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Contributing to scikit-bayes

Welcome to scikit-bayes! We appreciate your interest in contributing to this project. We aim to build a robust, scalable library for Bayesian classifiers within the Python ecosystem.

How to Contribute

  1. Bug Reports & Feature Requests: Use the GitHub issue tracker to report bugs or suggest new features. Please include minimal reproducible examples for bugs.

  2. Pull Requests:

    • Fork the repository.
    • Create a feature branch (git checkout -b feature/my-new-feature).
    • Implement your changes.
    • Ensure all tests pass. We strictly adhere to the scikit-learn estimator API.
    • Run the linting and formatting tools.
    • Submit a pull request.

Development Environment

We use pixi for environment management, but you can also use conda or standard Python virtual environments.

Setup (using Pixi)

git clone https://github.com/ptorrijos99/scikit-bayes.git
cd scikit-bayes
pixi install

Running Tests

We use pytest for unit testing. Our goal is to maintain >95% test coverage.

# Run tests
pixi run test

# Alternatively, using native pytest
pytest skbn/

Code Style (Linting & Formatting)

We strictly follow black for formatting and ruff for linting.

pixi run lint

Adding New Estimators

If you are adding a new estimator:

  • It must inherit from sklearn.base.BaseEstimator.
  • It must pass sklearn.utils.estimator_checks.check_estimator.
  • It must have comprehensive docstrings in numpy style.
  • It must not mutate self state inside predict or predict_proba methods.

Thank you for helping us improve scikit-bayes!