diff options
| author | Slasnista <stan.chambon@gmail.com> | 2017-09-05 10:00:01 +0200 |
|---|---|---|
| committer | Slasnista <stan.chambon@gmail.com> | 2017-09-05 10:00:01 +0200 |
| commit | 49c100de34583329058b39d414d2aa49b7fd15bf (patch) | |
| tree | d435b7814c86d710610ca291580ed3d3206edfce /test/test_da.py | |
| parent | 8e4a7930cf1ff80edeb30021acaf7337a02d18a5 (diff) | |
test semi supervised mode ok written for all class | need different tolerance for EMDTransport
Diffstat (limited to 'test/test_da.py')
| -rw-r--r-- | test/test_da.py | 29 |
1 files changed, 18 insertions, 11 deletions
diff --git a/test/test_da.py b/test/test_da.py index 9fc42a3..3602db9 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -64,11 +64,11 @@ def test_sinkhorn_lpl1_transport_class(): 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) + otda_unsup = ot.da.SinkhornLpl1Transport() + otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt) n_unsup = np.sum(otda_unsup.cost_) - otda_semi = ot.da.SinkhornTransport() + otda_semi = ot.da.SinkhornLpl1Transport() 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_) @@ -136,11 +136,11 @@ def test_sinkhorn_l1l2_transport_class(): 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) + otda_unsup = ot.da.SinkhornL1l2Transport() + otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt) n_unsup = np.sum(otda_unsup.cost_) - otda_semi = ot.da.SinkhornTransport() + otda_semi = ot.da.SinkhornL1l2Transport() 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_) @@ -152,7 +152,9 @@ def test_sinkhorn_l1l2_transport_class(): # and labeled target samples mass_semi = np.sum( otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max]) - assert mass_semi == 0, "semisupervised mode not working" + mass_semi = otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max] + assert_allclose(mass_semi, np.zeros_like(mass_semi), + rtol=1e-9, atol=1e-9) # check everything runs well with log=True otda = ot.da.SinkhornL1l2Transport(log=True) @@ -289,11 +291,11 @@ def test_emd_transport_class(): 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) + otda_unsup = ot.da.EMDTransport() + otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt) n_unsup = np.sum(otda_unsup.cost_) - otda_semi = ot.da.SinkhornTransport() + otda_semi = ot.da.EMDTransport() 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_) @@ -305,7 +307,11 @@ def test_emd_transport_class(): # and labeled target samples mass_semi = np.sum( otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max]) - assert mass_semi == 0, "semisupervised mode not working" + mass_semi = otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max] + + # we need to use a small tolerance here, otherwise the test breaks + assert_allclose(mass_semi, np.zeros_like(mass_semi), + rtol=1e-2, atol=1e-2) def test_mapping_transport_class(): @@ -491,3 +497,4 @@ def test_otda(): # test_sinkhorn_l1l2_transport_class() # test_sinkhorn_lpl1_transport_class() # test_mapping_transport_class() + |