python-som

Implementation of Kohonen's 2-D self-organizing map, built on NumPy, with scikit-learn for PCA and standardization. Accepts NumPy arrays, pandas DataFrames and plain lists.

Documentation · Changelog

Install

pip install python-som              # requires Python 3.10+
pip install "python-som[cli]"       # adds tqdm progress bars

Quick start

import numpy as np
import python_som

rng = np.random.default_rng(0)
data = rng.normal(size=(150, 4))

som = python_som.SOM(x=20, y=None, input_len=4, data=data, random_seed=42)
som.weight_initialization(mode="linear", data=data)
error = som.train(data, n_iteration=len(data), mode="batch")

umatrix = som.distance_matrix()
winner = som.winner(data[0])

A full worked example with plots is in examples/iris.py and in the getting-started guide.

Features

• Stepwise and batch training
• Random, random-sampling and linear (PCA) weight initialization
• Automatic selection of the map size ratio, from PCA
• Cyclic arrays, for toroidal maps
• Gaussian, bubble and Mexican hat neighborhood functions
• Custom decay functions
• Visualization support: U-matrix, activation matrix
• Supervised labelling, via the label map
• Fully type-annotated, with a py.typed marker

Neighborhood functions

All three are functions of the distance between two nodes in the grid, sqdist(c, i) in Eq. (5) of Kohonen (2013).

The Mexican hat takes negative values, so it cannot be used with mode='batch': the batch update of Kohonen Eq. (8) is a weighted mean whose denominator is not sign-definite for a signed neighborhood function. Use mode='random' or mode='sequential'; mode='batch' raises a ValueError.

See Neighborhood functions for the derivations, including why the Mexican hat is not an outer product of two 1-D wavelets.

Upgrading

0.3.0 corrects several methodology defects, so numerical results are not comparable with earlier versions. In particular random_seed no longer reproduces pre-0.3.0 maps: the generator is now per-instance rather than a call to np.random.seed on NumPy's global state. To reproduce figures made with an older version, pin python-som==0.2.0.

Each change and the passage of Kohonen (2013) behind it is in the changelog.

Development

uv sync --all-extras
uv run pytest --cov          # tests and coverage
uv run ruff check .          # lint
uv run ruff format --check . # formatting
uv run mypy                  # type-check
uv run mkdocs serve          # docs, locally
pre-commit install           # optional, run the gates on commit

If you use the SonarQube for IDE (SonarLint) VS Code extension, it will also apply Sonar's Python rules locally; the ruff configuration is set up to cover most of the same ground.

References

Based on:

Teuvo Kohonen, Essentials of the self-organizing map, Neural Networks, Volume 37, 2013, Pages 52-65, ISSN 0893-6080, https://doi.org/10.1016/j.neunet.2012.09.018

The Mexican hat neighborhood follows the lateral-interaction formulation in:

O. J. Vrieze, Kohonen network, in: Artificial Neural Networks: An Introduction to ANN Theory and Practice, Lecture Notes in Computer Science, Volume 931, Springer, Berlin, Heidelberg, 1995, Pages 83-100, https://doi.org/10.1007/BFb0027024

License

MIT. See LICENSE.