skbel 2.1.14

A set of Python modules to implement the Bayesian Evidential Learning (BEL) framework

skbel is a Python module for implementing the Bayesian Evidential Learning framework built on top of scikit-learn and is distributed under the 3-Clause BSD license.

For more information, read the documentation and run the example notebook.

Installation

Dependencies

skbel requires:

• Python (>= 3.7)

• Scikit-Learn (>= 0.24.1)

• NumPy (>= 1.14.6)

• SciPy (>= 1.1.0)

• joblib (>= 0.11)

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Skbel plotting capabilities require Matplotlib (>= 2.2.2).

User installation

The easiest way to install skbel is using pip

pip install skbel

Development

We welcome new contributors of all experience levels.

Important links

• Official source code repo: https://github.com/robinthibaut/skbel/

• Download releases: https://pypi.org/project/skbel/

• Issue tracker: https://github.com/robinthibaut/skbel/issues

Source code

You can check the latest sources with the command:

git clone https://github.com/robinthibaut/skbel.git

Contributing

Contributors and feedback from users are welcome. Don’t hesitate to submit an issue or a PR, or request a new feature.

Testing

After installation, you can launch the test suite from outside the source directory (you will need to have pytest >= 5.0.1 installed):

pytest skbel

Help and Support

Documentation

• HTML documentation (latest release): https://skbel.readthedocs.io/en/latest/

Communication

• Github Discussions: https://github.com/robinthibaut/skbel/discussions

How to cite

Thibaut, Robin, & Maximilian Ramgraber. (2021). SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn (v2.0.0). Zenodo. https://doi.org/10.5281/zenodo.6205242

BibTeX:

@software{thibaut_skbel_2021,
author       = {Thibaut, Robin and Maximilian Ramgraber},
title        = {{SKBEL} - Bayesian Evidential Learning framework built on top of scikit-learn},
month        = {9},
year         = 2021,
publisher    = {Zenodo},
version      = {v2.0.0},
doi          = {10.5281/zenodo.6205242},
url          = {https://doi.org/10.5281/zenodo.6205242},
}

Notebooks and tutorials

Nolwenn Lesparre, Nicolas Compaire, Thomas Hermans and Robin Thibaut. (2022). 4D Temperature Monitoring with BEL. [Dataset]. Kaggle. doi: 10.34740/kaggle/ds/2275519. url: https://doi.org/10.34740/kaggle/ds/2275519

Thibaut, Robin (2021). WHPA Prediction. [Dataset]. Kaggle. doi:10.34740/kaggle/dsv/2648718. url: https://www.kaggle.com/dsv/2648718

Peer-reviewed publications using SKBEL

Thibaut, Robin, Nicolas Compaire, Nolwenn Lesparre, Maximilian Ramgraber, Eric Laloy, and Thomas Hermans (Nov. 2022). “Comparing Well and Geophysical Data for Temperature Monitoring Within a Bayesian Experimental Design Framework”. In: Water Resources Research 58 (11). issn: 0043-1397. doi: 10.1029/2022WR033045. url: https://onlinelibrary.wiley.com/doi/10.1029/2022WR033045.

Thibaut, Robin, Eric Laloy, and Thomas Hermans (Dec. 2021). “A new framework for experimental design using Bayesian Evidential Learning: The case of wellhead protection area”. In: Journal of Hydrology 603, p. 126903. issn: 00221694. doi: 10.1016/j.jhydrol.2021.126903. url: https://linkinghub.elsevier.com/retrieve/pii/S0022169421009537.

Research project

Logs and results of the research project are available on the project page.