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spaCy/spacy/matcher.pyx

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# cython: profile=True
# cython: infer_types=True
from __future__ import unicode_literals
from os import path
from .typedefs cimport attr_t
from .typedefs cimport hash_t
from .attrs cimport attr_id_t
from .structs cimport TokenC, LexemeC
from .lexeme cimport Lexeme
from cymem.cymem cimport Pool
from preshed.maps cimport PreshMap
from libcpp.vector cimport vector
from libcpp.pair cimport pair
from murmurhash.mrmr cimport hash64
from libc.stdint cimport int32_t
from .attrs cimport ID, LENGTH, ENT_TYPE, ORTH, NORM, LEMMA, LOWER, SHAPE
from . import attrs
from .tokens.doc cimport get_token_attr
from .tokens.doc cimport Doc
from .vocab cimport Vocab
from .attrs import FLAG61 as U_ENT
from .attrs import FLAG60 as B2_ENT
from .attrs import FLAG59 as B3_ENT
from .attrs import FLAG58 as B4_ENT
from .attrs import FLAG57 as B5_ENT
from .attrs import FLAG56 as B6_ENT
from .attrs import FLAG55 as B7_ENT
from .attrs import FLAG54 as B8_ENT
from .attrs import FLAG53 as B9_ENT
from .attrs import FLAG52 as B10_ENT
from .attrs import FLAG51 as I3_ENT
from .attrs import FLAG50 as I4_ENT
from .attrs import FLAG49 as I5_ENT
from .attrs import FLAG48 as I6_ENT
from .attrs import FLAG47 as I7_ENT
from .attrs import FLAG46 as I8_ENT
from .attrs import FLAG45 as I9_ENT
from .attrs import FLAG44 as I10_ENT
from .attrs import FLAG43 as L2_ENT
from .attrs import FLAG42 as L3_ENT
from .attrs import FLAG41 as L4_ENT
from .attrs import FLAG40 as L5_ENT
from .attrs import FLAG39 as L6_ENT
from .attrs import FLAG38 as L7_ENT
from .attrs import FLAG37 as L8_ENT
from .attrs import FLAG36 as L9_ENT
from .attrs import FLAG35 as L10_ENT
try:
import ujson as json
except ImportError:
import json
cpdef enum quantifier_t:
_META
ONE
ZERO
ZERO_ONE
ZERO_PLUS
cdef enum action_t:
REJECT
ADVANCE
REPEAT
ACCEPT
ADVANCE_ZERO
PANIC
cdef struct AttrValueC:
attr_id_t attr
attr_t value
cdef struct TokenPatternC:
AttrValueC* attrs
int32_t nr_attr
quantifier_t quantifier
ctypedef TokenPatternC* TokenPatternC_ptr
ctypedef pair[int, TokenPatternC_ptr] StateC
cdef TokenPatternC* init_pattern(Pool mem, attr_t entity_id, attr_t label,
object token_specs) except NULL:
pattern = <TokenPatternC*>mem.alloc(len(token_specs) + 1, sizeof(TokenPatternC))
cdef int i
for i, (quantifier, spec) in enumerate(token_specs):
pattern[i].quantifier = quantifier
pattern[i].attrs = <AttrValueC*>mem.alloc(len(spec), sizeof(AttrValueC))
pattern[i].nr_attr = len(spec)
for j, (attr, value) in enumerate(spec):
pattern[i].attrs[j].attr = attr
pattern[i].attrs[j].value = value
i = len(token_specs)
pattern[i].attrs = <AttrValueC*>mem.alloc(3, sizeof(AttrValueC))
pattern[i].attrs[0].attr = ID
pattern[i].attrs[0].value = entity_id
pattern[i].attrs[1].attr = ENT_TYPE
pattern[i].attrs[1].value = label
pattern[i].nr_attr = 0
return pattern
cdef int get_action(const TokenPatternC* pattern, const TokenC* token) nogil:
for attr in pattern.attrs[:pattern.nr_attr]:
if get_token_attr(token, attr.attr) != attr.value:
if pattern.quantifier == ONE:
return REJECT
elif pattern.quantifier == ZERO:
return ACCEPT if (pattern+1).nr_attr == 0 else ADVANCE
elif pattern.quantifier in (ZERO_ONE, ZERO_PLUS):
return ACCEPT if (pattern+1).nr_attr == 0 else ADVANCE_ZERO
else:
return PANIC
if pattern.quantifier == ZERO:
return REJECT
elif pattern.quantifier in (ONE, ZERO_ONE):
return ACCEPT if (pattern+1).nr_attr == 0 else ADVANCE
elif pattern.quantifier == ZERO_PLUS:
return REPEAT
else:
return PANIC
def _convert_strings(token_specs, string_store):
# Support 'syntactic sugar' operator '+', as combination of ONE, ZERO_PLUS
operators = {'!': (ZERO,), '*': (ZERO_PLUS,), '+': (ONE, ZERO_PLUS),
'?': (ZERO_ONE,), '1': (ONE,)}
tokens = []
op = ONE
for spec in token_specs:
token = []
ops = (ONE,)
for attr, value in spec.items():
if isinstance(attr, basestring) and attr.upper() == 'OP':
if value in operators:
ops = operators[value]
else:
raise KeyError(
"Unknown operator '%s'. Options: %s" % (value, ', '.join(operators.keys())))
if isinstance(attr, basestring):
attr = attrs.IDS.get(attr.upper())
if isinstance(value, basestring):
value = string_store[value]
if isinstance(value, bool):
value = int(value)
if attr is not None:
token.append((attr, value))
for op in ops:
tokens.append((op, token))
return tokens
cdef class Matcher:
'''Match sequences of tokens, based on pattern rules.'''
cdef Pool mem
cdef vector[TokenPatternC*] patterns
cdef readonly Vocab vocab
cdef public object _patterns
cdef public object _entities
cdef public object _callbacks
cdef public object _acceptors
@classmethod
def load(cls, path, vocab):
'''Load the matcher and patterns from a file path.
Arguments:
path (Path):
Path to a JSON-formatted patterns file.
vocab (Vocab):
The vocabulary that the documents to match over will refer to.
Returns:
Matcher: The newly constructed object.
'''
if (path / 'gazetteer.json').exists():
with (path / 'gazetteer.json').open('r', encoding='utf8') as file_:
patterns = json.load(file_)
else:
patterns = {}
return cls(vocab, patterns)
def __init__(self, vocab, patterns={}):
"""Create the Matcher.
Arguments:
vocab (Vocab):
The vocabulary object, which must be shared with the documents
the matcher will operate on.
patterns (dict): Patterns to add to the matcher.
Returns:
The newly constructed object.
"""
self._patterns = {}
self._entities = {}
self._acceptors = {}
self._callbacks = {}
self.vocab = vocab
self.mem = Pool()
for entity_key, (etype, attrs, specs) in sorted(patterns.items()):
self.add_entity(entity_key, attrs)
for spec in specs:
self.add_pattern(entity_key, spec, label=etype)
def __reduce__(self):
return (self.__class__, (self.vocab, self._patterns), None, None)
property n_patterns:
def __get__(self): return self.patterns.size()
def add_entity(self, entity_key, attrs=None, if_exists='raise',
acceptor=None, on_match=None):
"""Add an entity to the matcher.
Arguments:
entity_key (unicode or int):
An ID for the entity.
attrs:
Attributes to associate with the Matcher.
if_exists ('raise', 'ignore' or 'update'):
Controls what happens if the entity ID already exists. Defaults to 'raise'.
acceptor:
Callback function to filter matches of the entity.
on_match:
Callback function to act on matches of the entity.
Returns:
None
"""
if if_exists not in ('raise', 'ignore', 'update'):
raise ValueError(
"Unexpected value for if_exists: %s.\n"
"Expected one of: ['raise', 'ignore', 'update']" % if_exists)
if attrs is None:
attrs = {}
entity_key = self.normalize_entity_key(entity_key)
if self.has_entity(entity_key):
if if_exists == 'raise':
raise KeyError(
"Tried to add entity %s. Entity exists, and if_exists='raise'.\n"
"Set if_exists='ignore' or if_exists='update', or check with "
"matcher.has_entity()")
elif if_exists == 'ignore':
return
self._entities[entity_key] = dict(attrs)
self._patterns.setdefault(entity_key, [])
self._acceptors[entity_key] = acceptor
self._callbacks[entity_key] = on_match
def add_pattern(self, entity_key, token_specs, label=""):
"""Add a pattern to the matcher.
Arguments:
entity_key (unicode or int):
An ID for the entity.
token_specs:
Description of the pattern to be matched.
label:
Label to assign to the matched pattern. Defaults to "".
Returns:
None
"""
token_specs = list(token_specs)
if len(token_specs) == 0:
msg = ("Cannot add pattern for zero tokens to matcher.\n"
"entity_key: {entity_key}\n"
"label: {label}")
raise ValueError(msg.format(entity_key=entity_key, label=label))
entity_key = self.normalize_entity_key(entity_key)
if not self.has_entity(entity_key):
self.add_entity(entity_key)
if isinstance(label, basestring):
label = self.vocab.strings[label]
elif label is None:
label = 0
spec = _convert_strings(token_specs, self.vocab.strings)
self.patterns.push_back(init_pattern(self.mem, entity_key, label, spec))
self._patterns[entity_key].append((label, token_specs))
def add(self, entity_key, label, attrs, specs, acceptor=None, on_match=None):
self.add_entity(entity_key, attrs=attrs, if_exists='update',
acceptor=acceptor, on_match=on_match)
for spec in specs:
self.add_pattern(entity_key, spec, label=label)
def normalize_entity_key(self, entity_key):
if isinstance(entity_key, basestring):
return self.vocab.strings[entity_key]
else:
return entity_key
def has_entity(self, entity_key):
"""Check whether the matcher has an entity.
Arguments:
entity_key (string or int): The entity key to check.
Returns:
bool: Whether the matcher has the entity.
"""
entity_key = self.normalize_entity_key(entity_key)
return entity_key in self._entities
def get_entity(self, entity_key):
"""Retrieve the attributes stored for an entity.
Arguments:
entity_key (unicode or int): The entity to retrieve.
Returns:
The entity attributes if present, otherwise None.
"""
entity_key = self.normalize_entity_key(entity_key)
if entity_key in self._entities:
return self._entities[entity_key]
else:
return None
def __call__(self, Doc doc, acceptor=None):
"""Find all token sequences matching the supplied patterns on the Doc.
Arguments:
doc (Doc):
The document to match over.
Returns:
list
A list of (entity_key, label_id, start, end) tuples,
describing the matches. A match tuple describes a span doc[start:end].
The label_id and entity_key are both integers.
"""
if acceptor is not None:
raise ValueError(
"acceptor keyword argument to Matcher deprecated. Specify acceptor "
"functions when you add patterns instead.")
cdef vector[StateC] partials
cdef int n_partials = 0
cdef int q = 0
cdef int i, token_i
cdef const TokenC* token
cdef StateC state
matches = []
for token_i in range(doc.length):
token = &doc.c[token_i]
q = 0
# Go over the open matches, extending or finalizing if able. Otherwise,
# we over-write them (q doesn't advance)
for state in partials:
action = get_action(state.second, token)
if action == PANIC:
raise Exception("Error selecting action in matcher")
while action == ADVANCE_ZERO:
state.second += 1
action = get_action(state.second, token)
if action == REPEAT:
# Leave the state in the queue, and advance to next slot
# (i.e. we don't overwrite -- we want to greedily match more
# pattern.
q += 1
elif action == REJECT:
pass
elif action == ADVANCE:
partials[q] = state
partials[q].second += 1
q += 1
elif action == ACCEPT:
# TODO: What to do about patterns starting with ZERO? Need to
# adjust the start position.
start = state.first
end = token_i+1
ent_id = state.second[1].attrs[0].value
label = state.second[1].attrs[1].value
acceptor = self._acceptors.get(ent_id)
if acceptor is None:
matches.append((ent_id, label, start, end))
else:
match = acceptor(doc, ent_id, label, start, end)
if match:
matches.append(match)
partials.resize(q)
# Check whether we open any new patterns on this token
for pattern in self.patterns:
action = get_action(pattern, token)
if action == PANIC:
raise Exception("Error selecting action in matcher")
while action == ADVANCE_ZERO:
pattern += 1
action = get_action(pattern, token)
if action == REPEAT:
state.first = token_i
state.second = pattern
partials.push_back(state)
elif action == ADVANCE:
# TODO: What to do about patterns starting with ZERO? Need to
# adjust the start position.
state.first = token_i
state.second = pattern + 1
partials.push_back(state)
elif action == ACCEPT:
start = token_i
end = token_i+1
ent_id = pattern[1].attrs[0].value
label = pattern[1].attrs[1].value
acceptor = self._acceptors.get(ent_id)
if acceptor is None:
matches.append((ent_id, label, start, end))
else:
match = acceptor(doc, ent_id, label, start, end)
if match:
matches.append(match)
# Look for open patterns that are actually satisfied
for state in partials:
while state.second.quantifier in (ZERO, ZERO_PLUS):
state.second += 1
if state.second.nr_attr == 0:
start = state.first
end = len(doc)
ent_id = state.second.attrs[0].value
label = state.second.attrs[0].value
acceptor = self._acceptors.get(ent_id)
if acceptor is None:
matches.append((ent_id, label, start, end))
else:
match = acceptor(doc, ent_id, label, start, end)
if match:
matches.append(match)
for i, (ent_id, label, start, end) in enumerate(matches):
on_match = self._callbacks.get(ent_id)
if on_match is not None:
on_match(self, doc, i, matches)
return matches
def pipe(self, docs, batch_size=1000, n_threads=2):
"""Match a stream of documents, yielding them in turn.
Arguments:
docs: A stream of documents.
batch_size (int):
The number of documents to accumulate into a working set.
n_threads (int):
The number of threads with which to work on the buffer in parallel,
if the Matcher implementation supports multi-threading.
Yields:
Doc Documents, in order.
"""
for doc in docs:
self(doc)
yield doc
def get_bilou(length):
if length == 1:
return [U_ENT]
elif length == 2:
return [B2_ENT, L2_ENT]
elif length == 3:
return [B3_ENT, I3_ENT, L3_ENT]
elif length == 4:
return [B4_ENT, I4_ENT, I4_ENT, L4_ENT]
elif length == 5:
return [B5_ENT, I5_ENT, I5_ENT, I5_ENT, L5_ENT]
elif length == 6:
return [B6_ENT, I6_ENT, I6_ENT, I6_ENT, I6_ENT, L6_ENT]
elif length == 7:
return [B7_ENT, I7_ENT, I7_ENT, I7_ENT, I7_ENT, I7_ENT, L7_ENT]
elif length == 8:
return [B8_ENT, I8_ENT, I8_ENT, I8_ENT, I8_ENT, I8_ENT, I8_ENT, L8_ENT]
elif length == 9:
return [B9_ENT, I9_ENT, I9_ENT, I9_ENT, I9_ENT, I9_ENT, I9_ENT, I9_ENT, L9_ENT]
elif length == 10:
return [B10_ENT, I10_ENT, I10_ENT, I10_ENT, I10_ENT, I10_ENT, I10_ENT,
I10_ENT, I10_ENT, L10_ENT]
else:
raise ValueError("Max length currently 10 for phrase matching")
cdef class PhraseMatcher:
cdef Pool mem
cdef Vocab vocab
cdef Matcher matcher
cdef PreshMap phrase_ids
cdef int max_length
cdef attr_t* _phrase_key
def __init__(self, Vocab vocab, phrases, max_length=10):
self.mem = Pool()
self._phrase_key = <attr_t*>self.mem.alloc(max_length, sizeof(attr_t))
self.max_length = max_length
self.vocab = vocab
self.matcher = Matcher(self.vocab, {})
self.phrase_ids = PreshMap()
for phrase in phrases:
if len(phrase) < max_length:
self.add(phrase)
abstract_patterns = []
for length in range(1, max_length):
abstract_patterns.append([{tag: True} for tag in get_bilou(length)])
self.matcher.add('Candidate', 'MWE', {}, abstract_patterns, acceptor=self.accept_match)
def add(self, Doc tokens):
cdef int length = tokens.length
assert length < self.max_length
tags = get_bilou(length)
assert len(tags) == length, length
cdef int i
for i in range(self.max_length):
self._phrase_key[i] = 0
for i, tag in enumerate(tags):
lexeme = self.vocab[tokens.c[i].lex.orth]
lexeme.set_flag(tag, True)
self._phrase_key[i] = lexeme.orth
cdef hash_t key = hash64(self._phrase_key, self.max_length * sizeof(attr_t), 0)
self.phrase_ids[key] = True
def __call__(self, Doc doc):
matches = []
for ent_id, label, start, end in self.matcher(doc):
cand = doc[start : end]
start = cand[0].idx
end = cand[-1].idx + len(cand[-1])
matches.append((start, end, cand.root.tag_, cand.text, 'MWE'))
for match in matches:
doc.merge(*match)
return matches
def pipe(self, stream, batch_size=1000, n_threads=2):
for doc in stream:
self(doc)
yield doc
def accept_match(self, Doc doc, int ent_id, int label, int start, int end):
assert (end - start) < self.max_length
cdef int i, j
for i in range(self.max_length):
self._phrase_key[i] = 0
for i, j in enumerate(range(start, end)):
self._phrase_key[i] = doc.c[j].lex.orth
cdef hash_t key = hash64(self._phrase_key, self.max_length * sizeof(attr_t), 0)
if self.phrase_ids.get(key):
return (ent_id, label, start, end)
else:
return False
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