diff options
Diffstat (limited to 'test/test_da.py')
-rw-r--r-- | test/test_da.py | 66 |
1 files changed, 33 insertions, 33 deletions
diff --git a/test/test_da.py b/test/test_da.py index 93f7e83..196f4c4 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -28,14 +28,14 @@ def test_sinkhorn_lpl1_transport_class(): clf.fit(Xs=Xs, ys=ys, Xt=Xt) # test dimensions of coupling - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = clf.transform(Xs=Xs) @@ -64,13 +64,13 @@ def test_sinkhorn_lpl1_transport_class(): # test semi supervised mode clf = ot.da.SinkhornLpl1Transport() clf.fit(Xs=Xs, ys=ys, Xt=Xt) - n_unsup = np.sum(clf.Cost) + n_unsup = np.sum(clf.cost_) # test semi supervised mode clf = ot.da.SinkhornLpl1Transport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf.Cost) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.cost_) assert n_unsup != n_semisup, "semisupervised mode not working" @@ -91,14 +91,14 @@ def test_sinkhorn_l1l2_transport_class(): clf.fit(Xs=Xs, ys=ys, Xt=Xt) # test dimensions of coupling - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = clf.transform(Xs=Xs) @@ -127,13 +127,13 @@ def test_sinkhorn_l1l2_transport_class(): # test semi supervised mode clf = ot.da.SinkhornL1l2Transport() clf.fit(Xs=Xs, ys=ys, Xt=Xt) - n_unsup = np.sum(clf.Cost) + n_unsup = np.sum(clf.cost_) # test semi supervised mode clf = ot.da.SinkhornL1l2Transport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf.Cost) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.cost_) assert n_unsup != n_semisup, "semisupervised mode not working" @@ -154,14 +154,14 @@ def test_sinkhorn_transport_class(): clf.fit(Xs=Xs, Xt=Xt) # test dimensions of coupling - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = clf.transform(Xs=Xs) @@ -190,13 +190,13 @@ def test_sinkhorn_transport_class(): # test semi supervised mode clf = ot.da.SinkhornTransport() clf.fit(Xs=Xs, Xt=Xt) - n_unsup = np.sum(clf.Cost) + n_unsup = np.sum(clf.cost_) # test semi supervised mode clf = ot.da.SinkhornTransport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf.Cost) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.cost_) assert n_unsup != n_semisup, "semisupervised mode not working" @@ -217,14 +217,14 @@ def test_emd_transport_class(): clf.fit(Xs=Xs, Xt=Xt) # test dimensions of coupling - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = clf.transform(Xs=Xs) @@ -253,13 +253,13 @@ def test_emd_transport_class(): # test semi supervised mode clf = ot.da.EMDTransport() clf.fit(Xs=Xs, Xt=Xt) - n_unsup = np.sum(clf.Cost) + n_unsup = np.sum(clf.cost_) # test semi supervised mode clf = ot.da.EMDTransport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf.Cost) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.cost_) assert n_unsup != n_semisup, "semisupervised mode not working" @@ -326,9 +326,9 @@ def test_otda(): da_emd.predict(xs) # interpolation of source samples -if __name__ == "__main__": +# if __name__ == "__main__": - test_sinkhorn_transport_class() - test_emd_transport_class() - test_sinkhorn_l1l2_transport_class() - test_sinkhorn_lpl1_transport_class() +# test_sinkhorn_transport_class() +# test_emd_transport_class() +# test_sinkhorn_l1l2_transport_class() +# test_sinkhorn_lpl1_transport_class() |