def test_SBM_nparams(self):
e = self.estimator.fit(self.graph, y=self.labels)
assert e._n_parameters() == (4)
e = SBMEstimator()
e.fit(self.graph)
assert e._n_parameters() == (4 + 1)
e = SBMEstimator(directed=False)
e.fit(self.graph)
assert e._n_parameters() == (1 + 3)
def test_SBM_score(self):
# tests score() and score_sample()
B = np.array([[0.75, 0.25], [0.25, 0.75]])
n_verts = 100
n = np.array([n_verts, n_verts])
tau = _n_to_labels(n)
p_mat = _block_to_full(B, tau, shape=(n_verts * 2, n_verts * 2))
graph = sample_edges(p_mat, directed=True)
estimator = SBMEstimator(max_comm=4)
_test_score(estimator, p_mat, graph)
with pytest.raises(ValueError):
estimator.score_samples(graph=graph[1:100, 1:100])
def test_SBM_fit_unsupervised(self):
np.random.seed(12345)
n_verts = 1500
B = np.array([[0.7, 0.1, 0.1], [0.1, 0.9, 0.1], [0.05, 0.1, 0.75]])
n = np.array([500, 500, 500])
labels = _n_to_labels(n)
p_mat = _block_to_full(B, labels, (n_verts, n_verts))
p_mat -= np.diag(np.diag(p_mat))
graph = sample_edges(p_mat, directed=True, loops=False)
sbe = SBMEstimator(directed=True, loops=False)
sbe.fit(graph)
assert adjusted_rand_score(labels, sbe.vertex_assignments_) > 0.95
assert_allclose(p_mat, sbe.p_mat_, atol=0.12)
def test_SBM_fit_supervised(self):
np.random.seed(8888)
B = np.array([
[0.9, 0.2, 0.05, 0.1],
[0.1, 0.7, 0.1, 0.1],
[0.2, 0.4, 0.8, 0.5],
[0.1, 0.2, 0.1, 0.7],
])
n = np.array([500, 500, 250, 250])
g = sbm(n, B, directed=True, loops=False)
sbe = SBMEstimator(directed=True, loops=False)
labels = _n_to_labels(n)
sbe.fit(g, y=labels)
B_hat = sbe.block_p_
assert_allclose(B_hat, B, atol=0.01)
def setup_class(cls):
estimator = SBMEstimator(directed=True, loops=False)
B = np.array([[0.9, 0.1], [0.1, 0.9]])
g = sbm([50, 50], B, directed=True)
labels = _n_to_labels([50, 50])
p_mat = _block_to_full(B, labels, (100, 100))
p_mat -= np.diag(np.diag(p_mat))
cls.estimator = estimator
cls.p_mat = p_mat
cls.graph = g
cls.labels = labels
def setUp(self) -> None:
estimator = SBMEstimator(directed=True, loops=False)
B = np.array([[0.9, 0.1], [0.1, 0.9]])
g = sbm([50, 50], B, directed=True)
labels = _n_to_labels([50, 50])
p_mat = _block_to_full(B, labels, (100, 100))
p_mat -= np.diag(np.diag(p_mat))
self.estimator = estimator
self.p_mat = p_mat
self.graph = g
self.labels = labels
def test_SBM_inputs(self):
with pytest.raises(TypeError):
SBMEstimator(directed="hey")
with pytest.raises(TypeError):
SBMEstimator(loops=6)
with pytest.raises(TypeError):
SBMEstimator(n_components="XD")
with pytest.raises(ValueError):
SBMEstimator(n_components=-1)
with pytest.raises(TypeError):
SBMEstimator(min_comm="1")
with pytest.raises(ValueError):
SBMEstimator(min_comm=-1)
with pytest.raises(TypeError):
SBMEstimator(max_comm="ay")
with pytest.raises(ValueError):
SBMEstimator(max_comm=-1)
with pytest.raises(ValueError):
SBMEstimator(min_comm=4, max_comm=2)
graph = er_np(100, 0.5)
bad_y = np.zeros(99)
sbe = SBMEstimator()
with pytest.raises(ValueError):
sbe.fit(graph, y=bad_y)
with pytest.raises(ValueError):
sbe.fit(graph[:, :99])
with pytest.raises(ValueError):
sbe.fit(graph[..., np.newaxis])
with pytest.raises(TypeError):
SBMEstimator(cluster_kws=1)
with pytest.raises(TypeError):
SBMEstimator(embed_kws=1)