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        "author": "Konstantinos Lampridis",
        "author_email": "k.lampridis@hotmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "Topic Modeling Toolkit - Python Library\n=========================================================================\n\nThis library aims to automate Topic Modeling research-related activities.\n\n* Data preprocessing and dataset computing\n* Model training (with parameter grid-search), evaluating and comparing\n* Graph building\n* Computing KL-divergence between p(c|t) distributions\n* Datasets/models/kl-distances reporting\n\n\n.. start-badges\n\n.. list-table::\n    :stub-columns: 1\n\n    * - tests\n      - | |travis|\n        | |coverage|\n        | |scrutinizer_code_quality|\n        | |code_intelligence|\n    * - package\n      - |version| |python_versions|\n\n.. |travis| image:: https://travis-ci.org/boromir674/topic-modeling-toolkit.svg?branch=dev\n    :alt: Travis-CI Build Status\n    :target: https://travis-ci.org/boromir674/topic-modeling-toolkit\n\n.. |coverage| image:: https://img.shields.io/codecov/c/github/boromir674/topic-modeling-toolkit/dev?style=flat-square\n    :alt: Coverage Status\n    :target: https://codecov.io/gh/boromir674/topic-modeling-toolkit/branch/dev\n\n.. |scrutinizer_code_quality| image:: https://scrutinizer-ci.com/g/boromir674/topic-modeling-toolkit/badges/quality-score.png?b=dev\n    :alt: Code Quality\n    :target: https://scrutinizer-ci.com/g/boromir674/topic-modeling-toolkit/?branch=dev\n\n.. |code_intelligence| image:: https://scrutinizer-ci.com/g/boromir674/topic-modeling-toolkit/badges/code-intelligence.svg?b=dev\n    :alt: Code Intelligence\n    :target: https://scrutinizer-ci.com/code-intelligence\n\n.. |version| image:: https://img.shields.io/pypi/v/topic-modeling-toolkit.svg\n    :alt: PyPI Package latest release\n    :target: https://pypi.org/project/topic-modeling-toolkit\n\n.. |python_versions| image:: https://img.shields.io/pypi/pyversions/topic-modeling-toolkit.svg\n    :alt: Supported versions\n    :target: https://pypi.org/project/topic-modeling-toolkit\n\n\n========\nOverview\n========\n\nThis library serves as a higher level API around the BigARTM_ (artm python interface) library and exposes it conviniently through the command line.\n\nKey features of the Library:\n\n* Flexible preprocessing pipelines\n* Optimization of classification scheme with an evolutionary algorithm\n* Fast model inference with parallel/multicore execution\n* Persisting of models and experimental results\n* Visualization\n\n.. _BigARTM: https://github.com/bigartm\n\n\nInstallation\n------------\n| The Topic Modeling Toolkit depends on the BigARTM C++ library. Therefore first you should first build and install it\n| either by following the instructions `here <https://bigartm.readthedocs.io/en/stable/installation/index.html>`_ or by using\n| the 'build_artm.sh' script provided. For example, for python3 you can use the following\n\n::\n\n    $ git clone https://github.com/boromir674/topic-modeling-toolkit.git\n    $ chmod +x topic-modeling-toolkit/build_artm.sh\n    $ # build and install BigARTM library in /usr/local and create python3 wheel\n    $ topic-modeling-toolkit/build_artm.sh\n    $ ls bigartm/build/python/bigartm*.whl\n\n| Now you should have the 'bigartm' executable in PATH and you can find a built python wheel in 'bigartm/build/python/'\n| You should install the wheel in your environment, for example with command\n\n::\n\n    python -m pip install bigartm/build/python/path-python-wheel\n\n| You can install the package with the following command\n| When the package gets hosted on PyPI, it should be installed\n\n::\n\n    $ cd topic-modeling-toolkit\n    $ pip install .\n\nIf the above fails try again including manual installation of dependencies\n\n::\n\n    $ cd topic-modeling-toolkit\n    $ pip install -r requirements.txt\n    $ pip install .\n\n\nUsage\n-----\nA sample example is below.\n\n::\n\n    $ current_dir=$(echo $PWD)\n    $ export COLLECTIONS_DIR=$current_dir/datasets-dir\n    $ mkdir $COLLECTIONS_DIR\n\n    $ transform posts pipeline.cfg my-dataset\n    $ train my-dataset train.cfg plsa-model --save\n    $ make-graphs --model-labels \"plsa-model\" --allmetrics --no-legend\n    $ xdg-open $COLLECTIONS_DIR/plsa-model/graphs/plsa*prpl*\n\nCitation\n--------\n\n1. 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\u2013323.\n\n\n",
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