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"""Tests for module sliced"""
# Author: Adrien Corenflos <adrien.corenflos@aalto.fi>
# Nicolas Courty <ncourty@irisa.fr>
#
# License: MIT License
import numpy as np
import pytest
import ot
from ot.sliced import get_random_projections
def test_get_random_projections():
rng = np.random.RandomState(0)
projections = get_random_projections(1000, 50, rng)
np.testing.assert_almost_equal(np.sum(projections ** 2, 0), 1.)
def test_sliced_same_dist():
n = 100
rng = np.random.RandomState(0)
x = rng.randn(n, 2)
u = ot.utils.unif(n)
res = ot.sliced_wasserstein_distance(x, x, u, u, 10, seed=rng)
np.testing.assert_almost_equal(res, 0.)
def test_sliced_bad_shapes():
n = 100
rng = np.random.RandomState(0)
x = rng.randn(n, 2)
y = rng.randn(n, 4)
u = ot.utils.unif(n)
with pytest.raises(ValueError):
_ = ot.sliced_wasserstein_distance(x, y, u, u, 10, seed=rng)
def test_sliced_log():
n = 100
rng = np.random.RandomState(0)
x = rng.randn(n, 4)
y = rng.randn(n, 4)
u = ot.utils.unif(n)
res, log = ot.sliced_wasserstein_distance(x, y, u, u, 10, p=1, seed=rng, log=True)
assert len(log) == 2
projections = log["projections"]
projected_emds = log["projected_emds"]
assert projections.shape[1] == len(projected_emds) == 10
for emd in projected_emds:
assert emd > 0
def test_sliced_different_dists():
n = 100
rng = np.random.RandomState(0)
x = rng.randn(n, 2)
u = ot.utils.unif(n)
y = rng.randn(n, 2)
res = ot.sliced_wasserstein_distance(x, y, u, u, 10, seed=rng)
assert res > 0.
def test_1d_sliced_equals_emd():
n = 100
m = 120
rng = np.random.RandomState(0)
x = rng.randn(n, 1)
a = rng.uniform(0, 1, n)
a /= a.sum()
y = rng.randn(m, 1)
u = ot.utils.unif(m)
res = ot.sliced_wasserstein_distance(x, y, a, u, 10, seed=42)
expected = ot.emd2_1d(x.squeeze(), y.squeeze(), a, u)
np.testing.assert_almost_equal(res ** 2, expected)
def test_max_sliced_same_dist():
n = 100
rng = np.random.RandomState(0)
x = rng.randn(n, 2)
u = ot.utils.unif(n)
res = ot.max_sliced_wasserstein_distance(x, x, u, u, 10, seed=rng)
np.testing.assert_almost_equal(res, 0.)
def test_max_sliced_different_dists():
n = 100
rng = np.random.RandomState(0)
x = rng.randn(n, 2)
u = ot.utils.unif(n)
y = rng.randn(n, 2)
res, log = ot.max_sliced_wasserstein_distance(x, y, u, u, 10, seed=rng, log=True)
assert res > 0.
def test_sliced_backend(nx):
n = 100
rng = np.random.RandomState(0)
x = rng.randn(n, 2)
y = rng.randn(2 * n, 2)
P = rng.randn(2, 20)
P = P / np.sqrt((P**2).sum(0, keepdims=True))
n_projections = 20
xb = nx.from_numpy(x)
yb = nx.from_numpy(y)
Pb = nx.from_numpy(P)
val0 = ot.sliced_wasserstein_distance(x, y, projections=P)
val = ot.sliced_wasserstein_distance(xb, yb, n_projections=n_projections, seed=0)
val2 = ot.sliced_wasserstein_distance(xb, yb, n_projections=n_projections, seed=0)
assert val > 0
assert val == val2
valb = nx.to_numpy(ot.sliced_wasserstein_distance(xb, yb, projections=Pb))
assert np.allclose(val0, valb)
def test_sliced_backend_type_devices(nx):
n = 100
rng = np.random.RandomState(0)
x = rng.randn(n, 2)
y = rng.randn(2 * n, 2)
P = rng.randn(2, 20)
P = P / np.sqrt((P**2).sum(0, keepdims=True))
for tp in nx.__type_list__:
print(nx.dtype_device(tp))
xb = nx.from_numpy(x, type_as=tp)
yb = nx.from_numpy(y, type_as=tp)
Pb = nx.from_numpy(P, type_as=tp)
valb = ot.sliced_wasserstein_distance(xb, yb, projections=Pb)
nx.assert_same_dtype_device(xb, valb)
def test_max_sliced_backend(nx):
n = 100
rng = np.random.RandomState(0)
x = rng.randn(n, 2)
y = rng.randn(2 * n, 2)
P = rng.randn(2, 20)
P = P / np.sqrt((P**2).sum(0, keepdims=True))
n_projections = 20
xb = nx.from_numpy(x)
yb = nx.from_numpy(y)
Pb = nx.from_numpy(P)
val0 = ot.max_sliced_wasserstein_distance(x, y, projections=P)
val = ot.max_sliced_wasserstein_distance(xb, yb, n_projections=n_projections, seed=0)
val2 = ot.max_sliced_wasserstein_distance(xb, yb, n_projections=n_projections, seed=0)
assert val > 0
assert val == val2
valb = nx.to_numpy(ot.max_sliced_wasserstein_distance(xb, yb, projections=Pb))
assert np.allclose(val0, valb)
def test_max_sliced_backend_type_devices(nx):
n = 100
rng = np.random.RandomState(0)
x = rng.randn(n, 2)
y = rng.randn(2 * n, 2)
P = rng.randn(2, 20)
P = P / np.sqrt((P**2).sum(0, keepdims=True))
for tp in nx.__type_list__:
print(nx.dtype_device(tp))
xb = nx.from_numpy(x, type_as=tp)
yb = nx.from_numpy(y, type_as=tp)
Pb = nx.from_numpy(P, type_as=tp)
valb = ot.max_sliced_wasserstein_distance(xb, yb, projections=Pb)
nx.assert_same_dtype_device(xb, valb)
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