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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](http://scikit-learn.org/stable/developers/contributing.html)
for how to contribute to an open-source project.
If you would like to open a bug report, please [open one here](https://github.com/trevorstephens/gplearn/issues).
Please try to provide a [Short, Self Contained, Example](http://sscce.org/)
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](https://github.com/trevorstephens/gplearn/issues) 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](https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt)
& an example, if appropriate and illustrative.
- Any bug fix has regression tests.
- Comply with [PEP8](https://pypi.python.org/pypi/pep8).
Currently ``gplearn`` uses [GitHub workflows](https://github.com/trevorstephens/gplearn/actions/workflows/build.yml)
for testing, [Coveralls](https://coveralls.io/github/trevorstephens/gplearn)
for code coverage reports, and [Codacy](https://app.codacy.com/gh/trevorstephens/gplearn/dashboard)
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.
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