DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation

• Published in: PloS one Vol. 18; no. 4; p. e0284443
• Main Authors: Al Hajj, Ghadi S., Pensar, Johan, Sandve, Geir K.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.04.2023; PLOS; Public Library of Science (PLoS)
• Subjects: Analysis; Biology and Life Sciences; Computational linguistics; Computer and Information Sciences; Computer Simulation; Constraint modelling; Data mining; Data simulation; Diacylglycerol; Evaluation; Graph theory; Inference; Language processing; Learning algorithms; Machine learning; Medicine and Health Sciences; Natural language interfaces; Parents & parenting; Physical Sciences; Random variables; Regression analysis; Research and Analysis Methods; Simulation; Simulation models; Social Sciences; Software; Source code; Norway
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0284443

Data simulation is fundamental for machine learning and causal inference, as it allows exploration of scenarios and assessment of methods in settings with full control of ground truth. Directed acyclic graphs (DAGs) are well established for encoding the dependence structure over a collection of variables in both inference and simulation settings. However, while modern machine learning is applied to data of an increasingly complex nature, DAG-based simulation frameworks are still confined to settings with relatively simple variable types and functional forms. We here present DagSim, a Python-based framework for DAG-based data simulation without any constraints on variable types or functional relations. A succinct YAML format for defining the simulation model structure promotes transparency, while separate user-provided functions for generating each variable based on its parents ensure simulation code modularization. We illustrate the capabilities of DagSim through use cases where metadata variables control shapes in an image and patterns in bio-sequences. DagSim is available as a Python package at PyPI. Source code and documentation are available at: https://github.com/uio-bmi/dagsim