| Name | multipers JSON |
| Version |
2.3.2
JSON |
| download |
| home_page | None |
| Summary | Multiparameter Topological Persistence for Machine Learning |
| upload_time | 2025-06-01 11:44:06 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
tda
persistence
multiparameter
sklearn
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# multipers : Multiparameter Persistence for Machine Learning
[](https://doi.org/10.21105/joss.06773) [](https://davidlapous.github.io/multipers) [](https://github.com/DavidLapous/multipers/actions/workflows/python_PR.yml)
<br>
Scikit-style PyTorch-autodiff multiparameter persistent homology python library.
This library aims to provide easy to use and performant strategies for applied multiparameter topology.
<br> Meant to be integrated in the [Gudhi](https://gudhi.inria.fr/) library.
## Compiled packages
| Source | Version | Downloads | Platforms |
| --- | --- | --- | --- |
| [](https://anaconda.org/conda-forge/multipers)| [](https://anaconda.org/conda-forge/multipers) | [](https://anaconda.org/conda-forge/multipers) |[](https://anaconda.org/conda-forge/multipers) |
| [](https:///pypi.org/project/multipers) | [](https://pypi.org/project/multipers) | [](https://pepy.tech/project/multipers) | |
## Quick start
This library allows computing several representations from "geometrical datasets", e.g., point clouds, images, graphs, that have multiple scales.
We provide some *nice* pictures in the [documentation](https://davidlapous.github.io/multipers/index.html).
A non-exhaustive list of features can be found in the **Features** section.
This library is available on pip and conda-forge for (reasonably up to date) Linux, macOS and Windows, via
```sh
pip install multipers
```
or
```sh
conda install multipers -c conda-forge
```
Windows support is experimental, and some core dependencies are not available on Windows.
We hence recommend Windows user to use [WSL](https://learn.microsoft.com/en-us/windows/wsl/).
<br>
A documentation and building instructions are available
[here](https://davidlapous.github.io/multipers/compilation.html).
## Features, and linked projects
This library features a bunch of different functions and helpers. See below for a non-exhaustive list.
<br>Filled box refers to implemented or interfaced code.
- [x] [[Multiparameter Module Approximation]](https://arxiv.org/abs/2206.02026) provides the multiparameter simplicial structure, as well as technics for approximating modules, via interval-decomposable modules. It is also very useful for visualization.
- [x] [[Stable Vectorization of Multiparameter Persistent Homology using Signed Barcodes as Measures, NeurIPS2023]](https://proceedings.neurips.cc/paper_files/paper/2023/hash/d75c474bc01735929a1fab5d0de3b189-Abstract-Conference.html) provides fast representations of multiparameter persistence modules, by using their signed barcodes decompositions encoded into signed measures. Implemented decompositions : Euler surfaces, Hilbert function, rank invariant (i.e. rectangles). It also provides representation technics for Machine Learning, i.e., Sliced Wasserstein kernels, and Vectorizations.
- [x] [[A Framework for Fast and Stable Representations of Multiparameter Persistent Homology Decompositions, NeurIPS2023]](https://proceedings.neurips.cc/paper_files/paper/2023/hash/702b67152ec4435795f681865b67999c-Abstract-Conference.html) Provides a vectorization framework for interval decomposable modules, for Machine Learning. Currently implemented as an extension of MMA.
- [x] [[Differentiability and Optimization of Multiparameter Persistent Homology, ICML2024]](https://proceedings.mlr.press/v235/scoccola24a.html) An approach to compute a (clarke) gradient for any reasonable multiparameter persistent invariant. Currently, any `multipers` computation is auto-differentiable using this strategy, provided that the input are pytorch gradient capable tensor.
- [x] [[Multiparameter Persistence Landscapes, JMLR]](https://jmlr.org/papers/v21/19-054.html) A vectorization technic for multiparameter persistence modules.
- [x] [[Filtration-Domination in Bifiltered Graphs, ALENEX2023]](https://doi.org/10.1137/1.9781611977561.ch3) Allows for 2-parameter edge collapses for 1-critical clique complexes. Very useful to speed up, e.g., Rips-Codensity bifiltrations.
- [x] [[Chunk Reduction for Multi-Parameter Persistent Homology, SOCG2019]](https://doi.org/10.4230/LIPIcs.SoCG.2019.37) Multi-filtration preprocessing algorithm for homology computations.
- [x] [[Computing Minimal Presentations and Bigraded Betti Numbers of 2-Parameter Persistent Homology, JAAG]](https://doi.org/10.1137/20M1388425) Minimal presentation of multiparameter persistence modules, using [mpfree](https://bitbucket.org/mkerber/mpfree/src/master/). Hilbert, Rank Decomposition Signed Measures, and MMA decompositions can be computed using the mpfree backend.
- [x] [[Delaunay Bifiltrations of Functions on Point Clouds, SODA2024]](https://epubs.siam.org/doi/10.1137/1.9781611977912.173) Provides an alternative to function rips bifiltrations, using Delaunay complexes. Very good alternative to Rips-Density like bifiltrations.
- [x] [[Delaunay Core Bifiltration]](https://arxiv.org/abs/2405.01214) Bifiltration for point clouds, taking into account the density. Similar to Rips-Density.
- [x] [[Rivet]](https://github.com/rivetTDA/rivet) Interactive two parameter persistence
- [x] [[Kernel Operations on the GPU, with Autodiff, without Memory Overflows, JMLR]](http://jmlr.org/papers/v22/20-275.html) Although not linked, at first glance, to persistence in any way, this library allows computing blazingly fast signed measures convolutions (and more!) with custom kernels.
- [ ] [Backend only] [[Projected distances for multi-parameter persistence modules]](https://arxiv.org/abs/2206.08818) Provides a strategy to estimate the convolution distance between multiparameter persistence module using projected barcodes. Implementation is a WIP.
- [ ] [Partial, and experimental] [[Efficient Two-Parameter Persistence Computation via Cohomology, SoCG2023]](https://doi.org/10.4230/LIPIcs.SoCG.2023.15) Minimal presentations for 2-parameter persistence algorithm.
If I missed something, or you want to add something, feel free to open an issue.
## Authors
[David Loiseaux](https://davidlapous.github.io/),<br>
[Hannah Schreiber](https://github.com/hschreiber) (Persistence backend code),<br>
[Luis Scoccola](https://luisscoccola.com/)
(Möbius inversion in python, degree-rips using [persistable](https://github.com/LuisScoccola/persistable) and [RIVET](https://github.com/rivetTDA/rivet/)),<br>
[Mathieu Carrière](https://www-sop.inria.fr/members/Mathieu.Carriere/) (Sliced Wasserstein),<br>
[Odin Hoff Gardå](https://odinhg.github.io/) (Delaunay Core bifiltration).<br>
## Citation
Please cite this library when using it in scientific publications;
you can use the following journal bibtex entry
```bib
@article{multipers,
title = {Multipers: {{Multiparameter Persistence}} for {{Machine Learning}}},
shorttitle = {Multipers},
author = {Loiseaux, David and Schreiber, Hannah},
year = {2024},
month = nov,
journal = {Journal of Open Source Software},
volume = {9},
number = {103},
pages = {6773},
issn = {2475-9066},
doi = {10.21105/joss.06773},
langid = {english},
}
```
## Contributions
Feel free to contribute, report a bug on a pipeline, or ask for documentation by opening an issue.<br>
In particular, if you have a nice example or application that is not taken care in the documentation (see the `./docs/notebooks/` folder), please contact me to add it there.
Raw data
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"description": "# multipers : Multiparameter Persistence for Machine Learning\n[](https://doi.org/10.21105/joss.06773) [](https://davidlapous.github.io/multipers) [](https://github.com/DavidLapous/multipers/actions/workflows/python_PR.yml)\n<br>\nScikit-style PyTorch-autodiff multiparameter persistent homology python library. \nThis library aims to provide easy to use and performant strategies for applied multiparameter topology.\n<br> Meant to be integrated in the [Gudhi](https://gudhi.inria.fr/) library.\n\n## Compiled packages\n| Source | Version | Downloads | Platforms | \n| --- | --- | --- | --- | \n| [](https://anaconda.org/conda-forge/multipers)| [](https://anaconda.org/conda-forge/multipers) | [](https://anaconda.org/conda-forge/multipers) |[](https://anaconda.org/conda-forge/multipers) | \n| [](https:///pypi.org/project/multipers) | [](https://pypi.org/project/multipers) | [](https://pepy.tech/project/multipers) | | \n\n\n\n## Quick start\nThis library allows computing several representations from \"geometrical datasets\", e.g., point clouds, images, graphs, that have multiple scales.\nWe provide some *nice* pictures in the [documentation](https://davidlapous.github.io/multipers/index.html). \nA non-exhaustive list of features can be found in the **Features** section.\n\nThis library is available on pip and conda-forge for (reasonably up to date) Linux, macOS and Windows, via\n```sh\npip install multipers\n```\nor \n```sh\nconda install multipers -c conda-forge\n```\n\nWindows support is experimental, and some core dependencies are not available on Windows.\nWe hence recommend Windows user to use [WSL](https://learn.microsoft.com/en-us/windows/wsl/).\n<br>\nA documentation and building instructions are available\n[here](https://davidlapous.github.io/multipers/compilation.html).\n\n\n## Features, and linked projects\nThis library features a bunch of different functions and helpers. See below for a non-exhaustive list.\n<br>Filled box refers to implemented or interfaced code.\n - [x] [[Multiparameter Module Approximation]](https://arxiv.org/abs/2206.02026) provides the multiparameter simplicial structure, as well as technics for approximating modules, via interval-decomposable modules. It is also very useful for visualization.\n - [x] [[Stable Vectorization of Multiparameter Persistent Homology using Signed Barcodes as Measures, NeurIPS2023]](https://proceedings.neurips.cc/paper_files/paper/2023/hash/d75c474bc01735929a1fab5d0de3b189-Abstract-Conference.html) provides fast representations of multiparameter persistence modules, by using their signed barcodes decompositions encoded into signed measures. Implemented decompositions : Euler surfaces, Hilbert function, rank invariant (i.e. rectangles). It also provides representation technics for Machine Learning, i.e., Sliced Wasserstein kernels, and Vectorizations.\n - [x] [[A Framework for Fast and Stable Representations of Multiparameter Persistent Homology Decompositions, NeurIPS2023]](https://proceedings.neurips.cc/paper_files/paper/2023/hash/702b67152ec4435795f681865b67999c-Abstract-Conference.html) Provides a vectorization framework for interval decomposable modules, for Machine Learning. Currently implemented as an extension of MMA.\n - [x] [[Differentiability and Optimization of Multiparameter Persistent Homology, ICML2024]](https://proceedings.mlr.press/v235/scoccola24a.html) An approach to compute a (clarke) gradient for any reasonable multiparameter persistent invariant. Currently, any `multipers` computation is auto-differentiable using this strategy, provided that the input are pytorch gradient capable tensor.\n - [x] [[Multiparameter Persistence Landscapes, JMLR]](https://jmlr.org/papers/v21/19-054.html) A vectorization technic for multiparameter persistence modules.\n - [x] [[Filtration-Domination in Bifiltered Graphs, ALENEX2023]](https://doi.org/10.1137/1.9781611977561.ch3) Allows for 2-parameter edge collapses for 1-critical clique complexes. Very useful to speed up, e.g., Rips-Codensity bifiltrations.\n - [x] [[Chunk Reduction for Multi-Parameter Persistent Homology, SOCG2019]](https://doi.org/10.4230/LIPIcs.SoCG.2019.37) Multi-filtration preprocessing algorithm for homology computations.\n - [x] [[Computing Minimal Presentations and Bigraded Betti Numbers of 2-Parameter Persistent Homology, JAAG]](https://doi.org/10.1137/20M1388425) Minimal presentation of multiparameter persistence modules, using [mpfree](https://bitbucket.org/mkerber/mpfree/src/master/). Hilbert, Rank Decomposition Signed Measures, and MMA decompositions can be computed using the mpfree backend.\n - [x] [[Delaunay Bifiltrations of Functions on Point Clouds, SODA2024]](https://epubs.siam.org/doi/10.1137/1.9781611977912.173) Provides an alternative to function rips bifiltrations, using Delaunay complexes. Very good alternative to Rips-Density like bifiltrations.\n - [x] [[Delaunay Core Bifiltration]](https://arxiv.org/abs/2405.01214) Bifiltration for point clouds, taking into account the density. Similar to Rips-Density. \n - [x] [[Rivet]](https://github.com/rivetTDA/rivet) Interactive two parameter persistence\n - [x] [[Kernel Operations on the GPU, with Autodiff, without Memory Overflows, JMLR]](http://jmlr.org/papers/v22/20-275.html) Although not linked, at first glance, to persistence in any way, this library allows computing blazingly fast signed measures convolutions (and more!) with custom kernels. \n - [ ] [Backend only] [[Projected distances for multi-parameter persistence modules]](https://arxiv.org/abs/2206.08818) Provides a strategy to estimate the convolution distance between multiparameter persistence module using projected barcodes. Implementation is a WIP.\n - [ ] [Partial, and experimental] [[Efficient Two-Parameter Persistence Computation via Cohomology, SoCG2023]](https://doi.org/10.4230/LIPIcs.SoCG.2023.15) Minimal presentations for 2-parameter persistence algorithm.\n\nIf I missed something, or you want to add something, feel free to open an issue.\n\n## Authors\n[David Loiseaux](https://davidlapous.github.io/),<br>\n[Hannah Schreiber](https://github.com/hschreiber) (Persistence backend code),<br>\n[Luis Scoccola](https://luisscoccola.com/) \n(M\u00f6bius inversion in python, degree-rips using [persistable](https://github.com/LuisScoccola/persistable) and [RIVET](https://github.com/rivetTDA/rivet/)),<br>\n[Mathieu Carri\u00e8re](https://www-sop.inria.fr/members/Mathieu.Carriere/) (Sliced Wasserstein),<br>\n[Odin Hoff Gard\u00e5](https://odinhg.github.io/) (Delaunay Core bifiltration).<br>\n\n## Citation\nPlease cite this library when using it in scientific publications;\nyou can use the following journal bibtex entry\n```bib\n@article{multipers,\n title = {Multipers: {{Multiparameter Persistence}} for {{Machine Learning}}},\n shorttitle = {Multipers},\n author = {Loiseaux, David and Schreiber, Hannah},\n year = {2024},\n month = nov,\n journal = {Journal of Open Source Software},\n volume = {9},\n number = {103},\n pages = {6773},\n issn = {2475-9066},\n doi = {10.21105/joss.06773},\n langid = {english},\n}\n```\n## Contributions\nFeel free to contribute, report a bug on a pipeline, or ask for documentation by opening an issue.<br>\nIn particular, if you have a nice example or application that is not taken care in the documentation (see the `./docs/notebooks/` folder), please contact me to add it there.\n\n",
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