Metadata-Version: 1.2
Name: koolsla
Version: 0.2.1
Summary: Food recommendation tool with Machine learning
Home-page: https://github.com/abdullahselek/koolsla
Author: Abdullah Selek
Author-email: abdullahselek@gmail.com
Maintainer: Abdullah Selek
Maintainer-email: abdullahselek@gmail.com
License: MIT License
Download-URL: https://pypi.org/pypi/koolsla/
Description: |logo| koolsla
        ==============
        
        .. |logo| image:: https://images1-focus-opensocial.googleusercontent.com/gadgets/proxy?url=https://raw.githubusercontent.com/abdullahselek/koolsla/master/resources/logo.png&container=focus&resize_w=20&resize_h=20
        
        .. image:: https://img.shields.io/pypi/v/koolsla.svg
            :target: https://pypi.python.org/pypi/koolsla/
        
        .. image:: https://img.shields.io/pypi/pyversions/koolsla.svg
            :target: https://pypi.org/project/koolsla
        
        .. image:: https://readthedocs.org/projects/koolsla/badge/?version=latest
            :target: http://koolsla.readthedocs.org/en/latest/?badge=latest
        
        .. image:: https://codecov.io/gh/abdullahselek/koolsla/branch/master/graph/badge.svg
            :target: https://codecov.io/gh/abdullahselek/koolsla
        
        +--------------------------------------------------------------------------+------------------------------------------------------------------------------------+
        |                                Linux                                     |                                       Windows                                      |
        +==========================================================================+====================================================================================+
        | .. image:: https://travis-ci.org/abdullahselek/koolsla.svg?branch=master | .. image:: https://ci.appveyor.com/api/projects/status/l5bt8yw7n35cvsov?svg=true   |
        |   :target: https://travis-ci.org/abdullahselek/koolsla                   |    :target: https://ci.appveyor.com/project/abdullahselek/koolsla                  |
        +--------------------------------------------------------------------------+------------------------------------------------------------------------------------+
        
        Description
        ===========
        
        koolsla (`Coleslaw <https://en.wikipedia.org/wiki/Coleslaw>`_) is a recommendation tool based on Machine Learning with contents.
        Developed with the power of `tf-idf <https://en.wikipedia.org/wiki/Tf%E2%80%93idf>`_ and `Cosine Similarity <https://en.wikipedia.org/wiki/Cosine_similarity>`_.
        
        The user gives a natural number that corresponds to the ID of a unique dish name. Through `tf-idf` the plot summaries of 424508 different dishes that reside in the dataset, are analyzed and vectorized. 
        Set of dishes (number set by user) is chosen as recommendations based on their `cosine similarity` with the vectorized input.
        
        koolsla is mainly an educational project.
        
        Installation
        ============
        
        You can install koolsla using::
        
            $ pip install koolsla
        
        Getting the code
        ================
        
        The code is hosted at https://github.com/abdullahselek/koolsla
        
        Check out the latest development version anonymously with::
        
            $ git clone git://github.com/abdullahselek/koolsla.git
            $ cd koolsla
        
        To install test dependencies, run either::
        
            $ pip install -Ur requirements.testing.txt
        
        Running Tests
        =============
        
        The test suite can be run against a single Python version which requires ``pip install pytest`` and optionally ``pip install pytest-cov``
        (these are included if you have installed dependencies from ``requirements.testing.txt``)
        
        To run the unit tests with a single Python version::
        
            $ py.test -v
        
        To also run code coverage::
        
            $ py.test --cov=koolsla
        
        To run the unit tests against a set of Python versions::
        
            $ tox
        
        Sample Usage
        ============
        
        Import recommender::
        
            from koolsla import recommender
        
        Getting recommendations with dish id and recommendation count::
        
            // Returns dictionary of tuples [(dish_id_1, similarity_ratio1), (dish_id_2, similarity_ratio2), (dish_id_3, similarity_ratio3)]
            recommendatons = recommender.recommend(82, 3)
        
        CLI
        ===
        
        After getting the code from https://github.com/abdullahselek/koolsla, run command::
        
            $ pip install -r requirements.txt
        
        And it's ready to use, there is detailed help menu which you can follow. One of the most used function for recommendation::
        
            $ python koolsla.py -d 25 --recommend 3
        
        For the help menu::
        
            $ python koolsla.py --help
        
Keywords: machine learning,food suggestion,tf-idf,cosine similarity
Platform: Any
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, <4
