diff options
Diffstat (limited to 'test/test_da.py')
| -rw-r--r-- | test/test_da.py | 210 |
1 files changed, 207 insertions, 3 deletions
diff --git a/test/test_da.py b/test/test_da.py index 2a5e50e..3b28119 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -5,7 +5,7 @@ # License: MIT License import numpy as np -from numpy.testing.utils import assert_allclose, assert_equal +from numpy.testing import assert_allclose, assert_equal import ot from ot.datasets import make_data_classif @@ -65,6 +65,16 @@ def test_sinkhorn_lpl1_transport_class(): transp_Xs = otda.fit_transform(Xs=Xs, ys=ys, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) + # test unsupervised vs semi-supervised mode otda_unsup = ot.da.SinkhornLpl1Transport() otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt) @@ -129,6 +139,16 @@ def test_sinkhorn_l1l2_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) @@ -210,6 +230,16 @@ def test_sinkhorn_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) @@ -271,6 +301,16 @@ def test_unbalanced_sinkhorn_transport_class(): transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) + Xs_new, _ = make_data_classif('3gauss', ns + 1) transp_Xs_new = otda.transform(Xs_new) @@ -353,6 +393,16 @@ def test_emd_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) @@ -510,7 +560,6 @@ def test_mapping_transport_class(): def test_linear_mapping(): - ns = 150 nt = 200 @@ -528,7 +577,6 @@ def test_linear_mapping(): def test_linear_mapping_class(): - ns = 150 nt = 200 @@ -549,3 +597,159 @@ def test_linear_mapping_class(): Cst = np.cov(Xst.T) np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2) + + +def test_jcpot_transport_class(): + """test_jcpot_transport + """ + + ns1 = 150 + ns2 = 150 + nt = 200 + + Xs1, ys1 = make_data_classif('3gauss', ns1) + Xs2, ys2 = make_data_classif('3gauss', ns2) + + Xt, yt = make_data_classif('3gauss2', nt) + + Xs = [Xs1, Xs2] + ys = [ys1, ys2] + + otda = ot.da.JCPOTTransport(reg_e=1, max_iter=10000, tol=1e-9, verbose=True, log=True) + + # test its computed + otda.fit(Xs=Xs, ys=ys, Xt=Xt) + + assert hasattr(otda, "coupling_") + assert hasattr(otda, "proportions_") + assert hasattr(otda, "log_") + + # test dimensions of coupling + for i, xs in enumerate(Xs): + assert_equal(otda.coupling_[i].shape, ((xs.shape[0], Xt.shape[0]))) + + # test all margin constraints + mu_t = unif(nt) + + for i in range(len(Xs)): + # test margin constraints w.r.t. uniform target weights for each coupling matrix + assert_allclose( + np.sum(otda.coupling_[i], axis=0), mu_t, rtol=1e-3, atol=1e-3) + + # test margin constraints w.r.t. modified source weights for each source domain + + assert_allclose( + np.dot(otda.log_['D1'][i], np.sum(otda.coupling_[i], axis=1)), otda.proportions_, rtol=1e-3, + atol=1e-3) + + # test transform + transp_Xs = otda.transform(Xs=Xs) + [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)] + + Xs_new, _ = make_data_classif('3gauss', ns1 + 1) + transp_Xs_new = otda.transform(Xs_new) + + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) + + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + [assert_equal(x.shape[0], y.shape[0]) for x, y in zip(transp_ys, ys)] + [assert_equal(x.shape[1], len(np.unique(y))) for x, y in zip(transp_ys, ys)] + + +def test_jcpot_barycenter(): + """test_jcpot_barycenter + """ + + ns1 = 150 + ns2 = 150 + nt = 200 + + sigma = 0.1 + np.random.seed(1985) + + ps1 = .2 + ps2 = .9 + pt = .4 + + Xs1, ys1 = make_data_classif('2gauss_prop', ns1, nz=sigma, p=ps1) + Xs2, ys2 = make_data_classif('2gauss_prop', ns2, nz=sigma, p=ps2) + Xt, yt = make_data_classif('2gauss_prop', nt, nz=sigma, p=pt) + + Xs = [Xs1, Xs2] + ys = [ys1, ys2] + + prop = ot.bregman.jcpot_barycenter(Xs, ys, Xt, reg=.5, metric='sqeuclidean', + numItermax=10000, stopThr=1e-9, verbose=False, log=False) + + np.testing.assert_allclose(prop, [1 - pt, pt], rtol=1e-3, atol=1e-3) + + +def test_emd_laplace_class(): + """test_emd_laplace_transport + """ + ns = 150 + nt = 200 + + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) + + otda = ot.da.EMDLaplaceTransport(reg_lap=0.01, max_iter=1000, tol=1e-9, verbose=False, log=True) + + # test its computed + otda.fit(Xs=Xs, ys=ys, Xt=Xt) + + assert hasattr(otda, "coupling_") + assert hasattr(otda, "log_") + + # test dimensions of coupling + assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + + # test all margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + + assert_allclose( + np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = otda.transform(Xs=Xs) + [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)] + + 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) + + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) |