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Commit 9d00f96

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[MRG] ot.lp reorganizing (#714)
* ot.lp reorganise to avoid def in __init__ * pr number + enabled pre-commit * added barycenter.py imports * fixed wrong import in ot.gmm * ruff fix attempt * removed ot bar contribs -> only o.lp reorganisation in this PR * add check_number_threads to ot/lp/__init__.py __all__ * update releases * made barycenter_solvers and network_simplex hidden + deprecated ot.lp.cvx * fix ref to lp.cvx in test * lp.cvx now imports barycenter and gives a warnings.warning * cvx import barycenter
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‎CONTRIBUTORS.md‎

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@@ -48,7 +48,7 @@ The contributors to this library are:
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* [Camille Le Coz](https://www.linkedin.com/in/camille-le-coz-8593b91a1/) (EMD2 debug)
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* [Eduardo Fernandes Montesuma](https://eddardd.github.io/my-personal-blog/) (Free support sinkhorn barycenter)
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* [Theo Gnassounou](https://github.com/tgnassou) (OT between Gaussian distributions)
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* [Clément Bonet](https://clbonet.github.io) (Wassertstein on circle, Spherical Sliced-Wasserstein)
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* [Clément Bonet](https://clbonet.github.io) (Wasserstein on circle, Spherical Sliced-Wasserstein)
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* [Ronak Mehta](https://ronakrm.github.io) (Efficient Discrete Multi Marginal Optimal Transport Regularization)
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* [Xizheng Yu](https://github.com/x12hengyu) (Efficient Discrete Multi Marginal Optimal Transport Regularization)
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* [Sonia Mazelet](https://github.com/SoniaMaz8) (Template based GNN layers)

‎README.md‎

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@@ -51,7 +51,7 @@ POT provides the following generic OT solvers (links to examples):
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* [Efficient Discrete Multi Marginal Optimal Transport Regularization](https://pythonot.github.io/auto_examples/others/plot_demd_gradient_minimize.html) [50].
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* [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.
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* [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].
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* Gaussian Mixture Model OT [69]
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* [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].
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* [Co-Optimal Transport](https://pythonot.github.io/auto_examples/others/plot_COOT.html) [49] and
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[unbalanced Co-Optimal Transport](https://pythonot.github.io/auto_examples/others/plot_learning_weights_with_COOT.html) [71].
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* Fused unbalanced Gromov-Wasserstein [70].

‎RELEASES.md‎

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- Implement CG solvers for partial FGW (PR #687)
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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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#### Closed issues
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- Fixed `ot.mapping` solvers which depended on deprecated `cvxpy` `ECOS` solver (PR #692, Issue #668)

‎ot/gmm.py‎

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

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

‎ot/gromov/_quantized.py‎

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