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