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

• URL: http://arxiv.org/abs/2107.03863v4
• Date: Mon, 4 Dec 2023 13:24:31 GMT
• Title: Benchpress: A Scalable and Versatile Workflow for Benchmarking Structure Learning Algorithms
• Authors: Felix L. Rios, Giusi Moffa, Jack Kuipers
• Abstract summary: Probabilistic graphical models are one common approach to modelling the data generating mechanism. We present a novel Snakemake workflow called Benchpress for producing scalable, reproducible, and platform-independent benchmarks. We demonstrate the applicability of this workflow for learning Bayesian networks in five typical data scenarios.
• Score: 1.7188280334580197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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