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)
See also examples/movielens.py
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.