Somap is a flexible, fast and scalable Self-Organizing Maps library in python. It allows you to define and run different flavors of SOMs (Kohonen, DSOM or your custom ones) on square or hexagonal 2D grid, with or without toroidal topology.

There are already a few open-source libraries for Self-Organizing Maps in python, of which MiniSom and SOMPY seem to be the most popular. I developped Somap to overcome what I believe to be two shortcomings of existing libraries for my research on bio-inspired AI:

• Ability to easily customize the SOM algorithm (e.g. distance, neighborhood, learning rate and update functions).
• Capacity to vectorize the computations over many SOMs (e.g. for distributed learning over 2D maps of SOMs).

Thanks to JAX's jit and vmap functions, it turned out that performance was also significantly better compared to other frameworks. Under the hood, it relies indirectly on JAX via the Equinox library that offers an easy-to-use PyTorch-like syntax.

Requires Python 3.10+ and a working installation of JAX 0.4.20+. You can follow these instructions to install JAX with the relevant hardware acceleration support. I am currently working on different ways to extend the basic SOM algorithm:

pip install somap

The classic workflow goes as follow:

import somap as smp

# Load the MNIST dataset as a Numpy array of shape (60000, 28, 28)
data = smp.datasets.MNIST().data

# Initialize the 2D map
model = smp.StaticKsom(
    shape = (11, 13), 
    topography = "hex", 
    borderless = False, 
    input_shape = (28, 28), 
    params = smp.StaticKsomParams(sigma=0.3, alpha=0.5)
)

# Train (see documentation to understand the "bu_v" dict key)
model, aux = smp.make_steps(model, {"bu_v": data})

# Plot the 2D map 
smp.plot(model)

# Retrieve the errors from all steps
quantization_errors = aux["metrics"]["quantization_error"]
topographic_errors = aux["metrics"]["topographic_error"]

You can also define your custom SOM:

import somap as smp
from jaxtyping import Array, Float

class MyCustomSomParams(smp.AbstractSomParams):
    sigma: float | Float[Array, "..."]
    alpha: float | Float[Array, "..."]

class MyCustomSom(smp.AbstractSom):

    @staticmethod
    def generate_algo(p: MyCustomSomParams) -> smp.SomAlgo:
        return smp.SomAlgo(
            f_dist=smp.EuclidianDist(),
            f_nbh=smp.GaussianNbh(sigma=p.sigma),
            f_lr=smp.ConstantLr(alpha=p.alpha),
            f_update=smp.SomUpdate(),
        )

If you need custom distance, neighborhood, learning rate and update functions for your SOM, you can define them by inheriting from smp.AbstractDist, smp.AbstractNbh, smp.AbstractLr and smp.AbstractUpdate. See the library source code for how to do it.

See: https://mthiboust.github.io/somap/

I am currently working on different ways to extend the basic SOM algorithm:

• Inputs: In addition to classic bottom-up driving inputs, a SOM could also receive lateral contextual or top-down modulatory inputs.
• Weighted inputs: Each data point from inputs can be weighted so that fuzzy data is weighted less for the winner selection.
• Dynamics: When receiving continuous inputs in time, past activations can influence the computation of the next step.
• Supervised and self-supervised learning: Top-down inputs and next inputs in time can act as teaching signal for supervised and self-supervised learning.
• Multi-agent system: Each SOM is an agent of a mutli-agent system where thousands of SOMs interact with each other.

Some of these features will land on an other library that depends on Somap.

If you found this library to be useful in academic work, then please cite:

@misc{thiboust2023somap,
  title={Somap: a flexible, fast and scalable python library for Self-Organizing Maps.},
  author={Matthieu Thiboust},
  year={2023},
  url={https://github.com/mthiboust/somap/},
}