Function test_emd_laplace

test/test_da.py:928–963

Complements :code:`test_emd_laplace_class` for uncovered options in :code:`emd_laplace`

(nx)

@pytest.skip_backend("jax")
@pytest.skip_backend("tf")
def test_emd_laplace(nx):
    """Complements :code:`test_emd_laplace_class` for uncovered options in :code:`emd_laplace`"""
    ns = 50
    nt = 50

    Xs, ys = make_data_classif("3gauss", ns)
    Xt, yt = make_data_classif("3gauss2", nt)

    Xs, ys, Xt, yt = nx.from_numpy(Xs, ys, Xt, yt)
    M = ot.dist(Xs, Xt)
    with pytest.raises(ValueError):
        ot.da.emd_laplace(
            ot.unif(ns), ot.unif(nt), Xs, Xt, M, sim_param=["INVALID", "INPUT", 2]
        )
    with pytest.raises(ValueError):
        ot.da.emd_laplace(
            ot.unif(ns), ot.unif(nt), Xs, Xt, M, sim=["INVALID", "INPUT", 2]
        )

    # test all margin constraints with gaussian similarity and disp regularisation
    coupling = ot.da.emd_laplace(
        ot.unif(ns, type_as=Xs),
        ot.unif(nt, type_as=Xs),
        Xs,
        Xt,
        M,
        sim="gauss",
        reg="disp",
    )

    assert_allclose(
        nx.to_numpy(nx.sum(coupling, axis=0)), unif(nt), rtol=1e-3, atol=1e-3
    )
    assert_allclose(
        nx.to_numpy(nx.sum(coupling, axis=1)), unif(ns), rtol=1e-3, atol=1e-3
    )


@pytest.skip_backend("jax")