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Fix d_separated deprecation warning #229

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Merged
merged 1 commit into from
Mar 14, 2025
Merged

Fix d_separated deprecation warning #229

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3 changes: 1 addition & 2 deletions causallearn/utils/DAG2PAG.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,6 @@

import numpy as np
import networkx as nx
from networkx.algorithms import d_separated

from causallearn.graph.Dag import Dag
from causallearn.graph.Edge import Edge
Expand Down Expand Up @@ -66,7 +65,7 @@ def dag2pag(dag: Dag, islatent: List[Node], isselection: List[Node] = []) -> Gen
for Z in combinations(observed_nodes, l):
if nodex in Z or nodey in Z:
continue
if d_separated(dg, {nodes_ids[nodex]}, {nodes_ids[nodey]}, set(nodes_ids[z] for z in Z) | set([nodes_ids[s] for s in isselection])):
if nx.is_d_separator(dg, {nodes_ids[nodex]}, {nodes_ids[nodey]}, set(nodes_ids[z] for z in Z) | set([nodes_ids[s] for s in isselection])):
if edge:
PAG.remove_edge(edge)
sepset[(nodes_ids[nodex], nodes_ids[nodey])] |= set(Z)
Expand Down
2 changes: 1 addition & 1 deletion causallearn/utils/PCUtils/Helper.py
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Expand Up @@ -419,7 +419,7 @@ def draw_graph(nx_graph):
def is_dsep(nx_graph, x, y, Z):
"Return True if x and y are d-separated by the set Z in nx_graph (networkx graph object) and False otherwise"
S = set([str(i) for i in Z])
return nx.d_separated(nx_graph, {str(x)}, {str(y)}, S)
return nx.is_d_separator(nx_graph, {str(x)}, {str(y)}, S)


#######################################################################################################################
Expand Down
4 changes: 2 additions & 2 deletions causallearn/utils/cit.py
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Expand Up @@ -529,8 +529,8 @@ def __init__(self, data, true_dag=None, **kwargs):
def __call__(self, X, Y, condition_set=None):
Xs, Ys, condition_set, cache_key = self.get_formatted_XYZ_and_cachekey(X, Y, condition_set)
if cache_key in self.pvalue_cache: return self.pvalue_cache[cache_key]
p = float(nx.d_separated(self.true_dag, {Xs[0]}, {Ys[0]}, set(condition_set)))
# pvalue is bool here: 1 if is_d_separated and 0 otherwise. So heuristic comparison-based uc_rules will not work.
p = float(nx.is_d_separator(self.true_dag, {Xs[0]}, {Ys[0]}, set(condition_set)))
# pvalue is bool here: 1 if is_d_separator and 0 otherwise. So heuristic comparison-based uc_rules will not work.

# here we use networkx's d_separation implementation.
# an alternative is to use causal-learn's own d_separation implementation in graph class:
Expand Down