Contributing

gplearn welcomes your contributions! Whether it is a bug report, bug fix, new feature or documentation enhancements, please help to improve the project!

In general, please follow the scikit-learn contribution guidelines for how to contribute to an open-source project.

If you would like to open a bug report, please open one here. Please try to provide a Short, Self Contained, Example so that the root cause can be pinned down and corrected more easily.

If you would like to contribute a new feature or fix an existing bug, the basic workflow to follow (as detailed more at the scikit-learn link above) is:

  • Open an issue with what you would like to contribute to the project and its merits. Some features may be out of scope for gplearn, so be sure to get the go-ahead before working on something that is outside of the project’s goals.
  • Fork the gplearn repository, clone it locally, and create your new feature branch.
  • Make your code changes on the branch, commit them, and push to your fork.
  • Open a pull request.

Please ensure that:

  • Only data-dependent arguments should be passed to the fit/transform methods (X, y, sample_weight), and conversely, no data should be passed to the estimator initialization.
  • No input validation occurs before fitting the estimator.
  • Any new feature has great test coverage.
  • Any new feature is well documented with numpy-style docstrings & an example, if appropriate and illustrative.
  • Any bug fix has regression tests.
  • Comply with PEP8.

Currently gplearn uses Travis CI and AppVeyor for testing, Coveralls for code coverage reports, and Codacy for code quality checks. These applications should automatically run on your new pull request to give you guidance on any problems in the new code.