[MRG] Add release version information to each example in the examples gallery (#743)

NailKhelifa · web-flow · commit 05dabf6ffaa7 · 2025-07-11T15:45:15.000+02:00
* release version for examples

* release indication on examples

* change added in release.md + minor modifs on examples added in 0.9.6
diff --git a/RELEASES.md b/RELEASES.md
@@ -17,6 +17,7 @@
 - Backend implementation of `ot.dist` for (PR #701)
 - Updated documentation Quickstart guide and User guide with new API (PR #726)
 - Fix jax version for auto-grad (PR #732)
+- Added to each example in the examples gallery the information about the release version in which it was introduced (PR #743)
 
 #### Closed issues
 - Fixed `ot.mapping` solvers which depended on deprecated `cvxpy` `ECOS` solver (PR #692, Issue #668)
diff --git a/examples/backends/plot_Sinkhorn_gradients.py b/examples/backends/plot_Sinkhorn_gradients.py
@@ -6,6 +6,9 @@
 
 This example illustrates the differences in terms of computation time between the gradient options for the Sinkhorn solver.
 
+.. note::
+    Example added in release: 0.9.6
+
 """
 
 # Author: Sonia Mazelet <sonia.mazelet@polytechnique.edu>
diff --git a/examples/backends/plot_dual_ot_pytorch.py b/examples/backends/plot_dual_ot_pytorch.py
@@ -4,6 +4,8 @@
 Dual OT solvers for entropic and quadratic regularized OT with Pytorch
 ======================================================================
 
+.. note::
+    Example added in release: 0.8.2.
 
 """
 
diff --git a/examples/backends/plot_optim_gromov_pytorch.py b/examples/backends/plot_optim_gromov_pytorch.py
@@ -3,6 +3,9 @@
 Optimizing the Gromov-Wasserstein distance with PyTorch
 =======================================================
 
+.. note::
+    Example added in release: 0.8.0.
+
 In this example, we use the pytorch backend to optimize the Gromov-Wasserstein
 (GW) loss between two graphs expressed as empirical distribution.
 
diff --git a/examples/backends/plot_sliced_wass_grad_flow_pytorch.py b/examples/backends/plot_sliced_wass_grad_flow_pytorch.py
@@ -3,6 +3,9 @@
 Sliced Wasserstein barycenter and gradient flow with PyTorch
 ============================================================
 
+.. note::
+    Example added in release: 0.8.0.
+
 In this example we use the pytorch backend to optimize the sliced Wasserstein
 loss between two empirical distributions [31].
 
diff --git a/examples/backends/plot_stoch_continuous_ot_pytorch.py b/examples/backends/plot_stoch_continuous_ot_pytorch.py
@@ -4,6 +4,8 @@
 Continuous OT plan estimation with Pytorch
 ======================================================================
 
+.. note::
+    Example added in release: 0.8.2.
 
 """
 
diff --git a/examples/backends/plot_unmix_optim_torch.py b/examples/backends/plot_unmix_optim_torch.py
@@ -4,6 +4,9 @@
 Wasserstein unmixing with PyTorch
 =================================
 
+.. note::
+    Example added in release: 0.8.0.
+
 In this example we estimate mixing parameters from distributions that minimize
 the Wasserstein distance. In other words we suppose that a target
 distribution :math:`\mu^t` can be expressed as a weighted sum of source
diff --git a/examples/backends/plot_wass2_gan_torch.py b/examples/backends/plot_wass2_gan_torch.py
@@ -4,6 +4,9 @@
 Wasserstein 2 Minibatch GAN with PyTorch
 ========================================
 
+.. note::
+    Example added in release: 0.8.0.
+
 In this example we train a Wasserstein GAN using Wasserstein 2 on minibatches
 as a distribution fitting term.
 
diff --git a/examples/barycenters/plot_debiased_barycenter.py b/examples/barycenters/plot_debiased_barycenter.py
@@ -4,6 +4,9 @@
 Debiased Sinkhorn barycenter demo
 =================================
 
+.. note::
+    Example added in release: 0.8.0.
+
 This example illustrates the computation of the debiased Sinkhorn barycenter
 as proposed in [37]_.
 
diff --git a/examples/barycenters/plot_free_support_sinkhorn_barycenter.py b/examples/barycenters/plot_free_support_sinkhorn_barycenter.py
@@ -4,6 +4,9 @@
 2D free support Sinkhorn barycenters of distributions
 ========================================================
 
+.. note::
+    Example added in release: 0.9.1.
+
 Illustration of Sinkhorn barycenter calculation between empirical distributions understood as point clouds
 
 """
diff --git a/examples/barycenters/plot_gaussian_barycenter.py b/examples/barycenters/plot_gaussian_barycenter.py
@@ -4,6 +4,9 @@
 Gaussian Bures-Wasserstein barycenters
 ========================================================
 
+.. note::
+    Example added in release: 0.9.2.
+
 Illustration of Gaussian Bures-Wasserstein barycenters.
 
 """
diff --git a/examples/barycenters/plot_generalized_free_support_barycenter.py b/examples/barycenters/plot_generalized_free_support_barycenter.py
@@ -4,6 +4,9 @@
 Generalized Wasserstein Barycenter Demo
 =======================================
 
+.. note::
+    Example added in release: 0.9.1.
+
 This example illustrates the computation of Generalized Wasserstein Barycenter
 as proposed in [42].
 
diff --git a/examples/domain-adaptation/plot_otda_classes.py b/examples/domain-adaptation/plot_otda_classes.py
@@ -4,6 +4,9 @@
 OT for domain adaptation
 ========================
 
+.. note::
+    Example added in release: 0.1.9.
+
 This example introduces a domain adaptation in a 2D setting and the 4 OTDA
 approaches currently supported in POT.
 
diff --git a/examples/domain-adaptation/plot_otda_color_images.py b/examples/domain-adaptation/plot_otda_color_images.py
@@ -4,6 +4,9 @@
 OT for image color adaptation
 =============================
 
+.. note::
+    Example added in release: 0.1.9.
+
 This example presents a way of transferring colors between two images
 with Optimal Transport as introduced in [6]
 
diff --git a/examples/domain-adaptation/plot_otda_d2.py b/examples/domain-adaptation/plot_otda_d2.py
@@ -4,6 +4,9 @@
 OT for domain adaptation on empirical distributions
 ===================================================
 
+.. note::
+    Example added in release: 0.1.9.
+
 This example introduces a domain adaptation in a 2D setting. It explicit
 the problem of domain adaptation and introduces some optimal transport
 approaches to solve it.
diff --git a/examples/domain-adaptation/plot_otda_jcpot.py b/examples/domain-adaptation/plot_otda_jcpot.py
@@ -4,6 +4,9 @@
 OT for multi-source target shift
 ================================
 
+.. note::
+    Example added in release: 0.7.0.
+
 This example introduces a target shift problem with two 2D source and 1 target domain.
 
 """
diff --git a/examples/domain-adaptation/plot_otda_laplacian.py b/examples/domain-adaptation/plot_otda_laplacian.py
@@ -4,6 +4,9 @@
 OT with Laplacian regularization for domain adaptation
 ======================================================
 
+.. note::
+    Example added in release: 0.7.0.
+
 This example introduces a domain adaptation in a 2D setting and OTDA
 approach with Laplacian regularization.
 
diff --git a/examples/domain-adaptation/plot_otda_linear_mapping.py b/examples/domain-adaptation/plot_otda_linear_mapping.py
@@ -5,6 +5,8 @@
 Linear OT mapping estimation
 ============================
 
+.. note::
+    Example updated in release: 0.9.1.
 
 """
 
diff --git a/examples/domain-adaptation/plot_otda_mapping.py b/examples/domain-adaptation/plot_otda_mapping.py
@@ -4,6 +4,9 @@
 OT mapping estimation for domain adaptation
 ===========================================
 
+.. note::
+    Example added in release: 0.1.9.
+
 This example presents how to use MappingTransport to estimate at the same
 time both the coupling transport and approximate the transport map with either
 a linear or a kernelized mapping as introduced in [8].
diff --git a/examples/domain-adaptation/plot_otda_mapping_colors_images.py b/examples/domain-adaptation/plot_otda_mapping_colors_images.py
@@ -4,6 +4,9 @@
 OT for image color adaptation with mapping estimation
 =====================================================
 
+.. note::
+    Example added in release: 0.1.9.
+
 OT for domain adaptation with image color adaptation [6] with mapping
 estimation [8].
 
diff --git a/examples/domain-adaptation/plot_otda_semi_supervised.py b/examples/domain-adaptation/plot_otda_semi_supervised.py
@@ -4,6 +4,9 @@
 OTDA unsupervised vs semi-supervised setting
 ============================================
 
+.. note::
+    Example added in release: 0.1.9.
+
 This example introduces a semi supervised domain adaptation in a 2D setting.
 It explicit the problem of semi supervised domain adaptation and introduces
 some optimal transport approaches to solve it.
diff --git a/examples/gromov/plot_fgw_solvers.py b/examples/gromov/plot_fgw_solvers.py
@@ -4,6 +4,9 @@
 Comparison of Fused Gromov-Wasserstein solvers
 ==============================
 
+.. note::
+    Example added in release: 0.9.1.
+
 This example illustrates the computation of FGW for attributed graphs
 using 4 different solvers to estimate the distance based on Conditional
 Gradient [24], Sinkhorn projections [12, 51] and alternated Bregman
diff --git a/examples/gromov/plot_gnn_TFGW.py b/examples/gromov/plot_gnn_TFGW.py
@@ -4,6 +4,9 @@
 Graph classification with Template Based Fused Gromov Wasserstein
 ==============================
 
+.. note::
+    Example added in release: 0.9.1.
+
 This example first illustrates how to train a graph classification gnn based on the Template Fused Gromov Wasserstein layer as proposed in [52] .
 
 [53] C. Vincent-Cuaz, R. Flamary, M. Corneli, T. Vayer, N. Courty (2022).Template based graph neural network with optimal transport distances. Advances in Neural Information Processing Systems, 35.
diff --git a/examples/gromov/plot_gromov.py b/examples/gromov/plot_gromov.py
@@ -3,6 +3,10 @@
 ==========================
 Gromov-Wasserstein example
 ==========================
+
+.. note::
+    Example added in release: 0.8.0.
+
 This example is designed to show how to use the Gromov-Wasserstein distance
 computation in POT.
 We first compare 3 solvers to estimate the distance based on
diff --git a/examples/gromov/plot_gromov_wasserstein_dictionary_learning.py b/examples/gromov/plot_gromov_wasserstein_dictionary_learning.py
@@ -5,6 +5,9 @@
 (Fused) Gromov-Wasserstein Linear Dictionary Learning
 =====================================================
 
+.. note::
+    Example added in release: 0.8.2.
+
 In this example, we illustrate how to learn a Gromov-Wasserstein dictionary on
 a dataset of structured data such as graphs, denoted
 :math:`\{ \mathbf{C_s} \}_{s \in [S]}` where every nodes have uniform weights.
diff --git a/examples/gromov/plot_partial_fgw.py b/examples/gromov/plot_partial_fgw.py
@@ -4,6 +4,9 @@
 Plot partial FGW for subgraph matching
 =================================
 
+.. note::
+    Example added in release: 0.9.6.
+
 This example illustrates the computation of partial (Fused) Gromov-Wasserstein
 divergences for subgraph matching tasks, using the exact formulation $p(F)GW$ and
 the entropically regularized one $p(F)GW_e$ [18, 29].
diff --git a/examples/gromov/plot_quantized_gromov_wasserstein.py b/examples/gromov/plot_quantized_gromov_wasserstein.py
@@ -4,6 +4,9 @@
 Quantized Fused Gromov-Wasserstein examples
 ===============================================
 
+.. note::
+    Examples added in release: 0.9.4.
+
 These examples show how to use the quantized (Fused) Gromov-Wasserstein
 solvers (qFGW) [68]. POT provides a generic solver `quantized_fused_gromov_wasserstein_partitioned`
 that takes as inputs partitioned graphs potentially endowed with node features,
diff --git a/examples/gromov/plot_semirelaxed_gromov_wasserstein_barycenter.py b/examples/gromov/plot_semirelaxed_gromov_wasserstein_barycenter.py
@@ -5,6 +5,9 @@
 Semi-relaxed (Fused) Gromov-Wasserstein Barycenter as Dictionary Learning
 =====================================================
 
+.. note::
+    Example added in release: 0.9.5.
+
 In this example, we illustrate how to learn a semi-relaxed Gromov-Wasserstein
 (srGW) barycenter using a Block-Coordinate Descent algorithm, on a dataset of
 structured data such as graphs, denoted :math:`\{ \mathbf{C_s} \}_{s \in [S]}`
diff --git a/examples/others/plot_EWCA.py b/examples/others/plot_EWCA.py
@@ -4,6 +4,9 @@
 Entropic Wasserstein Component Analysis
 =======================================
 
+.. note::
+    Example added in release: 0.9.1.
+
 This example illustrates the use of EWCA as proposed in [52].
 
 
diff --git a/examples/others/plot_SSNB.py b/examples/others/plot_SSNB.py
@@ -4,6 +4,9 @@
 Smooth and Strongly Convex Nearest Brenier Potentials
 =====================================================
 
+.. note::
+    Example added in release: 0.9.2.
+
 This example is designed to show how to use SSNB [58] in POT.
 SSNB computes an l-strongly convex potential :math:`\varphi` with an L-Lipschitz gradient such that
 :math:`\nabla \varphi \# \mu \approx \nu`. This regularity can be enforced only on the components of a partition
diff --git a/examples/others/plot_WDA.py b/examples/others/plot_WDA.py
@@ -4,6 +4,9 @@
 Wasserstein Discriminant Analysis
 =================================
 
+.. note::
+    Example added in release: 0.3.0.
+
 This example illustrate the use of WDA as proposed in [11].
 
 
diff --git a/examples/others/plot_WeakOT_VS_OT.py b/examples/others/plot_WeakOT_VS_OT.py
@@ -4,6 +4,9 @@
 Weak Optimal Transport VS exact Optimal Transport
 ====================================================
 
+.. note::
+    Example added in release: 0.8.2.
+
 Illustration of 2D optimal transport between distributions that are weighted
 sum of Diracs. The OT matrix is plotted with the samples.
 
diff --git a/examples/others/plot_dmmot.py b/examples/others/plot_dmmot.py
@@ -4,6 +4,9 @@
 Computing d-dimensional Barycenters via d-MMOT
 ===============================================================================
 
+.. note::
+    Example added in release: 0.9.1.
+
 When the cost is discretized (Monge), the d-MMOT solver can more quickly
 compute and minimize the distance between many distributions without the need
 for intermediate barycenter computations. This example compares the time to
diff --git a/examples/others/plot_factored_coupling.py b/examples/others/plot_factored_coupling.py
@@ -4,6 +4,9 @@
 Optimal transport with factored couplings
 ==========================================
 
+.. note::
+    Example added in release: 0.8.2.
+
 Illustration of the factored coupling OT between 2D empirical distributions
 
 """
diff --git a/examples/others/plot_logo.py b/examples/others/plot_logo.py
@@ -4,6 +4,9 @@
 Logo of the POT toolbox
 =======================
 
+.. note::
+    Example added in release: 0.8.2.
+
 In this example we plot the logo of the POT toolbox.
 
 This logo is that it is done 100% in Python and generated using
diff --git a/examples/others/plot_lowrank_GW.py b/examples/others/plot_lowrank_GW.py
@@ -4,6 +4,9 @@
 Low rank Gromov-Wasterstein between samples
 ========================================
 
+.. note::
+    Example added in release: 0.9.4.
+
 Comparison between entropic Gromov-Wasserstein and Low Rank Gromov Wasserstein [67]
 on two curves in 2D and 3D, both sampled with 200 points.
 
diff --git a/examples/others/plot_lowrank_sinkhorn.py b/examples/others/plot_lowrank_sinkhorn.py
@@ -4,6 +4,9 @@
 Low rank Sinkhorn
 ========================================
 
+.. note::
+    Example added in release: 0.9.2.
+
 This example illustrates the computation of Low Rank Sinkhorn [26].
 
 [65] Scetbon, M., Cuturi, M., & Peyré, G. (2021).
diff --git a/examples/others/plot_outlier_detection_with_COOT_and_unbalanced_COOT.py b/examples/others/plot_outlier_detection_with_COOT_and_unbalanced_COOT.py
@@ -4,6 +4,9 @@
 Detecting outliers by learning sample marginal distribution with CO-Optimal Transport and by using unbalanced Co-Optimal Transport
 ======================================================================================================================================
 
+.. note::
+    Example added in release: 0.9.5.
+
 In this example, we consider two point clouds living in different Euclidean spaces, where the outliers
 are artificially injected into the target data. We illustrate two methods which allow to filter out
 these outliers.
diff --git a/examples/others/plot_screenkhorn_1D.py b/examples/others/plot_screenkhorn_1D.py
@@ -4,6 +4,9 @@
 Screened optimal transport (Screenkhorn)
 ========================================
 
+.. note::
+    Example added in release: 0.7.0.
+
 This example illustrates the computation of Screenkhorn [26].
 
 [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019).
diff --git a/examples/plot_OT_1D_smooth.py b/examples/plot_OT_1D_smooth.py
@@ -4,6 +4,9 @@
 Smooth and sparse OT example
 ================================
 
+.. note::
+    Example updated in release: 0.9.1.
+
 This example illustrates the computation of
 Smooth and Sparse (KL an L2 reg.) OT and
 sparsity-constrained OT, together with their visualizations.
diff --git a/examples/plot_quickstart_guide.py b/examples/plot_quickstart_guide.py
diff --git a/examples/sliced-wasserstein/plot_variance.py b/examples/sliced-wasserstein/plot_variance.py
diff --git a/examples/sliced-wasserstein/plot_variance_ssw.py b/examples/sliced-wasserstein/plot_variance_ssw.py
diff --git a/examples/unbalanced-partial/plot_conv_sinkhorn_ti.py b/examples/unbalanced-partial/plot_conv_sinkhorn_ti.py
diff --git a/examples/unbalanced-partial/plot_partial_wass_and_gromov.py b/examples/unbalanced-partial/plot_partial_wass_and_gromov.py
diff --git a/examples/unbalanced-partial/plot_regpath.py b/examples/unbalanced-partial/plot_regpath.py
diff --git a/examples/unbalanced-partial/plot_unbalanced_OT.py b/examples/unbalanced-partial/plot_unbalanced_OT.py
