diff options
Diffstat (limited to 'test/test_partial.py')
| -rwxr-xr-x | test/test_partial.py | 72 |
1 files changed, 70 insertions, 2 deletions
diff --git a/test/test_partial.py b/test/test_partial.py index 8b1ca89..510e081 100755 --- a/test/test_partial.py +++ b/test/test_partial.py @@ -8,6 +8,74 @@ 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(): @@ -32,7 +100,7 @@ def test_partial_wasserstein(): 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) + log=True, verbose=True) # check constratints np.testing.assert_equal( @@ -92,7 +160,7 @@ def test_partial_gromov_wasserstein(): m = 2 / 3 res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C3, p, q, m=m, - log=True) + log=True, verbose=True) np.testing.assert_allclose(res0, 0, atol=1e-1, rtol=1e-1) C1 = sp.spatial.distance.cdist(xs, xs) |