mirror of https://github.com/explosion/spaCy.git
482 lines
17 KiB
Cython
482 lines
17 KiB
Cython
# cython: profile=True
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# cython: infer_types=True
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# coding: utf8
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from __future__ import unicode_literals
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import ujson
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from .typedefs cimport attr_t
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from .typedefs cimport hash_t
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from .attrs cimport attr_id_t
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from .structs cimport TokenC
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from cymem.cymem cimport Pool
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from preshed.maps cimport PreshMap
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from libcpp.vector cimport vector
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from libcpp.pair cimport pair
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from murmurhash.mrmr cimport hash64
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from libc.stdint cimport int32_t
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from .attrs cimport ID, ENT_TYPE
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from . import attrs
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from .tokens.doc cimport get_token_attr
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from .tokens.doc cimport Doc
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from .vocab cimport Vocab
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from .attrs import FLAG61 as U_ENT
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from .attrs import FLAG60 as B2_ENT
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from .attrs import FLAG59 as B3_ENT
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from .attrs import FLAG58 as B4_ENT
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from .attrs import FLAG57 as B5_ENT
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from .attrs import FLAG56 as B6_ENT
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from .attrs import FLAG55 as B7_ENT
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from .attrs import FLAG54 as B8_ENT
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from .attrs import FLAG53 as B9_ENT
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from .attrs import FLAG52 as B10_ENT
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from .attrs import FLAG51 as I3_ENT
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from .attrs import FLAG50 as I4_ENT
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from .attrs import FLAG49 as I5_ENT
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from .attrs import FLAG48 as I6_ENT
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from .attrs import FLAG47 as I7_ENT
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from .attrs import FLAG46 as I8_ENT
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from .attrs import FLAG45 as I9_ENT
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from .attrs import FLAG44 as I10_ENT
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from .attrs import FLAG43 as L2_ENT
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from .attrs import FLAG42 as L3_ENT
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from .attrs import FLAG41 as L4_ENT
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from .attrs import FLAG40 as L5_ENT
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from .attrs import FLAG39 as L6_ENT
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from .attrs import FLAG38 as L7_ENT
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from .attrs import FLAG37 as L8_ENT
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from .attrs import FLAG36 as L9_ENT
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from .attrs import FLAG35 as L10_ENT
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cpdef enum quantifier_t:
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_META
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ONE
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ZERO
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ZERO_ONE
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ZERO_PLUS
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cdef enum action_t:
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REJECT
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ADVANCE
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REPEAT
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ACCEPT
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ADVANCE_ZERO
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PANIC
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cdef struct AttrValueC:
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attr_id_t attr
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attr_t value
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cdef struct TokenPatternC:
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AttrValueC* attrs
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int32_t nr_attr
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quantifier_t quantifier
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ctypedef TokenPatternC* TokenPatternC_ptr
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ctypedef pair[int, TokenPatternC_ptr] StateC
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cdef TokenPatternC* init_pattern(Pool mem, attr_t entity_id,
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object token_specs) except NULL:
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pattern = <TokenPatternC*>mem.alloc(len(token_specs) + 1, sizeof(TokenPatternC))
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cdef int i
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for i, (quantifier, spec) in enumerate(token_specs):
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pattern[i].quantifier = quantifier
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pattern[i].attrs = <AttrValueC*>mem.alloc(len(spec), sizeof(AttrValueC))
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pattern[i].nr_attr = len(spec)
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for j, (attr, value) in enumerate(spec):
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pattern[i].attrs[j].attr = attr
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pattern[i].attrs[j].value = value
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i = len(token_specs)
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pattern[i].attrs = <AttrValueC*>mem.alloc(2, sizeof(AttrValueC))
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pattern[i].attrs[0].attr = ID
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pattern[i].attrs[0].value = entity_id
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pattern[i].nr_attr = 0
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return pattern
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cdef attr_t get_pattern_key(const TokenPatternC* pattern) except 0:
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while pattern.nr_attr != 0:
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pattern += 1
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id_attr = pattern[0].attrs[0]
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assert id_attr.attr == ID
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return id_attr.value
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cdef int get_action(const TokenPatternC* pattern, const TokenC* token) nogil:
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for attr in pattern.attrs[:pattern.nr_attr]:
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if get_token_attr(token, attr.attr) != attr.value:
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if pattern.quantifier == ONE:
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return REJECT
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elif pattern.quantifier == ZERO:
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return ACCEPT if (pattern+1).nr_attr == 0 else ADVANCE
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elif pattern.quantifier in (ZERO_ONE, ZERO_PLUS):
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return ACCEPT if (pattern+1).nr_attr == 0 else ADVANCE_ZERO
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else:
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return PANIC
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if pattern.quantifier == ZERO:
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return REJECT
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elif pattern.quantifier in (ONE, ZERO_ONE):
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return ACCEPT if (pattern+1).nr_attr == 0 else ADVANCE
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elif pattern.quantifier == ZERO_PLUS:
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return REPEAT
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else:
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return PANIC
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def _convert_strings(token_specs, string_store):
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# Support 'syntactic sugar' operator '+', as combination of ONE, ZERO_PLUS
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operators = {'!': (ZERO,), '*': (ZERO_PLUS,), '+': (ONE, ZERO_PLUS),
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'?': (ZERO_ONE,), '1': (ONE,)}
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tokens = []
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op = ONE
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for spec in token_specs:
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token = []
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ops = (ONE,)
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for attr, value in spec.items():
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if isinstance(attr, basestring) and attr.upper() == 'OP':
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if value in operators:
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ops = operators[value]
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else:
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raise KeyError(
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"Unknown operator '%s'. Options: %s" % (value, ', '.join(operators.keys())))
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if isinstance(attr, basestring):
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attr = attrs.IDS.get(attr.upper())
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if isinstance(value, basestring):
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value = string_store[value]
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if isinstance(value, bool):
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value = int(value)
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if attr is not None:
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token.append((attr, value))
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for op in ops:
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tokens.append((op, token))
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return tokens
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def merge_phrase(matcher, doc, i, matches):
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"""Callback to merge a phrase on match."""
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ent_id, label, start, end = matches[i]
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span = doc[start : end]
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span.merge(ent_type=label, ent_id=ent_id)
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cdef class Matcher:
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"""Match sequences of tokens, based on pattern rules."""
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cdef Pool mem
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cdef vector[TokenPatternC*] patterns
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cdef readonly Vocab vocab
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cdef public object _patterns
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cdef public object _entities
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cdef public object _callbacks
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cdef public object _acceptors
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def __init__(self, vocab):
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"""Create the Matcher.
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vocab (Vocab): The vocabulary object, which must be shared with the
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documents the matcher will operate on.
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RETURNS (Matcher): The newly constructed object.
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"""
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self._patterns = {}
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self._entities = {}
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self._acceptors = {}
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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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return (self.__class__, (self.vocab, self._patterns), None, None)
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def __len__(self):
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"""Get the number of rules added to the matcher.
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RETURNS (int): The number of rules.
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"""
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return len(self._patterns)
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def __contains__(self, key):
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"""Check whether the matcher contains rules for a match ID.
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key (unicode): The match ID.
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RETURNS (bool): Whether the matcher contains rules for this match ID.
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"""
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return len(self._patterns)
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def add(self, key, on_match, *patterns):
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"""Add a match-rule to the matcher.
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A match-rule consists of: an ID key, an on_match callback, and one or
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more patterns. If the key exists, the patterns are appended to the
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previous ones, and the previous on_match callback is replaced. The
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`on_match` callback will receive the arguments `(matcher, doc, i,
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matches)`. You can also set `on_match` to `None` to not perform any
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actions. A pattern consists of one or more `token_specs`, where a
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`token_spec` is a dictionary mapping attribute IDs to values. Token
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descriptors can also include quantifiers. There are currently important
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known problems with the quantifiers – see the docs.
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"""
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for pattern in patterns:
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if len(pattern) == 0:
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msg = ("Cannot add pattern for zero tokens to matcher.\n"
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"key: {key}\n")
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raise ValueError(msg.format(key=key))
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key = self._normalize_key(key)
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self._patterns.setdefault(key, [])
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self._callbacks[key] = on_match
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for pattern in patterns:
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specs = _convert_strings(pattern, self.vocab.strings)
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self.patterns.push_back(init_pattern(self.mem, key, specs))
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self._patterns[key].append(specs)
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def remove(self, key):
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"""Remove a rule from the matcher. A KeyError is raised if the key does
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not exist.
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key (unicode): The ID of the match rule.
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"""
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key = self._normalize_key(key)
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self._patterns.pop(key)
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self._callbacks.pop(key)
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cdef int i = 0
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while i < self.patterns.size():
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pattern_key = get_pattern_key(self.patterns.at(i))
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if pattern_key == key:
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self.patterns.erase(self.patterns.begin()+i)
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else:
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i += 1
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def has_key(self, key):
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"""Check whether the matcher has a rule with a given key.
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key (string or int): The key to check.
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RETURNS (bool): Whether the matcher has the rule.
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"""
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key = self._normalize_key(key)
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return key in self._patterns
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def get(self, key, default=None):
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"""Retrieve the pattern stored for an entity.
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key (unicode or int): The key to retrieve.
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RETURNS (tuple): The rule, as an (on_match, patterns) tuple.
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"""
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key = self._normalize_key(key)
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if key not in self._patterns:
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return default
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return (self._callbacks[key], self._patterns[key])
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def pipe(self, docs, batch_size=1000, n_threads=2):
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"""Match a stream of documents, yielding them in turn.
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docs (iterable): A stream of documents.
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batch_size (int): The number of documents to accumulate into a working set.
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n_threads (int): The number of threads with which to work on the buffer
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in parallel, if the `Matcher` implementation supports multi-threading.
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YIELDS (Doc): Documents, in order.
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"""
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for doc in docs:
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self(doc)
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yield doc
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def __call__(self, Doc doc):
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"""Find all token sequences matching the supplied patterns on the `Doc`.
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doc (Doc): The document to match over.
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RETURNS (list): A list of `(key, label_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 `label_id` and `key` are both integers.
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"""
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cdef vector[StateC] partials
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cdef int n_partials = 0
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cdef int q = 0
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cdef int i, token_i
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cdef const TokenC* token
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cdef StateC state
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matches = []
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for token_i in range(doc.length):
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token = &doc.c[token_i]
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q = 0
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# Go over the open matches, extending or finalizing if able. Otherwise,
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# we over-write them (q doesn't advance)
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for state in partials:
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action = get_action(state.second, token)
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if action == PANIC:
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raise Exception("Error selecting action in matcher")
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while action == ADVANCE_ZERO:
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state.second += 1
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action = get_action(state.second, token)
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if action == REPEAT:
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# Leave the state in the queue, and advance to next slot
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# (i.e. we don't overwrite -- we want to greedily match more
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# pattern.
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q += 1
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elif action == REJECT:
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pass
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elif action == ADVANCE:
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partials[q] = state
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partials[q].second += 1
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q += 1
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elif action == ACCEPT:
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# TODO: What to do about patterns starting with ZERO? Need to
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# adjust the start position.
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start = state.first
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end = token_i+1
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ent_id = state.second[1].attrs[0].value
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label = state.second[1].attrs[1].value
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matches.append((ent_id, start, end))
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partials.resize(q)
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# Check whether we open any new patterns on this token
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for pattern in self.patterns:
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action = get_action(pattern, token)
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if action == PANIC:
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raise Exception("Error selecting action in matcher")
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while action == ADVANCE_ZERO:
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pattern += 1
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action = get_action(pattern, token)
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if action == REPEAT:
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state.first = token_i
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state.second = pattern
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partials.push_back(state)
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elif action == ADVANCE:
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# TODO: What to do about patterns starting with ZERO? Need to
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# adjust the start position.
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state.first = token_i
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state.second = pattern + 1
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partials.push_back(state)
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elif action == ACCEPT:
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start = token_i
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end = token_i+1
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ent_id = pattern[1].attrs[0].value
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label = pattern[1].attrs[1].value
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matches.append((ent_id, start, end))
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# Look for open patterns that are actually satisfied
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for state in partials:
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while state.second.quantifier in (ZERO, ZERO_PLUS):
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state.second += 1
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if state.second.nr_attr == 0:
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start = state.first
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end = len(doc)
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ent_id = state.second.attrs[0].value
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label = state.second.attrs[0].value
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matches.append((ent_id, start, end))
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for i, (ent_id, label, start, end) in enumerate(matches):
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on_match = self._callbacks.get(ent_id)
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if on_match is not None:
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on_match(self, doc, i, matches)
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# TODO: only return (match_id, start, end)
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return matches
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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[key]
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else:
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return key
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def get_bilou(length):
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if length == 1:
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return [U_ENT]
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elif length == 2:
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return [B2_ENT, L2_ENT]
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elif length == 3:
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return [B3_ENT, I3_ENT, L3_ENT]
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elif length == 4:
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return [B4_ENT, I4_ENT, I4_ENT, L4_ENT]
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elif length == 5:
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return [B5_ENT, I5_ENT, I5_ENT, I5_ENT, L5_ENT]
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elif length == 6:
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return [B6_ENT, I6_ENT, I6_ENT, I6_ENT, I6_ENT, L6_ENT]
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elif length == 7:
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return [B7_ENT, I7_ENT, I7_ENT, I7_ENT, I7_ENT, I7_ENT, L7_ENT]
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elif length == 8:
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return [B8_ENT, I8_ENT, I8_ENT, I8_ENT, I8_ENT, I8_ENT, I8_ENT, L8_ENT]
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elif length == 9:
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return [B9_ENT, I9_ENT, I9_ENT, I9_ENT, I9_ENT, I9_ENT, I9_ENT, I9_ENT, L9_ENT]
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elif length == 10:
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return [B10_ENT, I10_ENT, I10_ENT, I10_ENT, I10_ENT, I10_ENT, I10_ENT,
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I10_ENT, I10_ENT, L10_ENT]
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else:
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raise ValueError("Max length currently 10 for phrase matching")
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cdef class PhraseMatcher:
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cdef Pool mem
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cdef Vocab vocab
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cdef Matcher matcher
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cdef PreshMap phrase_ids
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cdef int max_length
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cdef attr_t* _phrase_key
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def __init__(self, Vocab vocab, phrases, max_length=10):
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self.mem = Pool()
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self._phrase_key = <attr_t*>self.mem.alloc(max_length, sizeof(attr_t))
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self.max_length = max_length
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self.vocab = vocab
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self.matcher = Matcher(self.vocab, {})
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self.phrase_ids = PreshMap()
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for phrase in phrases:
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if len(phrase) < max_length:
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self.add(phrase)
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abstract_patterns = []
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for length in range(1, max_length):
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abstract_patterns.append([{tag: True} for tag in get_bilou(length)])
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self.matcher.add('Candidate', 'MWE', {}, abstract_patterns, acceptor=self.accept_match)
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def add(self, Doc tokens):
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cdef int length = tokens.length
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assert length < self.max_length
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tags = get_bilou(length)
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assert len(tags) == length, length
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cdef int i
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for i in range(self.max_length):
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self._phrase_key[i] = 0
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for i, tag in enumerate(tags):
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lexeme = self.vocab[tokens.c[i].lex.orth]
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lexeme.set_flag(tag, True)
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self._phrase_key[i] = lexeme.orth
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cdef hash_t key = hash64(self._phrase_key, self.max_length * sizeof(attr_t), 0)
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self.phrase_ids[key] = True
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def __call__(self, Doc doc):
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matches = []
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for ent_id, label, start, end in self.matcher(doc):
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cand = doc[start : end]
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start = cand[0].idx
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end = cand[-1].idx + len(cand[-1])
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matches.append((start, end, cand.root.tag_, cand.text, 'MWE'))
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for match in matches:
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doc.merge(*match)
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return matches
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def pipe(self, stream, batch_size=1000, n_threads=2):
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for doc in stream:
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self(doc)
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yield doc
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def accept_match(self, Doc doc, int ent_id, int label, int start, int end):
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assert (end - start) < self.max_length
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cdef int i, j
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for i in range(self.max_length):
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self._phrase_key[i] = 0
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for i, j in enumerate(range(start, end)):
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self._phrase_key[i] = doc.c[j].lex.orth
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cdef hash_t key = hash64(self._phrase_key, self.max_length * sizeof(attr_t), 0)
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if self.phrase_ids.get(key):
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return (ent_id, label, start, end)
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else:
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return False
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