Contributing

We welcome contributions to onlinerake! This guide will help you get started.

Development Setup

Requirements

  • Python 3.10 or later

  • Git

Setup Steps

  1. Fork the repository on GitHub

  2. Clone your fork locally:

git clone https://github.com/your-username/onlinerake.git
cd onlinerake

  1. Install in development mode:

pip install -e .
pip install pytest black flake8 sphinx sphinx-rtd-theme myst-parser

Code Style

We use black for code formatting and flake8 for linting:

# Format code
black onlinerake/

# Check formatting
black --check onlinerake/

# Run linting
flake8 onlinerake/

Running Tests

Run the test suite to make sure everything works:

# Run all tests
pytest test_onlinerake.py -v

# Run with coverage
pytest test_onlinerake.py --cov=onlinerake --cov-report=term

# Run simulation tests
python examples/simulation.py
python realistic_examples.py

Types of Contributions

Bug Reports

Found a bug? Please open an issue with: - Clear description of the problem - Minimal code example to reproduce - Your Python version and package versions

Feature Requests

Have an idea for improvement? Open an issue with: - Description of the proposed feature - Use case and motivation - Proposed API (if applicable)

Code Contributions

Ready to contribute code? Here’s the process:

  1. Check existing issues or open a new one

  2. Create a feature branch: git checkout -b feature-name

  3. Make your changes with tests

  4. Run the test suite and style checks

  5. Commit with descriptive messages

  6. Push and create a pull request

Documentation

Documentation improvements are always welcome:

# Build docs locally
cd docs/
make html

# View in browser
open _build/html/index.html

Documentation is written in reStructuredText and built with Sphinx.

Areas for Contribution

Here are some areas where contributions would be especially valuable:

Algorithm Enhancements

  • Support for multi-level categorical variables

  • Adaptive learning rate schedules

  • Alternative loss functions

  • Regularization techniques

Performance Optimizations

  • Vectorized operations for batch processing

  • Memory-efficient implementations

  • GPU acceleration (optional)

Additional Features

  • Integration with popular survey platforms

  • Visualization tools for weight evolution

  • Export functionality for different formats

  • Real-time monitoring dashboards

Testing & Quality

  • More edge case tests

  • Performance benchmarks

  • Integration tests

  • Property-based testing

Pull Request Guidelines

To ensure smooth review process:

  1. Focus: Keep changes focused and atomic

  2. Tests: Add tests for new functionality

  3. Documentation: Update docs for API changes

  4. Style: Follow existing code conventions

  5. Commit Messages: Use descriptive commit messages

Example commit message:

Add support for custom weight initialization

- Allow users to provide initial weights via new parameter
- Add validation for weight dimensions and positivity
- Update documentation and examples
- Add comprehensive tests for edge cases

Review Process

All contributions go through code review:

  1. Automated checks (CI/CD) must pass

  2. Manual review by maintainers

  3. Discussion and iteration as needed

  4. Merge when approved

Questions?

Feel free to:

  • Open an issue for questions

  • Start a discussion for broader topics

  • Reach out to maintainers directly

Thank you for contributing to onlinerake!