PyAgrum (pyagrum)

Module name	pyagrum
Package	pyagrum
Version	1.14.0
Language	Python
Docs	here
Paper	Verny et al.[1]
Graph type	DAG
MCMC	No
Edge constraints	No
Data type	B
Data missingness
Intervention type
Docker	bpimages/pyagrum:1.14.0

PyAgrum

pyAgrum is a scientific C++ and Python library dedicated to Bayesian networks (BN) and other Probabilistic Graphical Models. Based on the C++ aGrUM library, it provides a high-level interface to the C++ part of aGrUM allowing to create, manage and perform efficient computations with Bayesian networks and others probabilistic graphical models : Markov random fields (MRF), influence diagrams (ID) and LIMIDs, credal networks (CN), dynamic BN (dBN), probabilistic relational models (PRM).

Example

Config file: config.json

Command:

snakemake --cores all --use-singularity --configfile workflow/rules/structure_learning_algorithms/pyagrum/config.json

The following figure shows FP/P vs. TP/P for pattern graphs based on 5 datsets corresponding to 5 realisations of a 80-variables random binary Bayesian network, with an average indegree of 4.

pyAgrum FP/P vs. TP/P example — Fig. 51 FP/P vs. TP/P. for pattern graphs

Example JSON

[
  {
    "id": "pyagrum",
    "useMDLCorrection": true,
    "useSmoothingPrior": [
      true,
      false
    ],
    "timeout": null
  }
]