Contributing¶

Note¶

A large part of our guidelines come directly from scikit-learn. For more detail, refer to the documentation there.

git clone git://github.com/sklearn-theano/sklearn-theano.git

How to contribute¶

The preferred way to contribute to sklearn-theano is to fork the main repository on GitHub, then submit a “pull request” (PR):

1. Create an account on GitHub if you do not already have one.

2. 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.

3. Clone this copy to your local disk:

   $ git clone git@github.com:YourLogin/sklearn-theano.git
   

4. Create a branch to hold your changes:

   $ git checkout -b my-feature
   

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

5. 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 the your fork of the scikit-learn repo, and click ‘Pull request’ to send your changes to the maintainers for review. request. This will send an email to the committers, but might also send an email to the mailing list in order to get more visibility.

$ git remote add upstream https://github.com/sklearn-theano/sklearn-theano.git

(If any of the above seems like magic to you, then look up the Git documentation on the web.)

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

• Follow the coding-guidelines (see below).

• All public methods should have informative docstrings with sample usage presented as doctests when appropriate.

• 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 sklearn-theano.

• 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 a good unittest coverage (at least 90%, better 100%), check with:

  $ pip install nose coverage
  $ nosetests --with-coverage path/to/tests_for_package
  

  see also testing_coverage

• 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
  

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 wiki).

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 html-noplot 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.

Any math and equations, followed by references, can be added to further the documentation. Not starting the documentation with the maths makes it more friendly towards users that are just interested in what the feature will do, as opposed to how it works “under the hood”.

Issue Tracker Tags¶

All issues and pull requests on the Github issue tracker should have (at least) one of the following tags:

There are two other tags to help new contributors:

Additional Help¶

See the scikit-learn developer documentation for more details. In general, we try to follow the scikit-learn guidelines as closely as possible.

Working notes¶

For unresolved issues, TODOs, and remarks on ongoing work, developers are advised to maintain notes on the GitHub wiki.