spaCy/spacy/matcher/dependencymatcher.pyx
Ines Montani 1ea4df459d 💫 Break up large matcher.pyx (#3236)
* Break up large matcher.pyx

* Remove unused function
2019-02-07 19:42:25 +11:00

355 lines
13 KiB
Cython

# cython: infer_types=True
# cython: profile=True
from __future__ import unicode_literals
from cymem.cymem cimport Pool
from preshed.maps cimport PreshMap
from .matcher cimport Matcher
from ..vocab cimport Vocab
from ..tokens.doc cimport Doc
from .matcher import unpickle_matcher
from ..errors import Errors
DELIMITER = '||'
INDEX_HEAD = 1
INDEX_RELOP = 0
cdef class DependencyTreeMatcher:
"""Match dependency parse tree based on pattern rules."""
cdef Pool mem
cdef readonly Vocab vocab
cdef readonly Matcher token_matcher
cdef public object _patterns
cdef public object _keys_to_token
cdef public object _root
cdef public object _entities
cdef public object _callbacks
cdef public object _nodes
cdef public object _tree
def __init__(self, vocab):
"""Create the DependencyTreeMatcher.
vocab (Vocab): The vocabulary object, which must be shared with the
documents the matcher will operate on.
RETURNS (DependencyTreeMatcher): The newly constructed object.
"""
size = 20
self.token_matcher = Matcher(vocab)
self._keys_to_token = {}
self._patterns = {}
self._root = {}
self._nodes = {}
self._tree = {}
self._entities = {}
self._callbacks = {}
self.vocab = vocab
self.mem = Pool()
def __reduce__(self):
data = (self.vocab, self._patterns,self._tree, self._callbacks)
return (unpickle_matcher, data, None, None)
def __len__(self):
"""Get the number of rules, which are edges ,added to the dependency tree matcher.
RETURNS (int): The number of rules.
"""
return len(self._patterns)
def __contains__(self, key):
"""Check whether the matcher contains rules for a match ID.
key (unicode): The match ID.
RETURNS (bool): Whether the matcher contains rules for this match ID.
"""
return self._normalize_key(key) in self._patterns
def validateInput(self, pattern, key):
idx = 0
visitedNodes = {}
for relation in pattern:
if 'PATTERN' not in relation or 'SPEC' not in relation:
raise ValueError(Errors.E098.format(key=key))
if idx == 0:
if not('NODE_NAME' in relation['SPEC'] and 'NBOR_RELOP' not in relation['SPEC'] and 'NBOR_NAME' not in relation['SPEC']):
raise ValueError(Errors.E099.format(key=key))
visitedNodes[relation['SPEC']['NODE_NAME']] = True
else:
if not('NODE_NAME' in relation['SPEC'] and 'NBOR_RELOP' in relation['SPEC'] and 'NBOR_NAME' in relation['SPEC']):
raise ValueError(Errors.E100.format(key=key))
if relation['SPEC']['NODE_NAME'] in visitedNodes or relation['SPEC']['NBOR_NAME'] not in visitedNodes:
raise ValueError(Errors.E101.format(key=key))
visitedNodes[relation['SPEC']['NODE_NAME']] = True
visitedNodes[relation['SPEC']['NBOR_NAME']] = True
idx = idx + 1
def add(self, key, on_match, *patterns):
for pattern in patterns:
if len(pattern) == 0:
raise ValueError(Errors.E012.format(key=key))
self.validateInput(pattern,key)
key = self._normalize_key(key)
_patterns = []
for pattern in patterns:
token_patterns = []
for i in range(len(pattern)):
token_pattern = [pattern[i]['PATTERN']]
token_patterns.append(token_pattern)
# self.patterns.append(token_patterns)
_patterns.append(token_patterns)
self._patterns.setdefault(key, [])
self._callbacks[key] = on_match
self._patterns[key].extend(_patterns)
# Add each node pattern of all the input patterns individually to the matcher.
# This enables only a single instance of Matcher to be used.
# Multiple adds are required to track each node pattern.
_keys_to_token_list = []
for i in range(len(_patterns)):
_keys_to_token = {}
# TODO : Better ways to hash edges in pattern?
for j in range(len(_patterns[i])):
k = self._normalize_key(unicode(key)+DELIMITER+unicode(i)+DELIMITER+unicode(j))
self.token_matcher.add(k,None,_patterns[i][j])
_keys_to_token[k] = j
_keys_to_token_list.append(_keys_to_token)
self._keys_to_token.setdefault(key, [])
self._keys_to_token[key].extend(_keys_to_token_list)
_nodes_list = []
for pattern in patterns:
nodes = {}
for i in range(len(pattern)):
nodes[pattern[i]['SPEC']['NODE_NAME']]=i
_nodes_list.append(nodes)
self._nodes.setdefault(key, [])
self._nodes[key].extend(_nodes_list)
# Create an object tree to traverse later on.
# This datastructure enable easy tree pattern match.
# Doc-Token based tree cannot be reused since it is memory heavy and
# tightly coupled with doc
self.retrieve_tree(patterns,_nodes_list,key)
def retrieve_tree(self,patterns,_nodes_list,key):
_heads_list = []
_root_list = []
for i in range(len(patterns)):
heads = {}
root = -1
for j in range(len(patterns[i])):
token_pattern = patterns[i][j]
if('NBOR_RELOP' not in token_pattern['SPEC']):
heads[j] = ('root',j)
root = j
else:
heads[j] = (token_pattern['SPEC']['NBOR_RELOP'],_nodes_list[i][token_pattern['SPEC']['NBOR_NAME']])
_heads_list.append(heads)
_root_list.append(root)
_tree_list = []
for i in range(len(patterns)):
tree = {}
for j in range(len(patterns[i])):
if(_heads_list[i][j][INDEX_HEAD] == j):
continue
head = _heads_list[i][j][INDEX_HEAD]
if(head not in tree):
tree[head] = []
tree[head].append( (_heads_list[i][j][INDEX_RELOP],j) )
_tree_list.append(tree)
self._tree.setdefault(key, [])
self._tree[key].extend(_tree_list)
self._root.setdefault(key, [])
self._root[key].extend(_root_list)
def has_key(self, key):
"""Check whether the matcher has a rule with a given key.
key (string or int): The key to check.
RETURNS (bool): Whether the matcher has the rule.
"""
key = self._normalize_key(key)
return key in self._patterns
def get(self, key, default=None):
"""Retrieve the pattern stored for a key.
key (unicode or int): The key to retrieve.
RETURNS (tuple): The rule, as an (on_match, patterns) tuple.
"""
key = self._normalize_key(key)
if key not in self._patterns:
return default
return (self._callbacks[key], self._patterns[key])
def __call__(self, Doc doc):
matched_trees = []
matches = self.token_matcher(doc)
for key in list(self._patterns.keys()):
_patterns_list = self._patterns[key]
_keys_to_token_list = self._keys_to_token[key]
_root_list = self._root[key]
_tree_list = self._tree[key]
_nodes_list = self._nodes[key]
length = len(_patterns_list)
for i in range(length):
_keys_to_token = _keys_to_token_list[i]
_root = _root_list[i]
_tree = _tree_list[i]
_nodes = _nodes_list[i]
id_to_position = {}
for i in range(len(_nodes)):
id_to_position[i]=[]
# This could be taken outside to improve running time..?
for match_id, start, end in matches:
if match_id in _keys_to_token:
id_to_position[_keys_to_token[match_id]].append(start)
_node_operator_map = self.get_node_operator_map(doc,_tree,id_to_position,_nodes,_root)
length = len(_nodes)
if _root in id_to_position:
candidates = id_to_position[_root]
for candidate in candidates:
isVisited = {}
self.dfs(candidate,_root,_tree,id_to_position,doc,isVisited,_node_operator_map)
# To check if the subtree pattern is completely identified. This is a heuristic.
# This is done to reduce the complexity of exponential unordered subtree matching.
# Will give approximate matches in some cases.
if(len(isVisited) == length):
matched_trees.append((key,list(isVisited)))
for i, (ent_id, nodes) in enumerate(matched_trees):
on_match = self._callbacks.get(ent_id)
if on_match is not None:
on_match(self, doc, i, matches)
return matched_trees
def dfs(self,candidate,root,tree,id_to_position,doc,isVisited,_node_operator_map):
if(root in id_to_position and candidate in id_to_position[root]):
# color the node since it is valid
isVisited[candidate] = True
if root in tree:
for root_child in tree[root]:
if candidate in _node_operator_map and root_child[INDEX_RELOP] in _node_operator_map[candidate]:
candidate_children = _node_operator_map[candidate][root_child[INDEX_RELOP]]
for candidate_child in candidate_children:
result = self.dfs(
candidate_child.i,
root_child[INDEX_HEAD],
tree,
id_to_position,
doc,
isVisited,
_node_operator_map
)
# Given a node and an edge operator, to return the list of nodes
# from the doc that belong to node+operator. This is used to store
# all the results beforehand to prevent unnecessary computation while
# pattern matching
# _node_operator_map[node][operator] = [...]
def get_node_operator_map(self,doc,tree,id_to_position,nodes,root):
_node_operator_map = {}
all_node_indices = nodes.values()
all_operators = []
for node in all_node_indices:
if node in tree:
for child in tree[node]:
all_operators.append(child[INDEX_RELOP])
all_operators = list(set(all_operators))
all_nodes = []
for node in all_node_indices:
all_nodes = all_nodes + id_to_position[node]
all_nodes = list(set(all_nodes))
for node in all_nodes:
_node_operator_map[node] = {}
for operator in all_operators:
_node_operator_map[node][operator] = []
# Used to invoke methods for each operator
switcher = {
'<':self.dep,
'>':self.gov,
'>>':self.dep_chain,
'<<':self.gov_chain,
'.':self.imm_precede,
'$+':self.imm_right_sib,
'$-':self.imm_left_sib,
'$++':self.right_sib,
'$--':self.left_sib
}
for operator in all_operators:
for node in all_nodes:
_node_operator_map[node][operator] = switcher.get(operator)(doc,node)
return _node_operator_map
def dep(self,doc,node):
return list(doc[node].head)
def gov(self,doc,node):
return list(doc[node].children)
def dep_chain(self,doc,node):
return list(doc[node].ancestors)
def gov_chain(self,doc,node):
return list(doc[node].subtree)
def imm_precede(self,doc,node):
if node>0:
return [doc[node-1]]
return []
def imm_right_sib(self,doc,node):
for idx in range(list(doc[node].head.children)):
if idx == node-1:
return [doc[idx]]
return []
def imm_left_sib(self,doc,node):
for idx in range(list(doc[node].head.children)):
if idx == node+1:
return [doc[idx]]
return []
def right_sib(self,doc,node):
candidate_children = []
for idx in range(list(doc[node].head.children)):
if idx < node:
candidate_children.append(doc[idx])
return candidate_children
def left_sib(self,doc,node):
candidate_children = []
for idx in range(list(doc[node].head.children)):
if idx > node:
candidate_children.append(doc[idx])
return candidate_children
def _normalize_key(self, key):
if isinstance(key, basestring):
return self.vocab.strings.add(key)
else:
return key