fastersom

A small, fast batch self-organising map (SOM) in PyTorch: a reimplementation of Kohonen's batch map with a rectangular grid, gaussian neighbourhood and exponentially decaying neighbourhood width. Its defaults reproduce the batch-trained maps of XPySom with a gaussian neighbourhood of initial width half the grid. Data stays on the device (CUDA, MPS or CPU) and the per-epoch update smooths per-node sums with the separable gaussian, one small matrix product along each grid axis, so an epoch costs little more than one BMU search.

Install

uv add fastersom    # or: pip install fastersom

Usage

import numpy as np
from fastersom import SOM

X = np.load("pixels.npy")  # (n, d) or (..., d); numpy, array-like or torch
som = SOM(n_rows=30, n_cols=30, n_epochs=10, random_state=0)  # device: cuda > mps > cpu
som.fit(X)
som.weights_  # (900, d) node weights, node = row * n_cols + col
som.labels_  # BMU index per training sample
labels = som.predict(X)  # BMU index per sample
labels, dist = som.predict(X, return_distance=True)  # and the distance to it, in the same pass
dist = som.transform(X)  # (n, 900) distance to every node
umatrix = som.distance_map()  # (30, 30) summed distance to the grid neighbours, max 1
print(som.quantization_error(X), som.topographic_error(X))

som = SOM.from_weights(som.weights_, n_rows=30, n_cols=30)  # fitted map from saved weights, no data needed

Options:

• metric sets the distance used for BMUs, distances and error measures: "euclidean" (default), "manhattan" (L1), "cosine" (1 - cosine similarity) or "correlation" (1 - Pearson correlation). Nodes are always updated to the neighbourhood-weighted mean of the raw samples (not the median under manhattan, nor centred samples under correlation). Zero-norm samples under cosine and constant samples under correlation have no BMU: they get label -1 and distance NaN in predict, distance 1 in transform, and are left out of the update and the error measures.
• init="sample" initialises the nodes from random samples instead of seeded random unit vectors. An array of shape (n_rows * n_cols, d) or (n_rows, n_cols, d) gives the initial weights instead, e.g. for a warm start (SOM(init=som.weights_).fit(X)); with n_epochs=0, fit keeps them unchanged. The sigma schedule restarts at sigma_initial, which with the default largely retrains the map; to continue training instead, pass a small sigma_initial, e.g. the previous map's sigma_final.
• sigma_initial and sigma_final set the neighbourhood width schedule; the defaults reproduce XPySom's.
• batch_size bounds memory (about batch_size x n_nodes floats) without changing the algorithm. On CUDA and MPS it defaults to the largest batch that still helps speed and fits in memory_fraction (default 0.9 on CUDA, 0.5 on MPS) of the free GPU memory, so it rarely needs tuning.

Fitted maps pickle without device tensors and load on the configured device if available, else on the default one, so a map fitted on a GPU loads on a CPU-only machine. BatchSOM is an alias for SOM.

Benchmark

480,000 x 34 pixels, 10 epochs, mean of 3 seeds; peak GPU memory includes the data (benchmarks/memory.py).

At equal or less GPU memory fastersom is about 4x faster than XPySom on a 30 x 30 map and 5x faster on a 100 x 100 map (with a seventh of the memory), and over 20x faster on the CPU. torchsom updates the map per mini-batch, so it needs small batches and is far slower.

Algorithm

fastersom follows Kohonen's batch map (T. Kohonen, Self-Organizing Maps, 3rd ed., Springer, 2001). Each epoch assigns every sample to its best matching unit and sets every node to the neighbourhood-weighted mean of the samples, with neighbourhood h(j, k) = exp(-||r_j - r_k||^2 / (2 sigma(t)^2)) on the grid coordinates r and sigma(t) = sigma_initial * (sigma_final / sigma_initial) ** (t / n_epochs). There is no learning rate, because it cancels in the batch update. Only the rectangular topology, gaussian neighbourhood and exponential decay are supported.

Development

uv sync                      # installs the dev group: pytest, ruff, pre-commit, twine
uv run pre-commit install    # ruff, uv-lock and file checks on every commit
uv run pytest
uv build && uv run twine check --strict dist/*

License

Apache-2.0