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).

Important

Since pyAgrum 2.0.0, the package name follows PEP8 rules and is now pyagrum (lowercase). Please use import pyagrum instead of import pyAgrum in your code.

See the CHANGELOG for more details.

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 pearl2018why

Reference manual

1- Fundamental components

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

2- Bayesian networks

• Model for Bayesian Network
  • BayesNet
• Input/Output Formats for Bayesian Networks
  • Supported Formats
  • Other Supported Formats
• Inference
  • Exact Inference
  • Approximated Inference
• Learning
  • Class BNLearner
• Classifier with Bayesian Networks
  • Classifier using Bayesian networks
• 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

3- Causality

• Causal Model in pyAgrum
  • CausalModel
• Causal computation in pyAgrum
  • Causal Inference (a.k.a. do-calculus)
  • Counterfactual reasoning
• Notebook’s tools for causality
  • getCausalModel()
  • showCausalModel()
  • getCausalImpact()
  • showCausalImpact()
• Abstract Syntax Tree for Do-Calculus
  • Accessing the AST
• Causal Effect Estimation
  • CausalEffectEstimation

4- 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

5- pyAgrum's (experimentals) models

• Continuous-Time Bayesian Networks
  • The CTBN model
  • Inference in CTBN
  • Graphical tools for CTBN
  • Learning a CTBN
• Continuous Linear Gaussian Bayesian Networks
  • Gaussian Variables
  • The CLG model
  • Inference : Variable Elimination
  • Graphical tools for CLG
  • Learning a CLG
• Bayesian Network Mixture
  • Mixture Model
  • Inference on mixtures
  • Learning mixtures
• Quantum Bayesian Network Sampling
  • qBNMC — Circuit encoding
  • qBNRejection — Quantum inference

6- pyagrum's modules

• pyagrum.explain
• Jupyter notebook with Graphical Models
• DiscreteTypeProcessor for graphical Models
• Export and visualize Graphical models and Tensors (png, pdf)
• pyagrum.lib.dynamicBN
• other pyagrum.lib modules

7- pyAgrum's tools

• Random numbers from aGrUM
• fast syntax
• JGUM / BGUM Format Reference
• Functions from pyagrum
• Listeners
• Exceptions from aGrUM

8- Customizing pyAgrum

• Configuration for pyAgrum

9- Appendices

• Bibliography
• Glossary
• Index alphabétique