[MRG] batch FUGW loss

RELEASES.md

@@ -27,7 +27,10 @@ This new release adds support for sparse cost matrices and a new lazy EMD solver
   a callable, or a no-op (PR #808)
 - Add optional `scaler` parameter to `sliced_wasserstein_distance` and  `max_sliced_wasserstein_distance` (PR #808)
 - Add a numerically stable log-domain solver for entropic partial Wasserstein, selectable via the new `method` parameter of `entropic_partial_wasserstein` (`method='sinkhorn_log'`) or directly through `entropic_partial_wasserstein_logscale` (Issue #723)
-- Add cost functions between linear operators following [A Spectral-Grassmann Wasserstein metric for operator representations of dynamical systems](https://arxiv.org/pdf/2509.24920),  implemented in `ot.sgot` (PR #792)
+- Add cost functions between linear operators following  
+  [A Spectral-Grassmann Wasserstein metric for operator representations of dynamical systems](https://arxiv.org/pdf/2509.24920),  
+  implemented in `ot.sgot` (PR #792)
+- Add batch FUGW loss to `ot.batch` and fix issues in some default parameters in the batch module (PR #775)
 - Build wheels on ubuntu ARM to avoid QEMU emulation (PR #818)
 - Add new methods to compute the linear transport map and the related 2-Wasserstein distance betweeen high-dimensional (HD) Gaussian distributions as described in [88], implemented in  `ot.gaussian.bures_wasserstein_mapping_hd` and `ot.gaussian.bures_wasserstein_distance_hd`, respectively. Two additional methods estimate the same quantities from the source and destination observed data and are implemented in `ot.gaussian.empirical_bures_wasserstein_mapping_hd` and `ot.gaussian.empirical_bures_wasserstein_distance_hd`, respectively (PR #814)   
 

examples/backends/plot_gradient_descent.py

@@ -0,0 +1,266 @@
+# -*- coding: utf-8 -*-
+r"""
+===============================================================================
+Solve Fused Unbalanced Gromov Wasserstein with Adam
+===============================================================================
+
+Since the FUGW loss is differentiable, it can be minimized with first-order optimization.
+We show how to do this with the `loss_fugw_batch` function and compare the results with
+the dedicated FUGW solver `fused_unbalanced_gromov_wasserstein`.
+"""
+
+# Author: Rémi Flamary <remi.flamary@polytechnique.edu>
+#         Sonia Mazelet <sonia.mazelet@polytechnique.edu>
+#
+# License: MIT License
+
+# sphinx_gallery_thumbnail_number = 3
+
+import numpy as np
+import matplotlib.pylab as pl
+import torch
+from time import perf_counter
+import ot
+from ot.batch._quadratic import loss_quadratic_batch, tensor_batch
+from ot.gromov import fused_unbalanced_gromov_wasserstein
+from sklearn.manifold import MDS
+
+
+# %%
+# Generation of source and target graphs
+# ----------------
+
+rng = np.random.RandomState(42)
+
+
+def get_sbm(n, nc, ratio, P):
+    nbpc = np.round(n * ratio).astype(int)
+    n = np.sum(nbpc)
+    C = np.zeros((n, n))
+    for c1 in range(nc):
+        for c2 in range(c1 + 1):
+            if c1 == c2:
+                for i in range(np.sum(nbpc[:c1]), np.sum(nbpc[: c1 + 1])):
+                    for j in range(np.sum(nbpc[:c2]), i):
+                        if rng.rand() <= P[c1, c2]:
+                            C[i, j] = 1
+            else:
+                for i in range(np.sum(nbpc[:c1]), np.sum(nbpc[: c1 + 1])):
+                    for j in range(np.sum(nbpc[:c2]), np.sum(nbpc[: c2 + 1])):
+                        if rng.rand() <= P[c1, c2]:
+                            C[i, j] = 1
+
+    return C + C.T
+
+
+def plot_graph(x, C, color="C0", s=100):
+    for j in range(C.shape[0]):
+        for i in range(j):
+            if C[i, j] > 0:
+                pl.plot([x[i, 0], x[j, 0]], [x[i, 1], x[j, 1]], alpha=0.2, color="k")
+    pl.scatter(x[:, 0], x[:, 1], c=color, s=s, zorder=10, edgecolors="k")
+
+
+def get_sbm_labels(n, ratio):
+    nbpc = np.round(n * ratio).astype(int)
+    return np.concatenate(
+        [np.full(count, label, dtype=int) for label, count in enumerate(nbpc)]
+    )
+
+
+def get_noisy_one_hot(labels, n_classes, noise_level=0.1):
+    x = np.eye(n_classes)[labels]
+    x += noise_level * rng.randn(*x.shape)
+    return x
+
+
+n1 = 15
+n2 = 10
+nc1 = 3
+nc2 = 2
+ratio1 = np.array([0.33, 0.33, 0.33])
+ratio2 = np.array([0.5, 0.5])
+
+P1 = np.array([[0.8, 0.03, 0.0], [0.08, 0.8, 0.03], [0.0, 0.08, 0.8]])
+P2 = np.array(0.8 * np.eye(2) + 0.01 * np.ones((2, 2)))
+C1 = get_sbm(n1, nc1, ratio1, P1)
+C2 = get_sbm(n2, nc2, ratio2, P2)
+labels1 = get_sbm_labels(n1, ratio1)
+labels2 = get_sbm_labels(n2, ratio2)
+
+# Use noisy one-hot encodings of the SBM classes as node features.
+feature_dim = max(nc1, nc2)
+x1 = get_noisy_one_hot(labels1, feature_dim)
+x2 = get_noisy_one_hot(labels2, feature_dim)
+all_features = np.vstack([x1, x2])
+feature_min = all_features[:, :3].min(axis=0, keepdims=True)
+feature_max = all_features[:, :3].max(axis=0, keepdims=True)
+
+# get 2d positions for visualization
+pos1 = MDS(dissimilarity="precomputed", random_state=0, n_init=1).fit_transform(1 - C1)
+pos2 = MDS(dissimilarity="precomputed", random_state=0, n_init=1).fit_transform(1 - C2)
+
+colors1 = np.clip(
+    (x1 - feature_min) / np.maximum(feature_max - feature_min, 1e-15), 0.0, 1.0
+)
+colors2 = np.clip(
+    (x2 - feature_min) / np.maximum(feature_max - feature_min, 1e-15), 0.0, 1.0
+)
+
+
+pl.figure(1, (10, 5))
+pl.clf()
+pl.subplot(1, 2, 1)
+plot_graph(pos1, C1, color=colors1)
+pl.title("SBM source graph")
+pl.axis("off")
+pl.subplot(1, 2, 2)
+plot_graph(pos2, C2, color=colors2)
+pl.title("SBM target graph")
+_ = pl.axis("off")
+
+
+# %%
+# Solve FUGW with Adam
+# ----------------
+
+# Even though `loss_fugw_batch` supports batches of problems, we use a
+# batch of size 1 here for clarity.
+
+a = ot.unif(C1.shape[0])
+b = ot.unif(C2.shape[0])
+M = ot.dist(x1, x2)
+M /= M.max()
+
+a_torch = torch.tensor(a[None, :])
+b_torch = torch.tensor(b[None, :])
+C1_torch = torch.tensor(C1[None, :, :])
+C2_torch = torch.tensor(C2[None, :, :])
+M_torch = torch.tensor(M[None, :, :])
+L = tensor_batch(a_torch, b_torch, C1_torch, C2_torch, loss="sqeuclidean")
+
+alpha = 0.5
+reg_marginals = 0.5
+lr = 5e-2
+nb_iter_max = 1500
+tol = 1e-7
+
+T0_torch = a_torch[:, :, None] * b_torch[:, None, :]
+T_torch = torch.log(torch.expm1(T0_torch)).clone().requires_grad_(True)
+optimizer = torch.optim.Adam([T_torch], lr=lr)
+loss_iter = []
+mass_iter = []
+previous_plan_torch = None
+
+tic = perf_counter()
+for i in range(nb_iter_max):
+    optimizer.zero_grad()
+    # Positive transport plan parameterized as log(1 + exp(T)).
+    plan_torch = torch.nn.functional.softplus(T_torch)
+    loss = loss_quadratic_batch(
+        a_torch,
+        b_torch,
+        C1_torch,
+        C2_torch,
+        plan_torch,
+        M_torch,
+        alpha=alpha,
+        unbalanced=reg_marginals,
+        unbalanced_type="kl",
+        recompute_const=True,
+    )[0]
+
+    loss_iter.append(float(loss.detach()))
+    mass_iter.append(float(plan_torch.detach().sum()))
+    if previous_plan_torch is not None:
+        err = float(torch.sum(torch.abs(plan_torch.detach() - previous_plan_torch)))
+        if err < tol:
+            break
+    previous_plan_torch = plan_torch.detach().clone()
+    loss.backward()
+    optimizer.step()
+time_adam = perf_counter() - tic
+
+T_adam = torch.nn.functional.softplus(T_torch).detach().cpu().numpy()[0]
+
+
+# %%
+# Compare with the dedicated FUGW solver
+# -------------------------------------
+#
+# The dedicated solver uses a block coordinate descent (BCD) scheme. We compare
+# the coupling it returns with the one obtained by direct Adam minimization of
+# `loss_fugw_batch`.
+
+
+def evaluate_batch_fugw_loss(plan):
+    plan_torch = torch.tensor(plan[None, :, :], dtype=M_torch.dtype)
+    loss = loss_quadratic_batch(
+        a_torch,
+        b_torch,
+        C1_torch,
+        C2_torch,
+        plan_torch,
+        M_torch,
+        alpha=alpha,
+        unbalanced=reg_marginals,
+        unbalanced_type="kl",
+        recompute_const=True,
+    )[0]
+    return float(loss.detach())
+
+
+tic = perf_counter()
+result = ot.solve_gromov(
+    C1, C2, M, a, b, alpha=alpha, reg=0, unbalanced_type="kl", unbalanced=reg_marginals
+)
+time_bcd = perf_counter() - tic
+
+loss_adam_final = evaluate_batch_fugw_loss(T_adam)
+T_bcd = result.plan
+loss_bcd_final = evaluate_batch_fugw_loss(T_bcd)
+mass_bcd = T_bcd.sum()
+
+pl.figure(2, (10, 4))
+pl.clf()
+pl.subplot(1, 2, 1)
+pl.plot(loss_iter, label="Adam")
+pl.axhline(loss_bcd_final, color="C1", linestyle="--", label="BCD solver")
+pl.grid()
+pl.title("FUGW loss along iterations")
+pl.xlabel("Iterations")
+pl.legend()
+pl.subplot(1, 2, 2)
+pl.plot(mass_iter, label="Adam")
+pl.axhline(mass_bcd, color="C1", linestyle="--", label="BCD solver")
+pl.grid()
+pl.title("Transport mass")
+pl.xlabel("Iterations")
+_ = pl.legend()
+
+
+# %%
+# Visualize the learned couplings
+# -------------------------------
+# We visualize the couplings obtained by both methods to compare them.  On this example, both methods recover similar couplings,
+# but direct minimization reaches a lower `loss_fugw_batch` value at the cost
+# of a longer runtime.
+
+vmin = min(T_adam.min(), T_bcd.min())
+vmax = max(T_adam.max(), T_bcd.max())
+pl.figure(3, (10, 4))
+pl.clf()
+pl.subplot(1, 2, 1)
+pl.imshow(T_adam, interpolation="nearest", cmap="Blues", vmin=vmin, vmax=vmax)
+pl.title(
+    f"Coupling from direct minimization\nloss={loss_adam_final:.3f}, time={time_adam:.2f}s"
+)
+pl.xlabel("Target nodes")
+pl.ylabel("Source nodes")
+pl.colorbar()
+pl.subplot(1, 2, 2)
+pl.imshow(T_bcd, interpolation="nearest", cmap="Blues", vmin=vmin, vmax=vmax)
+pl.title(f"Coupling from BCD solver\nloss={loss_bcd_final:.3f}, time={time_bcd:.2f}s")
+pl.xlabel("Target nodes")
+pl.ylabel("Source nodes")
+_ = pl.colorbar()

ot/batch/_linear.py

@@ -147,7 +147,7 @@ def loss_linear_batch(M, T, nx=None):
     return nx.sum(M * T, axis=(1, 2))
 
 
-def loss_linear_samples_batch(X, Y, T, metric="l2"):
+def loss_linear_samples_batch(X, Y, T, metric="sqeuclidean"):
     r"""Computes the linear optimal transport loss given samples and transport plan. This is the equivalent of
     calling `dist_batch` and then `loss_linear_batch`.