Python/machine_learning/ridge_regression/model.py
2024-10-23 20:55:20 +05:30

83 lines
2.6 KiB
Python

import numpy as np
import pandas as pd
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)
# 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:
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
# computing gradient with L2 regularization
gradient = (
x_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:
X_scaled, _, _ = self.feature_scaling(X)
return X_scaled.dot(self.theta)
def compute_cost(self, x: np.ndarray, y: np.ndarray) -> float:
x_scaled, _, _ = self.feature_scaling(x)
m = len(y)
predictions = x_scaled.dot(self.theta)
cost = (
1 / (2 * m)) * np.sum((predictions - y) ** 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:
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]
# initialize and train the ridge regression model
model = RidgeRegression(alpha=0.01, regularization_param=0.1, num_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))