Tensor FM

Second Order Factorization Machine implemented with Tensorflow and scikit-learn.

tensor-fm

A scikit-learn compatible order 2 Factorization Machine, implemented atop TensorFlow 2. The algorithm is described in http://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf. For an higher level overview of the method see http://nowave.it/factorization-machines-with-tensorflow.html.

This package is a port to Tensorflow 2 of the code presented in that blog post. The goal of this project is to experiment with different optimization strategies for classical ML models, and scalability of TF2 backends.

Install

The latest development version of tensorfm can be installed from its github repo with:

pip install git+https://github.com/gmodena/tensor-fm

Usage examples

Tensorlow and scikit-learn APIs are provided.

Tensorflow

The tensorflow implementation of Factorization Machines lives under tensor-fm/tensorfm/base.py. An example of how to work with this API can be found in tensor-fm/tests/test_base.py.

Scikit-learn estimator

tensorfm.sklearn exposes two sklearn compatible estimators: FactorizationMachineRegressor and FactorizationMachineClassifier.

Example

from tensorfm.sklearn import FactorizationMachineRegressor
...
fm = FactorizationMachineRegressor()
fm.fit(X, y)
fm.predict(X)

Performance

All parameters and settings being equal, I noticed a considerable performance degradation of FactorizationMachineRegressor (MSE on train/test) on movielens compared to the tensorflow 1 implementation from http://nowave.it/factorization-machines-with-tensorflow.html. Possibly related, a test in the check_regressors_train suite (sklearn) fails due to a low R^2. As a workaround FactorizationMachineRegressor sets the poor_score tag to True.

Limitations and known issues

Operations on sparse matrices are currently not supported. Training continues till max_iter is reached, we should stop if performance does not improve for a certain number of iterations.

API

sklearn

Tensorflow

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.