mirror of
https://github.com/explosion/spaCy.git
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419 lines
16 KiB
Cython
419 lines
16 KiB
Cython
# cython: infer_types=True, profile=True
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from typing import List
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import numpy
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from cymem.cymem cimport Pool
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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 ..errors import Errors
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from ..tokens import Span
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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 matcher
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cdef public object _patterns
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cdef public object _raw_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 _callbacks
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cdef public object _nodes
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cdef public object _tree
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cdef public object _ops
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def __init__(self, vocab, *, validate=False):
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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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validate (bool): Whether patterns should be validated, passed to
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Matcher as `validate`
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"""
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size = 20
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self.matcher = Matcher(vocab, validate=validate)
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self._keys_to_token = {}
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self._patterns = {}
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self._raw_patterns = {}
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self._root = {}
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self._nodes = {}
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self._tree = {}
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self._callbacks = {}
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self.vocab = vocab
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self.mem = Pool()
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self._ops = {
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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.precede,
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";": self.imm_follow,
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";*": self.follow,
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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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def __reduce__(self):
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data = (self.vocab, self._raw_patterns, 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 not isinstance(relation, dict):
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raise ValueError(Errors.E1008)
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if "RIGHT_ATTRS" not in relation and "RIGHT_ID" 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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"RIGHT_ID" in relation
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and "REL_OP" not in relation
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and "LEFT_ID" not in relation
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):
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raise ValueError(Errors.E099.format(key=key))
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visited_nodes[relation["RIGHT_ID"]] = True
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else:
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if not(
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"RIGHT_ID" in relation
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and "RIGHT_ATTRS" in relation
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and "REL_OP" in relation
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and "LEFT_ID" in relation
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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["RIGHT_ID"] in visited_nodes
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or relation["LEFT_ID"] not in visited_nodes
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):
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raise ValueError(Errors.E101.format(key=key))
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if relation["REL_OP"] not in self._ops:
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raise ValueError(Errors.E1007.format(op=relation["REL_OP"]))
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visited_nodes[relation["RIGHT_ID"]] = True
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visited_nodes[relation["LEFT_ID"]] = True
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idx = idx + 1
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def add(self, key, 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 on_match is not None and not hasattr(on_match, "__call__"):
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raise ValueError(Errors.E171.format(arg_type=type(on_match)))
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if patterns is None or not isinstance(patterns, List): # old API
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raise ValueError(Errors.E948.format(arg_type=type(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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self._raw_patterns.setdefault(key, [])
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self._raw_patterns[key].extend(patterns)
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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]["RIGHT_ATTRS"]]
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token_patterns.append(token_pattern)
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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.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]["RIGHT_ID"]] = 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 ("REL_OP" not in token_pattern):
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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["REL_OP"],
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_nodes_list[i][token_pattern["LEFT_ID"]]
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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._raw_patterns:
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return default
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return (self._callbacks[key], self._raw_patterns[key])
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def remove(self, key):
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key = self._normalize_key(key)
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if not key in self._patterns:
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raise ValueError(Errors.E175.format(key=key))
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self._patterns.pop(key)
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self._raw_patterns.pop(key)
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self._nodes.pop(key)
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self._tree.pop(key)
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self._root.pop(key)
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def __call__(self, object doclike):
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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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if isinstance(doclike, Doc):
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doc = doclike
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elif isinstance(doclike, Span):
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doc = doclike.as_doc()
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else:
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raise ValueError(Errors.E195.format(good="Doc or Span", got=type(doclike).__name__))
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matched_key_trees = []
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matches = self.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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for matched_tree in matched_trees:
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matched_key_trees.append((key, matched_tree))
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for i, (match_id, nodes) in enumerate(matched_key_trees):
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on_match = self._callbacks.get(match_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 bint 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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for operator in all_operators:
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for node in all_nodes:
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_node_operator_map[node][operator] = self._ops.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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if doc[node].head == doc[node]:
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return []
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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 [t for t in doc[node].subtree if t != doc[node]]
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def imm_precede(self, doc, node):
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sent = self._get_sent(doc[node])
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if node < len(doc) - 1 and doc[node + 1] in sent:
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return [doc[node + 1]]
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return []
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def precede(self, doc, node):
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sent = self._get_sent(doc[node])
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return [doc[i] for i in range(node + 1, sent.end)]
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def imm_follow(self, doc, node):
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sent = self._get_sent(doc[node])
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if node > 0 and doc[node - 1] in sent:
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return [doc[node - 1]]
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return []
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def follow(self, doc, node):
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sent = self._get_sent(doc[node])
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return [doc[i] for i in range(sent.start, node)]
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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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def _get_sent(self, token):
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root = (list(token.ancestors) or [token])[-1]
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return token.doc[root.left_edge.i:root.right_edge.i + 1]
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def unpickle_matcher(vocab, patterns, callbacks):
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matcher = DependencyMatcher(vocab)
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for key, pattern in patterns.items():
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callback = callbacks.get(key, None)
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matcher.add(key, pattern, on_match=callback)
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return matcher
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