8000 [MRG] Implementation of two news algorithms: SaGroW and PoGroW. by Hv0nnus · Pull Request #275 · PythonOT/POT · GitHub
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[MRG] Implementation of two news algorithms: SaGroW and PoGroW. #275

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Merged
merged 6 commits into from
Sep 17, 2021
Merged

[MRG] Implementation of two news algorithms: SaGroW and PoGroW. #275

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5a19690
Add two new algorithms to solve Gromov Wasserstein: Sampled Gromov Wa…
Hv0nnus Jul 10, 2021
870107e
Correct some lines in SaGroW and PoGroW to follow pep8 guide.
Hv0nnus Sep 6, 2021
8f9e708
Merge branch 'master' into SaGroW
rflamary Sep 6, 2021
a8b3437
Change nb_samples name. Use rdm state. Change symmetric check.
Hv0nnus Sep 9, 2021
12897bb
Change names of len(p) and len(q) in SaGroW and PoGroW.
Hv0nnus Sep 9, 2021
10df3f6
Re-add some deleted lines in the comments of gromov.py
Hv0nnus Sep 10, 2021
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4 changes: 4 additions & 0 deletions README.md
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Expand Up @@ -28,6 +28,7 @@ POT provides the following generic OT solvers (links to examples):
* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_gromov_barycenter.html) (exact [13] and regularized [12])
* [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_barycenter_fgw.html) [24]
* [Stochastic solver](https://pythonot.github.io/auto_examples/plot_stochastic.html) for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
* [Sto 8000 chastic solver of Gromov Wasserstein](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) for large-scale problem with any loss functions [33]
* Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter.html) [20].
* [Unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html) [10, 25].
* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
Expand Down Expand Up @@ -198,6 +199,7 @@ The contributors to this library are
* [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn)
* [Ievgen Redko](https://ievred.github.io/) (Laplacian DA, JCPOT)
* [Adrien Corenflos](https://adriencorenflos.github.io/) (Sliced Wasserstein Distance)
* [Tanguy Kerdoncuff](https://hv0nnus.github.io/) (Sampled Gromov Wasserstein)
* [Minhui Huang](https://mhhuang95.github.io) (Projection Robust Wasserstein Distance)

This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages):
Expand Down Expand Up @@ -286,3 +288,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t
[31] Bonneel, Nicolas, et al. [Sliced and radon wasserstein barycenters of measures](https://perso.liris.cnrs.fr/nicolas.bonneel/WassersteinSliced-JMIV.pdf), Journal of Mathematical Imaging and Vision 51.1 (2015): 22-45

[32] Huang, M., Ma S., Lai, L. (2021). [A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance](http://proceedings.mlr.press/v139/huang21e.html), Proceedings of the 38th International Conference on Machine Learning (ICML).

[33] Kerdoncuff T., Emonet R., Marc S. [Sampled Gromov Wasserstein](https://hal.archives-ouvertes.fr/hal-03232509/document), Machine Learning Journal (MJL), 2021
34 changes: 34 additions & 0 deletions examples/gromov/plot_gromov.py
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Expand Up @@ -104,3 +104,37 @@
pl.title('Entropic Gromov Wasserstein')

pl.show()

#############################################################################
#
# Compute GW with a scalable stochastic method with any loss function
# ----------------------------------------------------------------------


def loss(x, y):
return np.abs(x - y)


pgw, plog = ot.gromov.pointwise_gromov_wasserstein(C1, C2, p, q, loss, max_iter=100,
log=True)

sgw, slog = ot.gromov.sampled_gromov_wasserstein(C1, C2, p, q, loss, epsilon=0.1, max_iter=100,
log=True)

print('Pointwise Gromov-Wasserstein distance estimated: ' + str(plog['gw_dist_estimated']))
print('Variance estimated: ' + str(plog['gw_dist_std']))
print('Sampled Gromov-Wasserstein distance: ' + str(slog['gw_dist_estimated']))
print('Variance estimated: ' + str(slog['gw_dist_std']))


pl.figure(1, (10, 5))

pl.subplot(1, 2, 1)
pl.imshow(pgw.toarray(), cmap='jet')
pl.title('Pointwise Gromov Wasserstein')

pl.subplot(1, 2, 2)
pl.imshow(sgw, cmap='jet')
pl.title('Sampled Gromov Wasserstein')

pl.show()
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