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-rw-r--r--test/test_da.py112
1 files changed, 109 insertions, 3 deletions
diff --git a/test/test_da.py b/test/test_da.py
index 2a5e50e..372ebd4 100644
--- a/test/test_da.py
+++ b/test/test_da.py
@@ -5,7 +5,7 @@
# License: MIT License
import numpy as np
-from numpy.testing.utils import assert_allclose, assert_equal
+from numpy.testing import assert_allclose, assert_equal
import ot
from ot.datasets import make_data_classif
@@ -510,7 +510,6 @@ def test_mapping_transport_class():
def test_linear_mapping():
-
ns = 150
nt = 200
@@ -528,7 +527,6 @@ def test_linear_mapping():
def test_linear_mapping_class():
-
ns = 150
nt = 200
@@ -549,3 +547,111 @@ def test_linear_mapping_class():
Cst = np.cov(Xst.T)
np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2)
+
+
+def test_jcpot_transport_class():
+ """test_jcpot_transport
+ """
+
+ ns1 = 150
+ ns2 = 150
+ nt = 200
+
+ Xs1, ys1 = make_data_classif('3gauss', ns1)
+ Xs2, ys2 = make_data_classif('3gauss', ns2)
+
+ Xt, yt = make_data_classif('3gauss2', nt)
+
+ Xs = [Xs1, Xs2]
+ ys = [ys1, ys2]
+
+ otda = ot.da.JCPOTTransport(reg_e=0.01, max_iter=1000, tol=1e-9, verbose=True, log=True)
+
+ # test its computed
+ otda.fit(Xs=Xs, ys=ys, Xt=Xt)
+
+ assert hasattr(otda, "coupling_")
+ assert hasattr(otda, "proportions_")
+ assert hasattr(otda, "log_")
+
+ # test dimensions of coupling
+ for i, xs in enumerate(Xs):
+ assert_equal(otda.coupling_[i].shape, ((xs.shape[0], Xt.shape[0])))
+
+ # test all margin constraints
+ mu_t = unif(nt)
+
+ for i in range(len(Xs)):
+ # test margin constraints w.r.t. uniform target weights for each coupling matrix
+ assert_allclose(
+ np.sum(otda.coupling_[i], axis=0), mu_t, rtol=1e-3, atol=1e-3)
+
+ # test margin constraints w.r.t. modified source weights for each source domain
+
+ assert_allclose(
+ np.dot(otda.log_['all_domains'][i]['D1'], np.sum(otda.coupling_[i], axis=1)), otda.proportions_, rtol=1e-3,
+ atol=1e-3)
+
+ # test transform
+ transp_Xs = otda.transform(Xs=Xs)
+ [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)]
+
+ Xs_new, _ = make_data_classif('3gauss', ns1 + 1)
+ transp_Xs_new = otda.transform(Xs_new)
+
+ # check that the oos method is working
+ assert_equal(transp_Xs_new.shape, Xs_new.shape)
+
+
+def test_emd_laplace_class():
+ """test_emd_laplace_transport
+ """
+ ns = 150
+ nt = 200
+
+ Xs, ys = make_data_classif('3gauss', ns)
+ Xt, yt = make_data_classif('3gauss2', nt)
+
+ otda = ot.da.EMDLaplaceTransport(reg_lap=0.01, max_iter=1000, tol=1e-9, verbose=False, log=True)
+
+ # test its computed
+ otda.fit(Xs=Xs, ys=ys, Xt=Xt)
+
+ assert hasattr(otda, "coupling_")
+ assert hasattr(otda, "log_")
+
+ # test dimensions of coupling
+ assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
+
+ # test all 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(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)]
+
+ 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)