Archived !

I've decided to merge everything with my multipers repo, as most of the code is shared.
Install this one instead ! It is also available on PyPI, i.e., you can easily install precompiled versions using pip install multipers.

Multiparameter Persistence using Signed Measures

Scikit-style multiparameter persistent homology python library, using signed measure and their representation for machine learning. Note: Next update after the release of Gudhi 3.9.

Installation

Dependencies
There are a few installation dependencies that can be installed with, e.g., conda.

conda create -n python311
conda activate python311
conda install python=3.11 cxx-compiler tbb tbb-devel numpy matplotlib gudhi cython shapely cycler tqdm boost-cpp setuptools pytest llvm-openmp cmake scikit-learn -c conda-forge
pip install filtration-domination

Installation
The following installs the library

pip install .

It has been tested with python 3.11 on Linux (gcc12,13,14) and Macos (clang14,16).
If the build fails on macos see a fix at the end of the readme.

How to use : tutorial notebooks

We provide a few notebooks, which explains, in different scenarios, how to use our library. Take a look at them ! They are in the tutorial folder.

References

This library is a fork of the MMA library, which handles multiparameter simplicial complex structures, aswell as approximation of multiparameter persistence modules via interval decompositions [arxiv].

The usage of signed measures for the vectorization of multiparameter persistence modules was introduced in Stable Vectorization of Multiparameter Persistent Homology using Signed Barcodes as Measures. They are fast, and easily usable in a machine learning context.

2-parameter edge collapses are realized using filtration_domination.

Authors

David Loiseaux, Luis Scoccola (Möbius inversion in python, degree-rips using persistable and RIVET), Mathieu Carrière (Sliced Wasserstein).

Contributions

Hannah Schreiber

Feel free to contribute, report a bug on a pipeline, or ask for documentation by opening an issue.
Any contribution is welcome.

For mac users

Due to the clang compiler, one may have to disable a compilator optimization to compile multipers: in the setup.py file, add the

-fno-aligned-new

line in the extra_compile_args list. You should have should end up with something like the following.

extensions = [Extension(f"multipers.{module}",
		sources=[f"multipers/{module}.pyx"],
		language='c++',
		extra_compile_args=[
			"-Ofast",
			"-std=c++20",
			"-fno-aligned-new", 
			"-Wall",
		],
		define_macros=[("NPY_NO_DEPRECATED_API", "NPY_1_7_API_VERSION")],
		libraries=["tbb"]
	) for module in cython_modules
]

Alternatives

One may try to use the clang compiler provided by conda or brew. If you have a simpler alternative, please let me know ;)