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)
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.
1 maintainer
Metadata
1 maintainer
Release files for skbel 2.1.14
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| skbel-2.1.14.tar.gz | 38.7 MB | Without provenance | Details for skbel-2.1.14.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| skbel-2.1.14-py3-none-any.whl | Python 3 | none | any | Details for skbel-2.1.14-py3-none-any.whl |
Total release size: 52.2 MB
Release files / skbel-2.1.14.tar.gz
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|---|---|
| Size | 38.7 MB |
| Tags | Source |
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No |
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twine/4.0.2 CPython/3.10.4
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Release files / skbel-2.1.14-py3-none-any.whl
| Download URL | skbel-2.1.14-py3-none-any.whl |
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| Tags | Python 3 |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.10.4
|
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