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This example demonstrates how to use the prototype module and the distance functions from ProtoTorch together with the `GLVQLoss` module to implement a GLVQ model and train it on the Iris dataset from scikit-learn. |
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ProtoTorch
ProtoTorch is a PyTorch-based Python toolbox for bleeding-edge research in prototype-based machine learning algorithms.
Description
This is a Python toolbox brewed at the Mittweida University of Applied Sciences in Germany for bleeding-edge research in Learning Vector Quantization (LVQ) and potentially other prototype-based methods. Although, there are other (perhaps more extensive) LVQ toolboxes available out there, the focus of ProtoTorch is ease-of-use, extensibility and speed.
Many popular prototype-based Machine Learning (ML) algorithms like K-Nearest Neighbors (KNN), Generalized Learning Vector Quantization (GLVQ) and Generalized Matrix Learning Vector Quantization (GMLVQ) are implemented using the "nn" API provided by PyTorch.
Installation
ProtoTorch can be installed using pip.
pip install prototorch
To install the bleeding-edge features and improvements:
git clone https://github.com/si-cim/prototorch.git
git checkout dev
cd prototorch
pip install -e .
Usage
ProtoTorch is modular. It is very easy to use the modular pieces provided by ProtoTorch, like the layers, losses, callbacks and metrics to build your own prototype-based(instance-based) models. These pieces blend-in seamlessly with numpy and PyTorch to allow you mix and match the modules from ProtoTorch with other PyTorch modules.
ProtoTorch comes prepackaged with many popular LVQ algorithms in a convenient API, with more algorithms and techniques coming soon. If you would simply like to be able to use those algorithms to train large ML models on a GPU, ProtoTorch lets you do this without requiring a black-belt in high-performance Tensor computation.