Merge branch 'master' into better-dist

Author: rflamary

.github/workflows/labeler.yml

@@ -1,7 +1,7 @@
 name: "Pull Request Labeler"
 
 on:
-  pull_request:
+  pull_request_target:
     branches:
       - 'master'
 jobs:
@@ -11,4 +11,4 @@ jobs:
       pull-requests: write
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/labeler@v5
+    - uses: actions/labeler@v5

CONTRIBUTORS.md

@@ -48,7 +48,7 @@ The contributors to this library are:
 * [Camille Le Coz](https://www.linkedin.com/in/camille-le-coz-8593b91a1/) (EMD2 debug)
 * [Eduardo Fernandes Montesuma](https://eddardd.github.io/my-personal-blog/) (Free support sinkhorn barycenter)
 * [Theo Gnassounou](https://github.com/tgnassou) (OT between Gaussian distributions)
-* [Clément Bonet](https://clbonet.github.io) (Wassertstein on circle, Spherical Sliced-Wasserstein)
+* [Clément Bonet](https://clbonet.github.io) (Wasserstein on circle, Spherical Sliced-Wasserstein)
 * [Ronak Mehta](https://ronakrm.github.io) (Efficient Discrete Multi Marginal Optimal Transport Regularization)
 * [Xizheng Yu](https://github.com/x12hengyu) (Efficient Discrete Multi Marginal Optimal Transport Regularization)
 * [Sonia Mazelet](https://github.com/SoniaMaz8) (Template based GNN layers)

README.md

@@ -40,8 +40,7 @@ POT provides the following generic OT solvers (links to examples):
 * [Sampled 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].
 * [One dimensional 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]. Also [exact unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_unbalanced_ot.html) with KL and quadratic regularization and the [regularization path of UOT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_regpath.html) [41]
-* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
-  formulations).
+* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) and [Partial Fused Gromov-Wasserstein](https://pythonot.github.io/auto_examples/gromov/plot_partial_fgw.html) (exact [29] and entropic [3] formulations).
 * [Sliced Wasserstein](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance.html) [31, 32] and Max-sliced Wasserstein [35] that can be used for gradient flows [36].
 * [Wasserstein distance on the circle](https://pythonot.github.io/auto_examples/plot_compute_wasserstein_circle.html) [44, 45]
 * [Spherical Sliced Wasserstein](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance_ssw.html) [46]
@@ -51,7 +50,7 @@ POT provides the following generic OT solvers (links to examples):
 * [Efficient Discrete Multi Marginal Optimal Transport Regularization](https://pythonot.github.io/auto_examples/others/plot_demd_gradient_minimize.html) [50].
 * [Several backends](https://pythonot.github.io/quickstart.html#solving-ot-with-multiple-backends) for easy use of POT with  [Pytorch](https://pytorch.org/)/[jax](https://github.com/google/jax)/[Numpy](https://numpy.org/)/[Cupy](https://cupy.dev/)/[Tensorflow](https://www.tensorflow.org/) arrays.
 * [Smooth Strongly Convex Nearest Brenier Potentials](https://pythonot.github.io/auto_examples/others/plot_SSNB.html#sphx-glr-auto-examples-others-plot-ssnb-py) [58], with an extension to bounding potentials using [59].
-* Gaussian Mixture Model OT [69]
+* [Gaussian Mixture Model OT](https://pythonot.github.io/auto_examples/others/plot_GMMOT_plan.html#sphx-glr-auto-examples-others-plot-gmmot-plan-py) [69].
 * [Co-Optimal Transport](https://pythonot.github.io/auto_examples/others/plot_COOT.html) [49] and
 [unbalanced Co-Optimal Transport](https://pythonot.github.io/auto_examples/others/plot_learning_weights_with_COOT.html) [71].
 * Fused unbalanced Gromov-Wasserstein [70].
@@ -391,3 +390,7 @@ Artificial Intelligence.
 [72] Thibault Séjourné, François-Xavier Vialard, and Gabriel Peyré (2021). [The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation](https://proceedings.neurips.cc/paper/2021/file/4990974d150d0de5e6e15a1454fe6b0f-Paper.pdf). Neural Information Processing Systems (NeurIPS).
 
 [73] Séjourné, T., Vialard, F. X., & Peyré, G. (2022). [Faster Unbalanced Optimal Transport: Translation Invariant Sinkhorn and 1-D Frank-Wolfe](https://proceedings.mlr.press/v151/sejourne22a.html). In International Conference on Artificial Intelligence and Statistics (pp. 4995-5021). PMLR.
+
+[74] Chewi, S., Maunu, T., Rigollet, P., & Stromme, A. J. (2020). [Gradient descent algorithms for Bures-Wasserstein barycenters](https://proceedings.mlr.press/v125/chewi20a.html). In Conference on Learning Theory (pp. 1276-1304). PMLR.
+
+[75] Altschuler, J., Chewi, S., Gerber, P. R., & Stromme, A. (2021). [Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent](https://papers.neurips.cc/paper_files/paper/2021/hash/b9acb4ae6121c941324b2b1d3fac5c30-Abstract.html). Advances in Neural Information Processing Systems, 34, 22132-22145.

RELEASES.md

@@ -6,12 +6,22 @@
 - Implement CG solvers for partial FGW (PR #687)
 - Added feature `grad=last_step` for `ot.solvers.solve` (PR #693)
 - Automatic PR labeling and release file update check (PR #704)
+- Reorganize sub-module `ot/lp/__init__.py` into separate files (PR #714)
+- Fix warning raise when import the library (PR #716)
+- Implement projected gradient descent solvers for entropic partial FGW (PR #702)
+- Fix documentation in the module `ot.gaussian` (PR #718)
+- Refactored `ot.bregman._convolutional` to improve readability (PR #709)
+- Added `ot.gaussian.bures_barycenter_gradient_descent` (PR #680)
+- Added `ot.gaussian.bures_wasserstein_distance` (PR #680)
+- `ot.gaussian.bures_wasserstein_distance` can be batched (PR #680)
 
 #### Closed issues
 - Fixed `ot.mapping` solvers which depended on deprecated `cvxpy` `ECOS` solver (PR #692, Issue #668)
 - Fixed numerical errors in `ot.gmm` (PR #690, Issue #689)
 - Add version number to the documentation (PR #696)
 - Update doc for default regularization in `ot.unbalanced` sinkhorn solvers (Issue #691, PR #700)
+- Clean documentation for `gromov`, `lp` and `unbalanced` folders (PR #710)
+- Clean references in documentation (PR #722)
 
 ## 0.9.5
 

examples/gromov/plot_barycenter_fgw.py

@@ -91,7 +91,7 @@ def build_noisy_circular_graph(
     g = nx.Graph()
     g.add_nodes_from(list(range(N)))
     for i in range(N):
-        noise = float(np.random.normal(mu, sigma, 1))
+        noise = np.random.normal(mu, sigma, 1)[0]
         if with_noise:
             g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise)
         else:
@@ -107,7 +107,7 @@ def build_noisy_circular_graph(
                 if i == N - 1:
                     g.add_edge(i, 1)
     g.add_edge(N, 0)
-    noise = float(np.random.normal(mu, sigma, 1))
+    noise = np.random.normal(mu, sigma, 1)[0]
     if with_noise:
         g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise)
     else:
@@ -157,7 +157,7 @@ def graph_colors(nx_graph, vmin=0, vmax=7):
     plt.subplot(3, 3, i + 1)
     g = X0[i]
     pos = nx.kamada_kawai_layout(g)
-    nx.draw(
+    nx.draw_networkx(
         g,
         pos=pos,
         node_color=graph_colors(g, vmin=-1, vmax=1),
@@ -173,7 +173,7 @@ def graph_colors(nx_graph, vmin=0, vmax=7):
 
 # %% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph
 # Features distances are the euclidean distances
-Cs = [shortest_path(nx.adjacency_matrix(x).todense()) for x in X0]
+Cs = [shortest_path(nx.adjacency_matrix(x).toarray()) for x in X0]
 ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0]
 Ys = [
     np.array([v for (k, v) in nx.get_node_attributes(x, "attr_name").items()]).reshape(
@@ -199,7 +199,7 @@ def graph_colors(nx_graph, vmin=0, vmax=7):
 
 # %%
 pos = nx.kamada_kawai_layout(bary)
-nx.draw(
+nx.draw_networkx(
     bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False
 )
 plt.suptitle("Barycenter", fontsize=20)