[MRG] Free support Sinkhorn barycenters

CONTRIBUTORS.md

@@ -39,6 +39,7 @@ The contributors to this library are:
 * [Cédric Vincent-Cuaz](https://github.com/cedricvincentcuaz) (Graph Dictionary Learning)
 * [Eloi Tanguy](https://github.com/eloitanguy) (Generalized Wasserstein Barycenters)
 * [Camille Le Coz](https://www.linkedin.com/in/camille-le-coz-8593b91a1/) (EMD2 debug)
+* [Eduardo Fernandes Montesuma](https://eddardd.github.io/my-personal-blog/) (Free support sinkhorn barycenter)
 
 ## Acknowledgments
 

RELEASES.md

@@ -5,6 +5,7 @@
 #### New features
 
 - Added Generalized Wasserstein Barycenter solver + example (PR #372), fixed graphical details on the example (PR #376)
+- Added Free Support Sinkhorn Barycenter + example (PR #387)
 
 #### Closed issues
 

examples/barycenters/plot_free_support_barycenter.py

@@ -4,13 +4,14 @@
 2D free support Wasserstein barycenters of distributions
 ========================================================
 
-Illustration of 2D Wasserstein barycenters if distributions are weighted
+Illustration of 2D Wasserstein and Sinkhorn barycenters if distributions are weighted
 sum of diracs.
 
 """
 
 # Authors: Vivien Seguy <vivien.seguy@iip.ist.i.kyoto-u.ac.jp>
 #          Rémi Flamary <remi.flamary@polytechnique.edu>
+#          Eduardo Fernandes Montesuma <eduardo.fernandes-montesuma@universite-paris-saclay.fr>
 #
 # License: MIT License
 
@@ -48,7 +49,7 @@
 
 
 # %%
-# Compute free support barycenter
+# Compute free support Wasserstein barycenter
 # -------------------------------
 
 k = 200  # number of Diracs of the barycenter
@@ -58,7 +59,28 @@
 X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b)
 
 # %%
-# Plot the barycenter
+# Plot the Wasserstein barycenter
+# ---------
+
+pl.figure(2, (8, 3))
+pl.scatter(x1[:, 0], x1[:, 1], alpha=0.5)
+pl.scatter(x2[:, 0], x2[:, 1], alpha=0.5)
+pl.scatter(X[:, 0], X[:, 1], s=b * 1000, marker='s', label='2-Wasserstein barycenter')
+pl.title('Data measures and their barycenter')
+pl.legend(loc="lower right")
+pl.show()
+
+# %%
+# Compute free support Sinkhorn barycenter
+
+k = 200  # number of Diracs of the barycenter
+X_init = np.random.normal(0., 1., (k, d))  # initial Dirac locations
+b = np.ones((k,)) / k  # weights of the barycenter (it will not be optimized, only the locations are optimized)
+
+X = ot.bregman.free_support_sinkhorn_barycenter(measures_locations, measures_weights, X_init, 20, b, numItermax=15)
+
+# %%
+# Plot the Wasserstein barycenter
 # ---------
 
 pl.figure(2, (8, 3))

examples/barycenters/plot_free_support_sinkhorn_barycenter.py

@@ -0,0 +1,151 @@
+# -*- coding: utf-8 -*-
+"""
+========================================================
+2D free support Sinkhorn barycenters of distributions
+========================================================
+
+Illustration of Sinkhorn barycenter calculation between empirical distributions understood as point clouds
+
+"""
+
+# Authors: Eduardo Fernandes Montesuma <eduardo.fernandes-montesuma@universite-paris-saclay.fr>
+#
+# License: MIT License
+
+import numpy as np
+import matplotlib.pyplot as plt
+import ot
+
+# %%
+# General Parameters
+# ------------------
+reg = 1e-2  # Entropic Regularization
+numItermax = 20  # Maximum number of iterations for the Barycenter algorithm
+numInnerItermax = 50  # Maximum number of sinkhorn iterations
+n_samples = 200
+
+# %%
+# Generate Data
+# -------------
+
+X1 = np.random.randn(200, 2)
+X2 = 2 * np.concatenate([
+    np.concatenate([- np.ones([50, 1]), np.linspace(-1, 1, 50)[:, None]], axis=1),
+    np.concatenate([np.linspace(-1, 1, 50)[:, None], np.ones([50, 1])], axis=1),
+    np.concatenate([np.ones([50, 1]), np.linspace(1, -1, 50)[:, None]], axis=1),
+    np.concatenate([np.linspace(1, -1, 50)[:, None], - np.ones([50, 1])], axis=1),
+], axis=0)
+X3 = np.random.randn(200, 2)
+X3 = 2 * (X3 / np.linalg.norm(X3, axis=1)[:, None])
+X4 = np.random.multivariate_normal(np.array([0, 0]), np.array([[1., 0.5], [0.5, 1.]]), size=200)
+
+a1, a2, a3, a4 = ot.unif(len(X1)), ot.unif(len(X1)), ot.unif(len(X1)), ot.unif(len(X1))
+
+# %%
+# Inspect generated distributions
+# -------------------------------
+
+fig, axes = plt.subplots(1, 4, figsize=(16, 4))
+
+axes[0].scatter(x=X1[:, 0], y=X1[:, 1], c='steelblue', edgecolor='k')
+axes[1].scatter(x=X2[:, 0], y=X2[:, 1], c='steelblue', edgecolor='k')
+axes[2].scatter(x=X3[:, 0], y=X3[:, 1], c='steelblue', edgecolor='k')
+axes[3].scatter(x=X4[:, 0], y=X4[:, 1], c='steelblue', edgecolor='k')
+
+axes[0].set_xlim([-3, 3])
+axes[0].set_ylim([-3, 3])
+axes[0].set_title('Distribution 1')
+
+axes[1].set_xlim([-3, 3])
+axes[1].set_ylim([-3, 3])
+axes[1].set_title('Distribution 2')
+
+axes[2].set_xlim([-3, 3])
+axes[2].set_ylim([-3, 3])
+axes[2].set_title('Distribution 3')
+
+axes[3].set_xlim([-3, 3])
+axes[3].set_ylim([-3, 3])
+axes[3].set_title('Distribution 4')
+
+plt.tight_layout()
+plt.show()
+
+# %%
+# Interpolating Empirical Distributions
+# -------------------------------------
+
+fig = plt.figure(figsize=(10, 10))
+
+weights = np.array([
+    [3 / 3, 0 / 3],
+    [2 / 3, 1 / 3],
+    [1 / 3, 2 / 3],
+    [0 / 3, 3 / 3],
+]).astype(np.float32)
+
+for k in range(4):
+    XB_init = np.random.randn(n_samples, 2)
+    XB = ot.bregman.free_support_sinkhorn_barycenter(
+        measures_locations=[X1, X2],
+        measures_weights=[a1, a2],
+        weights=weights[k],
+        X_init=XB_init,
+        reg=reg,
+        numItermax=numItermax,
+        numInnerItermax=numInnerItermax
+    )
+    ax = plt.subplot2grid((4, 4), (0, k))
+    ax.scatter(XB[:, 0], XB[:, 1], color='steelblue', edgecolor='k')
+    ax.set_xlim([-3, 3])
+    ax.set_ylim([-3, 3])
+
+for k in range(1, 4, 1):
+    XB_init = np.random.randn(n_samples, 2)
+    XB = ot.bregman.free_support_sinkhorn_barycenter(
+        measures_locations=[X1, X3],
+        measures_weights=[a1, a2],
+        weights=weights[k],
+        X_init=XB_init,
+        reg=reg,
+        numItermax=numItermax,
+        numInnerItermax=numInnerItermax
+    )
+    ax = plt.subplot2grid((4, 4), (k, 0))
+    ax.scatter(XB[:, 0], XB[:, 1], color='steelblue', edgecolor='k')
+    ax.set_xlim([-3, 3])
+    ax.set_ylim([-3, 3])
+
+for k in range(1, 4, 1):
+    XB_init = np.random.randn(n_samples, 2)
+    XB = ot.bregman.free_support_sinkhorn_barycenter(
+        measures_locations=[X3, X4],
+        measures_weights=[a1, a2],
+        weights=weights[k],
+        X_init=XB_init,
+        reg=reg,
+        numItermax=numItermax,
+        numInnerItermax=numInnerItermax
+    )
+    ax = plt.subplot2grid((4, 4), (3, k))
+    ax.scatter(XB[:, 0], XB[:, 1], color='steelblue', edgecolor='k')
+    ax.set_xlim([-3, 3])
+    ax.set_ylim([-3, 3])
+
+for k in range(1, 3, 1):
+    XB_init = np.random.randn(n_samples, 2)
+    XB = ot.bregman.free_support_sinkhorn_barycenter(
+        measures_locations=[X2, X4],
+        measures_weights=[a1, a2],
+        weights=weights[k],
+        X_init=XB_init,
+        reg=reg,
+        numItermax=numItermax,
+        numInnerItermax=numInnerItermax
+    )
+    ax = plt.subplot2grid((4, 4), (k, 3))
+    ax.scatter(XB[:, 0], XB[:, 1], color='steelblue', edgecolor='k')
+    ax.set_xlim([-3, 3])
+    ax.set_ylim([-3, 3])
+
+plt.show()

ot/bregman.py

@@ -1540,6 +1540,126 @@ def barycenter_sinkhorn(A, M, reg, weights=None, numItermax=1000,
         return geometricBar(weights, UKv)
 
 
+def free_support_sinkhorn_barycenter(measures_locations, measures_weights, X_init, reg, b=None, weights=None,
+                                     numItermax=100, numInnerItermax=1000, stopThr=1e-7, verbose=False, log=None,
+                                     **kwargs):
+    r"""
+    Solves the free support (locations of the barycenters are optimized, not the weights) regularized Wasserstein barycenter problem (i.e. the weighted Frechet mean for the 2-Sinkhorn divergence), formally:
+
+    .. math::
+        \min_\mathbf{X} \quad \sum_{i=1}^N w_i W_{reg}^2(\mathbf{b}, \mathbf{X}, \mathbf{a}_i, \mathbf{X}_i)
+
+    where :
+
+    - :math:`w \in \mathbb{(0, 1)}^{N}`'s are the barycenter weights and sum to one
+    - `measure_weights` denotes the :math:`\mathbf{a}_i \in \mathbb{R}^{k_i}`: empirical measures weights (on simplex)
+    - `measures_locations` denotes the :math:`\mathbf{X}_i \in \mathbb{R}^{k_i, d}`: empirical measures atoms locations
+    - :math:`\mathbf{b} \in \mathbb{R}^{k}` is the desired weights vector of the barycenter
+
+    This problem is considered in :ref:`[20] <references-free-support-barycenter>` (Algorithm 2).
+    There are two differences with the following codes:
+
+    - we do not optimize over the weights
+    - we do not do line search for the locations updates, we use i.e. :math:`\theta = 1` in
+      :ref:`[20] <references-free-support-barycenter>` (Algorithm 2). This can be seen as a discrete
+      implementation of the fixed-point algorithm of
+      :ref:`[43] <references-free-support-barycenter>` proposed in the continuous setting.
+    - at each iteration, instead of solving an exact OT problem, we use the Sinkhorn algorithm for calculating the
+      transport plan in :ref:`[20] <references-free-support-barycenter>` (Algorithm 2).
+
+    Parameters
+    ----------
+    measures_locations : list of N (k_i,d) array-like
+        The discrete support of a measure supported on :math:`k_i` locations of a `d`-dimensional space
+        (:math:`k_i` can be different for each element of the list)
+    measures_weights : list of N (k_i,) array-like
+        Numpy arrays where each numpy array has :math:`k_i` non-negatives values summing to one
+        representing the weights of each discrete input measure
+
+    X_init : (k,d) array-like
+        Initialization of the support locations (on `k` atoms) of the barycenter
+    reg : float
+        Regularization term >0
+    b : (k,) array-like
+        Initialization of the weights of the barycenter (non-negatives, sum to 1)
+    weights : (N,) array-like
+        Initialization of the coefficients of the barycenter (non-negatives, sum to 1)
+
+    numItermax : int, optional
+        Max number of iterations
+    numInnerItermax : int, optional
+        Max number of iterations when calculating the transport plans with Sinkhorn
+    stopThr : float, optional
+        Stop threshold on error (>0)
+    verbose : bool, optional
+        Print information along iterations
+    log : bool, optional
+        record log if True
+
+    Returns
+    -------
+    X : (k,d) array-like
+        Support locations (on k atoms) of the barycenter
+
+    See Also
+    --------
+    ot.bregman.sinkhorn : Entropic regularized OT solver
+    ot.lp.free_support_barycenter : Barycenter solver based on Linear Programming
+
+    .. _references-free-support-barycenter:
+    References
+    ----------
+    .. [20] Cuturi, Marco, and Arnaud Doucet. "Fast computation of Wasserstein barycenters." International Conference on Machine Learning. 2014.
+
+    .. [43] Álvarez-Esteban, Pedro C., et al. "A fixed-point approach to barycenters in Wasserstein space." Journal of Mathematical Analysis and Applications 441.2 (2016): 744-762.
+
+    """
+    nx = get_backend(*measures_locations, *measures_weights, X_init)
+
+    iter_count = 0
+
+    N = len(measures_locations)
+    k = X_init.shape[0]
+    d = X_init.shape[1]
+    if b is None:
+        b = nx.ones((k,), type_as=X_init) / k
+    if weights is None:
+        weights = nx.ones((N,), type_as=X_init) / N
+
+    X = X_init
+
+    log_dict = {}
+    displacement_square_norms = []
+
+    displacement_square_norm = stopThr + 1.
+
+    while (displacement_square_norm > stopThr and iter_count < numItermax):
+
+        T_sum = nx.zeros((k, d), type_as=X_init)
+
+        for (measure_locations_i, measure_weights_i, weight_i) in zip(measures_locations, measures_weights, weights):
+            M_i = dist(X, measure_locations_i)
+            T_i = sinkhorn(b, measure_weights_i, M_i, reg=reg, numItermax=numInnerItermax, **kwargs)
+            T_sum = T_sum + weight_i * 1. / b[:, None] * nx.dot(T_i, measure_locations_i)
+
+        displacement_square_norm = nx.sum((T_sum - X) ** 2)
+        if log:
+            displacement_square_norms.append(displacement_square_norm)
+
+        X = T_sum
+
+        if verbose:
+            print('iteration %d, displacement_square_norm=%f\n', iter_count, displacement_square_norm)
+
+        iter_count += 1
+
+    if log:
+        log_dict['displacement_square_norms'] = displacement_square_norms
+        return X, log_dict
+    else:
+        return X
+
+
 def _barycenter_sinkhorn_log(A, M, reg, weights=None, numItermax=1000,
                              stopThr=1e-4, verbose=False, log=False, warn=True):
     r"""Compute the entropic wasserstein barycenter in log-domain

test/test_bregman.py

@@ -3,6 +3,7 @@
 # Author: Remi Flamary <remi.flamary@unice.fr>
 #         Kilian Fatras <kilian.fatras@irisa.fr>
 #         Quang Huy Tran <quang-huy.tran@univ-ubs.fr>
+#         Eduardo Fernandes Montesuma <eduardo.fernandes-montesuma@universite-paris-saclay.fr>
 #
 # License: MIT License
 
@@ -490,6 +491,31 @@ def test_barycenter(nx, method, verbose, warn):
         ot.bregman.barycenter(A_nx, M_nx, reg, log=True)
 
 
+def test_free_support_sinkhorn_barycenter():
+    measures_locations = [
+        np.array([-1.]).reshape((1, 1)),  # First dirac support
+        np.array([1.]).reshape((1, 1))  # Second dirac support
+    ]
+
+    measures_weights = [
+        np.array([1.]),  # First dirac sample weights
+        np.array([1.])  # Second dirac sample weights
+    ]
+
+    # Barycenter initialization
+    X_init = np.array([-12.]).reshape((1, 1))
+
+    # Obvious barycenter locations. Take a look on test_ot.py, test_free_support_barycenter
+    bar_locations = np.array([0.]).reshape((1, 1))
+
+    # Calculate free support barycenter w/ Sinkhorn algorithm. We set the entropic regularization
+    # term to 1, but this should be, in general, fine-tuned to the problem.
+    X = ot.bregman.free_support_sinkhorn_barycenter(measures_locations, measures_weights, X_init, reg=1)
+
+    # Verifies if calculated barycenter matches ground-truth
+    np.testing.assert_allclose(X, bar_locations, rtol=1e-5, atol=1e-7)
+
+
 @pytest.mark.parametrize("method, verbose, warn",
                          product(["sinkhorn", "sinkhorn_stabilized", "sinkhorn_log"],
                                  [True, False], [True, False]))