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"""Tests for module gromov """
# Author: Erwan Vautier <erwan.vautier@gmail.com>
# Nicolas Courty <ncourty@irisa.fr>
# Titouan Vayer <titouan.vayer@irisa.fr>
#
# License: MIT License
import numpy as np
import ot
def test_gromov():
n_samples = 50 # 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=4)
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()
G = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss', verbose=True)
# check constratints
np.testing.assert_allclose(
p, G.sum(1), atol=1e-04) # cf convergence gromov
np.testing.assert_allclose(
q, G.sum(0), atol=1e-04) # cf convergence gromov
Id = (1 / (1.0 * n_samples)) * np.eye(n_samples, n_samples)
np.testing.assert_allclose(
G, np.flipud(Id), atol=1e-04)
gw, log = ot.gromov.gromov_wasserstein2(C1, C2, p, q, 'kl_loss', log=True)
G = log['T']
np.testing.assert_allclose(gw, 0, atol=1e-1, rtol=1e-1)
# check constratints
np.testing.assert_allclose(
p, G.sum(1), atol=1e-04) # cf convergence gromov
np.testing.assert_allclose(
q, G.sum(0), atol=1e-04) # cf convergence gromov
def test_entropic_gromov():
n_samples = 50 # 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()
G = ot.gromov.entropic_gromov_wasserstein(
C1, C2, p, q, 'square_loss', epsilon=5e-4, verbose=True)
# check constratints
np.testing.assert_allclose(
p, G.sum(1), atol=1e-04) # cf convergence gromov
np.testing.assert_allclose(
q, G.sum(0), atol=1e-04) # cf convergence gromov
gw, log = ot.gromov.entropic_gromov_wasserstein2(
C1, C2, p, q, 'kl_loss', epsilon=1e-2, log=True)
G = log['T']
np.testing.assert_allclose(gw, 0, atol=1e-1, rtol=1e-1)
# check constratints
np.testing.assert_allclose(
p, G.sum(1), atol=1e-04) # cf convergence gromov
np.testing.assert_allclose(
q, G.sum(0), atol=1e-04) # cf convergence gromov
def test_gromov_barycenter():
ns = 50
nt = 60
Xs, ys = ot.datasets.make_data_classif('3gauss', ns, random_state=42)
Xt, yt = ot.datasets.make_data_classif('3gauss2', nt, random_state=42)
C1 = ot.dist(Xs)
C2 = ot.dist(Xt)
n_samples = 3
Cb = ot.gromov.gromov_barycenters(n_samples, [C1, C2],
[ot.unif(ns), ot.unif(nt)
], ot.unif(n_samples), [.5, .5],
'square_loss', # 5e-4,
max_iter=100, tol=1e-3,
verbose=True)
np.testing.assert_allclose(Cb.shape, (n_samples, n_samples))
Cb2 = ot.gromov.gromov_barycenters(n_samples, [C1, C2],
[ot.unif(ns), ot.unif(nt)
], ot.unif(n_samples), [.5, .5],
'kl_loss', # 5e-4,
max_iter=100, tol=1e-3)
np.testing.assert_allclose(Cb2.shape, (n_samples, n_samples))
def test_gromov_entropic_barycenter():
ns = 50
nt = 60
Xs, ys = ot.datasets.make_data_classif('3gauss', ns, random_state=42)
Xt, yt = ot.datasets.make_data_classif('3gauss2', nt, random_state=42)
C1 = ot.dist(Xs)
C2 = ot.dist(Xt)
n_samples = 3
Cb = ot.gromov.entropic_gromov_barycenters(n_samples, [C1, C2],
[ot.unif(ns), ot.unif(nt)
], ot.unif(n_samples), [.5, .5],
'square_loss', 2e-3,
max_iter=100, tol=1e-3,
verbose=True)
np.testing.assert_allclose(Cb.shape, (n_samples, n_samples))
Cb2 = ot.gromov.entropic_gromov_barycenters(n_samples, [C1, C2],
[ot.unif(ns), ot.unif(nt)
], ot.unif(n_samples), [.5, .5],
'kl_loss', 2e-3,
max_iter=100, tol=1e-3)
np.testing.assert_allclose(Cb2.shape, (n_samples, n_samples))
def test_fgw():
n_samples = 50 # 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()
ys = np.random.randn(xs.shape[0], 2)
yt = ys[::-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()
M = ot.dist(ys, yt)
M /= M.max()
G = ot.gromov.fused_gromov_wasserstein(M, C1, C2, p, q, 'square_loss', alpha=0.5)
# check constratints
np.testing.assert_allclose(
p, G.sum(1), atol=1e-04) # cf convergence fgw
np.testing.assert_allclose(
q, G.sum(0), atol=1e-04) # cf convergence fgw
Id = (1 / (1.0 * n_samples)) * np.eye(n_samples, n_samples)
np.testing.assert_allclose(
G, np.flipud(Id), atol=1e-04) # cf convergence gromov
fgw, log = ot.gromov.fused_gromov_wasserstein2(M, C1, C2, p, q, 'square_loss', alpha=0.5, log=True)
G = log['T']
np.testing.assert_allclose(fgw, 0, atol=1e-1, rtol=1e-1)
# check constratints
np.testing.assert_allclose(
p, G.sum(1), atol=1e-04) # cf convergence gromov
np.testing.assert_allclose(
q, G.sum(0), atol=1e-04) # cf convergence gromov
def test_fgw_barycenter():
np.random.seed(42)
ns = 50
nt = 60
Xs, ys = ot.datasets.make_data_classif('3gauss', ns, random_state=42)
Xt, yt = ot.datasets.make_data_classif('3gauss2', nt, random_state=42)
ys = np.random.randn(Xs.shape[0], 2)
yt = np.random.randn(Xt.shape[0], 2)
C1 = ot.dist(Xs)
C2 = ot.dist(Xt)
n_samples = 3
X, C = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], [ot.unif(ns), ot.unif(nt)], [.5, .5], 0.5,
fixed_structure=False, fixed_features=False,
p=ot.unif(n_samples), loss_fun='square_loss',
max_iter=100, tol=1e-3)
np.testing.assert_allclose(C.shape, (n_samples, n_samples))
np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1]))
xalea = np.random.randn(n_samples, 2)
init_C = ot.dist(xalea, xalea)
X, C = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], ps=[ot.unif(ns), ot.unif(nt)], lambdas=[.5, .5], alpha=0.5,
fixed_structure=True, init_C=init_C, fixed_features=False,
p=ot.unif(n_samples), loss_fun='square_loss',
max_iter=100, tol=1e-3)
np.testing.assert_allclose(C.shape, (n_samples, n_samples))
np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1]))
init_X = np.random.randn(n_samples, ys.shape[1])
X, C = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], [ot.unif(ns), ot.unif(nt)], [.5, .5], 0.5,
fixed_structure=False, fixed_features=True, init_X=init_X,
p=ot.unif(n_samples), loss_fun='square_loss',
max_iter=100, tol=1e-3)
np.testing.assert_allclose(C.shape, (n_samples, n_samples))
np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1]))
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