.") | ||
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| features_scaled, _, _ = self.feature_scaling( | ||
| features | ||
| ) # Scale features using training data | ||
| return features_scaled.dot(self.theta) | ||
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| def compute_cost(self, features: np.ndarray, target: np.ndarray) -> float: | ||
| """ | ||
| Compute the cost function with regularization. | ||
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| :param features: Input features, shape (m, n) | ||
| :param target: Target values, shape (m,) | ||
| :return: Computed cost | ||
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| Example: | ||
| >>> rr = RidgeRegression(alpha=0.01, lambda_=0.1, iterations=10) | ||
| >>> features = np.array([[1, 2], [2, 3], [4, 6]]) | ||
| >>> target = np.array([1, 2, 3]) | ||
| >>> rr.fit(features, target) | ||
| >>> cost = rr.compute_cost(features, target) | ||
| >>> isinstance(cost, float) | ||
| True | ||
| """ | ||
| if self.theta is None: | ||
| raise ValueError("Model is not trained yet. Call the `fit` method first.") | ||
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| features_scaled, _, _ = self.feature_scaling( | ||
| features | ||
| ) # Scale features using training data | ||
| m = len(target) | ||
| predictions = features_scaled.dot(self.theta) | ||
| cost = (1 / (2 * m)) * np.sum((predictions - target) ** 2) + ( | ||
| self.lambda_ / (2 * m) | ||
| ) * np.sum(self.theta**2) | ||
| return cost | ||
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| def mean_absolute_error(self, y_true: np.ndarray, y_pred: np.ndarray) -> float: | ||
| """ | ||
| Compute Mean Absolute Error (MAE) between true and predicted values. | ||
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| :param y_true: Actual target values, shape (m,) | ||
| :param y_pred: Predicted target values, shape (m,) | ||
| :return: MAE | ||
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| Example: | ||
| >>> rr = RidgeRegression(alpha=0.01, lambda_=0.1, iterations=10) | ||
| >>> y_true = np.array([1, 2, 3]) | ||
| >>> y_pred = np.array([1.1, 2.1, 2.9]) | ||
| >>> mae = rr.mean_absolute_error(y_true, y_pred) | ||
| >>> isinstance(mae, float) | ||
| True | ||
| """ | ||
| return np.mean(np.abs(y_true - y_pred)) | ||
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| # Example usage | ||
| if __name__ == "__main__": | ||
| # Load dataset | ||
| data = pd.read_csv( | ||
| "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/master/Week1/ADRvsRating.csv" | ||
| ) | ||
| data_x = data[["Rating"]].to_numpy() # Feature: Rating | ||
| data_y = data["ADR"].to_numpy() # Target: ADR | ||
| data_y = (data_y - np.mean(data_y)) / np.std(data_y) | ||
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| # Add bias term (intercept) to the feature matrix | ||
| data_x = np.c_[np.ones(data_x.shape[0]), data_x] # Add intercept term | ||
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| # Initialize and train the Ridge Regression model | ||
| model = RidgeRegression(alpha=0.01, lambda_=0.1, iterations=1000) | ||
| model.fit(data_x, data_y) | ||
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| # Predictions | ||
| predictions = model.predict(data_x) | ||
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| # Results | ||
| print("Optimized Weights:", model.theta) | ||
| print("Cost:", model.compute_cost(data_x, data_y)) | ||
| print("Mean Absolute Error:", model.mean_absolute_error(data_y, predictions)) | ||
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As there is no test file in this pull request nor any test function or class in the file
machine_learning/ridge_regression.py, please provide doctest for the functionmean_absolute_error