Skip to content

Latest commit

 

History

History
187 lines (129 loc) · 6.84 KB

CONTRIBUTING.md

File metadata and controls

187 lines (129 loc) · 6.84 KB
Raw

Contributing with pymfe

How to contribute

The preferred way to contribute to pymfe 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 [email protected]:YourLogin/pymfe.git
 $ cd pymfe

  3. Create a branch to hold your changes. Never work in the master branch!

     $ git checkout -b my-feature

  4. Work on your copy using Git to do the version control. When you're done editing, do:

     $ git add modified_files
 $ git commit -m "Add a simple message explaining your modifications."

    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 pymfe repo, and click 'Pull request' to send your changes to the maintainers for review. This will send an email to the Collaborators.

See examples in our Git Repository or Documentation that we made to any person that wishes to contribute for the development of the package or understand more about it. We expect that these examples show you the basics of PYMFE architecture and inspire you to contribute.

(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 cited below (pytest, pylint and mypy).

  • Avoid reinventing the wheel, reuse the functions from the pymfe._internal submodule.

  • If your pull request addresses an issue, please use the title to describe the issue and mention the issue number (using #) 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 a broad review of functionality or API, or seek collaborators.

  • Don't forget to activate your virtual environment, if any:

     $ source venv/bin/activate

  • All other tests pass when everything is rebuilt from scratch. On Unix-like systems, check with (from the top-level source folder):

     $ make

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

  • At least one paragraph of 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 unit test coverage (at least 90%), check with:

     $ pip install pytest pytest-cov
 $ pytest tests/ --showlocals -v --cov=pymfe/

  • For avoiding source-code bug and keep quality, check with:

     $ pip install pylint
 $ pylint path/to/module.py -d 'C0103, R0913, R0902, R0914, C0302, R0904, R0801, E1101'

  • Python typing, check with:

     $ pip install mypy
 $ mypy path/to/module.py --ignore-missing-imports

We added a Makefile to execute all this command in a simple way:

  • For installing all necessary libraries:

      $ make install-dev

  • For checking typing, source code style and code quality:

      $ make code-check

  • For executing all the tests:

      $ make test-cov

  • For executing all the tests:

      $ make all

Report 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 on 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, pandas, 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 pandas; print("pandas", pandas.__version__)
import patsy; print("patsy", pandas.__version__)
import pymfe; print("pymfe", pymfe.__version__)

  • If you wish, you can use a predefined issue template.

Documentation

We are glad to accept any 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 docs/ directory. The resulting HTML files will be placed in _build/html/ and are viewable in a web browser. See the README file in the docs/ directory for more information.

For building the documentation, you will need sphinx.

When you are writing documentation, it is essential 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.

Notes

This guide is adapted from scikit-learn and imbalanced-learn.