GeoSOM — Self-Organising Maps on geodesic domes for Python

mt.geosom provides Self-Organising Maps (SOMs) whose neurons live on the lattices of GeodesicDome: a geodesic sphere (spherical SOM, GeoSOM) or a flat hexagonal/rectilinear grid, with borders or as a torus.

The map view — the whole attribute vectors on a sphere you rotate with the mouse; click a neuron to see its attributes. The triangles between neurons are coloured by the Euclidean distance between their attribute vectors: dark basins are clusters, bright ridges are the borders between them.

The matrix view — one map per attribute, coloured by how much that attribute changes between neighbouring neurons. Drag any map and every map rotates with it.

What you get

All interactive code is in the subpackage mt.geosom.gui (needs matplotlib); everything else needs only numpy and geodesicdomes. The step-by-step guide is in docs/index.md.

1. Installation

Requires Python ≥ 3.10, NumPy and geodesicdomes.

pip install geosom                  # the library (numpy + geodesicdomes)
pip install "geosom[interactive]"   # + matplotlib, for the interactive views (mt.geosom.gui)
pip install "geosom[gpu]"           # + torch, to train on an NVIDIA (CUDA) or Apple Silicon (MPS) GPU

Training uses a GPU automatically when torch (with CUDA or MPS) or CuPy is installed, and every CPU core otherwise — see GPU and multi-core below.

Check that it works:

python -c "from mt.geosom.GeoSOM import GeoSOM; print(GeoSOM(4))"

Working on this repository: setup_env.sh (recommended)

cd GeoSOM
./setup_env.sh                             # once
python examples/01_plane_som.py       # works straight away, in the same terminal

It works like GeodesicDome's script (environment in ~/.venvs/GeoSOM, outside Google Drive; an activated shell at the end; auto-activation in new terminals; safe to re-run; the GPU packages this machine can use — torch for CUDA, Apple Silicon or ROCm, plus CuPy on NVIDIA, skip with --gpu none; --help for options), with one addition:

• It uses your local GeodesicDome. If ../GeodesicDome exists next to this repository, it is installed in editable mode, so changes to both libraries are picked up at once. Use --dome PATH (or $GEODESICDOME_DIR) for another location. Without a checkout, geodesicdomes is installed from PyPI.

Other ways to install

pip install "git+https://github.com/takatsuka/GeoSOM.git"   # latest development version
pip install -e ".[dev]"                                            # from a local checkout, editable

Optional extras: interactive (matplotlib, via geodesicdomes), notebooks (Jupyter, ipywidgets, ipympl), dev (pytest, ruff, build, twine), and all.

Notebooks

Every example is also a Jupyter notebook in examples/notebooks/, explained step by step, with controls under every viewer and a "Try it" section to retrain with other settings. With ipympl the viewers are live in the notebook: drag the spheres, click neurons, use the buttons on the figures.

pip install -e ".[notebooks]"           # setup_env.sh already includes it
jupyter lab examples/notebooks

2. Quick start

2.1 Train a GeoSOM and visualise it, step by step

Train. Every neuron gets a weight vector with one value per attribute of your data.

import numpy as np
from mt.geosom import datasets
from mt.geosom.GeoSOM import GeoSOM
from mt.geosom.som import InitializationType

# 1. data: an array (samples, attributes); NaN marks a missing value
data, labels, names = datasets.load_sample('clusters') # demo: 7 labelled clusters, 12 attributes
# data, labels, names = datasets.load_sample('wine')                              # other samples: animals, iris, penguins, wine
# data, labels, names = datasets.read_csv('mydata.csv', label_column='species')   # your own data
x = datasets.standardise(data)                        # zero mean, unit variance per attribute

# 2. a spherical SOM on GeodesicDome(10): 10*10**2 + 2 = 1002 neurons, no borders
som = GeoSOM(10, seed=0)
som.initialise(InitializationType.Linear, x)          # spread over the first 3 principal components

# 3. train
som.train(x, epochs=30)                               # batch training (mode='online' also works)
print(som)                                            # GeoSOM(frequency=10, neurons=1002, dim=12)
print(f'QE {som.quantisation_error(x):.3f}  TE {som.topographic_error(x):.3f}')

# 4. use it
som.weights                                           # (1002, 12): one weight vector per neuron
bmu = som.bmu(x)                                      # the best matching neuron of every sample
hits = som.hits(x)                                    # how many samples each neuron wins

Visualise. Two interactive views, both built on GeodesicDome's rotatable map projections (needs matplotlib):

import matplotlib.pyplot as plt
from mt.geosom.gui import ComponentMatrixViewer, SOMViewer, link_views

# 5. the whole attribute vectors on one rotatable map of the sphere,
#    with each neuron labelled by the samples it is the best matching unit of
viewer = SOMViewer(som, x, labels=labels, feature_names=names, node_labels='majority')

# 6. the matrix view: one map per attribute, all rotating together
matrix = ComponentMatrixViewer(som, names, data=x, labels=labels)

# 7. keep the two windows in step (rotation and selected neuron), then open them
link_views(viewer, matrix)
plt.show()

Steps 5–7 in one line: explore(som, x, labels, names) (from mt.geosom.gui).

What you see in SOMViewer. The triangles between neighbouring neurons are coloured by the Euclidean distance between their attribute vectors: dark basins are clusters, bright ridges are the borders between them. Drag to rotate the sphere; click a neuron to see its attribute vector on the right; switch layers on the left (U-matrix, one attribute's value or difference, PCA colour, hits, classes). With node_labels, every neuron that is the best matching unit of some samples shows their label (majority label, all labels, or labels with counts). Turn the labels and the sample dots on and off with the check boxes below the colour bar (or l and s); the style button next to them (or L) switches the label style, and A− / A+ (or - / +) change the label size.

What you see in the matrix view (ComponentMatrixViewer). One map per attribute. Each triangle is coloured by how much that attribute changes between its three neurons (mean |w_a[k] − w_b[k]|), so you can see which attributes form which cluster borders; the first map is the distance over all attributes. Drag any map and every map rotates; click marks a neuron on every map, with its value of each attribute in the titles; double-click opens that attribute on its own; v switches to the attribute values, c to one colour scale for all maps.

Save images instead of opening windows (e.g. on a server):

import matplotlib
matplotlib.use('Agg')                                  # before any window is created
from mt.geosom.gui import explore

views = explore(som, x, labels, names, select=int(hits.argmax()), show=False)
views.map.save('som_map.png', dpi=110)
views.matrix.save('som_matrix.png', dpi=90)

Complete scripts: examples/02_geosom_interactive.py (single map), examples/03_component_matrix.py (matrix view), examples/04_explore_all.py (both, linked).

2.2 Everything at once, from a terminal

python -m mt.geosom                                  # demo: 7 labelled clusters, 12 attributes, on a sphere
python -m mt.geosom --data wine                      # a sample shipped with the package: animals, iris, penguins, wine
python -m mt.geosom --data mydata.csv --label-column species
python -m mt.geosom --freq 8 --epochs 50 --projection "Wagner VI"
python -m mt.geosom --only matrix --matrix-mode value
python -m mt.geosom --data iris --node-labels counts --label-size 9   # label neurons with the samples they win
python -m mt.geosom --lattice hexagonal              # a flat 20 x 30 hexagonal grid instead
python -m mt.geosom --save out/som --no-gui          # writes out/som_map.png and out/som_matrix.png
python -m mt.geosom --help

This standardises the data, trains, and opens both views, linked. The installed command geosom is the same, and so is python main.py in this repository. From Python, train_and_explore(data, labels, names) does the same.

2.3 Flat SOM (PlaneSOM)

import numpy as np
from mt.geodesicdome.grid.plane import Lattice, Topology
from mt.geosom.PlaneSOM import PlaneSOM
from mt.geosom.gui import explore                     # needs matplotlib

data = np.random.rand(500, 6)                              # 500 samples, 6 attributes

plane = PlaneSOM(row=20, col=30, lattice=Lattice.Hexagonal, topology=Topology.Plane)   # or Topology.Donut: a torus
plane.initialise(dataset=data)                             # linear (PCA) initialisation
plane.train(data, epochs=30)
print(plane.quantisation_error(data), plane.topographic_error(data))

explore(plane, data, feature_names=list('abcdef'))         # PlaneSOMViewer + PlaneComponentMatrixViewer

python examples/01_plane_som.py does all this with labelled demo data (--topology torus, --lattice rectilinear, --data file.csv). Each neuron's weight vector is also stored on its lattice vertex (vertex.data), so the neighbourhood searches of GeodesicDome (som.grid.get_neighbours_in_distance(v, d)) work directly on the map.

2.4 Kohonen's animals: a semantic map

from mt.geosom import datasets
from mt.geosom.GeoSOM import GeoSOM
from mt.geosom.gui import explore

x, animals, names = datasets.animals(symbols=True)    # 16 animals; 16 symbol values + 13 attributes each
som = GeoSOM(4, seed=3)                          # 162 neurons
som.initialise(dataset=x)
som.train(x, epochs=60)                               # binary attributes: no standardising
explore(som, x, labels=animals, feature_names=names, node_labels='all', matrix=False)

Sixteen animals are described by 13 yes/no attributes (small, medium, big; two legs, four legs, hair, hooves, mane, feathers; likes to hunt, run, fly, swim) — the data of Ritter & Kohonen's "Self-organizing semantic maps" (1989). Nothing says which animals are birds, hunters or grazers, yet the trained map puts them in those groups, and the neuron labels show which animal each neuron stands for. datasets.load_sample('animals') gives the 13 attributes alone. Owl and hawk have the same attributes, and so do horse and zebra; animals(symbols=True) sets the data up as in the paper, adding a small "symbol" part (0.2 for the animal itself) to attribute vectors scaled to unit length. python examples/05_kohonen_animals.py runs it (--symbols; --lattice hexagonal for the paper's flat 10 × 15 map).

3. API

SOM(grid) — mt.geosom.som

Abstract base class. grid is any mt.geodesicdome Manifold (GeodesicDome or Plane); subclasses implement initialise(type, ...) and train(). InitializationType is Random or Linear.

GeoSOM and PlaneSOM both derive from LatticeSOM (mt.geosom.lattice_som), which holds the training and every measurement, so the members in the GeoSOM table below apply to both. Distances between neurons are measured in neighbour spacings (1 = the distance between neighbouring neurons).

PlaneSOM(row, col, dim=None, lattice=Lattice.Hexagonal, topology=Topology.Plane, seed=None, backend=None) — mt.geosom.PlaneSOM

SOM on GeodesicDome's Plane: Lattice.Hexagonal (6 neighbours, odd rows shifted by half a spacing) or Lattice.Rectilinear (4 neighbours); Topology.Plane (with borders) or Topology.Donut (a torus: no borders, like the sphere; use an even number of rows with a hexagonal torus). Neuron i sits at column i % col, row i // col.

Neuron(vertex, weights=None, dimension=0) — mt.geosom.neuron

vertex · weights · dimension · labels · distance(other): Euclidean distance that skips components where other is missing (mt.geosom.util.MISSING, i.e. NaN).

GeoSOM(grid=8, seed=None, backend=None) — mt.geosom.GeoSOM

Spherical SOM whose neurons are the unique points of a GeodesicDome (grid is a dome or its frequency). Every neuron has a weight vector with one value per attribute. Distances between neurons are great-circle distances in rings (1 ring = mean spacing between neighbours), so sigma means the same at any frequency.

Missing values (NaN) are ignored when finding best matching units and when updating weights. Standardise attributes that are on different scales before training, or the largest ones dominate the Euclidean distance.

GPU and multi-core

Best-matching-unit search, distances, the error measures and both training modes run on the compute backend of GeodesicDome (mt.geodesicdome.backend): an NVIDIA GPU through torch or CuPy, the Apple Silicon GPU through torch (MPS), otherwise NumPy on every CPU core. The choice is automatic; override it per map or globally:

som = GeoSOM(32, backend='cuda')      # or 'mps', 'cupy', 'cpu', 'numpy', 'torch:cpu'; None = automatic
som.to('cpu')                              # move an existing map
print(som.backend)                         # Backend(torch, cuda:0, float32)

MTGEODESIC_BACKEND=cpu python train.py     # default for every map; also MTGEODESIC_NUM_THREADS, MTGEODESIC_DTYPE

Distances between neurons are computed on the device when needed rather than stored as a dense matrix, so very large maps fit in memory (frequency 64, 40 962 neurons, trains in under 1 GB); the matrix is kept whole only when it fits comfortably, and node_distance builds it on first use. The CPU computes in float64 and gives the same results as before; a GPU uses float32 by default (set MTGEODESIC_DTYPE=float64 for double precision on CUDA).

SOMViewer(som, data=None, labels=None, feature_names=None, ...) — mt.geosom.gui

Built on GeodesicDome's ProjectionViewer, so the sphere rotates the same way (drag; arrows; r, p, g, e).

Click a neuron to see its attribute vector (standardised against all neurons) with its raw values, hits and labels. Samples are drawn as dots at their best matching units (s hides them). select_node(i), set_layer(name, component) and save(path) do the same from code.

Neuron labels. SOMViewer(..., node_labels='majority') or viewer.set_node_labels(mode, sample_names=None) writes, on every neuron that is the best matching unit of at least one sample, the label(s) of those samples: 'majority', 'all', 'counts' or 'first' (see bmu_labels), coloured by class; None removes them. By default the text comes from labels; pass sample_names (any text per sample, e.g. names or IDs) to label neurons with those instead. The labels rotate with the sphere. On and off: the neuron labels check box below the colour bar, the l key, or viewer.show_node_labels(True/False) / toggle_node_labels(), which keep the style; the labels: button, L or set_node_labels(style) change the style (majority → all → counts). Size: the A− / A+ buttons, - / +, viewer.set_label_size(points), or label_size= when opening the viewer (default 7 pt, kept between 3 and 30); the labels are resized in place. The sample dots check box, s or set_show_samples(on) do the same for the sample dots. PlaneSOMViewer has the same.

ComponentMatrixViewer(som, feature_names=None, mode='difference', ...) — mt.geosom.gui

A grid with one rotatable map per attribute (plus, first, the Euclidean distance over all attributes). In the default 'difference' mode each triangle between three neighbouring neurons is coloured by how much that attribute changes between them, so you can see which attributes make which cluster borders; 'value' shows the attribute itself.

Options: include_total, shared_scale, ncols, projection, view, and data/labels (passed to the viewers a double-click opens). Methods: rotate, set_view, set_rotation, set_mode, set_shared_scale, select_node, open_single(k), on_rotate, save, show.

A drag frame redraws every map. At frequency 10 (1 002 neurons) with 13 maps a frame takes about 0.15 s, so use a lower frequency for smoother rotation with many attributes.

PlaneSOMViewer(som, data=None, labels=None, feature_names=None, ...), PlaneComponentMatrixViewer(som, ...) — mt.geosom.gui

The two views for a PlaneSOM, with the same layers, inspector, keys and options as SOMViewer and ComponentMatrixViewer (there is nothing to rotate). The faces between neurons are triangles on a hexagonal grid and squares on a rectilinear one; e shows their edges. In the matrix, click marks a neuron on every map and double-click opens one attribute in a PlaneSOMViewer, selection linked.

explore(som, data=None, labels=None, feature_names=None, ...) — mt.geosom.gui

Opens the two views that suit the SOM (SOMViewer + ComponentMatrixViewer for a GeoSOM, PlaneSOMViewer + PlaneComponentMatrixViewer for a PlaneSOM), linked, and returns Views(map, matrix). Options: single, matrix (which views), link, layer, matrix_mode, projection, view, select (a neuron to select), node_labels and sample_names and label_size (neuron labels on the single map), show=False (build without opening windows, e.g. to save). train_and_explore(data, labels, feature_names, lattice='sphere', frequency=10, rows=20, cols=30, topology='plane', epochs=30, standardise=True, ...) trains first and returns (som, views). python -m mt.geosom / geosom is the command-line version (--help for its options).

datasets — mt.geosom.datasets

Sample data shipped with the package (CSV files in mt/geosom/data):

load_sample(name) loads one, samples() lists them, sample_path(name) gives the CSV file's path, and load(name_or_path, label_column=None) accepts a sample name, 'colours' or a CSV path (as --data does). Sources, citations and licences are in data/README.md.

animals(symbols=False, symbol_weight=0.2) loads the animals, with symbols=True set up as Ritter & Kohonen's semantic map. clusters(n_clusters=7, dim=12, per_cluster=80, seed=3) (labelled Gaussian clusters) and colours() (random RGB) generate data; read_csv(path, label_column=None) reads your own (numeric columns become attributes, empty cells and NA are missing values). All return Dataset(data, labels, feature_names); standardise(x) gives every attribute zero mean and unit variance.

link_views(*viewers, rotation=True, selection=True) — mt.geosom.gui

Keeps viewers in step, in the same or separate windows: turning one turns the others (SOMViewer, ComponentMatrixViewer, GeodesicDome's ProjectionViewer), and selecting a neuron in one selects it in the others (all four SOM viewers).

from mt.geosom.gui import SOMViewer, link_views

a = SOMViewer(som, x, layer='Component difference', component=0)   # som: the trained GeoSOM of section 2.1
b = SOMViewer(som, x, layer='Component difference', component=1)
link_views(a, b)

4. Changelog

See CHANGELOG.md.

5. Citing

If you use GeoSOM in research, please cite:

Y. Wu and M. Takatsuka, "Spherical self-organizing map using efficient indexed geodesic data structure," Neural Networks, vol. 19, no. 6–7, pp. 900–910, 2006. doi:10.1016/j.neunet.2006.05.021

@article{wu2006spherical,
  author  = {Wu, Yingxin and Takatsuka, Masahiro},
  title   = {Spherical self-organizing map using efficient indexed geodesic data structure},
  journal = {Neural Networks},
  volume  = {19},
  number  = {6--7},
  pages   = {900--910},
  year    = {2006},
  month   = {07},
  doi     = {10.1016/j.neunet.2006.05.021}
}

The repository's CITATION.cff also lets GitHub's "Cite this repository" button produce a reference to the software itself.

6. Licence

Copyright © 2022–2026 Masahiro Takatsuka.

GeoSOM is free software under the GNU Affero General Public License v3.0 or later (LICENSE), with an additional attribution term (NOTICE). In short:

• You may use, study, modify and share it, including commercially.
• If you distribute it, or a modified version, or software that includes it, or let people use a modified version over a network, you must release the complete source code of that work under the same licence.
• You must keep the attribution to the author and to the 2006 paper, both in the source code and in the legal notices your software displays.
• There is no warranty.

Commercial licence. To use GeoSOM in proprietary software without these obligations, contact masa@takatsuka.org about a commercial licence.

Contributing. Contributions are welcome under the terms in CONTRIBUTING.md.