Package: pykale
Very cool library with lots of great ideas on moving toward 'green', efficient multimodal machine learning and AI.Kevin Carlberg, AI Research Science Manager at Facebook Reality Labs (quoted fromtweet).Getting Started|Documentation|Contributing|Discussions|ChangelogPyKale is a library in thePyTorch ecosystemaiming to make machine learning more accessible to interdisciplinary research by bridging gaps between data, software, and end users. Both machine learning experts and end users can do better research with our accessible, scalable, and sustainable design, guided by green machine learning principles. PyKale has a unifiedpipeline-basedAPI and focuses onmultimodal learningandtransfer learningfor graphs, images, and videos at the moment, with supporting models ondeep learninganddimensionality reduction.PyKale enforcesstandardizationandminimalism, via green machine learning concepts ofreducingrepetitions and redundancy,reusingexisting resources, andrecyclinglearning models across areas. PyKale will enable and accelerateinterdisciplinary,knowledge-awaremachine learning research for graphs, images, and videos in applications including bioinformatics, graph analysis, image/video recognition, and medical imaging, with an overarching theme of leveraging knowledge from multiple sources for accurate andinterpretableprediction.See ourarXiv preprintand four short introductory videos on YouTube:Why build PyKale?How was PyKale built?What's in PyKale?anda 5-min summary.Pipeline-based APIloaddataloads data from disk or online resources as inputprepdatapreprocesses data to fit machine learning modules below (transforms)embedembeds data in a new space to learn a new representation (feature extraction/selection)predictpredicts a desired outputevaluateevaluates the performance using some metricsinterpretinterprets the features and outputs via post-prediction analysis mainly via visualizationpipelinespecifies a machine learning workflow by combining several other modulesExample usageexamplesdemonstrate real applications on specific datasets with a standardized structure.How to UseStep 0: InstallationPyKale supports Python 3.8, 3.9, or 3.10. Before installingpykale, we suggest you to firstinstall PyTorchmatching your hardware, and if graphs will be used, installPyTorch Geometricfollowing itsofficial instructions.Simple installation ofpykalefromPyPI:pipinstallpykaleFor more details and other options, please refer tothe installation guide.Step 1: Tutorials and ExamplesStart with a brieftutorialwalking through API usage in examples orinteractiveJupyter notebook tutorials, e.g.orfor a basic digit classification problem.Browse through theexamplesto see the usage of PyKale in performing various prediction tasks in a wide range of applications, using a variety of settings, e.g. with or withoutPyTorch Lightning.Ask questions onPyKale's GitHub Discussions tabif you need help or create an issue if you find somStep 2: Building and ContributingBuild new modules and/or projects with PyKale referring to thetutorial, e.g., on how to modify an existing pipeline or build a new one.This is an open-source project welcoming your contributions. You can contribute in three ways:StarandforkPyKale to follow its latest developments, share it with your networks, andask questionsabout it.Use PyKale in your project and let us know any bugs (& fixes) and feature requests/suggestions via creating anissue.Contribute viabranch, fork, and pullfor minor fixes and new features, functions, or examples to become one of thecontributors.Seecontributing guidelinesfor more details. You can also reach us viaemailif needed. The participation in this open source project is subject toCode of Conduct.Who We AreThe TeamPyKale is maintained byHaiping Lu,Shuo Zhou,Xianyuan Liu, andPeizhen Bai, with contributions from many othercontributors.Citation@inproceedings{pykale-cikm2022,
      title     = {{PyKale}: Knowledge-Aware Machine Learning from Multiple Sources in {Python}},
      author    = {Haiping Lu and Xianyuan Liu and Shuo Zhou and Robert Turner and Peizhen Bai and Raivo Koot and Mustafa Chasmai and Lawrence Schobs and Hao Xu},
      booktitle = {Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM)},
      doi       = {10.1145/3511808.3557676},
      year      = {2022}
    }Please consider citing ourCIKM2022 paperabove if you findPyKaleuseful to your research.AcknowledgementsThe 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".