[MRG] Fix issue 317 (#318)

* Fix issue 317

* Update with docs and tests

Co-authored-by: Rémi Flamary <[email protected]>

Author: cshjin

ot/gromov.py

@@ -1368,6 +1368,8 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon,
     -------
     C : array-like, shape (`N`, `N`)
         Similarity matrix in the barycenter space (permutated arbitrarily)
+    log : dict
+        Log dictionary of error during iterations. Return only if `log=True` in parameters.
 
     References
     ----------
@@ -1401,7 +1403,7 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon,
         Cprev = C
 
         T = [entropic_gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon,
-                                         max_iter, 1e-4, verbose, log) for s in range(S)]
+                                         max_iter, 1e-4, verbose, log=False) for s in range(S)]
         if loss_fun == 'square_loss':
             C = update_square_loss(p, lambdas, T, Cs)
 
@@ -1414,9 +1416,6 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon,
             err = nx.norm(C - Cprev)
             error.append(err)
 
-            if log:
-                log['err'].append(err)
-
             if verbose:
                 if cpt % 200 == 0:
                     print('{:5s}|{:12s}'.format(
@@ -1425,7 +1424,10 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon,
 
         cpt += 1
 
-    return C
+    if log:
+        return C, {"err": error}
+    else:
+        return C
 
 
 def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun,
@@ -1479,6 +1481,8 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun,
     -------
     C : array-like, shape (`N`, `N`)
         Similarity matrix in the barycenter space (permutated arbitrarily)
+    log : dict
+        Log dictionary of error during iterations. Return only if `log=True` in parameters.
 
     References
     ----------
@@ -1513,7 +1517,7 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun,
         Cprev = C
 
         T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun,
-                                numItermax=max_iter, stopThr=1e-5, verbose=verbose, log=log) for s in range(S)]
+                                numItermax=max_iter, stopThr=1e-5, verbose=verbose, log=False) for s in range(S)]
         if loss_fun == 'square_loss':
             C = update_square_loss(p, lambdas, T, Cs)
 
@@ -1526,9 +1530,6 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun,
             err = nx.norm(C - Cprev)
             error.append(err)
 
-            if log:
-                log['err'].append(err)
-
             if verbose:
                 if cpt % 200 == 0:
                     print('{:5s}|{:12s}'.format(
@@ -1537,7 +1538,10 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun,
 
         cpt += 1
 
-    return C
+    if log:
+        return C, {"err": error}
+    else:
+        return C
 
 
 def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_features=False,

test/test_gromov.py

@@ -385,6 +385,20 @@ def test_gromov_barycenter(nx):
     np.testing.assert_allclose(Cb, Cbb, atol=1e-06)
     np.testing.assert_allclose(Cbb.shape, (n_samples, n_samples))
 
+    # test of gromov_barycenters with `log` on
+    Cb_, err_ = ot.gromov.gromov_barycenters(
+        n_samples, [C1, C2], [p1, p2], p, [.5, .5],
+        'square_loss', max_iter=100, tol=1e-3, verbose=True, random_state=42, log=True
+    )
+    Cbb_, errb_ = ot.gromov.gromov_barycenters(
+        n_samples, [C1b, C2b], [p1b, p2b], pb, [.5, .5],
+        'square_loss', max_iter=100, tol=1e-3, verbose=True, random_state=42, log=True
+    )
+    Cbb_ = nx.to_numpy(Cbb_)
+    np.testing.assert_allclose(Cb_, Cbb_, atol=1e-06)
+    np.testing.assert_array_almost_equal(err_['err'], errb_['err'])
+    np.testing.assert_allclose(Cbb_.shape, (n_samples, n_samples))
+
     Cb2 = ot.gromov.gromov_barycenters(
         n_samples, [C1, C2], [p1, p2], p, [.5, .5],
         'kl_loss', max_iter=100, tol=1e-3, random_state=42
@@ -396,6 +410,20 @@ def test_gromov_barycenter(nx):
     np.testing.assert_allclose(Cb2, Cb2b, atol=1e-06)
     np.testing.assert_allclose(Cb2b.shape, (n_samples, n_samples))
 
+    # test of gromov_barycenters with `log` on
+    Cb2_, err2_ = ot.gromov.gromov_barycenters(
+        n_samples, [C1, C2], [p1, p2], p, [.5, .5],
+        'kl_loss', max_iter=100, tol=1e-3, verbose=True, random_state=42, log=True
+    )
+    Cb2b_, err2b_ = ot.gromov.gromov_barycenters(
+        n_samples, [C1b, C2b], [p1b, p2b], pb, [.5, .5],
+        'kl_loss', max_iter=100, tol=1e-3, verbose=True, random_state=42, log=True
+    )
+    Cb2b_ = nx.to_numpy(Cb2b_)
+    np.testing.assert_allclose(Cb2_, Cb2b_, atol=1e-06)
+    np.testing.assert_array_almost_equal(err2_['err'], err2_['err'])
+    np.testing.assert_allclose(Cb2b_.shape, (n_samples, n_samples))
+
 
 @pytest.mark.filterwarnings("ignore:divide")
 def test_gromov_entropic_barycenter(nx):
@@ -429,6 +457,20 @@ def test_gromov_entropic_barycenter(nx):
     np.testing.assert_allclose(Cb, Cbb, atol=1e-06)
     np.testing.assert_allclose(Cbb.shape, (n_samples, n_samples))
 
+    # test of entropic_gromov_barycenters with `log` on
+    Cb_, err_ = ot.gromov.entropic_gromov_barycenters(
+        n_samples, [C1, C2], [p1, p2], p, [.5, .5],
+        'square_loss', 1e-3, max_iter=100, tol=1e-3, verbose=True, random_state=42, log=True
+    )
+    Cbb_, errb_ = ot.gromov.entropic_gromov_barycenters(
+        n_samples, [C1b, C2b], [p1b, p2b], pb, [.5, .5],
+        'square_loss', 1e-3, max_iter=100, tol=1e-3, verbose=True, random_state=42, log=True
+    )
+    Cbb_ = nx.to_numpy(Cbb_)
+    np.testing.assert_allclose(Cb_, Cbb_, atol=1e-06)
+    np.testing.assert_array_almost_equal(err_['err'], errb_['err'])
+    np.testing.assert_allclose(Cbb_.shape, (n_samples, n_samples))
+
     Cb2 = ot.gromov.entropic_gromov_barycenters(
         n_samples, [C1, C2], [p1, p2], p, [.5, .5],
         'kl_loss', 1e-3, max_iter=100, tol=1e-3, random_state=42
@@ -440,6 +482,20 @@ def test_gromov_entropic_barycenter(nx):
     np.testing.assert_allclose(Cb2, Cb2b, atol=1e-06)
     np.testing.assert_allclose(Cb2b.shape, (n_samples, n_samples))
 
+    # test of entropic_gromov_barycenters with `log` on
+    Cb2_, err2_ = ot.gromov.entropic_gromov_barycenters(
+        n_samples, [C1, C2], [p1, p2], p, [.5, .5],
+        'kl_loss', 1e-3, max_iter=100, tol=1e-3, verbose=True, random_state=42, log=True
+    )
+    Cb2b_, err2b_ = ot.gromov.entropic_gromov_barycenters(
+        n_samples, [C1b, C2b], [p1b, p2b], pb, [.5, .5],
+        'kl_loss', 1e-3, max_iter=100, tol=1e-3, verbose=True, random_state=42, log=True
+    )
+    Cb2b_ = nx.to_numpy(Cb2b_)
+    np.testing.assert_allclose(Cb2_, Cb2b_, atol=1e-06)
+    np.testing.assert_array_almost_equal(err2_['err'], err2_['err'])
+    np.testing.assert_allclose(Cb2b_.shape, (n_samples, n_samples))
+
 
 def test_fgw(nx):
     n_samples = 50  # nb samples