https://github.com/mthiboust/somap
A flexible, fast and scalable python library for Self-Organizing Maps
https://github.com/mthiboust/somap
ai bio-inspired equinox jax machine-learning python self-organizing-map unsupervised-clustering unsupervised-learning
Last synced: 2 months ago
JSON representation
A flexible, fast and scalable python library for Self-Organizing Maps
- Host: GitHub
- URL: https://github.com/mthiboust/somap
- Owner: mthiboust
- License: apache-2.0
- Created: 2023-11-23T15:37:26.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-07T20:01:01.000Z (over 1 year ago)
- Last Synced: 2024-08-09T20:43:08.757Z (over 1 year ago)
- Topics: ai, bio-inspired, equinox, jax, machine-learning, python, self-organizing-map, unsupervised-clustering, unsupervised-learning
- Language: Jupyter Notebook
- Homepage: https://mthiboust.github.io/somap/
- Size: 4.6 MB
- Stars: 9
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Somap
***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.
# Why a new SOM library?
There are already a few open-source libraries for Self-Organizing Maps in python, of which [MiniSom](https://github.com/JustGlowing/minisom) and [SOMPY](https://github.com/sevamoo/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](https://github.com/google/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](https://github.com/patrick-kidger/equinox) library that offers an easy-to-use PyTorch-like syntax.
# Installation
Requires Python 3.10+ and a working installation of JAX 0.4.20+. You can follow [these instructions](https://github.com/google/jax#installation) to install JAX with the relevant hardware acceleration support.
I am currently working on different ways to extend the basic SOM algorithm:
```bash
pip install somap
```
# Quick example
The classic workflow goes as follow:
```python
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:
```python
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.
# Documentation
See: [https://mthiboust.github.io/somap/](https://mthiboust.github.io/somap/)
# Next steps
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***.
# Citation
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/},
}
```