Thank you for choosing to contribute in HyperPy. There are a ton of great open-source projects out there, so we appreciate your interest in contributing to HyperPy. It is an automatic hyperparameter optimization framework, low-code machine learning library in Python developed and open-sourced in October 2021 by Mora Sergio sergiomora823@gmail.com and is now maintained by awesome community members just like you. In this documentation we will cover couple of ways you can contribute to this project.
There is always a room for improvement in documentation. We welcome all the pull requests to fix typo / improve grammar or semantic structuring of documents. Here are few documents you can work on:
- Official Tutorials: https://github.com/sergiomora03/py-hyperpy/tree/master/tutorials
- README.md https://github.com/sergiomora03/py-hyperpy/blob/master/README.md
- Functional Documentation / Docstrings: https://github.com/sergiomora03/py-hyperpy/tree/master/hyperpy
If you would like to help in working on open issues. Lookout for following tags: good first issue help wanted open for contribution
If you are interested or have already written Medium story covering PyCaret. You can submit your story in a markdown format. Submit a PR to https://github.com/sergiomora03/py-hyperpy/tree/master/resources. To convert medium stories into markdown format please download this chrome extension: https://chrome.google.com/webstore/detail/export-to-markdown/dodkihcbgpjblncjahodbnlgkkflliim
If you are willing to make major contribution you can always look out for the active sprint under Projects and discuss the proposal with sprint leader. Current active sprint is 2.2 - major refactoring. This sprint is led by Yard1.
- Improving unit-test cases https://github.com/sergiomora03/py-hyperpy/tree/master/hyperpy/tests
- Major refactoring in
preprocess.pyto accomodate distributed processing - Example Notebooks required. Send PR to https://github.com/sergiomora03/py-hyperpy/tree/master/examples