susi 1.0.5

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

SUSI: SUpervised Self-organIzing maps in Python

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

Description

We present the SUSI package for Python. It includes a fully functional SOM for unsupervised and supervised tasks. The class structure is set up as follows:

• SOMClustering: Unsupervised SOM for clustering

  • SOMEstimator: Base class for 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

Documentation:

Documentation

Installation:

Installation guidelines

Paper:

arXiv:1903.11114

Installation

pip3 install susi

More information can be found in the installation guidelines.

Examples

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

• examples/SOMClustering

• examples/SOMRegressor_Hyperspectral

• examples/SOMClassifier

• examples/SOMClassifier_Salinas

Citation

The bibtex file including both references is available here.

Paper:

Felix M. Riese and S. Keller, “SUSI: Supervised Self-Organizing Maps for Regression and Classification in Python”, arXiv:1903.11114, 2019. Submitted to an ISPRS conference.

@article{riesekeller2019susi,
    author = {Riese, Felix~M. and Keller, Sina},
    title = {SUSI: Supervised Self-Organizing Maps for Regression and Classification in Python},
    year = {2019},
    notes = {Submitted to an ISPRS conference},
    archivePrefix = {arXiv},
    eprint = {1903.11114},
    primaryClass = {cs.LG},
    url = {https://arxiv.org/abs/1903.11114}
}

Code:

Felix M. Riese, “SUSI: SUpervised Self-organIzing maps in Python”, 10.5281/zenodo.2609130, 2019.

@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}}
}

Change Log

[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

• [CHANGED] Moved decreasing_rate() out of SOM classes

• [FIXED] Removed duplicate constructor for SOMRegressor, fixed fit() params

• [ADDED] Codecov, Codacy

[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