def test_bic():
"""
Expect 3 clusters from a 3 block model
"""
np.random.seed(3)
num_sims = 10
# Generate adjacency and labels
n = 50
n_communites = [n, n, n]
p = np.array([[0.8, 0.3, 0.2], [0.3, 0.8, 0.3], [0.2, 0.3, 0.8]])
y = np.repeat([1, 2, 3], repeats=n)
for _ in range(num_sims):
A = sbm(n=n_communites, p=p)
# Embed to get latent positions
ase = AdjacencySpectralEmbed(n_components=5)
X_hat = ase.fit_transform(A)
# Compute clusters
gclust = GaussianCluster(min_components=10)
gclust.fit(X_hat, y)
bics = gclust.bic_
aris = gclust.ari_
bic_argmin = bics.iloc[:, 0].values.argmin()
assert_equal(2, bic_argmin)
# The plus one is to adjust the index by min_components
assert_allclose(1, aris.iloc[:, 0][bic_argmin + 1])
def test_ase_three_blocks():
"""
Expect 3 clusters from a 3 block model
"""
np.random.seed(3)
num_sims = 10
# Generate adjacency and labels
n = 50
n_communites = [n, n, n]
p = np.array([[0.8, 0.3, 0.2], [0.3, 0.8, 0.3], [0.2, 0.3, 0.8]])
y = np.repeat([1, 2, 3], repeats=n)
for _ in range(num_sims):
A = sbm(n=n_communites, p=p)
# Embed to get latent positions
ase = AdjacencySpectralEmbed(n_components=5)
X_hat = ase.fit_transform(A)
# Compute clusters
gclust = GaussianCluster(min_components=10)
gclust.fit(X_hat, y)
n_components = gclust.n_components_
# Assert that the three cluster model is the best
assert_equal(n_components, 3)
# Asser that we get perfect clustering
assert_allclose(gclust.ari_.loc[n_components], 1)
def test_ase_three_blocks():
"""
Expect 3 clusters from a 3 block model
"""
np.random.seed(1)
# Generate adjacency and labels
n = 50
n_communites = [n, n, n]
p = np.array([[0.8, 0.3, 0.2], [0.3, 0.8, 0.3], [0.2, 0.3, 0.8]])
y = np.repeat([1, 2, 3], repeats=n)
A = sbm(n=n_communites, p=p)
# Embed to get latent positions
ase = AdjacencySpectralEmbed(n_components=5)
X_hat = ase.fit_transform(A)
# Compute clusters
AutoGMM = AutoGMMCluster(max_components=10)
AutoGMM.fit(X_hat, y)
n_components = AutoGMM.n_components_
# Assert that the three cluster model is the best
assert_equal(n_components, 3)
# Asser that we get perfect clustering
assert_allclose(AutoGMM.ari_, 1)
def test_input_checks(self):
with self.assertRaises(TypeError):
ase = AdjacencySpectralEmbed(diag_aug="over 9000")
ase.fit()
def test_unconnected_warning(self):
A = er_nm(100, 10)
with self.assertWarns(UserWarning):
ase = AdjacencySpectralEmbed()
ase.fit(A)