diff options
Diffstat (limited to 'test/test_bregman.py')
| -rw-r--r-- | test/test_bregman.py | 119 |
1 files changed, 119 insertions, 0 deletions
diff --git a/test/test_bregman.py b/test/test_bregman.py index c8e9179..7f4972c 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -1,6 +1,7 @@ """Tests for module bregman on OT with bregman projections """ # Author: Remi Flamary <remi.flamary@unice.fr> +# Kilian Fatras <kilian.fatras@irisa.fr> # # License: MIT License @@ -71,11 +72,39 @@ def test_sinkhorn_variants(): Ges = ot.sinkhorn( u, u, M, 1, method='sinkhorn_epsilon_scaling', stopThr=1e-10) Gerr = ot.sinkhorn(u, u, M, 1, method='do_not_exists', stopThr=1e-10) + G_green = ot.sinkhorn(u, u, M, 1, method='greenkhorn', stopThr=1e-10) # check values np.testing.assert_allclose(G0, Gs, atol=1e-05) np.testing.assert_allclose(G0, Ges, atol=1e-05) np.testing.assert_allclose(G0, Gerr) + np.testing.assert_allclose(G0, G_green, atol=1e-5) + print(G0, G_green) + + +def test_sinkhorn_variants_log(): + # test sinkhorn + n = 100 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G0, log0 = ot.sinkhorn(u, u, M, 1, method='sinkhorn', stopThr=1e-10, log=True) + Gs, logs = ot.sinkhorn(u, u, M, 1, method='sinkhorn_stabilized', stopThr=1e-10, log=True) + Ges, loges = ot.sinkhorn( + u, u, M, 1, method='sinkhorn_epsilon_scaling', stopThr=1e-10, log=True) + Gerr, logerr = ot.sinkhorn(u, u, M, 1, method='do_not_exists', stopThr=1e-10, log=True) + G_green, loggreen = ot.sinkhorn(u, u, M, 1, method='greenkhorn', stopThr=1e-10, log=True) + + # check values + np.testing.assert_allclose(G0, Gs, atol=1e-05) + np.testing.assert_allclose(G0, Ges, atol=1e-05) + np.testing.assert_allclose(G0, Gerr) + np.testing.assert_allclose(G0, G_green, atol=1e-5) + print(G0, G_green) def test_bary(): @@ -105,6 +134,30 @@ def test_bary(): ot.bregman.barycenter(A, M, reg, log=True, verbose=True) +def test_wasserstein_bary_2d(): + + size = 100 # size of a square image + a1 = np.random.randn(size, size) + a1 += a1.min() + a1 = a1 / np.sum(a1) + a2 = np.random.randn(size, size) + a2 += a2.min() + a2 = a2 / np.sum(a2) + # creating matrix A containing all distributions + A = np.zeros((2, size, size)) + A[0, :, :] = a1 + A[1, :, :] = a2 + + # wasserstein + reg = 1e-2 + bary_wass = ot.bregman.convolutional_barycenter2d(A, reg) + + np.testing.assert_allclose(1, np.sum(bary_wass)) + + # help in checking if log and verbose do not bug the function + ot.bregman.convolutional_barycenter2d(A, reg, log=True, verbose=True) + + def test_unmix(): n_bins = 50 # nb bins @@ -135,3 +188,69 @@ def test_unmix(): ot.bregman.unmix(a, D, M, M0, h0, reg, 1, alpha=0.01, log=True, verbose=True) + + +def test_empirical_sinkhorn(): + # test sinkhorn + n = 100 + a = ot.unif(n) + b = ot.unif(n) + + X_s = np.reshape(np.arange(n), (n, 1)) + X_t = np.reshape(np.arange(0, n), (n, 1)) + M = ot.dist(X_s, X_t) + M_m = ot.dist(X_s, X_t, metric='minkowski') + + G_sqe = ot.bregman.empirical_sinkhorn(X_s, X_t, 1) + sinkhorn_sqe = ot.sinkhorn(a, b, M, 1) + + G_log, log_es = ot.bregman.empirical_sinkhorn(X_s, X_t, 0.1, log=True) + sinkhorn_log, log_s = ot.sinkhorn(a, b, M, 0.1, log=True) + + G_m = ot.bregman.empirical_sinkhorn(X_s, X_t, 1, metric='minkowski') + sinkhorn_m = ot.sinkhorn(a, b, M_m, 1) + + loss_emp_sinkhorn = ot.bregman.empirical_sinkhorn2(X_s, X_t, 1) + loss_sinkhorn = ot.sinkhorn2(a, b, M, 1) + + # check constratints + np.testing.assert_allclose( + sinkhorn_sqe.sum(1), G_sqe.sum(1), atol=1e-05) # metric sqeuclidian + np.testing.assert_allclose( + sinkhorn_sqe.sum(0), G_sqe.sum(0), atol=1e-05) # metric sqeuclidian + np.testing.assert_allclose( + sinkhorn_log.sum(1), G_log.sum(1), atol=1e-05) # log + np.testing.assert_allclose( + sinkhorn_log.sum(0), G_log.sum(0), atol=1e-05) # log + np.testing.assert_allclose( + sinkhorn_m.sum(1), G_m.sum(1), atol=1e-05) # metric euclidian + np.testing.assert_allclose( + sinkhorn_m.sum(0), G_m.sum(0), atol=1e-05) # metric euclidian + np.testing.assert_allclose(loss_emp_sinkhorn, loss_sinkhorn, atol=1e-05) + + +def test_empirical_sinkhorn_divergence(): + #Test sinkhorn divergence + n = 10 + a = ot.unif(n) + b = ot.unif(n) + X_s = np.reshape(np.arange(n), (n, 1)) + X_t = np.reshape(np.arange(0, n * 2, 2), (n, 1)) + M = ot.dist(X_s, X_t) + M_s = ot.dist(X_s, X_s) + M_t = ot.dist(X_t, X_t) + + emp_sinkhorn_div = ot.bregman.empirical_sinkhorn_divergence(X_s, X_t, 1) + sinkhorn_div = (ot.sinkhorn2(a, b, M, 1) - 1 / 2 * ot.sinkhorn2(a, a, M_s, 1) - 1 / 2 * ot.sinkhorn2(b, b, M_t, 1)) + + emp_sinkhorn_div_log, log_es = ot.bregman.empirical_sinkhorn_divergence(X_s, X_t, 1, log=True) + sink_div_log_ab, log_s_ab = ot.sinkhorn2(a, b, M, 1, log=True) + sink_div_log_a, log_s_a = ot.sinkhorn2(a, a, M_s, 1, log=True) + sink_div_log_b, log_s_b = ot.sinkhorn2(b, b, M_t, 1, log=True) + sink_div_log = sink_div_log_ab - 1 / 2 * (sink_div_log_a + sink_div_log_b) + + # check constratints + np.testing.assert_allclose( + emp_sinkhorn_div, sinkhorn_div, atol=1e-05) # cf conv emp sinkhorn + np.testing.assert_allclose( + emp_sinkhorn_div_log, sink_div_log, atol=1e-05) # cf conv emp sinkhorn |