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Felix Rios: Benchpress: A Scalable and Versatile Workflow for Benchmarking Structure Learning Algorithms for Graphical Models

Time: Tue 2022-05-24 10.15

Location: 3721, Lindstedtsvägen 25, and Zoom

Video link: Meeting ID: 659 3743 5667

Lecturer: Felix Rios (University of Basel)

Abstract

Describing the relationship between the variables in a study domain and modeling 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 and reproducible 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, gCastle, GOBNILP, pcalg, r.blip, scikit-learn, TETRAD, and trilearn as well as a variety of methods for data generating models and performance evaluation. The source code and documentation is publicly available from http://github.com/felixleopoldo/benchpress