2.0.0 requirements and readme update

.github/workflows/bamtcodecov.yml

@@ -10,7 +10,7 @@ jobs:
     - uses: actions/checkout@v3
     - uses: actions/setup-python@v4
       with:
-        python-version: '3.9'
+        python-version: '3.11'
     - name: Update pip
       run: python -m pip install --upgrade pip
     - name: Install requirements

.gitignore

@@ -14,4 +14,165 @@ tutorials/bamt
 /temp.ipynb
 /bamt_house_price_example_feature_analysis.ipynb
 /bamt_house_price_example_data_sampling.ipynb
-/tmp.ipynb
+/tmp.ipynb
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/

README.md

@@ -0,0 +1,160 @@
+![BAMT framework logo](docs/images/BAMT_white_bg.png)
+
+# BAMT - Bayesian Analytical and Modelling Toolkit
+
+Repository of a data modeling and analysis tool based on Bayesian networks.
+
+## Badges
+
+| team       | ![ITMO](https://raw.githubusercontent.com/ITMO-NSS-team/open-source-ops/cd771018e80e9164f7b661bd2191061ab58f94de/badges/ITMO_badge.svg) ![NCCR](https://raw.githubusercontent.com/ITMO-NSS-team/open-source-ops/cd771018e80e9164f7b661bd2191061ab58f94de/badges/NCCR_badge.svg) |
+|------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| package    | ![pypi](https://badge.fury.io/py/bamt.svg) ![Supported Python Versions](https://img.shields.io/badge/python_3.9-passing-success) ![Supported Python Versions](https://img.shields.io/badge/python_3.10-passing-success)                             |
+| tests      | ![Build](https://github.com/ITMO-NSS-team/BAMT/actions/workflows/bamtcodecov.yml/badge.svg) ![coverage](https://codecov.io/github/aimclub/BAMT/branch/master/graph/badge.svg?token=fA4qsxGqTC)                                                   |
+| docs       | ![Documentation Status](https://readthedocs.org/projects/bamt/badge/?version=latest)                                                                                                                                                              |
+| license    | ![license](https://img.shields.io/github/license/ITMO-NSS-team/BAMT)                                                                                                                                                                              |
+| stats      | ![downloads](https://static.pepy.tech/personalized-badge/bamt?period=total&units=international_system&left_color=grey&right_color=blue&left_text=downloads) ![downloads/month](https://static.pepy.tech/personalized-badge/bamt?period=month&units=international_system&left_color=grey&right_color=blue&left_text=downloads/month) ![downloads/week](https://static.pepy.tech/personalized-badge/bamt?period=week&units=international_system&left_color=grey&right_color=blue&left_text=downloads/week) |
+| style      | ![Black](https://img.shields.io/badge/code%20style-black-000000.svg)                                                                                                                                                                               |
+
+## 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.
+
+![bamt readme scheme](docs/images/bamt_readme_scheme.png)
+
+## Installation
+
+BAMT package is available via PyPi:
+
+```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 (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.
+
+![bn example gif](img/BN_gif.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.
+
+![synthetics generation](img/synth_gen.png)
+
+## How to use
+
+Then the necessary classes are imported from the library:
+
+```python
+from bamt.networks.hybrid_bn import HybridBN
+```
+
+Next, a network instance is created and training (structure and parameters) is performed:
+
+```python
+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](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 directories:
+
+- [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 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](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)
+
+## Citation
+
+```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},
+}
+```