BAMT - Bayesian Analytical and Modelling Toolkit

Repository of a data modeling and analysis tool based on Bayesian networks.

Badges

team
package
tests
docs
license
stats
style

Introduction

BAMT - Bayesian Analytical and Modelling Toolkit. This repository contains a data modeling and analysis tool based on Bayesian networks. It can be divided into two main parts - algorithms for constructing and training Bayesian networks on data and algorithms for applying Bayesian networks for filling gaps, generating synthetic data, assessing edge strength, etc.

Installation

BAMT package is available via PyPi:

pip install bamt

BAMT Features

The following algorithms for Bayesian Networks learning are implemented:

Building the structure of a Bayesian network based on expert knowledge by directly specifying the structure of the network.
Building the structure of a Bayesian network on data using three algorithms - Hill Climbing, evolutionary, and PC (PC is currently under development). For Hill Climbing, the following score functions are implemented - MI, K2, BIC, AIC. The algorithms work on both discrete and mixed data.
Learning the parameters of distributions in the nodes of the network based on Gaussian distribution and Mixture Gaussian distribution with automatic selection of the number of components.
Non-parametric learning of distributions at nodes using classification and regression models.
BigBraveBN - algorithm for structural learning of Bayesian networks with a large number of nodes. Tested on networks with up to 500 nodes.

Difference from existing implementations:

Algorithms work on mixed data.
Structural learning implements score-functions for mixed data.
Parametric learning implements the use of a mixture of Gaussian distributions to approximate continuous distributions.
Non-parametric learning of distributions with various user-specified regression and classification models.
The algorithm for structural training of large Bayesian networks (> 10 nodes) is based on local training of small networks with their subsequent algorithmic connection.

For example, in terms of data analysis and modeling using Bayesian networks, a pipeline has been implemented to generate synthetic data by sampling from Bayesian networks.

How to use

Then the necessary classes are imported from the library:

from bamt.networks.hybrid_bn import HybridBN

Next, a network instance is created and training (structure and parameters) is performed:

bn = HybridBN(has_logit=False, use_mixture=True)
bn.add_edges(preprocessed_data)
bn.fit_parameters(data)

Examples & Tutorials

More examples can be found in Documentation.

Publications about BAMT

We have published several articles about BAMT:

Project structure

The latest stable version of the library is available in the master branch.

It includes the following modules and directories:

bamt - directory with the framework code:
- Preprocessing - module for data preprocessing
- Networks - module for building and training Bayesian networks
- Nodes - module for nodes support of Bayesian networks
- Utilities - module for mathematical and graph utilities
data - directory with data for experiments and tests
tests - directory with unit and integration tests
tutorials - directory with tutorials
docs - directory with RTD documentation

Preprocessing

Preprocessor module allows users to transform data according to the pipeline (similar to the pipeline in scikit-learn).

Networks

Three types of networks are implemented:

HybridBN - Bayesian network with mixed data
DiscreteBN - Bayesian network with discrete data
ContinuousBN - Bayesian network with continuous data

They are inherited from the abstract class BaseNetwork.

Nodes

Contains classes for nodes of Bayesian networks.

Utilities

Utilities module contains mathematical and graph utilities to support the main functionality of the library.

Web-BAMT

A web interface for BAMT is currently under development. The repository is available at web-BAMT.

Contacts

If you have questions or suggestions, you can contact us at the following address: ideeva@itmo.ru (Irina Deeva)

Our resources:

Citation

@misc{BAMT,
  author={BAMT},
  title = {Repository experiments and data},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/ITMO-NSS-team/BAMT.git}},
  url = {https://github.com/ITMO-NSS-team/BAMT.git}
}

@article{deeva2023advanced,
  title={Advanced Approach for Distributions Parameters Learning in Bayesian Networks with Gaussian Mixture Models and Discriminative Models},
  author={Deeva, Irina and Bubnova, Anna and Kalyuzhnaya, Anna V},
  journal={Mathematics},
  volume={11},
  number={2},
  pages={343},
  year={2023},
}

aimclub/BAMT