Merge branch 'master' into doc_srgw

RELEASES.md

@@ -21,6 +21,8 @@
 + Add `stop_criterion` feature to (un)regularized (f)gw barycenter solvers (PR #578)
 + Add `fixed_structure` and `fixed_features` to entropic fgw barycenter solver (PR #578)
 + Add new entropic BAPG solvers for GW and FGW (PR #581)
++ Add Bures-Wasserstein barycenter in `ot.gaussian` (PR #582)
+
 
 #### Closed issues
 - Fix line search evaluating cost outside of the interpolation range (Issue #502, PR #504)

ot/gaussian.py

@@ -344,6 +344,188 @@ def empirical_bures_wasserstein_distance(xs, xt, reg=1e-6, ws=None,
         return W
 
 
+def bures_wasserstein_barycenter(m, C, weights=None, num_iter=1000, eps=1e-7, log=False):
+    r"""Return OT linear operator between samples.
+
+    The function estimates the optimal barycenter of the
+    empirical distributions. This is equivalent to resolving the fixed point
+     algorithm for multiple Gaussian distributions :math:`\left{\mathcal{N}(\mu,\Sigma)\right}_{i=1}^n`
+    :ref:`[1] <references-OT-mapping-linear-barycenter>`.
+
+    The barycenter still following a Gaussian distribution :math:`\mathcal{N}(\mu_b,\Sigma_b)`
+    where :
+
+    .. math::
+        \mu_b = \sum_{i=1}^n w_i \mu_i
+
+    And the barycentric covariance is the solution of the following fixed-point algorithm:
+
+    .. math::
+        \Sigma_b = \sum_{i=1}^n w_i \left(\Sigma_b^{1/2}\Sigma_i^{1/2}\Sigma_b^{1/2}\right)^{1/2}
+
+
+    Parameters
+    ----------
+    m : array-like (k,d)
+        mean of k distributions
+    C : array-like (k,d,d)
+        covariance of k distributions
+    weights : array-like (k), optional
+        weights for each distribution
+    num_iter : int, optional
+        number of iteration for the fixed point algorithm
+    eps : float, optional
+        tolerance for the fixed point algorithm
+    log : bool, optional
+        record log if True
+
+
+    Returns
+    -------
+    mb : (d,) array-like
+        mean of the barycenter
+    Cb : (d, d) array-like
+        covariance of the barycenter
+    log : dict
+        log dictionary return only if log==True in parameters
+
+
+    .. _references-OT-mapping-linear-barycenter:
+    References
+    ----------
+    .. [1] M. Agueh and G. Carlier, "Barycenters in the Wasserstein space",
+        SIAM Journal on Mathematical Analysis, vol. 43, no. 2, pp. 904-924,
+        2011.
+    """
+    nx = get_backend(*C, *m,)
+
+    # Compute the mean barycenter
+    mb = nx.mean(m)
+
+    # Init the covariance barycenter
+    Cb = nx.mean(C, axis=0)
+
+    if weights is None:
+        weights = nx.ones(len(C), type_as=C[0]) / len(C)
+
+    for it in range(num_iter):
+        # fixed point update
+        Cb12 = nx.sqrtm(Cb)
+
+        Cnew = Cb12 @ C @ Cb12
+        C_ = []
+        for i in range(len(C)):
+            C_.append(nx.sqrtm(Cnew[i]))
+        Cnew = nx.stack(C_, axis=0)
+        Cnew *= weights[:, None, None]
+        Cnew = nx.sum(Cnew, axis=0)
+
+        # check convergence
+        diff = nx.norm(Cb - Cnew)
+        if diff <= eps:
+            break
+        Cb = Cnew
+    else:
+        print("Dit not converge.")
+
+    if log:
+        log = {}
+        log['num_iter'] = it
+        log['final_diff'] = diff
+        return mb, Cb, log
+    else:
+        return mb, Cb
+
+
+def empirical_bures_wasserstein_barycenter(
+    X, reg=1e-6, weights=None, num_iter=1000, eps=1e-7,
+    w=None, bias=True, log=False
+):
+    r"""Return OT linear operator between samples.
+
+    The function estimates the optimal barycenter of the
+    empirical distributions. This is equivalent to resolving the fixed point
+     algorithm for multiple Gaussian distributions :math:`\left{\mathcal{N}(\mu,\Sigma)\right}_{i=1}^n`
+    :ref:`[1] <references-OT-mapping-linear-barycenter>`.
+
+    The barycenter still following a Gaussian distribution :math:`\mathcal{N}(\mu_b,\Sigma_b)`
+    where :
+
+    .. math::
+        \mu_b = \sum_{i=1}^n w_i \mu_i
+
+    And the barycentric covariance is the solution of the following fixed-point algorithm:
+
+    .. math::
+        \Sigma_b = \sum_{i=1}^n w_i \left(\Sigma_b^{1/2}\Sigma_i^{1/2}\Sigma_b^{1/2}\right)^{1/2}
+
+
+    Parameters
+    ----------
+    X : list of array-like (n,d)
+        samples in each distribution
+    reg : float,optional
+        regularization added to the diagonals of covariances (>0)
+    weights : array-like (n,), optional
+        weights for each distribution
+    num_iter : int, optional
+        number of iteration for the fixed point algorithm
+    eps : float, optional
+        tolerance for the fixed point algorithm
+    w : list of array-like (n,), optional
+        weights for each sample in each distribution
+    bias: boolean, optional
+        estimate bias :math:`\mathbf{b}` else :math:`\mathbf{b} = 0` (default:True)
+    log : bool, optional
+        record log if True
+
+
+    Returns
+    -------
+    mb : (d,) array-like
+        mean of the barycenter
+    Cb : (d, d) array-like
+        covariance of the barycenter
+    log : dict
+        log dictionary return only if log==True in parameters
+
+
+    .. _references-OT-mapping-linear-barycenter:
+    References
+    ----------
+    .. [1] M. Agueh and G. Carlier, "Barycenters in the Wasserstein space",
+        SIAM Journal on Mathematical Analysis, vol. 43, no. 2, pp. 904-924,
+        2011.
+    """
+    X = list_to_array(*X)
+    nx = get_backend(*X)
+
+    k = len(X)
+    d = [X[i].shape[1] for i in range(k)]
+
+    if bias:
+        m = [nx.mean(X[i], axis=0)[None, :] for i in range(k)]
+        X = [X[i] - m[i] for i in range(k)]
+    else:
+        m = [nx.zeros((1, d[i]), type_as=X[i]) for i in range(k)]
+
+    if w is None:
+        w = [nx.ones((X[i].shape[0], 1), type_as=X[i]) / X[i].shape[0] for i in range(k)]
+
+    C = [
+        nx.dot((X[i] * w[i]).T, X[i]) / nx.sum(w[i]) + reg * nx.eye(d[i], type_as=X[i])
+        for i in range(k)
+    ]
+    m = nx.stack(m, axis=0)
+    C = nx.stack(C, axis=0)
+    if log:
+        mb, Cb, log = bures_wasserstein_barycenter(m, C, weights=weights, num_iter=num_iter, eps=eps, log=log)
+        return mb, Cb, log
+    else:
+        mb, Cb = bures_wasserstein_barycenter(m, C, weights=weights, num_iter=num_iter, eps=eps, log=log)
+        return mb, Cb
+
+
 def gaussian_gromov_wasserstein_distance(Cov_s, Cov_t, log=False):
     r""" Return the Gaussian Gromov-Wasserstein value from [57].
 

test/test_gaussian.py

@@ -108,6 +108,71 @@ def test_empirical_bures_wasserstein_distance(nx, bias):
     np.testing.assert_allclose(10 * bias, nx.to_numpy(Wb), rtol=1e-2, atol=1e-2)
 
 
+def test_bures_wasserstein_barycenter(nx):
+    n = 50
+    k = 10
+    X = []
+    y = []
+    m = []
+    C = []
+    for _ in range(k):
+        X_, y_ = make_data_classif('3gauss', n)
+        m_ = np.mean(X_, axis=0)[None, :]
+        C_ = np.cov(X_.T)
+        X.append(X_)
+        y.append(y_)
+        m.append(m_)
+        C.append(C_)
+    m = np.array(m)
+    C = np.array(C)
+    X = nx.from_numpy(*X)
+    m = nx.from_numpy(m)
+    C = nx.from_numpy(C)
+
+    mblog, Cblog, log = ot.gaussian.bures_wasserstein_barycenter(m, C, log=True)
+    mb, Cb = ot.gaussian.bures_wasserstein_barycenter(m, C, log=False)
+
+    np.testing.assert_allclose(Cb, Cblog, rtol=1e-2, atol=1e-2)
+    np.testing.assert_allclose(mb, mblog, rtol=1e-2, atol=1e-2)
+
+    # Test weights argument
+    weights = nx.ones(k) / k
+    mbw, Cbw = ot.gaussian.bures_wasserstein_barycenter(m, C, weights=weights, log=False)
+    np.testing.assert_allclose(Cbw, Cb, rtol=1e-2, atol=1e-2)
+
+    # test with closed form for diagonal covariance matrices
+    Cdiag = [nx.diag(nx.diag(C[i])) for i in range(k)]
+    Cdiag = nx.stack(Cdiag, axis=0)
+    mbdiag, Cbdiag = ot.gaussian.bures_wasserstein_barycenter(m, Cdiag, log=False)
+
+    Cdiag_sqrt = [nx.sqrtm(C) for C in Cdiag]
+    Cdiag_sqrt = nx.stack(Cdiag_sqrt, axis=0)
+    Cdiag_mean = nx.mean(Cdiag_sqrt, axis=0)
+    Cdiag_cf = Cdiag_mean @ Cdiag_mean
+
+    np.testing.assert_allclose(Cbdiag, Cdiag_cf, rtol=1e-2, atol=1e-2)
+
+
+@pytest.mark.parametrize("bias", [True, False])
+def test_empirical_bures_wasserstein_barycenter(nx, bias):
+    n = 50
+    k = 10
+    X = []
+    y = []
+    for _ in range(k):
+        X_, y_ = make_data_classif('3gauss', n)
+        X.append(X_)
+        y.append(y_)
+
+    X = nx.from_numpy(*X)
+
+    mblog, Cblog, log = ot.gaussian.empirical_bures_wasserstein_barycenter(X, log=True, bias=bias)
+    mb, Cb = ot.gaussian.empirical_bures_wasserstein_barycenter(X, log=False, bias=bias)
+
+    np.testing.assert_allclose(Cb, Cblog, rtol=1e-2, atol=1e-2)
+    np.testing.assert_allclose(mb, mblog, rtol=1e-2, atol=1e-2)
+
+
 @pytest.mark.parametrize("d_target", [1, 2, 3, 10])
 def test_gaussian_gromov_wasserstein_distance(nx, d_target):
     ns = 400