mirror of https://github.com/explosion/spaCy.git
345 lines
14 KiB
Cython
345 lines
14 KiB
Cython
# cython: infer_types=True, profile=True
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from libc.stdint cimport uintptr_t
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from preshed.maps cimport map_init, map_set, map_get, map_clear, map_iter
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import warnings
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from ..attrs cimport ORTH, POS, TAG, DEP, LEMMA, MORPH
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from ..attrs import IDS
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from ..structs cimport TokenC
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from ..tokens.token cimport Token
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from ..tokens.span cimport Span
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from ..typedefs cimport attr_t
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from ..schemas import TokenPattern
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from ..errors import Errors, Warnings
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cdef class PhraseMatcher:
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"""Efficiently match large terminology lists. While the `Matcher` matches
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sequences based on lists of token descriptions, the `PhraseMatcher` accepts
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match patterns in the form of `Doc` objects.
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DOCS: https://nightly.spacy.io/api/phrasematcher
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USAGE: https://nightly.spacy.io/usage/rule-based-matching#phrasematcher
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Adapted from FlashText: https://github.com/vi3k6i5/flashtext
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MIT License (see `LICENSE`)
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Copyright (c) 2017 Vikash Singh (vikash.duliajan@gmail.com)
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"""
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def __init__(self, Vocab vocab, attr="ORTH", validate=False):
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"""Initialize the PhraseMatcher.
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vocab (Vocab): The shared vocabulary.
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attr (int / str): Token attribute to match on.
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validate (bool): Perform additional validation when patterns are added.
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DOCS: https://nightly.spacy.io/api/phrasematcher#init
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"""
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self.vocab = vocab
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self._callbacks = {}
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self._docs = {}
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self._validate = validate
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self.mem = Pool()
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self.c_map = <MapStruct*>self.mem.alloc(1, sizeof(MapStruct))
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self._terminal_hash = 826361138722620965
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map_init(self.mem, self.c_map, 8)
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if isinstance(attr, (int, long)):
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self.attr = attr
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else:
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attr = attr.upper()
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if attr == "TEXT":
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attr = "ORTH"
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if attr == "IS_SENT_START":
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attr = "SENT_START"
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if attr.lower() not in TokenPattern().dict():
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raise ValueError(Errors.E152.format(attr=attr))
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self.attr = IDS.get(attr)
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def __len__(self):
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"""Get the number of match IDs added to the matcher.
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RETURNS (int): The number of rules.
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DOCS: https://nightly.spacy.io/api/phrasematcher#len
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"""
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return len(self._callbacks)
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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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DOCS: https://nightly.spacy.io/api/phrasematcher#contains
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"""
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return key in self._callbacks
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def __reduce__(self):
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data = (self.vocab, self._docs, self._callbacks, self.attr)
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return (unpickle_matcher, data, None, None)
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def remove(self, key):
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"""Remove a rule from the matcher by match ID. A KeyError is raised if
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the key does not exist.
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key (str): The match ID.
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DOCS: https://nightly.spacy.io/api/phrasematcher#remove
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"""
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if key not in self._docs:
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raise KeyError(key)
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cdef MapStruct* current_node
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cdef MapStruct* terminal_map
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cdef MapStruct* node_pointer
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cdef void* result
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cdef key_t terminal_key
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cdef void* value
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cdef int c_i = 0
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cdef vector[MapStruct*] path_nodes
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cdef vector[key_t] path_keys
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cdef key_t key_to_remove
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for keyword in sorted(self._docs[key], key=lambda x: len(x), reverse=True):
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current_node = self.c_map
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path_nodes.clear()
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path_keys.clear()
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for token in keyword:
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result = map_get(current_node, token)
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if result:
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path_nodes.push_back(current_node)
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path_keys.push_back(token)
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current_node = <MapStruct*>result
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else:
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# if token is not found, break out of the loop
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current_node = NULL
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break
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# remove the tokens from trie node if there are no other
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# keywords with them
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result = map_get(current_node, self._terminal_hash)
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if current_node != NULL and result:
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terminal_map = <MapStruct*>result
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terminal_keys = []
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c_i = 0
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while map_iter(terminal_map, &c_i, &terminal_key, &value):
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terminal_keys.append(self.vocab.strings[terminal_key])
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# if this is the only remaining key, remove unnecessary paths
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if terminal_keys == [key]:
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while not path_nodes.empty():
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node_pointer = path_nodes.back()
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path_nodes.pop_back()
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key_to_remove = path_keys.back()
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path_keys.pop_back()
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result = map_get(node_pointer, key_to_remove)
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if node_pointer.filled == 1:
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map_clear(node_pointer, key_to_remove)
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self.mem.free(result)
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else:
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# more than one key means more than 1 path,
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# delete not required path and keep the others
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map_clear(node_pointer, key_to_remove)
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self.mem.free(result)
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break
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# otherwise simply remove the key
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else:
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result = map_get(current_node, self._terminal_hash)
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if result:
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map_clear(<MapStruct*>result, self.vocab.strings[key])
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del self._callbacks[key]
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del self._docs[key]
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def add(self, key, docs, *_docs, on_match=None):
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"""Add a match-rule to the phrase-matcher. A match-rule consists of: an ID
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key, an on_match callback, and one or more patterns.
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As of spaCy v2.2.2, PhraseMatcher.add supports the future API, which
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makes the patterns the second argument and a list (instead of a variable
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number of arguments). The on_match callback becomes an optional keyword
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argument.
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key (str): The match ID.
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docs (list): List of `Doc` objects representing match patterns.
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on_match (callable): Callback executed on match.
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*_docs (Doc): For backwards compatibility: list of patterns to add
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as variable arguments. Will be ignored if a list of patterns is
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provided as the second argument.
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DOCS: https://nightly.spacy.io/api/phrasematcher#add
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"""
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if docs is None or hasattr(docs, "__call__"): # old API
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on_match = docs
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docs = _docs
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_ = self.vocab[key]
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self._callbacks[key] = on_match
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self._docs.setdefault(key, set())
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cdef MapStruct* current_node
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cdef MapStruct* internal_node
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cdef void* result
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if isinstance(docs, Doc):
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raise ValueError(Errors.E179.format(key=key))
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for doc in docs:
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if len(doc) == 0:
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continue
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if isinstance(doc, Doc):
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attrs = (TAG, POS, MORPH, LEMMA, DEP)
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has_annotation = {attr: doc.has_annotation(attr) for attr in attrs}
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for attr in attrs:
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if self.attr == attr and not has_annotation[attr]:
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if attr == TAG:
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pipe = "tagger"
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elif attr in (POS, MORPH):
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pipe = "morphologizer"
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elif attr == LEMMA:
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pipe = "lemmatizer"
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elif attr == DEP:
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pipe = "parser"
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error_msg = Errors.E155.format(pipe=pipe, attr=self.vocab.strings.as_string(attr))
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raise ValueError(error_msg)
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if self._validate and any(has_annotation.values()) \
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and self.attr not in attrs:
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string_attr = self.vocab.strings[self.attr]
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warnings.warn(Warnings.W012.format(key=key, attr=string_attr))
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keyword = self._convert_to_array(doc)
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else:
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keyword = doc
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self._docs[key].add(tuple(keyword))
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current_node = self.c_map
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for token in keyword:
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if token == self._terminal_hash:
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warnings.warn(Warnings.W021)
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break
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result = <MapStruct*>map_get(current_node, token)
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if not result:
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internal_node = <MapStruct*>self.mem.alloc(1, sizeof(MapStruct))
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map_init(self.mem, internal_node, 8)
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map_set(self.mem, current_node, token, internal_node)
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result = internal_node
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current_node = <MapStruct*>result
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result = <MapStruct*>map_get(current_node, self._terminal_hash)
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if not result:
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internal_node = <MapStruct*>self.mem.alloc(1, sizeof(MapStruct))
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map_init(self.mem, internal_node, 8)
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map_set(self.mem, current_node, self._terminal_hash, internal_node)
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result = internal_node
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map_set(self.mem, <MapStruct*>result, self.vocab.strings[key], NULL)
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def __call__(self, doc, *, as_spans=False):
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"""Find all sequences matching the supplied patterns on the `Doc`.
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doc (Doc): The document to match over.
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as_spans (bool): Return Span objects with labels instead of (match_id,
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start, end) tuples.
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RETURNS (list): A list of `(match_id, start, end)` tuples,
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describing the matches. A match tuple describes a span
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`doc[start:end]`. The `match_id` is an integer. If as_spans is set
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to True, a list of Span objects is returned.
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DOCS: https://nightly.spacy.io/api/phrasematcher#call
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"""
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matches = []
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if doc is None or len(doc) == 0:
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# if doc is empty or None just return empty list
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return matches
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cdef vector[SpanC] c_matches
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self.find_matches(doc, &c_matches)
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for i in range(c_matches.size()):
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matches.append((c_matches[i].label, c_matches[i].start, c_matches[i].end))
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for i, (ent_id, start, end) in enumerate(matches):
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on_match = self._callbacks.get(self.vocab.strings[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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if as_spans:
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return [Span(doc, start, end, label=key) for key, start, end in matches]
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else:
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return matches
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cdef void find_matches(self, Doc doc, vector[SpanC] *matches) nogil:
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cdef MapStruct* current_node = self.c_map
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cdef int start = 0
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cdef int idx = 0
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cdef int idy = 0
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cdef key_t key
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cdef void* value
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cdef int i = 0
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cdef SpanC ms
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cdef void* result
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while idx < doc.length:
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start = idx
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token = Token.get_struct_attr(&doc.c[idx], self.attr)
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# look for sequences from this position
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result = map_get(current_node, token)
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if result:
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current_node = <MapStruct*>result
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idy = idx + 1
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while idy < doc.length:
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result = map_get(current_node, self._terminal_hash)
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if result:
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i = 0
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while map_iter(<MapStruct*>result, &i, &key, &value):
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ms = make_spanstruct(key, start, idy)
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matches.push_back(ms)
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inner_token = Token.get_struct_attr(&doc.c[idy], self.attr)
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result = map_get(current_node, inner_token)
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if result:
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current_node = <MapStruct*>result
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idy += 1
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else:
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break
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else:
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# end of doc reached
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result = map_get(current_node, self._terminal_hash)
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if result:
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i = 0
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while map_iter(<MapStruct*>result, &i, &key, &value):
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ms = make_spanstruct(key, start, idy)
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matches.push_back(ms)
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current_node = self.c_map
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idx += 1
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def pipe(self, stream, batch_size=1000, return_matches=False, as_tuples=False):
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"""Match a stream of documents, yielding them in turn. Deprecated as of
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spaCy v3.0.
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"""
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warnings.warn(Warnings.W105.format(matcher="PhraseMatcher"), DeprecationWarning)
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if as_tuples:
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for doc, context in stream:
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matches = self(doc)
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if return_matches:
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yield ((doc, matches), context)
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else:
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yield (doc, context)
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else:
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for doc in stream:
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matches = self(doc)
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if return_matches:
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yield (doc, matches)
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else:
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yield doc
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def _convert_to_array(self, Doc doc):
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return [Token.get_struct_attr(&doc.c[i], self.attr) for i in range(len(doc))]
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def unpickle_matcher(vocab, docs, callbacks, attr):
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matcher = PhraseMatcher(vocab, attr=attr)
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for key, specs in docs.items():
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callback = callbacks.get(key, None)
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matcher.add(key, specs, on_match=callback)
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return matcher
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cdef SpanC make_spanstruct(attr_t label, int start, int end) nogil:
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cdef SpanC spanc
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spanc.label = label
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spanc.start = start
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spanc.end = end
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return spanc
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