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pyfms

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

pyfms supports 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/pyfms/blob/master/documentation.md>__.

Example Usage

See example.py <https://github.com/dstein64/pyfms/blob/master/example.py>__.

scikit-learn>=0.18 is required to run the example code.

Tests

Tests are in tests/ <https://github.com/dstein64/pyfms/blob/master/tests>__.

::

# Run tests
$ python -m unittest discover tests -v

License

pyfms has an MIT License <https://en.wikipedia.org/wiki/MIT_License>__.

See LICENSE <https://github.com/dstein64/pyfms/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.