Introduction to 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).

The module is generated using the SWIG interface generator. Custom-written code was added to make the interface more user friendly.

pyAgrum aims to allow to easily use (as well as to prototype new algorithms on) Bayesian network and other graphical models.

pyAgrum contains :

• tutorials as jupyter notebooks.

• a comprehensive API documentation.

• a gitlab repository.

• and a website.

Tutorials and notebooks

• Tutorials on pyAgrum
• Inference in Bayesian networks
• Learning Bayesian networks
• Different Graphical Models
• Bayesian networks as scikit-learn compliant classifiers
• Causal Bayesian Networks
• Examples
• pyAgrum’s specific features

Reference manual

1- Fundamental components

• Graphs manipulation
  • Edges and Arcs
  • Directed Graphs
  • Undirected Graphs
  • Mixed Graph
  • Partially Directed Graph (DAG)
• Random Variables
  • Common API for Random Discrete Variables
  • Concrete classes for Random Discrete Variables
• Potential and Instantiation
  • Instantiation
  • Potential

2- Graphical Models

• Bayesian network
• Influence Diagram
• Credal Network
• Markov random field
• Probabilistic Relational Models

3- Causality

• pyAgrum.causal documentation
  • Causal Model
  • Causal Formula
  • Causal Inference
  • Other functions
  • Abstract Syntax Tree for Do-Calculus
  • Exceptions
  • Notebook’s tools for causality

4- scikit-learn-like BN Classifiers

• pyAgrum.skbn documentation
  • Classifier using Bayesian networks
  • Discretizer for Bayesian networks

5- pyAgrum.lib modules

• pyAgrum.lib.notebook
• pyAgrum.lib.image
• pyAgrum.lib.explain
• pyAgrum.lib.dynamicBN
• other pyAgrum.lib modules

6- Miscellaneous

• Functions from pyAgrum
• Other functions from aGrUM
  • Listeners
• Exceptions from aGrUM

7- Customizing pyAgrum

• Configuration for pyAgrum

Indices and tables

• Index

• Module Index

• Search Page