null = np.sort(null)
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)