Metadata-Version: 1.1
Name: simplebayes
Version: 1.5.8
Summary: A memory-based, optional-persistence naïve bayesian text classifier.
Home-page: https://github.com/hickeroar/simplebayes
Author: Ryan Vennell
Author-email: ryan.vennell@gmail.com
License: MIT
Description: simplebayes
        ===========
        A memory-based, optional-persistence naïve bayesian text classifier.
        --------------------------------------------------------------------
        ::
        
            This work is heavily inspired by the python "redisbayes" module found here:
            [https://github.com/jart/redisbayes] and [https://pypi.python.org/pypi/redisbayes]
        
            I've elected to write this to alleviate the network/time requirements when
            using the bayesian classifier to classify large sets of text, or when
            attempting to train with very large sets of sample data.
        
        Build Status
        ------------
        .. image:: https://travis-ci.org/hickeroar/simplebayes.svg?branch=master
        .. image:: https://img.shields.io/badge/coverage-100%-brightgreen.svg?style=flat
        .. image:: https://img.shields.io/badge/pylint-10.00/10-brightgreen.svg?style=flat
        .. image:: https://img.shields.io/badge/flake8-passing-brightgreen.svg?style=flat
        
        Installation
        ------------
        ::
        
            sudo pip install simplebayes
        
        Basic Usage
        -----------
        .. code-block:: python
        
            import simplebayes
            bayes = simplebayes.SimpleBayes()
        
            bayes.train('good', 'sunshine drugs love sex lobster sloth')
            bayes.train('bad', 'fear death horror government zombie')
        
            assert bayes.classify('sloths are so cute i love them') == 'good'
            assert bayes.classify('i would fear a zombie and love the government') == 'bad'
        
            print bayes.score('i fear zombies and love the government')
        
            bayes.untrain('bad', 'fear death')
        
            assert bayes.tally('bad') == 3
        
        Cache Usage
        -----------
        .. code-block:: python
        
            import simplebayes
            bayes = simplebayes.SimpleBayes(cache_path='/my/cache/')
            # Cache file is '/my/cache/_simplebayes.pickle'
            # Default cache_path is '/tmp/'
        
            if not bayes.cache_train():
                # Unable to load cache data, so we're training it
                bayes.train('good', 'sunshine drugs love sex lobster sloth')
                bayes.train('bad', 'fear death horror government zombie')
        
                # Saving the cache so next time the training won't be needed
                bayes.persist_cache()
        
        Tokenizer Override
        ------------------
        .. code-block:: python
        
            import simplebayes
        
            def my_tokenizer(sample):
                return sample.split()
        
            bayes = simplebayes.SimpleBayes(tokenizer=my_tokenizer)
        
        License
        -------
        ::
        
            The MIT License (MIT)
        
            Copyright (c) 2015 Ryan Vennell
        
            Permission is hereby granted, free of charge, to any person obtaining a copy
            of this software and associated documentation files (the "Software"), to deal
            in the Software without restriction, including without limitation the rights
            to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
            copies of the Software, and to permit persons to whom the Software is
            furnished to do so, subject to the following conditions:
        
            The above copyright notice and this permission notice shall be included in all
            copies or substantial portions of the Software.
        
            THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
            IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
            FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
            AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
            LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
            OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
            SOFTWARE.
        
        API Documentation
        -----------------
        `<http://hickeroar.github.io/simplebayes/simplebayes.html>`_
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Topic :: Utilities
