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Contributing to auto-bayesian

Thanks for your interest in improving auto-bayesian. This project values small, readable, explicit code. Contributions that keep it that way are very welcome.

Development setup

This project uses uv for environment and dependency management.

uv sync --dev

Workflow

  1. Fork the repository and create a feature branch.
  2. Make your change, keeping modules focused and public APIs typed.
  3. Run the checks below before opening a pull request.
  4. Open a pull request describing the motivation and the change.

Checks

uv run ruff check .                                  # lint
uv run pytest                                        # tests
uv run auto-bayesian train examples/lead_scoring.toml  # smoke test

Please add or update tests for any behavior change.

Design principles

These guidelines keep the codebase approachable:

  • Prefer direct code over abstraction layers.
  • Each module has one clear responsibility.
  • Keep public APIs typed and small, and defaults deterministic.
  • Avoid hidden behavior and side effects.
  • Add a dependency only when it removes real complexity.

License

By contributing, you agree that your contributions will be licensed under the Apache License 2.0.