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

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SuSi: Supervised Self-organizing maps in Python

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. The class structure is set up as follows:

  • SOMClustering: Unsupervised SOM for clustering
    • SOMEstimator: Base class for supervised and semi-supervised SOMs
      • SOMRegressor: Regression SOM
      • SOMClassifier: Classification SOM
License:3-Clause BSD license
Author:Felix M. Riese
Citation:see Citation and in the bibtex file
Installation:Installation guidelines
Paper:F. M. Riese, S. Keller and S. Hinz in Remote Sensing, 2020


pip3 install susi

More information can be found in the installation guidelines.


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


  • How should I set the initial hyperparameters of a SOM? For more details on the hyperparameters, see in documentation/hyperparameters.
  • How can I optimize the hyperparameters? The SuSi hyperparameters can be optimized, for example, with scikit-learn.model_selection.GridSearchCV, since the SuSi package is developed according to several scikit-learn guidelines.


The bibtex file including both references is available in bibliography.bib.


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

    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 = {},
    ISSN = {2072-4292},
    DOI = {10.3390/rs12010007}


Felix M. Riese, “SuSi: SUpervised Self-organIzing maps in Python”, Zenodo, 2019. DOI:10.5281/zenodo.2609130
    author = {Riese, Felix~M.},
    title = {{SuSi: Supervised Self-Organizing Maps in Python}},
    year = {2019},
    DOI = {10.5281/zenodo.2609130},
    publisher = {Zenodo},
    howpublished = {\href{}{}}


This project is published under the 3-Clause BSD license.

PyPI - License

Change Log

[1.1.0] - 2020-08-31

  • [ADDED] Logo.
  • [ADDED] SOMPlots documentation.
  • [REMOVED] Python 3.5 support. Now, only 3.6-3.8 are supported.
  • [FIXED] Scikit-learn warnings regarding validation of positional arguments.
  • [FIXED] Sphinx documentation warnings.

[1.0.10] - 2020-04-21

  • [ADDED] Support for Python 3.8.x.
  • [ADDED] Test coverage and MultiOutput test.
  • [CHANGED] Function setPlaceholder to set_placeholder.
  • [FIXED] Documentation links

[1.0.9] - 2020-04-07

  • [ADDED] Documentation of the hyperparameters.
  • [ADDED] Plot scripts.
  • [CHANGED] Structure of the module files.

[1.0.8] - 2020-01-20

  • [FIXED] Replaced scikit-learn sklearn.utils.fixes.parallel_helper, see #12.

[1.0.7] - 2019-11-28

  • [ADDED] Optional tqdm visualization of the SOM training
  • [ADDED] New init_mode_supervised called random_minmax.
  • [CHANGED] Official name of package changes from SUSI to SuSi.
  • [CHANGED] Docstrings for functions are now according to guidelines.
  • [FIXED] Semi-supervised classification handling, sample weights
  • [FIXED] Supervised classification SOM initalization of n_iter_supervised
  • [FIXED] Code refactored according to prospector
  • [FIXED] Resolved bug in get_datapoints_from_node() for unsupervised SOM.

[1.0.6] - 2019-09-11

  • [ADDED] Semi-supervised abilities for classifier and regressor
  • [ADDED] Example notebooks for semi-supervised applications
  • [ADDED] Tests for example notebooks
  • [CHANGED] Requirements for the SuSi package
  • [REMOVED] Support for Python 3.4
  • [FIXED] Code looks better in documentation with sphinx.ext.napoleon

[1.0.5] - 2019-04-23

  • [ADDED] PCA initialization of the SOM weights with 2 principal components
  • [ADDED] Variable variance
  • [CHANGED] Moved installation guidelines and examples to documentation

[1.0.4] - 2019-04-21

  • [ADDED] Batch algorithm for unsupervised and supervised SOM
  • [ADDED] Calculation of the unified distance matrix (u-matrix)
  • [FIXED] Added estimator_check of scikit-learn and fixed recognized issues

[1.0.3] - 2019-04-09

  • [ADDED] Link to arXiv paper
  • [ADDED] Mexican-hat neighborhood distance weight
  • [ADDED] Possibility for different initialization modes
  • [CHANGED] Simplified initialization of estimators
  • [FIXED] URLs and styles in documentation
  • [FIXED] Colormap in Salinas example

[1.0.2] - 2019-03-27

  • [ADDED] Codecov, Codacy
  • [CHANGED] Moved decreasing_rate() out of SOM classes
  • [FIXED] Removed duplicate constructor for SOMRegressor, fixed fit() params

[1.0.1] - 2019-03-26

  • [ADDED] Config file for Travis
  • [ADDED] Requirements for read-the-docs documentation

[1.0.0] - 2019-03-26

  • Initial release

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