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"""Tests for gromov._estimators.py"""
# Author: Rémi Flamary <remi.flamary@unice.fr>
# Tanguy Kerdoncuff <tanguy.kerdoncuff@laposte.net>
# Cédric Vincent-Cuaz <cedvincentcuaz@gmail.com>
#
# License: MIT License
import numpy as np
import pytest
import ot
from ot.backend import NumpyBackend
def test_pointwise_gromov(nx):
n_samples = 5 # nb samples
mu_s = np.array([0, 0])
cov_s = np.array([[1, 0], [0, 1]])
xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=42)
xt = xs[::-1].copy()
p = ot.unif(n_samples)
q = ot.unif(n_samples)
C1 = ot.dist(xs, xs)
C2 = ot.dist(xt, xt)
C1 /= C1.max()
C2 /= C2.max()
C1b, C2b, pb, qb = nx.from_numpy(C1, C2, p, q)
def loss(x, y):
return np.abs(x - y)
def lossb(x, y):
return nx.abs(x - y)
G, log = ot.gromov.pointwise_gromov_wasserstein(
C1, C2, p, q, loss, max_iter=100, log=True, verbose=True, random_state=42
)
G = NumpyBackend().todense(G)
Gb, logb = ot.gromov.pointwise_gromov_wasserstein(
C1b, C2b, pb, qb, lossb, max_iter=100, log=True, verbose=True, random_state=42
)
Gb = nx.to_numpy(nx.todense(Gb))
# check constraints
np.testing.assert_allclose(G, Gb, atol=1e-06)
np.testing.assert_allclose(p, Gb.sum(1), atol=1e-04) # cf convergence gromov
np.testing.assert_allclose(q, Gb.sum(0), atol=1e-04) # cf convergence gromov
np.testing.assert_allclose(float(logb["gw_dist_estimated"]), 0.0, atol=1e-08)
np.testing.assert_allclose(float(logb["gw_dist_std"]), 0.0, atol=1e-08)
G, log = ot.gromov.pointwise_gromov_wasserstein(
C1,
C2,
p,
q,
loss,
max_iter=100,
alpha=0.1,
log=True,
verbose=True,
random_state=42,
)
G = NumpyBackend().todense(G)
Gb, logb = ot.gromov.pointwise_gromov_wasserstein(
C1b,
C2b,
pb,
qb,
lossb,
max_iter=100,
alpha=0.1,
log=True,
verbose=True,
random_state=42,
)
Gb = nx.to_numpy(nx.todense(Gb))
np.testing.assert_allclose(G, Gb, atol=1e-06)
@pytest.skip_backend("tf", reason="test very slow with tf backend")
@pytest.skip_backend("jax", reason="test very slow with jax backend")
def test_sampled_gromov(nx):
n_samples = 5 # nb samples
mu_s = np.array([0, 0], dtype=np.float64)
cov_s = np.array([[1, 0], [0, 1]], dtype=np.float64)
xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=42)
xt = xs[::-1].copy()
p = ot.unif(n_samples)
q = ot.unif(n_samples)
C1 = ot.dist(xs, xs)
C2 = ot.dist(xt, xt)
C1 /= C1.max()
C2 /= C2.max()
C1b, C2b, pb, qb = nx.from_numpy(C1, C2, p, q)
def loss(x, y):
return np.abs(x - y)
def lossb(x, y):
return nx.abs(x - y)
G, log = ot.gromov.sampled_gromov_wasserstein(
C1,
C2,
p,
q,
loss,
max_iter=20,
nb_samples_grad=2,
epsilon=1,
log=True,
verbose=True,
random_state=42,
)
Gb, logb = ot.gromov.sampled_gromov_wasserstein(
C1b,
C2b,
pb,
qb,
lossb,
max_iter=20,
nb_samples_grad=2,
epsilon=1,
log=True,
verbose=True,
random_state=42,
)
Gb = nx.to_numpy(Gb)
# check constraints
np.testing.assert_allclose(G, Gb, atol=1e-06)
np.testing.assert_allclose(p, Gb.sum(1), atol=1e-04) # cf convergence gromov
np.testing.assert_allclose(q, Gb.sum(0), atol=1e-04) # cf convergence gromov
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