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"""Tests for gromov._lowrank.py"""
# Author: Laurène DAVID <laurene.david@ip-paris.fr>
#
# License: MIT License
import ot
import numpy as np
import pytest
def test__flat_product_operator():
# test flat product operator
n, d = 100, 2
X = np.reshape(1.0 * np.arange(2 * n), (n, d))
A1, A2 = ot.lowrank.compute_lr_sqeuclidean_matrix(X, X, rescale_cost=False)
A1_ = ot.gromov._lowrank._flat_product_operator(A1)
A2_ = ot.gromov._lowrank._flat_product_operator(A2)
cost = ot.dist(X, X)
# test value
np.testing.assert_allclose(cost**2, np.dot(A1_, A2_.T), atol=1e-05)
def test_lowrank_gromov_wasserstein_samples():
# test low rank gromov wasserstein
n_samples = 20 # nb samples
mu_s = np.array([0, 0])
cov_s = np.array([[1, 0], [0, 1]])
X_s = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=1)
X_t = X_s[::-1].copy()
a = ot.unif(n_samples)
b = ot.unif(n_samples)
Q, R, g, log = ot.gromov.lowrank_gromov_wasserstein_samples(
X_s, X_t, a, b, reg=0.1, log=True, rescale_cost=False
)
P = log["lazy_plan"][:]
# check constraints for P
np.testing.assert_allclose(a, P.sum(1), atol=1e-04)
np.testing.assert_allclose(b, P.sum(0), atol=1e-04)
# check if lazy_plan is equal to the fully computed plan
P_true = np.dot(Q, np.dot(np.diag(1 / g), R.T))
np.testing.assert_allclose(P, P_true, atol=1e-05)
# check warn parameter when low rank GW algorithm doesn't converge
with pytest.warns(UserWarning):
ot.gromov.lowrank_gromov_wasserstein_samples(
X_s,
X_t,
a,
b,
reg=0.1,
stopThr=0,
numItermax=1,
warn=True,
warn_dykstra=False,
)
# check warn parameter when Dykstra algorithm doesn't converge
with pytest.warns(UserWarning):
ot.gromov.lowrank_gromov_wasserstein_samples(
X_s,
X_t,
a,
b,
reg=0.1,
stopThr_dykstra=0,
numItermax_dykstra=1,
warn=False,
warn_dykstra=True,
)
@pytest.mark.parametrize(("alpha, rank"), ((0.8, 2), (0.5, 3), (0.2, 6), (0.1, -1)))
def test_lowrank_gromov_wasserstein_samples_alpha_error(alpha, rank):
# Test warning for value of alpha and rank
n_samples = 20 # nb samples
mu_s = np.array([0, 0])
cov_s = np.array([[1, 0], [0, 1]])
X_s = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=1)
X_t = X_s[::-1].copy()
a = ot.unif(n_samples)
b = ot.unif(n_samples)
with pytest.raises(ValueError):
ot.gromov.lowrank_gromov_wasserstein_samples(
X_s, X_t, a, b, reg=0.1, rank=rank, alpha=alpha, warn=False
)
@pytest.mark.parametrize(("gamma_init"), ("rescale", "theory", "other"))
def test_lowrank_wasserstein_samples_gamma_init(gamma_init):
# Test lr sinkhorn with different init strategies
n_samples = 20 # nb samples
mu_s = np.array([0, 0])
cov_s = np.array([[1, 0], [0, 1]])
X_s = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=1)
X_t = X_s[::-1].copy()
a = ot.unif(n_samples)
b = ot.unif(n_samples)
if gamma_init not in ["rescale", "theory"]:
with pytest.raises(NotImplementedError):
ot.gromov.lowrank_gromov_wasserstein_samples(
X_s, X_t, a, b, reg=0.1, gamma_init=gamma_init, log=True
)
else:
Q, R, g, log = ot.gromov.lowrank_gromov_wasserstein_samples(
X_s, X_t, a, b, reg=0.1, gamma_init=gamma_init, log=True
)
P = log["lazy_plan"][:]
# check constraints for P
np.testing.assert_allclose(a, P.sum(1), atol=1e-04)
np.testing.assert_allclose(b, P.sum(0), atol=1e-04)
@pytest.skip_backend("tf")
def test_lowrank_gromov_wasserstein_samples_backends(nx):
# Test low rank sinkhorn for different backends
n_samples = 20 # nb samples
mu_s = np.array([0, 0])
cov_s = np.array([[1, 0], [0, 1]])
X_s = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=1)
X_t = X_s[::-1].copy()
a = ot.unif(n_samples)
b = ot.unif(n_samples)
ab, bb, X_sb, X_tb = nx.from_numpy(a, b, X_s, X_t)
Q, R, g, log = ot.gromov.lowrank_gromov_wasserstein_samples(
X_sb, X_tb, ab, bb, reg=0.1, log=True
)
lazy_plan = log["lazy_plan"]
P = lazy_plan[:]
np.testing.assert_allclose(ab, P.sum(1), atol=1e-04)
np.testing.assert_allclose(bb, P.sum(0), atol=1e-04)
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