Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent

This repository provides a Python library that implements the Elastic Boolean Matrix Factorization (Elbmf) algorithm using PyTorch. It provides an efficient methods for factorizing binary matrices into low-rank matrices using a continuous relaxation, an elastic-net inspired Boolean regularization, and proximal gradient descent.

Acknowledgments

This project is based on the research paper 'Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent'

@inproceedings{dalleiger2022efficiently,
    title={Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent},
    author={Sebastian Dalleiger and Jilles Vreeken},
    booktitle={Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS)},
    year={2022}
}

Installation

pip install torch
pip install git+https://github.com/sdall/elbmf-python

Usage

import torch
from elbmf import elbmf

X = torch.randint(0, 2, (100, 100))

U, V = elbmf(
    X                   = X,                  # a Boolean n*m matrix  
    n_components        = 20,                 # number of components
    l1reg               = 0.01,               # l1 coefficient
    l2reg               = 0.02,               # l2 coefficient
    regularization_rate = lambda t: 1.02**t,  # monotonically increasing regularization-rate function
    maxiter             = 3000,               # maximum number of iterations
    tolerance           = 1e-8,               # the threshold to the absolute difference between the current and previous losses determines the convergence
    beta                = 0.0001,             # inertial coefficient of iPALM
    callback            = None,               # e.g. lambda t, U, V, fn: print(t, fn)
    with_rounding       = True)               # rounds U and V in case of early stopping

If the resulting reconstruction is unexpected or not ideal, you might want to try to tweak l1reg, l2reg, maxiter, and most importantly the regularization_rate, with disabled with_rounding (for debugging purposes) and enabled callback.

Contributing

Contributions to Elbmf are welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request on the GitHub repository: https://github.com/sdall/elbmf-python

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

If you have any questions or inquiries, please contact sdalleig@mpi-inf.mpg.de.

Feel free to reach out with any feedback or suggestions.