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"""Tests for gromov._utils.py"""
# Author: Cédric Vincent-Cuaz <cedvincentcuaz@gmail.com>
#
# License: MIT License
import itertools
import numpy as np
import pytest
import ot
from ot.gromov._utils import networkx_import, sklearn_import
def test_update_barycenter(nx):
ns = 5
nt = 10
Xs, ys = ot.datasets.make_data_classif("3gauss", ns, random_state=42)
Xt, yt = ot.datasets.make_data_classif("3gauss2", nt, random_state=42)
rng = np.random.RandomState(42)
ys = rng.randn(Xs.shape[0], 2)
yt = rng.randn(Xt.shape[0], 2)
C1 = ot.dist(Xs)
C2 = ot.dist(Xt)
C1 /= C1.max()
C2 /= C2.max()
p1, p2 = ot.unif(ns), ot.unif(nt)
n_samples = 3
ysb, ytb, C1b, C2b, p1b, p2b = nx.from_numpy(ys, yt, C1, C2, p1, p2)
lambdas = [0.5, 0.5]
Csb = [C1b, C2b]
Ysb = [ysb, ytb]
Tb = [nx.ones((m, n_samples), type_as=C1b) / (m * n_samples) for m in [ns, nt]]
pb = nx.concatenate([nx.sum(elem, 0)[None, :] for elem in Tb], axis=0)
# test edge cases for the update of the barycenter with `p != None`
# and `target=False`
Cb = ot.gromov.update_barycenter_structure(
[elem.T for elem in Tb], Csb, lambdas, pb, target=False
)
Xb = ot.gromov.update_barycenter_feature(
[elem.T for elem in Tb], Ysb, lambdas, pb, target=False
)
Cbt = ot.gromov.update_barycenter_structure(
Tb, Csb, lambdas, None, target=True, check_zeros=False
)
Xbt = ot.gromov.update_barycenter_feature(
Tb, Ysb, lambdas, None, target=True, check_zeros=False
)
np.testing.assert_allclose(Cb, Cbt)
np.testing.assert_allclose(Xb, Xbt)
# test not supported metrics
with pytest.raises(ValueError):
Cbt = ot.gromov.update_barycenter_structure(
Tb, Csb, lambdas, None, loss_fun="unknown", target=True
)
with pytest.raises(ValueError):
Xbt = ot.gromov.update_barycenter_feature(
Tb, Ysb, lambdas, None, loss_fun="unknown", target=True
)
def test_semirelaxed_init_plan(nx):
ns = 5
nt = 10
Xs, ys = ot.datasets.make_data_classif("3gauss", ns, random_state=42)
Xt, yt = ot.datasets.make_data_classif("3gauss2", nt, random_state=42)
rng = np.random.RandomState(42)
ys = rng.randn(Xs.shape[0], 2)
yt = rng.randn(Xt.shape[0], 2)
C1 = ot.dist(Xs)
C2 = ot.dist(Xt)
C1 /= C1.max()
C2 /= C2.max()
p1, p2 = ot.unif(ns), ot.unif(nt)
ysb, ytb, C1b, C2b, p1b, p2b = nx.from_numpy(ys, yt, C1, C2, p1, p2)
# test not supported method
with pytest.raises(ValueError):
_ = ot.gromov.semirelaxed_init_plan(C1b, C2b, p1b, method="unknown")
if sklearn_import:
# tests consistency across backends with m > n
for method in ["kmeans", "spectral"]:
T = ot.gromov.semirelaxed_init_plan(C1b, C2b, p1b, method=method)
Tb = ot.gromov.semirelaxed_init_plan(C1b, C2b, p1b, method=method)
np.testing.assert_allclose(T, Tb)
# tests consistency across backends with m = n
T = ot.gromov.semirelaxed_init_plan(C1b, C1b, p1b, method=method)
Tb = ot.gromov.semirelaxed_init_plan(C1b, C1b, p1b, method=method)
np.testing.assert_allclose(T, Tb)
if networkx_import:
# tests consistency across backends with m > n
T = ot.gromov.semirelaxed_init_plan(C1b, C2b, p1b, method="fluid")
Tb = ot.gromov.semirelaxed_init_plan(C1b, C2b, p1b, method="fluid")
np.testing.assert_allclose(T, Tb)
# tests consistency across backends with m = n
T = ot.gromov.semirelaxed_init_plan(C1b, C1b, p1b, method="fluid")
Tb = ot.gromov.semirelaxed_init_plan(C1b, C1b, p1b, method="fluid")
np.testing.assert_allclose(T, Tb)
@pytest.mark.parametrize("divergence", ["kl", "l2"])
def test_div_between_product(nx, divergence):
ns = 5
nt = 10
ps, pt = ot.unif(ns), ot.unif(nt)
ps, pt = nx.from_numpy(ps), nx.from_numpy(pt)
ps1, pt1 = 2 * ps, 2 * pt
res_nx = ot.gromov.div_between_product(ps, pt, ps1, pt1, divergence, nx=nx)
res = ot.gromov.div_between_product(ps, pt, ps1, pt1, divergence, nx=None)
np.testing.assert_allclose(res_nx, res, atol=1e-06)
@pytest.mark.parametrize(
"divergence, mass", itertools.product(["kl", "l2"], [True, False])
)
def test_div_to_product(nx, divergence, mass):
ns = 5
nt = 10
a, b = ot.unif(ns), ot.unif(nt)
a, b = nx.from_numpy(a), nx.from_numpy(b)
pi = 2 * a[:, None] * b[None, :]
pi1, pi2 = nx.sum(pi, 1), nx.sum(pi, 0)
res = ot.gromov.div_to_product(
pi, a, b, pi1=None, pi2=None, divergence=divergence, mass=mass, nx=None
)
res1 = ot.gromov.div_to_product(
pi, a, b, pi1=None, pi2=None, divergence=divergence, mass=mass, nx=nx
)
res2 = ot.gromov.div_to_product(
pi, a, b, pi1=pi1, pi2=pi2, divergence=divergence, mass=mass, nx=None
)
res3 = ot.gromov.div_to_product(
pi, a, b, pi1=pi1, pi2=pi2, divergence=divergence, mass=mass, nx=nx
)
np.testing.assert_allclose(res1, res, atol=1e-06)
np.testing.assert_allclose(res2, res, atol=1e-06)
np.testing.assert_allclose(res3, res, atol=1e-06)
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