pykale 0.1.0b3

Knowledge-aware machine learning from multiple sources in Python

PyKale

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Getting Started | Documentation | Contributing | Discussions | Changelog

PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos. By adopting a unified pipeline-based API design, PyKale enforces standardization and minimalism, via reusing existing resources, reducing repetitions and redundancy, and recycling learning models across areas. PyKale aims to facilitate interdisciplinary, knowledge-aware machine learning research for graphs, images, texts, and videos in applications including bioinformatics, graph analysis, image/video recognition, and medical imaging. It focuses on leveraging knowledge from multiple sources for accurate and interpretable prediction. See a 12-minute introduction video on YouTube.

Pipeline-based core API (generic and reusable)

• loaddata loads data from disk or online resources as in input
• prepdata preprocesses data to fit machine learning modules below (transforms)
• embed embeds data in a new space to learn a new representation (feature extraction/selection)
• predict predicts a desired output
• evaluate evaluates the performance using some metrics
• interpret interprets the features and outputs via post-prediction analysis mainly via visualisation
• pipeline specifies a machine learning workflow by combining several other modules

Example usage

• examples demonstrate real applications on specific datasets.

Installation

Simple installation from PyPI:

pip install pykale

For more details and other options, please refer to the installation guide.

Examples, Tutorials, and Discussions

See our numerous examples (and tutorials) on how to perform various prediction tasks in a wide range of applications using PyKale.

Ask and answer questions on PyKale's GitHub Discussions tab.

Contributing

We appreciate all contributions. You can contribute in three ways:

• Star and fork PyKale to follow its latest developments, share it with your networks, and ask questions about it.
• Use PyKale in your project and let us know any bugs (& fixes) and feature requests/suggestions via creating an issue.
• Contribute via branch, fork, and pull for minor fixes and new features, functions, or examples to become one of the contributors.

See contributing guidelines for more details. You can also reach us via email if needed. The participation in this open source project is subject to Code of Conduct.

The Team

PyKale is primarily maintained by a group of researchers at the University of Sheffield: Haiping Lu, Raivo Koot, Xianyuan Liu, Shuo Zhou, Peizhen Bai, and Robert Turner.

We would like to thank our other contributors including (but not limited to) Cameron McWilliam, David Jones, and Will Furnass.

Citation

    @Misc{pykale2021,
      author =   {Haiping Lu and Raivo Koot and Xianyuan Liu and Shuo Zhou and Peizhen Bai and Robert Turner},
      title =    {{PyKale}: Knowledge-aware machine learning from multiple sources in Python},
      howpublished = {\url{https://github.com/pykale/pykale}},
      year = {2021}
    }

Acknowledgements

The development of PyKale is partially supported by the following project(s).

• Wellcome Trust Innovator Awards: Digital Technologies Ref 215799/Z/19/Z "Developing a Machine Learning Tool to Improve Prognostic and Treatment Response Assessment on Cardiac MRI Data".