Hybrid Restricted Master Problem for Boolean Matrix Factorisation

We present bfact, a Python package for performing accurate low-rank Boolean matrix factorisation (BMF). bfact uses a hybrid combinatorial optimisation approach based on \emph{a priori} candidate factors generated from clustering algorithms. It selects the best disjoint factors, before performing either a second combinatorial, or heuristic algorithm to recover the BMF. We show that bfact does particularly well at estimating the true rank of matrices in simulated settings. In real benchmarks, using a collation of single-cell RNA-sequencing datasets from the Human Lung Cell Atlas, we show that bfact achieves strong signal recovery, with a much lower rank.