Skip to main content

A Theano-based Python implementation of Factorization Machines

Project description

A 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.

> conda install theano

Other operating systems may be compatible if Theano can be properly installed.

Installation

pyfms is available on PyPI, the Python Package Index.

$ pip install pyfms

Documentation

See documentation.md.

Example Usage

See example.py.

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

License

PyFactorizationMachines has an MIT License.

See LICENSE.

Acknowledgments

RMSprop code is from Newmu/Theano-Tutorials.

Adam code is from Newmu/dcgan_code.

References

Rendle, S. 2010. “Factorization Machines.” In 2010 IEEE 10th International Conference on Data Mining (ICDM), 995–1000. doi:10.1109/ICDM.2010.127.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
pyfms-0.3.3.tar.gz (8.7 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page