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CONTRIBUTING.md

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Thank you for considering contributing to PrivacyRaven! Feel free to ask any questions if something is unclear. We’ve codified a set of guidelines and instructions to make contributing to PrivacyRaven as easy as possible. Please note that these guidelines are not rigid and can be broken if necessary.

  • Build instructions are contained in the README. Fork your own repository from GitHub and rebase your fork with new changes from master.
  • Raise or be assigned to an issue before submitting a pull request. After submitting a pull request, one of our core maintainers must approve it before it is pushed.
  • When reporting a bug, provide as much information as possible with regards to your setup.
  • When suggesting a feature, feel free to leave it open-ended. Provide links to similar implementations or reference papers as applicable.
  • Ensure that the code you’ve contributed passes all tests and formatting checks. PrivacyRaven uses Python black, isort, flake8, and nox.
  • Be generous with comments and explanations
  • Create tests and documentation for your contribution
  • For now, documentation merely entails incorporating docstrings like so:
def example(a):
   """Does something

   Parameters:
      a: data type that represents something

   Returns:
      a data type that represents something else"""

class another(object):
    """Does another thing

    Attributes:
       a: data type that represents something else"""
  • Clearly disclose all known limitations
  • Center the user while developing. Simplify the API as much as possible
  • Make sure that a user can quickly understand what your code does even without understanding how it works. Reference the Python API Checklist to confirm usability.
  • Build from the fundamental building blocks.
  • To add a new attack in attack.py, make sure to build functions for any novel synthesizer, robustness metrics, or subset sampling strategies, and that the new attack is included in the run-all attacks feature.
  • To add a new synthesizer for synthesis.py or robustness metric for robust.py, maintain the same function signature.
  • When building classes, use attrs.
  • We prefer data-specific code to be written in PyTorch and generally adhere to the PyTorch Style Guide.