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
• Exact and Approximated Inference
• Learning Bayesian networks
• Different Graphical Models
• Bayesian networks as scikit-learn compliant classifiers
• Causal Bayesian Networks
• pyAgrum’s (experimental) models
• pyAgrum’s specific features
• Examples
• Examples from ‘The Book of Why’ (J. Pearl, 2018)

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- Bayesian networks

• Model for Bayesian Network
  • BayesNet
• Inference
  • Exact Inference
  • Approximated Inference
• Learning
  • BNLearner
• Tools for Bayesian networks
  • Random generation of Bayesian network
  • Generation of database
  • Information Theory of Bayesian network
  • Meek Rules Algorithm
  • Comparison of Bayesian networks
  • Explanation and analysis
  • Fragment of Bayesian networks
• Causality
  • Causal Model
  • Causal Formula
  • Causal Inference
  • Other functions
  • Abstract Syntax Tree for Do-Calculus
  • Exceptions
  • Notebook’s tools for causality
• Classifier
  • Classifier using Bayesian networks

3- Other graphical models

• Influence Diagram and LIMIDS
  • Model for Decision in PGM
  • Inference for Influence Diagram
• Credal Network
  • CN Model
  • CN Inference
• Markov random field
  • Undirected Graphical Model
  • Inference in Markov random fields
• Probabilistic Relational Models
  • PRMexplorer

4- pyAgrum.lib modules

• Discretizer for graphical Models
• Jupyter notebook with Graphical Models
• Export and visualize Graphical models and Potentials (png, pdf)
• pyAgrum.lib.explain
• pyAgrum.lib.dynamicBN
• other pyAgrum.lib modules

5- pyAgrum's (experimentals) models

• Continuous-Time Bayesian Networks
  • The CTBN model
  • Inference
  • Graphical tools
  • Learning a CTBN
• Continuous Linear Gaussian Bayesian Networks
  • Gaussian Variables
  • The CLG model
  • Inference : Variable Elimination
  • Graphical tools
  • Learning a CLG

5- Miscellaneous

• Random numbers from aGrUM
• fast syntax
• Functions from pyAgrum
• Listeners
• Exceptions from aGrUM

6- Customizing pyAgrum

• Configuration for pyAgrum

Indices and tables

• Index