merge

Author: cedricvincentcuaz

.github/labeler.yml

@@ -74,7 +74,7 @@ ot.solvers:
 
 ot.partial:
 - changed-files:
-  - any-glob-to-any-file: ot/partial.py
+  - any-glob-to-any-file: ot/partial/**
 
 ot.sliced:
 - changed-files:
@@ -94,4 +94,4 @@ ot.dr:
 
 ot.gnn:
 - changed-files:
-  - any-glob-to-any-file: ot/gnn/**
+  - any-glob-to-any-file: ot/gnn/**

.github/workflows/build_doc.yml

@@ -26,7 +26,7 @@ jobs:
         python -m pip install --user --upgrade --progress-bar off pip
         python -m pip install --user --upgrade --progress-bar off -r requirements_all.txt
         python -m pip install --user --upgrade --progress-bar off -r docs/requirements.txt
-        python -m pip install --user --upgrade --progress-bar off ipython "https://api.github.com/repos/sphinx-gallery/sphinx-gallery/zipball/master" memory_profiler
+        python -m pip install --user --upgrade --progress-bar off ipython sphinx-gallery memory_profiler
         python -m pip install --user -e .
     # Look at what we have and fail early if there is some library conflict
     - name: Check installation

.github/workflows/build_tests.yml

@@ -47,7 +47,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: ["3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
     - uses: actions/checkout@v4
@@ -78,7 +78,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v5
       with:
-        python-version: "3.12"
+        python-version: "3.13"
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip setuptools
@@ -98,7 +98,7 @@ jobs:
      strategy:
        max-parallel: 4
        matrix:
-         os: [macos-latest, macos-13]
+         os: [macos-latest]
          python-version: ["3.12"]
 
      steps:

.github/workflows/build_wheels.yml

@@ -30,7 +30,7 @@ jobs:
 
     - name: Install cibuildwheel
       run: |
-        python -m pip install cibuildwheel==2.23.3
+        python -m pip install cibuildwheel==3.1.4
 
     - name: Build wheels
       env:
@@ -65,7 +65,7 @@ jobs:
 
     - name: Install cibuildwheel
       run: |
-        python -m pip install cibuildwheel==2.16.4
+        python -m pip install cibuildwheel==3.1.4
 
     - name: Set up QEMU
       if: runner.os == 'Linux'

.github/workflows/build_wheels_weekly.yml

@@ -29,7 +29,7 @@ jobs:
 
     - name: Install cibuildwheel
       run: |
-        python -m pip install cibuildwheel==2.23.3
+        python -m pip install cibuildwheel==3.1.4
 
     - name: Set up QEMU
       if: runner.os == 'Linux'

.github/workflows/labeler.yml

@@ -9,6 +9,7 @@ jobs:
     permissions:
       contents: read
       pull-requests: write
+      issues: write
     runs-on: ubuntu-latest
     steps:
     - uses: actions/labeler@v5

.gitignore

@@ -15,6 +15,7 @@ docs/modules/
 
 # Cython output
 ot/lp/emd_wrap.cpp
+ot/partial/partial_cython.cpp
 
 # Distribution / packaging
 .Python
@@ -122,3 +123,5 @@ debug
 
 # pytest cahche
 .pytest_cache
+
+docs/source/

CONTRIBUTORS.md

@@ -12,6 +12,11 @@ It is currently maintained by :
 
 ## Contributors
 
+<a href="https://github.com/PythonOT/POT/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=PythonOT/POT" />
+</a>
+
+
 The contributors to this library are:
 
 * [Rémi Flamary](http://remi.flamary.com/) (EMD wrapper, Pytorch backend, DA
@@ -42,7 +47,7 @@ The contributors to this library are:
 * [Cédric Vincent-Cuaz](https://github.com/cedricvincentcuaz) (Graph Dictionary Learning, FGW,
   semi-relaxed FGW, quantized FGW, partial FGW)
 * [Eloi Tanguy](https://github.com/eloitanguy) (Generalized Wasserstein
-  Barycenters, GMMOT)
+  Barycenters, GMMOT, Barycenters for General Transport Costs)
 * [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)
@@ -54,6 +59,7 @@ The contributors to this library are:
 * [Julie Delon](https://judelo.github.io/) (GMM OT)
 * [Samuel Boïté](https://samuelbx.github.io/) (GMM OT)
 
+
 ## Acknowledgments
 
 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):

MANIFEST.in

@@ -10,3 +10,4 @@ include ot/lp/full_bipartitegraph.h
 include ot/lp/full_bipartitegraph_omp.h
 include ot/lp/network_simplex_simple.h
 include ot/lp/network_simplex_simple_omp.h
+include ot/partial/partial_cython.pyx

README.md

@@ -14,21 +14,36 @@ learning.
 
 Website and documentation: [https://PythonOT.github.io/](https://PythonOT.github.io/)
 
-Source Code (MIT): [https://github.com/PythonOT/POT](https://github.com/PythonOT/POT)
+Source Code (MIT):
+[https://github.com/PythonOT/POT](https://github.com/PythonOT/POT)
 
-POT provides the following generic OT solvers (links to examples):
+
+POT has the following main features:
+* A large set of differentiable solvers for optimal transport problems, including:
+  *  Exact linear OT, entropic and quadratic regularized OT,
+  *  Gromov-Wasserstein (GW) distances, Fused GW distances and variants of
+     quadratic OT,
+  *  Unbalanced and partial OT for different divergences,
+*  OT barycenters (Wasserstein and GW) for fixed and free support,
+*  Fast OT solvers in 1D, on the circle and between Gaussian Mixture Models (GMMs),
+*  Many ML related solvers, such as domain adaptation, optimal transport mapping
+   estimation, subspace learning, Graph Neural Networks (GNNs) layers.
+*  Several backends for easy use with Pytorch, Jax, Tensorflow, Numpy and Cupy arrays.
+
+### Implemented Features
+
+POT provides the following generic OT solvers:
 
 * [OT Network Simplex solver](https://pythonot.github.io/auto_examples/plot_OT_1D.html) for the linear program/ Earth Movers Distance [1] .
 * [Conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) [6] and [Generalized conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) for regularized OT [7].
 * Entropic regularization OT solver with [Sinkhorn Knopp
   Algorithm](https://pythonot.github.io/auto_examples/plot_OT_1D.html) [2] ,
-  stabilized version [9] [10] [34], lazy CPU/GPU solver from geomloss [60] [61], greedy Sinkhorn [22] and [Screening
-  Sinkhorn [26]
-  ](https://pythonot.github.io/auto_examples/plot_screenkhorn_1D.html).
+  stabilized version [9] [10] [34], lazy CPU/GPU solver from geomloss [60] [61], greedy Sinkhorn [22] and Screening
+  Sinkhorn [26].
 * Bregman projections for [Wasserstein barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html) [3], [convolutional barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_convolutional_barycenter.html) [21]  and unmixing [4].
 * Sinkhorn divergence [23] and entropic regularization OT from empirical data.
 * Debiased Sinkhorn barycenters [Sinkhorn divergence barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_debiased_barycenter.html) [37]
-* [Smooth optimal transport solvers](https://pythonot.github.io/auto_examples/plot_OT_1D_smooth.html) (dual and semi-dual) for KL and squared L2 regularizations [17].
+* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17].
 * Weak OT solver between empirical distributions [39]
 * Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html) with LP solver (only small scale).
 * [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 [12,51]), differentiable using gradients from Graph Dictionary Learning [38]
@@ -42,18 +57,21 @@ POT provides the following generic OT solvers (links to examples):
 * [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) 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]
+* [Wasserstein distance on the
+  circle](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_compute_wasserstein_circle.html)
+  [44, 45] and  [Spherical Sliced Wasserstein](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance_ssw.html) [46]
 * [Graph Dictionary Learning solvers](https://pythonot.github.io/auto_examples/gromov/plot_gromov_wasserstein_dictionary_learning.html) [38].
 * [Semi-relaxed (Fused) Gromov-Wasserstein divergences](https://pythonot.github.io/auto_examples/gromov/plot_semirelaxed_fgw.html) with corresponding [barycenter solvers](https://pythonot.github.io/auto_examples/gromov/plot_semirelaxed_gromov_wasserstein_barycenter.hmtl) (exact and regularized [48]).
 * [Quantized (Fused) Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/gromov/plot_quantized_gromov_wasserstein.html) [68].
 * [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](https://pythonot.github.io/auto_examples/others/plot_GMMOT_plan.html#sphx-glr-auto-examples-others-plot-gmmot-plan-py) [69].
+* [Gaussian Mixture Model OT](https://pythonot.github.io/auto_examples/gaussian_gmm/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].
+* [Optimal Transport Barycenters for Generic Costs](https://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter_generic_cost.html) [77]
+* [Barycenters between Gaussian Mixture Models](https://pythonot.github.io/auto_examples/barycenters/plot_gmm_barycenter.html) [69, 77]
 
 POT provides the following Machine Learning related solvers:
 
@@ -173,6 +191,12 @@ import ot
 ```python
 # a,b are 1D histograms (sum to 1 and positive)
 # M is the ground cost matrix
+
+# With the unified  API :
+Wd = ot.solve(M, a, b).value # exact linear program
+Wd_reg = ot.solve(M, a, b, reg=reg).value # entropic regularized OT
+
+# With the old API :
 Wd = ot.emd2(a, b, M) # exact linear program
 Wd_reg = ot.sinkhorn2(a, b, M, reg) # entropic regularized OT
 # if b is a matrix compute all distances to a and return a vector
@@ -183,10 +207,29 @@ Wd_reg = ot.sinkhorn2(a, b, M, reg) # entropic regularized OT
 ```python
 # a,b are 1D histograms (sum to 1 and positive)
 # M is the ground cost matrix
+
+# With the unified API :
+T = ot.solve(M, a, b).plan # exact linear program
+T_reg = ot.solve(M, a, b, reg=reg).plan # entropic regularized OT
+
+# With the old API :
 T = ot.emd(a, b, M) # exact linear program
 T_reg = ot.sinkhorn(a, b, M, reg) # entropic regularized OT
 ```
 
+* Compute OT on empirical distributions
+
+```python
+# X and Y are two 2D arrays of shape (n_samples, n_features)
+
+# with squared euclidean metric
+T = ot.solve_sample(X, Y).plan # exact linear program
+T_reg = ot.solve_sample(X, Y, reg=reg).plan # entropic regularized OT
+
+Wass_2 = ot.solve_sample(X, Y).value # Squared Wasserstein_2
+Wass_1 = ot.solve_sample(X, Y, metric='euclidean').value # Wasserstein 1
+```
+
 * Compute Wasserstein barycenter
 
 ```python
@@ -209,7 +252,11 @@ It is currently maintained by :
 * [Rémi Flamary](https://remi.flamary.com/)
 * [Cédric Vincent-Cuaz](https://cedricvincentcuaz.github.io/)
 
-The numerous contributors to this library are listed [here](CONTRIBUTORS.md).
+The POT contributors to this library are listed [here](CONTRIBUTORS.md).
+
+<a href="https://github.com/PythonOT/POT/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=PythonOT/POT" />
+</a>
 
 POT has benefited from the financing or manpower from the following partners:
 
@@ -336,7 +383,7 @@ Dictionary Learning](https://arxiv.org/pdf/2102.06555.pdf), International Confer
 
 [50] Liu, T., Puigcerver, J., & Blondel, M. (2023). [Sparsity-constrained optimal transport](https://openreview.net/forum?id=yHY9NbQJ5BP). Proceedings of the Eleventh International Conference on Learning Representations (ICLR).
 
-[51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019). [Gromov-wasserstein learning for graph matching and node embedding](http://proceedings.mlr.press/v97/xu19b.html). In International Conference on Machine Learning (ICML), 2019.
+[51] Xu, H., Luo, D., Zha, H., & Carin, L. (2019). [Gromov-wasserstein learning for graph matching and node embedding](http://proceedings.mlr.press/v97/xu19b.html). In International Conference on Machine Learning (ICML), 2019.
 
 [52] Collas, A., Vayer, T., Flamary, F., & Breloy, A. (2023). [Entropic Wasserstein Component Analysis](https://arxiv.org/abs/2303.05119). ArXiv.
 
@@ -389,3 +436,18 @@ Artificial Intelligence.
 [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.
+
+[76] Chapel, L., Tavenard, R. (2025). [One for all and all for one: Efficient computation of partial Wasserstein distances on the line](https://iclr.cc/virtual/2025/poster/28547). In International Conference on Learning Representations.
+
+[77] Tanguy, Eloi and Delon, Julie and Gozlan, Nathaël (2024). [Computing Barycentres of Measures for Generic Transport Costs](https://arxiv.org/abs/2501.04016). arXiv preprint 2501.04016 (2024)
+
+[78] Martin, R. D., Medri, I., Bai, Y., Liu, X., Yan, K., Rohde, G. K., & Kolouri, S. (2024). [LCOT: Linear Circular Optimal Transport](https://openreview.net/forum?id=49z97Y9lMq). International Conference on Learning Representations.
+
+[79] Liu, X., Bai, Y., Martín, R. D., Shi, K., Shahbazi, A., Landman, B. A., Chang, C., & Kolouri, S. (2025). [Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data](https://openreview.net/forum?id=fgUFZAxywx). International Conference on Learning Representations.
+
+[80] Altschuler, J., Bach, F., Rudi, A., Niles-Weed, J., [Massively scalable Sinkhorn distances via the Nyström method](https://proceedings.neurips.cc/paper_files/paper/2019/file/f55cadb97eaff2ba1980e001b0bd9842-Paper.pdf), Advances in Neural Information Processing Systems, 2019.
+
+[81] Xu, H., Luo, D., & Carin, L. (2019). [Scalable Gromov-Wasserstein learning for graph partitioning and matching](https://proceedings.neurips.cc/paper/2019/hash/6e62a992c676f611616097dbea8ea030-Abstract.html). Neural Information Processing Systems (NeurIPS).
+
+
+```