Diffusion Earth Mover's Distance embeds the Wasserstein distance between two distributions on a graph into L^1 in log-linear time.
DiffusionEMD is available in pypi. Install by running the following:
pip install DiffusionEMD
DiffusionEMD is written following the sklearn estimator framework. We provide two functions that operate quite differently. First the Chebyshev approxiamtion of the operator in DiffusionCheb, which we recommend when the number of distributions is small compared to the number of points. Second, the Interpolative Decomposition method that computes dyadic powers of
import numpy as np from DiffusionEMD import DiffusionCheb # Setup an adjacency matrix and a set of distributions to embed adj = np.ones((10, 10)) distributions = np.random.randn(10, 5) dc = DiffusionCheb() # Embeddings where the L1 distance approximates the Earth Mover's Distance embeddings = dc.fit_transform(adj, distributions) # Shape: (5, 60)
Requirements can be found in requirements.txt
Examples are in the notebooks directory.
Take a look at the examples provided there to get a sense of how the parameters behave on simple examples that are easy to visualize.
This code implements the algorithms described in this paper:
ArXiv Link: http://arxiv.org/abs/2102.12833:
@InProceedings{pmlr-v139-tong21a,
title = {Diffusion Earth Mover’s Distance and Distribution Embeddings},
author = {Tong, Alexander Y and Huguet, Guillaume and Natik, Amine and Macdonald, Kincaid and Kuchroo, Manik and Coifman, Ronald and Wolf, Guy and Krishnaswamy, Smita},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {10336--10346},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v139/tong21a/tong21a.pdf},
url = {http://proceedings.mlr.press/v139/tong21a.html},
}