Metadata-Version: 1.1
Name: topic-modeling-toolkit
Version: 0.5.6
Summary: Topic Modeling Toolkit
Home-page: https://github.com/boromir674/topic-modeling-toolkit
Author: Konstantinos Lampridis
Author-email: k.lampridis@hotmail.com
License: GNU GPLv3
Download-URL: https://github.com/boromir674/topic-modeling-toolkit/archive/v0.5.6.tar.gz
Description: Topic Modeling Toolkit - Python Library
        =========================================================================
        
        This library aims to automate Topic Modeling research-related activities.
        
        * Data preprocessing and dataset computing
        * Model training (with parameter grid-search), evaluating and comparing
        * Graph building
        * Computing KL-divergence between p(c|t) distributions
        * Datasets/models/kl-distances reporting
        
        
        .. start-badges
        
        .. list-table::
            :stub-columns: 1
        
            * - tests
              - | |travis|
                | |coverage|
                | |scrutinizer_code_quality|
                | |code_intelligence|
            * - package
              - |version| |python_versions|
        
        .. |travis| image:: https://travis-ci.org/boromir674/topic-modeling-toolkit.svg?branch=dev
            :alt: Travis-CI Build Status
            :target: https://travis-ci.org/boromir674/topic-modeling-toolkit
        
        .. |coverage| image:: https://img.shields.io/codecov/c/github/boromir674/topic-modeling-toolkit/dev?style=flat-square
            :alt: Coverage Status
            :target: https://codecov.io/gh/boromir674/topic-modeling-toolkit/branch/dev
        
        .. |scrutinizer_code_quality| image:: https://scrutinizer-ci.com/g/boromir674/topic-modeling-toolkit/badges/quality-score.png?b=dev
            :alt: Code Quality
            :target: https://scrutinizer-ci.com/g/boromir674/topic-modeling-toolkit/?branch=dev
        
        .. |code_intelligence| image:: https://scrutinizer-ci.com/g/boromir674/topic-modeling-toolkit/badges/code-intelligence.svg?b=dev
            :alt: Code Intelligence
            :target: https://scrutinizer-ci.com/code-intelligence
        
        .. |version| image:: https://img.shields.io/pypi/v/topic-modeling-toolkit.svg
            :alt: PyPI Package latest release
            :target: https://pypi.org/project/topic-modeling-toolkit
        
        .. |python_versions| image:: https://img.shields.io/pypi/pyversions/topic-modeling-toolkit.svg
            :alt: Supported versions
            :target: https://pypi.org/project/topic-modeling-toolkit
        
        
        ========
        Overview
        ========
        
        This library serves as a higher level API around the BigARTM_ (artm python interface) library and exposes it conviniently through the command line.
        
        Key features of the Library:
        
        * Flexible preprocessing pipelines
        * Optimization of classification scheme with an evolutionary algorithm
        * Fast model inference with parallel/multicore execution
        * Persisting of models and experimental results
        * Visualization
        
        .. _BigARTM: https://github.com/bigartm
        
        
        Installation
        ------------
        | The Topic Modeling Toolkit depends on the BigARTM C++ library. Therefore first you should first build and install it
        | either by following the instructions `here <https://bigartm.readthedocs.io/en/stable/installation/index.html>`_ or by using
        | the 'build_artm.sh' script provided. For example, for python3 you can use the following
        
        ::
        
            $ git clone https://github.com/boromir674/topic-modeling-toolkit.git
            $ chmod +x topic-modeling-toolkit/build_artm.sh
            $ # build and install BigARTM library in /usr/local and create python3 wheel
            $ topic-modeling-toolkit/build_artm.sh
            $ ls bigartm/build/python/bigartm*.whl
        
        | Now you should have the 'bigartm' executable in PATH and you can find a built python wheel in 'bigartm/build/python/'
        | You should install the wheel in your environment, for example with command
        
        ::
        
            python -m pip install bigartm/build/python/path-python-wheel
        
        | You can install the package with the following command
        | When the package gets hosted on PyPI, it should be installed
        
        ::
        
            $ cd topic-modeling-toolkit
            $ pip install .
        
        If the above fails try again including manual installation of dependencies
        
        ::
        
            $ cd topic-modeling-toolkit
            $ pip install -r requirements.txt
            $ pip install .
        
        
        Usage
        -----
        A sample example is below.
        
        ::
        
            $ current_dir=$(echo $PWD)
            $ export COLLECTIONS_DIR=$current_dir/datasets-dir
            $ mkdir $COLLECTIONS_DIR
        
            $ transform posts pipeline.cfg my-dataset
            $ train my-dataset train.cfg plsa-model --save
            $ make-graphs --model-labels "plsa-model" --allmetrics --no-legend
            $ xdg-open $COLLECTIONS_DIR/plsa-model/graphs/plsa*prpl*
        
        Citation
        --------
        
        1. Vorontsov, K. and Potapenko, A. (2015). `Additive regularization of topic models <http://machinelearning.ru/wiki/images/4/47/Voron14mlj.pdf>`_. Machine Learning, 101(1):303–323.
        
Keywords: topic modeling machine learning
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Intended Audience :: Science/Research
