def test_transform_correct_types(self):
lse = LaplacianSpectralEmbed(n_components=2)
for graph in self.testgraphs.values():
A, a = remove_vertices(graph, 1, return_removed=True)
lse.fit(A)
directed = lse.latent_right_ is not None
w = lse.transform(a)
if directed:
self.assertIsInstance(w, tuple)
self.assertIsInstance(w[0], np.ndarray)
self.assertIsInstance(w[1], np.ndarray)
elif not directed:
self.assertIsInstance(w, np.ndarray)
self.assertEqual(np.atleast_2d(w).shape[1], 2)
def test_transform_correct_types(self):
ase = AdjacencySpectralEmbed(n_components=2)
for graph in self.testgraphs.values():
A, a = remove_vertices(graph, 1, return_removed=True)
ase.fit(A)
directed = ase.latent_right_ is not None
weighted = not np.array_equal(A, A.astype(bool))
w = ase.transform(a)
if directed:
self.assertIsInstance(w, tuple)
self.assertIsInstance(w[0], np.ndarray)
self.assertIsInstance(w[1], np.ndarray)
elif not directed:
self.assertIsInstance(w, np.ndarray)
self.assertEqual(np.atleast_2d(w).shape[1], 2)
def test_exceptions(self):
ase = clone(self.ase)
with pytest.raises(Exception):
ase.fit(self.testgraphs["Gwd"])
ase.transform("9001")
with pytest.raises(Exception):
Guwd = self.testgraphs["Guwd"]
ase.fit(Guwd)
ase.transform(np.ones(len(Guwd)))
with pytest.raises(ValueError):
A, a = remove_vertices(self.testgraphs["Gw"], [0, 1], return_removed=True)
a = a.T
ase.fit(A)
ase.transform(a)
def test_embedding(self):
epsilon = 0.1
nodes_per_community = 100
P = np.array([[0.8, 0.2], [0.2, 0.8]])
undirected, labels_ = sbm(2 * [nodes_per_community],
P,
return_labels=True)
oos_idx = 0
A, a = remove_vertices(undirected,
indices=oos_idx,
return_removed=True)
lse = LaplacianSpectralEmbed(n_components=2)
X_hat = lse.fit_transform(A)
w = lse.transform(a)
self.assertTrue(
X_hat[0][0] - epsilon < w[0][0] < X_hat[0][0] + epsilon)
self.assertTrue(
X_hat[0][1] - epsilon < w[0][1] < X_hat[0][1] + epsilon)
def test_directed_correct_latent_positions(self):
# setup
ase = AdjacencySpectralEmbed(n_components=3)
P = np.array([[0.9, 0.1, 0.1], [0.3, 0.6, 0.1], [0.1, 0.5, 0.6]])
M, labels = sbm([200, 200, 200], P, directed=True, return_labels=True)
# one node from each community
oos_idx = np.nonzero(np.r_[1, np.diff(labels)[:-1]])[0]
labels = list(labels)
oos_labels = [labels.pop(i) for i in oos_idx]
# Grab out-of-sample, fit, transform
A, a = remove_vertices(M, indices=oos_idx, return_removed=True)
latent_left, latent_right = ase.fit_transform(A)
oos_left, oos_right = ase.transform(a)
# separate into communities
for i, latent in enumerate([latent_left, latent_right]):
left = i == 0
df = pd.DataFrame(
{
"Type": labels,
"Dimension 1": latent[:, 0],
"Dimension 2": latent[:, 1],
"Dimension 3": latent[:, 2],
}
)
# make sure that oos vertices are closer to their true community averages than other community averages
means = df.groupby("Type").mean()
if left:
avg_dist_within = np.diag(pairwise_distances(means, oos_left))
avg_dist_between = np.diag(pairwise_distances(means, oos_right))
self.assertTrue(all(avg_dist_within < avg_dist_between))
elif not left:
avg_dist_within = np.diag(pairwise_distances(means, oos_right))
avg_dist_between = np.diag(pairwise_distances(means, oos_left))
self.assertTrue(all(avg_dist_within < avg_dist_between))
def test_directed_vertex_direction(self):
M = self.testgraphs["Guwd"]
oos_idx = 0
A, a = remove_vertices(M, indices=oos_idx, return_removed=True)
assert_equal(np.delete(M[:, 0], oos_idx), a[0])