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bamt-1.1.40
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مشاهده بیشترتوضیحات
data modeling and analysis tool based on Bayesian networks
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | bamt-1.1.40 |
| نام | bamt |
| نسخه کتابخانه | 1.1.40 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Roman Netrogolov |
| ایمیل نویسنده | romius2001@gmail.com |
| آدرس صفحه اصلی | https://github.com/ITMO-NSS-team/BAMT |
| آدرس اینترنتی | https://pypi.org/project/bamt/ |
| مجوز | BSD-3-Clause |
.. image:: /docs/images/BAMT_white_bg.png
:align: center
:alt: BAMT framework logo
.. start-badges
.. list-table::
:stub-columns: 1
* - package
- | |pypi| |py_8| |py_9| |py_10|
* - tests
- | |Build| |coverage|
* - docs
- |docs|
* - license
- | |license|
* - stats
- | |downloads_stats| |downloads_monthly| |downloads_weekly|
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, assessing edges strength e.t.c.
.. image:: docs/images/bamt_readme_scheme.png
:target: docs/images/bamt_readme_scheme.png
:align: center
:alt: bamt readme scheme
Installation
^^^^^^^^^^^^
BAMT package is available via PyPi:
.. code-block:: bash
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 (evolutionary and PC are 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.
.. image:: img/BN_gif.gif
:target: img/BN_gif.gif
:align: center
:alt: bn example gif
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.
.. image:: img/synth_gen.png
:target: img/synth_gen.png
:align: center
:height: 300px
:width: 600px
:alt: synthetics generation
How to use
^^^^^^^^^^
Then the necessary classes are imported from the library:
.. code-block:: python
import bamt.networks as Nets
Next, a network instance is created and training (structure and parameters) is performed:
.. code-block:: python
bn = Nets.HybridBN(has_logit=False, use_mixture=True)
bn.add_edges(discretized_data, scoring_function=('K2',K2Score))
bn.fit_parameters(data)
Examples & Tutorials
^^^^^^^^^^^^^^^^^^^^^^
More examples can be found in `tutorials <https://github.com/ITMO-NSS-team/BAMT/tree/master/tutorials>`__ and `Documentation <https://bamt.readthedocs.io/en/latest/examples/learn_save.html>`__.
Publications about BAMT
^^^^^^^^^^^^^^^^^^^^^^^
We have published several articles about BAMT:
* `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)
Project structure
^^^^^^^^^^^^^^^^^
The latest stable version of the library is available in the master branch.
It includes the following modules and direcotries:
* `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
Preprocessing
=============
Preprocessor module allows user to transform data according pipeline (similar to 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 <https://github.com/aimclub/Web-BAMT>`__
Contacts
^^^^^^^^
If you have questions or suggestions, you can contact us at the following address: ideeva@itmo.ru (Irina Deeva)
Our resources:
* `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>`__
.. |docs| image:: https://readthedocs.org/projects/bamt/badge/?version=latest
:target: https://bamt.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. |pypi| image:: https://badge.fury.io/py/bamt.svg
:target: https://badge.fury.io/py/bamt
.. |py_10| image:: https://img.shields.io/badge/python_3.10-passing-success
:alt: Supported Python Versions
:target: https://img.shields.io/badge/python_3.10-passing-success
.. |py_8| image:: https://img.shields.io/badge/python_3.8-passing-success
:alt: Supported Python Versions
:target: https://img.shields.io/badge/python_3.8-passing-success
.. |py_9| image:: https://img.shields.io/badge/python_3.9-passing-success
:alt: Supported Python Versions
:target: https://img.shields.io/badge/python_3.9-passing-success
.. |license| image:: https://img.shields.io/github/license/ITMO-NSS-team/BAMT
:alt: Supported Python Versions
:target: https://github.com/ITMO-NSS-team/BAMT/blob/master/LICENCE
.. |downloads_stats| image:: https://static.pepy.tech/personalized-badge/bamt?period=total&units=international_system&left_color=grey&right_color=blue&left_text=downloads
:target: https://pepy.tech/project/bamt
.. |downloads_monthly| image:: https://static.pepy.tech/personalized-badge/bamt?period=month&units=international_system&left_color=grey&right_color=blue&left_text=downloads/month
:target: https://pepy.tech/project/bamt
.. |downloads_weekly| image:: https://static.pepy.tech/personalized-badge/bamt?period=week&units=international_system&left_color=grey&right_color=blue&left_text=downloads/week
:target: https://pepy.tech/project/bamt
.. |Build| image:: https://github.com/ITMO-NSS-team/BAMT/actions/workflows/bamtcodecov.yml/badge.svg
:target: https://github.com/ITMO-NSS-team/BAMT/actions/workflows/bamtcodecov.yml
.. |coverage| image:: https://codecov.io/github/ITMO-NSS-team/BAMT/branch/master/graph/badge.svg?token=9ZX37JNIYZ
:target: https://codecov.io/github/ITMO-NSS-team/BAMT
نیازمندی
| مقدار | نام |
|---|---|
| ==65.6.3 | setuptools |
| >=1.24.2 | numpy |
| ==3.6.2 | matplotlib |
| ==1.5.2 | pandas |
| ==0.14.8 | pomegranate |
| ==1.6.2 | gmr |
| ==1.2.0 | scikit-learn |
| >=1.8.0,<2.0.0 | scipy |
| >=0.2.1 | pyvis |
| >=0.5.1,<0.6.0 | missingno |
| ==0.1.20 | pgmpy |
| ==0.2.2 | pyitlib |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.9,<3.11 | Python |
نحوه نصب
نصب پکیج whl bamt-1.1.40:
pip install bamt-1.1.40.whl
نصب پکیج tar.gz bamt-1.1.40:
pip install bamt-1.1.40.tar.gz