rustgression 0.5.0

Fast OLS and TLS regression using Rust backend

rustgression

This project provides fast regression analysis (OLS, TLS) as a Python package.

• WebSite: https://pypi.org/project/rustgression/
• Documentation: https://github.com/connect0459/rustgression/blob/main/README.md
• Source code: https://github.com/connect0459/rustgression
• Bug reports and security issues: https://github.com/connect0459/rustgression/issues

Overview

rustgression provides high-performance regression analysis tools implemented in Rust as a Python package. It includes the following features.

• Ordinary Least Squares (OLS): Traditional least squares method. Minimizes errors only in the y-direction.
• Total Least Squares (TLS): Orthogonal regression. Considers errors in both variables (x-axis and y-axis).

This package targets Python version 3.11 and above.

Installation

Using pip:

pip install rustgression

Using uv:

uv add rustgression

Usage

import numpy as np
from rustgression import OlsRegressor, TlsRegressor

def generate_sample_data(size: int = 100, noise_std: float = 0.5) -> tuple[np.ndarray, np.ndarray]:
    """Generate sample data for regression example.

    Args:
        size: Number of data points
        noise_std: Standard deviation of noise

    Returns:
        Tuple of (x, y) arrays
    """
    x = np.linspace(0, 10, size)
    true_slope, true_intercept = 2.0, 1.0
    y = true_slope * x + true_intercept + np.random.normal(0, noise_std, size)
    return x, y

def main():
    # Generate sample data
    x, y = generate_sample_data()

    # Ordinary Least Squares (OLS) Regression
    print("=== Ordinary Least Squares (OLS) Results ===")
    ols_model = OlsRegressor(x, y)
    print(f"Slope: {ols_model.slope():.4f}")
    print(f"Intercept: {ols_model.intercept():.4f}")
    print(f"R-value: {ols_model.r_value():.4f}")
    print(f"P-value: {ols_model.p_value():.4e}")
    print(f"Standard Error: {ols_model.stderr():.4f}")
    print(f"Intercept Standard Error: {ols_model.intercept_stderr():.4f}\n")

    # Total Least Squares (TLS) Regression
    print("=== Total Least Squares (TLS) Results ===")
    tls_model = TlsRegressor(x, y)
    print(f"Slope: {tls_model.slope():.4f}")
    print(f"Intercept: {tls_model.intercept():.4f}")
    print(f"R-value: {tls_model.r_value():.4f}")

if __name__ == "__main__":
    main()

Documentation

• API Reference
• Development Guide
• Changelog

Author

connect0459