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76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
import numpy as np
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import pandas as pd
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class RidgeRegression:
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def __init__(self, alpha=0.001, regularization_param=0.1, num_iterations=1000):
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self.alpha = alpha
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self.regularization_param = regularization_param
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self.num_iterations = num_iterations
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self.theta = None
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def feature_scaling(self, X):
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mean = np.mean(X, axis=0)
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std = np.std(X, axis=0)
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# avoid division by zero for constant features (std = 0)
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std[std == 0] = 1 # set std=1 for constant features to avoid NaN
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X_scaled = (X - mean) / std
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return X_scaled, mean, std
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def fit(self, X, y):
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X_scaled, mean, std = self.feature_scaling(X)
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m, n = X_scaled.shape
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self.theta = np.zeros(n) # initializing weights to zeros
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for i in range(self.num_iterations):
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predictions = X_scaled.dot(self.theta)
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error = predictions - y
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# computing gradient with L2 regularization
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gradient = (
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X_scaled.T.dot(error) + self.regularization_param * self.theta
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) / m
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self.theta -= self.alpha * gradient # updating weights
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def predict(self, X):
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X_scaled, _, _ = self.feature_scaling(X)
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return X_scaled.dot(self.theta)
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def compute_cost(self, X, y):
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X_scaled, _, _ = self.feature_scaling(X)
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m = len(y)
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predictions = X_scaled.dot(self.theta)
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cost = (1 / (2 * m)) * np.sum((predictions - y) ** 2) + (
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self.regularization_param / (2 * m)
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) * np.sum(self.theta**2)
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return cost
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def mean_absolute_error(self, y_true, y_pred):
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return np.mean(np.abs(y_true - y_pred))
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# Example usage
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if __name__ == "__main__":
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df = pd.read_csv("ADRvsRating.csv")
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X = df[["Rating"]].values
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y = df["ADR"].values
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y = (y - np.mean(y)) / np.std(y)
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# Add bias term (intercept) to the feature matrix
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X = np.c_[np.ones(X.shape[0]), X]
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# initialize and train the Ridge Regression model
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model = RidgeRegression(alpha=0.01, regularization_param=0.1, num_iterations=1000)
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model.fit(X, y)
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# predictions
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predictions = model.predict(X)
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# results
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print("Optimized Weights:", model.theta)
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print("Cost:", model.compute_cost(X, y))
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print("Mean Absolute Error:", model.mean_absolute_error(y, predictions))
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