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        "author": "Daniel Steinberg",
        "author_email": "ds@dannyadam.com",
        "bugtrack_url": null,
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            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX :: Linux",
            "Operating System :: Unix",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Information Analysis"
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        "description": "A `Theano <http://deeplearning.net/software/theano/>`__-based Python implementation of\nfactorization machines, based on the model presented in *Factorization Machines* (Rendle 2010).\n\nFeatures\n--------\n\n-  Sample weighting\n-  For binary classification, this implementation uses a logit function\n   combined with a cross entropy loss function.\n-  Extensibility of algorithms for: regularization, loss function optimization, and the error\n   function\n-  Support for sparse data\n\nRequirements\n------------\n\nPyFactorizationMachines supports Python 2.7 and Python 3.x.\n\nLinux and Mac are supported.\n\nWindows is supported with Theano properly installed. The recommended way to install Theano on\nWindows is using `Anaconda <https://www.continuum.io/anaconda-overview>`__.\n\n::\n\n    > conda install theano\n\nOther operating systems may be compatible if Theano can be properly installed.\n\nInstallation\n------------\n\n`pyfms <https://pypi.python.org/pypi/pyfms>`__ is available on PyPI, the Python Package Index.\n\n::\n\n    $ pip install pyfms\n\nDocumentation\n-------------\n\nSee `documentation.md <https://github.com/dstein64/PyFactorizationMachines/blob/master/documentation.md>`__.\n\nExample Usage\n-------------\n\nSee `example.py <https://github.com/dstein64/PyFactorizationMachines/blob/master/example.py>`__.\n\nscikit-learn>=0.18 is required to run the example code.\n\nLicense\n-------\n\nPyFactorizationMachines has an `MIT License <https://en.wikipedia.org/wiki/MIT_License>`__.\n\nSee `LICENSE <https://github.com/dstein64/PyFactorizationMachines/blob/master/LICENSE>`__.\n\nAcknowledgments\n---------------\n\nRMSprop code is from\n`Newmu/Theano-Tutorials <https://github.com/Newmu/Theano-Tutorials/blob/master/4_modern_net.py>`__.\n\nAdam code is from\n`Newmu/dcgan_code <https://github.com/Newmu/dcgan_code/blob/master/lib/updates.py>`__.\n\nReferences\n----------\n\nRendle, S. 2010. \u201cFactorization Machines.\u201d In 2010 IEEE 10th\nInternational Conference on Data Mining (ICDM), 995\u20131000.\ndoi:10.1109/ICDM.2010.127.\n",
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        "keywords": "factorization-machines",
        "license": "MIT",
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        "name": "pyfms",
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