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https://github.com/aidos-lab/pytorch-topological
A topological machine learning framework based on PyTorch
https://github.com/aidos-lab/pytorch-topological
deep-learning pytorch topological-data-analysis topological-machine-learning
Last synced: about 1 month ago
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A topological machine learning framework based on PyTorch
- Host: GitHub
- URL: https://github.com/aidos-lab/pytorch-topological
- Owner: aidos-lab
- License: bsd-3-clause
- Created: 2021-12-12T14:02:06.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-08-22T09:05:32.000Z (3 months ago)
- Last Synced: 2024-09-28T14:42:55.943Z (about 1 month ago)
- Topics: deep-learning, pytorch, topological-data-analysis, topological-machine-learning
- Language: Python
- Homepage: https://pytorch-topological.readthedocs.io/
- Size: 314 KB
- Stars: 147
- Watchers: 5
- Forks: 21
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md
- Roadmap: ROADMAP.md
Awesome Lists containing this project
README
# `pytorch-topological`: A topological machine learning framework for `pytorch`
[](https://pytorch-topological.readthedocs.io/en/latest/?badge=latest) [](https://codeclimate.com/github/aidos-lab/pytorch-topological/maintainability)    [](https://github.com/aidos-lab/pytorch-topological/actions/workflows/run_tests.yaml)
`pytorch-topological` (or `torch_topological`) is a topological machine
learning framework for [PyTorch](https://pytorch.org). 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*](https://doi.org/10.1002/dvdy.175), Developmental Dynamics
Volume 249, Issue 7, pp. 816--833, 2020.
- Hensel et al., [*A Survey of Topological Machine Learning Methods*](https://www.frontiersin.org/articles/10.3389/frai.2021.681108/full),
Frontiers in Artificial Intelligence, 2021.
# Installation and requirements
`torch_topological` requires Python 3.9. More recent versions might work
but necessitate building some dependencies by yourself; Python 3.9
currently offers the smoothest experience.
It is recommended to use the excellent [`poetry`](https://python-poetry.org) framework
to install `torch_topological`:
```
poetry add torch-topological
```
Alternatively, use `pip` to install the package:
```
pip install -U torch-topological
```
**A note on older versions.** Older versions of Python are not
explicitly supported, and things may break in unexpected ways.
If you want to use a different version, check `pyproject.toml`
and adjust the Python requirement to your preference. This may
or may not work, good luck!
# Usage
`torch_topological` is still a work in progress. You can [browse the documentation](https://pytorch-topological.readthedocs.io)
or, if code reading is more your thing, dive directly into [some example
code](./torch_topological/examples).
Here is a list of *other* projects that are using `torch_topological`:
- [SHAPR](https://github.com/marrlab/SHAPR_torch), a method for for
predicting the 3D cell shape of individual cells based on 2D
microscopy images
This list is incomplete---you can help expanding it by using
`torch_topological` in your own projects! :innocent:
# Contributing
Check out the [contribution guidelines](CONTRIBUTING.md) or the [road
map](ROADMAP.md) 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`](https://github.com/giotto-ai/giotto-tda) | [`gudhi`](https://github.com/GUDHI/gudhi-devel)
|
|---------------------------------------------------------------|-------------------------------------------------------------|
| 
| 
|
Furthermore, `pytorch-topological` draws inspiration from several
projects that provide a glimpse into the wonderful world of topological
machine learning:
- [`difftda`](https://github.com/MathieuCarriere/difftda) by [Mathieu Carrière](https://github.com/MathieuCarriere)
- [`Ripser`](https://github.com/Ripser/ripser) by [Ulrich Bauer](https://github.com/ubauer)
- [`Teaspoon`](https://lizliz.github.io/teaspoon/) by [Elizabeth Munch](https://elizabethmunch.com/) and her team
- [`TopologyLayer`](https://github.com/bruel-gabrielsson/TopologyLayer) by [Rickard Brüel Gabrielsson](https://github.com/bruel-gabrielsson)
- [`topological-autoencoders`](https://github.com/BorgwardtLab/topological-autoencoders) by [Michael Moor](https://github.com/mi92), [Max Horn](https://github.com/ExpectationMax), and [Bastian Rieck](https://github.com/Pseudomanifold)
- [`torchph`](https://github.com/c-hofer/torchph) by [Christoph Hofer](https://github.com/c-hofer) and [Roland Kwitt](https://github.com/rkwitt)
Finally, `pytorch-topological` makes heavy use of [`POT`](https://pythonot.github.io), the Python Optimal Transport Library.
We are indebted to the many contributors of all these projects.