CONTRIBUTING.md

Contributing to scikit-learn

Note: This document is a 'getting started' summary for contributing code, documentation, testing, and filing issues. Visit the Contributing page for the full contributor's guide. Please read it carefully to help make the code review process go as smoothly as possible and maximize the likelihood of your contribution being merged.

How to contribute

The preferred workflow for contributing to scikit-learn is to fork the main repository on GitHub, clone, and develop on a branch. Steps:

1. Fork the project repository by clicking on the 'Fork' button near the top right of the page. This creates a copy of the code under your GitHub user account. For more details on how to fork a repository see this guide.

2. Clone your fork of the scikit-learn repo from your GitHub account to your local disk:

   $ git clone git@github.com:YourLogin/scikit-learn.git
   $ cd scikit-learn

3. Create a feature branch to hold your development changes:

   $ git checkout -b my-feature

   Always use a feature branch. It's good practice to never work on the master branch!

4. Develop the feature on your feature branch. Add changed files using git add and then git commit files:

   $ git add modified_files
   $ git commit

   to record your changes in Git, then push the changes to your GitHub account with:

   $ git push -u origin my-feature

5. Follow these instructions to create a pull request from your fork. This will send an email to the committers.

(If any of the above seems like magic to you, please look up the Git documentation on the web, or ask a friend or another contributor for help.)

Pull Request Checklist

We recommended that your contribution complies with the following rules before you submit a pull request:

• Follow the coding-guidelines.

• Use, when applicable, the validation tools and scripts in the sklearn.utils submodule. A list of utility routines available for developers can be found in the Utilities for Developers page.

• Give your pull request a helpful title that summarises what your contribution does. In some cases Fix <ISSUE TITLE> is enough. Fix #<ISSUE NUMBER> is not enough.

• Often pull requests resolve one or more other issues (or pull requests). If merging your pull request means that some other issues/PRs should be closed, you should use keywords to create link to them (e.g., Fixes #1234; multiple issues/PRs are allowed as long as each one is preceded by a keyword). Upon merging, those issues/PRs will automatically be closed by GitHub. If your pull request is simply related to some other issues/PRs, create a link to them without using the keywords (e.g., See also #1234).

• 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] (Ready for Merge), if the contribution is complete and ready for a detailed review. Two core developers will review your code and change the prefix of the pull request to [MRG + 1] and [MRG + 2] on approval, making it eligible for merging. An incomplete contribution -- where you expect to do more work before receiving a full review -- 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

• 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.

• Documentation and high-coverage tests are necessary for enhancements to be accepted. Bug-fixes or new features should be provided with non-regression tests. These tests verify the correct behavior of the fix or feature. In this manner, further modifications on the code base are granted to be consistent with the desired behavior. For the Bug-fixes case, at the time of the PR, this tests should fail for the code base in master and pass for the PR code.

• 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: