Note: This document is just to get started, visit Contributing page for the full contributor's guide. Please be sure to read it carefully to make the code review process go as smoothly as possible and maximize the likelihood of your contribution being merged.
How to contribute
The preferred way to contribute to scikit-learn is to fork the main repository on GitHub:
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/scikit-learn.git
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 the your fork of the scikit-learn repo, and click 'Pull request' to send your changes to the maintainers for review. request. This will send an email to the committers.
(If any of the above seems like magic to you, then look up the Git documentation on the web.)
It is recommended to check that your contribution complies with the following rules before submitting a pull request:
All public methods should have informative docstrings with sample usage presented as doctests when appropriate.
All other tests pass when everything is rebuilt from scratch. On Unix-like systems, check with (from the toplevel source folder):
When adding additional functionality, provide at least one example script in the
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 scikit-learn.
At least one paragraph of narrative documentation with links to ```` references in the literature (with PDF links when possible) and the example.
The documentation should also include expected time and space complexity of the algorithm and scalability, e.g. "this algorithm can scale to a large number of samples > 100000, but does not scale in dimensionality: n_features is expected to be lower than 100".
You can also check for common programming errors with the following tools:
Code with good unittest coverage (at least 80%), check with:
$ pip install nose coverage $ nosetests --with-coverage path/to/tests_for_package
No pyflakes warnings, check with:
$ pip install pyflakes $ pyflakes path/to/module.py
No PEP8 warnings, check with:
$ pip install pep8 $ pep8 path/to/module.py
AutoPEP8 can help you fix some of the easy redundant errors:
$ pip install autopep8 $ autopep8 path/to/pep8.py
Bonus points for contributions that include a performance analysis with a benchmark script and profiling output (please report on the mailing list or on the GitHub issue).
A great way to start contributing to scikit-learn is to pick an item from the list of Easy issues in the issue tracker. Resolving these issues allow you to start contributing to the project without much prior knowledge. Your assistance in this area will be greatly appreciated by the more experienced developers as it helps free up their time to concentrate on other issues.
We are glad to accept any sort of documentation: function docstrings, reStructuredText documents (like this one), tutorials, etc. reStructuredText documents live in the source code repository under the doc/ directory.
You can edit the documentation using any text editor and then generate
the HTML output by typing
make html from the doc/ directory.
make can be used to quickly generate the
documentation without the example gallery. 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.
Visit the Contributing Code section of the website for more information including conforming to the API spec and profiling contributed code.