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| -rw-r--r-- | docs/source/all.rst | 6 | ||||
| -rw-r--r-- | docs/source/readme.rst | 8 | ||||
| -rwxr-xr-x | examples/plot_partial_wass_and_gromov.py | 163 | ||||
| -rw-r--r-- | ot/__init__.py | 81 | ||||
| -rwxr-xr-x | ot/partial.py | 1014 | ||||
| -rwxr-xr-x | test/test_partial.py | 141 |
6 files changed, 1332 insertions, 81 deletions
diff --git a/docs/source/all.rst b/docs/source/all.rst index c968aa1..a6d9790 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -86,3 +86,9 @@ ot.unbalanced .. automodule:: ot.unbalanced :members: + +ot.partial +------------- + +.. automodule:: ot.partial + :members: diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 0871779..d5f2161 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -391,6 +391,14 @@ of the 36th International Conference on Machine Learning (ICML). `Learning with a Wasserstein Loss <http://cbcl.mit.edu/wasserstein/>`__ Advances in Neural Information Processing Systems (NIPS). +[26] Caffarelli, L. A., McCann, R. J. (2020). `Free boundaries in optimal transport and +Monge-Ampere obstacle problems <http://www.math.toronto.edu/~mccann/papers/annals2010.pdf>`__, +Annals of mathematics, 673-730. + +[27] Chapel, L., Alaya, M., Gasso, G. (2019). `Partial Gromov-Wasserstein with Applications +on Positive-Unlabeled Learning <https://arxiv.org/abs/2002.08276>`__. arXiv preprint +arXiv:2002.08276. + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg diff --git a/examples/plot_partial_wass_and_gromov.py b/examples/plot_partial_wass_and_gromov.py new file mode 100755 index 0000000..30b3fc0 --- /dev/null +++ b/examples/plot_partial_wass_and_gromov.py @@ -0,0 +1,163 @@ +# -*- coding: utf-8 -*-
+"""
+==========================
+Partial Wasserstein and Gromov-Wasserstein example
+==========================
+
+This example is designed to show how to use the Partial (Gromov-)Wassertsein
+distance computation in POT.
+"""
+
+# Author: Laetitia Chapel <laetitia.chapel@irisa.fr>
+# License: MIT License
+
+import scipy as sp
+import numpy as np
+import matplotlib.pylab as pl
+import ot
+
+
+#############################################################################
+#
+# Sample two 2D Gaussian distributions and plot them
+# --------------------------------------------------
+#
+# For demonstration purpose, we sample two Gaussian distributions in 2-d
+# spaces and add some random noise.
+
+
+n_samples = 20 # nb samples (gaussian)
+n_noise = 20 # nb of samples (noise)
+
+mu = np.array([0, 0])
+cov = np.array([[1, 0], [0, 2]])
+
+xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
+xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2))
+xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
+xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2))
+
+M = sp.spatial.distance.cdist(xs, xt)
+
+fig = pl.figure()
+ax1 = fig.add_subplot(131)
+ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+ax2 = fig.add_subplot(132)
+ax2.scatter(xt[:, 0], xt[:, 1], color='r')
+ax3 = fig.add_subplot(133)
+ax3.imshow(M)
+pl.show()
+
+#############################################################################
+#
+# Compute partial Wasserstein plans and distance,
+# by transporting 50% of the mass
+# ----------------------------------------------
+
+p = ot.unif(n_samples + n_noise)
+q = ot.unif(n_samples + n_noise)
+
+w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=0.5, log=True)
+w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=0.1, m=0.5,
+ log=True)
+
+print('Partial Wasserstein distance (m = 0.5): ' + str(log0['partial_w_dist']))
+print('Entropic partial Wasserstein distance (m = 0.5): ' +
+ str(log['partial_w_dist']))
+
+pl.figure(1, (10, 5))
+pl.subplot(1, 2, 1)
+pl.imshow(w0, cmap='jet')
+pl.title('Partial Wasserstein')
+pl.subplot(1, 2, 2)
+pl.imshow(w, cmap='jet')
+pl.title('Entropic partial Wasserstein')
+pl.show()
+
+
+#############################################################################
+#
+# Sample one 2D and 3D Gaussian distributions and plot them
+# ---------------------------------------------------------
+#
+# The Gromov-Wasserstein distance allows to compute distances with samples that
+# do not belong to the same metric space. For demonstration purpose, we sample
+# two Gaussian distributions in 2- and 3-dimensional spaces.
+
+n_samples = 20 # nb samples
+n_noise = 10 # nb of samples (noise)
+
+p = ot.unif(n_samples + n_noise)
+q = ot.unif(n_samples + n_noise)
+
+mu_s = np.array([0, 0])
+cov_s = np.array([[1, 0], [0, 1]])
+
+mu_t = np.array([0, 0, 0])
+cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
+
+
+xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)
+xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0)
+P = sp.linalg.sqrtm(cov_t)
+xt = np.random.randn(n_samples, 3).dot(P) + mu_t
+xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0)
+
+fig = pl.figure()
+ax1 = fig.add_subplot(121)
+ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+ax2 = fig.add_subplot(122, projection='3d')
+ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')
+pl.show()
+
+
+#############################################################################
+#
+# Compute partial Gromov-Wasserstein plans and distance,
+# by transporting 100% and 2/3 of the mass
+# -----------------------------------------------------
+
+C1 = sp.spatial.distance.cdist(xs, xs)
+C2 = sp.spatial.distance.cdist(xt, xt)
+
+print('-----m = 1')
+m = 1
+res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m,
+ log=True)
+res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,
+ m=m, log=True)
+
+print('Partial Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist']))
+print('Entropic partial Wasserstein distance (m = 1): ' +
+ str(log['partial_gw_dist']))
+
+pl.figure(1, (10, 5))
+pl.title("mass to be transported m = 1")
+pl.subplot(1, 2, 1)
+pl.imshow(res0, cmap='jet')
+pl.title('Partial Wasserstein')
+pl.subplot(1, 2, 2)
+pl.imshow(res, cmap='jet')
+pl.title('Entropic partial Wasserstein')
+pl.show()
+
+print('-----m = 2/3')
+m = 2 / 3
+res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True)
+res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,
+ m=m, log=True)
+
+print('Partial Wasserstein distance (m = 2/3): ' +
+ str(log0['partial_gw_dist']))
+print('Entropic partial Wasserstein distance (m = 2/3): ' +
+ str(log['partial_gw_dist']))
+
+pl.figure(1, (10, 5))
+pl.title("mass to be transported m = 2/3")
+pl.subplot(1, 2, 1)
+pl.imshow(res0, cmap='jet')
+pl.title('Partial Wasserstein')
+pl.subplot(1, 2, 2)
+pl.imshow(res, cmap='jet')
+pl.title('Entropic partial Wasserstein')
+pl.show()
diff --git a/ot/__init__.py b/ot/__init__.py deleted file mode 100644 index 89c7936..0000000 --- a/ot/__init__.py +++ /dev/null @@ -1,81 +0,0 @@ -""" - -This is the main module of the POT toolbox. It provides easy access to -a number of sub-modules and functions described below. - -.. note:: - - - Here is a list of the submodules and short description of what they contain. - - - :any:`ot.lp` contains OT solvers for the exact (Linear Program) OT problems. - - :any:`ot.bregman` contains OT solvers for the entropic OT problems using - Bregman projections. - - :any:`ot.lp` contains OT solvers for the exact (Linear Program) OT problems. - - :any:`ot.smooth` contains OT solvers for the regularized (l2 and kl) smooth OT - problems. - - :any:`ot.gromov` contains solvers for Gromov-Wasserstein and Fused Gromov - Wasserstein problems. - - :any:`ot.optim` contains generic solvers OT based optimization problems - - :any:`ot.da` contains classes and function related to Monge mapping - estimation and Domain Adaptation (DA). - - :any:`ot.gpu` contains GPU (cupy) implementation of some OT solvers - - :any:`ot.dr` contains Dimension Reduction (DR) methods such as Wasserstein - Discriminant Analysis. - - :any:`ot.utils` contains utility functions such as distance computation and - timing. - - :any:`ot.datasets` contains toy dataset generation functions. - - :any:`ot.plot` contains visualization functions - - :any:`ot.stochastic` contains stochastic solvers for regularized OT. - - :any:`ot.unbalanced` contains solvers for regularized unbalanced OT. - -.. warning:: - The list of automatically imported sub-modules is as follows: - :py:mod:`ot.lp`, :py:mod:`ot.bregman`, :py:mod:`ot.optim` - :py:mod:`ot.utils`, :py:mod:`ot.datasets`, - :py:mod:`ot.gromov`, :py:mod:`ot.smooth` - :py:mod:`ot.stochastic` - - The following sub-modules are not imported due to additional dependencies: - - - :any:`ot.dr` : depends on :code:`pymanopt` and :code:`autograd`. - - :any:`ot.gpu` : depends on :code:`cupy` and a CUDA GPU. - - :any:`ot.plot` : depends on :code:`matplotlib` - -""" - -# Author: Remi Flamary <remi.flamary@unice.fr> -# Nicolas Courty <ncourty@irisa.fr> -# -# License: MIT License - - -# All submodules and packages -from . import lp -from . import bregman -from . import optim -from . import utils -from . import datasets -from . import da -from . import gromov -from . import smooth -from . import stochastic -from . import unbalanced - -# OT functions -from .lp import emd, emd2, emd_1d, emd2_1d, wasserstein_1d -from .bregman import sinkhorn, sinkhorn2, barycenter -from .unbalanced import sinkhorn_unbalanced, barycenter_unbalanced, sinkhorn_unbalanced2 -from .da import sinkhorn_lpl1_mm - -# utils functions -from .utils import dist, unif, tic, toc, toq - -__version__ = "0.6.0" - -__all__ = ['emd', 'emd2', 'emd_1d', 'sinkhorn', 'sinkhorn2', 'utils', 'datasets', - 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', - 'emd_1d', 'emd2_1d', 'wasserstein_1d', - 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim', - 'sinkhorn_unbalanced', 'barycenter_unbalanced', - 'sinkhorn_unbalanced2'] diff --git a/ot/partial.py b/ot/partial.py new file mode 100755 index 0000000..3425acb --- /dev/null +++ b/ot/partial.py @@ -0,0 +1,1014 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Partial OT +""" + +# Author: Laetitia Chapel <laetitia.chapel@irisa.fr> +# License: MIT License + +import numpy as np + +from .lp import emd + + +def partial_wasserstein_lagrange(a, b, M, reg_m=None, nb_dummies=1, log=False, + **kwargs): + r""" + Solves the partial optimal transport problem for the quadratic cost + and returns the OT plan + + The function considers the following problem: + + .. math:: + \gamma = \arg\min_\gamma <\gamma,(M-\lambda)>_F + + s.t. + \gamma\geq 0 \\ + \gamma 1 \leq a\\ + \gamma^T 1 \leq b\\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} + + + or equivalently: + + .. math:: + \gamma = \arg\min_\gamma <\gamma,M>_F + \sqrt(\lambda/2) + (\|\gamma 1 - a\|_1 + \|\gamma^T 1 - b\|_1) + + s.t. + \gamma\geq 0 \\ + + + where : + + - M is the metric cost matrix + - a and b are source and target unbalanced distributions + - :math:`\lambda` is the lagragian cost. Tuning its value allows attaining + a given mass to be transported m + + The formulation of the problem has been proposed in [26]_ + + Parameters + ---------- + a : np.ndarray (dim_a,) + Unnormalized histogram of dimension dim_a + b : np.ndarray (dim_b,) + Unnormalized histograms of dimension dim_b + M : np.ndarray (dim_a, dim_b) + cost matrix for the quadratic cost + reg_m : float, optional + Lagragian cost + log : bool, optional + record log if True + + + Returns + ------- + gamma : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + + + Examples + -------- + + >>> import ot + >>> a = [.1, .2] + >>> b = [.1, .1] + >>> M = [[0., 1.], [2., 3.]] + >>> np.round(partial_wasserstein_lagrange(a,b,M), 2) + array([[0.1, 0. ], + [0. , 0.1]]) + >>> np.round(partial_wasserstein_lagrange(a,b,M,reg_m=2), 2) + array([[0.1, 0. ], + [0. , 0. ]]) + + References + ---------- + + .. [26] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in + optimal transport and Monge-Ampere obstacle problems. Annals of + mathematics, 673-730. + + See Also + -------- + ot.partial.partial_wasserstein : Partial Wasserstein with fixed mass + """ + + if np.sum(a) > 1 or np.sum(b) > 1: + raise ValueError("Problem infeasible. Check that a and b are in the " + "simplex") + + if reg_m is None: + reg_m = np.max(M) + 1 + if reg_m < -np.max(M): + return np.zeros((len(a), len(b))) + + eps = 1e-20 + M = np.asarray(M, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + a = np.asarray(a, dtype=np.float64) + + M_star = M - reg_m # modified cost matrix + + # trick to fasten the computation: select only the subset of columns/lines + # that can have marginals greater than 0 (that is to say M < 0) + idx_x = np.where(np.min(M_star, axis=1) < eps)[0] + idx_y = np.where(np.min(M_star, axis=0) < eps)[0] + + # extend a, b, M with "reservoir" or "dummy" points + M_extended = np.zeros((len(idx_x) + nb_dummies, len(idx_y) + nb_dummies)) + M_extended[:len(idx_x), :len(idx_y)] = M_star[np.ix_(idx_x, idx_y)] + + a_extended = np.append(a[idx_x], [(np.sum(a) - np.sum(a[idx_x]) + + np.sum(b)) / nb_dummies] * nb_dummies) + b_extended = np.append(b[idx_y], [(np.sum(b) - np.sum(b[idx_y]) + + np.sum(a)) / nb_dummies] * nb_dummies) + + gamma_extended, log_emd = emd(a_extended, b_extended, M_extended, log=True, + **kwargs) + gamma = np.zeros((len(a), len(b))) + gamma[np.ix_(idx_x, idx_y)] = gamma_extended[:-nb_dummies, :-nb_dummies] + + if log_emd['warning'] is not None: + raise ValueError("Error in the EMD resolution: try to increase the" + " number of dummy points") + log_emd['cost'] = np.sum(gamma * M) + if log: + return gamma, log_emd + else: + return gamma + + +def partial_wasserstein(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): + r""" + Solves the partial optimal transport problem for the quadratic cost + and returns the OT plan + + The function considers the following problem: + + .. math:: + \gamma = \arg\min_\gamma <\gamma,M>_F + + s.t. + \gamma\geq 0 \\ + \gamma 1 \leq a\\ + \gamma^T 1 \leq b\\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} + + + where : + + - M is the metric cost matrix + - a and b are source and target unbalanced distributions + - m is the amount of mass to be transported + + Parameters + ---------- + a : np.ndarray (dim_a,) + Unnormalized histogram of dimension dim_a + b : np.ndarray (dim_b,) + Unnormalized histograms of dimension dim_b + M : np.ndarray (dim_a, dim_b) + cost matrix for the quadratic cost + m : float, optional + amount of mass to be transported + nb_dummies : int, optional, default:1 + number of reservoir points to be added (to avoid numerical + instabilities, increase its value if an error is raised) + log : bool, optional + record log if True + + + Returns + ------- + :math:`gamma` : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + + + Examples + -------- + + >>> import ot + >>> a = [.1, .2] + >>> b = [.1, .1] + >>> M = [[0., 1.], [2., 3.]] + >>> np.round(partial_wasserstein(a,b,M), 2) + array([[0.1, 0. ], + [0. , 0.1]]) + >>> np.round(partial_wasserstein(a,b,M,m=0.1), 2) + array([[0.1, 0. ], + [0. , 0. ]]) + + References + ---------- + .. [26] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in + optimal transport and Monge-Ampere obstacle problems. Annals of + mathematics, 673-730. + .. [27] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + Wasserstein with Applications on Positive-Unlabeled Learning". + arXiv preprint arXiv:2002.08276. + + See Also + -------- + ot.partial.partial_wasserstein_lagrange: Partial Wasserstein with + regularization on the marginals + ot.partial.entropic_partial_wasserstein: Partial Wasserstein with a + entropic regularization parameter + """ + + 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.") + elif m > np.min((np.sum(a), np.sum(b))): + raise ValueError("Problem infeasible. Parameter m should lower or" + " equal than min(|a|_1, |b|_1).") + + b_extended = np.append(b, [(np.sum(a) - m) / nb_dummies] * nb_dummies) + a_extended = np.append(a, [(np.sum(b) - m) / nb_dummies] * nb_dummies) + M_extended = np.ones((len(a_extended), len(b_extended))) * 0 + M_extended[-1, -1] = np.max(M) * 1e5 + M_extended[:len(a), :len(b)] = M + + gamma, log_emd = emd(a_extended, b_extended, M_extended, log=True, + **kwargs) + if log_emd['warning'] is not None: + raise ValueError("Error in the EMD resolution: try to increase the" + " number of dummy points") + log_emd['partial_w_dist'] = np.sum(M * gamma[:len(a), :len(b)]) + + if log: + return gamma[:len(a), :len(b)], log_emd + else: + return gamma[:len(a), :len(b)] + + +def partial_wasserstein2(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): + r""" + Solves the partial optimal transport problem for the quadratic cost + and returns the partial GW discrepancy + + The function considers the following problem: + + .. math:: + \gamma = \arg\min_\gamma <\gamma,M>_F + + s.t. + \gamma\geq 0 \\ + \gamma 1 \leq a\\ + \gamma^T 1 \leq b\\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} + + + where : + + - M is the metric cost matrix + - a and b are source and target unbalanced distributions + - m is the amount of mass to be transported + + Parameters + ---------- + a : np.ndarray (dim_a,) + Unnormalized histogram of dimension dim_a + b : np.ndarray (dim_b,) + Unnormalized histograms of dimension dim_b + M : np.ndarray (dim_a, dim_b) + cost matrix for the quadratic cost + m : float, optional + amount of mass to be transported + nb_dummies : int, optional, default:1 + number of reservoir points to be added (to avoid numerical + instabilities, increase its value if an error is raised) + log : bool, optional + record log if True + + + Returns + ------- + :math:`gamma` : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + + + Examples + -------- + + >>> import ot + >>> a=[.1, .2] + >>> b=[.1, .1] + >>> M=[[0., 1.], [2., 3.]] + >>> np.round(partial_wasserstein2(a, b, M), 1) + 0.3 + >>> np.round(partial_wasserstein2(a,b,M,m=0.1), 1) + 0.0 + + References + ---------- + .. [26] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in + optimal transport and Monge-Ampere obstacle problems. Annals of + mathematics, 673-730. + .. [27] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + Wasserstein with Applications on Positive-Unlabeled Learning". + arXiv preprint arXiv:2002.08276. + """ + + partial_gw, log_w = partial_wasserstein(a, b, M, m, nb_dummies, log=True, + **kwargs) + + log_w['T'] = partial_gw + + if log: + return np.sum(partial_gw * M), log_w + else: + return np.sum(partial_gw * M) + + +def gwgrad_partial(C1, C2, T): + """Compute the GW gradient. Note: we can not use the trick in [12]_ as + the marginals may not sum to 1. + + Parameters + ---------- + C1: array of shape (n_p,n_p) + intra-source (P) cost matrix + + C2: array of shape (n_u,n_u) + intra-target (U) cost matrix + + T : array of shape(n_p+nb_dummies, n_u) (default: None) + Transport matrix + + Returns + ------- + numpy.array of shape (n_p+nb_dummies, n_u) + gradient + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + """ + cC1 = np.dot(C1 ** 2 / 2, np.dot(T, np.ones(C2.shape[0]).reshape(-1, 1))) + cC2 = np.dot(np.dot(np.ones(C1.shape[0]).reshape(1, -1), T), C2 ** 2 / 2) + constC = cC1 + cC2 + A = -np.dot(C1, T).dot(C2.T) + tens = constC + A + return tens * 2 + + +def gwloss_partial(C1, C2, T): + """Compute the GW loss. + + Parameters + ---------- + C1: array of shape (n_p,n_p) + intra-source (P) cost matrix + + C2: array of shape (n_u,n_u) + intra-target (U) cost matrix + + T : array of shape(n_p+nb_dummies, n_u) (default: None) + Transport matrix + + Returns + ------- + GW loss + """ + g = gwgrad_partial(C1, C2, T) * 0.5 + return np.sum(g * T) + + +def partial_gromov_wasserstein(C1, C2, p, q, m=None, nb_dummies=1, G0=None, + thres=1, numItermax=1000, tol=1e-7, + log=False, verbose=False, **kwargs): + r""" + Solves the partial optimal transport problem + and returns the OT plan + + The function considers the following problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + + s.t. \gamma 1 \leq a \\ + \gamma^T 1 \leq b \\ + \gamma\geq 0 \\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} \\ + + where : + + - M is the metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma) + =\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are the sample weights + - m is the amount of mass to be transported + + The formulation of the problem has been proposed in [27]_ + + + Parameters + ---------- + C1 : ndarray, shape (ns, ns) + Metric cost matrix in the source space + C2 : ndarray, shape (nt, nt) + Metric costfr matrix in the target space + p : ndarray, shape (ns,) + Distribution in the source space + q : ndarray, shape (nt,) + Distribution in the target space + m : float, optional + Amount of mass to be transported (default: min (|p|_1, |q|_1)) + nb_dummies : int, optional + Number of dummy points to add (avoid instabilities in the EMD solver) + G0 : ndarray, shape (ns, nt), optional + Initialisation of the transportation matrix + thres : float, optional + quantile of the gradient matrix to populate the cost matrix when 0 + (default: 1) + numItermax : int, optional + Max number of iterations + log : bool, optional + return log if True + verbose : bool, optional + Print information along iterations + armijo : bool, optional + If True the steps of the line-search is found via an armijo research. Else closed form is used. + If there is convergence issues use False. + **kwargs : dict + parameters can be directly passed to the emd solver + + + Returns + ------- + gamma : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + + + Examples + -------- + >>> import ot + >>> import scipy as sp + >>> a = np.array([0.25] * 4) + >>> b = np.array([0.25] * 4) + >>> x = np.array([1,2,100,200]).reshape((-1,1)) + >>> y = np.array([3,2,98,199]).reshape((-1,1)) + >>> C1 = sp.spatial.distance.cdist(x, x) + >>> C2 = sp.spatial.distance.cdist(y, y) + >>> np.round(partial_gromov_wasserstein(C1, C2, a, b),2) + array([[0. , 0.25, 0. , 0. ], + [0.25, 0. , 0. , 0. ], + [0. , 0. , 0.25, 0. ], + [0. , 0. , 0. , 0.25]]) + >>> np.round(partial_gromov_wasserstein(C1, C2, a, b, m=0.25),2) + array([[0. , 0. , 0. , 0. ], + [0. , 0. , 0. , 0. ], + [0. , 0. , 0. , 0. ], + [0. , 0. , 0. , 0.25]]) + + References + ---------- + .. [27] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + Wasserstein with Applications on Positive-Unlabeled Learning". + arXiv preprint arXiv:2002.08276. + + """ + + 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.") + elif m > np.min((np.sum(p), np.sum(q))): + raise ValueError("Problem infeasible. Parameter m should lower or" + " equal than min(|a|_1, |b|_1).") + + if G0 is None: + G0 = np.outer(p, q) + + dim_G_extended = (len(p) + nb_dummies, len(q) + nb_dummies) + q_extended = np.append(q, [(np.sum(p) - m) / nb_dummies] * nb_dummies) + p_extended = np.append(p, [(np.sum(q) - m) / nb_dummies] * nb_dummies) + + cpt = 0 + err = 1 + eps = 1e-20 + if log: + log = {'err': []} + + while (err > tol and cpt < numItermax): + + Gprev = G0 + + M = gwgrad_partial(C1, C2, G0) + M[M < eps] = np.quantile(M[M > eps], thres) + + M_emd = np.ones(dim_G_extended) * np.max(M) * 1e2 + M_emd[:len(p), :len(q)] = M + M_emd[-nb_dummies:, -nb_dummies:] = np.max(M) * 1e5 + M_emd = np.asarray(M_emd, dtype=np.float64) + + Gc, logemd = emd(p_extended, q_extended, M_emd, log=True, **kwargs) + + if logemd['warning'] is not None: + raise ValueError("Error in the EMD resolution: try to increase the" + " number of dummy points") + + G0 = Gc[:len(p), :len(q)] + + if cpt % 10 == 0: # to speed up the computations + err = np.linalg.norm(G0 - Gprev) + if log: + log['err'].append(err) + if verbose: + if cpt % 200 == 0: + print('{:5s}|{:12s}|{:12s}'.format( + 'It.', 'Err', 'Loss') + '\n' + '-' * 31) + print('{:5d}|{:8e}|{:8e}'.format(cpt, err, + gwloss_partial(C1, C2, G0))) + + cpt += 1 + + if log: + log['partial_gw_dist'] = gwloss_partial(C1, C2, G0) + return G0[:len(p), :len(q)], log + else: + return G0[:len(p), :len(q)] + + +def partial_gromov_wasserstein2(C1, C2, p, q, m=None, nb_dummies=1, G0=None, + thres=0.75, numItermax=1000, tol=1e-7, + log=False, verbose=False, **kwargs): + r""" + Solves the partial optimal transport problem + and returns the partial Gromov-Wasserstein discrepancy + + The function considers the following problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + + s.t. \gamma 1 \leq a \\ + \gamma^T 1 \leq b \\ + \gamma\geq 0 \\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} \\ + + where : + + - M is the metric cost matrix + - :math:`\Omega` is the entropic regularization term + :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are the sample weights + - m is the amount of mass to be transported + + The formulation of the problem has been proposed in [27]_ + + + Parameters + ---------- + C1 : ndarray, shape (ns, ns) + Metric cost matrix in the source space + C2 : ndarray, shape (nt, nt) + Metric costfr matrix in the target space + p : ndarray, shape (ns,) + Distribution in the source space + q : ndarray, shape (nt,) + Distribution in the target space + m : float, optional + Amount of mass to be transported (default: min (|p|_1, |q|_1)) + nb_dummies : int, optional + Number of dummy points to add (avoid instabilities in the EMD solver) + G0 : ndarray, shape (ns, nt), optional + Initialisation of the transportation matrix + thres : float, optional + quantile of the gradient matrix to populate the cost matrix when 0 + (default: 1) + numItermax : int, optional + Max number of iterations + log : bool, optional + return log if True + verbose : bool, optional + Print information along iterations + **kwargs : dict + parameters can be directly passed to the emd solver + + + Returns + ------- + partial_gw_dist : (dim_a x dim_b) ndarray + partial GW discrepancy + log : dict + log dictionary returned only if `log` is `True` + + + Examples + -------- + >>> import ot + >>> import scipy as sp + >>> a = np.array([0.25] * 4) + >>> b = np.array([0.25] * 4) + >>> x = np.array([1,2,100,200]).reshape((-1,1)) + >>> y = np.array([3,2,98,199]).reshape((-1,1)) + >>> C1 = sp.spatial.distance.cdist(x, x) + >>> C2 = sp.spatial.distance.cdist(y, y) + >>> np.round(partial_gromov_wasserstein2(C1, C2, a, b),2) + 1.69 + >>> np.round(partial_gromov_wasserstein2(C1, C2, a, b, m=0.25),2) + 0.0 + + References + ---------- + .. [27] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + Wasserstein with Applications on Positive-Unlabeled Learning". + arXiv preprint arXiv:2002.08276. + + """ + + partial_gw, log_gw = partial_gromov_wasserstein(C1, C2, p, q, m, + nb_dummies, G0, thres, + numItermax, tol, True, + verbose, **kwargs) + + log_gw['T'] = partial_gw + + if log: + return log_gw['partial_gw_dist'], log_gw + else: + return log_gw['partial_gw_dist'] + + +def entropic_partial_wasserstein(a, b, M, reg, m=None, numItermax=1000, + stopThr=1e-100, verbose=False, log=False): + r""" + Solves the partial optimal transport problem + and returns the OT plan + + The function considers the following problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + s.t. \gamma 1 \leq a \\ + \gamma^T 1 \leq b \\ + \gamma\geq 0 \\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} \\ + + where : + + - M is the metric cost matrix + - :math:`\Omega` is the entropic regularization term + :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are the sample weights + - m is the amount of mass to be transported + + The formulation of the problem has been proposed in [3]_ + + + Parameters + ---------- + a : np.ndarray (dim_a,) + Unnormalized histogram of dimension dim_a + b : np.ndarray (dim_b,) + Unnormalized histograms of dimension dim_b + M : np.ndarray (dim_a, dim_b) + cost matrix + reg : float + Regularization term > 0 + m : float, optional + Amount of mass to be transported + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshold on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + + + Examples + -------- + >>> import ot + >>> a = [.1, .2] + >>> b = [.1, .1] + >>> M = [[0., 1.], [2., 3.]] + >>> np.round(entropic_partial_wasserstein(a, b, M, 1, 0.1), 2) + array([[0.06, 0.02], + [0.01, 0. ]]) + + + References + ---------- + .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. + (2015). Iterative Bregman projections for regularized transportation + problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + + See Also + -------- + ot.partial.partial_wasserstein: exact Partial Wasserstein + """ + + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + M = np.asarray(M, dtype=np.float64) + + dim_a, dim_b = M.shape + dx = np.ones(dim_a) + dy = np.ones(dim_b) + + if len(a) == 0: + a = np.ones(dim_a, dtype=np.float64) / dim_a + if len(b) == 0: + b = np.ones(dim_b, dtype=np.float64) / dim_b + + if m is None: + m = np.min((np.sum(a), np.sum(b))) + if m < 0: + raise ValueError("Problem infeasible. Parameter m should be greater" + " than 0.") + if m > np.min((np.sum(a), np.sum(b))): + raise ValueError("Problem infeasible. Parameter m should lower or" + " equal than min(|a|_1, |b|_1).") + + log_e = {'err': []} + + # Next 3 lines equivalent to K=np.exp(-M/reg), but faster to compute + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + np.multiply(K, m / np.sum(K), out=K) + + err, cpt = 1, 0 + + while (err > stopThr and cpt < numItermax): + Kprev = K + K1 = np.dot(np.diag(np.minimum(a / np.sum(K, axis=1), dx)), K) + K2 = np.dot(K1, np.diag(np.minimum(b / np.sum(K1, axis=0), dy))) + K = K2 * (m / np.sum(K2)) + + if np.any(np.isnan(K)) or np.any(np.isinf(K)): + print('Warning: numerical errors at iteration', cpt) + break + if cpt % 10 == 0: + err = np.linalg.norm(Kprev - K) + if log: + log_e['err'].append(err) + if verbose: + if cpt % 200 == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 11) + print('{:5d}|{:8e}|'.format(cpt, err)) + + cpt = cpt + 1 + log_e['partial_w_dist'] = np.sum(M * K) + if log: + return K, log_e + else: + return K + + +def entropic_partial_gromov_wasserstein(C1, C2, p, q, reg, m=None, G0=None, + numItermax=1000, tol=1e-7, log=False, + verbose=False): + r""" + Returns the partial Gromov-Wasserstein transport between (C1,p) and (C2,q) + + The function solves the following optimization problem: + + .. math:: + GW = \arg\min_{\gamma} \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})\cdot + \gamma_{i,j}\cdot\gamma_{k,l} + reg\cdot\Omega(\gamma) + + s.t. + \gamma\geq 0 \\ + \gamma 1 \leq a\\ + \gamma^T 1 \leq b\\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} + + where : + + - C1 is the metric cost matrix in the source space + - C2 is the metric cost matrix in the target space + - p and q are the sample weights + - L : quadratic loss function + - :math:`\Omega` is the entropic regularization term + :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - m is the amount of mass to be transported + + The formulation of the problem has been proposed in [12]. + + Parameters + ---------- + C1 : ndarray, shape (ns, ns) + Metric cost matrix in the source space + C2 : ndarray, shape (nt, nt) + Metric costfr matrix in the target space + p : ndarray, shape (ns,) + Distribution in the source space + q : ndarray, shape (nt,) + Distribution in the target space + reg: float + entropic regularization parameter + m : float, optional + Amount of mass to be transported (default: min (|p|_1, |q|_1)) + G0 : ndarray, shape (ns, nt), optional + Initialisation of the transportation matrix + numItermax : int, optional + Max number of iterations + log : bool, optional + return log if True + verbose : bool, optional + Print information along iterations + + Examples + -------- + >>> import ot + >>> import scipy as sp + >>> a = np.array([0.25] * 4) + >>> b = np.array([0.25] * 4) + >>> x = np.array([1,2,100,200]).reshape((-1,1)) + >>> y = np.array([3,2,98,199]).reshape((-1,1)) + >>> C1 = sp.spatial.distance.cdist(x, x) + >>> C2 = sp.spatial.distance.cdist(y, y) + >>> np.round(entropic_partial_gromov_wasserstein(C1, C2, a, b,50), 2) + array([[0.12, 0.13, 0. , 0. ], + [0.13, 0.12, 0. , 0. ], + [0. , 0. , 0.25, 0. ], + [0. , 0. , 0. , 0.25]]) + >>> np.round(entropic_partial_gromov_wasserstein(C1, C2, a, b, 50, m=0.25) + , 2) + array([[0.02, 0.03, 0. , 0.03], + [0.03, 0.03, 0. , 0.03], + [0. , 0. , 0.03, 0. ], + [0.02, 0.02, 0. , 0.03]]) + + Returns + ------- + :math: `gamma` : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + + See Also + -------- + ot.partial.partial_gromov_wasserstein: exact Partial Gromov-Wasserstein + """ + + if G0 is None: + G0 = np.outer(p, q) + + 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.") + elif m > np.min((np.sum(p), np.sum(q))): + raise ValueError("Problem infeasible. Parameter m should lower or" + " equal than min(|a|_1, |b|_1).") + + cpt = 0 + err = 1 + + loge = {'err': []} + + while (err > tol and cpt < numItermax): + Gprev = G0 + M_entr = gwgrad_partial(C1, C2, G0) + G0 = entropic_partial_wasserstein(p, q, M_entr, reg, m) + if cpt % 10 == 0: # to speed up the computations + err = np.linalg.norm(G0 - Gprev) + if log: + loge['err'].append(err) + if verbose: + if cpt % 200 == 0: + print('{:5s}|{:12s}|{:12s}'.format( + 'It.', 'Err', 'Loss') + '\n' + '-' * 31) + print('{:5d}|{:8e}|{:8e}'.format(cpt, err, + gwloss_partial(C1, C2, G0))) + + cpt += 1 + + if log: + loge['partial_gw_dist'] = gwloss_partial(C1, C2, G0) + return G0, loge + else: + return G0 + + +def entropic_partial_gromov_wasserstein2(C1, C2, p, q, reg, m=None, G0=None, + numItermax=1000, tol=1e-7, log=False, + verbose=False): + r""" + Returns the partial Gromov-Wasserstein discrepancy between (C1,p) and + (C2,q) + + The function solves the following optimization problem: + + .. math:: + GW = \arg\min_{\gamma} \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})\cdot + \gamma_{i,j}\cdot\gamma_{k,l} + reg\cdot\Omega(\gamma) + + s.t. + \gamma\geq 0 \\ + \gamma 1 \leq a\\ + \gamma^T 1 \leq b\\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} + + where : + + - C1 is the metric cost matrix in the source space + - C2 is the metric cost matrix in the target space + - p and q are the sample weights + - L : quadratic loss function + - :math:`\Omega` is the entropic regularization term + :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - m is the amount of mass to be transported + + The formulation of the problem has been proposed in [12]. + + + Parameters + ---------- + C1 : ndarray, shape (ns, ns) + Metric cost matrix in the source space + C2 : ndarray, shape (nt, nt) + Metric costfr matrix in the target space + p : ndarray, shape (ns,) + Distribution in the source space + q : ndarray, shape (nt,) + Distribution in the target space + reg: float + entropic regularization parameter + m : float, optional + Amount of mass to be transported (default: min (|p|_1, |q|_1)) + G0 : ndarray, shape (ns, nt), optional + Initialisation of the transportation matrix + numItermax : int, optional + Max number of iterations + log : bool, optional + return log if True + verbose : bool, optional + Print information along iterations + + + Returns + ------- + partial_gw_dist: float + Gromov-Wasserstein distance + log : dict + log dictionary returned only if `log` is `True` + + Examples + -------- + >>> import ot + >>> import scipy as sp + >>> a = np.array([0.25] * 4) + >>> b = np.array([0.25] * 4) + >>> x = np.array([1,2,100,200]).reshape((-1,1)) + >>> y = np.array([3,2,98,199]).reshape((-1,1)) + >>> C1 = sp.spatial.distance.cdist(x, x) + >>> C2 = sp.spatial.distance.cdist(y, y) + >>> np.round(entropic_partial_gromov_wasserstein2(C1, C2, a, b,50), 2) + 1.87 + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + """ + + partial_gw, log_gw = entropic_partial_gromov_wasserstein(C1, C2, p, q, reg, + m, G0, numItermax, + tol, True, + verbose) + + log_gw['T'] = partial_gw + + if log: + return log_gw['partial_gw_dist'], log_gw + else: + return log_gw['partial_gw_dist'] diff --git a/test/test_partial.py b/test/test_partial.py new file mode 100755 index 0000000..fbcd3c2 --- /dev/null +++ b/test/test_partial.py @@ -0,0 +1,141 @@ +"""Tests for module partial """ + +# Author: +# Laetitia Chapel <laetitia.chapel@irisa.fr> +# +# License: MIT License + +import numpy as np +import scipy as sp +import ot + + +def test_partial_wasserstein(): + + n_samples = 20 # nb samples (gaussian) + n_noise = 20 # nb of samples (noise) + + mu = np.array([0, 0]) + cov = np.array([[1, 0], [0, 2]]) + + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) + xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2)) + xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) + xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2)) + + M = ot.dist(xs, xt) + + p = ot.unif(n_samples + n_noise) + q = ot.unif(n_samples + n_noise) + + m = 0.5 + + w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=m, log=True) + w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=1, m=m, + log=True) + + # check constratints + np.testing.assert_equal( + w0.sum(1) - p <= 1e-5, [True] * len(p)) # cf convergence wasserstein + np.testing.assert_equal( + w0.sum(0) - q <= 1e-5, [True] * len(q)) # cf convergence wasserstein + np.testing.assert_equal( + w.sum(1) - p <= 1e-5, [True] * len(p)) # cf convergence wasserstein + np.testing.assert_equal( + w.sum(0) - q <= 1e-5, [True] * len(q)) # cf convergence wasserstein + + # check transported mass + np.testing.assert_allclose( + np.sum(w0), m, atol=1e-04) + np.testing.assert_allclose( + np.sum(w), m, atol=1e-04) + + w0, log0 = ot.partial.partial_wasserstein2(p, q, M, m=m, log=True) + w0_val = ot.partial.partial_wasserstein2(p, q, M, m=m, log=False) + + G = log0['T'] + + np.testing.assert_allclose(w0, w0_val, atol=1e-1, rtol=1e-1) + + # check constratints + np.testing.assert_equal( + G.sum(1) <= p, [True] * len(p)) # cf convergence wasserstein + np.testing.assert_equal( + G.sum(0) <= q, [True] * len(q)) # cf convergence wasserstein + np.testing.assert_allclose( + np.sum(G), m, atol=1e-04) + + +def test_partial_gromov_wasserstein(): + n_samples = 20 # nb samples + n_noise = 10 # nb of samples (noise) + + p = ot.unif(n_samples + n_noise) + q = ot.unif(n_samples + n_noise) + + mu_s = np.array([0, 0]) + cov_s = np.array([[1, 0], [0, 1]]) + + mu_t = np.array([0, 0, 0]) + cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) + + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) + xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0) + P = sp.linalg.sqrtm(cov_t) + xt = np.random.randn(n_samples, 3).dot(P) + mu_t + xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0) + xt2 = xs[::-1].copy() + + C1 = ot.dist(xs, xs) + C2 = ot.dist(xt, xt) + C3 = ot.dist(xt2, xt2) + + m = 2 / 3 + res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C3, p, q, m=m, + log=True) + res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C3, p, q, 10, + m=m, log=True) + np.testing.assert_allclose(res0, 0, atol=1e-1, rtol=1e-1) + np.testing.assert_allclose(res, 0, atol=1e-1, rtol=1e-1) + + C1 = sp.spatial.distance.cdist(xs, xs) + C2 = sp.spatial.distance.cdist(xt, xt) + + m = 1 + res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, + log=True) + G = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss') + np.testing.assert_allclose(G, res0, atol=1e-04) + + res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, + m=m, log=True) + G = ot.gromov.entropic_gromov_wasserstein( + C1, C2, p, q, 'square_loss', epsilon=10) + np.testing.assert_allclose(G, res, atol=1e-02) + + w0, log0 = ot.partial.partial_gromov_wasserstein2(C1, C2, p, q, m=m, + log=True) + w0_val = ot.partial.partial_gromov_wasserstein2(C1, C2, p, q, m=m, + log=False) + G = log0['T'] + np.testing.assert_allclose(w0, w0_val, atol=1e-1, rtol=1e-1) + + m = 2 / 3 + res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, + log=True) + res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, + m=m, log=True) + # check constratints + np.testing.assert_equal( + res0.sum(1) <= p, [True] * len(p)) # cf convergence wasserstein + np.testing.assert_equal( + res0.sum(0) <= q, [True] * len(q)) # cf convergence wasserstein + np.testing.assert_allclose( + np.sum(res0), m, atol=1e-04) + + np.testing.assert_equal( + res.sum(1) <= p, [True] * len(p)) # cf convergence wasserstein + np.testing.assert_equal( + res.sum(0) <= q, [True] * len(q)) # cf convergence wasserstein + np.testing.assert_allclose( + np.sum(res), m, atol=1e-04) |