def abstract_atoms(tree):
check_tree_top_level(tree)
new_tree = HypergraphTree(node=tree.node,
children=[abstract_atoms(t) for t in tree])
check_tree_top_level(new_tree)
for t_id in tree.node.terminal_ids():
# this modifies the tree.node object too
new_child = abstract_atom(tree.node, t_id)
child_tree = HypergraphTree(node=new_child)
new_tree.append(child_tree)
check_tree_top_level(new_tree)
return new_tree
def dict_tree_to_graph_tree(tree):
out_children = []
if 'children' in tree:
for child in tree['children'].values():
out_children.append(dict_tree_to_graph_tree(child))
out_tree = HypergraphTree(node=tree['node'], children=out_children)
return out_tree
def dict_tree_to_graph_tree(tree):
out_children = []
if "children" in tree:
for child in tree["children"].values():
out_children.append(dict_tree_to_graph_tree(child))
out_tree = HypergraphTree(node=tree["node"], children=out_children)
return out_tree
def abstract_atoms(tree):
check_tree_top_level(tree)
new_tree = HypergraphTree(node=tree.node,
children=[abstract_atoms(t) for t in tree])
check_tree_top_level(new_tree)
for t_id in tree.node.terminal_ids():
# this modifies the tree.node object too
new_child = abstract_atom(tree.node, t_id)
child_tree = HypergraphTree(node=new_child)
new_tree.append(child_tree)
check_tree_top_level(new_tree)
return new_tree
# check that the number of a tree's children still equals the number of nonterminals in its node,
# and the number of edges match (should also check edge type)
return new_tree
def abstract_ring_atoms(tree):
new_children = [abstract_ring_atoms(x) for x in tree]
if len(tree.node.terminal_ids()) >= 2:
for t_id in tree.node.terminal_ids():
# this modifies the tree.node object too, appending a nonterminal at the end, instead of the abstracted atom
new_child, subtree_inds = abstract_ring_atom(tree.node, t_id)
if new_child is not None:
grandchildren = [
] # the abstracted node takes over some children
for ind in subtree_inds:
grandchildren.append(new_children.pop(ind))
child_tree = HypergraphTree(node=new_child,
children=grandchildren)
new_children.append(child_tree)
new_tree = HypergraphTree(node=tree.node, children=new_children)
return new_tree
def normalize_tree(self, tree):
# as we're replacing the original hypergraph from the parser with an equivalent node from our rules list,
# which could have a different order of nonterminals, need to reorder subtrees to match
new_subtrees = [self.normalize_tree(subtree) for subtree in tree]
child_id_to_subtree = {child_id: subtree for child_id, subtree in zip(tree.node.child_ids(), new_subtrees)}
rule_id, node_id_map = self.rule_to_index(tree.node)
reordered_subtrees = [child_id_to_subtree[node_id_map[child_id]] for child_id in self.rules[rule_id].child_ids()]
new_tree = HypergraphTree(node=self.rules[rule_id], children=reordered_subtrees)
new_tree.node.rule_id = rule_id
self.last_tree_processed = new_tree
return new_tree
def _remove_largest_clique(cliques, tree):
hypergraph = tree.node
clique = sorted(cliques, key=lambda x: len(x))[-1]
clique_nodes = []
clique_children = []
clique_idxs = []
for i, node_id in enumerate(hypergraph.child_ids()):
if node_id in clique:
clique_nodes.append(hypergraph.node[node_id])
clique_children.append(tree[i])
clique_idxs.append(i)
# Clean parent
for node_id in clique:
del hypergraph.node[node_id]
for idx in sorted(clique_idxs, reverse=True):
hypergraph.child_ids
tree.pop(idx)
# Add new non-terminal
# Find external edges from clique
clique_edges = set()
for node in clique_nodes:
clique_edges.update(set(node.edge_ids))
external_edge_ids = []
external_edges = []
for node in hypergraph.node.values():
for edge_id, edge in zip(node.edge_ids, node.edges):
if edge_id in clique_edges:
external_edge_ids.append(edge_id)
external_edges.append(edge)
new_nt = Node(edge_ids=external_edge_ids, edges=external_edges)
hypergraph.add_node(new_nt)
remaining_clique_edges = clique_edges - set(external_edge_ids)
for edge_id in remaining_clique_edges:
hypergraph.edges.pop(edge_id)
hypergraph.validate()
# Make new child from clique
external_edges_ids_cpy = copy.deepcopy(external_edge_ids)
external_edges_cpy = copy.deepcopy(external_edges)
new_nt_child = Node(edge_ids=external_edges_ids_cpy,
edges=external_edges_cpy)
new_child = HyperGraph()
new_child.add_parent_node(new_nt_child)
edge_id_map = {}
for node in clique_nodes:
new_child.add_node(node)
for i, (edge_id, edge) in enumerate(zip(node.edge_ids, node.edges)):
new_id = edge_id_map.get(edge_id)
if not new_id:
new_id = new_child.add_edge(edge)
edge_id_map[edge_id] = new_id
node.edge_ids[i] = new_id
if edge_id in external_edge_ids:
idx = external_edge_ids.index(edge_id)
new_nt_child.edge_ids[idx] = new_id
new_child.validate()
child_graph = new_child.to_nx()
child_graph.remove_node(new_child.parent_node_id)
assert len(make_max_clique_graph(child_graph)) == 1
tree.append(HypergraphTree(new_child, clique_children))