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Describing the relationship between the variables in a study domain andmodelling the data generating mechanism is a fundamental problem in manyempirical sciences. Probabilistic graphical models are one common approach totackle the problem. Learning the graphical structure for such models iscomputationally challenging and a fervent area of current research with aplethora of algorithms being developed. To facilitate the benchmarking ofdifferent methods, we present a novel Snakemake workflow, called Benchpress forproducing scalable, reproducible, and platform-independent benchmarks ofstructure learning algorithms for probabilistic graphical models. Benchpress isinterfaced 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 tocontribute additional methodologies. Benchpress currently provides an interfaceto a large number of state-of-the-art algorithms from libraries such asBDgraph, BiDAG, bnlearn, causal-learn, gCastle, GOBNILP, pcalg, r.blip,scikit-learn, TETRAD, and trilearn as well as a variety of methods for datagenerating models and performance evaluation. Alongside user-defined models andrandomly generated datasets, the workflow also includes a number of standarddatasets and graphical models from the literature, which may be included in abenchmarking study. We demonstrate the applicability of this workflow forlearning Bayesian networks in five typical data scenarios. The source code anddocumentation is publicly available from http://benchpressdocs.readthedocs.io.

Benchpress: A Scalable and Versatile Workflow for Benchmarking Structure Learning Algorithms