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"""Tests for module da on Domain Adaptation """
# Author: Remi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
import numpy as np
import ot
from numpy.testing.utils import assert_allclose, assert_equal
from ot.datasets import get_data_classif
from ot.utils import unif
np.random.seed(42)
def test_sinkhorn_transport():
"""test_sinkhorn_transport
"""
ns = 150
nt = 200
Xs, ys = get_data_classif('3gauss', ns)
Xt, yt = get_data_classif('3gauss2', nt)
clf = ot.da.SinkhornTransport()
# test its computed
clf.fit(Xs=Xs, Xt=Xt)
# test dimensions of coupling
assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0])))
assert_equal(clf.gamma_.shape, ((Xs.shape[0], Xt.shape[0])))
# test margin constraints
mu_s = unif(ns)
mu_t = unif(nt)
assert_allclose(np.sum(clf.gamma_, axis=0), mu_t, rtol=1e-3, atol=1e-3)
assert_allclose(np.sum(clf.gamma_, axis=1), mu_s, rtol=1e-3, atol=1e-3)
# test transform
transp_Xs = clf.transform(Xs=Xs)
assert_equal(transp_Xs.shape, Xs.shape)
Xs_new, _ = get_data_classif('3gauss', ns + 1)
transp_Xs_new = clf.transform(Xs_new)
# check that the oos method is not working
assert_equal(transp_Xs_new, Xs_new)
# test inverse transform
transp_Xt = clf.inverse_transform(Xt=Xt)
assert_equal(transp_Xt.shape, Xt.shape)
Xt_new, _ = get_data_classif('3gauss2', nt + 1)
transp_Xt_new = clf.inverse_transform(Xt=Xt_new)
# check that the oos method is not working and returns the input data
assert_equal(transp_Xt_new, Xt_new)
def test_otda():
n_samples = 150 # nb samples
np.random.seed(0)
xs, ys = ot.datasets.get_data_classif('3gauss', n_samples)
xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples)
a, b = ot.unif(n_samples), ot.unif(n_samples)
# LP problem
da_emd = ot.da.OTDA() # init class
da_emd.fit(xs, xt) # fit distributions
da_emd.interp() # interpolation of source samples
da_emd.predict(xs) # interpolation of source samples
np.testing.assert_allclose(a, np.sum(da_emd.G, 1))
np.testing.assert_allclose(b, np.sum(da_emd.G, 0))
# sinkhorn regularization
lambd = 1e-1
da_entrop = ot.da.OTDA_sinkhorn()
da_entrop.fit(xs, xt, reg=lambd)
da_entrop.interp()
da_entrop.predict(xs)
np.testing.assert_allclose(a, np.sum(da_entrop.G, 1), rtol=1e-3, atol=1e-3)
np.testing.assert_allclose(b, np.sum(da_entrop.G, 0), rtol=1e-3, atol=1e-3)
# non-convex Group lasso regularization
reg = 1e-1
eta = 1e0
da_lpl1 = ot.da.OTDA_lpl1()
da_lpl1.fit(xs, ys, xt, reg=reg, eta=eta)
da_lpl1.interp()
da_lpl1.predict(xs)
np.testing.assert_allclose(a, np.sum(da_lpl1.G, 1), rtol=1e-3, atol=1e-3)
np.testing.assert_allclose(b, np.sum(da_lpl1.G, 0), rtol=1e-3, atol=1e-3)
# True Group lasso regularization
reg = 1e-1
eta = 2e0
da_l1l2 = ot.da.OTDA_l1l2()
da_l1l2.fit(xs, ys, xt, reg=reg, eta=eta, numItermax=20, verbose=True)
da_l1l2.interp()
da_l1l2.predict(xs)
np.testing.assert_allclose(a, np.sum(da_l1l2.G, 1), rtol=1e-3, atol=1e-3)
np.testing.assert_allclose(b, np.sum(da_l1l2.G, 0), rtol=1e-3, atol=1e-3)
# linear mapping
da_emd = ot.da.OTDA_mapping_linear() # init class
da_emd.fit(xs, xt, numItermax=10) # fit distributions
da_emd.predict(xs) # interpolation of source samples
# nonlinear mapping
da_emd = ot.da.OTDA_mapping_kernel() # init class
da_emd.fit(xs, xt, numItermax=10) # fit distributions
da_emd.predict(xs) # interpolation of source samples
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