This is a TensorFlow 2.0 implementation of an arbitrary order (>=2) Factorization Machine based on paper Factorization Machines with libFM.

It supports:

• different (gradient-based) optimization methods
• classification/regression via different loss functions (logistic and mse implemented)

The inference time is linear with respect to the number of features.

Tested on Python3.6

Dependencies

• scikit-learn
• numpy
• tqdm
• tensorflow 2.0+ (tested on 2.2)

Installation

Stable version can be installed via pip install tffm2.

Usage

The interface is similar to scikit-learn models. To train a 6-order FM model with rank=10 for 100 iterations with learning_rate=0.01 use the following sample

from tffm2 import TFFMClassifier
model = TFFMClassifier(
    order=6,
    rank=10,
    optimizer=tf.keras.optimizers.Adam(learning_rate=0.00001),
    n_epochs=100,
    init_std=0.001
)
model.fit(X_tr, y_tr, show_progress=True)

See example.ipynb and gpu_benchmark.ipynb for more details.

It's highly recommended to read tffm/core.py for help.

Testing

Run python test.py from the terminal.

Reference

This code is ported from https://github.com/geffy/tffm