correction of semi supervised mode

Author: Slasnista

ot/da.py

@@ -1088,26 +1088,23 @@ def fit(self, Xs=None, ys=None, Xt=None, yt=None):
         # pairwise distance
         self.Cost = dist(Xs, Xt, metric=self.metric)
 
-        if self.mode == "semisupervised":
-
-            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
+        if (ys is not None) and (yt is not None):
+
+            if self.limit_max != np.infty:
+                self.limit_max = self.limit_max * np.max(self.Cost)
+
+            # assumes labeled source samples occupy the first rows
+            # and labeled target samples occupy the first columns
+            classes = np.unique(ys)
+            for c in classes:
+                idx_s = np.where((ys != c) & (ys != -1))
+                idx_t = np.where(yt == c)
+
+                # all the coefficients corresponding to a source sample
+                # and a target sample :
+                # with different labels get a infinite
+                for j in idx_t[0]:
+                    self.Cost[idx_s[0], j] = self.limit_max
 
         # distribution estimation
         self.mu_s = self.distribution_estimation(Xs)
@@ -1243,6 +1240,9 @@ class SinkhornTransport(BaseTransport):
         Controls the verbosity of the optimization algorithm
     log : int, optional (default=0)
         Controls the logs of the optimization algorithm
+    limit_max: float, optional (defaul=np.infty)
+        Controls the semi supervised mode. Transport between labeled source
+        and target samples of different classes will exhibit an infinite cost
     Attributes
     ----------
     Coupling_ : the optimal coupling
@@ -1257,19 +1257,19 @@ class SinkhornTransport(BaseTransport):
            26, 2013
     """
 
-    def __init__(self, reg_e=1., mode="unsupervised", max_iter=1000,
+    def __init__(self, reg_e=1., max_iter=1000,
                  tol=10e-9, verbose=False, log=False,
                  metric="sqeuclidean",
                  distribution_estimation=distribution_estimation_uniform,
-                 out_of_sample_map='ferradans'):
+                 out_of_sample_map='ferradans', limit_max=np.infty):
 
         self.reg_e = reg_e
-        self.mode = mode
         self.max_iter = max_iter
         self.tol = tol
         self.verbose = verbose
         self.log = log
         self.metric = metric
+        self.limit_max = limit_max
         self.distribution_estimation = distribution_estimation
         self.out_of_sample_map = out_of_sample_map
 
@@ -1326,6 +1326,10 @@ class EMDTransport(BaseTransport):
         Controls the verbosity of the optimization algorithm
     log : int, optional (default=0)
         Controls the logs of the optimization algorithm
+    limit_max: float, optional (default=10)
+        Controls the semi supervised mode. Transport between labeled source
+        and target samples of different classes will exhibit an infinite cost
+        (10 times the maximum value of the cost matrix)
     Attributes
     ----------
     Coupling_ : the optimal coupling
@@ -1337,15 +1341,15 @@ class EMDTransport(BaseTransport):
            on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
     """
 
-    def __init__(self, mode="unsupervised", verbose=False,
+    def __init__(self, verbose=False,
                  log=False, metric="sqeuclidean",
                  distribution_estimation=distribution_estimation_uniform,
-                 out_of_sample_map='ferradans'):
+                 out_of_sample_map='ferradans', limit_max=10):
 
-        self.mode = mode
         self.verbose = verbose
         self.log = log
         self.metric = metric
+        self.limit_max = limit_max
         self.distribution_estimation = distribution_estimation
         self.out_of_sample_map = out_of_sample_map
 
@@ -1414,6 +1418,10 @@ class SinkhornLpl1Transport(BaseTransport):
         Controls the verbosity of the optimization algorithm
     log : int, optional (default=0)
         Controls the logs of the optimization algorithm
+    limit_max: float, optional (defaul=np.infty)
+        Controls the semi supervised mode. Transport between labeled source
+        and target samples of different classes will exhibit an infinite cost
+
     Attributes
     ----------
     Coupling_ : the optimal coupling
@@ -1431,16 +1439,15 @@ class SinkhornLpl1Transport(BaseTransport):
 
     """
 
-    def __init__(self, reg_e=1., reg_cl=0.1, mode="unsupervised",
+    def __init__(self, reg_e=1., reg_cl=0.1,
                  max_iter=10, max_inner_iter=200,
                  tol=10e-9, verbose=False, log=False,
                  metric="sqeuclidean",
                  distribution_estimation=distribution_estimation_uniform,
-                 out_of_sample_map='ferradans'):
+                 out_of_sample_map='ferradans', limit_max=np.infty):
 
         self.reg_e = reg_e
         self.reg_cl = reg_cl
-        self.mode = mode
         self.max_iter = max_iter
         self.max_inner_iter = max_inner_iter
         self.tol = tol
@@ -1449,6 +1456,7 @@ def __init__(self, reg_e=1., reg_cl=0.1, mode="unsupervised",
         self.metric = metric
         self.distribution_estimation = distribution_estimation
         self.out_of_sample_map = out_of_sample_map
+        self.limit_max = limit_max
 
     def fit(self, Xs, ys=None, Xt=None, yt=None):
         """Build a coupling matrix from source and target sets of samples
@@ -1514,6 +1522,11 @@ class SinkhornL1l2Transport(BaseTransport):
         Controls the verbosity of the optimization algorithm
     log : int, optional (default=0)
         Controls the logs of the optimization algorithm
+    limit_max: float, optional (default=10)
+        Controls the semi supervised mode. Transport between labeled source
+        and target samples of different classes will exhibit an infinite cost
+        (10 times the maximum value of the cost matrix)
+
     Attributes
     ----------
     Coupling_ : the optimal coupling
@@ -1531,16 +1544,15 @@ class SinkhornL1l2Transport(BaseTransport):
 
     """
 
-    def __init__(self, reg_e=1., reg_cl=0.1, mode="unsupervised",
+    def __init__(self, reg_e=1., reg_cl=0.1,
                  max_iter=10, max_inner_iter=200,
                  tol=10e-9, verbose=False, log=False,
                  metric="sqeuclidean",
                  distribution_estimation=distribution_estimation_uniform,
-                 out_of_sample_map='ferradans'):
+                 out_of_sample_map='ferradans', limit_max=10):
 
         self.reg_e = reg_e
         self.reg_cl = reg_cl
-        self.mode = mode
         self.max_iter = max_iter
         self.max_inner_iter = max_inner_iter
         self.tol = tol
@@ -1549,6 +1561,7 @@ def __init__(self, reg_e=1., reg_cl=0.1, mode="unsupervised",
         self.metric = metric
         self.distribution_estimation = distribution_estimation
         self.out_of_sample_map = out_of_sample_map
+        self.limit_max = limit_max
 
     def fit(self, Xs, ys=None, Xt=None, yt=None):
         """Build a coupling matrix from source and target sets of samples

test/test_da.py

@@ -63,12 +63,12 @@ def test_sinkhorn_lpl1_transport_class():
     assert_equal(transp_Xs.shape, Xs.shape)
 
     # test semi supervised mode
-    clf = ot.da.SinkhornTransport(mode="semisupervised")
-    clf.fit(Xs=Xs, Xt=Xt)
+    clf = ot.da.SinkhornLpl1Transport()
+    clf.fit(Xs=Xs, ys=ys, Xt=Xt)
     n_unsup = np.sum(clf.Cost)
 
     # test semi supervised mode
-    clf = ot.da.SinkhornTransport(mode="semisupervised")
+    clf = ot.da.SinkhornLpl1Transport()
     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)
@@ -126,12 +126,12 @@ def test_sinkhorn_l1l2_transport_class():
     assert_equal(transp_Xs.shape, Xs.shape)
 
     # test semi supervised mode
-    clf = ot.da.SinkhornTransport(mode="semisupervised")
-    clf.fit(Xs=Xs, Xt=Xt)
+    clf = ot.da.SinkhornL1l2Transport()
+    clf.fit(Xs=Xs, ys=ys, Xt=Xt)
     n_unsup = np.sum(clf.Cost)
 
     # test semi supervised mode
-    clf = ot.da.SinkhornTransport(mode="semisupervised")
+    clf = ot.da.SinkhornL1l2Transport()
     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)
@@ -189,12 +189,12 @@ def test_sinkhorn_transport_class():
     assert_equal(transp_Xs.shape, Xs.shape)
 
     # test semi supervised mode
-    clf = ot.da.SinkhornTransport(mode="semisupervised")
+    clf = ot.da.SinkhornTransport()
     clf.fit(Xs=Xs, Xt=Xt)
     n_unsup = np.sum(clf.Cost)
 
     # test semi supervised mode
-    clf = ot.da.SinkhornTransport(mode="semisupervised")
+    clf = ot.da.SinkhornTransport()
     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)
@@ -252,12 +252,12 @@ def test_emd_transport_class():
     assert_equal(transp_Xs.shape, Xs.shape)
 
     # test semi supervised mode
-    clf = ot.da.SinkhornTransport(mode="semisupervised")
+    clf = ot.da.EMDTransport()
     clf.fit(Xs=Xs, Xt=Xt)
     n_unsup = np.sum(clf.Cost)
 
     # test semi supervised mode
-    clf = ot.da.SinkhornTransport(mode="semisupervised")
+    clf = ot.da.EMDTransport()
     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)