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.
The preferred workflow for contributing to scikit-learn is to fork the main repository on GitHub, clone, and develop on a branch. Steps:
-
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.
-
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 -
Create a
featurebranch to hold your development changes:$ git checkout -b my-feature
Always use a
featurebranch. It's good practice to never work on themasterbranch! -
Develop the feature on your feature branch. Add changed files using
git addand thengit commitfiles:$ 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
-
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.)
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.utilssubmodule. 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:
- Code with good unittest coverage (at least 80%), check with:
$ pip install pytest pytest-cov
$ pytest --cov sklearn path/to/tests_for_package- No flake8 warnings, check with:
$ pip install flake8
$ flake8 path/to/module.pyBonus 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).
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:
-
Verify that your issue is not being currently addressed by other issues or pull requests.
-
If you are submitting an algorithm or feature request, please verify that the algorithm fulfills our new algorithm requirements.
-
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:
import platform; print(platform.platform())
import sys; print("Python", sys.version)
import numpy; print("NumPy", numpy.__version__)
import scipy; print("SciPy", scipy.__version__)
import sklearn; print("Scikit-Learn", sklearn.__version__)- 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.
A great way to start contributing to scikit-learn is to pick an item from the list of good first issues. If you have already contributed to scikit-learn look at Easy issues instead. 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.
Alternatively, make can be used to quickly generate the
documentation without the example gallery. The resulting HTML files will
be placed in _build/html/stable and are viewable in a web browser. See the
README file in the doc/ directory for more information.
For building the documentation, you will need sphinx, matplotlib, and pillow.
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.