def test_kmcca_n_components():
Xs = next(generate_mcca_test_data())
n_features = sum([X.shape[1] for X in Xs])
kmcca = KMCCA(n_components=n_features + 1)
with pytest.raises(AttributeError):
kmcca.n_components_
with pytest.warns(None):
kmcca.fit(Xs)
assert kmcca.n_components_ == n_features
for load in kmcca.dual_vars_:
assert kmcca.n_components_ == load.shape[1]
def test_pgso():
Xs = next(generate_mcca_test_data())
N = Xs[0].shape[0]
ranks = []
corrs = []
for tol in [0, 0.1, 0.5, 1]:
kmcca = KMCCA(kernel='rbf', pgso=True, tol=tol, n_components=2)
scores = kmcca.fit(Xs).transform(Xs)
# kmcca2 = KMCCA(kernel='rbf', pgso=True, tol=tol)
# scores2 = kmcca2.fit(Xs).transform(Xs)
for v in range(len(Xs)):
assert len(
set([
kmcca.pgso_Ls_[v].shape[1], kmcca.pgso_norms_[v].shape[0],
kmcca.pgso_idxs_[v].shape[0], kmcca.pgso_Xs_[v].shape[0]
])) == 1
R = kmcca._get_kernel(Xs[v], v) - \
kmcca.pgso_Ls_[v] @ kmcca.pgso_Ls_[v].T
R = R - kmcca.kernel_col_means_[v] - \
kmcca.kernel_col_means_[v].T + kmcca.kernel_mat_means_[v]
assert np.trace(R) / N <= tol
ranks.append(kmcca.pgso_ranks_)
corrs.append(kmcca.canon_corrs(scores))
if tol == 0:
assert np.all(ranks[-1] == [N for _ in Xs])
assert np.all(np.diff(corrs, axis=0) <= 1e-10), corrs
assert np.all(np.diff(corrs, axis=1) <= 1e-10), corrs
assert np.all(np.diff(ranks, axis=0) <= 1e-10), ranks