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| # BAMT - Bayesian Analytical and Modelling Toolkit | ||
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| Repository of a data modeling and analysis tool based on Bayesian networks. | ||
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| ## Badges | ||
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| | team |   | | ||
| |------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ||
| | package |    | | ||
| | tests |   | | ||
| | docs |  | | ||
| | license |  | | ||
| | stats |    | | ||
| | style |  | | ||
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| ## Introduction | ||
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| 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. | ||
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| ## Installation | ||
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| BAMT package is available via PyPi: | ||
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| ```bash | ||
| pip install bamt | ||
| ``` | ||
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| ## BAMT Features | ||
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| The following algorithms for Bayesian Networks learning are implemented: | ||
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| - 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. | ||
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| ### Difference from existing implementations: | ||
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| - 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. | ||
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| 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. | ||
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| ## How to use | ||
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| Then the necessary classes are imported from the library: | ||
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| ```python | ||
| from bamt.networks.hybrid_bn import HybridBN | ||
| ``` | ||
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| Next, a network instance is created and training (structure and parameters) is performed: | ||
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| ```python | ||
| bn = HybridBN(has_logit=False, use_mixture=True) | ||
| bn.add_edges(preprocessed_data) | ||
| bn.fit_parameters(data) | ||
| ``` | ||
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| ## Examples & Tutorials | ||
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| More examples can be found in [Documentation](https://bamt.readthedocs.io/en/latest/examples/learn_save.html). | ||
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| ## Publications about BAMT | ||
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| We have published several articles about BAMT: | ||
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| - [Advanced Approach for Distributions Parameters Learning in Bayesian Networks with Gaussian Mixture Models and Discriminative Models](https://www.mdpi.com/2227-7390/11/2/343) (2023) | ||
| - [BigBraveBN: algorithm of structural learning for bayesian networks with a large number of nodes](https://www.sciencedirect.com/science/article/pii/S1877050922016945) (2022) | ||
| - [MIxBN: Library for learning Bayesian networks from mixed data](https://www.sciencedirect.com/science/article/pii/S1877050921020925) (2021) | ||
| - [Oil and Gas Reservoirs Parameters Analysis Using Mixed Learning of Bayesian Networks](https://link.springer.com/chapter/10.1007/978-3-030-77961-0_33) (2021) | ||
| - [Bayesian Networks-based personal data synthesis](https://dl.acm.org/doi/abs/10.1145/3411170.3411243) (2020) | ||
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| ## Project structure | ||
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| The latest stable version of the library is available in the master branch. | ||
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| It includes the following modules and directories: | ||
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| - [bamt](https://github.com/ITMO-NSS-team/BAMT/tree/master/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](https://github.com/ITMO-NSS-team/BAMT/tree/master/data) - directory with data for experiments and tests | ||
| - [tests](https://github.com/ITMO-NSS-team/BAMT/tree/master/tests) - directory with unit and integration tests | ||
| - [tutorials](https://github.com/ITMO-NSS-team/BAMT/tree/master/tutorials) - directory with tutorials | ||
| - [docs](https://github.com/ITMO-NSS-team/BAMT/tree/master/docs) - directory with RTD documentation | ||
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| ### Preprocessing | ||
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| Preprocessor module allows users to transform data according to the pipeline (similar to the pipeline in scikit-learn). | ||
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| ### Networks | ||
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| Three types of networks are implemented: | ||
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| - HybridBN - Bayesian network with mixed data | ||
| - DiscreteBN - Bayesian network with discrete data | ||
| - ContinuousBN - Bayesian network with continuous data | ||
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| They are inherited from the abstract class BaseNetwork. | ||
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| ### Nodes | ||
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| Contains classes for nodes of Bayesian networks. | ||
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| ### Utilities | ||
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| Utilities module contains mathematical and graph utilities to support the main functionality of the library. | ||
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| ## Web-BAMT | ||
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| A web interface for BAMT is currently under development. The repository is available at [web-BAMT](https://github.com/aimclub/Web-BAMT). | ||
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| ## Contacts | ||
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| If you have questions or suggestions, you can contact us at the following address: [email protected] (Irina Deeva) | ||
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| Our resources: | ||
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| - [Natural Systems Simulation Team](https://itmo-nss-team.github.io/) | ||
| - [NSS team Telegram channel](https://t.me/NSS_group) | ||
| - [NSS lab YouTube channel](https://www.youtube.com/@nsslab/videos) | ||
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| ## Citation | ||
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| ```bibtex | ||
| @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}, | ||
| } | ||
| ``` |
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