Metadata-Version: 1.1
Name: pyfpgrowth
Version: 1.0
Summary: A Python implementation of the Frequent Pattern Growth algorithm.
Home-page: https://github.com/evandempsey/fp-growth
Author: Evan Dempsey
Author-email: me@evandempsey.io
License: ISCL
Description: ===============================
        FP-Growth
        ===============================
        
        .. image:: https://img.shields.io/pypi/v/pyfpgrowth.svg
                :target: https://pypi.python.org/pypi/pyfpgrowth
        
        .. image:: https://img.shields.io/travis/evandempsey/fp-growth.svg
                :target: https://travis-ci.org/evandempsey/fp-growth
        
        .. image:: https://readthedocs.org/projects/fp-growth/badge/?version=latest
                :target: https://readthedocs.org/projects/fp-growth/?badge=latest
                :alt: Documentation Status
        
        
        A Python implementation of the Frequent Pattern Growth algorithm.
        
        * Free software: ISC license
        * Documentation: https://fp-growth.readthedocs.org.
        
        Getting Started
        ---------------
        
        You can install the package with pip::
        
            pip install pyfpgrowth
        
        Then, to use it in a project, inport it and use the find_frequent_patterns and generate_association_rules functions::
        
            import pyfpgrowth
        
        It is assumed that your transactions are a sequence of sequences representing items in baskets. The item IDs are integers::
        
            transactions = [[1, 2, 5],
                            [2, 4],
                            [2, 3],
                            [1, 2, 4],
                            [1, 3],
                            [2, 3],
                            [1, 3],
                            [1, 2, 3, 5],
                            [1, 2, 3]]
        
        Use find_frequent_patterns to find patterns in baskets that occur over the support threshold::
        
            patterns = pyfpgrowth.find_frequent_patterns(transactions, 2)
        
        Use generate_association_rules to find patterns that are associated with another with a certain minimum probability::
        
            rules = pyfpgrowth.generate_association_rules(patterns, 0.7)
        
        Credits
        ---------
        
        This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
        
        .. _Cookiecutter: https://github.com/audreyr/cookiecutter
        .. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
        
        
        =======
        History
        =======
        
        0.1.0 (2016-04-25)
        ------------------
        
        * First release on PyPI.
        
Keywords: pyfpgrowth
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: ISC License (ISCL)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
