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Commit 79eb337

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cedricvincentcuazrflamary
andauthored
[MRG] Partial Entropic FGW solvers (#702)
* merge * new dev version * first commit partial fgw * complete tests + solve_gromov * complete tests + solve_gromov * release * partial entropic fgw solvers * add tests * complete solve_gromov * update * fix solvers * fix solvers * fix solvers * improve example and doc * update readme * reset solve_gromov behavior for unbalanced=none * merge * merge --------- Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>
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‎README.md‎

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@@ -40,8 +40,7 @@ POT provides the following generic OT solvers (links to examples):
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* [Sampled solver of Gromov Wasserstein](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) for large-scale problem with any loss functions [33]
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* Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter.html) [20].
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* [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]
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* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
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formulations).
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* [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).
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* [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].
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* [Wasserstein distance on the circle](https://pythonot.github.io/auto_examples/plot_compute_wasserstein_circle.html) [44, 45]
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* [Spherical Sliced Wasserstein](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance_ssw.html) [46]

‎RELEASES.md‎

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- Added feature `grad=last_step` for `ot.solvers.solve` (PR #693)
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- Automatic PR labeling and release file update check (PR #704)
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- Reorganize sub-module `ot/lp/__init__.py` into separate files (PR #714)
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- Implement projected gradient descent solvers for entropic partial FGW (PR #702)
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- Fix documentation in the module `ot.gaussian` (PR #718)
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#### Closed issues

‎examples/gromov/plot_barycenter_fgw.py‎

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

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