A Theano-based Python implementation of Factorization Machines
A Theano-based Python implementation of factorization machines, based on the model presented in Factorization Machines (Rendle 2010).
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
pyfms 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.
pyfms is available on PyPI, the Python Package Index.
$ pip install pyfms
scikit-learn>=0.18 is required to run the example code.
Tests are in tests/.
# Run tests $ python -m unittest discover tests -v
pyfms has an MIT License.
RMSprop code is from Newmu/Theano-Tutorials.
Adam code is from Newmu/dcgan_code.
Rendle, S. 2010. “Factorization Machines.” In 2010 IEEE 10th International Conference on Data Mining (ICDM), 995–1000. doi:10.1109/ICDM.2010.127.
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