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CONTRIBUTING.md

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Contributing

We love your input! We want to make contributing to this project as easy and transparent as possible, whether it's:

  • Reporting a bug
  • Discussing the current state of the code
  • Submitting a fix
  • Proposing new features
  • Becoming a maintainer

Development Process

We use GitHub to host code, to track issues and feature requests, as well as accept pull requests.

Pull Requests

  1. Fork the repo and create your branch from main.
  2. If you've added code that should be tested, add tests.
  3. If you've changed APIs, update the documentation.
  4. Ensure the test suite passes.
  5. Make sure your code lints.
  6. Issue that pull request!

Development Setup

# Clone your fork
git clone https://github.com/yourusername/machine-learning-model.git
cd "Machine Learning Model"

# Create virtual environment (if not exists)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
make install-dev

# Install pre-commit hooks
pre-commit install

# Run tests
make test

# Check code quality
make check

Code Style

We use several tools to maintain code quality:

  • Black for code formatting
  • isort for import sorting
  • flake8 for linting
  • mypy for type checking
  • pytest for testing
  • ruff for fast linting

Run all checks with:

make check

Project-Specific Guidelines

Machine Learning Code

  • Document all model architectures and hyperparameters
  • Include data preprocessing steps
  • Add model performance metrics in docstrings
  • Use type hints for tensor shapes where applicable

Scripts

  • All scripts should have proper argument parsing
  • Include help text and examples
  • Test scripts with edge cases
  • Document script dependencies

Commit Message Convention

We follow the Conventional Commits specification:

<type>[optional scope]: <description>

[optional body]

[optional footer(s)]

Types:

  • feat: A new feature
  • fix: A bug fix
  • docs: Documentation only changes
  • style: Changes that do not affect the meaning of the code
  • refactor: A code change that neither fixes a bug nor adds a feature
  • perf: A code change that improves performance
  • test: Adding missing tests or correcting existing tests
  • chore: Changes to the build process or auxiliary tools

Examples:

feat(model): add transformer architecture
fix(scripts): resolve project creation path issue
docs(readme): update installation instructions

Testing

  • Write tests for new features
  • Ensure all tests pass
  • Aim for high test coverage
  • Include both unit and integration tests
  • Test ML models with sample data
  • Mock external dependencies

Documentation

  • Update docstrings for new functions/classes
  • Update README.md if needed
  • Add examples for new features
  • Document model architectures
  • Include performance benchmarks

Working with Virtual Environments

Since this project uses virtual environments:

  1. Always activate the virtual environment before working
  2. Use pip install -e . for development installs
  3. Update requirements files when adding dependencies
  4. Test in clean environments before submitting

License

By contributing, you agree that your contributions will be licensed under the same license as the project.