Benchpress: A Scalable and Versatile Workflow for Benchmarking Structure Learning Algorithms
This tool facilitates reproducible benchmarking for researchers in fields like machine learning and empirical sciences, though it is incremental as it builds on existing algorithms and workflows.
The authors tackled the challenge of benchmarking structure learning algorithms for probabilistic graphical models by developing Benchpress, a scalable and reproducible Snakemake workflow that interfaces with numerous state-of-the-art algorithms and datasets, demonstrating its applicability in five typical data scenarios.
Describing the relationship between the variables in a study domain and modelling the data generating mechanism is a fundamental problem in many empirical sciences. Probabilistic graphical models are one common approach to tackle the problem. Learning the graphical structure for such models is computationally challenging and a fervent area of current research with a plethora of algorithms being developed. To facilitate the benchmarking of different methods, we present a novel Snakemake workflow, called Benchpress for producing scalable, reproducible, and platform-independent benchmarks of structure learning algorithms for probabilistic graphical models. Benchpress is interfaced via a simple JSON-file, which makes it accessible for all users, while the code is designed in a fully modular fashion to enable researchers to contribute additional methodologies. Benchpress currently provides an interface to a large number of state-of-the-art algorithms from libraries such as BDgraph, BiDAG, bnlearn, causal-learn, gCastle, GOBNILP, pcalg, r.blip, scikit-learn, TETRAD, and trilearn as well as a variety of methods for data generating models and performance evaluation. Alongside user-defined models and randomly generated datasets, the workflow also includes a number of standard datasets and graphical models from the literature, which may be included in a benchmarking study. We demonstrate the applicability of this workflow for learning Bayesian networks in five typical data scenarios. The source code and documentation is publicly available from http://benchpressdocs.readthedocs.io.