def test_DCER_score(self):
p_mat = self.p_mat
graph = self.graph
estimator = DCEREstimator()
_test_score(estimator, p_mat, graph)
with pytest.raises(ValueError):
estimator.score_samples(graph=graph[1:500, 1:500])
def test_DCER_inputs(self):
with pytest.raises(TypeError):
DCEREstimator(directed="hey")
with pytest.raises(TypeError):
DCEREstimator(loops=6)
graph = er_np(100, 0.5)
dcere = DCEREstimator()
with pytest.raises(ValueError):
dcere.fit(graph[:, :99])
with pytest.raises(ValueError):
dcere.fit(graph[..., np.newaxis])
def test_DCER_sample(self):
np.random.seed(8888)
estimator = DCEREstimator(directed=True, loops=False)
g = self.graph
p_mat = self.p_mat
with pytest.raises(NotFittedError):
estimator.sample()
estimator.fit(g)
with pytest.raises(ValueError):
estimator.sample(n_samples=-1)
with pytest.raises(TypeError):
estimator.sample(n_samples="nope")
B = 0.5
dc = np.random.uniform(0.25, 0.75, size=100)
p_mat = np.outer(dc, dc) * B
p_mat -= np.diag(np.diag(p_mat))
g = sample_edges(p_mat, directed=True)
estimator.fit(g)
estimator.p_mat_ = p_mat
_test_sample(estimator, p_mat, n_samples=1000, atol=0.2)
def test_DCER_nparams(self):
n_verts = 1000
graph = self.graph
e = DCEREstimator(directed=True)
e.fit(graph)
assert e._n_parameters() == (n_verts + 1)
return null
def compute_pvalues(p_upper, null):
row = {}
row["estimated_p_upper"] = p_upper
ind = np.searchsorted(null, p_upper)
row["pvalue"] = 1 - ind / len(
null) # TODO make more exact but this is roughly right for one sided
return row
edge_types = ["ad", "aa", "dd", "da"]
null_estimators = {
"ER": EREstimator(directed=True, loops=False),
"DCER": DCEREstimator(directed=True, loops=False, degree_directed=False),
}
rerun_test = False
if rerun_test:
currtime = time.time()
n_null_samples = 100
statistics = []
for edge_type in edge_types:
print(f"Edge type = {edge_type}")
edge_type_adj = mg.to_edge_type_graph(edge_type).adj
edge_type_adj = binarize(edge_type_adj)
largest_connected_component(edge_type_adj)