diff options
| -rw-r--r-- | .travis.yml | 2 | ||||
| -rw-r--r-- | docs/source/all.rst | 6 | ||||
| -rw-r--r-- | docs/source/quickstart.rst | 64 | ||||
| -rw-r--r-- | docs/source/readme.rst | 98 | ||||
| -rwxr-xr-x | examples/plot_partial_wass_and_gromov.py | 163 | ||||
| -rw-r--r-- | ot/__init__.py | 2 | ||||
| -rwxr-xr-x | ot/partial.py | 1056 | ||||
| -rwxr-xr-x | test/test_partial.py | 140 |
8 files changed, 1495 insertions, 36 deletions
diff --git a/.travis.yml b/.travis.yml index 072bc55..7ff1b3c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -16,7 +16,7 @@ matrix: python: 3.7 - os: linux sudo: required - python: 2.7 + python: 3.8 # - os: osx # sudo: required # language: generic 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/quickstart.rst b/docs/source/quickstart.rst index 978eaff..d56f812 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -645,6 +645,53 @@ implemented the main function :any:`ot.barycenter_unbalanced`. - :any:`auto_examples/plot_UOT_barycenter_1D` +Partial optimal transport +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Partial OT is a variant of the optimal transport problem when only a fixed amount of mass m +is to be transported. The partial OT metric between two histograms a and b is defined as [28]_: + +.. 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\} + + +Interestingly the problem can be casted into a regular OT problem by adding reservoir points +in which the surplus mass is sent [29]_. We provide a solver for partial OT +in :any:`ot.partial`. The exact resolution of the problem is computed in :any:`ot.partial.partial_wasserstein` +and :any:`ot.partial.partial_wasserstein2` that return respectively the OT matrix and the value of the +linear term. The entropic solution of the problem is computed in :any:`ot.partial.entropic_partial_wasserstein` +(see [3]_). + +The partial Gromov-Wasserstein formulation of the problem + +.. math:: + GW = \min_\gamma \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*\gamma_{i,j}*\gamma_{k,l} + + 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\} + +is computed in :any:`ot.partial.partial_gromov_wasserstein` and in +:any:`ot.partial.entropic_partial_gromov_wasserstein` when considering the entropic +regularization of the problem. + + +.. hint:: + + Examples of the use of :any:`ot.partial` are available in : + + - :any:`auto_examples/plot_partial` + + + Gromov-Wasserstein ^^^^^^^^^^^^^^^^^^ @@ -921,3 +968,20 @@ References .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : Learning with a Wasserstein Loss, Advances in Neural Information Processing Systems (NIPS) 2015 + +.. [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). Screening Sinkhorn + Algorithm for Regularized Optimal Transport <https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport>, + Advances in Neural Information Processing Systems 33 (NeurIPS). + +.. [27] Redko I., Courty N., Flamary R., Tuia D. (2019). Optimal Transport for Multi-source + Domain Adaptation under Target Shift <http://proceedings.mlr.press/v89/redko19a.html>, + Proceedings of the Twenty-Second International Conference on Artificial Intelligence + and Statistics (AISTATS) 22, 2019. + +.. [28] 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. + +.. [29] 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. diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 0871779..6d98dc5 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -36,6 +36,9 @@ It provides the following solvers: problem [18] and dual problem [19]) - Non regularized free support Wasserstein barycenters [20]. - Unbalanced OT with KL relaxation distance and barycenter [10, 25]. +- Screening Sinkhorn Algorithm for OT [26]. +- JCPOT algorithm for multi-source domain adaptation with target shift + [27]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -48,19 +51,19 @@ POT using the following bibtex reference: :: - @misc{flamary2017pot, - title={POT Python Optimal Transport library}, - author={Flamary, R{'e}mi and Courty, Nicolas}, - url={https://github.com/rflamary/POT}, - year={2017} - } + @misc{flamary2017pot, + title={POT Python Optimal Transport library}, + author={Flamary, R{'e}mi and Courty, Nicolas}, + url={https://github.com/rflamary/POT}, + year={2017} + } Installation ------------ The library has been tested on Linux, MacOSX and Windows. It requires a -C++ compiler for using the EMD solver and relies on the following Python -modules: +C++ compiler for building/installing the EMD solver and relies on the +following Python modules: - Numpy (>=1.11) - Scipy (>=1.0) @@ -75,19 +78,19 @@ be installed prior to installing POT. This can be done easily with :: - pip install numpy cython + pip install numpy cython You can install the toolbox through PyPI with: :: - pip install POT + pip install POT or get the very latest version by downloading it and then running: :: - python setup.py install --user # for user install (no root) + python setup.py install --user # for user install (no root) Anaconda installation with conda-forge ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -98,7 +101,7 @@ required dependencies: :: - conda install -c conda-forge pot + conda install -c conda-forge pot Post installation check ^^^^^^^^^^^^^^^^^^^^^^^ @@ -108,7 +111,7 @@ without errors: .. code:: python - import ot + import ot Note that for easier access the module is name ot instead of pot. @@ -121,9 +124,9 @@ below - **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: - :: +:: - pip install pymanopt autograd + pip install pymanopt autograd - **ot.gpu** (GPU accelerated OT) depends on cupy that have to be installed following instructions on `this @@ -139,36 +142,36 @@ Short examples - Import the toolbox - .. code:: python +.. code:: python - import ot + import ot - Compute Wasserstein distances - .. code:: python +.. code:: python - # a,b are 1D histograms (sum to 1 and positive) - # M is the ground cost matrix - Wd=ot.emd2(a,b,M) # exact linear program - Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT - # if b is a matrix compute all distances to a and return a vector + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + Wd=ot.emd2(a,b,M) # exact linear program + Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT + # if b is a matrix compute all distances to a and return a vector - Compute OT matrix - .. code:: python +.. code:: python - # a,b are 1D histograms (sum to 1 and positive) - # M is the ground cost matrix - T=ot.emd(a,b,M) # exact linear program - T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + T=ot.emd(a,b,M) # exact linear program + T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT - Compute Wasserstein barycenter - .. code:: python +.. code:: python - # A is a n*d matrix containing d 1D histograms - # M is the ground cost matrix - ba=ot.barycenter(A,M,reg) # reg is regularization parameter + # A is a n*d matrix containing d 1D histograms + # M is the ground cost matrix + ba=ot.barycenter(A,M,reg) # reg is regularization parameter Examples and Notebooks ~~~~~~~~~~~~~~~~~~~~~~ @@ -207,6 +210,10 @@ want a quick look: Wasserstein <https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb>`__ - `Gromov Wasserstein Barycenter <https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb>`__ +- `Fused Gromov + Wasserstein <https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb>`__ +- `Fused Gromov Wasserstein + Barycenter <https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb>`__ You can also see the notebooks with `Jupyter nbviewer <https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/>`__. @@ -237,6 +244,7 @@ The contributors to this library are - `Vayer Titouan <https://tvayer.github.io/>`__ - `Hicham Janati <https://hichamjanati.github.io/>`__ (Unbalanced OT) - `Romain Tavenard <https://rtavenar.github.io/>`__ (1d Wasserstein) +- `Mokhtar Z. Alaya <http://mzalaya.github.io/>`__ (Screenkhorn) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -274,11 +282,11 @@ References [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). `Displacement interpolation using Lagrangian mass transport <https://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf>`__. -In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. +In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. [2] Cuturi, M. (2013). `Sinkhorn distances: Lightspeed computation of optimal transport <https://arxiv.org/pdf/1306.0895.pdf>`__. In Advances -in Neural Information Processing Systems (pp. 2292-2300). +in Neural Information Processing Systems (pp. 2292-2300). [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). `Iterative Bregman projections for regularized transportation @@ -387,10 +395,30 @@ and Statistics, (AISTATS) 21, 2018 graphs <http://proceedings.mlr.press/v97/titouan19a.html>`__ Proceedings of the 36th International Conference on Machine Learning (ICML). -[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2019). +[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). `Learning with a Wasserstein Loss <http://cbcl.mit.edu/wasserstein/>`__ Advances in Neural Information Processing Systems (NIPS). +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). +`Screening Sinkhorn Algorithm for Regularized Optimal +Transport <https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport>`__, +Advances in Neural Information Processing Systems 33 (NeurIPS). + +[27] Redko I., Courty N., Flamary R., Tuia D. (2019). `Optimal Transport +for Multi-source Domain Adaptation under Target +Shift <http://proceedings.mlr.press/v89/redko19a.html>`__, Proceedings +of the Twenty-Second International Conference on Artificial Intelligence +and Statistics (AISTATS) 22, 2019. + +[28] 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. + +[29] 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 index 89c7936..4fcb800 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -28,6 +28,7 @@ a number of sub-modules and functions described below. - :any:`ot.plot` contains visualization functions - :any:`ot.stochastic` contains stochastic solvers for regularized OT. - :any:`ot.unbalanced` contains solvers for regularized unbalanced OT. + - :any:`ot.partial` contains solvers for partial OT. .. warning:: The list of automatically imported sub-modules is as follows: @@ -61,6 +62,7 @@ from . import gromov from . import smooth from . import stochastic from . import unbalanced +from . import partial # OT functions from .lp import emd, emd2, emd_1d, emd2_1d, wasserstein_1d diff --git a/ot/partial.py b/ot/partial.py new file mode 100755 index 0000000..5f4b836 --- /dev/null +++ b/ot/partial.py @@ -0,0 +1,1056 @@ +# -*- 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 (see Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. + (2018). An interpolating distance between optimal transport and Fisher–Rao + metrics. Foundations of Computational Mathematics, 18(1), 1-44.) + + .. 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 [28]_ + + + 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 + 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 + + .. warning:: + When dealing with a large number of points, the EMD solver may face + some instabilities, especially when the mass associated to the dummy + point is large. To avoid them, increase the number of dummy points + (allows a smoother repartition of the mass over the points). + + 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 + ---------- + + .. [28] 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 + + + .. warning:: + When dealing with a large number of points, the EMD solver may face + some instabilities, especially when the mass associated to the dummy + point is large. To avoid them, increase the number of dummy points + (allows a smoother repartition of the mass over the points). + + + 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 + ---------- + .. [28] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in + optimal transport and Monge-Ampere obstacle problems. Annals of + mathematics, 673-730. + .. [29] 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.zeros((len(a_extended), len(b_extended))) + 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 + + + .. warning:: + When dealing with a large number of points, the EMD solver may face + some instabilities, especially when the mass associated to the dummy + point is large. To avoid them, increase the number of dummy points + (allows a smoother repartition of the mass over the points). + + + 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 + ---------- + .. [28] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in + optimal transport and Monge-Ampere obstacle problems. Annals of + mathematics, 673-730. + .. [29] 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 [29]_ + + + 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 + tol : float, optional + tolerance for stopping 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 + ------- + 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 + ---------- + .. [29] 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, thres) + + M_emd = np.zeros(dim_G_extended) + 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=1, 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 [29]_ + + + 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 + tol : float, optional + tolerance for stopping 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 + + + .. warning:: + When dealing with a large number of points, the EMD solver may face + some instabilities, especially when the mass associated to the dummy + point is large. To avoid them, increase the number of dummy points + (allows a smoother repartition of the mass over the points). + + + 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 + ---------- + .. [29] 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]_ (prop. 5) + + + 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, dtype=np.float64) + dy = np.ones(dim_b, dtype=np.float64) + + 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))) * 1.0 + 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 GW problem has been proposed in [12]_ and the + partial GW in [29]_. + + 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 + tol : float, optional + Stop threshold on error (>0) + 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. + .. [29] 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_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 GW problem has been proposed in [12]_ and the + partial GW in [29]_. + + + 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 + tol : float, optional + Stop threshold on error (>0) + 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. + .. [29] 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 = 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..8b1ca89 --- /dev/null +++ b/test/test_partial.py @@ -0,0 +1,140 @@ +"""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) + np.testing.assert_allclose(res0, 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, + 100, 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) |