Metadata-Version: 1.1
Name: pyfms
Version: 0.3.3
Summary: A Theano-based Python implementation of Factorization Machines
Home-page: https://github.com/dstein64/PyFactorizationMachines
Author: Daniel Steinberg
Author-email: ds@dannyadam.com
License: MIT
Description: A `Theano <http://deeplearning.net/software/theano/>`__-based Python implementation of
        factorization machines, based on the model presented in *Factorization Machines* (Rendle 2010).
        
        Features
        --------
        
        -  Sample weighting
        -  For binary classification, this implementation uses a logit function
           combined with a cross entropy loss function.
        -  Extensibility of algorithms for: regularization, loss function optimization, and the error
           function
        -  Support for sparse data
        
        Requirements
        ------------
        
        PyFactorizationMachines supports Python 2.7 and Python 3.x.
        
        Linux and Mac are supported.
        
        Windows is supported with Theano properly installed. The recommended way to install Theano on
        Windows is using `Anaconda <https://www.continuum.io/anaconda-overview>`__.
        
        ::
        
            > conda install theano
        
        Other operating systems may be compatible if Theano can be properly installed.
        
        Installation
        ------------
        
        `pyfms <https://pypi.python.org/pypi/pyfms>`__ is available on PyPI, the Python Package Index.
        
        ::
        
            $ pip install pyfms
        
        Documentation
        -------------
        
        See `documentation.md <https://github.com/dstein64/PyFactorizationMachines/blob/master/documentation.md>`__.
        
        Example Usage
        -------------
        
        See `example.py <https://github.com/dstein64/PyFactorizationMachines/blob/master/example.py>`__.
        
        scikit-learn>=0.18 is required to run the example code.
        
        License
        -------
        
        PyFactorizationMachines has an `MIT License <https://en.wikipedia.org/wiki/MIT_License>`__.
        
        See `LICENSE <https://github.com/dstein64/PyFactorizationMachines/blob/master/LICENSE>`__.
        
        Acknowledgments
        ---------------
        
        RMSprop code is from
        `Newmu/Theano-Tutorials <https://github.com/Newmu/Theano-Tutorials/blob/master/4_modern_net.py>`__.
        
        Adam code is from
        `Newmu/dcgan_code <https://github.com/Newmu/dcgan_code/blob/master/lib/updates.py>`__.
        
        References
        ----------
        
        Rendle, S. 2010. “Factorization Machines.” In 2010 IEEE 10th
        International Conference on Data Mining (ICDM), 995–1000.
        doi:10.1109/ICDM.2010.127.
        
Keywords: factorization-machines,machine-learning
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: Unix
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
