aGrUM/pyAgrum

aGrUM is a fast and powerful C++ library for developing applications based on graphical models such as Bayesian networks, influence diagrams, decision trees, GAI networks, and Markov decision processes. It provides fundamental building blocks for tasks such as:

• Graphical model learning/elicitation
• Probabilistic inference with graphical models
• Planning and decision-making

pyAgrum is a Python wrapper for the C++ aGrUM library (built using SWIG). It offers a high-level interface to simplify the creation, modeling, learning, inference, and embedding of Bayesian Networks and other graphical models. Additionally, it includes extensions for:

• Scikit-learn-compatible probabilistic classifiers based on Bayesian networks
• Causal analysis tools, including causal networks and do-calculus
• Dynamic Bayesian networks
• Explainability tools for Bayesian networks

Quick Start

Installation

Install pyAgrum via pip or conda:

bash pip install pyagrum

bash conda install -c conda-forge pyagrum

Learning Resources

• Documentation
• Tutorials & Examples
• Interactive Examples on Binder

Dependencies

aGrUM is designed to minimize external dependencies in its C++ codebase. All essential dependencies are included in the source code. The external libraries currently used by aGrUM are:

• nanodbc: A lightweight C++ wrapper for ODBC
• lrs: A vertex enumeration program
• tinyxml: A simple and lightweight XML parser
• CxxTest: A unit testing framework for C++

pyAgrum Dependencies

The Python wrapper pyAgrum introduces additional dependencies. These are managed separately and specified in the following files:

• requirements.txt: Mandatory dependencies
• optional_requirements.txt: Optional dependencies for advanced features

For most users, installation via pip or conda will automatically handle these dependencies.

Project Structure

The project is organized as follows:

bash /acttools # aGrUM Compiler Tool (ACT) implementation /apps # Application examples using aGrUM or pyAgrum /src # aGrUM's C++ source code /wrappers # Wrappers for aGrUM (Python, Java, etc.)

Key Directories

• /src/agrum: Core C++ library
• /wrappers/pyAgrum: pyAgrum files, including tests and Sphinx documentation
• /src/docs: aGrUM documentation
• /src/testunits: Unit tests

Philosophy & Design

aGrUM was initially developed to support the research of the Graphical Models and Decision team at LIP6. Over time, it evolved into a comprehensive open-source library to aid both research and practical applications in decision support and data science.

Key design principles include:

• Modern C++20 development for cross-platform compatibility (GCC 8.0, Clang, MSVC)
• Emphasis on performance (multi-threaded algorithms for faster learning/inference)
• Support for fine-grained customization in learning, inference, and modeling

For more details, visit the aGrUM feature list.

Wrappers

To make aGrUM more accessible, various wrappers have been developed, including:

• pyAgrum: The primary Python wrapper
• jAgrum: An experimental Java wrapper

Wrappers are built using SWIG.

Building aGrUM

The recommended build tool is ACT (aGrUM Compilation Tool). It requires Python ( 3.9) and platform-specific tools:

• Linux: Python, g++, CMake
• macOS: Python, clang or g++, CMake
• Windows: Python, MSVC 2022, CMake

ACT Commands

bash act [target] [version] [action]

• Targets: aGrUM, pyAgrum, jAgrum
• Versions: release, debug
• Actions: install, uninstall, test, lib, doc, wheel, etc.

Example:

bash act test release pyAgrum

For more details, run act --help.

Contributions

We welcome contributions! Please fork the repository, make your changes, and submit a merge request.

Note: Contributors must sign a contribution policy before their changes can be merged.

Continuous Integration

Every commit is tested (with several compilers) on:

• Ubuntu (22.04, x84-64)
• macOS (15.x, x86-64 & ARM)
• Windows (11, x84-64)

The CI pipeline builds and tests both aGrUM and pyAgrum to ensure cross-platform stability.

Testing

Run tests with the act test command:

• aGrUM: Test individual modules using -m MODULES or specific suites using -t TESTS.
• pyAgrum: Choose between quick tests (-t quick) or comprehensive tests including Jupyter notebooks (-t all).

Bibliography

For academic references, see the aGrUM bibliography.