Contributing

We welcome all forms of contribution, please consider contributing in any way you can!

Bugs, issues, input, and questions

Please use the issue tracker to share any of the following:

• Bugs

• Issues

• Questions

• Feature requests

• Ideas

• Input

Having these reported and saved in the issue tracker is very helpful to make sure that they are properly addressed. Please make sure to be as descriptive and neat as possible when opening up an issue.

Developing guidelines

If you have written code or want to start writing new code that you think will improve sparse-lm then please follow the steps below to make a contribution.

• All code should have unit tests.

• Code should be well documented following google style docstrings.

• All code should pass the pre-commit hook. The code follows the black code style.

• Estimators should follow scikit-learn’s developing estimator guidelines.

Adding code contributions

1. If you are contributing for the first time:

   • Fork the repository and then clone your fork to your local workspace.

   • Make sure to add the upstream repository as a remote:

     git remote add upstream https://github.com/CederGroupHub/sparse-lm.git
     

   • You should always keep your main branch or any feature branch up to date with the upstream repository main branch. Be good about doing fast forward merges of the upstream main into your fork branches while developing.

2. In order to have changes available without having to re-install the package:

   • Install the package in editable mode:

     pip install -e .
     

3. To develop your contributions you are free to do so in your main branch or any feature branch in your fork.

   • We recommend to only your forks main branch for short/easy fixes and additions.

   • For more complex features, try to use a feature branch with a descriptive name.

   • For very complex feautres feel free to open up a PR even before your contribution is finished with [WIP] in its name, and optionally mark it as a draft.

4. While developing we recommend you use the pre-commit hook that is setup to ensure that your code will satisfy all lint, documentation and black requirements. To do so install pre-commit, and run in your clones top directory:

   pre-commit install
   

   • All code should use google style docstrings and black style formatting.

5. Make sure to test your contribution and write unit tests for any new features. All tests should go in the sparse-lm\tests directory. The CI will run tests upon opening a PR, but running them locally will help find problems before:

   pytests tests