Add Ridge Regression to Machine Learning #12111
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| import numpy as np | ||
| import requests | ||
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| def collect_dataset(): | ||
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| """Collect dataset of CSGO | ||
| The dataset contains ADR vs Rating of a Player | ||
| :return : dataset obtained from the link, as matrix | ||
| """ | ||
| response = requests.get( | ||
| "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/" | ||
| "master/Week1/ADRvsRating.csv", | ||
| timeout=10, | ||
| ) | ||
| lines = response.text.splitlines() | ||
| data = [] | ||
| for item in lines: | ||
| item = item.split(",") | ||
| data.append(item) | ||
| data.pop(0) # This is for removing the labels from the list | ||
| dataset = np.matrix(data) | ||
| return dataset | ||
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| def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta, lambda_reg): | ||
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| """Run steep gradient descent and updates the Feature vector accordingly | ||
| :param data_x : contains the dataset | ||
| :param data_y : contains the output associated with each data-entry | ||
| :param len_data : length of the data | ||
| :param alpha : Learning rate of the model | ||
| :param theta : Feature vector (weights for our model) | ||
| :param lambda_reg: Regularization parameter | ||
| :return : Updated Features using | ||
| curr_features - alpha_ * gradient(w.r.t. feature) | ||
| """ | ||
| n = len_data | ||
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| prod = np.dot(theta, data_x.transpose()) | ||
| prod -= data_y.transpose() | ||
| sum_grad = np.dot(prod, data_x) | ||
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| # Add regularization to the gradient | ||
| theta_regularized = np.copy(theta) | ||
| theta_regularized[0, 0] = 0 # Don't regularize the bias term | ||
| sum_grad += lambda_reg * theta_regularized # Add regularization to gradient | ||
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| theta = theta - (alpha / n) * sum_grad | ||
| return theta | ||
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| def sum_of_square_error(data_x, data_y, len_data, theta, lambda_reg): | ||
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| """Return sum of square error for error calculation | ||
| :param data_x : contains our dataset | ||
| :param data_y : contains the output (result vector) | ||
| :param len_data : len of the dataset | ||
| :param theta : contains the feature vector | ||
| :param lambda_reg: Regularization parameter | ||
| :return : sum of square error computed from given features | ||
| """ | ||
| prod = np.dot(theta, data_x.transpose()) | ||
| prod -= data_y.transpose() | ||
| sum_elem = np.sum(np.square(prod)) | ||
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| # Add regularization to the cost function | ||
| regularization_term = lambda_reg * np.sum( | ||
| np.square(theta[:, 1:]) | ||
| ) # Don't regularize the bias term | ||
| error = (sum_elem / (2 * len_data)) + (regularization_term / (2 * len_data)) | ||
| return error | ||
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| def run_ridge_regression(data_x, data_y, lambda_reg=1.0): | ||
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| """Implement Ridge Regression over the dataset | ||
| :param data_x : contains our dataset | ||
| :param data_y : contains the output (result vector) | ||
| :param lambda_reg: Regularization parameter | ||
| :return : feature for line of best fit (Feature vector) | ||
| """ | ||
| iterations = 100000 | ||
| alpha = 0.0001550 | ||
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| no_features = data_x.shape[1] | ||
| len_data = data_x.shape[0] | ||
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| theta = np.zeros((1, no_features)) | ||
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| for i in range(iterations): | ||
| theta = run_steep_gradient_descent( | ||
| data_x, data_y, len_data, alpha, theta, lambda_reg | ||
| ) | ||
| error = sum_of_square_error(data_x, data_y, len_data, theta, lambda_reg) | ||
| print(f"At Iteration {i + 1} - Error is {error:.5f}") | ||
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| return theta | ||
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| def mean_absolute_error(predicted_y, original_y): | ||
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There was a problem hiding this comment. As there is no test file in this pull request nor any test function or class in the file Please provide return type hint for the function: Please provide type hint for the parameter: Please provide type hint for the parameter: |
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| """Return mean absolute error for error calculation | ||
| :param predicted_y : contains the output of prediction (result vector) | ||
| :param original_y : contains values of expected outcome | ||
| :return : mean absolute error computed from given features | ||
| """ | ||
| total = sum(abs(y - predicted_y[i]) for i, y in enumerate(original_y)) | ||
| return total / len(original_y) | ||
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| def main(): | ||
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There was a problem hiding this comment. Please provide return type hint for the function: As there is no test file in this pull request nor any test function or class in the file There was a problem hiding this comment. As there is no test file in this pull request nor any test function or class in the file Please provide return type hint for the function: |
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| """Driver function""" | ||
| data = collect_dataset() | ||
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| len_data = data.shape[0] | ||
| data_x = np.c_[np.ones(len_data), data[:, :-1]].astype(float) | ||
| data_y = data[:, -1].astype(float) | ||
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| lambda_reg = 1.0 # Set your desired regularization parameter | ||
| theta = run_ridge_regression(data_x, data_y, lambda_reg) | ||
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| len_result = theta.shape[1] | ||
| print("Resultant Feature vector : ") | ||
| for i in range(len_result): | ||
| print(f"{theta[0, i]:.5f}") | ||
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| if __name__ == "__main__": | ||
| main() | ||
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