def test_linear_mapping():
ns = 150
nt = 200
Xs, ys = get_data_classif('3gauss', ns)
Xt, yt = get_data_classif('3gauss2', nt)
A, b = ot.da.OT_mapping_linear(Xs, Xt)
Xst = Xs.dot(A) + b
Ct = np.cov(Xt.T)
Cst = np.cov(Xst.T)
np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2)
def test_linear_mapping():
ns = 150
nt = 200
Xs, ys = get_data_classif('3gauss', ns)
Xt, yt = get_data_classif('3gauss2', nt)
A, b = ot.da.OT_mapping_linear(Xs, Xt)
Xst = Xs.dot(A) + b
Ct = np.cov(Xt.T)
Cst = np.cov(Xst.T)
np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2)
def test_linear_mapping_class():
ns = 150
nt = 200
Xs, ys = get_data_classif('3gauss', ns)
Xt, yt = get_data_classif('3gauss2', nt)
otmap = ot.da.LinearTransport()
otmap.fit(Xs=Xs, Xt=Xt)
assert hasattr(otmap, "A_")
assert hasattr(otmap, "B_")
assert hasattr(otmap, "A1_")
assert hasattr(otmap, "B1_")
Xst = otmap.transform(Xs=Xs)
Ct = np.cov(Xt.T)
Cst = np.cov(Xst.T)
np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2)
def test_linear_mapping_class():
ns = 150
nt = 200
Xs, ys = get_data_classif('3gauss', ns)
Xt, yt = get_data_classif('3gauss2', nt)
otmap = ot.da.LinearTransport()
otmap.fit(Xs=Xs, Xt=Xt)
assert hasattr(otmap, "A_")
assert hasattr(otmap, "B_")
assert hasattr(otmap, "A1_")
assert hasattr(otmap, "B1_")
Xst = otmap.transform(Xs=Xs)
Ct = np.cov(Xt.T)
Cst = np.cov(Xst.T)
np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2)
def test_sinkhorn_l1l2_transport_class():
"""test_sinkhorn_transport
"""
ns = 150
nt = 200
Xs, ys = get_data_classif('3gauss', ns)
Xt, yt = get_data_classif('3gauss2', nt)
otda = ot.da.SinkhornL1l2Transport()
# test its computed
otda.fit(Xs=Xs, ys=ys, 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, _ = get_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, _ = get_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, ys=ys, Xt=Xt)
assert_equal(transp_Xs.shape, Xs.shape)
# test unsupervised vs semi-supervised mode
otda_unsup = ot.da.SinkhornL1l2Transport()
otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt)
n_unsup = np.sum(otda_unsup.cost_)
otda_semi = ot.da.SinkhornL1l2Transport()
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 that the coupling forbids mass transport between labeled source
# and labeled target samples
mass_semi = np.sum(
otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max])
mass_semi = otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max]
assert_allclose(mass_semi, np.zeros_like(mass_semi), rtol=1e-9, atol=1e-9)
# check everything runs well with log=True
otda = ot.da.SinkhornL1l2Transport(log=True)
otda.fit(Xs=Xs, ys=ys, Xt=Xt)
assert len(otda.log_.keys()) != 0
def test_mapping_transport_class():
"""test_mapping_transport
"""
ns = 60
nt = 120
Xs, ys = get_data_classif('3gauss', ns)
Xt, yt = get_data_classif('3gauss2', nt)
Xs_new, _ = get_data_classif('3gauss', ns + 1)
##########################################################################
# kernel == linear mapping tests
##########################################################################
# check computation and dimensions if bias == False
otda = ot.da.MappingTransport(kernel="linear", bias=False)
otda.fit(Xs=Xs, Xt=Xt)
assert hasattr(otda, "coupling_")
assert hasattr(otda, "mapping_")
assert hasattr(otda, "log_")
assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
assert_equal(otda.mapping_.shape, ((Xs.shape[1], Xt.shape[1])))
# 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)
transp_Xs_new = otda.transform(Xs_new)
# check that the oos method is working
assert_equal(transp_Xs_new.shape, Xs_new.shape)
# check computation and dimensions if bias == True
otda = ot.da.MappingTransport(kernel="linear", bias=True)
otda.fit(Xs=Xs, Xt=Xt)
assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
assert_equal(otda.mapping_.shape, ((Xs.shape[1] + 1, Xt.shape[1])))
# 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)
transp_Xs_new = otda.transform(Xs_new)
# check that the oos method is working
assert_equal(transp_Xs_new.shape, Xs_new.shape)
##########################################################################
# kernel == gaussian mapping tests
##########################################################################
# check computation and dimensions if bias == False
otda = ot.da.MappingTransport(kernel="gaussian", bias=False)
otda.fit(Xs=Xs, Xt=Xt)
assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
assert_equal(otda.mapping_.shape, ((Xs.shape[0], Xt.shape[1])))
# 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)
transp_Xs_new = otda.transform(Xs_new)
# check that the oos method is working
assert_equal(transp_Xs_new.shape, Xs_new.shape)
# check computation and dimensions if bias == True
otda = ot.da.MappingTransport(kernel="gaussian", bias=True)
otda.fit(Xs=Xs, Xt=Xt)
assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
assert_equal(otda.mapping_.shape, ((Xs.shape[0] + 1, Xt.shape[1])))
# 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)
transp_Xs_new = otda.transform(Xs_new)
# check that the oos method is working
assert_equal(transp_Xs_new.shape, Xs_new.shape)
# check everything runs well with log=True
otda = ot.da.MappingTransport(kernel="gaussian", log=True)
otda.fit(Xs=Xs, Xt=Xt)
assert len(otda.log_.keys()) != 0
def test_sinkhorn_l1l2_transport_class():
"""test_sinkhorn_transport
"""
ns = 150
nt = 200
Xs, ys = get_data_classif('3gauss', ns)
Xt, yt = get_data_classif('3gauss2', nt)
otda = ot.da.SinkhornL1l2Transport()
# test its computed
otda.fit(Xs=Xs, ys=ys, 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, _ = get_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, _ = get_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, ys=ys, Xt=Xt)
assert_equal(transp_Xs.shape, Xs.shape)
# test unsupervised vs semi-supervised mode
otda_unsup = ot.da.SinkhornL1l2Transport()
otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt)
n_unsup = np.sum(otda_unsup.cost_)
otda_semi = ot.da.SinkhornL1l2Transport()
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 that the coupling forbids mass transport between labeled source
# and labeled target samples
mass_semi = np.sum(
otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max])
mass_semi = otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max]
assert_allclose(mass_semi, np.zeros_like(mass_semi),
rtol=1e-9, atol=1e-9)
# check everything runs well with log=True
otda = ot.da.SinkhornL1l2Transport(log=True)
otda.fit(Xs=Xs, ys=ys, Xt=Xt)
assert len(otda.log_.keys()) != 0
def test_mapping_transport_class():
"""test_mapping_transport
"""
ns = 60
nt = 120
Xs, ys = get_data_classif('3gauss', ns)
Xt, yt = get_data_classif('3gauss2', nt)
Xs_new, _ = get_data_classif('3gauss', ns + 1)
##########################################################################
# kernel == linear mapping tests
##########################################################################
# check computation and dimensions if bias == False
otda = ot.da.MappingTransport(kernel="linear", bias=False)
otda.fit(Xs=Xs, Xt=Xt)
assert hasattr(otda, "coupling_")
assert hasattr(otda, "mapping_")
assert hasattr(otda, "log_")
assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
assert_equal(otda.mapping_.shape, ((Xs.shape[1], Xt.shape[1])))
# 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)
transp_Xs_new = otda.transform(Xs_new)
# check that the oos method is working
assert_equal(transp_Xs_new.shape, Xs_new.shape)
# check computation and dimensions if bias == True
otda = ot.da.MappingTransport(kernel="linear", bias=True)
otda.fit(Xs=Xs, Xt=Xt)
assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
assert_equal(otda.mapping_.shape, ((Xs.shape[1] + 1, Xt.shape[1])))
# 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)
transp_Xs_new = otda.transform(Xs_new)
# check that the oos method is working
assert_equal(transp_Xs_new.shape, Xs_new.shape)
##########################################################################
# kernel == gaussian mapping tests
##########################################################################
# check computation and dimensions if bias == False
otda = ot.da.MappingTransport(kernel="gaussian", bias=False)
otda.fit(Xs=Xs, Xt=Xt)
assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
assert_equal(otda.mapping_.shape, ((Xs.shape[0], Xt.shape[1])))
# 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)
transp_Xs_new = otda.transform(Xs_new)
# check that the oos method is working
assert_equal(transp_Xs_new.shape, Xs_new.shape)
# check computation and dimensions if bias == True
otda = ot.da.MappingTransport(kernel="gaussian", bias=True)
otda.fit(Xs=Xs, Xt=Xt)
assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
assert_equal(otda.mapping_.shape, ((Xs.shape[0] + 1, Xt.shape[1])))
# 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)
transp_Xs_new = otda.transform(Xs_new)
# check that the oos method is working
assert_equal(transp_Xs_new.shape, Xs_new.shape)
# check everything runs well with log=True
otda = ot.da.MappingTransport(kernel="gaussian", log=True)
otda.fit(Xs=Xs, Xt=Xt)
assert len(otda.log_.keys()) != 0