CONTRIBUTING.md

Guidelines for Contributing

As a scientific community-driven software project, PyMC3 welcomes contributions from interested individuals or groups. These guidelines are provided to give potential contributors information to make their contribution compliant with the conventions of the PyMC3 project, and maximize the probability of such contributions to be merged as quickly and efficiently as possible.

There are 4 main ways of contributing to the PyMC3 project (in descending order of difficulty or scope):

• Adding new or improved functionality to the existing codebase
• Fixing outstanding issues (bugs) with the existing codebase. They range from low-level software bugs to higher-level design problems.
• Contributing or improving the documentation (docs) or examples (pymc3/examples)
• Submitting issues related to bugs or desired enhancements

Opening issues

We appreciate being notified of problems with the existing PyMC code. We prefer that issues be filed the on Gitub Issue Tracker, rather than on social media or by direct email to the developers.

Please verify that your issue is not being currently addressed by other issues or pull requests by using the GitHub search tool to look for key words in the project issue tracker.

Contributing code via pull requests

While issue reporting is valuable, we strongly encourage users who are inclined to do so to submit patches for new or existing issues via pull requests. This is particularly the case for simple fixes, such as typos or tweaks to documentation, which do not require a heavy investment of time and attention.

Contributors are also encouraged to contribute new code to enhance PyMC's functionality, also via pull requests. Please consult the PyMC3 documentation to ensure that any new contribution does not strongly overlap with existing functionality.

The preferred workflow for contributing to PyMC3 is to fork the GitHUb repository, clone it to your local machine, and develop on a feature branch.

Steps:

1. Fork the project repository by clicking on the 'Fork' button near the top right of the main repository page. This creates a copy of the code under your GitHub user account.

2. Clone your fork of the PyMC3 repo from your GitHub account to your local disk:

   $ git clone git@github.com:<your GitHub handle>/pymc3.git
   $ cd pymc3-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 routinely work on the master branch of any repository.

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 locally, then push the changes to your GitHub account with:

   $ git push -u origin my-feature

5. Go to the GitHub web page of your fork of the PyMC3 repo. Click the 'Pull request' button to send your changes to the project's maintainers fo review. This will send an email to the committers.

Pull request checklist

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

• If your pull request addresses an issue, please use the pull request title to describe the issue and mention the issue number in the pull request description. This will make sure a link back to the original issue is created.

• All public methods must have informative docstrings with sample usage when appropriate.

• Please prefix the title of incomplete contributions with [WIP] (to indicate a work in progress). WIPs may be useful to (1) indicate you are working on something to avoid duplicated work, (2) request broad review of functionality or API, or (3) seek collaborators.

• All other tests pass when everything is rebuilt from scratch. See Developing in Docker for information on running the test suite locally.

• When adding additional functionality, provide at least one example script or Jupyter Notebook in the pymc3/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 PyMC3.

• Documentation and high-coverage tests are necessary for enhancements to be accepted.

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

Developing in Docker

We have provided a Dockerfile which helps for isolating build problems, and local development. Install Docker for your operating system, clone this repo, then run ./scripts/start_container.sh. This should start a local docker container called pymc3, as well as a jupyter notebook server running on port 8888. You will have to open a browser at localhost:8888. The repo will be running the code from your local copy of pymc3, so it is good for development. You may also use it to run the test suite, with

$  docker exec -it pymc3  bash # logon to the container
$  cd ~/pymc3  
$  . ./scripts/test.sh # takes a while!

This should be quite close to how the tests run on TravisCI.

Style guide

Follow TensorFlow's style guide or the Google style guide for writing code, which more or less follows PEP 8.

This guide was derived from the scikit-learn guide to contributing