felixriese / Susi
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SuSi: Supervised Self-organizing maps in Python
Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)
Description
We present the SuSi package for Python. It includes a fully functional SOM for unsupervised, supervised and semi-supervised tasks:
- SOMClustering: Unsupervised SOM for clustering
- SOMRegressor: (Semi-)Supervised Regression SOM
- SOMClassifier: (Semi-)Supervised Classification SOM
:License:
3-Clause BSD license <LICENSE>
_
:Author:
Felix M. Riese <mailto:[email protected]>
_
:Citation:
see Citation
_ and in the bibtex <https://github.com/felixriese/susi/blob/master/bibliography.bib>
_ file
:Documentation:
Documentation <https://susi.readthedocs.io/en/latest/index.html>
_
:Installation:
Installation guidelines <https://susi.readthedocs.io/en/latest/install.html>
_
:Paper:
F. M. Riese, S. Keller and S. Hinz in Remote Sensing, 2020 <https://www.mdpi.com/2072-4292/12/1/7>
_
Installation
.. code:: bash
pip3 install susi
More information can be found in the installation guidelines <https://susi.readthedocs.io/en/latest/install.html>
_.
.. image:: https://static.pepy.tech/personalized-badge/susi?period=total&units=international_system&left_color=black&right_color=blue&left_text=Downloads :target: https://pepy.tech/project/susi :alt: Downloads
Examples
A collection of code examples can be found in the documentation <https://susi.readthedocs.io/en/latest/examples.html>
_.
Code examples as Jupyter Notebooks can be found here:
-
examples/SOMClustering <https://github.com/felixriese/susi/blob/master/examples/SOMClustering.ipynb>
_ -
examples/SOMRegressor <https://github.com/felixriese/susi/blob/master/examples/SOMRegressor.ipynb>
_ -
examples/SOMRegressor_semisupervised <https://github.com/felixriese/susi/blob/master/examples/SOMRegressor_semisupervised.ipynb>
_ -
examples/SOMClassifier <https://github.com/felixriese/susi/blob/master/examples/SOMClassifier.ipynb>
_ -
examples/SOMClassifier_semisupervised <https://github.com/felixriese/susi/blob/master/examples/SOMClassifier_semisupervised.ipynb>
_
FAQs
-
How should I set the initial hyperparameters of a SOM? For more details
on the hyperparameters, see in
documentation/hyperparameters <https://susi.readthedocs.io/en/latest/hyperparameters.html>
_. -
How can I optimize the hyperparameters? The SuSi hyperparameters
can be optimized, for example, with
scikit-learn.model_selection.GridSearchCV <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html>
_, since the SuSi package is developed according to several scikit-learn guidelines.
Citation
The bibtex file including both references is available in bibliography.bib <https://github.com/felixriese/susi/blob/master/bibliography.bib>
_.
Paper:
F. M. Riese, S. Keller and S. Hinz, "Supervised and Semi-Supervised Self-Organizing
Maps for Regression and Classification Focusing on Hyperspectral Data",
Remote Sensing, vol. 12, no. 1, 2020. DOI:10.3390/rs12010007 <https://doi.org/10.3390/rs12010007>
_
.. code:: bibtex
@article{riese2020supervised,
author = {Riese, Felix~M. and Keller, Sina and Hinz, Stefan},
title = {{Supervised and Semi-Supervised Self-Organizing Maps for
Regression and Classification Focusing on Hyperspectral Data}},
journal = {Remote Sensing},
year = {2020},
volume = {12},
number = {1},
article-number = {7},
URL = {https://www.mdpi.com/2072-4292/12/1/7},
ISSN = {2072-4292},
DOI = {10.3390/rs12010007}
}
Code:
Felix M. Riese, "SuSi: SUpervised Self-organIzing maps in Python",
Zenodo, 2019. DOI:10.5281/zenodo.2609130 <https://doi.org/10.5281/zenodo.2609130>
_
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2609130.svg :target: https://doi.org/10.5281/zenodo.2609130
.. code:: bibtex
@misc{riese2019susicode,
author = {Riese, Felix~M.},
title = {{SuSi: Supervised Self-Organizing Maps in Python}},
year = {2019},
DOI = {10.5281/zenodo.2609130},
publisher = {Zenodo},
howpublished = {\href{https://doi.org/10.5281/zenodo.2609130}{doi.org/10.5281/zenodo.2609130}}
}
License
This project is published under the 3-Clause BSD <LICENSE>
_ license.
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