Metadata-Version: 1.1
Name: kindred
Version: 1.0.0
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/
           :target: http://kindred.readthedocs.io/
           :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
        
        Kindred relies on the Stanford CoreNLP toolkit for parsing. By default it will attempt to connect to a local server (localhost:9000). If you want Kindred to download the CoreNLP files and run it as a subprocess when a server can't be found, use the following command:
        
        >>> import kindred
        >>> kindred.downloadCoreNLP()
        
        Usage
        -----
        
        Full documentation can be found at `readthedocs`_.
        
        BioNLP Shared Task Example
        ~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        >>> 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])
        
        
        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
        .. _`readthedocs`: http://kindred.readthedocs.io/
        
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 :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
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
