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import numpy as np
import ot
# import pytest
def test_OTDA():
n = 150 # nb bins
xs, ys = ot.datasets.get_data_classif('3gauss', n)
xt, yt = ot.datasets.get_data_classif('3gauss2', n)
a, b = ot.unif(n), ot.unif(n)
# 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
assert np.allclose(a, np.sum(da_emd.G, 1))
assert np.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)
assert np.allclose(a, np.sum(da_entrop.G, 1), rtol=1e-3, atol=1e-3)
assert np.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)
assert np.allclose(a, np.sum(da_lpl1.G, 1), rtol=1e-3, atol=1e-3)
assert np.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)
assert np.allclose(a, np.sum(da_l1l2.G, 1), rtol=1e-3, atol=1e-3)
assert np.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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