fixes for travis, added test, minor nits

Author: ngayraud

.travis.yml

@@ -13,15 +13,14 @@ matrix:
           python: 3.5
         - os: linux
           sudo: required
-          python: 3.6        
+          python: 3.6
         - os: linux
           sudo: required
           python: 2.7
 before_install:
   - ./.travis/before_install.sh
 before_script: # configure a headless display to test plot generation
   - "export DISPLAY=:99.0"
-  - "sh -e /etc/init.d/xvfb start"
   - sleep 3 # give xvfb some time to start
 # command to install dependencies
 install:
@@ -30,6 +29,8 @@ install:
   - pip install flake8 pytest "pytest-cov<2.6"
   - pip install .
 # command to run tests + check syntax style
+services:
+  - xvfb
 script:
   - python setup.py develop
   - flake8 examples/ ot/ test/

ot/da.py

@@ -1852,7 +1852,7 @@ class UnbalancedSinkhornTransport(BaseTransport):
     """
 
     def __init__(self, reg_e=1., reg_m=0.1, method='sinkhorn',
-                 max_iter=10, tol=10e-9, verbose=False, log=False,
+                 max_iter=10, tol=1e-9, verbose=False, log=False,
                  metric="sqeuclidean", norm=None,
                  distribution_estimation=distribution_estimation_uniform,
                  out_of_sample_map='ferradans', limit_max=10):

ot/utils.py

@@ -178,7 +178,9 @@ def cost_normalization(C, norm=None):
         The input cost matrix normalized according to given norm.
     """
 
-    if norm == "median":
+    if norm is None:
+        pass
+    elif norm == "median":
         C /= float(np.median(C))
     elif norm == "max":
         C /= float(np.max(C))

test/test_da.py

@@ -245,6 +245,79 @@ def test_sinkhorn_transport_class():
     assert len(otda.log_.keys()) != 0
 
 
+def test_unbalanced_sinkhorn_transport_class():
+    """test_sinkhorn_transport
+    """
+
+    ns = 150
+    nt = 200
+
+    Xs, ys = make_data_classif('3gauss', ns)
+    Xt, yt = make_data_classif('3gauss2', nt)
+
+    otda = ot.da.UnbalancedSinkhornTransport()
+
+    # test its computed
+    otda.fit(Xs=Xs, Xt=Xt)
+    assert hasattr(otda, "cost_")
+    assert hasattr(otda, "coupling_")
+    assert hasattr(otda, "log_")
+
+    # test dimensions of coupling
+    assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
+    assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
+
+    # test margin constraints
+    mu_s = unif(ns)
+    mu_t = unif(nt)
+    assert_allclose(
+        np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3)
+    assert_allclose(
+        np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3)
+
+    # test transform
+    transp_Xs = otda.transform(Xs=Xs)
+    assert_equal(transp_Xs.shape, Xs.shape)
+
+    Xs_new, _ = make_data_classif('3gauss', ns + 1)
+    transp_Xs_new = otda.transform(Xs_new)
+
+    # check that the oos method is working
+    assert_equal(transp_Xs_new.shape, Xs_new.shape)
+
+    # test inverse transform
+    transp_Xt = otda.inverse_transform(Xt=Xt)
+    assert_equal(transp_Xt.shape, Xt.shape)
+
+    Xt_new, _ = make_data_classif('3gauss2', nt + 1)
+    transp_Xt_new = otda.inverse_transform(Xt=Xt_new)
+
+    # check that the oos method is working
+    assert_equal(transp_Xt_new.shape, Xt_new.shape)
+
+    # test fit_transform
+    transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt)
+    assert_equal(transp_Xs.shape, Xs.shape)
+
+    # test unsupervised vs semi-supervised mode
+    otda_unsup = ot.da.SinkhornTransport()
+    otda_unsup.fit(Xs=Xs, Xt=Xt)
+    n_unsup = np.sum(otda_unsup.cost_)
+
+    otda_semi = ot.da.SinkhornTransport()
+    otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt)
+    assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
+    n_semisup = np.sum(otda_semi.cost_)
+
+    # check that the cost matrix norms are indeed different
+    assert n_unsup != n_semisup, "semisupervised mode not working"
+
+    # check everything runs well with log=True
+    otda = ot.da.SinkhornTransport(log=True)
+    otda.fit(Xs=Xs, ys=ys, Xt=Xt)
+    assert len(otda.log_.keys()) != 0
+
+
 def test_emd_transport_class():
     """test_sinkhorn_transport
     """