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CONTRIBUTING.md

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Contributing

Scikit-Optimize is an open-source project, and contributions of all kinds are welcome. We believe in this code of conduct.

You can contribute documentation, examples or code, review open pull requests, help answer questions in the forum, create visualizations, maintain project infrastructure, write new user stories ... All efforts are equally important.

Issues

When opening a new issue, please follow the chosen issue template. See How to Report Bugs Effectively.

When describing a bug, please provide a minimal working example, and full traceback, if applicable, sufficient to understand and reproduce the issue on our end.

Wrap code/verbatim/output text in fenced code blocks.

Pull requests

Contributions should roughly follow the GitHub flow and these guidelines:

  • all changes by pull request (PR);
  • create a PR early while you work on it (good to avoid duplicated work, get broad review of functionality or API, or seek collaborators);
  • mark your pull request as draft until it's ready for review;
  • a PR should solve one problem with a minimal set of changes (don't mix problems together in one issue/PR);
  • describe why you are proposing the changes you are proposing;
  • try to not rush changes (the definition of rush depends on how big your changes are);
  • someone else has to merge your PR;
  • new code needs to come with tests that cover it;
  • apply PEP8 as much as possible, but not too much (flake8 should pass);
  • no merging if CI checks are red.

These are guidelines rather than hard rules to be enforced by 🚓

All contributors are welcome, but note that scikit-optimize is a mature project, with thousands of users, and thus a responsibility to move slow, particularly when it comes to backwards-incompatible changes or vastly expanded functionality/scope.

Before making a PR for a new feature, do weigh the cost-benefit assumptions. Make sure your proposal is discussed and that it received community support. Avoid expending effort that would go unmerged. The inclusion criteria for new functionality is as strict as that of scikit-learn.