semi supervised mode supported

Author: Slasnista

ot/da.py

@@ -1089,8 +1089,25 @@ def fit(self, Xs=None, ys=None, Xt=None, yt=None):
         self.Cost = dist(Xs, Xt, metric=self.metric)
 
         if self.mode == "semisupervised":
-            print("TODO: modify cost matrix accordingly")
-            pass
+
+            if (ys is not None) and (yt is not None):
+
+                # assumes labeled source samples occupy the first rows
+                # and labeled target samples occupy the first columns
+                classes = np.unique(ys)
+                for c in classes:
+                    ids = np.where(ys == c)
+                    idt = np.where(yt == c)
+
+                    # all the coefficients corresponding to a source sample
+                    # and a target sample with the same label gets a 0
+                    # transport cost
+                    for j in idt[0]:
+                        self.Cost[ids[0], j] = 0
+            else:
+                print("Warning: using unsupervised mode\
+                       \nto use semisupervised mode, please provide ys and yt")
+                pass
 
         # distribution estimation
         self.mu_s = self.distribution_estimation(Xs)

test/test_da.py

@@ -62,6 +62,19 @@ def test_sinkhorn_lpl1_transport_class():
     transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt)
     assert_equal(transp_Xs.shape, Xs.shape)
 
+    # test semi supervised mode
+    clf = ot.da.SinkhornTransport(mode="semisupervised")
+    clf.fit(Xs=Xs, Xt=Xt)
+    n_unsup = np.sum(clf.Cost)
+
+    # test semi supervised mode
+    clf = ot.da.SinkhornTransport(mode="semisupervised")
+    clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt)
+    assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0])))
+    n_semisup = np.sum(clf.Cost)
+
+    assert n_unsup != n_semisup, "semisupervised mode not working"
+
 
 def test_sinkhorn_l1l2_transport_class():
     """test_sinkhorn_transport
@@ -112,6 +125,19 @@ def test_sinkhorn_l1l2_transport_class():
     transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt)
     assert_equal(transp_Xs.shape, Xs.shape)
 
+    # test semi supervised mode
+    clf = ot.da.SinkhornTransport(mode="semisupervised")
+    clf.fit(Xs=Xs, Xt=Xt)
+    n_unsup = np.sum(clf.Cost)
+
+    # test semi supervised mode
+    clf = ot.da.SinkhornTransport(mode="semisupervised")
+    clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt)
+    assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0])))
+    n_semisup = np.sum(clf.Cost)
+
+    assert n_unsup != n_semisup, "semisupervised mode not working"
+
 
 def test_sinkhorn_transport_class():
     """test_sinkhorn_transport
@@ -162,6 +188,19 @@ def test_sinkhorn_transport_class():
     transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt)
     assert_equal(transp_Xs.shape, Xs.shape)
 
+    # test semi supervised mode
+    clf = ot.da.SinkhornTransport(mode="semisupervised")
+    clf.fit(Xs=Xs, Xt=Xt)
+    n_unsup = np.sum(clf.Cost)
+
+    # test semi supervised mode
+    clf = ot.da.SinkhornTransport(mode="semisupervised")
+    clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt)
+    assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0])))
+    n_semisup = np.sum(clf.Cost)
+
+    assert n_unsup != n_semisup, "semisupervised mode not working"
+
 
 def test_emd_transport_class():
     """test_sinkhorn_transport
@@ -212,6 +251,19 @@ def test_emd_transport_class():
     transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt)
     assert_equal(transp_Xs.shape, Xs.shape)
 
+    # test semi supervised mode
+    clf = ot.da.SinkhornTransport(mode="semisupervised")
+    clf.fit(Xs=Xs, Xt=Xt)
+    n_unsup = np.sum(clf.Cost)
+
+    # test semi supervised mode
+    clf = ot.da.SinkhornTransport(mode="semisupervised")
+    clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt)
+    assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0])))
+    n_semisup = np.sum(clf.Cost)
+
+    assert n_unsup != n_semisup, "semisupervised mode not working"
+
 
 def test_otda():