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

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 vari...

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Bibliographic Details
Published in:PloS one 2023-04, Vol.18 (4), p.e0284443-e0284443
Main Authors: Al Hajj, Ghadi S, Pensar, Johan, Sandve, Geir K
Format: Article
Language:English
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
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0284443