torchsom: The Reference PyTorch Library for Self-Organizing Maps

The most comprehensive, scalable, and PyTorch-native implementation of Self-Organizing Maps

📚 Documentation | 🚀 Quick Start | 📊 Examples | 🤝 Contributing

⭐ If you find torchsom valuable, please consider starring this repository ⭐

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🎯 Why torchsom?

torchsom is the reference PyTorch library for Self-Organizing Maps (SOMs), purpose-built for seamless integration with modern deep learning and scientific workflows. Unlike legacy SOM packages, torchsom is engineered from the ground up to fully leverage PyTorch’s ecosystem—offering native GPU acceleration, scalable performance, and compatibility with neural network pipelines. Whether you are a researcher or practitioner, torchsom empowers you to efficiently incorporate SOMs into your machine learning projects, from exploratory data analysis to advanced model architectures.

torchsom is the official implementation accompanying the paper: torchsom: The Reference PyTorch Library for Self-Organizing Maps. If you use torchsom in academic or industrial work, please cite the paper and the software (see CITATION).

Note: Check the table below for a comprehensive comparison of how torchsom differs from existing SOM libraries, and explore our Visualization Gallery for examples of the rich visual outputs available.

⚡ Key Advantages

Aspect	torchsom	MiniSom	SimpSOM	SOMPY	somoclu	som-pbc
Framework	PyTorch	NumPy	NumPy	NumPy	C++/CUDA	NumPy
GPU Acceleration	CUDA	❌	CuPy/CUML	❌	CUDA	❌
API Design	scikit-learn	Custom	Custom	MATLAB	Custom	custom
Maintenance	Active	Active	Minimal	Minimal	Minimal	❌
Documentation	Rich	❌	Basic	❌	Basic	Basic
Test Coverage	~86%	❌	~53%	❌	Minimal	❌
PyPI Distribution	✅	✅	✅	❌	✅	❌
SOM Variants	Multiple	❌	PBC	❌	PBC	PBC (only)
Visualization	Advanced	❌	Moderate	Moderate	Basic	Basic
Extensibility	High	Moderate	Low	Low	Low	Low

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📑 Table of Contents

• Quick Start
• Tutorials
• Installation
• Documentation
• Citation
• Contributing
• Acknowledgments
• License
• References

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🚀 Quick Start

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

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.plot_training_errors(quantization_errors=QE, topographic_errors=TE, save_path=None)
visualizer.plot_distance_map(save_path=None)
visualizer.plot_hit_map(data=X, save_path=None)

📓 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

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💾 Installation

📦 PyPI

pip install torchsom

🔧 Development Version

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

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📚 Documentation

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

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📝 Citation

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

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

@software{Berthier_TorchSOM_The_Reference_2025,
    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.

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🤝 Contributing

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

• GitHub Issues: Report bugs or request features

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🙏 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

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📄 License

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

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📚 References

📖 Core Papers

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

🔗 Related Projects

• 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

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