[MRG] ot.lp reorganizing

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

@@ -51,7 +51,7 @@ 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].

RELEASES.md

@@ -6,6 +6,7 @@
 - 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)
 
 #### Closed issues
 - Fixed `ot.mapping` solvers which depended on deprecated `cvxpy` `ECOS` solver (PR #692, Issue #668)

ot/gmm.py

@@ -12,7 +12,7 @@
 from .backend import get_backend
 from .lp import emd2, emd
 import numpy as np
-from .lp import dist
+from .utils import dist
 from .gaussian import bures_wasserstein_mapping
 
 

ot/gromov/_partial.py

@@ -185,7 +185,7 @@ def partial_gromov_wasserstein(
     if m is None:
         m = min(np.sum(p), np.sum(q))
     elif m < 0:
-        raise ValueError("Problem infeasible. Parameter m should be greater" " than 0.")
+        raise ValueError("Problem infeasible. Parameter m should be greater than 0.")
     elif m > min(np.sum(p), np.sum(q)):
         raise ValueError(
             "Problem infeasible. Parameter m should lower or"
@@ -654,7 +654,7 @@ def partial_fused_gromov_wasserstein(
     if m is None:
         m = min(np.sum(p), np.sum(q))
     elif m < 0:
-        raise ValueError("Problem infeasible. Parameter m should be greater" " than 0.")
+        raise ValueError("Problem infeasible. Parameter m should be greater than 0.")
     elif m > min(np.sum(p), np.sum(q)):
         raise ValueError(
             "Problem infeasible. Parameter m should lower or"
@@ -1213,7 +1213,7 @@ def entropic_partial_gromov_wasserstein(
     if m is None:
         m = min(nx.sum(p), nx.sum(q))
     elif m < 0:
-        raise ValueError("Problem infeasible. Parameter m should be greater" " than 0.")
+        raise ValueError("Problem infeasible. Parameter m should be greater than 0.")
     elif m > min(nx.sum(p), nx.sum(q)):
         raise ValueError(
             "Problem infeasible. Parameter m should lower or"

ot/gromov/_quantized.py

@@ -375,7 +375,7 @@ def get_graph_partition(
         raise ValueError(
             f"""
             Unknown `part_method='{part_method}'`. Use one of:
-            {'random', 'louvain', 'fluid', 'spectral', 'GW', 'FGW'}.
+            {"random", "louvain", "fluid", "spectral", "GW", "FGW"}.
             """
         )
     return nx.from_numpy(part, type_as=C0)
@@ -447,7 +447,7 @@ def get_graph_representants(C, part, rep_method="pagerank", random_state=0, nx=N
         raise ValueError(
             f"""
             Unknown `rep_method='{rep_method}'`. Use one of:
-            {'random', 'pagerank'}.
+            {"random", "pagerank"}.
             """
         )
 
@@ -953,7 +953,7 @@ def get_partition_and_representants_samples(
     else:
         raise ValueError(
             f"""
-            Unknown `method='{method}'`. Use one of: {'random', 'kmeans'}
+            Unknown `method='{method}'`. Use one of: {"random", "kmeans"}
             """
         )