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Contributing to dislib

How to contribute

The preferred workflow for contributing to dislib 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 dislib repo from your GitHub account to your local disk:

    $ git clone git@github.com:YourLogin/dislib.git
$ cd dislib

  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:

  • Run the tests before attempting to merge. You can run them locally with:
./run_tests.sh # it may ask your password to start the ssh daemon
  • Check the code coverage, it should at least do not decrease due to the PR. You can run them locally with:
pip3 install coverage
./run_coverage.sh # it may ask your password to start the ssh daemon
  • Check the code style before attempting to merge. If there is any warning the PR will be rejected. You can run them locally with:
pip3 install flake8
flake8 ./dislib
  • Docker image. All tests and code checks are run inside a docker image. If you want to run the tests in the same environment that travis will use:
    docker build --pull --cache-from bscwdc/dislib --tag bscwdc/dislib .
    docker run -d --name dislib bscwdc/dislib 
    docker exec dislib bash run_tests.sh
    docker exec dislib bash run_coverage.sh
    docker exec dislib flake8 dislib

  • Follow the coding-guidelines defined by default flake8.

  • 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 [WIP] to indicate a work in progress where you expect feedback before doing more work. 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.

  • 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.

You can also check for common programming errors with the following tools:

  • Code with good unittest coverage (at least 80%), check with:
$ pip3 install coverage
$ coverage run --source dislib tests/tests.py
  • No flake8 warnings, check with:
$ pip install flake8
$ flake8 dislib

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).

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 PyCOMPSs version (or docker image 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.

New contributor tips

A great way to start contributing to dislib is to choose a possible algorithm from sklearn or one of your interest and get in touch with us! We are open to research collaborations.

Documentation

We use numpy doc style. 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.

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.