Function test_mapping_transport_class

test/test_da.py:593–641

test_mapping_transport

(nx, kernel, bias)

@pytest.mark.parametrize("kernel", ["linear", "gaussian"])
@pytest.mark.parametrize("bias", ["unbiased", "biased"])
def test_mapping_transport_class(nx, kernel, bias):
    """test_mapping_transport"""

    ns = 20
    nt = 30

    Xs, ys = make_data_classif("3gauss", ns)
    Xt, yt = make_data_classif("3gauss2", nt)
    Xs_new, _ = make_data_classif("3gauss", ns + 1)

    Xs, Xt, Xs_new = nx.from_numpy(Xs, Xt, Xs_new)

    # Mapping tests
    bias = bias == "biased"
    otda = ot.da.MappingTransport(kernel=kernel, bias=bias)
    otda.fit(Xs=Xs, Xt=Xt)
    assert hasattr(otda, "coupling_")
    assert hasattr(otda, "mapping_")
    assert hasattr(otda, "log_")

    assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
    S = Xs.shape[0] if kernel == "gaussian" else Xs.shape[1]  # if linear
    if bias:
        S += 1
    assert_equal(otda.mapping_.shape, ((S, Xt.shape[1])))

    # test margin constraints
    mu_s = unif(ns)
    mu_t = unif(nt)
    assert_allclose(
        nx.to_numpy(nx.sum(otda.coupling_, axis=0)), mu_t, rtol=1e-3, atol=1e-3
    )
    assert_allclose(
        nx.to_numpy(nx.sum(otda.coupling_, axis=1)), mu_s, rtol=1e-3, atol=1e-3
    )

    # test transform
    transp_Xs = otda.transform(Xs=Xs)
    assert_equal(transp_Xs.shape, Xs.shape)

    transp_Xs_new = otda.transform(Xs_new)

    # check that the oos method is working
    assert_equal(transp_Xs_new.shape, Xs_new.shape)

    # check everything runs well with log=True
    otda = ot.da.MappingTransport(kernel=kernel, bias=bias, log=True)
    otda.fit(Xs=Xs, Xt=Xt)
    assert len(otda.log_.keys()) != 0


@pytest.skip_backend("jax")