take into account comments

Author: cedricvincentcuaz

ot/gromov/_gw.py

@@ -166,10 +166,6 @@ def df(G):
         def df(G):
             return 0.5 * (gwggrad(constC, hC1, hC2, G, np_) + gwggrad(constCt, hC1t, hC2t, G, np_))
 
-    # removed since 0.9.2
-    # if loss_fun == 'kl_loss':
-    #    armijo = True # there is no closed form line-search with KL
-
     if armijo:
         def line_search(cost, G, deltaG, Mi, cost_G, **kwargs):
             return line_search_armijo(cost, G, deltaG, Mi, cost_G, nx=np_, **kwargs)
@@ -478,10 +474,6 @@ def df(G):
         def df(G):
             return 0.5 * (gwggrad(constC, hC1, hC2, G, np_) + gwggrad(constCt, hC1t, hC2t, G, np_))
 
-    # removed since 0.9.2
-    # if loss_fun == 'kl_loss':
-    #    armijo = True  # there is no closed form line-search with KL
-
     if armijo:
         def line_search(cost, G, deltaG, Mi, cost_G, **kwargs):
             return line_search_armijo(cost, G, deltaG, Mi, cost_G, nx=np_, **kwargs)
@@ -827,10 +819,6 @@ def gromov_barycenters(
     else:
         C = init_C
 
-    # removed since 0.9.2
-    # if loss_fun == 'kl_loss':
-    #    armijo = True
-
     cpt = 0
     err = 1
 
@@ -1014,10 +1002,6 @@ def fgw_barycenters(
     if warmstartT:
         T = [nx.outer(p, q) for q in ps]
 
-    # removed since 0.9.2
-    # if loss_fun == 'kl_loss':
-    #    armijo = True
-
     cpt = 0
     err_feature = 1
     err_structure = 1

test/test_gromov.py

@@ -1425,39 +1425,38 @@ def test_fgw_barycenter(nx):
     init_C /= init_C.max()
     init_Cb = nx.from_numpy(init_C)
 
-    try:  # to raise warning when `fixed_structure=True`and `init_C=None`
+    with pytest.raises(ot.utils.UndefinedParameter):  # to raise warning when `fixed_structure=True`and `init_C=None`
         Xb, Cb = ot.gromov.fgw_barycenters(
             n_samples, [ysb, ytb], [C1b, C2b], ps=[p1b, p2b], lambdas=None,
             alpha=0.5, fixed_structure=True, init_C=None, fixed_features=False,
             p=None, loss_fun='square_loss', max_iter=100, tol=1e-3
         )
-    except ot.utils.UndefinedParameter:
-        Xb, Cb = ot.gromov.fgw_barycenters(
-            n_samples, [ysb, ytb], [C1b, C2b], ps=[p1b, p2b], lambdas=None,
-            alpha=0.5, fixed_structure=True, init_C=init_Cb, fixed_features=False,
-            p=None, loss_fun='square_loss', max_iter=100, tol=1e-3
-        )
+
+    Xb, Cb = ot.gromov.fgw_barycenters(
+        n_samples, [ysb, ytb], [C1b, C2b], ps=[p1b, p2b], lambdas=None,
+        alpha=0.5, fixed_structure=True, init_C=init_Cb, fixed_features=False,
+        p=None, loss_fun='square_loss', max_iter=100, tol=1e-3
+    )
     Xb, Cb = nx.to_numpy(Xb), nx.to_numpy(Cb)
     np.testing.assert_allclose(Cb.shape, (n_samples, n_samples))
     np.testing.assert_allclose(Xb.shape, (n_samples, ys.shape[1]))
 
     init_X = rng.randn(n_samples, ys.shape[1])
     init_Xb = nx.from_numpy(init_X)
 
-    try:  # to raise warning when `fixed_features=True`and `init_X=None`
+    with pytest.raises(ot.utils.UndefinedParameter):  # to raise warning when `fixed_features=True`and `init_X=None`
         Xb, Cb, logb = ot.gromov.fgw_barycenters(
             n_samples, [ysb, ytb], [C1b, C2b], [p1b, p2b], [.5, .5], 0.5,
             fixed_structure=False, fixed_features=True, init_X=None,
             p=pb, loss_fun='square_loss', max_iter=100, tol=1e-3,
             warmstartT=True, log=True, random_state=98765, verbose=True
         )
-    except ot.utils.UndefinedParameter:
-        Xb, Cb, logb = ot.gromov.fgw_barycenters(
-            n_samples, [ysb, ytb], [C1b, C2b], [p1b, p2b], [.5, .5], 0.5,
-            fixed_structure=False, fixed_features=True, init_X=init_Xb,
-            p=pb, loss_fun='square_loss', max_iter=100, tol=1e-3,
-            warmstartT=True, log=True, random_state=98765, verbose=True
-        )
+    Xb, Cb, logb = ot.gromov.fgw_barycenters(
+        n_samples, [ysb, ytb], [C1b, C2b], [p1b, p2b], [.5, .5], 0.5,
+        fixed_structure=False, fixed_features=True, init_X=init_Xb,
+        p=pb, loss_fun='square_loss', max_iter=100, tol=1e-3,
+        warmstartT=True, log=True, random_state=98765, verbose=True
+    )
 
     X, C = nx.to_numpy(Xb), nx.to_numpy(Cb)
     np.testing.assert_allclose(C.shape, (n_samples, n_samples))