ruff fix attempt

Author: eloitanguy

README.md

@@ -51,10 +51,11 @@ 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].
+* OT Barycenters for generic transport costs [].
 
 POT provides the following Machine Learning related solvers:
 
@@ -391,3 +392,7 @@ 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] 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)

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"}
             """
         )
 

ot/lp/__init__.py

@@ -12,9 +12,9 @@
 from .cvx import barycenter
 from .dmmot import dmmot_monge_1dgrid_loss, dmmot_monge_1dgrid_optimize
 from .network_simplex import emd, emd2
-from .barycenter import (
-    free_support_barycenter, 
-    generalized_free_support_barycenter
+from .barycenter_solvers import (
+    free_support_barycenter,
+    generalized_free_support_barycenter,
 )
 
 # import compiled emd

ot/lp/barycenter.py renamed to ot/lp/barycenter_solvers.py

@@ -126,7 +126,7 @@ def partial_wasserstein_lagrange(
     nx = get_backend(a, b, M)
 
     if nx.sum(a) > 1 + 1e-15 or nx.sum(b) > 1 + 1e-15:  # 1e-15 for numerical errors
-        raise ValueError("Problem infeasible. Check that a and b are in the " "simplex")
+        raise ValueError("Problem infeasible. Check that a and b are in the simplex")
 
     if reg_m is None:
         reg_m = float(nx.max(M)) + 1
@@ -171,7 +171,7 @@ def partial_wasserstein_lagrange(
 
     if log_emd["warning"] is not None:
         raise ValueError(
-            "Error in the EMD resolution: try to increase the" " number of dummy points"
+            "Error in the EMD resolution: try to increase the number of dummy points"
         )
     log_emd["cost"] = nx.sum(gamma * M0)
     log_emd["u"] = nx.from_numpy(log_emd["u"], type_as=a0)
@@ -287,7 +287,7 @@ def partial_wasserstein(a, b, M, m=None, nb_dummies=1, log=False, **kwargs):
     if m is None:
         return partial_wasserstein_lagrange(a, b, M, log=log, **kwargs)
     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 > nx.min(nx.stack((nx.sum(a), nx.sum(b)))):
         raise ValueError(
             "Problem infeasible. Parameter m should lower or"
@@ -315,7 +315,7 @@ def partial_wasserstein(a, b, M, m=None, nb_dummies=1, log=False, **kwargs):
 
     if log_emd["warning"] is not None:
         raise ValueError(
-            "Error in the EMD resolution: try to increase the" " number of dummy points"
+            "Error in the EMD resolution: try to increase the number of dummy points"
         )
     log_emd["partial_w_dist"] = nx.sum(M * gamma)
     log_emd["u"] = log_emd["u"][: len(a)]
@@ -522,7 +522,7 @@ def entropic_partial_wasserstein(
     if m is None:
         m = nx.min(nx.stack((nx.sum(a), nx.sum(b)))) * 1.0
     if m < 0:
-        raise ValueError("Problem infeasible. Parameter m should be greater" " than 0.")
+        raise ValueError("Problem infeasible. Parameter m should be greater than 0.")
     if m > nx.min(nx.stack((nx.sum(a), nx.sum(b)))):
         raise ValueError(
             "Problem infeasible. Parameter m should lower or"
@@ -780,7 +780,7 @@ def partial_gromov_wasserstein(
     if m is None:
         m = np.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 > np.min((np.sum(p), np.sum(q))):
         raise ValueError(
             "Problem infeasible. Parameter m should lower or"
@@ -1132,7 +1132,7 @@ def entropic_partial_gromov_wasserstein(
     if m is None:
         m = np.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 > np.min((np.sum(p), np.sum(q))):
         raise ValueError(
             "Problem infeasible. Parameter m should lower or"

ot/partial.py

@@ -517,7 +517,7 @@ def check_random_state(seed):
     if isinstance(seed, np.random.RandomState):
         return seed
     raise ValueError(
-        "{} cannot be used to seed a numpy.random.RandomState" " instance".format(seed)
+        "{} cannot be used to seed a numpy.random.RandomState instance".format(seed)
     )
 
 
@@ -787,7 +787,7 @@ def _update_doc(self, olddoc):
 def _is_deprecated(func):
     r"""Helper to check if func is wrapped by our deprecated decorator"""
     if sys.version_info < (3, 5):
-        raise NotImplementedError("This is only available for python3.5 " "or above")
+        raise NotImplementedError("This is only available for python3.5 or above")
     closures = getattr(func, "__closure__", [])
     if closures is None:
         closures = []