mirror of https://github.com/explosion/spaCy.git
363 lines
14 KiB
Cython
363 lines
14 KiB
Cython
# cython: infer_types=True, profile=True
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from cymem.cymem cimport Pool
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from preshed.maps cimport PreshMap
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from libcpp cimport bool
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import numpy
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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 DependencyMatcher:
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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 DependencyMatcher.
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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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"""
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size = 20
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# TODO: make matcher work with validation
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self.token_matcher = Matcher(vocab, validate=False)
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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
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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 (str): 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.has_key(key)
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def validate_input(self, pattern, key):
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idx = 0
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visited_nodes = {}
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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(
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"NODE_NAME" in relation["SPEC"]
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and "NBOR_RELOP" not in relation["SPEC"]
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and "NBOR_NAME" not in relation["SPEC"]
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):
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raise ValueError(Errors.E099.format(key=key))
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visited_nodes[relation["SPEC"]["NODE_NAME"]] = True
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else:
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if not(
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"NODE_NAME" in relation["SPEC"]
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and "NBOR_RELOP" in relation["SPEC"]
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and "NBOR_NAME" in relation["SPEC"]
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):
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raise ValueError(Errors.E100.format(key=key))
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if (
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relation["SPEC"]["NODE_NAME"] in visited_nodes
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or relation["SPEC"]["NBOR_NAME"] not in visited_nodes
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):
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raise ValueError(Errors.E101.format(key=key))
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visited_nodes[relation["SPEC"]["NODE_NAME"]] = True
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visited_nodes[relation["SPEC"]["NBOR_NAME"]] = True
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idx = idx + 1
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def add(self, key, patterns, *_patterns, on_match=None):
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"""Add a new matcher rule to the matcher.
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key (str): The match ID.
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patterns (list): The patterns to add for the given key.
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on_match (callable): Optional callback executed on match.
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"""
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if patterns is None or hasattr(patterns, "__call__"): # old API
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on_match = patterns
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patterns = _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.validate_input(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
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# matcher. 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, [_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. This data structure
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# enables easy tree pattern match. Doc-Token based tree cannot be
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# reused since it is memory-heavy and tightly coupled with the 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] = (
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token_pattern["SPEC"]["NBOR_RELOP"],
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_nodes_list[i][token_pattern["SPEC"]["NBOR_NAME"]]
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)
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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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return self._normalize_key(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 (str / 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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"""Find all token sequences matching the supplied pattern.
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doclike (Doc or Span): The document to match over.
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RETURNS (list): A list of `(key, start, end)` tuples,
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describing the matches. A match tuple describes a span
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`doc[start:end]`. The `label_id` and `key` are both integers.
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"""
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matched_key_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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# TODO: 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(
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doc,
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_tree,
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id_to_position,
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_nodes,_root
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)
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length = len(_nodes)
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matched_trees = []
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self.recurse(_tree,id_to_position,_node_operator_map,0,[],matched_trees)
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matched_key_trees.append((key,matched_trees))
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for i, (ent_id, nodes) in enumerate(matched_key_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, matched_key_trees)
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return matched_key_trees
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def recurse(self,tree,id_to_position,_node_operator_map,int patternLength,visited_nodes,matched_trees):
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cdef bool isValid;
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if(patternLength == len(id_to_position.keys())):
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isValid = True
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for node in range(patternLength):
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if(node in tree):
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for idx, (relop,nbor) in enumerate(tree[node]):
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computed_nbors = numpy.asarray(_node_operator_map[visited_nodes[node]][relop])
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isNbor = False
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for computed_nbor in computed_nbors:
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if(computed_nbor.i == visited_nodes[nbor]):
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isNbor = True
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isValid = isValid & isNbor
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if(isValid):
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matched_trees.append(visited_nodes)
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return
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allPatternNodes = numpy.asarray(id_to_position[patternLength])
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for patternNode in allPatternNodes:
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self.recurse(tree,id_to_position,_node_operator_map,patternLength+1,visited_nodes+[patternNode],matched_trees)
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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 [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 child in list(doc[node].head.children):
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if child.i == node - 1:
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return [doc[child.i]]
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return []
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def imm_left_sib(self, doc, node):
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for child in list(doc[node].head.children):
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if child.i == node + 1:
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return [doc[child.i]]
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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 child in list(doc[node].head.children):
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if child.i < node:
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candidate_children.append(doc[child.i])
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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 child in list(doc[node].head.children):
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if child.i > node:
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candidate_children.append(doc[child.i])
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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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