pytorch-topological - GitLite

KO | EN

gitlite — search

#javascript #react #typescript #llm #go #rust #deep-learning #docker #nodejs #java #cli #pytorch #mcp #openai

pytorch-topological

(★ 214)

A topological machine learning framework based on PyTorch

파일 탐색기

최종 버전 다운로드 (.zip)

- - run_tests.yaml
- - comparison.rst
  - conf.py
  - data.rst
  - index.rst
  - nn.rst
  - nutshell.rst
  - usage.rst
  - utils.rst
- .gitignore
- make.bat
- Makefile
- giotto.jpg
- gudhi.png
- __init__.py
- test_alpha_complex.py
- test_cubical_complex.py
- test_layers.py
- test_lower_star_persistence.py
- test_multi_scale_kernel.py
- test_ot.py
- test_perslay.py
- test_pllay.py
- test_pytorch_topological.py
- test_vietoris_rips_complex.py
- - __init__.py
  - shapes.py
  - utils.py
- - __init__.py
  - shapes.py
  - spheres.py
- - alpha_complex.py
  - alpha_complex_animated.py
  - alpha_complex_pd.py
  - autoencoders.py
  - benchmarking.py
  - classification.py
  - cubical_complex.py
  - distances.py
  - gan.py
  - image_smoothing.py
  - summary_statistics.py
  - weighted_euler_characteristic_transform.py
- - __init__.py
  - alpha_complex.py
  - cubical_complex.py
  - data.py
  - distances.py
  - graphs.py
  - layers.py
  - loss.py
  - lower_star_persistence.py
  - multi_scale_kernel.py
  - perslay.py
  - pllay.py
  - sliced_wasserstein_distance.py
  - sliced_wasserstein_kernel.py
  - vietoris_rips_complex.py
  - weighted_euler_characteristic_transform.py
- - __init__.py
  - filters.py
  - general.py
  - summary_statistics.py
- __init__.py
.gitignore
.readthedocs.yaml
CHANGELOG.md
CODE_OF_CONDUCT.md
CONTRIBUTING.md
LICENSE.md
pyproject.toml
README.md
ROADMAP.md
torch_topological.svg
uv.lock

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/aidos-lab/pytorch-topological

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd pytorch-topological

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. 공식 설치 스크립트

쉬움 추천

사전 준비물

Python 3 pip 명령어를 쓰려면 Python이 필요합니다.

pip install -U torch-topological

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

✅ 설치 후 새 터미널을 열고, 프로그램의 버전 확인 명령(예: --version)으로 정상 설치됐는지 확인하세요.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

3. Python

쉬움

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

poetry add torch-topological

이 명령어를 터미널에 그대로 입력해 실행하세요.

pip install -U torch-topological

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

<img src="https://raw.githubusercontent.com/aidos-lab/pytorch-topological/main/torch_topological.svg" height=128 alt="`pytorch-topological` icon" />

# `pytorch-topological`: A topological machine learning framework for `pytorch`

[![Documentation](https://readthedocs.org/projects/pytorch-topological/badge/?version=latest)](https://pytorch-topological.readthedocs.io/en/latest/?badge=latest) [![Maintainability](https://api.codeclimate.com/v1/badges/397f53d1968f01b86e74/maintainability)](https://codeclimate.com/github/aidos-lab/pytorch-topological/maintainability) ![GitHub contributors](https://img.shields.io/github/contributors/aidos-lab/pytorch-topological) ![PyPI - License](https://img.shields.io/pypi/l/torch_topological) ![PyPI](https://img.shields.io/pypi/v/torch_topological) [![Tests](https://github.com/aidos-lab/pytorch-topological/actions/workflows/run_tests.yaml/badge.svg)](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)<br />       |
|---------------------------------------------------------------|-------------------------------------------------------------|
| <img src="logos/giotto.jpg" height=128 alt="`giotto` icon" /> | <img src="logos/gudhi.png" height=128 alt="`GUDHI` icon" /> |

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.