torchsom: The Reference PyTorch Library for Self-Organizing Maps

🎯 Overview

Self-Organizing Maps (SOMs) remain highly relevant in modern machine learning (ML) due to their interpretability, topology preservation, and computational efficiency. They excel and are widely used in domains such as energy systems, biology, internet of things (IoT), environmental science, and industrial applications.

Despite their utility, the SOM ecosystem is fragmented. Existing implementations are often outdated, unmaintained, and lack GPU acceleration or modern deep learning (DL) framework integration, limiting adoption and scalability.

torchsom addresses these gaps as a reference PyTorch library for SOMs. It provides:

• GPU-accelerated training
• Advanced clustering capabilities
• A scikit-learn-style API for ease of use
• Rich visualization tools
• Robust software engineering practices

torchsom enables researchers and practitioners to integrate SOMs seamlessly into workflows, from exploratory data analysis to advanced model architectures.

This library accompanies the paper: torchsom: The Reference PyTorch Library for Self-Organizing Maps. If you use torchsom in academic or industrial work, please cite both the paper and the software (see CITATION).

Note: See the comparison table below to understand how torchsom differs from other SOM libraries, and explore our Visualization Gallery for example outputs.

⚡ Why torchsom?

Unlike legacy implementations, torchsom is engineered from the ground up for modern ML workflows:

| | torchsom | MiniSom | SimpSOM | SOMPY | somoclu | som-pbc | |---|---|---|---|---|---|---| | Architecture Section | | | | | | | | Framework | PyTorch | NumPy | NumPy | NumPy | C++/CUDA | NumPy | | GPU Acceleration | ✅ CUDA | ❌ | ✅ CuPy/CUML | ❌ | ✅ CUDA | ❌ | | API Design | scikit-learn | Custom | Custom | MATLAB | Custom | custom | | Development Quality Section | | | | | | | | Maintenance | ✅ Active | ✅ Active | ⚠️ Minimal | ⚠️ Minimal | ⚠️ Minimal | ❌ | | Documentation | ✅ Rich | ❌ | ⚠️ Basic | ❌ | ⚠️ Basic | ⚠️ Basic | | Test Coverage | ✅ ~86% | ❌ | 🟠 ~53% | ❌ | ⚠️ Minimal | ❌ | | PyPI Distribution | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | | Functionality Section | | | | | | | | Visualization | ✅ Advanced | ❌ | 🟠 Moderate | 🟠 Moderate | ⚠️ Basic | ⚠️ Basic | | Clustering | ✅ Advanced | ❌ | ❌ | ❌ | ❌ | ❌ | | JITL support | ✅ Built-in | ❌ | ❌ | ❌ | ❌ | ❌ | | SOM Variants | 🚧 In development | ❌ | 🟠 PBC | ❌ | 🟠 PBC | 🟠 PBC | | Extensibility | ✅ High | 🟠 Moderate | ⚠️ Low | ⚠️ Low | ⚠️ Low | ⚠️ Low |

Note: torchsom supports Just-In-Time Learning (JITL). Given an online query, JITL collects relevant datapoints to form a local buffer (selected first by topology, then by distance). A lightweight local model is then trained on this buffer, enabling efficient supervised learning (regression or classification).

📑 Table of Contents

• Quick Start
• Tutorials
• Installation
• Documentation
• Citation
• Contributing
• Acknowledgments
• License
• Related Work and References <!-- - Reproducibility --> <!-- - Performance Benchmarks -->

🚀 Quick Start

Get started with torchsom in just a few lines of code:

```python import torch from torchsom.core import SOM from torchsom.visualization import SOMVisualizer

Create a 10x10 map for 3D input

som = SOM(x=10, y=10, num_features=3, epochs=50)

Train SOM for 50 epochs on 1000 samples

X = torch.randn(1000, 3) som.initialize_weights(data=X, mode="pca") QE, TE = som.fit(data=X)

Visualize results

visualizer = SOMVisualizer(som=som, config=None) visualizer.plottrainingerrors(quantizationerrors=QE, topographicerrors=TE, savepath=None) visualizer.plothitmap(data=X, batchsize=256, savepath=None) visualizer.plotdistancemap( savepath=None, distancemetric=som.distancefnname, neighborhoodorder=som.neighborhood_order, scaling="sum" ) ```

📓 Tutorials

Explore our comprehensive collection of Jupyter notebooks:

• 📊 iris.ipynb: Multiclass classification
• 🍷 wine.ipynb: Multiclass classification
• 🏠 boston_housing.ipynb: Regression
• ⚡ energy_efficiency.ipynb: Multi-output regression
• 🎯 clustering.ipynb: SOM-based clustering analysis

🎨 Visualization Gallery

🗺️ D-Matrix Visualization Michelin production line (regression)	📍 Hit Map Visualization Michelin production line (regression)	📊 Mean Map Visualization Michelin production line (regression)
🗺️ Component Planes Visualization Another Michelin line (regression)		🏷️ Classification Map Wine dataset (multi-classification)
📊 Cluster Metrics Clustering analysis	📈 K-Means Elbow Optimal cluster selection	🎯 HDBSCAN Cluster Map Cluster visualization

💾 Installation

📦 PyPI

bash pip install torchsom

🔧 Development Version

bash git clone https://github.com/michelin/TorchSOM.git cd TorchSOM python3.9 -m venv .torchsom_env source .torchsom_env/bin/activate pip install -e ".[all]"

📚 Documentation

Comprehensive documentation is available at opensource.michelin.io/TorchSOM

📝 Citation

If you use torchsom in your academic, research or industrial work, please cite both the paper and software:

```bibtex @inproceedings{Berthier2025TorchSOM, title={torchsom: The Reference PyTorch Library for Self-Organizing Maps}, author={Berthier, Louis}, booktitle={Conference Name}, year={2025} }

@software{BerthierTorchSOMTheReference2025, author={Berthier, Louis}, title={torchsom: The Reference PyTorch Library for Self-Organizing Maps}, url={https://github.com/michelin/TorchSOM}, version={1.0.0}, year={2025} } ```

For more details, please refer to the CITATION file.

🤝 Contributing

We welcome contributions from the community! See our Contributing Guide and Code of Conduct for details.

• GitHub Issues: Report bugs or request features <!-- - GitHub Discussions: Ask questions and share ideas -->

🙏 Acknowledgments

• Centre de Mathématiques Appliquées (CMAP) at École Polytechnique
• Manufacture Française des Pneumatiques Michelin for collaboration
• Giuseppe Vettigli for MiniSom inspiration
• The PyTorch team for the amazing framework
• Logo created using DALL-E

📄 License

torchsom is licensed under the Apache License 2.0. See the LICENSE file for details.

📚 Related Work and References

📖 Foundational Literature Papers

• Kohonen, T. (2001). Self-Organizing Maps. Springer.

🔗 Related Softwares

• MiniSom: Minimalistic Python SOM
• SimpSOM:Simple Self-Organizing Maps
• SOMPY: Python SOM library
• somoclu: Massively Parallel Self-Organizing Maps
• som-pbc: A simple self-organizing map implementation in Python with periodic boundary conditions
• SOM Toolbox: MATLAB implementation