You're absolutely right! Dependencies are crucial for developers to understand and set up the environment correctly. Here’s the updated version with a properly detailed Dependencies section integrated:
aGrUM is a 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
Install pyAgrum via pip or conda:
pip install pyAgrumconda install -c conda-forge pyagrumaGrUM 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++
The Python wrapper pyAgrum introduces additional dependencies. These are managed separately and specified in the following files:
requirements.txt: Mandatory dependenciesoptional_requirements.txt: Optional dependencies for advanced features
For most users, installation via pip or conda will automatically handle these dependencies.
The project is organized as follows:
/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.)
/src/agrum: Core C++ library/wrappers/pyAgrum: pyAgrum files, including tests and Sphinx documentation/src/docs: aGrUM documentation/src/testunits: Unit tests
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.
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.
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 [target] [version] [action]- Targets:
aGrUM,pyAgrum,jAgrum - Versions:
release,debug - Actions:
install,uninstall,test,lib,doc,wheel, etc.
Example:
act test release pyAgrumFor more details, run act --help.
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.
Every commit is tested on:
- Ubuntu (20.04 & 22.04)
- macOS (Intel & ARM)
- Windows 11
The CI pipeline builds and tests both aGrUM and pyAgrum to ensure cross-platform stability.
Run tests with the act test command:
- aGrUM: Test individual modules using
-m MODULESor specific suites using-t TESTS. - pyAgrum: Choose between quick tests (
-t quick) or comprehensive tests including Jupyter notebooks (-t all).
For academic references, see the aGrUM bibliography.