def test_weighted_GCCA_methods():
# TODO we have view weighted GCCA and missing observation GCCA
latent_dims = 2
c = 0
unweighted_gcca = GCCA(latent_dims=latent_dims, c=[c, c]).fit([X, Y])
deweighted_gcca = GCCA(latent_dims=latent_dims,
c=[c, c],
view_weights=[0.5, 0.5]).fit([X, Y])
corr_unweighted_gcca = unweighted_gcca.score((X, Y))
corr_deweighted_gcca = deweighted_gcca.score((X, Y))
# Check the correlations from each unregularized method are the same
K = np.ones((2, X.shape[0]))
K[0, 200:] = 0
unobserved_gcca = GCCA(latent_dims=latent_dims, c=[c, c]).fit((X, Y), K=K)
assert (np.testing.assert_array_almost_equal(
corr_unweighted_gcca, corr_deweighted_gcca, decimal=1) is None)
def test_unregularized_multi():
# Tests unregularized CCA methods for more than 2 views. The idea is that all of these should give the same result.
latent_dims = 2
cca = rCCA(latent_dims=latent_dims).fit((X, Y, Z))
iter = CCA_ALS(latent_dims=latent_dims,
stochastic=False,
tol=1e-12,
random_state=rng).fit((X, Y, Z))
gcca = GCCA(latent_dims=latent_dims).fit((X, Y, Z))
mcca = MCCA(latent_dims=latent_dims).fit((X, Y, Z))
kcca = KCCA(latent_dims=latent_dims).fit((X, Y, Z))
corr_cca = cca.score((X, Y, Z))
corr_iter = iter.score((X, Y, Z))
corr_gcca = gcca.score((X, Y, Z))
corr_mcca = mcca.score((X, Y, Z))
corr_kcca = kcca.score((X, Y, Z))
# Check the correlations from each unregularized method are the same
assert np.testing.assert_array_almost_equal(corr_cca, corr_iter,
decimal=1) is None
assert np.testing.assert_array_almost_equal(corr_cca, corr_mcca,
decimal=1) is None
assert np.testing.assert_array_almost_equal(corr_cca, corr_gcca,
decimal=1) is None
assert np.testing.assert_array_almost_equal(corr_cca, corr_kcca,
decimal=1) is None
def test_sparse_input():
# Tests unregularized CCA methods. The idea is that all of these should give the same result.
latent_dims = 2
cca = CCA(latent_dims=latent_dims, centre=False).fit((X_sp, Y_sp))
iter = CCA_ALS(
latent_dims=latent_dims,
tol=1e-9,
stochastic=False,
centre=False,
random_state=rng,
).fit((X_sp, Y_sp))
iter_pls = PLS_ALS(latent_dims=latent_dims, tol=1e-9, centre=False).fit(
(X_sp, Y_sp))
gcca = GCCA(latent_dims=latent_dims, centre=False).fit((X_sp, Y_sp))
mcca = MCCA(latent_dims=latent_dims, centre=False).fit((X_sp, Y_sp))
kcca = KCCA(latent_dims=latent_dims, centre=False).fit((X_sp, Y_sp))
scca = SCCA(latent_dims=latent_dims, centre=False, c=0.001).fit(
(X_sp, Y_sp))
corr_cca = cca.score((X, Y))
corr_iter = iter.score((X, Y))
corr_gcca = gcca.score((X, Y))
corr_mcca = mcca.score((X, Y))
corr_kcca = kcca.score((X, Y))
# Check the correlations from each unregularized method are the same
assert np.testing.assert_array_almost_equal(
corr_iter, corr_mcca, decimal=1) is None
assert np.testing.assert_array_almost_equal(
corr_iter, corr_gcca, decimal=1) is None
assert np.testing.assert_array_almost_equal(
corr_iter, corr_kcca, decimal=1) is None
def test_unregularized_methods():
# Tests unregularized CCA methods. The idea is that all of these should give the same result.
latent_dims = 2
cca = CCA(latent_dims=latent_dims).fit([X, Y])
iter = CCA_ALS(latent_dims=latent_dims,
tol=1e-9,
stochastic=False,
random_state=rng).fit([X, Y])
gcca = GCCA(latent_dims=latent_dims).fit([X, Y])
mcca = MCCA(latent_dims=latent_dims, eps=1e-9).fit([X, Y])
kcca = KCCA(latent_dims=latent_dims).fit([X, Y])
kgcca = KGCCA(latent_dims=latent_dims).fit([X, Y])
tcca = TCCA(latent_dims=latent_dims).fit([X, Y])
corr_cca = cca.score((X, Y))
corr_iter = iter.score((X, Y))
corr_gcca = gcca.score((X, Y))
corr_mcca = mcca.score((X, Y))
corr_kcca = kcca.score((X, Y))
corr_kgcca = kgcca.score((X, Y))
corr_tcca = tcca.score((X, Y))
assert np.testing.assert_array_almost_equal(corr_cca, corr_iter,
decimal=1) is None
assert np.testing.assert_array_almost_equal(corr_cca, corr_mcca,
decimal=1) is None
assert np.testing.assert_array_almost_equal(corr_cca, corr_gcca,
decimal=1) is None
assert np.testing.assert_array_almost_equal(corr_cca, corr_kcca,
decimal=1) is None
assert np.testing.assert_array_almost_equal(corr_cca, corr_tcca,
decimal=1) is None
assert (np.testing.assert_array_almost_equal(
corr_kgcca, corr_gcca, decimal=1) is None)
# Check standardized models have standard outputs
assert (np.testing.assert_allclose(
np.linalg.norm(iter.transform(
(X, Y))[0], axis=0)**2, n, rtol=0.2) is None)
assert (np.testing.assert_allclose(
np.linalg.norm(cca.transform(
(X, Y))[0], axis=0)**2, n, rtol=0.2) is None)
assert (np.testing.assert_allclose(
np.linalg.norm(mcca.transform(
(X, Y))[0], axis=0)**2, n, rtol=0.2) is None)
assert (np.testing.assert_allclose(
np.linalg.norm(kcca.transform(
(X, Y))[0], axis=0)**2, n, rtol=0.2) is None)
assert (np.testing.assert_allclose(
np.linalg.norm(iter.transform(
(X, Y))[1], axis=0)**2, n, rtol=0.2) is None)
assert (np.testing.assert_allclose(
np.linalg.norm(cca.transform(
(X, Y))[1], axis=0)**2, n, rtol=0.2) is None)
assert (np.testing.assert_allclose(
np.linalg.norm(mcca.transform(
(X, Y))[1], axis=0)**2, n, rtol=0.2) is None)
assert (np.testing.assert_allclose(
np.linalg.norm(kcca.transform(
(X, Y))[1], axis=0)**2, n, rtol=0.2) is None)
def test_regularized_methods():
# Test that linear regularized methods match PLS solution when using maximum regularisation.
latent_dims = 2
c = 1
kernel = KCCA(latent_dims=latent_dims,
c=[c, c],
kernel=["linear", "linear"]).fit((X, Y))
pls = PLS(latent_dims=latent_dims).fit([X, Y])
gcca = GCCA(latent_dims=latent_dims, c=[c, c]).fit([X, Y])
mcca = MCCA(latent_dims=latent_dims, c=[c, c]).fit([X, Y])
rcca = rCCA(latent_dims=latent_dims, c=[c, c]).fit([X, Y])
corr_gcca = gcca.score((X, Y))
corr_mcca = mcca.score((X, Y))
corr_kernel = kernel.score((X, Y))
corr_pls = pls.score((X, Y))
corr_rcca = rcca.score((X, Y))
# Check the correlations from each unregularized method are the same
assert np.testing.assert_array_almost_equal(corr_pls, corr_mcca,
decimal=1) is None
assert (np.testing.assert_array_almost_equal(
corr_pls, corr_kernel, decimal=1) is None)
assert np.testing.assert_array_almost_equal(corr_pls, corr_rcca,
decimal=1) is None