diff options
author | Rémi Flamary <remi.flamary@gmail.com> | 2019-09-09 14:55:04 +0200 |
---|---|---|
committer | Rémi Flamary <remi.flamary@gmail.com> | 2019-09-09 14:55:04 +0200 |
commit | b2a7afb848a78570d01f35f9b239be8838520edc (patch) | |
tree | fc243208d24f5488d5ce06298b2ebb39b76be9bb /test | |
parent | c698e0aa20d28e36d25f87082855a490283f3c88 (diff) | |
parent | f251b4d080a577c2cee890ca43d8ec3658332021 (diff) |
merge new unbalanced
Diffstat (limited to 'test')
-rw-r--r-- | test/test_bregman.py | 97 | ||||
-rw-r--r-- | test/test_da.py | 65 | ||||
-rw-r--r-- | test/test_unbalanced.py | 163 |
3 files changed, 274 insertions, 51 deletions
diff --git a/test/test_bregman.py b/test/test_bregman.py index 7f4972c..f70df10 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -7,6 +7,7 @@ import numpy as np import ot +import pytest def test_sinkhorn(): @@ -71,13 +72,11 @@ def test_sinkhorn_variants(): Gs = ot.sinkhorn(u, u, M, 1, method='sinkhorn_stabilized', stopThr=1e-10) 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) @@ -96,18 +95,17 @@ def test_sinkhorn_variants_log(): 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(): +@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) +def test_barycenter(method): n_bins = 100 # nb bins @@ -126,14 +124,42 @@ def test_bary(): weights = np.array([1 - alpha, alpha]) # wasserstein - reg = 1e-3 - bary_wass = ot.bregman.barycenter(A, M, reg, weights) + reg = 1e-2 + bary_wass = ot.bregman.barycenter(A, M, reg, weights, method=method) np.testing.assert_allclose(1, np.sum(bary_wass)) ot.bregman.barycenter(A, M, reg, log=True, verbose=True) +def test_barycenter_stabilization(): + + n_bins = 100 # nb bins + + # Gaussian distributions + a1 = ot.datasets.make_1D_gauss(n_bins, m=30, s=10) # m= mean, s= std + a2 = ot.datasets.make_1D_gauss(n_bins, m=40, s=10) + + # creating matrix A containing all distributions + A = np.vstack((a1, a2)).T + + # loss matrix + normalization + M = ot.utils.dist0(n_bins) + M /= M.max() + + alpha = 0.5 # 0<=alpha<=1 + weights = np.array([1 - alpha, alpha]) + + # wasserstein + reg = 1e-2 + bar_stable = ot.bregman.barycenter(A, M, reg, weights, + method="sinkhorn_stabilized", + stopThr=1e-8) + bar = ot.bregman.barycenter(A, M, reg, weights, method="sinkhorn", + stopThr=1e-8) + np.testing.assert_allclose(bar, bar_stable) + + def test_wasserstein_bary_2d(): size = 100 # size of a square image @@ -254,3 +280,60 @@ def test_empirical_sinkhorn_divergence(): 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 + + +def test_stabilized_vs_sinkhorn_multidim(): + # test if stable version matches sinkhorn + # for multidimensional inputs + n = 100 + + # Gaussian distributions + a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std + b1 = ot.datasets.make_1D_gauss(n, m=60, s=8) + b2 = ot.datasets.make_1D_gauss(n, m=30, s=4) + + # creating matrix A containing all distributions + b = np.vstack((b1, b2)).T + + M = ot.utils.dist0(n) + M /= np.median(M) + epsilon = 0.1 + G, log = ot.bregman.sinkhorn(a, b, M, reg=epsilon, + method="sinkhorn_stabilized", + log=True) + G2, log2 = ot.bregman.sinkhorn(a, b, M, epsilon, + method="sinkhorn", log=True) + + np.testing.assert_allclose(G, G2) + + +def test_implemented_methods(): + IMPLEMENTED_METHODS = ['sinkhorn', 'sinkhorn_stabilized'] + ONLY_1D_methods = ['greenkhorn', 'sinkhorn_epsilon_scaling'] + NOT_VALID_TOKENS = ['foo'] + # test generalized sinkhorn for unbalanced OT barycenter + n = 3 + rng = np.random.RandomState(42) + + x = rng.randn(n, 2) + a = ot.utils.unif(n) + + # make dists unbalanced + b = ot.utils.unif(n) + A = rng.rand(n, 2) + M = ot.dist(x, x) + epsilon = 1. + + for method in IMPLEMENTED_METHODS: + ot.bregman.sinkhorn(a, b, M, epsilon, method=method) + ot.bregman.sinkhorn2(a, b, M, epsilon, method=method) + ot.bregman.barycenter(A, M, reg=epsilon, method=method) + with pytest.raises(ValueError): + for method in set(NOT_VALID_TOKENS): + ot.bregman.sinkhorn(a, b, M, epsilon, method=method) + ot.bregman.sinkhorn2(a, b, M, epsilon, method=method) + ot.bregman.barycenter(A, M, reg=epsilon, method=method) + for method in ONLY_1D_methods: + ot.bregman.sinkhorn(a, b, M, epsilon, method=method) + with pytest.raises(ValueError): + ot.bregman.sinkhorn2(a, b, M, epsilon, method=method) diff --git a/test/test_da.py b/test/test_da.py index f7f3a9d..2a5e50e 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -245,6 +245,71 @@ def test_sinkhorn_transport_class(): assert len(otda.log_.keys()) != 0 +def test_unbalanced_sinkhorn_transport_class(): + """test_sinkhorn_transport + """ + + ns = 150 + nt = 200 + + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) + + otda = ot.da.UnbalancedSinkhornTransport() + + # test its computed + otda.fit(Xs=Xs, Xt=Xt) + assert hasattr(otda, "cost_") + assert hasattr(otda, "coupling_") + assert hasattr(otda, "log_") + + # test dimensions of coupling + assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + + # test transform + transp_Xs = otda.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + Xs_new, _ = make_data_classif('3gauss', ns + 1) + transp_Xs_new = otda.transform(Xs_new) + + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) + + # test inverse transform + transp_Xt = otda.inverse_transform(Xt=Xt) + assert_equal(transp_Xt.shape, Xt.shape) + + Xt_new, _ = make_data_classif('3gauss2', nt + 1) + transp_Xt_new = otda.inverse_transform(Xt=Xt_new) + + # check that the oos method is working + assert_equal(transp_Xt_new.shape, Xt_new.shape) + + # test fit_transform + transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt) + assert_equal(transp_Xs.shape, Xs.shape) + + # test unsupervised vs semi-supervised mode + otda_unsup = ot.da.SinkhornTransport() + otda_unsup.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(otda_unsup.cost_) + + otda_semi = ot.da.SinkhornTransport() + otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(otda_semi.cost_) + + # check that the cost matrix norms are indeed different + assert n_unsup != n_semisup, "semisupervised mode not working" + + # check everything runs well with log=True + otda = ot.da.SinkhornTransport(log=True) + otda.fit(Xs=Xs, ys=ys, Xt=Xt) + assert len(otda.log_.keys()) != 0 + + def test_emd_transport_class(): """test_sinkhorn_transport """ diff --git a/test/test_unbalanced.py b/test/test_unbalanced.py index 1395fe1..ca1efba 100644 --- a/test/test_unbalanced.py +++ b/test/test_unbalanced.py @@ -7,9 +7,12 @@ import numpy as np import ot import pytest +from ot.unbalanced import barycenter_unbalanced +from scipy.special import logsumexp -@pytest.mark.parametrize("method", ["sinkhorn"]) + +@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) def test_unbalanced_convergence(method): # test generalized sinkhorn for unbalanced OT n = 100 @@ -23,29 +26,35 @@ def test_unbalanced_convergence(method): M = ot.dist(x, x) epsilon = 1. - alpha = 1. - K = np.exp(- M / epsilon) + reg_m = 1. - G, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, alpha=alpha, - stopThr=1e-10, method=method, + G, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, + reg_m=reg_m, + method=method, log=True) - loss = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, + loss = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, method=method) # check fixed point equations - fi = alpha / (alpha + epsilon) - v_final = (b / K.T.dot(log["u"])) ** fi - u_final = (a / K.dot(log["v"])) ** fi + # in log-domain + fi = reg_m / (reg_m + epsilon) + logb = np.log(b + 1e-16) + loga = np.log(a + 1e-16) + logKtu = logsumexp(log["logu"][None, :] - M.T / epsilon, axis=1) + logKv = logsumexp(log["logv"][None, :] - M / epsilon, axis=1) + + v_final = fi * (logb - logKtu) + u_final = fi * (loga - logKv) np.testing.assert_allclose( - u_final, log["u"], atol=1e-05) + u_final, log["logu"], atol=1e-05) np.testing.assert_allclose( - v_final, log["v"], atol=1e-05) + v_final, log["logv"], atol=1e-05) # check if sinkhorn_unbalanced2 returns the correct loss np.testing.assert_allclose((G * M).sum(), loss, atol=1e-5) -@pytest.mark.parametrize("method", ["sinkhorn"]) +@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) def test_unbalanced_multiple_inputs(method): # test generalized sinkhorn for unbalanced OT n = 100 @@ -59,28 +68,59 @@ def test_unbalanced_multiple_inputs(method): M = ot.dist(x, x) epsilon = 1. - alpha = 1. - K = np.exp(- M / epsilon) + reg_m = 1. loss, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, - alpha=alpha, - stopThr=1e-10, method=method, + reg_m=reg_m, + method=method, log=True) # check fixed point equations - fi = alpha / (alpha + epsilon) - v_final = (b / K.T.dot(log["u"])) ** fi - - u_final = (a[:, None] / K.dot(log["v"])) ** fi + # in log-domain + fi = reg_m / (reg_m + epsilon) + logb = np.log(b + 1e-16) + loga = np.log(a + 1e-16)[:, None] + logKtu = logsumexp(log["logu"][:, None, :] - M[:, :, None] / epsilon, + axis=0) + logKv = logsumexp(log["logv"][None, :] - M[:, :, None] / epsilon, axis=1) + v_final = fi * (logb - logKtu) + u_final = fi * (loga - logKv) np.testing.assert_allclose( - u_final, log["u"], atol=1e-05) + u_final, log["logu"], atol=1e-05) np.testing.assert_allclose( - v_final, log["v"], atol=1e-05) + v_final, log["logv"], atol=1e-05) assert len(loss) == b.shape[1] -def test_unbalanced_barycenter(): +def test_stabilized_vs_sinkhorn(): + # test if stable version matches sinkhorn + n = 100 + + # Gaussian distributions + a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std + b1 = ot.datasets.make_1D_gauss(n, m=60, s=8) + b2 = ot.datasets.make_1D_gauss(n, m=30, s=4) + + # creating matrix A containing all distributions + b = np.vstack((b1, b2)).T + + M = ot.utils.dist0(n) + M /= np.median(M) + epsilon = 0.1 + reg_m = 1. + G, log = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, reg=epsilon, + method="sinkhorn_stabilized", + reg_m=reg_m, + log=True) + G2, log2 = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, + method="sinkhorn", log=True) + + np.testing.assert_allclose(G, G2, atol=1e-5) + + +@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) +def test_unbalanced_barycenter(method): # test generalized sinkhorn for unbalanced OT barycenter n = 100 rng = np.random.RandomState(42) @@ -92,27 +132,56 @@ def test_unbalanced_barycenter(): A = A * np.array([1, 2])[None, :] M = ot.dist(x, x) epsilon = 1. - alpha = 1. - K = np.exp(- M / epsilon) + reg_m = 1. - q, log = ot.unbalanced.barycenter_unbalanced(A, M, reg=epsilon, alpha=alpha, - stopThr=1e-10, - log=True) + q, log = barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, + method=method, log=True) # check fixed point equations - fi = alpha / (alpha + epsilon) - v_final = (q[:, None] / K.T.dot(log["u"])) ** fi - u_final = (A / K.dot(log["v"])) ** fi + fi = reg_m / (reg_m + epsilon) + logA = np.log(A + 1e-16) + logq = np.log(q + 1e-16)[:, None] + logKtu = logsumexp(log["logu"][:, None, :] - M[:, :, None] / epsilon, + axis=0) + logKv = logsumexp(log["logv"][None, :] - M[:, :, None] / epsilon, axis=1) + v_final = fi * (logq - logKtu) + u_final = fi * (logA - logKv) np.testing.assert_allclose( - u_final, log["u"], atol=1e-05) + u_final, log["logu"], atol=1e-05) np.testing.assert_allclose( - v_final, log["v"], atol=1e-05) + v_final, log["logv"], atol=1e-05) + + +def test_barycenter_stabilized_vs_sinkhorn(): + # test generalized sinkhorn for unbalanced OT barycenter + n = 100 + rng = np.random.RandomState(42) + + x = rng.randn(n, 2) + A = rng.rand(n, 2) + + # make dists unbalanced + A = A * np.array([1, 4])[None, :] + M = ot.dist(x, x) + epsilon = 0.5 + reg_m = 10 + + qstable, log = barycenter_unbalanced(A, M, reg=epsilon, + reg_m=reg_m, log=True, + tau=100, + method="sinkhorn_stabilized", + ) + q, log = barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, + method="sinkhorn", + log=True) + + np.testing.assert_allclose( + q, qstable, atol=1e-05) def test_implemented_methods(): - IMPLEMENTED_METHODS = ['sinkhorn'] - TO_BE_IMPLEMENTED_METHODS = ['sinkhorn_stabilized', - 'sinkhorn_epsilon_scaling'] + IMPLEMENTED_METHODS = ['sinkhorn', 'sinkhorn_stabilized'] + TO_BE_IMPLEMENTED_METHODS = ['sinkhorn_reg_scaling'] NOT_VALID_TOKENS = ['foo'] # test generalized sinkhorn for unbalanced OT barycenter n = 3 @@ -123,24 +192,30 @@ def test_implemented_methods(): # make dists unbalanced b = ot.utils.unif(n) * 1.5 - + A = rng.rand(n, 2) M = ot.dist(x, x) epsilon = 1. - alpha = 1. + reg_m = 1. for method in IMPLEMENTED_METHODS: - ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, reg_m, method=method) - ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, method=method) + barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, + method=method) with pytest.warns(UserWarning, match='not implemented'): for method in set(TO_BE_IMPLEMENTED_METHODS): - ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, reg_m, method=method) - ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, method=method) + barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, + method=method) with pytest.raises(ValueError): for method in set(NOT_VALID_TOKENS): - ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, reg_m, method=method) - ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, method=method) + barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, + method=method) |