Metadata-Version: 1.1
Name: kindred
Version: 2.4.1
Summary: A relation extraction toolkit for biomedical text mining
Home-page: http://github.com/jakelever/kindred
Author: Jake Lever
Author-email: jake.lever@gmail.com
License: MIT
Description: =======
        Kindred
        =======
        
        |pypi| |build-status| |coverage| |docs| |license|
        
        .. |pypi| image:: https://img.shields.io/pypi/v/kindred.svg
           :target: https://pypi.python.org/pypi/kindred
           :alt: PyPI Release
           
        .. |build-status| image:: https://travis-ci.org/jakelever/kindred.svg?branch=master
           :target: https://travis-ci.org/jakelever/kindred
           :alt: Travis CI status
        
        .. |coverage| image:: https://coveralls.io/repos/github/jakelever/kindred/badge.svg?branch=master
           :target: https://coveralls.io/github/jakelever/kindred?branch=master
           :alt: Coverage status
           
        .. |docs| image:: https://readthedocs.org/projects/kindred/badge/?version=stable
           :target: http://kindred.readthedocs.io/en/stable/
           :alt: Documentation status
           
        .. |license| image:: https://img.shields.io/badge/License-MIT-blue.svg
           :target: https://opensource.org/licenses/MIT
           :alt: MIT license
        
        Kindred is a package for relation extraction in biomedical texts. Given some training data, it can build a model to identify relations between entities (e.g. drugs, genes, etc) in a sentence.
        
        Installation
        ------------
        
        You can install "kindred" via `pip`_ from `PyPI`_::
        
           $ pip install kindred
        
        As of v2, Kindred relies on the `Spacy`_ toolkit for parsing. After installing kindred (which also installs spacy), you will need to install a Spacy language model. For instance, the command below installs the english language model::
        
           $ python -m spacy download en 
        
        Usage
        -----
        
        Check out the `tutorial`_ that goes through a simple use case of extracting capital cities from text. More details and the full documentation can be found at `readthedocs`_.
        
        BioNLP Shared Task Example
        ~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        >>> import kindred
        >>> trainCorpus = kindred.bionlpst.load('2016-BB3-event-train')
        >>> devCorpus = kindred.bionlpst.load('2016-BB3-event-dev')
        >>> predictionCorpus = devCorpus.clone()
        >>> predictionCorpus.removeRelations()
        >>> classifier = kindred.RelationClassifier()
        >>> classifier.train(trainCorpus)
        >>> classifier.predict(predictionCorpus)
        >>> f1score = kindred.evaluate(devCorpus, predictionCorpus, metric='f1score')
        
        PubAnnotation Example
        ~~~~~~~~~~~~~~~~~~~~~
        
        >>> corpus = kindred.pubannotation.load('bionlp-st-gro-2013-development')
        
        PubTator Example
        ~~~~~~~~~~~~~~~~
        
        >>> corpus = kindred.pubtator.load([19894120,19894121])
        
        Input Formats
        ~~~~~~~~~~~~~
        
        Kindred can load several formats, including BioNLP Shared Task, JSON, BioC XML and a simple tag format. Check out the `file format documentation`_ for example data and code.
        
        Citing
        ------
        It would be wonderful if you could cite the `associated paper`_ for this package if used in any academic research.
        
        .. code-block:: bibtex
        
           @article{lever2017painless,
              title={Painless {R}elation {E}xtraction with {K}indred},
              author={Lever, Jake and Jones, Steven},
              journal={BioNLP 2017},
              pages={176--183},
              year={2017}
           }
        
        Contributing
        ------------
        Contributions are very welcome.
        
        License
        -------
        
        Distributed under the terms of the `MIT`_ license, "kindred" is free and open source software
        
        Issues
        ------
        
        If you encounter any problems, please `file an issue`_ along with a detailed description.
        
        .. _`MIT`: http://opensource.org/licenses/MIT
        .. _`file an issue`: https://github.com/jakelever/kindred/issues
        .. _`pip`: https://pypi.python.org/pypi/pip/
        .. _`PyPI`: https://pypi.python.org/pypi
        .. _`tutorial`: https://github.com/jakelever/kindred/tree/master/tutorial
        .. _`readthedocs`: http://kindred.readthedocs.io/
        .. _`Spacy`: https://spacy.io
        .. _`associated paper`: http://aclweb.org/anthology/W17-2322
        .. _`file format documentation`: https://kindred.readthedocs.io/en/stable/fileformats.html
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Human Machine Interfaces
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing
Classifier: Topic :: Text Processing :: General
Classifier: Topic :: Text Processing :: Indexing
Classifier: Topic :: Text Processing :: Linguistic
