def test_grouppca_inverse_index(
n_individual_components,
prewhiten,
whiten,
multiview_output,
index,
inverse_index,
):
# Test that the projection of data can be inverted
rng = np.random.RandomState(0)
n, p = 50, 3
X = rng.randn(n, p) # spherical data
X[:, 1] *= 0.00001 # make middle component relatively small
X += [5, 4, 3] # make a large mean
X2 = np.copy(X)
X2[:, 1] += rng.randn(n) * 0.00001
X2 = X2.dot(rng.rand(p, p))
X3 = np.copy(X)
X3[:, 1] += rng.randn(n) * 0.00001
X3 = X3.dot(rng.rand(p, p))
Xs = [X, X2, X3]
gpca = GroupPCA(
n_components=2,
prewhiten=prewhiten,
whiten=whiten,
n_individual_components=n_individual_components,
multiview_output=multiview_output,
).fit(Xs)
if index is not None:
index_ = np.atleast_1d(index)
Xs_transform = [Xs[i] for i in index_]
len_index = len(index_)
else:
len_index = 3
Xs_transform = np.copy(Xs)
if inverse_index is not None:
inverse_index_ = np.atleast_1d(inverse_index)
Xs_inverse = [Xs[i] for i in inverse_index_]
len_inverse_index = len(inverse_index_)
else:
len_inverse_index = 3
Xs_inverse = np.copy(Xs)
Y = gpca.transform(Xs_transform, index=index)
if multiview_output and len_index != len_inverse_index:
with pytest.raises(AssertionError):
Y_inverse = gpca.inverse_transform(Y, index=inverse_index)
elif multiview_output and index != inverse_index:
pass
else:
Y_inverse = gpca.inverse_transform(Y, index=inverse_index)
for X, X_estimated in zip(Xs_inverse, Y_inverse):
assert_allclose(X, X_estimated, atol=1e-4)
def test_grouppca_inverse(n_individual_components, prewhiten, whiten,
multiview_output):
# Test that the projection of data can be inverted
rng = np.random.RandomState(0)
n, p = 50, 3
X = rng.randn(n, p) # spherical data
X[:, 1] *= 0.00001 # make middle component relatively small
X += [5, 4, 3] # make a large mean
X2 = np.copy(X)
X2[:, 1] += rng.randn(n) * 0.00001
Xs = [X, X2]
gpca = GroupPCA(
n_components=2,
prewhiten=prewhiten,
whiten=whiten,
n_individual_components=n_individual_components,
multiview_output=multiview_output,
).fit(Xs)
Y = gpca.transform(Xs)
Y_inverse = gpca.inverse_transform(Y)
assert len(Y_inverse) == len(Xs)
for X, X_estimated in zip(Xs, Y_inverse):
assert_allclose(X, X_estimated, atol=1e-4)