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Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)

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

pip3 install susi

conda install -c conda-forge susi

More information can be found in theinstallation guidelines.

A collection of code examples can be found inthe documentation.Code examples as Jupyter Notebooks can be found here:

• examples/SOMClustering
• examples/SOMRegressor
• examples/SOMRegressor_semisupervised
• examples/SOMRegressor_multioutput
• examples/SOMClassifier
• examples/SOMClassifier_semisupervised

• How should I set the initial hyperparameters of a SOM? For more detailson the hyperparameters, see indocumentation/hyperparameters.
• How can I optimize the hyperparameters? The SuSi hyperparameterscan be optimized, for example, withscikit-learn.model_selection.GridSearchCV,since the SuSi package is developed according to several scikit-learnguidelines.

The bibtex file including both references is available inbibliography.bib.

Paper:

F. M. Riese, S. Keller and S. Hinz, "Supervised and Semi-Supervised Self-OrganizingMaps for Regression and Classification Focusing on Hyperspectral Data",Remote Sensing, vol. 12, no. 1, 2020.DOI:10.3390/rs12010007

@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

@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}}}

This project is published under the3-Clause BSD license.