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

It supports:

• dense and sparse inputs
• different (gradient-based) optimization methods
• Classification/regression via different loss functions (logistic and mse implemented)
• logging via TensorBoard

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

Tested on both Python2.7 and Python3.5.

Dependencies

• scikit-learn
• numpy
• tensorflow 0.8+
• tqdm

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 tffm import TFFMClassifier
model = TFFMClassifier(
    order=6,
    rank=10,
    optimizer=tf.train.AdamOptimizer(learning_rate=0.01),
    n_epochs=100,
    batch_size=-1,
    init_std=0.001,
    input_type='dense'
)
model.fit(X_tr, y_tr, show_progress=True)

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

Corresponding paper is coming soon.

Testing

Just run python test.py in the terminal. Nosetests works too, but for some reason it makes TensorFlow print insane amount of logs on the screen.