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Benchpress: a scalable and platform-independent workflow for benchmarking structure learning algorithms for graphical models

Rios, Felix L. and Moffa, Giusi and Kuipers, Jack. (2021) Benchpress: a scalable and platform-independent workflow for benchmarking structure learning algorithms for graphical models.

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Official URL: https://edoc.unibas.ch/84872/

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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 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 automated 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, 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 software tool also includes a number of standard datasets and graphical models from the literature, which may be included in a benchmarking workflow. We demonstrate the applicability of this workflow for learning Bayesian networks in four typical data scenarios.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Mathematik > Statistik (Moffa)
UniBasel Contributors:Moffa, Giusi and Rios, Felix Leopoldo
Item Type:Working Paper
Publisher:arXiv
Number of Pages:32
Note:Publication type according to Uni Basel Research Database: Discussion paper / Internet publication
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Last Modified:01 Nov 2021 10:37
Deposited On:01 Nov 2021 10:37

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