def directed_erdos(nnodes,
density=None,
exp_nbrs=None,
size=1,
as_list=False,
random_order=True) -> Union[DAG, List[DAG]]:
"""
Generate random Erdos-Renyi DAG(s) on `nnodes` nodes with density `density`.
Parameters
----------
nnodes:
Number of nodes in each graph.
density:
Probability of any edge.
size:
Number of graphs.
as_list:
If True, always return as a list, even if only one DAG is generated.
Examples
--------
>>> import causaldag as cd
>>> d = cd.rand.directed_erdos(5, .5)
"""
assert density is not None or exp_nbrs is not None
density = density if density is not None else exp_nbrs / (nnodes - 1)
if size == 1:
# if density < .01:
# print('here')
# random_nx = fast_gnp_random_graph(nnodes, density, directed=True)
# d = DAG(nodes=set(range(nnodes)), arcs=random_nx.edges)
# return [d] if as_list else d
bools = _coin(density, size=int(nnodes * (nnodes - 1) / 2))
arcs = {(i, j)
for (i, j), b in zip(itr.combinations(range(nnodes), 2), bools)
if b}
d = DAG(nodes=set(range(nnodes)), arcs=arcs)
if random_order:
nodes = list(range(nnodes))
d = d.rename_nodes(dict(enumerate(np.random.permutation(nodes))))
return [d] if as_list else d
else:
return [
directed_erdos(nnodes, density, random_order=random_order)
for _ in range(size)
]
def directed_erdos_with_confounders(
nnodes: int,
density: Optional[float] = None,
exp_nbrs: Optional[float] = None,
num_confounders: int = 1,
confounder_pervasiveness: float = 1,
size=1,
as_list=False,
random_order=True) -> Union[DAG, List[DAG]]:
assert density is not None or exp_nbrs is not None
density = density if density is not None else exp_nbrs / (nnodes - 1)
if size == 1:
confounders = list(range(num_confounders))
nonconfounders = list(range(num_confounders, nnodes + num_confounders))
bools = _coin(confounder_pervasiveness,
size=int(num_confounders * nnodes))
confounder_arcs = {
(i, j)
for (i,
j), b in zip(itr.product(confounders, nonconfounders), bools)
if b
}
bools = _coin(density, size=int(nnodes * (nnodes - 1) / 2))
local_arcs = {
(i, j)
for (i, j), b in zip(itr.combinations(nonconfounders, 2), bools)
if b
}
d = DAG(nodes=set(range(nnodes)), arcs=confounder_arcs | local_arcs)
if random_order:
nodes = list(range(nnodes + num_confounders))
d = d.rename_nodes(dict(enumerate(np.random.permutation(nodes))))
return [d] if as_list else d
else:
return [
directed_erdos_with_confounders(
nnodes,
density,
num_confounders=num_confounders,
confounder_pervasiveness=confounder_pervasiveness,
random_order=random_order) for _ in range(size)
]