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

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, and searching for anomalous values.

BAMT package is available via PyPi: pip install bamt

In terms of training Bayesian networks on data, the following algorithms 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. For Hill Climbing, the following score functions are implemented - MI, K2, BIC, AIC. The algorithms work on both discrete and mixed data.
• Training the parameters of distributions in the nodes of the network on the basis of data.

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;
• The algorithm for structural training of large Bayesian networks (> 10 nodes) is based on local training of small networks with their subsequent algorithmic connection.

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

Bayesian networks can be used to restore gaps in reservoirs data, search for anomalous values, and also to search for analogous reservoirs.

1. GraphUtils consist of functions for:

• Finding nodes types/signs
• Topological ordering

2. MathUtils consist of functions for:

• Additional function to support calculation of metrics from group1 ('MI', 'LL', 'BIC', 'AIC')

Preprocessor module allows user to transform data according pipeline (in analogy with pipeline in scikit-learn).

There are 3 general types of networks: discrete, gaussian, hybrid. All implemented in networks.py

Contains nodes' classes and their methods.