linear_regression.py

"""
Linear regression is the most basic type of regression commonly used for
predictive analysis. The idea is pretty simple: we have a dataset and we have
features associated with it. Features should be chosen very cautiously
as they determine how much our model will be able to make future predictions.
We try to set the weight of these features, over many iterations, so that they best
fit our dataset. In this particular code, I had used a CSGO dataset (ADR vs
Rating). We try to best fit a line through dataset and estimate the parameters.
"""

import numpy as np
import requests


def collect_dataset() -> np.matrix:
    """Collect dataset of CSGO
    The dataset contains ADR vs Rating of a Player
    :return : dataset obtained from the link, as matrix
    """
    """
    :return: Dataset obtained from the link, as a matrix
    :raises RuntimeError: If there's a network error or issue with the dataset
    ;raises ValueError: If the dataset is empty or malformed
    """
    try:
        response = requests.get(
            "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
            "master/Week1/ADRvsRating.csv",
            timeout=10,
        )
        response.raise_for_status()
    except requests.RequestException:
        raise RuntimeError("Failed to fetch dataset")

    lines = response.text.splitlines()
    if not lines or len(lines) < 2:
        raise ValueError("Dataset is empty or missing headers")

    try:
        data = [line.split(",") for line in lines[1:]]
        if not data:
            raise ValueError("No data found after header row")
        dataset = np.matrix(data, dtype=float)
    except ValueError:
        raise RuntimeError("Error converting data to float")
    except np.core._exception._ArrayMemoryError:
        raise MemoryError("Memory issues in matrix creation")

    return dataset


def run_steep_gradient_descent(
    data_x: np.matrix, data_y: np.matrix, len_data: int, alpha: float, theta: np.matrix
) -> np.matrix:
    """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 (weight's for our model)
    :param return    : Updated Feature's, using
                       curr_features - alpha_ * gradient(w.r.t. feature)
    ;raises ValueError: If dimensions of inputs are inconsisten
    """
    n = len_data
    prod = np.dot(theta, data_x.transpose())
    prod -= data_y.transpose()
    sum_grad = np.dot(prod, data_x)
    theta = theta - (alpha / n) * sum_grad
    if data_x.shape[0] != len_data or data_y.shape[0] != len_data:
        raise ValueError("The Dimensions of dataset and output vector are different")
    if data_x.shape[1] != theta.shape[1]:
        raise ValueError("The Dimensions of dataset and feature vector are not same")
    return theta


def sum_of_square_error(
    data_x: np.matrix, data_y: np.matrix, len_data: int, theta: np.matrix
) -> float:
    """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
    :return          : sum of square error computed from given feature's
    """
    prod = np.dot(theta, data_x.transpose())
    prod -= data_y.transpose()
    sum_elem = np.sum(np.square(prod))
    error = sum_elem / (2 * len_data)
    return error


def run_linear_regression(data_x: np.matrix, data_y: np.matrix) -> np.matrix:
    """Implement Linear regression over the dataset
    :param data_x  : contains our dataset
    :param data_y  : contains the output (result vector)
    :return        : feature for line of best fit (Feature vector)
    """
    iterations = 100000
    alpha = 0.0001550

    no_features = data_x.shape[1]
    len_data = data_x.shape[0]

    theta = np.zeros((1, no_features))

    for i in range(iterations):
        theta = run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta)
        error = sum_of_square_error(data_x, data_y, len_data, theta)
        print(f"At Iteration {i + 1} - Error is {error:.5f}")

    return theta


def mean_absolute_error(predicted_y, original_y) -> float:
    """Return sum of square 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 feature's
    """
    if len(predicted_y) != len(original_y):
        raise ValueError("The Values are too different There may be Some Big error")
    total = sum(abs(y - predicted_y[i]) for i, y in enumerate(original_y))
    return total / len(original_y)


def main() -> None:
    """Driver function"""

    data = collect_dataset()

    len_data = data.shape[0]
    data_x = np.c_[np.ones(len_data), data[:, :-1]].astype(float)
    data_y = data[:, -1].astype(float)

    theta = run_linear_regression(data_x, data_y)
    len_result = theta.shape[1]
    print("Resultant Feature vector : ")
    for i in range(len_result):
        print(f"{theta[0, i]:.5f}")


if __name__ == "__main__":
    main()