Metadata-Version: 2.1
Name: pykale
Version: 0.1.0b3
Summary: Knowledge-aware machine learning from multiple sources in Python
Home-page: https://github.com/pykale/pykale
Author: The PyKale team
Author-email: pykale-group@sheffield.ac.uk
License: MIT
Project-URL: Bug Tracker, https://github.com/pykale/pykale/issues
Project-URL: Documentation, https://pykale.readthedocs.io
Project-URL: Source, https://github.com/pykale/pykale
Description: <p align="center">
          <img src="https://github.com/pykale/pykale/raw/master/docs/images/pykale_logo.png" width="5%" alt='project-monai'> PyKale
        </p>
        
        -----------------------------------------
        
        ![tests](https://github.com/pykale/pykale/workflows/test/badge.svg)
        [![Documentation Status](https://readthedocs.org/projects/pykale/badge/?version=latest)](https://pykale.readthedocs.io/en/latest/?badge=latest)
        [![PyPI version](https://img.shields.io/pypi/v/pykale?color=blue)](https://pypi.org/project/pykale/)
        [![PyPI downloads](https://pepy.tech/badge/pykale)](https://pepy.tech/project/pykale)
        
        [Getting Started](https://github.com/pykale/pykale/tree/master/examples) |
        [Documentation](https://pykale.readthedocs.io/) |
        [Contributing](https://github.com/pykale/pykale/blob/master/.github/CONTRIBUTING.md) |
        [Discussions](https://github.com/pykale/pykale/discussions) |
        [Changelog](https://github.com/pykale/pykale/tree/master/.github/CHANGELOG.md)
        
         PyKale is a [PyTorch](https://pytorch.org/) library for [multimodal learning](https://en.wikipedia.org/wiki/Multimodal_learning) and [transfer learning](https://en.wikipedia.org/wiki/Transfer_learning) as well as [deep learning](https://en.wikipedia.org/wiki/Deep_learning) and [dimensionality reduction](https://en.wikipedia.org/wiki/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](https://youtu.be/i5BYdMfbpMQ).
        
        ### 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](https://pypi.org/project/pykale/):
        
        ```bash
        pip install pykale
        ```
        
        For more details and other options, please refer to [the installation guide](https://pykale.readthedocs.io/en/latest/installation.html).
        
        ## Examples, Tutorials, and Discussions
        
        See our numerous [**examples (and tutorials)**](https://github.com/pykale/pykale/tree/master/examples) 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](https://github.com/pykale/pykale/discussions).
        
        ## Contributing
        
        We appreciate all contributions. You can contribute in three ways:
        
        - [Star](https://docs.github.com/en/github/getting-started-with-github/saving-repositories-with-stars) and [fork](https://docs.github.com/en/github/getting-started-with-github/fork-a-repo) PyKale to follow its latest developments, share it with your networks, and [ask questions](https://github.com/pykale/pykale/discussions)  about it.
        - Use PyKale in your project and let us know any bugs (& fixes) and feature requests/suggestions via creating an [issue](https://github.com/pykale/pykale/issues).
        - Contribute via [branch, fork, and pull](https://github.com/pykale/pykale/blob/master/CONTRIBUTING.md#branch-fork-and-pull) for minor fixes and new features, functions, or examples to become one of the [contributors](https://github.com/pykale/pykale/graphs/contributors).
        
        See [contributing guidelines](https://github.com/pykale/pykale/blob/master/.github/CONTRIBUTING.md) for more details. You can also reach us via <a href="mailto:pykale-group&#64;sheffield.ac.uk">email</a> if needed. The participation in this open source project is subject to [Code of Conduct](https://github.com/pykale/pykale/blob/master/.github/CODE_OF_CONDUCT.md).
        
        ## The Team
        
        PyKale is primarily maintained by a group of researchers at the University of Sheffield: [Haiping Lu](http://staffwww.dcs.shef.ac.uk/people/H.Lu/), [Raivo Koot](https://github.com/RaivoKoot), [Xianyuan Liu](https://github.com/XianyuanLiu), [Shuo Zhou](https://sz144.github.io/), [Peizhen Bai](https://github.com/pz-white), and [Robert Turner](https://github.com/bobturneruk).
        
        We would like to thank our other contributors including (but not limited to) Cameron McWilliam, David Jones, and Will Furnass.
        
        ## Citation
        
        ```lang-latex
            @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".
        
Keywords: machine learning,pytorch,deep learning,multimodal learning,transfer learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Software Development :: Libraries
Classifier: Natural Language :: English
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: extras
Provides-Extra: dev
