Revert "Add node_names"

This reverts commit c2210d0.

Author: EvieQ01

causallearn/search/ConstraintBased/FCI.py

@@ -15,7 +15,7 @@
 from causallearn.utils.cit import *
 from causallearn.utils.FAS import fas
 from causallearn.utils.PCUtils.BackgroundKnowledge import BackgroundKnowledge
-
+from itertools import combinations
 
 def traverseSemiDirected(node: Node, edge: Edge) -> Node | None:
     if node == edge.get_node1():
@@ -320,8 +320,9 @@ def rulesR1R2cycle(graph: Graph, bk: BackgroundKnowledge | None, changeFlag: boo
 
 def isNoncollider(graph: Graph, sep_sets: Dict[Tuple[int, int], Set[int]], node_i: Node, node_j: Node,
                   node_k: Node) -> bool:
-    sep_set = sep_sets[(graph.get_node_map()[node_i], graph.get_node_map()[node_k])]
-    return sep_set is not None and sep_set.__contains__(graph.get_node_map()[node_j])
+    node_map = graph.get_node_map()
+    sep_set = sep_sets.get((node_map[node_i], node_map[node_k]))
+    return sep_set is not None and sep_set.__contains__(node_map[node_j])
 
 
 def ruleR3(graph: Graph, sep_sets: Dict[Tuple[int, int], Set[int]], bk: BackgroundKnowledge | None, changeFlag: bool,
@@ -542,6 +543,142 @@ def ruleR4B(graph: Graph, maxPathLength: int, data: ndarray, independence_test_m
     return change_flag
 
 
+
+def rule8(graph: Graph, nodes: List[Node]):
+    nodes = graph.get_nodes()
+    changeFlag = False
+    for node_B in nodes:
+        adj = graph.get_adjacent_nodes(node_B)
+        if len(adj) < 2:
+            continue
+
+        cg = ChoiceGenerator(len(adj), 2)
+        combination = cg.next()
+
+        while combination is not None:
+            node_A = adj[combination[0]]
+            node_C = adj[combination[1]]
+            combination = cg.next()
+            
+            if(graph.get_endpoint(node_A, node_B) == Endpoint.ARROW and graph.get_endpoint(node_B, node_A) == Endpoint.TAIL and \
+                graph.get_endpoint(node_B, node_C) == Endpoint.ARROW and graph.get_endpoint(node_C, node_B) == Endpoint.TAIL and \
+                    graph.is_adjacent_to(node_A, node_C) and \
+                        graph.get_endpoint(node_A, node_C) == Endpoint.ARROW and graph.get_endpoint(node_C, node_A)== Endpoint.CIRCLE) or \
+                        (graph.get_endpoint(node_A, node_B) == Endpoint.CIRCLE and graph.get_endpoint(node_B, node_A) == Endpoint.TAIL and \
+                graph.get_endpoint(node_B, node_C) == Endpoint.ARROW and graph.get_endpoint(node_C, node_B) == Endpoint.TAIL and \
+                    graph.is_adjacent_to(node_A, node_C) and \
+                        graph.get_endpoint(node_A, node_C) == Endpoint.ARROW and graph.get_endpoint(node_C, node_A)== Endpoint.CIRCLE):
+                edge1 = graph.get_edge(node_A, node_C)
+                graph.remove_edge(edge1)
+                graph.add_edge(Edge(node_A, node_C,Endpoint.TAIL, Endpoint.ARROW))
+                changeFlag = True
+
+    return changeFlag
+
+
+
+def is_possible_parent(graph: Graph, potential_parent_node, child_node):
+    if graph.node_map[potential_parent_node] == graph.node_map[child_node]:
+        return False
+    if not graph.is_adjacent_to(potential_parent_node, child_node):
+        return False
+
+    if graph.get_endpoint(child_node, potential_parent_node) == Endpoint.ARROW or \
+    graph.get_endpoint(potential_parent_node, child_node) == Endpoint.TAIL:
+        return False
+    else:
+        return True
+
+
+def find_possible_children(graph: Graph, parent_node, en_nodes=None):
+    if en_nodes is None:
+        nodes = graph.get_nodes()
+        en_nodes = [node for node in nodes if graph.node_map[node] != graph.node_map[parent_node]]
+
+    potential_child_nodes = set()
+    for potential_node in en_nodes:
+        if is_possible_parent(graph, potential_parent_node=parent_node, child_node=potential_node):
+            potential_child_nodes.add(potential_node)
+
+    return potential_child_nodes
+
+def rule9(graph: Graph, nodes: List[Node]):
+    changeFlag = False
+    nodes = graph.get_nodes()
+    for node_C in nodes:
+        intoCArrows = graph.get_nodes_into(node_C, Endpoint.ARROW)
+        for node_A in intoCArrows:
+            # we want A o--> C
+            if not graph.get_endpoint(node_C, node_A) == Endpoint.CIRCLE:
+                continue
+        
+            # look for a possibly directed uncovered path s.t. B and C are not connected (for the given A o--> C
+            a_node_idx = graph.node_map[node_A]
+            c_node_idx = graph.node_map[node_C]
+            a_adj_nodes = graph.get_adjacent_nodes(node_A)
+            nodes_set = [node for node in a_adj_nodes if graph.node_map[node] != a_node_idx and graph.node_map[node]!= c_node_idx]
+            possible_children = find_possible_children(graph, node_A, nodes_set)
+            for node_B in possible_children:
+                if graph.is_adjacent_to(node_B, node_C):
+                    continue
+                if existsSemiDirectedPath(node_from=node_B, node_to=node_C, G=graph):
+                    edge1 = graph.get_edge(node_A, node_C)
+                    graph.remove_edge(edge1)
+                    graph.add_edge(Edge(node_A, node_C, Endpoint.TAIL, Endpoint.ARROW))
+                    changeFlag = True
+                    break #once we found it, break out since we have already oriented Ao->C to A->C, we want to find the next A 
+    return changeFlag
+
+
+def rule10(graph: Graph):
+    changeFlag = False
+    nodes = graph.get_nodes()
+    for node_C in nodes:
+        intoCArrows = graph.get_nodes_into(node_C, Endpoint.ARROW)
+        if len(intoCArrows) < 2:
+                continue
+        # get all A where A o-> C
+        Anodes = [node_A for node_A in intoCArrows if graph.get_endpoint(node_C, node_A) == Endpoint.CIRCLE]
+        if len(Anodes) == 0:
+            continue
+        
+        for node_A in Anodes:
+            A_adj_nodes = graph.get_adjacent_nodes(node_A)
+            en_nodes = [i for i in A_adj_nodes if i is not node_C]
+            A_possible_children = find_possible_children(graph, parent_node=node_A, en_nodes=en_nodes)
+            if len(A_possible_children) < 2:
+                continue
+
+            gen = ChoiceGenerator(len(intoCArrows), 2)
+            choice = gen.next()
+            while choice is not None:
+                node_B = intoCArrows[choice[0]]
+                node_D = intoCArrows[choice[1]]
+
+                choice = gen.next()
+                # we want B->C<-D 
+                if graph.get_endpoint(node_C, node_B) != Endpoint.TAIL:
+                    continue
+
+                if graph.get_endpoint(node_C, node_D) != Endpoint.TAIL:
+                    continue
+
+                for children in combinations(A_possible_children, 2):
+                    child_one, child_two = children
+                    if not existsSemiDirectedPath(node_from=child_one, node_to=node_B, G=graph) or \
+                        not existsSemiDirectedPath(node_from=child_two, node_to=node_D, G=graph):
+                        continue
+
+                    if not graph.is_adjacent_to(child_one, child_two):
+                        edge1 = graph.get_edge(node_A, node_C)
+                        graph.remove_edge(edge1)
+                        graph.add_edge(Edge(node_A, node_C, Endpoint.TAIL, Endpoint.ARROW))
+                        changeFlag = True
+                        break #once we found it, break out since we have already oriented Ao->C to A->C, we want to find the next A 
+
+    return changeFlag
+
+
 def visibleEdgeHelperVisit(graph: Graph, node_c: Node, node_a: Node, node_b: Node, path: List[Node]) -> bool:
     if path.__contains__(node_a):
         return False
@@ -691,10 +828,8 @@ def _contains_all(set_a: Set[Node], set_b: Set[Node]):
                     break
 
 
-
 def fci(dataset: ndarray, independence_test_method: str=fisherz, alpha: float = 0.05, depth: int = -1,
-        max_path_length: int = -1, verbose: bool = False, background_knowledge: BackgroundKnowledge | None = None, 
-        show_progress: bool = True, node_names = None,
+        max_path_length: int = -1, verbose: bool = False, background_knowledge: BackgroundKnowledge | None = None, show_progress: bool = True,
         **kwargs) -> Tuple[Graph, List[Edge]]:
     """
     Perform Fast Causal Inference (FCI) algorithm for causal discovery
@@ -749,10 +884,8 @@ def fci(dataset: ndarray, independence_test_method: str=fisherz, alpha: float =
 
 
     nodes = []
-    if node_names is None:
-        node_names = [f"X{i + 1}" for i in range(dataset.shape[1])]
     for i in range(dataset.shape[1]):
-        node = GraphNode(node_names[i])
+        node = GraphNode(f"X{i + 1}")
         node.add_attribute("id", i)
         nodes.append(node)
 
@@ -790,6 +923,13 @@ def fci(dataset: ndarray, independence_test_method: str=fisherz, alpha: float =
             if verbose:
                 print("Epoch")
 
+        # rule 8
+        change_flag = rule8(graph,nodes)
+        # rule 9
+        change_flag = rule9(graph, nodes)
+        # rule 10
+        change_flag = rule10(graph)
+
     graph.set_pag(True)
 
     edges = get_color_edges(graph)