srlearn Contributing Guidelines
Our goal is to make BoostSRL more accessible to the Python community by creating general-purpose wrappers for the existing software. Any suggestions or contributions which further this goal are welcome.
Code of Conduct
We have adopted the Contributor Covenant Code of Conduct. Please read, follow, and report any incidents which violate this.
Submitting Issues (bugs and new features):
We use GitHub issues for tracking all bugs and feature requests; feel free to open an issue if you have found a bug or wish to see a feature implemented.
It is recommended to check that your issue complies with the following guidelines before submitting.
Documentation for requesting features is included in the feature_request template.
We use Github issues to track all bugs and feature requests; feel free to open an issue if you have found a bug or wish to see a feature implemented.
It is recommended to check that your issue complies with the following rules before submitting:
Follow the guidelines in the bug-report template.
Please ensure all code snippets and error messages are formatted in appropriate code blocks. See Creating and highlighting code blocks.
Please include your operating system type and version number, as well as your Python, scikit-learn, numpy, and scipy versions. This information can be found by running the following code snippet:
python -c "import srlearn; srlearn.show_versions()"
Please be specific about what estimators and/or functions are involved and the shape of the data, as appropriate; please include a reproducible code snippet or link to a gist. If an exception is raised, please provide the traceback.
This guide is adapted from imbalanced-learn.
How to contribute
The preferred way to contribute to
srlearn is to fork the
main repository on
Fork the project repository: click on the 'Fork' button near the top of the page. This creates a copy of the code under your account on the GitHub server.
Clone this copy to your local disk:
$ git clone firstname.lastname@example.org:YourLogin/srlearn.git $ cd srlearn
Create a branch to hold your changes:
$ git checkout -b my-feature
and start making changes. Never work in the
Work on this copy on your computer using Git to do the version control. When you're done editing, do:
$ git add modified_files $ git commit
to record your changes in Git, then push them to GitHub with:
$ git push -u origin my-feature
Finally, go to the web page of your fork of the srlearn repo, and click 'Pull request' to send your changes to the maintainers for review.
(If any of the above seems like magic to you, then look up the Git documentation on the web.)
Contributing Pull Requests
It is recommended to check that your contribution complies with the following rules before submitting a pull request:
If your pull request addresses an issue, please use the title to describe the issue and mention the issue number in the pull request description to ensure a link is created to the original issue.
All public methods should have informative docstrings with sample usage presented as doctests when appropriate.
Please prefix the title of your pull request with
[MRG]if the contribution is complete and should be subjected to a detailed review. Incomplete contributions should be prefixed
[WIP]to indicate a work in progress (and changed to
[MRG]when it matures). WIPs may be useful to: indicate you are working on something to avoid duplicated work, request broad review of functionality or API, or seek collaborators. WIPs often benefit from the inclusion of a task list in the PR description.
All other tests pass when everything is rebuilt from scratch. On Unix-like systems, check with (from the toplevel source folder):
$ make test
When adding additional functionality, provide at least one example script in the
docs/examples/folder. Have a look at other examples for reference. Examples should demonstrate why the new functionality is useful in practice and, if possible, compare it to other methods available in srlearn.
Documentation and high-coverage tests are necessary for enhancements to be accepted.
At least one paragraph of narrative documentation with links to references in the literature (with PDF links when possible) and the example.
You can also check for common programming errors with the following tools:
Code with good unittest coverage (at least 80%), check with:
$ pip install pytest pytest-cov $ pytest --cov=srlearn srlearn
Code style is formatted automatically with
$ pip install black $ black srlearn/
blackcannot automatically fix all style problems, see
pycodestylefor pep8 compliance
$ pip install pycodestyle $ pycodestyle srlearn/
pylinthelps catch common issues (keep rating above 9.0).
$ pip install pylint $ pylint srlearn/
We are glad to accept any sort of documentation: function docstrings,
reStructuredText documents, tutorials, etc. reStructuredText documents
live in the source code repository under the
You can edit the documentation using any text editor and then generate
the HTML output by typing
make html from the docs/ directory.
The resulting HTML files will be placed in
build/html/ and are viewable
in a web browser. See the README file in the doc/ directory for more information.
When you are writing documentation, it is important to keep a good compromise between mathematical and algorithmic details, and give intuition to the reader on what the algorithm does. It is best to always start with a small paragraph with a hand-waving explanation of what the method does to the data and a figure (coming from an example) illustrating it.