diff options
author | Rémi Flamary <remi.flamary@gmail.com> | 2022-04-07 14:18:54 +0200 |
---|---|---|
committer | GitHub <noreply@github.com> | 2022-04-07 14:18:54 +0200 |
commit | 0b223ff883fd73601984a92c31cb70d4aded16e8 (patch) | |
tree | bca74110049debfa35735f4618a9d1543690c2a4 | |
parent | ad02112d4288f3efdd5bc6fc6e45444313bba871 (diff) |
[MRG] Remove deprecated ot.gpu submodule (#361)
* remove all cpu submodule and tests
* speedup tests gromov
-rw-r--r-- | README.md | 2 | ||||
-rw-r--r-- | RELEASES.md | 1 | ||||
-rw-r--r-- | docs/source/quickstart.rst | 58 | ||||
-rw-r--r-- | ot/gpu/__init__.py | 50 | ||||
-rw-r--r-- | ot/gpu/bregman.py | 196 | ||||
-rw-r--r-- | ot/gpu/da.py | 144 | ||||
-rw-r--r-- | ot/gpu/utils.py | 101 | ||||
-rw-r--r-- | test/test_gpu.py | 106 | ||||
-rw-r--r-- | test/test_gromov.py | 129 |
9 files changed, 113 insertions, 674 deletions
@@ -185,7 +185,7 @@ The contributors to this library are * [Alexandre Gramfort](http://alexandre.gramfort.net/) (CI, documentation) * [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) (Partial OT) * [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) -* [Léo Gautheron](https://github.com/aje) (GPU implementation) +* [Léo Gautheron](https://github.com/aje) (Initial GPU implementation) * [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1) (DA classes) * [Stanislas Chambon](https://slasnista.github.io/) (DA classes) * [Antoine Rolet](https://arolet.github.io/) (EMD solver debug) diff --git a/RELEASES.md b/RELEASES.md index 7d458f3..b54a84a 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -5,6 +5,7 @@ #### New features +- remode deprecated `ot.gpu` submodule (PR #361) - Update examples in the gallery (PR #359). - Add stochastic loss and OT plan computation for regularized OT and backend examples(PR #360). diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index 09a362b..b4cc8ab 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -1028,15 +1028,6 @@ FAQ speedup can be obtained by using a GPU implementation since all operations are matrix/vector products. -4. **Using GPU fails with error: module 'ot' has no attribute 'gpu'** - - In order to limit import time and hard dependencies in POT. we do not import - some sub-modules automatically with :code:`import ot`. In order to use the - acceleration in :any:`ot.gpu` you need first to import is with - :code:`import ot.gpu`. - - See `Issue #85 <https://github.com/rflamary/POT/issues/85>`__ and :any:`ot.gpu` - for more details. References @@ -1172,3 +1163,52 @@ References .. [30] Flamary, Rémi, et al. "Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching." NIPS Workshop on Optimal Transport and Machine Learning OTML. 2014. + +.. [31] Bonneel, Nicolas, et al. `Sliced and radon wasserstein barycenters of + measures + <https://perso.liris.cnrs.fr/nicolas.bonneel/WassersteinSliced-JMIV.pdf>`_\ + , Journal of Mathematical Imaging and Vision 51.1 (2015): 22-45 + +.. [32] Huang, M., Ma S., Lai, L. (2021). `A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance <http://proceedings.mlr.press/v139/huang21e.html>`_\ , Proceedings of the 38th International Conference on Machine Learning (ICML). + +.. [33] Kerdoncuff T., Emonet R., Marc S. `Sampled Gromov Wasserstein + <https://hal.archives-ouvertes.fr/hal-03232509/document>`_\ , Machine + Learning Journal (MJL), 2021 + +.. [34] Feydy, J., Séjourné, T., Vialard, F. X., Amari, S. I., Trouvé, A., & + Peyré, G. (2019, April). `Interpolating between optimal transport and MMD + using Sinkhorn divergences + <http://proceedings.mlr.press/v89/feydy19a/feydy19a.pdf>`_. In The 22nd + International Conference on Artificial Intelligence and Statistics (pp. + 2681-2690). PMLR. + +.. [35] Deshpande, I., Hu, Y. T., Sun, R., Pyrros, A., Siddiqui, N., Koyejo, S., + & Schwing, A. G. (2019). `Max-sliced wasserstein distance and its use + for gans + <https://openaccess.thecvf.com/content_CVPR_2019/papers/Deshpande_Max-Sliced_Wasserstein_Distance_and_Its_Use_for_GANs_CVPR_2019_paper.pdf>`_. + In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10648-10656). + +.. [36] Liutkus, A., Simsekli, U., Majewski, S., Durmus, A., & Stöter, F. R. + (2019, May). `Sliced-Wasserstein flows: Nonparametric generative modeling via + optimal transport and diffusions + <http://proceedings.mlr.press/v97/liutkus19a/liutkus19a.pdf>`_. In International + Conference on Machine Learning (pp. 4104-4113). PMLR. + +.. [37] Janati, H., Cuturi, M., Gramfort, A. `Debiased sinkhorn barycenters + <http://proceedings.mlr.press/v119/janati20a/janati20a.pdf>`_ Proceedings of + the 37th International Conference on Machine Learning, PMLR 119:4692-4701, 2020 + +.. [38] C. Vincent-Cuaz, T. Vayer, R. Flamary, M. Corneli, N. Courty, `Online + Graph Dictionary Learning <https://arxiv.org/pdf/2102.06555.pdf>`_\ , + International Conference on Machine Learning (ICML), 2021. + +.. [39] Gozlan, N., Roberto, C., Samson, P. M., & Tetali, P. (2017). + `Kantorovich duality for general transport costs and applications + <https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.712.1825&rep=rep1&type=pdf>`_. + Journal of Functional Analysis, 273(11), 3327-3405. + +.. [40] Forrow, A., Hütter, J. C., Nitzan, M., Rigollet, P., Schiebinger, G., & + Weed, J. (2019, April). `Statistical optimal transport via factored + couplings <http://proceedings.mlr.press/v89/forrow19a/forrow19a.pdf>`_. In + The 22nd International Conference on Artificial Intelligence and Statistics + (pp. 2454-2465). PMLR. diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py deleted file mode 100644 index 12db605..0000000 --- a/ot/gpu/__init__.py +++ /dev/null @@ -1,50 +0,0 @@ -# -*- coding: utf-8 -*- -""" -GPU implementation for several OT solvers and utility -functions. - -The GPU backend in handled by `cupy -<https://cupy.chainer.org/>`_. - -.. warning:: - This module is now deprecated and will be removed in future releases. POT - now privides a backend mechanism that allows for solving prolem on GPU wth - the pytorch backend. - - -.. warning:: - Note that by default the module is not imported in :mod:`ot`. In order to - use it you need to explicitely import :mod:`ot.gpu` . - -By default, the functions in this module accept and return numpy arrays -in order to proide drop-in replacement for the other POT function but -the transfer between CPU en GPU comes with a significant overhead. - -In order to get the best performances, we recommend to give only cupy -arrays to the functions and desactivate the conversion to numpy of the -result of the function with parameter ``to_numpy=False``. - -""" - -# Author: Remi Flamary <remi.flamary@unice.fr> -# Leo Gautheron <https://github.com/aje> -# -# License: MIT License - -import warnings - -from . import bregman -from . import da -from .bregman import sinkhorn -from .da import sinkhorn_lpl1_mm - -from . import utils -from .utils import dist, to_gpu, to_np - - -warnings.warn('This module is deprecated and will be removed in the next minor release of POT', category=DeprecationWarning) - - -__all__ = ["utils", "dist", "sinkhorn", - "sinkhorn_lpl1_mm", 'bregman', 'da', 'to_gpu', 'to_np'] - diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py deleted file mode 100644 index 76af00e..0000000 --- a/ot/gpu/bregman.py +++ /dev/null @@ -1,196 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Bregman projections for regularized OT with GPU -""" - -# Author: Remi Flamary <remi.flamary@unice.fr> -# Leo Gautheron <https://github.com/aje> -# -# License: MIT License - -import cupy as np # np used for matrix computation -import cupy as cp # cp used for cupy specific operations -from . import utils - - -def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, - verbose=False, log=False, to_numpy=True, **kwargs): - r""" - Solve the entropic regularization optimal transport on GPU - - If the input matrix are in numpy format, they will be uploaded to the - GPU first which can incur significant time overhead. - - The function solves the following optimization problem: - - .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) - - s.t. \gamma 1 = a - - \gamma^T 1= b - - \gamma\geq 0 - where : - - - M is the (ns,nt) 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 source and target weights (sum to 1) - - The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_ - - - Parameters - ---------- - a : np.ndarray (ns,) - samples weights in the source domain - b : np.ndarray (nt,) or np.ndarray (nt,nbb) - samples in the target domain, compute sinkhorn with multiple targets - and fixed M if b is a matrix (return OT loss + dual variables in log) - M : np.ndarray (ns,nt) - loss matrix - reg : float - Regularization term >0 - 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 - to_numpy : boolean, optional (default True) - If true convert back the GPU array result to numpy format. - - - Returns - ------- - gamma : (ns x nt) ndarray - Optimal transportation matrix for the given parameters - log : dict - log dictionary return only if log==True in parameters - - - References - ---------- - - .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 - - - See Also - -------- - ot.lp.emd : Unregularized OT - ot.optim.cg : General regularized OT - - """ - - a = cp.asarray(a) - b = cp.asarray(b) - M = cp.asarray(M) - - if len(a) == 0: - a = np.ones((M.shape[0],)) / M.shape[0] - if len(b) == 0: - b = np.ones((M.shape[1],)) / M.shape[1] - - # init data - Nini = len(a) - Nfin = len(b) - - if len(b.shape) > 1: - nbb = b.shape[1] - else: - nbb = 0 - - if log: - log = {'err': []} - - # we assume that no distances are null except those of the diagonal of - # distances - if nbb: - u = np.ones((Nini, nbb)) / Nini - v = np.ones((Nfin, nbb)) / Nfin - else: - u = np.ones(Nini) / Nini - v = np.ones(Nfin) / Nfin - - # print(reg) - - # 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) - - # print(np.min(K)) - tmp2 = np.empty(b.shape, dtype=M.dtype) - - Kp = (1 / a).reshape(-1, 1) * K - cpt = 0 - err = 1 - while (err > stopThr and cpt < numItermax): - uprev = u - vprev = v - - KtransposeU = np.dot(K.T, u) - v = np.divide(b, KtransposeU) - u = 1. / np.dot(Kp, v) - - if (np.any(KtransposeU == 0) or - np.any(np.isnan(u)) or np.any(np.isnan(v)) or - np.any(np.isinf(u)) or np.any(np.isinf(v))): - # we have reached the machine precision - # come back to previous solution and quit loop - print('Warning: numerical errors at iteration', cpt) - u = uprev - v = vprev - break - if cpt % 10 == 0: - # we can speed up the process by checking for the error only all - # the 10th iterations - if nbb: - err = np.sqrt( - np.sum((u - uprev)**2) / np.sum((u)**2) - + np.sum((v - vprev)**2) / np.sum((v)**2) - ) - else: - # compute right marginal tmp2= (diag(u)Kdiag(v))^T1 - tmp2 = np.sum(u[:, None] * K * v[None, :], 0) - #tmp2=np.einsum('i,ij,j->j', u, K, v) - err = np.linalg.norm(tmp2 - b) # violation of marginal - if log: - log['err'].append(err) - - if verbose: - if cpt % 200 == 0: - print( - '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) - print('{:5d}|{:8e}|'.format(cpt, err)) - cpt = cpt + 1 - if log: - log['u'] = u - log['v'] = v - - if nbb: # return only loss - #res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) (explodes cupy memory) - res = np.empty(nbb) - for i in range(nbb): - res[i] = np.sum(u[:, None, i] * (K * M) * v[None, :, i]) - if to_numpy: - res = utils.to_np(res) - if log: - return res, log - else: - return res - - else: # return OT matrix - res = u.reshape((-1, 1)) * K * v.reshape((1, -1)) - if to_numpy: - res = utils.to_np(res) - if log: - return res, log - else: - return res - - -# define sinkhorn as sinkhorn_knopp -sinkhorn = sinkhorn_knopp diff --git a/ot/gpu/da.py b/ot/gpu/da.py deleted file mode 100644 index 7adb830..0000000 --- a/ot/gpu/da.py +++ /dev/null @@ -1,144 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Domain adaptation with optimal transport with GPU implementation -""" - -# Author: Remi Flamary <remi.flamary@unice.fr> -# Nicolas Courty <ncourty@irisa.fr> -# Michael Perrot <michael.perrot@univ-st-etienne.fr> -# Leo Gautheron <https://github.com/aje> -# -# License: MIT License - - -import cupy as np # np used for matrix computation -import cupy as cp # cp used for cupy specific operations -import numpy as npp -from . import utils - -from .bregman import sinkhorn - - -def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, - numInnerItermax=200, stopInnerThr=1e-9, verbose=False, - log=False, to_numpy=True): - """ - Solve the entropic regularization optimal transport problem with nonconvex - group lasso regularization on GPU - - If the input matrix are in numpy format, they will be uploaded to the - GPU first which can incur significant time overhead. - - - The function solves the following optimization problem: - - .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma) - + \eta \Omega_g(\gamma) - - s.t. \gamma 1 = a - - \gamma^T 1= b - - \gamma\geq 0 - where : - - - M is the (ns,nt) metric cost matrix - - :math:`\Omega_e` is the entropic regularization term - :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - :math:`\Omega_g` is the group lasso regulaization term - :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1` - where :math:`\mathcal{I}_c` are the index of samples from class c - in the source domain. - - a and b are source and target weights (sum to 1) - - The algorithm used for solving the problem is the generalised conditional - gradient as proposed in [5]_ [7]_ - - - Parameters - ---------- - a : np.ndarray (ns,) - samples weights in the source domain - labels_a : np.ndarray (ns,) - labels of samples in the source domain - b : np.ndarray (nt,) - samples weights in the target domain - M : np.ndarray (ns,nt) - loss matrix - reg : float - Regularization term for entropic regularization >0 - eta : float, optional - Regularization term for group lasso regularization >0 - numItermax : int, optional - Max number of iterations - numInnerItermax : int, optional - Max number of iterations (inner sinkhorn solver) - stopInnerThr : float, optional - Stop threshold on error (inner sinkhorn solver) (>0) - verbose : bool, optional - Print information along iterations - log : bool, optional - record log if True - to_numpy : boolean, optional (default True) - If true convert back the GPU array result to numpy format. - - - Returns - ------- - gamma : (ns x nt) ndarray - Optimal transportation matrix for the given parameters - log : dict - log dictionary return only if log==True in parameters - - - References - ---------- - - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE - Transactions on Pattern Analysis and Machine Intelligence , - vol.PP, no.99, pp.1-1 - .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). - Generalized conditional gradient: analysis of convergence - and applications. arXiv preprint arXiv:1510.06567. - - See Also - -------- - ot.lp.emd : Unregularized OT - ot.bregman.sinkhorn : Entropic regularized OT - ot.optim.cg : General regularized OT - - """ - - a, labels_a, b, M = utils.to_gpu(a, labels_a, b, M) - - p = 0.5 - epsilon = 1e-3 - - indices_labels = [] - labels_a2 = cp.asnumpy(labels_a) - classes = npp.unique(labels_a2) - for c in classes: - idxc = utils.to_gpu(*npp.where(labels_a2 == c)) - indices_labels.append(idxc) - - W = np.zeros(M.shape) - - for cpt in range(numItermax): - Mreg = M + eta * W - transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax, - stopThr=stopInnerThr, to_numpy=False) - # the transport has been computed. Check if classes are really - # separated - W = np.ones(M.shape) - for (i, c) in enumerate(classes): - - majs = np.sum(transp[indices_labels[i]], axis=0) - majs = p * ((majs + epsilon)**(p - 1)) - W[indices_labels[i]] = majs - - if to_numpy: - return utils.to_np(transp) - else: - return transp diff --git a/ot/gpu/utils.py b/ot/gpu/utils.py deleted file mode 100644 index 41e168a..0000000 --- a/ot/gpu/utils.py +++ /dev/null @@ -1,101 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Utility functions for GPU -""" - -# Author: Remi Flamary <remi.flamary@unice.fr> -# Nicolas Courty <ncourty@irisa.fr> -# Leo Gautheron <https://github.com/aje> -# -# License: MIT License - -import cupy as np # np used for matrix computation -import cupy as cp # cp used for cupy specific operations - - -def euclidean_distances(a, b, squared=False, to_numpy=True): - """ - Compute the pairwise euclidean distance between matrices a and b. - - If the input matrix are in numpy format, they will be uploaded to the - GPU first which can incur significant time overhead. - - Parameters - ---------- - a : np.ndarray (n, f) - first matrix - b : np.ndarray (m, f) - second matrix - to_numpy : boolean, optional (default True) - If true convert back the GPU array result to numpy format. - squared : boolean, optional (default False) - if True, return squared euclidean distance matrix - - Returns - ------- - c : (n x m) np.ndarray or cupy.ndarray - pairwise euclidean distance distance matrix - """ - - a, b = to_gpu(a, b) - - a2 = np.sum(np.square(a), 1) - b2 = np.sum(np.square(b), 1) - - c = -2 * np.dot(a, b.T) - c += a2[:, None] - c += b2[None, :] - - if not squared: - np.sqrt(c, out=c) - if to_numpy: - return to_np(c) - else: - return c - - -def dist(x1, x2=None, metric='sqeuclidean', to_numpy=True): - """Compute distance between samples in x1 and x2 on gpu - - Parameters - ---------- - - x1 : np.array (n1,d) - matrix with n1 samples of size d - x2 : np.array (n2,d), optional - matrix with n2 samples of size d (if None then x2=x1) - metric : str - Metric from 'sqeuclidean', 'euclidean', - - - Returns - ------- - - M : np.array (n1,n2) - distance matrix computed with given metric - - """ - if x2 is None: - x2 = x1 - if metric == "sqeuclidean": - return euclidean_distances(x1, x2, squared=True, to_numpy=to_numpy) - elif metric == "euclidean": - return euclidean_distances(x1, x2, squared=False, to_numpy=to_numpy) - else: - raise NotImplementedError - - -def to_gpu(*args): - """ Upload numpy arrays to GPU and return them""" - if len(args) > 1: - return (cp.asarray(x) for x in args) - else: - return cp.asarray(args[0]) - - -def to_np(*args): - """ convert GPU arras to numpy and return them""" - if len(args) > 1: - return (cp.asnumpy(x) for x in args) - else: - return cp.asnumpy(args[0]) diff --git a/test/test_gpu.py b/test/test_gpu.py deleted file mode 100644 index 8e62a74..0000000 --- a/test/test_gpu.py +++ /dev/null @@ -1,106 +0,0 @@ -"""Tests for module gpu for gpu acceleration """ - -# Author: Remi Flamary <remi.flamary@unice.fr> -# -# License: MIT License - -import numpy as np -import ot -import pytest - -try: # test if cudamat installed - import ot.gpu - nogpu = False -except ImportError: - nogpu = True - - -@pytest.mark.skipif(nogpu, reason="No GPU available") -def test_gpu_old_doctests(): - a = [.5, .5] - b = [.5, .5] - M = [[0., 1.], [1., 0.]] - G = ot.sinkhorn(a, b, M, 1) - np.testing.assert_allclose(G, np.array([[0.36552929, 0.13447071], - [0.13447071, 0.36552929]])) - - -@pytest.mark.skipif(nogpu, reason="No GPU available") -def test_gpu_dist(): - - rng = np.random.RandomState(0) - - for n_samples in [50, 100, 500, 1000]: - print(n_samples) - a = rng.rand(n_samples // 4, 100) - b = rng.rand(n_samples, 100) - - M = ot.dist(a.copy(), b.copy()) - M2 = ot.gpu.dist(a.copy(), b.copy()) - - np.testing.assert_allclose(M, M2, rtol=1e-10) - - M2 = ot.gpu.dist(a.copy(), b.copy(), metric='euclidean', to_numpy=False) - - # check raise not implemented wrong metric - with pytest.raises(NotImplementedError): - M2 = ot.gpu.dist(a.copy(), b.copy(), metric='cityblock', to_numpy=False) - - -@pytest.mark.skipif(nogpu, reason="No GPU available") -def test_gpu_sinkhorn(): - - rng = np.random.RandomState(0) - - for n_samples in [50, 100, 500, 1000]: - a = rng.rand(n_samples // 4, 100) - b = rng.rand(n_samples, 100) - - wa = ot.unif(n_samples // 4) - wb = ot.unif(n_samples) - - wb2 = np.random.rand(n_samples, 20) - wb2 /= wb2.sum(0, keepdims=True) - - M = ot.dist(a.copy(), b.copy()) - M2 = ot.gpu.dist(a.copy(), b.copy(), to_numpy=False) - - reg = 1 - - G = ot.sinkhorn(wa, wb, M, reg) - G1 = ot.gpu.sinkhorn(wa, wb, M, reg) - - np.testing.assert_allclose(G1, G, rtol=1e-10) - - # run all on gpu - ot.gpu.sinkhorn(wa, wb, M2, reg, to_numpy=False, log=True) - - # run sinkhorn for multiple targets - ot.gpu.sinkhorn(wa, wb2, M2, reg, to_numpy=False, log=True) - - -@pytest.mark.skipif(nogpu, reason="No GPU available") -def test_gpu_sinkhorn_lpl1(): - - rng = np.random.RandomState(0) - - for n_samples in [50, 100, 500]: - print(n_samples) - a = rng.rand(n_samples // 4, 100) - labels_a = np.random.randint(10, size=(n_samples // 4)) - b = rng.rand(n_samples, 100) - - wa = ot.unif(n_samples // 4) - wb = ot.unif(n_samples) - - M = ot.dist(a.copy(), b.copy()) - M2 = ot.gpu.dist(a.copy(), b.copy(), to_numpy=False) - - reg = 1 - - G = ot.da.sinkhorn_lpl1_mm(wa, labels_a, wb, M, reg) - G1 = ot.gpu.da.sinkhorn_lpl1_mm(wa, labels_a, wb, M, reg) - - np.testing.assert_allclose(G1, G, rtol=1e-10) - - ot.gpu.da.sinkhorn_lpl1_mm(wa, labels_a, wb, M2, reg, to_numpy=False, log=True) diff --git a/test/test_gromov.py b/test/test_gromov.py index 12fd2b9..9c85b92 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -188,7 +188,7 @@ def test_gromov2_gradients(): @pytest.skip_backend("jax", reason="test very slow with jax backend")
@pytest.skip_backend("tf", reason="test very slow with tf backend")
def test_entropic_gromov(nx):
- n_samples = 50 # nb samples
+ n_samples = 10 # nb samples
mu_s = np.array([0, 0])
cov_s = np.array([[1, 0], [0, 1]])
@@ -222,9 +222,9 @@ def test_entropic_gromov(nx): q, Gb.sum(0), atol=1e-04) # cf convergence gromov
gw, log = ot.gromov.entropic_gromov_wasserstein2(
- C1, C2, p, q, 'kl_loss', epsilon=1e-2, log=True)
+ C1, C2, p, q, 'kl_loss', max_iter=10, epsilon=1e-2, log=True)
gwb, logb = ot.gromov.entropic_gromov_wasserstein2(
- C1b, C2b, pb, qb, 'kl_loss', epsilon=1e-2, log=True)
+ C1b, C2b, pb, qb, 'kl_loss', max_iter=10, epsilon=1e-2, log=True)
gwb = nx.to_numpy(gwb)
G = log['T']
@@ -245,7 +245,7 @@ def test_entropic_gromov(nx): @pytest.skip_backend("tf", reason="test very slow with tf backend")
def test_entropic_gromov_dtype_device(nx):
# setup
- n_samples = 50 # nb samples
+ n_samples = 5 # nb samples
mu_s = np.array([0, 0])
cov_s = np.array([[1, 0], [0, 1]])
@@ -280,7 +280,7 @@ def test_entropic_gromov_dtype_device(nx): def test_pointwise_gromov(nx):
- n_samples = 50 # nb samples
+ n_samples = 5 # nb samples
mu_s = np.array([0, 0])
cov_s = np.array([[1, 0], [0, 1]])
@@ -331,14 +331,12 @@ def test_pointwise_gromov(nx): Gb = nx.to_numpy(nx.todense(Gb))
np.testing.assert_allclose(G, Gb, atol=1e-06)
- np.testing.assert_allclose(float(logb['gw_dist_estimated']), 0.10342276348494964, atol=1e-8)
- np.testing.assert_allclose(float(logb['gw_dist_std']), 0.0015952535464736394, atol=1e-8)
@pytest.skip_backend("tf", reason="test very slow with tf backend")
@pytest.skip_backend("jax", reason="test very slow with jax backend")
def test_sampled_gromov(nx):
- n_samples = 50 # nb samples
+ n_samples = 5 # nb samples
mu_s = np.array([0, 0], dtype=np.float64)
cov_s = np.array([[1, 0], [0, 1]], dtype=np.float64)
@@ -365,9 +363,9 @@ def test_sampled_gromov(nx): return nx.abs(x - y)
G, log = ot.gromov.sampled_gromov_wasserstein(
- C1, C2, p, q, loss, max_iter=100, epsilon=1, log=True, verbose=True, random_state=42)
+ C1, C2, p, q, loss, max_iter=20, nb_samples_grad=2, epsilon=1, log=True, verbose=True, random_state=42)
Gb, logb = ot.gromov.sampled_gromov_wasserstein(
- C1b, C2b, pb, qb, lossb, max_iter=100, epsilon=1, log=True, verbose=True, random_state=42)
+ C1b, C2b, pb, qb, lossb, max_iter=20, nb_samples_grad=2, epsilon=1, log=True, verbose=True, random_state=42)
Gb = nx.to_numpy(Gb)
# check constraints
@@ -377,13 +375,10 @@ def test_sampled_gromov(nx): np.testing.assert_allclose(
q, Gb.sum(0), atol=1e-04) # cf convergence gromov
- np.testing.assert_allclose(float(logb['gw_dist_estimated']), 0.05679474884977278, atol=1e-08)
- np.testing.assert_allclose(float(logb['gw_dist_std']), 0.0005986592106971995, atol=1e-08)
-
def test_gromov_barycenter(nx):
- ns = 10
- nt = 20
+ ns = 5
+ nt = 8
Xs, ys = ot.datasets.make_data_classif('3gauss', ns, random_state=42)
Xt, yt = ot.datasets.make_data_classif('3gauss2', nt, random_state=42)
@@ -450,8 +445,8 @@ def test_gromov_barycenter(nx): @pytest.mark.filterwarnings("ignore:divide")
def test_gromov_entropic_barycenter(nx):
- ns = 10
- nt = 20
+ ns = 5
+ nt = 10
Xs, ys = ot.datasets.make_data_classif('3gauss', ns, random_state=42)
Xt, yt = ot.datasets.make_data_classif('3gauss2', nt, random_state=42)
@@ -517,7 +512,7 @@ def test_gromov_entropic_barycenter(nx): def test_fgw(nx):
- n_samples = 50 # nb samples
+ n_samples = 20 # nb samples
mu_s = np.array([0, 0])
cov_s = np.array([[1, 0], [0, 1]])
@@ -579,7 +574,7 @@ def test_fgw(nx): def test_fgw2_gradients():
- n_samples = 50 # nb samples
+ n_samples = 20 # nb samples
mu_s = np.array([0, 0])
cov_s = np.array([[1, 0], [0, 1]])
@@ -625,8 +620,8 @@ def test_fgw2_gradients(): def test_fgw_barycenter(nx):
np.random.seed(42)
- ns = 50
- nt = 60
+ ns = 10
+ nt = 20
Xs, ys = ot.datasets.make_data_classif('3gauss', ns, random_state=42)
Xt, yt = ot.datasets.make_data_classif('3gauss2', nt, random_state=42)
@@ -674,7 +669,7 @@ def test_fgw_barycenter(nx): def test_gromov_wasserstein_linear_unmixing(nx):
- n = 10
+ n = 4
X1, y1 = ot.datasets.make_data_classif('3gauss', n, random_state=42)
X2, y2 = ot.datasets.make_data_classif('3gauss2', n, random_state=42)
@@ -709,10 +704,10 @@ def test_gromov_wasserstein_linear_unmixing(nx): tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200
)
- np.testing.assert_allclose(unmixing1, nx.to_numpy(unmixing1b), atol=1e-06)
- np.testing.assert_allclose(unmixing1, [1., 0.], atol=1e-01)
- np.testing.assert_allclose(unmixing2, nx.to_numpy(unmixing2b), atol=1e-06)
- np.testing.assert_allclose(unmixing2, [0., 1.], atol=1e-01)
+ np.testing.assert_allclose(unmixing1, nx.to_numpy(unmixing1b), atol=5e-06)
+ np.testing.assert_allclose(unmixing1, [1., 0.], atol=5e-01)
+ np.testing.assert_allclose(unmixing2, nx.to_numpy(unmixing2b), atol=5e-06)
+ np.testing.assert_allclose(unmixing2, [0., 1.], atol=5e-01)
np.testing.assert_allclose(C1_emb, nx.to_numpy(C1b_emb), atol=1e-06)
np.testing.assert_allclose(C2_emb, nx.to_numpy(C2b_emb), atol=1e-06)
np.testing.assert_allclose(reconstruction1, nx.to_numpy(reconstruction1b), atol=1e-06)
@@ -758,7 +753,7 @@ def test_gromov_wasserstein_linear_unmixing(nx): def test_gromov_wasserstein_dictionary_learning(nx):
# create dataset composed from 2 structures which are repeated 5 times
- shape = 10
+ shape = 4
n_samples = 2
n_atoms = 2
projection = 'nonnegative_symmetric'
@@ -795,7 +790,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): for i in range(n_samples):
_, _, _, reconstruction = ot.gromov.gromov_wasserstein_linear_unmixing(
Cs[i], Cdict_init, p=ps[i], q=q, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50
)
initial_total_reconstruction += reconstruction
@@ -803,7 +798,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): Cdict, log = ot.gromov.gromov_wasserstein_dictionary_learning(
Cs, D=n_atoms, nt=shape, ps=ps, q=q, Cdict_init=Cdict_init,
epochs=epochs, batch_size=2 * n_samples, learning_rate=1., reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200,
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50,
projection=projection, use_log=False, use_adam_optimizer=use_adam_optimizer, verbose=verbose
)
# > Compute reconstruction of samples on learned dictionary without backend
@@ -811,7 +806,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): for i in range(n_samples):
_, _, _, reconstruction = ot.gromov.gromov_wasserstein_linear_unmixing(
Cs[i], Cdict, p=None, q=None, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50
)
total_reconstruction += reconstruction
@@ -822,7 +817,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): Cdictb, log = ot.gromov.gromov_wasserstein_dictionary_learning(
Csb, D=n_atoms, nt=shape, ps=None, q=None, Cdict_init=Cdict_initb,
epochs=epochs, batch_size=n_samples, learning_rate=1., reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200,
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50,
projection=projection, use_log=False, use_adam_optimizer=use_adam_optimizer, verbose=verbose
)
# Compute reconstruction of samples on learned dictionary
@@ -830,7 +825,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): for i in range(n_samples):
_, _, _, reconstruction = ot.gromov.gromov_wasserstein_linear_unmixing(
Csb[i], Cdictb, p=psb[i], q=qb, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50
)
total_reconstruction_b += reconstruction
@@ -846,7 +841,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): Cdict_bis, log = ot.gromov.gromov_wasserstein_dictionary_learning(
Cs, D=n_atoms, nt=shape, ps=None, q=None, Cdict_init=None,
epochs=epochs, batch_size=n_samples, learning_rate=1., reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200,
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50,
projection=projection, use_log=False, use_adam_optimizer=use_adam_optimizer, verbose=verbose
)
# > Compute reconstruction of samples on learned dictionary
@@ -854,7 +849,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): for i in range(n_samples):
_, _, _, reconstruction = ot.gromov.gromov_wasserstein_linear_unmixing(
Cs[i], Cdict_bis, p=ps[i], q=q, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50
)
total_reconstruction_bis += reconstruction
@@ -865,7 +860,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): Cdictb_bis, log = ot.gromov.gromov_wasserstein_dictionary_learning(
Csb, D=n_atoms, nt=shape, ps=psb, q=qb, Cdict_init=None,
epochs=epochs, batch_size=n_samples, learning_rate=1., reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200,
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50,
projection=projection, use_log=False, use_adam_optimizer=use_adam_optimizer, verbose=verbose
)
# > Compute reconstruction of samples on learned dictionary
@@ -873,7 +868,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): for i in range(n_samples):
_, _, _, reconstruction = ot.gromov.gromov_wasserstein_linear_unmixing(
Csb[i], Cdictb_bis, p=None, q=None, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50
)
total_reconstruction_b_bis += reconstruction
@@ -892,7 +887,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): Cdict_bis2, log = ot.gromov.gromov_wasserstein_dictionary_learning(
Cs, D=n_atoms, nt=shape, ps=ps, q=q, Cdict_init=Cdict,
epochs=epochs, batch_size=n_samples, learning_rate=10., reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200,
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50,
projection=projection, use_log=use_log, use_adam_optimizer=use_adam_optimizer, verbose=verbose
)
# > Compute reconstruction of samples on learned dictionary
@@ -900,7 +895,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): for i in range(n_samples):
_, _, _, reconstruction = ot.gromov.gromov_wasserstein_linear_unmixing(
Cs[i], Cdict_bis2, p=ps[i], q=q, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50
)
total_reconstruction_bis2 += reconstruction
@@ -911,7 +906,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): Cdictb_bis2, log = ot.gromov.gromov_wasserstein_dictionary_learning(
Csb, D=n_atoms, nt=shape, ps=psb, q=qb, Cdict_init=Cdictb,
epochs=epochs, batch_size=n_samples, learning_rate=10., reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200,
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50,
projection=projection, use_log=use_log, use_adam_optimizer=use_adam_optimizer, verbose=verbose
)
# > Compute reconstruction of samples on learned dictionary
@@ -919,7 +914,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): for i in range(n_samples):
_, _, _, reconstruction = ot.gromov.gromov_wasserstein_linear_unmixing(
Csb[i], Cdictb_bis2, p=psb[i], q=qb, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50
)
total_reconstruction_b_bis2 += reconstruction
@@ -929,7 +924,7 @@ def test_gromov_wasserstein_dictionary_learning(nx): def test_fused_gromov_wasserstein_linear_unmixing(nx):
- n = 10
+ n = 4
X1, y1 = ot.datasets.make_data_classif('3gauss', n, random_state=42)
X2, y2 = ot.datasets.make_data_classif('3gauss2', n, random_state=42)
F, y = ot.datasets.make_data_classif('3gauss', n, random_state=42)
@@ -947,28 +942,28 @@ def test_fused_gromov_wasserstein_linear_unmixing(nx): unmixing1, C1_emb, Y1_emb, OT, reconstruction1 = ot.gromov.fused_gromov_wasserstein_linear_unmixing(
C1, F, Cdict, Ydict, p=p, q=p, alpha=0.5, reg=reg,
- tol_outer=10**(-6), tol_inner=10**(-6), max_iter_outer=20, max_iter_inner=200
+ tol_outer=10**(-6), tol_inner=10**(-6), max_iter_outer=10, max_iter_inner=50
)
unmixing1b, C1b_emb, Y1b_emb, OTb, reconstruction1b = ot.gromov.fused_gromov_wasserstein_linear_unmixing(
C1b, Fb, Cdictb, Ydictb, p=None, q=None, alpha=0.5, reg=reg,
- tol_outer=10**(-6), tol_inner=10**(-6), max_iter_outer=20, max_iter_inner=200
+ tol_outer=10**(-6), tol_inner=10**(-6), max_iter_outer=10, max_iter_inner=50
)
unmixing2, C2_emb, Y2_emb, OT, reconstruction2 = ot.gromov.fused_gromov_wasserstein_linear_unmixing(
C2, F, Cdict, Ydict, p=None, q=None, alpha=0.5, reg=reg,
- tol_outer=10**(-6), tol_inner=10**(-6), max_iter_outer=20, max_iter_inner=200
+ tol_outer=10**(-6), tol_inner=10**(-6), max_iter_outer=10, max_iter_inner=50
)
unmixing2b, C2b_emb, Y2b_emb, OTb, reconstruction2b = ot.gromov.fused_gromov_wasserstein_linear_unmixing(
C2b, Fb, Cdictb, Ydictb, p=pb, q=pb, alpha=0.5, reg=reg,
- tol_outer=10**(-6), tol_inner=10**(-6), max_iter_outer=20, max_iter_inner=200
+ tol_outer=10**(-6), tol_inner=10**(-6), max_iter_outer=10, max_iter_inner=50
)
- np.testing.assert_allclose(unmixing1, nx.to_numpy(unmixing1b), atol=1e-06)
- np.testing.assert_allclose(unmixing1, [1., 0.], atol=1e-01)
- np.testing.assert_allclose(unmixing2, nx.to_numpy(unmixing2b), atol=1e-06)
- np.testing.assert_allclose(unmixing2, [0., 1.], atol=1e-01)
+ np.testing.assert_allclose(unmixing1, nx.to_numpy(unmixing1b), atol=4e-06)
+ np.testing.assert_allclose(unmixing1, [1., 0.], atol=4e-01)
+ np.testing.assert_allclose(unmixing2, nx.to_numpy(unmixing2b), atol=4e-06)
+ np.testing.assert_allclose(unmixing2, [0., 1.], atol=4e-01)
np.testing.assert_allclose(C1_emb, nx.to_numpy(C1b_emb), atol=1e-03)
np.testing.assert_allclose(C2_emb, nx.to_numpy(C2b_emb), atol=1e-03)
np.testing.assert_allclose(Y1_emb, nx.to_numpy(Y1b_emb), atol=1e-03)
@@ -983,22 +978,22 @@ def test_fused_gromov_wasserstein_linear_unmixing(nx): unmixing1, C1_emb, Y1_emb, OT, reconstruction1 = ot.gromov.fused_gromov_wasserstein_linear_unmixing(
C1, F, Cdict, Ydict, p=p, q=p, alpha=0.5, reg=reg,
- tol_outer=10**(-6), tol_inner=10**(-6), max_iter_outer=20, max_iter_inner=200
+ tol_outer=10**(-6), tol_inner=10**(-6), max_iter_outer=10, max_iter_inner=50
)
unmixing1b, C1b_emb, Y1b_emb, OTb, reconstruction1b = ot.gromov.fused_gromov_wasserstein_linear_unmixing(
C1b, Fb, Cdictb, Ydictb, p=None, q=None, alpha=0.5, reg=reg,
- tol_outer=10**(-6), tol_inner=10**(-6), max_iter_outer=20, max_iter_inner=200
+ tol_outer=10**(-6), tol_inner=10**(-6), max_iter_outer=10, max_iter_inner=50
)
unmixing2, C2_emb, Y2_emb, OT, reconstruction2 = ot.gromov.fused_gromov_wasserstein_linear_unmixing(
C2, F, Cdict, Ydict, p=None, q=None, alpha=0.5, reg=reg,
- tol_outer=10**(-6), tol_inner=10**(-6), max_iter_outer=20, max_iter_inner=200
+ tol_outer=10**(-6), tol_inner=10**(-6), max_iter_outer=10, max_iter_inner=50
)
unmixing2b, C2b_emb, Y2b_emb, OTb, reconstruction2b = ot.gromov.fused_gromov_wasserstein_linear_unmixing(
C2b, Fb, Cdictb, Ydictb, p=pb, q=pb, alpha=0.5, reg=reg,
- tol_outer=10**(-6), tol_inner=10**(-6), max_iter_outer=20, max_iter_inner=200
+ tol_outer=10**(-6), tol_inner=10**(-6), max_iter_outer=10, max_iter_inner=50
)
np.testing.assert_allclose(unmixing1, nx.to_numpy(unmixing1b), atol=1e-06)
@@ -1018,7 +1013,7 @@ def test_fused_gromov_wasserstein_linear_unmixing(nx): def test_fused_gromov_wasserstein_dictionary_learning(nx):
# create dataset composed from 2 structures which are repeated 5 times
- shape = 10
+ shape = 4
n_samples = 2
n_atoms = 2
projection = 'nonnegative_symmetric'
@@ -1060,7 +1055,7 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): for i in range(n_samples):
_, _, _, _, reconstruction = ot.gromov.fused_gromov_wasserstein_linear_unmixing(
Cs[i], Ys[i], Cdict_init, Ydict_init, p=ps[i], q=q,
- alpha=alpha, reg=0., tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200
+ alpha=alpha, reg=0., tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50
)
initial_total_reconstruction += reconstruction
@@ -1069,7 +1064,7 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): Cdict, Ydict, log = ot.gromov.fused_gromov_wasserstein_dictionary_learning(
Cs, Ys, D=n_atoms, nt=shape, ps=ps, q=q, Cdict_init=Cdict_init, Ydict_init=Ydict_init,
epochs=epochs, batch_size=n_samples, learning_rate_C=1., learning_rate_Y=1., alpha=alpha, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200,
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50,
projection=projection, use_log=False, use_adam_optimizer=use_adam_optimizer, verbose=verbose
)
# > Compute reconstruction of samples on learned dictionary
@@ -1077,7 +1072,7 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): for i in range(n_samples):
_, _, _, _, reconstruction = ot.gromov.fused_gromov_wasserstein_linear_unmixing(
Cs[i], Ys[i], Cdict, Ydict, p=None, q=None, alpha=alpha, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50
)
total_reconstruction += reconstruction
# Compare both
@@ -1088,7 +1083,7 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): Cdictb, Ydictb, log = ot.gromov.fused_gromov_wasserstein_dictionary_learning(
Csb, Ysb, D=n_atoms, nt=shape, ps=None, q=None, Cdict_init=Cdict_initb, Ydict_init=Ydict_initb,
epochs=epochs, batch_size=2 * n_samples, learning_rate_C=1., learning_rate_Y=1., alpha=alpha, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200,
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50,
projection=projection, use_log=False, use_adam_optimizer=use_adam_optimizer, verbose=verbose
)
# > Compute reconstruction of samples on learned dictionary
@@ -1096,7 +1091,7 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): for i in range(n_samples):
_, _, _, _, reconstruction = ot.gromov.fused_gromov_wasserstein_linear_unmixing(
Csb[i], Ysb[i], Cdictb, Ydictb, p=psb[i], q=qb, alpha=alpha, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50
)
total_reconstruction_b += reconstruction
@@ -1111,7 +1106,7 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): Cdict_bis, Ydict_bis, log = ot.gromov.fused_gromov_wasserstein_dictionary_learning(
Cs, Ys, D=n_atoms, nt=shape, ps=None, q=None, Cdict_init=None, Ydict_init=None,
epochs=epochs, batch_size=n_samples, learning_rate_C=1., learning_rate_Y=1., alpha=alpha, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200,
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50,
projection=projection, use_log=False, use_adam_optimizer=use_adam_optimizer, verbose=verbose
)
# > Compute reconstruction of samples on learned dictionary
@@ -1119,7 +1114,7 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): for i in range(n_samples):
_, _, _, _, reconstruction = ot.gromov.fused_gromov_wasserstein_linear_unmixing(
Cs[i], Ys[i], Cdict_bis, Ydict_bis, p=ps[i], q=q, alpha=alpha, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50
)
total_reconstruction_bis += reconstruction
@@ -1130,7 +1125,7 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): Cdictb_bis, Ydictb_bis, log = ot.gromov.fused_gromov_wasserstein_dictionary_learning(
Csb, Ysb, D=n_atoms, nt=shape, ps=None, q=None, Cdict_init=None, Ydict_init=None,
epochs=epochs, batch_size=n_samples, learning_rate_C=1., learning_rate_Y=1., alpha=alpha, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200,
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50,
projection=projection, use_log=False, use_adam_optimizer=use_adam_optimizer, verbose=verbose
)
@@ -1139,7 +1134,7 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): for i in range(n_samples):
_, _, _, _, reconstruction = ot.gromov.fused_gromov_wasserstein_linear_unmixing(
Csb[i], Ysb[i], Cdictb_bis, Ydictb_bis, p=psb[i], q=qb, alpha=alpha, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50
)
total_reconstruction_b_bis += reconstruction
@@ -1156,7 +1151,7 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): Cdict_bis2, Ydict_bis2, log = ot.gromov.fused_gromov_wasserstein_dictionary_learning(
Cs, Ys, D=n_atoms, nt=shape, ps=ps, q=q, Cdict_init=Cdict, Ydict_init=Ydict,
epochs=epochs, batch_size=n_samples, learning_rate_C=10., learning_rate_Y=10., alpha=alpha, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200,
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50,
projection=projection, use_log=use_log, use_adam_optimizer=use_adam_optimizer, verbose=verbose
)
# > Compute reconstruction of samples on learned dictionary
@@ -1164,7 +1159,7 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): for i in range(n_samples):
_, _, _, _, reconstruction = ot.gromov.fused_gromov_wasserstein_linear_unmixing(
Cs[i], Ys[i], Cdict_bis2, Ydict_bis2, p=ps[i], q=q, alpha=alpha, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50
)
total_reconstruction_bis2 += reconstruction
@@ -1175,7 +1170,7 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): Cdictb_bis2, Ydictb_bis2, log = ot.gromov.fused_gromov_wasserstein_dictionary_learning(
Csb, Ysb, D=n_atoms, nt=shape, ps=None, q=None, Cdict_init=Cdictb, Ydict_init=Ydictb,
epochs=epochs, batch_size=n_samples, learning_rate_C=10., learning_rate_Y=10., alpha=alpha, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200,
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50,
projection=projection, use_log=use_log, use_adam_optimizer=use_adam_optimizer, verbose=verbose
)
@@ -1184,7 +1179,7 @@ def test_fused_gromov_wasserstein_dictionary_learning(nx): for i in range(n_samples):
_, _, _, _, reconstruction = ot.gromov.fused_gromov_wasserstein_linear_unmixing(
Csb[i], Ysb[i], Cdictb_bis2, Ydictb_bis2, p=None, q=None, alpha=alpha, reg=0.,
- tol_outer=tol, tol_inner=tol, max_iter_outer=20, max_iter_inner=200
+ tol_outer=tol, tol_inner=tol, max_iter_outer=10, max_iter_inner=50
)
total_reconstruction_b_bis2 += reconstruction
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