[FEAT] Add EWCA in to ot.dr

README.md

@@ -312,3 +312,5 @@ Dictionary Learning](https://arxiv.org/pdf/2102.06555.pdf), International Confer
 [50] Liu, T., Puigcerver, J., & Blondel, M. (2023). [Sparsity-constrained optimal transport](https://openreview.net/forum?id=yHY9NbQJ5BP). Proceedings of the Eleventh International Conference on Learning Representations (ICLR).
 
 [51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019). [Gromov-wasserstein learning for graph matching and node embedding](http://proceedings.mlr.press/v97/xu19b.html). In International Conference on Machine Learning (ICML), 2019.
+
+[52] Collas, A., Vayer, T., Flamary, F., & Breloy, A. (2023). [Entropic Wasserstein Component Analysis](https://arxiv.org/abs/2303.05119). ArXiv.

RELEASES.md

@@ -15,6 +15,7 @@
 - Added features `warmstartT` and `kwargs` to all CG and entropic (F)GW barycenter solvers (PR #455)
 - Added entropic semi-relaxed (Fused) Gromov-Wasserstein solvers in `ot.gromov` + examples (PR #455)
 - Make marginal parameters optional for (F)GW solvers in `._gw`, `._bregman` and `._semirelaxed` (PR #455)
+- Add Entropic Wasserstein Component Analysis (ECWA) in ot.dr (PR #486)
 
 #### Closed issues
 

examples/others/plot_EWCA.py

@@ -0,0 +1,187 @@
+# -*- coding: utf-8 -*-
+"""
+=======================================
+Entropic Wasserstein Component Analysis
+=======================================
+
+This example illustrates the use of EWCA as proposed in [52].
+
+
+[52] Collas, A., Vayer, T., Flamary, F., & Breloy, A. (2023).
+Entropic Wasserstein Component Analysis.
+
+"""
+
+# Author: Antoine Collas <antoine.collas@inria.fr>
+#
+# License: MIT License
+# sphinx_gallery_thumbnail_number = 2
+
+import numpy as np
+import matplotlib.pylab as pl
+from ot.dr import ewca
+from sklearn.datasets import make_blobs
+from matplotlib import ticker as mticker
+import matplotlib.patches as patches
+import matplotlib
+
+##############################################################################
+# Generate data
+# -------------
+
+n_samples = 20
+esp = 0.8
+centers = np.array([[esp, esp], [-esp, -esp]])
+cluster_std = 0.4
+
+rng = np.random.RandomState(42)
+X, y = make_blobs(
+    n_samples=n_samples,
+    n_features=2,
+    centers=centers,
+    cluster_std=cluster_std,
+    shuffle=False,
+    random_state=rng,
+)
+X = X - X.mean(0)
+
+##############################################################################
+# Plot data
+# -------------
+
+fig = pl.figure(figsize=(4, 4))
+cmap = matplotlib.colormaps.get_cmap("tab10")
+pl.scatter(
+    X[: n_samples // 2, 0],
+    X[: n_samples // 2, 1],
+    color=[cmap(y[i] + 1) for i in range(n_samples // 2)],
+    alpha=0.4,
+    label="Class 1",
+    zorder=30,
+    s=50,
+)
+pl.scatter(
+    X[n_samples // 2:, 0],
+    X[n_samples // 2:, 1],
+    color=[cmap(y[i] + 1) for i in range(n_samples // 2, n_samples)],
+    alpha=0.4,
+    label="Class 2",
+    zorder=30,
+    s=50,
+)
+x_y_lim = 2.5
+fs = 15
+pl.xlim(-x_y_lim, x_y_lim)
+pl.xticks([])
+pl.ylim(-x_y_lim, x_y_lim)
+pl.yticks([])
+pl.legend(fontsize=fs)
+pl.title("Data", fontsize=fs)
+pl.tight_layout()
+
+
+##############################################################################
+# Compute EWCA
+# -------------
+
+pi, U = ewca(X, k=2, reg=0.5)
+
+
+##############################################################################
+# Plot data, first component, and projected data
+# -------------
+
+fig = pl.figure(figsize=(4, 4))
+
+scale = 3
+u = U[:, 0]
+pl.plot(
+    [scale * u[0], -scale * u[0]],
+    [scale * u[1], -scale * u[1]],
+    color="grey",
+    linestyle="--",
+    lw=3,
+    alpha=0.3,
+    label=r"$\mathbf{U}$",
+)
+X1 = X @ u[:, None] @ u[:, None].T
+
+for i in range(n_samples):
+    for j in range(n_samples):
+        v = pi[i, j] / pi.max()
+        if v >= 0.15 or (i, j) == (n_samples - 1, n_samples - 1):
+            pl.plot(
+                [X[i, 0], X1[j, 0]],
+                [X[i, 1], X1[j, 1]],
+                alpha=v,
+                linestyle="-",
+                c="C0",
+                label=r"$\pi_{ij}$"
+                if (i, j) == (n_samples - 1, n_samples - 1)
+                else None,
+            )
+pl.scatter(
+    X[:, 0],
+    X[:, 1],
+    color=[cmap(y[i] + 1) for i in range(n_samples)],
+    alpha=0.4,
+    label=r"$\mathbf{x}_i$",
+    zorder=30,
+    s=50,
+)
+pl.scatter(
+    X1[:, 0],
+    X1[:, 1],
+    color=[cmap(y[i] + 1) for i in range(n_samples)],
+    alpha=0.9,
+    s=50,
+    marker="+",
+    label=r"$\mathbf{U}\mathbf{U}^{\top}\mathbf{x}_i$",
+    zorder=30,
+)
+pl.title("Data and projections", fontsize=fs)
+pl.xlim(-x_y_lim, x_y_lim)
+pl.xticks([])
+pl.ylim(-x_y_lim, x_y_lim)
+pl.yticks([])
+pl.legend(fontsize=fs, loc="upper left")
+pl.tight_layout()
+
+
+##############################################################################
+# Plot transport plan
+# -------------
+
+fig = pl.figure(figsize=(5, 5))
+
+norm = matplotlib.colors.PowerNorm(0.5, vmin=0, vmax=100)
+im = pl.imshow(n_samples * pi * 100, cmap=pl.cm.Blues, norm=norm, aspect="auto")
+cb = fig.colorbar(im, orientation="vertical", shrink=0.8)
+ticks_loc = cb.ax.get_yticks().tolist()
+cb.ax.yaxis.set_major_locator(mticker.FixedLocator(ticks_loc))
+cb.ax.set_yticklabels([f"{int(i)}%" for i in cb.get_ticks()])
+cb.ax.tick_params(labelsize=fs)
+for i, class_ in enumerate(np.sort(np.unique(y))):
+    indices = y == class_
+    idx_min = np.min(np.arange(len(y))[indices])
+    idx_max = np.max(np.arange(len(y))[indices])
+    width = idx_max - idx_min + 1
+    rect = patches.Rectangle(
+        (idx_min - 0.5, idx_min - 0.5),
+        width,
+        width,
+        linewidth=1,
+        edgecolor="r",
+        facecolor="none",
+    )
+    pl.gca().add_patch(rect)
+
+pl.title("OT plan", fontsize=fs)
+pl.ylabel(r"($\mathbf{x}_1, \cdots, \mathbf{x}_n$)")
+x_label = r"($\mathbf{U}\mathbf{U}^{\top}\mathbf{x}_1, \cdots,"
+x_label += r"\mathbf{U}\mathbf{U}^{\top}\mathbf{x}_n$)"
+pl.xlabel(x_label)
+pl.tight_layout()
+pl.axis("scaled")
+
+pl.show()

ot/dr.py

@@ -12,16 +12,21 @@
 # Author: Remi Flamary <remi.flamary@unice.fr>
 #         Minhui Huang <mhhuang@ucdavis.edu>
 #         Jakub Zadrozny <jakub.r.zadrozny@gmail.com>
+#         Antoine Collas <antoine.collas@inria.fr>
 #
 # License: MIT License
 
 from scipy import linalg
 import autograd.numpy as np
+from sklearn.decomposition import PCA
 
 import pymanopt
 import pymanopt.manifolds
 import pymanopt.optimizers
 
+from .bregman import sinkhorn as sinkhorn_bregman
+from .utils import dist as dist_utils
+
 
 def dist(x1, x2):
     r""" Compute squared euclidean distance between samples (autograd)
@@ -376,3 +381,153 @@ def Vpi(X, Y, a, b, pi):
         iter = iter + 1
 
     return pi, U
+
+
+def ewca(X, U0=None, reg=1, k=2, method='BCD', sinkhorn_method='sinkhorn', stopThr=1e-6, maxiter=100, maxiter_sink=1000, maxiter_MM=10, verbose=0):
+    r"""
+    Entropic Wasserstein Component Analysis :ref:`[52] <references-entropic-wasserstein-component_analysis>`.
+
+    The function solves the following optimization problem:
+
+    .. math::
+        \mathbf{U} = \mathop{\arg \min}_\mathbf{U} \quad
+        W(\mathbf{X}, \mathbf{U}\mathbf{U}^T \mathbf{X})
+
+    where :
+
+    - :math:`\mathbf{U}` is a matrix in the Stiefel(`p`, `d`) manifold
+    - :math:`W` is entropic regularized Wasserstein distances
+    - :math:`\mathbf{X}` are samples
+
+    Parameters
+    ----------
+    X : ndarray, shape (n, d)
+        Samples from measure :math:`\mu`.
+    U0 : ndarray, shape (d, k), optional
+        Initial starting point for projection.
+    reg : float, optional
+        Regularization term >0 (entropic regularization).
+    k : int, optional
+        Subspace dimension.
+    method : str, optional
+        Eather 'BCD' or 'MM' (Block Coordinate Descent or Majorization-Minimization).
+        Prefer MM when d is large.
+    sinkhorn_method : str
+        Method used for the Sinkhorn solver, see :ref:`ot.bregman.sinkhorn` for more details.
+    stopThr : float, optional
+        Stop threshold on error (>0).
+    maxiter : int, optional
+        Maximum number of iterations of the BCD/MM.
+    maxiter_sink : int, optional
+        Maximum number of iterations of the Sinkhorn solver.
+    maxiter_MM : int, optional
+        Maximum number of iterations of the MM (only used when method='MM').
+    verbose : int, optional
+        Print information along iterations.
+
+    Returns
+    -------
+    pi : ndarray, shape (n, n)
+        Optimal transportation matrix for the given parameters.
+    U : ndarray, shape (d, k)
+        Matrix Stiefel manifold.
+
+
+    .. _references-entropic-wasserstein-component_analysis:
+    References
+    ----------
+    .. [52] Collas, A., Vayer, T., Flamary, F., & Breloy, A. (2023).
+            Entropic Wasserstein Component Analysis.
+    """  # noqa
+    n, d = X.shape
+    X = X - X.mean(0)
+
+    if U0 is None:
+        pca_fitted = PCA(n_components=k).fit(X)
+        U = pca_fitted.components_.T
+        if method == 'MM':
+            lambda_scm = pca_fitted.explained_variance_[0]
+    else:
+        U = U0
+
+    # marginals
+    u0 = (1. / n) * np.ones(n)
+
+    # print iterations
+    if verbose > 0:
+        print('{:4s}|{:13s}|{:12s}|{:12s}'.format('It.', 'Loss', 'Crit.', 'Thres.') + '\n' + '-' * 40)
+
+    def compute_loss(M, pi, reg):
+        return np.sum(M * pi) + reg * np.sum(pi * (np.log(pi) - 1))
+
+    def grassmann_distance(U1, U2):
+        proj = U1.T @ U2
+        _, s, _ = np.linalg.svd(proj)
+        s[s > 1] = 1
+        s = np.arccos(s)
+        return np.linalg.norm(s)
+
+    # loop
+    it = 0
+    crit = np.inf
+    sinkhorn_warmstart = None
+
+    while (it < maxiter) and (crit > stopThr):
+        U_old = U
+
+        # Solve transport
+        M = dist_utils(X, (X @ U) @ U.T)
+        pi, log_sinkhorn = sinkhorn_bregman(
+            u0, u0, M, reg,
+            numItermax=maxiter_sink,
+            method=sinkhorn_method, warmstart=sinkhorn_warmstart,
+            warn=False, log=True
+        )
+        key_warmstart = 'warmstart'
+        if key_warmstart in log_sinkhorn:
+            sinkhorn_warmstart = log_sinkhorn[key_warmstart]
+        if (pi >= 1e-300).all():
+            loss = compute_loss(M, pi, reg)
+        else:
+            loss = np.inf
+
+        # Solve PCA
+        pi_sym = (pi + pi.T) / 2
+
+        if method == 'BCD':
+            # block coordinate descent
+            S = X.T @ (2 * pi_sym - (1. / n) * np.eye(n)) @ X
+            _, U = np.linalg.eigh(S)
+            U = U[:, ::-1][:, :k]
+
+        elif method == 'MM':
+            # majorization-minimization
+            eig, _ = np.linalg.eigh(pi_sym)
+            lambda_pi = eig[0]
+
+            for _ in range(maxiter_MM):
+                X_proj = X @ U
+                X_T_X_proj = X.T @ X_proj
+
+                R = (1 / n) * X_T_X_proj
+                alpha = 1 - 2 * n * lambda_pi
+                if alpha > 0:
+                    R = alpha * (R - lambda_scm * U)
+                else:
+                    R = alpha * R
+
+                R = R - (2 * X.T @ (pi_sym @ X_proj)) + (2 * lambda_pi * X_T_X_proj)
+                U, _ = np.linalg.qr(R)
+
+        else:
+            raise ValueError(f"Unknown method '{method}', use 'BCD' or 'MM'.")
+
+        # stop or not
+        it += 1
+        crit = grassmann_distance(U_old, U)
+
+        # print
+        if verbose > 0:
+            print('{:4d}|{:8e}|{:8e}|{:8e}'.format(it, loss, crit, stopThr))
+
+    return pi, U