[MRG] Fix UCOOT partial init_duals crash, replace prints with errors/warnings, docstring fixes

.pre-commit-config.yaml

@@ -1,5 +1,5 @@
 repos:
-  # Ruff skada
+  # Ruff
   - repo: https://github.com/astral-sh/ruff-pre-commit
     rev: v0.5.2
     hooks:
@@ -15,10 +15,11 @@ repos:
         name: ruff lint doc, tutorials, tests and examples
         # D103: missing docstring in public function
         # D400: docstring first line must end with period
-        args: ["--ignore=D103,D400", "--fix"]
-        files: ^docs/|^examples/^test/
+        # E402: module level import not at top of file (sphinx-gallery sections)
+        args: ["--ignore=D103,D400,E402", "--fix"]
+        files: ^docs/|^examples/|^test/
       - id: ruff-format
-        files: ^ot/|^docs/|^examples/|
+        files: ^ot/|^docs/|^examples/
 
   # Codespell
   - repo: https://github.com/codespell-project/codespell

README.md

@@ -62,7 +62,7 @@ POT provides the following generic OT solvers:
   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]).
+* [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.html) (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.

RELEASES.md

@@ -30,6 +30,9 @@
 - Fix `UnbalancedSinkhornTransport` `transform` failing with `AttributeError: 'NoneType' object has no attribute 'array_equal'` when `fit` was called with missing parameters (PR #837, Issue #650)
 - Fix quantized (F)GW solvers that ordered OT based on clusters and not initial node ordering (PR #857, Issue #786)
 - Update CircleCI config to use `version: 2.1` instead of the deprecated `version: 2.` (PR #882)
+- Fix `ot.gromov.fused_unbalanced_across_spaces_divergence` crashing with a `TypeError` when only one of the sample or feature dual pairs was passed in `init_duals` (PR #888)
+- Raise errors instead of printing a message and returning `None` or dummy values in `ot.datasets.make_data_classif` (unknown dataset), `ot.stochastic.solve_semi_dual_entropic` (unknown method) and `ot.mapping.nearest_brenier_potential_*` (missing CVXPY), and emit warnings instead of prints in `ot.gaussian.bures_barycenter_fixpoint`, `ot.regpath` and `ot.partial.entropic_partial_wasserstein` (PR #888)
+- Fix docstring errors (wrong or duplicated parameters, typos) and fix the file patterns of the ruff hooks in `.pre-commit-config.yaml` (PR #888)
 
 ## 0.9.7.post1
 

examples/backends/plot_gradient_descent.py

@@ -22,7 +22,6 @@
 from time import perf_counter
 import ot
 from ot.batch._quadratic import loss_quadratic_batch, tensor_batch
-from ot.gromov import fused_unbalanced_gromov_wasserstein
 from sklearn.manifold import MDS
 
 

examples/backends/plot_ot_batch.py

@@ -152,17 +152,15 @@ def benchmark_naive(samples_source, samples_target):
     start = perf_counter()
     for i in range(n_problems):
         M = ot.dist(samples_source[i], samples_target[i])
-        res = ot.solve(M, reg=reg, max_iter=max_iter, tol=tol, reg_type="entropy")
+        ot.solve(M, reg=reg, max_iter=max_iter, tol=tol, reg_type="entropy")
     end = perf_counter()
     return end - start
 
 
 def benchmark_batch(samples_source, samples_target):
     start = perf_counter()
     M_batch = ot.dist_batch(samples_source, samples_target)
-    res_batch = ot.solve_batch(
-        M=M_batch, reg=reg, max_iter=max_iter, tol=tol, reg_type="entropy"
-    )
+    ot.solve_batch(M=M_batch, reg=reg, max_iter=max_iter, tol=tol, reg_type="entropy")
     end = perf_counter()
     return end - start
 

examples/barycenters/plot_solve_barycenter_variants.py

@@ -122,7 +122,6 @@ def plot_list_res(
 
         X = lst_res[i].X
         list_P = [lst_res[i].list_res[k].plan for k in range(2)]
-        loss = lst_res[i].value
 
         plot2D_samples_mat(x1, X, list_P[0])
         plot2D_samples_mat(x2, X, list_P[1])

examples/gromov/plot_partial_fgw.py

@@ -38,7 +38,6 @@
 import pylab as pl
 import networkx as nx
 import math
-from scipy.sparse.csgraph import shortest_path
 import matplotlib.colors as mcol
 from matplotlib import cm
 from ot.gromov import (

examples/others/plot_sgot.py

@@ -28,7 +28,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-from ot.sgot import sgot_metric, sgot_cost_matrix
+from ot.sgot import sgot_metric
 
 from scipy.linalg import eig
 

examples/plot_bsp_ot.py

@@ -20,7 +20,6 @@
 import ot.utils
 import ot.bsp
 import numpy as np
-import time
 import matplotlib.pyplot as plt
 from matplotlib.animation import FuncAnimation
 

examples/plot_debias_sink_div.py

@@ -21,7 +21,6 @@
 import matplotlib.pylab as pl
 import ot
 import ot.plot
-from ot.datasets import make_1D_gauss as gauss
 
 ##############################################################################
 # Generate data

examples/sliced-wasserstein/plot_uniortho_high_dim.py

@@ -137,7 +137,6 @@
 # Wasserstein in 3D") are. We use the same exact closed-form reference as
 # below (a pure translation).
 
-from ot.sliced import get_projections_spiral  # noqa: E402 (kept local to this section)
 
 d3 = 3
 rng3 = np.random.RandomState(1)

examples/unbalanced-partial/plot_UOT_sliced.py

@@ -147,9 +147,10 @@ def generate_dataset(n_samples):
 
 # %%
 
-get_rot = lambda theta: np.array(
-    [[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]
-)
+
+def get_rot(theta):
+    return np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])
+
 
 # visu parameters
 nb_slices = 180  # 60
@@ -480,9 +481,10 @@ def plot_slices(
 
 # %%
 
-get_rot = lambda theta: np.array(
-    [[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]
-)
+
+def get_rot(theta):
+    return np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])
+
 
 n_samples = 500
 nb_outliers = 200

ot/batch/_quadratic.py

@@ -568,7 +568,7 @@ def solve_gromov_batch(
         Gromov problem (when M is not provided). By default ``alpha=None``
         corresponds to ``alpha=1`` for Gromov problem (``M==None``) and
         ``alpha=0.5`` for Fused Gromov-Wasserstein problem (``M!=None``)
-    epsilon : float, optional
+    reg : float, optional
         Regularization parameter for proximal gradient descent. Default is 1e-2.
     T_init : array-like, shape (B, n, m), optional
         Initial transport plan. If None, it is initialized to uniform distribution.
@@ -584,11 +584,11 @@ def solve_gromov_batch(
         Type of gradient computation, either or 'autodiff', 'envelope' or 'detach'. 'autodiff' provides gradients wrt all
         outputs (`plan, value, value_linear`) but with important memory cost.
         'envelope' provides gradients only for (`value, value_linear`)`.  `detach`` is the fastest option but
-        provides no gradients. Default is 'detach'.
-    assume_inner_convergence : bool, optional
-        If True, assumes that the inner Bregman projection always converged i.e. the transport plan satisfies the marginal constraints.
-        This enables faster computations of the tensor product but might results in inaccurate results (e.g. negative values of the loss).
-        Default is True.
+        provides no gradients. Default is 'envelope'.
+    logits : bool, optional
+        For KL loss, whether Ca and Cb are logits (unnormalized log
+        probabilities). Default is None.
+
     Returns
     -------
     res : OTResult()

ot/bregman/_empirical.py

@@ -124,7 +124,7 @@ def empirical_sinkhorn(
         samples weights in the source domain
     b : array-like, shape (n_samples_b,)
         samples weights in the target domain
-    numItermax : int, optional
+    numIterMax : int, optional
         Max number of iterations
     stopThr : float, optional
         Stop threshold on error (>0)
@@ -359,7 +359,7 @@ def empirical_sinkhorn2(
         samples weights in the source domain
     b : array-like, shape (n_samples_b,)
         samples weights in the target domain
-    numItermax : int, optional
+    numIterMax : int, optional
         Max number of iterations
     stopThr : float, optional
         Stop threshold on error (>0)
@@ -598,7 +598,7 @@ def empirical_sinkhorn_divergence(
         samples weights in the source domain
     b : array-like, shape (n_samples_b,)
         samples weights in the target domain
-    numItermax : int, optional
+    numIterMax : int, optional
         Max number of iterations
     stopThr : float, optional
         Stop threshold on error (>0)

ot/bregman/_geomloss.py

@@ -35,7 +35,7 @@ def get_sinkhorn_geomloss_lazytensor(
     ----------
     X_a : array-like, shape (n_samples_a, dim)
         samples in the source domain
-    X_torch: array-like, shape (n_samples_b, dim)
+    X_b : array-like, shape (n_samples_b, dim)
         samples in the target domain
     f : array-like, shape (n_samples_a,)
         First dual potentials (log space)

ot/bregman/_sinkhorn.py

@@ -293,7 +293,7 @@ def sinkhorn2(
     of the regularization (and using warm start) sometimes leads to better
     solutions. Note that the greedy version of the sinkhorn
     :py:func:`ot.bregman.greenkhorn` can also lead to a speedup and the screening
-    version of the sinkhorn :py:func:`ot.bregman.screenkhorn` aim a providing  a
+    version of the sinkhorn :py:func:`ot.bregman.screenkhorn` aims at providing a
     fast approximation of the Sinkhorn problem. For use of GPU and gradient
     computation with small number of iterations we strongly recommend the
     :py:func:`ot.bregman.sinkhorn_log` solver that will no need to check for
@@ -1491,9 +1491,9 @@ def sinkhorn_epsilon_scaling(
     dim_a = len(a)
     dim_b = len(b)
 
-    # nrelative umerical precision with 64 bits
+    # relative numerical precision with 64 bits
     numItermin = 35
-    numItermax = max(numItermin, numItermax)  # ensure that last velue is exact
+    numItermax = max(numItermin, numItermax)  # ensure that last value is exact
 
     ii = 0
     if log:

ot/da.py

@@ -889,7 +889,7 @@ class LinearTransport(BaseTransport):
     Parameters
     ----------
     reg : float,optional
-        regularization added to the daigonals of covariances (>0)
+        regularization added to the diagonals of covariances (>0)
     bias: boolean, optional
         estimate bias :math:`\mathbf{b}` else :math:`\mathbf{b} = 0` (default:True)
     log : bool, optional

ot/datasets.py

@@ -170,9 +170,7 @@ def make_data_classif(dataset, n, nz=0.5, theta=0, p=0.5, random_state=None, **k
         x[:, 1] += kwargs["bias"][1]
 
     else:
-        x = np.array(0)
-        y = np.array(0)
-        print("unknown dataset")
+        raise ValueError("Unknown dataset '{}'".format(dataset))
 
     return x, y.astype(int)
 
@@ -197,7 +195,7 @@ def make_gauss_hd(
     p       : int
             dimension of the ambient space the data live in
     dim     : (int,int) or int
-            the intrinsic dimensions of the source and target Gaussian HD distriutions. If a single int the intrinsic dimension is assumed to be the same
+            the intrinsic dimensions of the source and target Gaussian HD distributions. If a single int the intrinsic dimension is assumed to be the same
     m_diff  : float
             the shift in the first coordinate of the means of the Gaussian HD distributions, i.e. ms_0 and mt_0, respectively (see code)
     a       : (float, float)

ot/dr.py

@@ -462,7 +462,7 @@ def ewca(
     k : int, optional
         Subspace dimension.
     method : str, optional
-        Eather 'BCD' or 'MM' (Block Coordinate Descent or Majorization-Minimization).
+        Either 'BCD' or 'MM' (Block Coordinate Descent or Majorization-Minimization).
         Prefer MM when d is large.
     sinkhorn_method : str
         Method used for the Sinkhorn solver, see :ref:`ot.bregman.sinkhorn` for more details.

ot/gaussian.py

@@ -346,7 +346,7 @@ def empirical_bures_wasserstein_mapping_hd(
         samples in the source domain
     xt : array-like (nt,p)
         samples in the target domain
-    d_instrinsic : int or list of two ints
+    d_intrinsic : int or list of two ints
                   the intrinsic dimensions of the source and destination distribution, respectively. If d_intrinsic is int it is assumed the intrinsic dimension is the same                            
     reg : float,optional
         regularization added to the diagonals of covariances (null by default)
@@ -818,7 +818,7 @@ def empirical_bures_wasserstein_distance_hd(
         samples in the source domain
     xt : array-like (nt,p)
         samples in the target domain
-    d_instrinsic : int or list of two ints
+    d_intrinsic : int or list of two ints
                   the intrinsic dimensions of the source and destination distribution, respectively. If d_intrinsic is int it is assumed the intrinsic dimension is the same     
     reg : float,optional
         regularization added to the diagonals of covariances (null by default)
@@ -937,8 +937,6 @@ def bures_barycenter_fixpoint(
         covariance of k distributions
     weights : array-like (k), optional
         weights for each distribution
-    method : str
-        method used for the solver, either 'fixed_point' or 'gradient_descent'
     num_iter : int, optional
         number of iteration for the fixed point algorithm
     eps : float, optional
@@ -990,7 +988,10 @@ def bures_barycenter_fixpoint(
         Cb = Cnew
 
     if diff > eps:
-        print("Dit not converge.")
+        warnings.warn(
+            "Bures barycenter fixed point did not converge, consider "
+            "increasing num_iter or eps."
+        )
 
     if log:
         log = {}
@@ -1027,8 +1028,6 @@ def bures_barycenter_gradient_descent(
         covariance of k distributions
     weights : array-like (k), optional
         weights for each distribution
-    method : str
-        method used for the solver, either 'fixed_point' or 'gradient_descent'
     num_iter : int, optional
         number of iteration for the fixed point algorithm
     eps : float, optional

ot/gnn/_utils.py

@@ -93,7 +93,7 @@ def FGW_distance_to_templates(
         Graph node features.
     tplt_features : list of torch.Tensor, shape (n_templates, n_template_nodes, n_features)
         List of the node features of the templates.
-    weights : torch.Tensor, shape (n_templates, n_template_nodes)
+    tplt_weights : torch.Tensor, shape (n_templates, n_template_nodes)
         Weights on the nodes of the templates.
     alpha : float, optional
         Trade-off parameter (0 < alpha < 1).
@@ -243,7 +243,7 @@ def wasserstein_distance_to_templates(
         Node features of the graph.
     tplt_features : list of torch.Tensor, shape (n_templates, n_template_nodes, n_features)
         List of the node features of the templates.
-    weights : torch.Tensor, shape (n_templates, n_template_nodes)
+    tplt_weights : torch.Tensor, shape (n_templates, n_template_nodes)
         Weights on the nodes of the templates.
     batch: torch.Tensor, optional
         Batch vector which assigns each node to its graph.

ot/gromov/_dictionary.py

@@ -86,8 +86,8 @@ def gromov_wasserstein_dictionary_learning(
     projection: str , optional
         If 'nonnegative' and/or 'symmetric' is in projection, the corresponding projection will be performed at each stochastic update of the dictionary
         Else the set of atoms is :math:`R^{nt * nt}`. Default is 'nonnegative_symmetric'
-    log: bool, optional
-        If set to True, losses evolution by batches and epochs are tracked. Default is False.
+    use_log: bool, optional
+        If set to True, losses evolution by batches and epochs are tracked. Default is True.
     use_adam_optimizer: bool, optional
         If set to True, adam optimizer with default settings is used as adaptative learning rate strategy.
         Else perform SGD with fixed learning rate. Default is True.
@@ -670,7 +670,7 @@ def fused_gromov_wasserstein_dictionary_learning(
     projection: str, optional
         If 'nonnegative' and/or 'symmetric' is in projection, the corresponding projection will be performed at each stochastic update of the dictionary
         Else the set of atoms is :math:`R^{nt * nt}`. Default is 'nonnegative_symmetric'
-    log: bool, optional
+    use_log: bool, optional
         If set to True, losses evolution by batches and epochs are tracked. Default is False.
     use_adam_optimizer: bool, optional
         If set to True, adam optimizer with default settings is used as adaptative learning rate strategy.

ot/gromov/_quantized.py

@@ -118,7 +118,7 @@ def quantized_fused_gromov_wasserstein_partitioned(
         List of relations between representants and their associated samples in the target space.
     list_p1 : list of npart1 arrays,
         List of node distributions within each partition of the source space.
-    list_p : list of npart2 arrays,
+    list_p2 : list of npart2 arrays,
         List of node distributions within each partition of the target space.
     part1 : list of npart1 arrays, optional. Default is None.
         List of arrays containing the indices of nodes in each partition of the source space.

ot/gromov/_unbalanced.py

@@ -203,11 +203,13 @@ def fused_unbalanced_across_spaces_divergence(
     # MAIN FUNCTION
 
     if reg_type not in ["joint", "independent"]:
-        raise (NotImplementedError('Unknown reg_type="{}"'.format(reg_type)))
+        raise NotImplementedError('Unknown reg_type="{}"'.format(reg_type))
     if divergence not in ["kl", "l2"]:
-        raise (NotImplementedError('Unknown divergence="{}"'.format(divergence)))
+        raise NotImplementedError('Unknown divergence="{}"'.format(divergence))
     if unbalanced_solver not in ["sinkhorn", "sinkhorn_log", "mm", "lbfgsb"]:
-        raise (NotImplementedError('Unknown method="{}"'.format(unbalanced_solver)))
+        raise NotImplementedError(
+            'Unknown unbalanced_solver="{}"'.format(unbalanced_solver)
+        )
 
     # hyperparameters
     alpha_samp, alpha_feat = get_parameter_pair(alpha)
@@ -242,9 +244,9 @@ def fused_unbalanced_across_spaces_divergence(
 
     arr = [X, Y]
 
-    for tuple in [duals_samp, duals_feat]:
-        if tuple is not None:
-            d1, d2 = duals_feat
+    for duals in [duals_samp, duals_feat]:
+        if duals is not None:
+            d1, d2 = duals
             if d1 is not None:
                 arr.append(list_to_array(d1))
             if d2 is not None: