[MRG] Fix bug in regularized OTDA l1lp with log (#413)

* correct bug in DA l1lp with log

* better tests and speedup with smaller dataset size

* remove jax for log test

* remove trndorflow for log test

* pep8!

Author: rflamary

RELEASES.md

@@ -27,7 +27,8 @@ roughly 2^31) (PR #381)
 - Fixed an issue where the doc could not be built due to some changes in matplotlib's API (Issue #403, PR #402)
 - Replaced Numpy C Compiler with Setuptools C Compiler due to deprecation issues (Issue #408, PR #409)
 - Fixed weak optimal transport docstring (Issue #404, PR #410)
-
+- Fixed error whith parameter `log=True`for `SinkhornLpl1Transport` (Issue #412, 
+PR #413)
 
 ## 0.8.2
 

ot/da.py

@@ -126,8 +126,12 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
     W = nx.zeros(M.shape, type_as=M)
     for cpt in range(numItermax):
         Mreg = M + eta * W
-        transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax,
-                          stopThr=stopInnerThr)
+        if log:
+            transp, log = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax,
+                                   stopThr=stopInnerThr, log=True)
+        else:
+            transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax,
+                              stopThr=stopInnerThr)
         # the transport has been computed. Check if classes are really
         # separated
         W = nx.ones(M.shape, type_as=M)
@@ -136,7 +140,10 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
             majs = p * ((majs + epsilon) ** (p - 1))
             W[indices_labels[i]] = majs
 
-    return transp
+    if log:
+        return transp, log
+    else:
+        return transp
 
 
 def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10,

test/test_da.py

@@ -42,14 +42,34 @@ def test_class_jax_tf():
             otda.fit(Xs=Xs, ys=ys, Xt=Xt)
 
 
+@pytest.skip_backend("jax")
+@pytest.skip_backend("tf")
+@pytest.mark.parametrize("class_to_test", [ot.da.EMDTransport, ot.da.SinkhornTransport, ot.da.SinkhornLpl1Transport, ot.da.SinkhornL1l2Transport, ot.da.SinkhornL1l2Transport])
+def test_log_da(nx, class_to_test):
+
+    ns = 50
+    nt = 50
+
+    Xs, ys = make_data_classif('3gauss', ns)
+    Xt, yt = make_data_classif('3gauss2', nt)
+
+    Xs, ys, Xt, yt = nx.from_numpy(Xs, ys, Xt, yt)
+
+    otda = class_to_test(log=True)
+
+    # test its computed
+    otda.fit(Xs=Xs, ys=ys, Xt=Xt)
+    assert hasattr(otda, "log_")
+
+
 @pytest.skip_backend("jax")
 @pytest.skip_backend("tf")
 def test_sinkhorn_lpl1_transport_class(nx):
     """test_sinkhorn_transport
     """
 
-    ns = 150
-    nt = 200
+    ns = 50
+    nt = 50
 
     Xs, ys = make_data_classif('3gauss', ns)
     Xt, yt = make_data_classif('3gauss2', nt)
@@ -136,7 +156,7 @@ def test_sinkhorn_l1l2_transport_class(nx):
     """
 
     ns = 50
-    nt = 100
+    nt = 50
 
     Xs, ys = make_data_classif('3gauss', ns)
     Xt, yt = make_data_classif('3gauss2', nt)
@@ -230,8 +250,8 @@ def test_sinkhorn_transport_class(nx):
     """test_sinkhorn_transport
     """
 
-    ns = 150
-    nt = 200
+    ns = 50
+    nt = 50
 
     Xs, ys = make_data_classif('3gauss', ns)
     Xt, yt = make_data_classif('3gauss2', nt)
@@ -323,8 +343,8 @@ def test_unbalanced_sinkhorn_transport_class(nx):
     """test_sinkhorn_transport
     """
 
-    ns = 150
-    nt = 200
+    ns = 50
+    nt = 50
 
     Xs, ys = make_data_classif('3gauss', ns)
     Xt, yt = make_data_classif('3gauss2', nt)
@@ -402,8 +422,8 @@ def test_emd_transport_class(nx):
     """test_sinkhorn_transport
     """
 
-    ns = 150
-    nt = 200
+    ns = 50
+    nt = 50
 
     Xs, ys = make_data_classif('3gauss', ns)
     Xt, yt = make_data_classif('3gauss2', nt)
@@ -558,8 +578,8 @@ def test_mapping_transport_class_specific_seed(nx):
 @pytest.skip_backend("jax")
 @pytest.skip_backend("tf")
 def test_linear_mapping(nx):
-    ns = 150
-    nt = 200
+    ns = 50
+    nt = 50
 
     Xs, ys = make_data_classif('3gauss', ns)
     Xt, yt = make_data_classif('3gauss2', nt)
@@ -579,8 +599,8 @@ def test_linear_mapping(nx):
 @pytest.skip_backend("jax")
 @pytest.skip_backend("tf")
 def test_linear_mapping_class(nx):
-    ns = 150
-    nt = 200
+    ns = 50
+    nt = 50
 
     Xs, ys = make_data_classif('3gauss', ns)
     Xt, yt = make_data_classif('3gauss2', nt)
@@ -609,9 +629,9 @@ def test_jcpot_transport_class(nx):
     """test_jcpot_transport
     """
 
-    ns1 = 150
-    ns2 = 150
-    nt = 200
+    ns1 = 50
+    ns2 = 50
+    nt = 50
 
     Xs1, ys1 = make_data_classif('3gauss', ns1)
     Xs2, ys2 = make_data_classif('3gauss', ns2)
@@ -681,9 +701,9 @@ def test_jcpot_barycenter(nx):
     """test_jcpot_barycenter
     """
 
-    ns1 = 150
-    ns2 = 150
-    nt = 200
+    ns1 = 50
+    ns2 = 50
+    nt = 50
 
     sigma = 0.1
     np.random.seed(1985)
@@ -713,8 +733,8 @@ def test_jcpot_barycenter(nx):
 def test_emd_laplace_class(nx):
     """test_emd_laplace_transport
     """
-    ns = 150
-    nt = 200
+    ns = 50
+    nt = 50
 
     Xs, ys = make_data_classif('3gauss', ns)
     Xt, yt = make_data_classif('3gauss2', nt)