def test_jcpot_barycenter():
"""test_jcpot_barycenter
"""
ns1 = 150
ns2 = 150
nt = 200
sigma = 0.1
np.random.seed(1985)
ps1 = .2
ps2 = .9
pt = .4
Xs1, ys1 = make_data_classif('2gauss_prop', ns1, nz=sigma, p=ps1)
Xs2, ys2 = make_data_classif('2gauss_prop', ns2, nz=sigma, p=ps2)
Xt, yt = make_data_classif('2gauss_prop', nt, nz=sigma, p=pt)
Xs = [Xs1, Xs2]
ys = [ys1, ys2]
prop = ot.bregman.jcpot_barycenter(Xs,
ys,
Xt,
reg=.5,
metric='sqeuclidean',
numItermax=10000,
stopThr=1e-9,
verbose=False,
log=False)
np.testing.assert_allclose(prop, [1 - pt, pt], rtol=1e-3, atol=1e-3)
def toy(n_samples_source, n_samples_target, nz=0.8, random_state=None):
Xs, ys = make_data_classif('3gauss',
n_samples_source,
nz=nz,
random_state=random_state)
Xt, yt = make_data_classif('3gauss2',
n_samples_target,
nz=nz,
random_state=random_state)
return Xs, ys, Xt, yt
def test_mapping_transport_class_specific_seed(nx):
# check that it does not crash when derphi is very close to 0
ns = 20
nt = 30
np.random.seed(39)
Xs, ys = make_data_classif('3gauss', ns)
Xt, yt = make_data_classif('3gauss2', nt)
otda = ot.da.MappingTransport(kernel="gaussian", bias=False)
otda.fit(Xs=nx.from_numpy(Xs), Xt=nx.from_numpy(Xt))
np.random.seed(None)
def test_linear_mapping():
ns = 150
nt = 200
Xs, ys = make_data_classif('3gauss', ns)
Xt, yt = make_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_mapping_transport_class(nx, kernel, bias):
"""test_mapping_transport
"""
ns = 20
nt = 30
Xs, ys = make_data_classif('3gauss', ns)
Xt, yt = make_data_classif('3gauss2', nt)
Xs_new, _ = make_data_classif('3gauss', ns + 1)
Xs, Xt, Xs_new = nx.from_numpy(Xs, Xt, Xs_new)
# Mapping tests
bias = bias == "biased"
otda = ot.da.MappingTransport(kernel=kernel, bias=bias)
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])))
S = Xs.shape[0] if kernel == "gaussian" else Xs.shape[1] # if linear
if bias:
S += 1
assert_equal(otda.mapping_.shape, ((S, Xt.shape[1])))
# test margin constraints
mu_s = unif(ns)
mu_t = unif(nt)
assert_allclose(
nx.to_numpy(nx.sum(otda.coupling_, axis=0)), mu_t, rtol=1e-3, atol=1e-3)
assert_allclose(
nx.to_numpy(nx.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=kernel, bias=bias, log=True)
otda.fit(Xs=Xs, Xt=Xt)
assert len(otda.log_.keys()) != 0
def test_linear_mapping(nx):
ns = 150
nt = 200
Xs, ys = make_data_classif('3gauss', ns)
Xt, yt = make_data_classif('3gauss2', nt)
Xsb, Xtb = nx.from_numpy(Xs, Xt)
A, b = ot.da.OT_mapping_linear(Xsb, Xtb)
Xst = nx.to_numpy(nx.dot(Xsb, 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 = make_data_classif('3gauss', ns)
Xt, yt = make_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_class_jax_tf():
backends = []
from ot.backend import jax, tf
if jax:
backends.append(ot.backend.JaxBackend())
if tf:
backends.append(ot.backend.TensorflowBackend())
for nx in backends:
ns = 150
nt = 200
Xs, ys = make_data_classif('3gauss', ns)
Xt, yt = make_data_classif('3gauss2', nt)
Xs, ys, Xt, yt = nx.from_numpy(Xs, ys, Xt, yt)
otda = ot.da.SinkhornLpl1Transport()
with pytest.raises(TypeError):
otda.fit(Xs=Xs, ys=ys, Xt=Xt)
def test_sinkhorn_l1l2_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.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, _ = 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)
# check label propagation
transp_yt = otda.transform_labels(ys)
assert_equal(transp_yt.shape[0], yt.shape[0])
assert_equal(transp_yt.shape[1], len(np.unique(ys)))
# check inverse label propagation
transp_ys = otda.inverse_transform_labels(yt)
assert_equal(transp_ys.shape[0], ys.shape[0])
assert_equal(transp_ys.shape[1], len(np.unique(yt)))
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, 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_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=1,
max_iter=10000,
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_['D1'][i],
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)
# check label propagation
transp_yt = otda.transform_labels(ys)
assert_equal(transp_yt.shape[0], yt.shape[0])
assert_equal(transp_yt.shape[1], len(np.unique(ys)))
# check inverse label propagation
transp_ys = otda.inverse_transform_labels(yt)
[assert_equal(x.shape[0], y.shape[0]) for x, y in zip(transp_ys, ys)]
[
assert_equal(x.shape[1], len(np.unique(y)))
for x, y in zip(transp_ys, ys)
]
def test_mapping_transport_class():
"""test_mapping_transport
"""
ns = 60
nt = 120
Xs, ys = make_data_classif('3gauss', ns)
Xt, yt = make_data_classif('3gauss2', nt)
Xs_new, _ = make_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_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 transform
transp_Xs = otda.transform(Xs=Xs)
assert_equal(transp_Xs.shape, Xs.shape)
# check label propagation
transp_yt = otda.transform_labels(ys)
assert_equal(transp_yt.shape[0], yt.shape[0])
assert_equal(transp_yt.shape[1], len(np.unique(ys)))
# check inverse label propagation
transp_ys = otda.inverse_transform_labels(yt)
assert_equal(transp_ys.shape[0], ys.shape[0])
assert_equal(transp_ys.shape[1], len(np.unique(yt)))
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
# Generate data
# -------------
n = 50
sigma = 0.3
np.random.seed(1985)
p1 = .2
dec1 = [0, 2]
p2 = .9
dec2 = [0, -2]
pt = .4
dect = [4, 0]
xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1)
xs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2)
xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect)
all_Xr = [xs1, xs2]
all_Yr = [ys1, ys2]
# %%
da = 1.5
def plot_ax(dec, name):
pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5)
pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5)
pl.text(dec[0] - .5, dec[1] + 2, name)
def test_emd_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.EMDTransport()
# test its computed
otda.fit(Xs=Xs, Xt=Xt)
assert hasattr(otda, "cost_")
assert hasattr(otda, "coupling_")
# 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.EMDTransport()
otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt)
n_unsup = np.sum(otda_unsup.cost_)
otda_semi = ot.da.EMDTransport()
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]
# we need to use a small tolerance here, otherwise the test breaks
assert_allclose(mass_semi, np.zeros_like(mass_semi),
rtol=1e-2, atol=1e-2)
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)
# check label propagation
transp_yt = otda.transform_labels(ys)
assert_equal(transp_yt.shape[0], yt.shape[0])
assert_equal(transp_yt.shape[1], len(np.unique(ys)))
# check inverse label propagation
transp_ys = otda.inverse_transform_labels(yt)
assert_equal(transp_ys.shape[0], ys.shape[0])
assert_equal(transp_ys.shape[1], len(np.unique(yt)))
