Contribute#

Thank you for choosing to contribute to PyCaret. There are a ton of great open-source projects out there, so we appreciate your interest in contributing to PyCaret. It is an open-source, low-code machine learning library in Python developed and open-sourced in April 2020 by Moez Ali moez.ali@queensu.ca and is now maintained by awesome community members just like you. In this documentation, we will cover a couple of ways you can contribute to this project.

Documentation#

There is always room for improvement in documentation. We welcome all the pull requests to fix typo / improve grammar or semantic structuring of documents. Here are a few documents you can work on:

Open Issues#

If you would like to help in working on open issues. Look out for following tags: good first issue help wanted open for contribution

Major Contribution#

If you are willing to make a major contribution you can always lookout for the active sprint under Projects and discuss the proposal with sprint leader.

What we currently need help on?#

  • Improving unit-test cases and test coverage sktime/pycaret

  • Refactor preprocessing pipeline to support GPU

  • Dask Integration

Development setup#

Follow installation instructions to first create a virtual environment. Then, install development version of the package:

pip install -e .[test]

We use pre-commit with black for code formatting. It runs automatically before you make a new commit. To set up pre-commit, follow these steps:

  1. Install pre-commit:

pip install pre-commit

  1. Set up pre-commit:

pre-commit install

Unit testing#

Install development version of the package with additional extra dependencies required for unit testing:

pip install -e .[test]

We use pytest for unit testing.

To run tests, except skipped ones (search for @pytest.mark.skip decorator over test functions), run:

pytest pycaret

Documentation#

We use sphinx to build our documentation and readthedocs to host it. The source files can be found in docs/source/. The main configuration file for sphinx is conf.py and the main page is index.rst.

To build the documentation locally, you need to install the documentation dependency set.

pip install -e ".[docs]"

To build the website locally, run:

sh make.sh

You can find the generated files in the docs/build/ folder. To view the website, open docs/build/index.html with your preferred web browser.