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Contributing code

This guide is adapted from scikit-learn.

How to contribute

The preferred way to contribute to imbalanced-learn is to fork the main repository on GitHub:

  1. 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.

  2. Clone this copy to your local disk:

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

  3. Create a branch to hold your changes:

     $ git checkout -b my-feature

    and start making changes. Never work in the master branch!

  4. 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 imbalanced-learn repo, and click 'Pull request' to send your changes to the maintainers for review. 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.)

Contributing Pull Requests

It is recommended to check that your contribution complies with the following rules before submitting a pull request:

  • Follow the coding-guidelines as for scikit-learn.

  • When applicable, use the validation tools and other code in the sklearn.utils submodule. A list of utility routines available for developers can be found in the Utilities for Developers page.

  • 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

  • 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.

  • 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=imblearn imblearn

  • 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

Filing bugs

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.

  • 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 runnning 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__)
import imblearn; print("Imbalanced-Learn", imblearn.__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.

Documentation

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/ 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.