torchsom: The Reference PyTorch Library for Self-Organizing Maps
This provides a tool for researchers and practitioners using PyTorch to implement SOMs more efficiently, but it is incremental as it adapts existing methods to a new framework.
The paper introduces torchsom, an open-source PyTorch library for Self-Organizing Maps, offering dimensionality reduction, clustering, and visualization features with GPU acceleration and scikit-learn API compatibility.
This paper introduces torchsom, an open-source Python library that provides a reference implementation of the Self-Organizing Map (SOM) in PyTorch. This package offers three main features: (i) dimensionality reduction, (ii) clustering, and (iii) friendly data visualization. It relies on a PyTorch backend, enabling (i) fast and efficient training of SOMs through GPU acceleration, and (ii) easy and scalable integrations with PyTorch ecosystem. Moreover, torchsom follows the scikit-learn API for ease of use and extensibility. The library is released under the Apache 2.0 license with 90% test coverage, and its source code and documentation are available at https://github.com/michelin/TorchSOM.