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"""Tests for module partial """
# Author:
# Laetitia Chapel <laetitia.chapel@irisa.fr>
#
# License: MIT License
import numpy as np
import scipy as sp
import ot
import pytest
def test_raise_errors():
n_samples = 20 # nb samples (gaussian)
n_noise = 20 # nb of samples (noise)
mu = np.array([0, 0])
cov = np.array([[1, 0], [0, 2]])
xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2))
xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2))
M = ot.dist(xs, xt)
p = ot.unif(n_samples + n_noise)
q = ot.unif(n_samples + n_noise)
with pytest.raises(ValueError):
ot.partial.partial_wasserstein_lagrange(p + 1, q, M, 1, log=True)
with pytest.raises(ValueError):
ot.partial.partial_wasserstein(p, q, M, m=2, log=True)
with pytest.raises(ValueError):
ot.partial.partial_wasserstein(p, q, M, m=-1, log=True)
with pytest.raises(ValueError):
ot.partial.entropic_partial_wasserstein(p, q, M, reg=1, m=2, log=True)
with pytest.raises(ValueError):
ot.partial.entropic_partial_wasserstein(p, q, M, reg=1, m=-1, log=True)
with pytest.raises(ValueError):
ot.partial.partial_gromov_wasserstein(M, M, p, q, m=2, log=True)
with pytest.raises(ValueError):
ot.partial.partial_gromov_wasserstein(M, M, p, q, m=-1, log=True)
with pytest.raises(ValueError):
ot.partial.entropic_partial_gromov_wasserstein(M, M, p, q, reg=1, m=2, log=True)
with pytest.raises(ValueError):
ot.partial.entropic_partial_gromov_wasserstein(M, M, p, q, reg=1, m=-1, log=True)
def test_partial_wasserstein_lagrange():
n_samples = 20 # nb samples (gaussian)
n_noise = 20 # nb of samples (noise)
mu = np.array([0, 0])
cov = np.array([[1, 0], [0, 2]])
xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2))
xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2))
M = ot.dist(xs, xt)
p = ot.unif(n_samples + n_noise)
q = ot.unif(n_samples + n_noise)
w0, log0 = ot.partial.partial_wasserstein_lagrange(p, q, M, 1, log=True)
def test_partial_wasserstein():
n_samples = 20 # nb samples (gaussian)
n_noise = 20 # nb of samples (noise)
mu = np.array([0, 0])
cov = np.array([[1, 0], [0, 2]])
xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2))
xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2))
M = ot.dist(xs, xt)
p = ot.unif(n_samples + n_noise)
q = ot.unif(n_samples + n_noise)
m = 0.5
w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=m, log=True)
w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=1, m=m,
log=True, verbose=True)
# check constratints
np.testing.assert_equal(
w0.sum(1) - p <= 1e-5, [True] * len(p)) # cf convergence wasserstein
np.testing.assert_equal(
w0.sum(0) - q <= 1e-5, [True] * len(q)) # cf convergence wasserstein
np.testing.assert_equal(
w.sum(1) - p <= 1e-5, [True] * len(p)) # cf convergence wasserstein
np.testing.assert_equal(
w.sum(0) - q <= 1e-5, [True] * len(q)) # cf convergence wasserstein
# check transported mass
np.testing.assert_allclose(
np.sum(w0), m, atol=1e-04)
np.testing.assert_allclose(
np.sum(w), m, atol=1e-04)
w0, log0 = ot.partial.partial_wasserstein2(p, q, M, m=m, log=True)
w0_val = ot.partial.partial_wasserstein2(p, q, M, m=m, log=False)
G = log0['T']
np.testing.assert_allclose(w0, w0_val, atol=1e-1, rtol=1e-1)
# check constratints
np.testing.assert_equal(
G.sum(1) <= p, [True] * len(p)) # cf convergence wasserstein
np.testing.assert_equal(
G.sum(0) <= q, [True] * len(q)) # cf convergence wasserstein
np.testing.assert_allclose(
np.sum(G), m, atol=1e-04)
def test_partial_gromov_wasserstein():
n_samples = 20 # nb samples
n_noise = 10 # nb of samples (noise)
p = ot.unif(n_samples + n_noise)
q = ot.unif(n_samples + n_noise)
mu_s = np.array([0, 0])
cov_s = np.array([[1, 0], [0, 1]])
mu_t = np.array([0, 0, 0])
cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)
xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0)
P = sp.linalg.sqrtm(cov_t)
xt = np.random.randn(n_samples, 3).dot(P) + mu_t
xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0)
xt2 = xs[::-1].copy()
C1 = ot.dist(xs, xs)
C2 = ot.dist(xt, xt)
C3 = ot.dist(xt2, xt2)
m = 2 / 3
res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C3, p, q, m=m,
log=True, verbose=True)
np.testing.assert_allclose(res0, 0, atol=1e-1, rtol=1e-1)
C1 = sp.spatial.distance.cdist(xs, xs)
C2 = sp.spatial.distance.cdist(xt, xt)
m = 1
res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m,
log=True)
G = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss')
np.testing.assert_allclose(G, res0, atol=1e-04)
res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,
m=m, log=True)
G = ot.gromov.entropic_gromov_wasserstein(
C1, C2, p, q, 'square_loss', epsilon=10)
np.testing.assert_allclose(G, res, atol=1e-02)
w0, log0 = ot.partial.partial_gromov_wasserstein2(C1, C2, p, q, m=m,
log=True)
w0_val = ot.partial.partial_gromov_wasserstein2(C1, C2, p, q, m=m,
log=False)
G = log0['T']
np.testing.assert_allclose(w0, w0_val, atol=1e-1, rtol=1e-1)
m = 2 / 3
res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m,
log=True)
res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q,
100, m=m,
log=True)
# check constratints
np.testing.assert_equal(
res0.sum(1) <= p, [True] * len(p)) # cf convergence wasserstein
np.testing.assert_equal(
res0.sum(0) <= q, [True] * len(q)) # cf convergence wasserstein
np.testing.assert_allclose(
np.sum(res0), m, atol=1e-04)
np.testing.assert_equal(
res.sum(1) <= p, [True] * len(p)) # cf convergence wasserstein
np.testing.assert_equal(
res.sum(0) <= q, [True] * len(q)) # cf convergence wasserstein
np.testing.assert_allclose(
np.sum(res), m, atol=1e-04)
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