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scikit-learn-contrib / polylearn

A library for factorization machines and polynomial networks for classification and regression in Python.
http://contrib.scikit-learn.org/polylearn/
BSD 2-Clause "Simplified" License
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factorization-machines machine-learning polynomial-networks polynomial-regression

readme

.. -- mode: rst --

polylearn

A library for factorization machines and polynomial networks for classification and regression in Python.

Github repository. <https://github.com/scikit-learn-contrib/polylearn/>_

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Factorization machines and polynomial networks are machine learning models that can capture feature interaction (co-occurrence) through polynomial terms. Because feature interactions can be very sparse, it's common to use low rank, factorized representations; this way, we can learn weights even for feature co-occurrences that haven't been observed at training time.

Factorization machines are popular for recommender systems, as they are a generalization of matrix completion models.

This package provides:

Installation

Binary packages are not yet available.

The development version of polylearn can be installed from its git repository. In this case it is assumed that you have a working C++ compiler.

  1. Obtain the sources by::

    git clone https://github.com/scikit-learn-contrib/polylearn.git

or, if git is unavailable, download as a ZIP from GitHub <https://github.com/scikit-learn-contrib/polylearn/archive/master.zip>_.

  1. Install the dependencies::

    via pip

    pip install numpy scipy scikit-learn nose pip install sklearn-contrib-lightning

    via conda

    conda install numpy scipy scikit-learn nose conda install -c conda-forge sklearn-contrib-lightning

  2. Build and install polylearn::

    cd polylearn python setup.py build sudo python setup.py install

References

The solvers implemented are introduced in [1]. Factorization machines are introduced in [2] and polynomial networks in [3]_.

.. [1] Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda. Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms. In: Proc. of ICML 2016. [PDF <http://mblondel.org/publications/mblondel-icml2016.pdf>_]

.. [2] Steffen Rendle. Factorization machines. In: Proc. of IEEE ICDM 2010. [PDF <https://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle2010FM.pdf>_]

.. [3] Roi Livni, Shai Shalev-Shwartz, Ohad Shamir. On the computational efficiency of training neural networks. In: Proc. of NIPS 2014. [arXiv <http://arxiv.org/abs/1410.1141>_]

Authors