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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/main/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

Pip

.. code:: bash

    pip3 install susi
    
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Conda

.. code:: bash

conda install -c conda-forge susi

More information can be found in the installation guidelines <https://susi.readthedocs.io/en/latest/install.html>_.

.. image:: https://img.shields.io/conda/dn/conda-forge/susi.svg :target: https://anaconda.org/conda-forge/susi :alt: Conda-Forge 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/main/examples/SOMClustering.ipynb>_
• examples/SOMRegressor <https://github.com/felixriese/susi/blob/main/examples/SOMRegressor.ipynb>_
• examples/SOMRegressor_semisupervised <https://github.com/felixriese/susi/blob/main/examples/SOMRegressor_semisupervised.ipynb>_
• examples/SOMRegressor_multioutput <https://github.com/felixriese/susi/blob/main/examples/SOMRegressor_multioutput.ipynb>_
• examples/SOMClassifier <https://github.com/felixriese/susi/blob/main/examples/SOMClassifier.ipynb>_
• examples/SOMClassifier_semisupervised <https://github.com/felixriese/susi/blob/main/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/main/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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