torch-topological 0.1.2

A framework for topological machine learning based on `pytorch`.

pytorch-topological: A topological machine learning framework for pytorch

pytorch-topological (or torch_topological) is a topological machine learning framework for PyTorch. It aims to collect loss terms and neural network layers in order to simplify building the next generation of topology-based machine learning tools.

Topological machine learning in a nutshell

Topological machine learning refers to a new class of machine learning algorithms that are able to make use of topological features in data sets. In contrast to methods based on a purely geometrical point of view, topological features are capable of focusing on connectivity aspects of a data set. This provides an interesting fresh perspective that can be used to create powerful hybrid algorithms, capable of yielding more insights into data.

This is an emerging research field, firmly rooted in computational topology and topological data analysis. If you want to learn more about how topology and geometry can work in tandem, here are a few resources to get you started:

• Amézquita et al., The Shape of Things to Come: Topological Data Analysis and Biology, from Molecules to Organisms, Developmental Dynamics Volume 249, Issue 7, pp. 816--833, 2020.

• Hensel et al., A Survey of Topological Machine Learning Methods, Frontiers in Artificial Intelligence, 2021.

Installation

It is recommended to use the excellent poetry framework to install torch_topological:

poetry add torch-topological

Alternatively, use pip to install the package:

pip install -U torch-topological

Usage

torch_topological is still a work in progress. You can browse the documentation or, if code reading is more your thing, dive directly into some example code.

Contributing

Check out the contribution guidelines or the road map of the project.

Acknowledgements

Our software and research does not exist in a vacuum. pytorch-topological is standing on the shoulders of proverbial giants. In particular, we want to thank the following projects for constituting the technical backbone of the project:

giotto-tda	gudhi

Furthermore, pytorch-topological draws inspiration from several projects that provide a glimpse into the wonderful world of topological machine learning:

• difftda by Mathieu Carrière

• Ripser by Ulrich Bauer

• TopologyLayer by Rickard Brüel Gabrielsson

• topological-autoencoders by Michael Moor, Max Horn, and Bastian Rieck

• torchph by Christoph Hofer and Roland Kwitt

Finally, pytorch-topological makes heavy use of POT, the Python Optimal Transport Library. We are indebted to the many contributors of all these projects.