def alter_weights(gdag: GaussDAG,
prob_altered: float = None,
num_altered: int = None,
rand_weight_fn=unif_away_original):
"""
Return a copy of a GaussDAG with some of its arc weights randomly altered by `rand_weight_fn`.
Parameters
----------
gdag:
GaussDAG
prob_altered:
Probability each arc has its weight altered.
num_altered:
Number of arcs whose weights are altered.
rand_weight_fn:
Function to generate random weights, given the original weight.
"""
if num_altered is None and prob_altered is None:
raise ValueError(
"Must specify at least one of `percent_altered` or `num_altered`.")
num_altered = num_altered if num_altered is not None else np.random.binomial(
gdag.num_arcs, prob_altered)
altered_arcs = random.sample(list(gdag.arcs), num_altered)
new_gdag = gdag.copy()
weights = gdag.arc_weights
for i, j in altered_arcs:
new_gdag.set_arc_weight(i, j, rand_weight_fn(weights[(i, j)]))
return new_gdag
def alter_weights(gdag: GaussDAG,
prob_altered: float = None,
num_altered: int = None,
prob_added: float = None,
num_added: int = None,
prob_removed: float = None,
num_removed: int = None,
rand_weight_fn=unif_away_zero,
rand_change_fn=unif_away_original):
"""
Return a copy of a GaussDAG with some of its arc weights randomly altered by `rand_weight_fn`.
Parameters
----------
gdag:
GaussDAG
prob_altered:
Probability each arc has its weight altered.
num_altered:
Number of arcs whose weights are altered.
prob_added:
Probability that each missing arc is added.
num_added:
Number of missing arcs added.
prob_removed:
Probability that each arc is removed.
num_removed:
Number of arcs removed.
rand_weight_fn:
Function that returns a random weight for each new edge.
rand_change_fn:
Function that takes the current weight of an edge and returns the new weight.
"""
if num_altered is None and prob_altered is None:
raise ValueError(
"Must specify at least one of `prob_altered` or `num_altered`.")
if num_added is None and prob_added is None:
raise ValueError(
"Must specify at least one of `prob_added` or `num_added`.")
if num_removed is None and prob_removed is None:
raise ValueError(
"Must specify at least one of `prob_removed` or `num_removed`.")
if num_altered + num_removed > gdag.num_arcs:
raise ValueError(
f"Tried altering {num_altered} arcs and removing {num_removed} arcs, but there are only {gdag.num_arcs} arcs in this DAG."
)
num_missing_arcs = comb(gdag.nnodes, 2) - gdag.num_arcs
if num_added > num_missing_arcs:
raise ValueError(
f"Tried adding {num_added} arcs but there are only {num_missing_arcs} arcs missing from the DAG."
)
# GET NUMBER ADDED/CHANGED/REMOVED
num_altered = num_altered if num_altered is not None else np.random.binomial(
gdag.num_arcs, prob_altered)
num_removed = num_removed if num_removed is not None else np.random.binomial(
gdag.num_arcs, prob_removed)
num_removed = min(num_removed, gdag.num_arcs - num_altered)
num_added = num_added if num_added is not None else np.random.binomial(
num_missing_arcs, prob_added)
# GET ACTUAL ARCS THAT ARE ADDED/CHANGED/REMOVED
altered_arcs = random.sample(list(gdag.arcs), num_altered)
removed_arcs = random.sample(list(gdag.arcs - set(altered_arcs)),
num_removed)
valid_arcs_to_add = set(itr.combinations(gdag.topological_sort(),
2)) - gdag.arcs
added_arcs = random.sample(list(valid_arcs_to_add), num_added)
# CREATE NEW DAG
new_gdag = gdag.copy()
weights = gdag.arc_weights
for i, j in altered_arcs:
new_gdag.set_arc_weight(i, j, rand_change_fn(weights[(i, j)]))
for i, j in removed_arcs:
new_gdag.remove_arc(i, j)
new_weights = rand_weight_fn(size=num_added)
for (i, j), val in zip(added_arcs, new_weights):
new_gdag.set_arc_weight(i, j, val)
return new_gdag