Merge pull request #87 from hichamjanati/unbalanced-ot

[MRG] Add Unbalanced KL Wasserstein distance + barycenter

Author: rflamary

README.md

@@ -27,6 +27,7 @@ It provides the following solvers:
 * Gromov-Wasserstein distances and barycenters ([13] and regularized [12])
 * Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
 * Non regularized free support Wasserstein barycenters [20].
+* Unbalanced OT with KL relaxation distance and barycenter [10, 25].
 
 Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.
 
@@ -165,6 +166,7 @@ The contributors to this library are:
 * [Kilian Fatras](https://kilianfatras.github.io/)
 * [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home)
 * [Vayer Titouan](https://tvayer.github.io/)
+* [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT)
 
 This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages):
 
@@ -236,3 +238,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t
 [23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https://arxiv.org/abs/1706.00292), Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018
 
 [24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on 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). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/)  Advances in Neural Information Processing Systems (NIPS).

docs/source/all.rst

@@ -43,7 +43,7 @@ ot.da
 
 .. automodule:: ot.da
   :members:
-  
+
 ot.gpu
 --------
 
@@ -80,3 +80,9 @@ ot.stochastic
 
 .. automodule:: ot.stochastic
    :members:
+
+ot.unbalanced
+-------------
+
+.. automodule:: ot.unbalanced
+  :members:

examples/plot_UOT_1D.py

@@ -0,0 +1,76 @@
+# -*- coding: utf-8 -*-
+"""
+===============================
+1D Unbalanced optimal transport
+===============================
+
+This example illustrates the computation of Unbalanced Optimal transport
+using a Kullback-Leibler relaxation.
+"""
+
+# Author: Hicham Janati <hicham.janati@inria.fr>
+#
+# License: MIT License
+
+import numpy as np
+import matplotlib.pylab as pl
+import ot
+import ot.plot
+from ot.datasets import make_1D_gauss as gauss
+
+##############################################################################
+# Generate data
+# -------------
+
+
+#%% parameters
+
+n = 100  # nb bins
+
+# bin positions
+x = np.arange(n, dtype=np.float64)
+
+# Gaussian distributions
+a = gauss(n, m=20, s=5)  # m= mean, s= std
+b = gauss(n, m=60, s=10)
+
+# make distributions unbalanced
+b *= 5.
+
+# loss matrix
+M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
+M /= M.max()
+
+
+##############################################################################
+# Plot distributions and loss matrix
+# ----------------------------------
+
+#%% plot the distributions
+
+pl.figure(1, figsize=(6.4, 3))
+pl.plot(x, a, 'b', label='Source distribution')
+pl.plot(x, b, 'r', label='Target distribution')
+pl.legend()
+
+# plot distributions and loss matrix
+
+pl.figure(2, figsize=(5, 5))
+ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')
+
+
+##############################################################################
+# Solve Unbalanced Sinkhorn
+# --------------
+
+
+# Sinkhorn
+
+epsilon = 0.1  # entropy parameter
+alpha = 1.  # Unbalanced KL relaxation parameter
+Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True)
+
+pl.figure(4, figsize=(5, 5))
+ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn')
+
+pl.show()

examples/plot_UOT_barycenter_1D.py

@@ -0,0 +1,164 @@
+# -*- coding: utf-8 -*-
+"""
+===========================================================
+1D Wasserstein barycenter demo for Unbalanced distributions
+===========================================================
+
+This example illustrates the computation of regularized Wassersyein Barycenter
+as proposed in [10] for Unbalanced inputs.
+
+
+[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816.
+
+"""
+
+# Author: Hicham Janati <hicham.janati@inria.fr>
+#
+# License: MIT License
+
+import numpy as np
+import matplotlib.pylab as pl
+import ot
+# necessary for 3d plot even if not used
+from mpl_toolkits.mplot3d import Axes3D  # noqa
+from matplotlib.collections import PolyCollection
+
+##############################################################################
+# Generate data
+# -------------
+
+# parameters
+
+n = 100  # nb bins
+
+# bin positions
+x = np.arange(n, dtype=np.float64)
+
+# Gaussian distributions
+a1 = ot.datasets.make_1D_gauss(n, m=20, s=5)  # m= mean, s= std
+a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)
+
+# make unbalanced dists
+a2 *= 3.
+
+# creating matrix A containing all distributions
+A = np.vstack((a1, a2)).T
+n_distributions = A.shape[1]
+
+# loss matrix + normalization
+M = ot.utils.dist0(n)
+M /= M.max()
+
+##############################################################################
+# Plot data
+# ---------
+
+# plot the distributions
+
+pl.figure(1, figsize=(6.4, 3))
+for i in range(n_distributions):
+    pl.plot(x, A[:, i])
+pl.title('Distributions')
+pl.tight_layout()
+
+##############################################################################
+# Barycenter computation
+# ----------------------
+
+# non weighted barycenter computation
+
+weight = 0.5  # 0<=weight<=1
+weights = np.array([1 - weight, weight])
+
+# l2bary
+bary_l2 = A.dot(weights)
+
+# wasserstein
+reg = 1e-3
+alpha = 1.
+
+bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)
+
+pl.figure(2)
+pl.clf()
+pl.subplot(2, 1, 1)
+for i in range(n_distributions):
+    pl.plot(x, A[:, i])
+pl.title('Distributions')
+
+pl.subplot(2, 1, 2)
+pl.plot(x, bary_l2, 'r', label='l2')
+pl.plot(x, bary_wass, 'g', label='Wasserstein')
+pl.legend()
+pl.title('Barycenters')
+pl.tight_layout()
+
+##############################################################################
+# Barycentric interpolation
+# -------------------------
+
+# barycenter interpolation
+
+n_weight = 11
+weight_list = np.linspace(0, 1, n_weight)
+
+
+B_l2 = np.zeros((n, n_weight))
+
+B_wass = np.copy(B_l2)
+
+for i in range(0, n_weight):
+    weight = weight_list[i]
+    weights = np.array([1 - weight, weight])
+    B_l2[:, i] = A.dot(weights)
+    B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)
+
+
+# plot interpolation
+
+pl.figure(3)
+
+cmap = pl.cm.get_cmap('viridis')
+verts = []
+zs = weight_list
+for i, z in enumerate(zs):
+    ys = B_l2[:, i]
+    verts.append(list(zip(x, ys)))
+
+ax = pl.gcf().gca(projection='3d')
+
+poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])
+poly.set_alpha(0.7)
+ax.add_collection3d(poly, zs=zs, zdir='y')
+ax.set_xlabel('x')
+ax.set_xlim3d(0, n)
+ax.set_ylabel(r'$\alpha$')
+ax.set_ylim3d(0, 1)
+ax.set_zlabel('')
+ax.set_zlim3d(0, B_l2.max() * 1.01)
+pl.title('Barycenter interpolation with l2')
+pl.tight_layout()
+
+pl.figure(4)
+cmap = pl.cm.get_cmap('viridis')
+verts = []
+zs = weight_list
+for i, z in enumerate(zs):
+    ys = B_wass[:, i]
+    verts.append(list(zip(x, ys)))
+
+ax = pl.gcf().gca(projection='3d')
+
+poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])
+poly.set_alpha(0.7)
+ax.add_collection3d(poly, zs=zs, zdir='y')
+ax.set_xlabel('x')
+ax.set_xlim3d(0, n)
+ax.set_ylabel(r'$\alpha$')
+ax.set_ylim3d(0, 1)
+ax.set_zlabel('')
+ax.set_zlim3d(0, B_l2.max() * 1.01)
+pl.title('Barycenter interpolation with Wasserstein')
+pl.tight_layout()
+
+pl.show()

ot/__init__.py

@@ -20,10 +20,12 @@
 from . import gromov
 from . import smooth
 from . import stochastic
+from . import unbalanced
 
 # OT functions
 from .lp import emd, emd2
 from .bregman import sinkhorn, sinkhorn2, barycenter
+from .unbalanced import sinkhorn_unbalanced, barycenter_unbalanced
 from .da import sinkhorn_lpl1_mm
 
 # utils functions
@@ -33,4 +35,5 @@
 
 __all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets',
            'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov',
-           'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim']
+           'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim',
+           'sinkhorn_unbalanced', "barycenter_unbalanced"]

ot/bregman.py

@@ -241,7 +241,7 @@ def sink():
 
     b = np.asarray(b, dtype=np.float64)
     if len(b.shape) < 2:
-        b = b.reshape((-1, 1))
+        b = b[:, None]
 
     return sink()