mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 01:46:28 +03:00
576 lines
20 KiB
Cython
576 lines
20 KiB
Cython
cimport cython
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from libc.string cimport memcpy, memset
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from libc.stdint cimport uint32_t
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import numpy
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import numpy.linalg
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import struct
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cimport numpy as np
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import math
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import six
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from ..lexeme cimport Lexeme
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from ..lexeme cimport EMPTY_LEXEME
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from ..typedefs cimport attr_t, flags_t
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from ..attrs cimport attr_id_t
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from ..attrs cimport ID, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX, LENGTH, CLUSTER
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from ..attrs cimport POS, LEMMA, TAG, DEP, HEAD, SPACY, ENT_IOB, ENT_TYPE
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from ..parts_of_speech cimport CONJ, PUNCT, NOUN
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from ..parts_of_speech cimport univ_pos_t
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from ..lexeme cimport Lexeme
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from .span cimport Span
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from .token cimport Token
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from ..serialize.bits cimport BitArray
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from ..util import normalize_slice
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DEF PADDING = 5
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cdef int bounds_check(int i, int length, int padding) except -1:
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if (i + padding) < 0:
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raise IndexError
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if (i - padding) >= length:
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raise IndexError
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cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) nogil:
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if feat_name == LEMMA:
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return token.lemma
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elif feat_name == POS:
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return token.pos
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elif feat_name == TAG:
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return token.tag
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elif feat_name == DEP:
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return token.dep
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elif feat_name == HEAD:
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return token.head
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elif feat_name == SPACY:
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return token.spacy
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elif feat_name == ENT_IOB:
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return token.ent_iob
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elif feat_name == ENT_TYPE:
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return token.ent_type
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else:
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return Lexeme.get_struct_attr(token.lex, feat_name)
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cdef class Doc:
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"""
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Container class for annotated text. Constructed via English.__call__ or
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Tokenizer.__call__.
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"""
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def __init__(self, Vocab vocab, orths_and_spaces=None):
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self.vocab = vocab
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size = 20
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self.mem = Pool()
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# Guarantee self.lex[i-x], for any i >= 0 and x < padding is in bounds
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# However, we need to remember the true starting places, so that we can
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# realloc.
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data_start = <TokenC*>self.mem.alloc(size + (PADDING*2), sizeof(TokenC))
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cdef int i
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for i in range(size + (PADDING*2)):
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data_start[i].lex = &EMPTY_LEXEME
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data_start[i].l_edge = i
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data_start[i].r_edge = i
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self.c = data_start + PADDING
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self.max_length = size
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self.length = 0
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self.is_tagged = False
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self.is_parsed = False
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self._py_tokens = []
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self._vector = None
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def __getitem__(self, object i):
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"""Get a Token or a Span from the Doc.
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Returns:
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token (Token) or span (Span):
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"""
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if isinstance(i, slice):
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start, stop = normalize_slice(len(self), i.start, i.stop, i.step)
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return Span(self, start, stop, label=0)
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if i < 0:
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i = self.length + i
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bounds_check(i, self.length, PADDING)
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if self._py_tokens[i] is not None:
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return self._py_tokens[i]
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else:
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return Token.cinit(self.vocab, &self.c[i], i, self)
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def __iter__(self):
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"""Iterate over the tokens.
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Yields:
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token (Token):
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"""
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cdef int i
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for i in range(self.length):
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if self._py_tokens[i] is not None:
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yield self._py_tokens[i]
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else:
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yield Token.cinit(self.vocab, &self.c[i], i, self)
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def __len__(self):
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return self.length
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def __unicode__(self):
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return u''.join([t.text_with_ws for t in self])
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def __bytes__(self):
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return u''.join([t.text_with_ws for t in self]).encode('utf-8')
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def __str__(self):
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if six.PY3:
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return self.__unicode__()
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return self.__bytes__()
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def __repr__(self):
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return self.__str__()
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def similarity(self, other):
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if self.vector_norm == 0 or other.vector_norm == 0:
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return 0.0
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return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
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property vector:
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def __get__(self):
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if self._vector is None:
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self._vector = sum(t.vector for t in self) / len(self)
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return self._vector
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def __set__(self, value):
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self._vector = value
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property vector_norm:
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def __get__(self):
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cdef float value
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if self._vector_norm is None:
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self._vector_norm = 1e-20
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for value in self.vector:
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self._vector_norm += value * value
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self._vector_norm = math.sqrt(self._vector_norm)
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return self._vector_norm
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def __set__(self, value):
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self._vector_norm = value
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@property
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def string(self):
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return self.text_with_ws
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@property
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def text_with_ws(self):
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return u''.join([t.text_with_ws for t in self])
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@property
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def text(self):
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return u' '.join(t.text for t in self)
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property ents:
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def __get__(self):
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"""Yields named-entity Span objects.
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Iterate over the span to get individual Token objects, or access the label:
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>>> from spacy.en import English
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>>> nlp = English()
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>>> tokens = nlp(u'Mr. Best flew to New York on Saturday morning.')
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>>> ents = list(tokens.ents)
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>>> ents[0].label, ents[0].label_, ''.join(t.orth_ for t in ents[0])
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(112504, u'PERSON', u'Best ')
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"""
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cdef int i
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cdef const TokenC* token
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cdef int start = -1
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cdef int label = 0
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output = []
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for i in range(self.length):
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token = &self.c[i]
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if token.ent_iob == 1:
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assert start != -1
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elif token.ent_iob == 2 or token.ent_iob == 0:
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if start != -1:
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output.append(Span(self, start, i, label=label))
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start = -1
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label = 0
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elif token.ent_iob == 3:
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if start != -1:
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output.append(Span(self, start, i, label=label))
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start = i
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label = token.ent_type
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if start != -1:
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output.append(Span(self, start, self.length, label=label))
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return tuple(output)
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def __set__(self, ents):
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# TODO:
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# 1. Allow negative matches
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# 2. Ensure pre-set NERs are not over-written during statistical prediction
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# 3. Test basic data-driven ORTH gazetteer
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# 4. Test more nuanced date and currency regex
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cdef int i
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for i in range(self.length):
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self.c[i].ent_type = 0
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self.c[i].ent_iob = 0
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cdef attr_t ent_type
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cdef int start, end
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for ent_type, start, end in ents:
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if ent_type is None or ent_type < 0:
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# Mark as O
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for i in range(start, end):
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self.c[i].ent_type = 0
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self.c[i].ent_iob = 2
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else:
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# Mark (inside) as I
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for i in range(start, end):
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self.c[i].ent_type = ent_type
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self.c[i].ent_iob = 1
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# Set start as B
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self.c[start].ent_iob = 3
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@property
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def noun_chunks(self):
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"""Yield spans for base noun phrases."""
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if not self.is_parsed:
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raise ValueError(
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"noun_chunks requires the dependency parse, which "
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"requires data to be installed. If you haven't done so, run: "
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"\npython -m spacy.en.download all\n"
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"to install the data")
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cdef const TokenC* word
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labels = ['nsubj', 'dobj', 'nsubjpass', 'pcomp', 'pobj',
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'attr', 'root']
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np_deps = [self.vocab.strings[label] for label in labels]
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conj = self.vocab.strings['conj']
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np_label = self.vocab.strings['NP']
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for i in range(self.length):
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word = &self.c[i]
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if word.pos == NOUN and word.dep in np_deps:
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yield Span(self, word.l_edge, i+1, label=np_label)
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elif word.pos == NOUN and word.dep == conj:
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head = word+word.head
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while head.dep == conj and head.head < 0:
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head += head.head
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# If the head is an NP, and we're coordinated to it, we're an NP
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if head.dep in np_deps:
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yield Span(self, word.l_edge, i+1, label=np_label)
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@property
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def sents(self):
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"""
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Yield a list of sentence Span objects, calculated from the dependency parse.
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"""
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if not self.is_parsed:
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raise ValueError(
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"sentence boundary detection requires the dependency parse, which "
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"requires data to be installed. If you haven't done so, run: "
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"\npython -m spacy.en.download all\n"
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"to install the data")
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cdef int i
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start = 0
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for i in range(1, self.length):
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if self.c[i].sent_start:
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yield Span(self, start, i)
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start = i
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yield Span(self, start, self.length)
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cdef int push_back(self, LexemeOrToken lex_or_tok, bint has_space) except -1:
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if self.length == self.max_length:
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self._realloc(self.length * 2)
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cdef TokenC* t = &self.c[self.length]
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if LexemeOrToken is const_TokenC_ptr:
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t[0] = lex_or_tok[0]
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else:
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t.lex = lex_or_tok
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if self.length == 0:
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t.idx = 0
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else:
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t.idx = (t-1).idx + (t-1).lex.length + (t-1).spacy
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t.l_edge = self.length
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t.r_edge = self.length
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assert t.lex.orth != 0
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t.spacy = has_space
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self.length += 1
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self._py_tokens.append(None)
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return t.idx + t.lex.length + t.spacy
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@cython.boundscheck(False)
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cpdef np.ndarray to_array(self, object py_attr_ids):
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"""Given a list of M attribute IDs, export the tokens to a numpy ndarray
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of shape N*M, where N is the length of the sentence.
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Arguments:
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attr_ids (list[int]): A list of attribute ID ints.
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Returns:
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feat_array (numpy.ndarray[long, ndim=2]):
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A feature matrix, with one row per word, and one column per attribute
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indicated in the input attr_ids.
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"""
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cdef int i, j
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cdef attr_id_t feature
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cdef np.ndarray[attr_t, ndim=2] output
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# Make an array from the attributes --- otherwise our inner loop is Python
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# dict iteration.
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cdef np.ndarray[attr_t, ndim=1] attr_ids = numpy.asarray(py_attr_ids, dtype=numpy.int32)
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output = numpy.ndarray(shape=(self.length, len(attr_ids)), dtype=numpy.int32)
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for i in range(self.length):
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for j, feature in enumerate(attr_ids):
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output[i, j] = get_token_attr(&self.c[i], feature)
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return output
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def count_by(self, attr_id_t attr_id, exclude=None, PreshCounter counts=None):
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"""Produce a dict of {attribute (int): count (ints)} frequencies, keyed
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by the values of the given attribute ID.
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>>> from spacy.en import English, attrs
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>>> nlp = English()
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>>> tokens = nlp(u'apple apple orange banana')
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>>> tokens.count_by(attrs.ORTH)
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{12800L: 1, 11880L: 2, 7561L: 1}
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>>> tokens.to_array([attrs.ORTH])
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array([[11880],
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[11880],
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[ 7561],
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[12800]])
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"""
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cdef int i
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cdef attr_t attr
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cdef size_t count
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if counts is None:
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counts = PreshCounter()
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output_dict = True
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else:
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output_dict = False
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# Take this check out of the loop, for a bit of extra speed
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if exclude is None:
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for i in range(self.length):
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counts.inc(get_token_attr(&self.c[i], attr_id), 1)
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else:
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for i in range(self.length):
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if not exclude(self[i]):
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attr = get_token_attr(&self.c[i], attr_id)
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counts.inc(attr, 1)
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if output_dict:
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return dict(counts)
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def _realloc(self, new_size):
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self.max_length = new_size
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n = new_size + (PADDING * 2)
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# What we're storing is a "padded" array. We've jumped forward PADDING
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# places, and are storing the pointer to that. This way, we can access
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# words out-of-bounds, and get out-of-bounds markers.
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# Now that we want to realloc, we need the address of the true start,
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# so we jump the pointer back PADDING places.
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cdef TokenC* data_start = self.c - PADDING
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data_start = <TokenC*>self.mem.realloc(data_start, n * sizeof(TokenC))
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self.c = data_start + PADDING
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cdef int i
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for i in range(self.length, self.max_length + PADDING):
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self.c[i].lex = &EMPTY_LEXEME
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cdef int set_parse(self, const TokenC* parsed) except -1:
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# TODO: This method is fairly misleading atm. It's used by Parser
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# to actually apply the parse calculated. Need to rethink this.
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# Probably we should use from_array?
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self.is_parsed = True
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for i in range(self.length):
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self.c[i] = parsed[i]
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assert self.c[i].l_edge <= i
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assert self.c[i].r_edge >= i
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def from_array(self, attrs, array):
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cdef int i, col
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cdef attr_id_t attr_id
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cdef TokenC* tokens = self.c
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cdef int length = len(array)
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cdef attr_t[:] values
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for col, attr_id in enumerate(attrs):
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values = array[:, col]
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if attr_id == HEAD:
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for i in range(length):
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tokens[i].head = values[i]
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if values[i] >= 1:
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tokens[i + values[i]].l_kids += 1
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elif values[i] < 0:
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tokens[i + values[i]].r_kids += 1
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if not self.is_parsed and tokens[i].head != 0:
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self.is_parsed = True
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elif attr_id == TAG:
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for i in range(length):
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self.vocab.morphology.assign_tag(&tokens[i],
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self.vocab.morphology.reverse_index[values[i]])
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if not self.is_tagged and tokens[i].tag != 0:
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self.is_tagged = True
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elif attr_id == POS:
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for i in range(length):
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tokens[i].pos = <univ_pos_t>values[i]
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elif attr_id == DEP:
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for i in range(length):
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tokens[i].dep = values[i]
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elif attr_id == ENT_IOB:
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for i in range(length):
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tokens[i].ent_iob = values[i]
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elif attr_id == ENT_TYPE:
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for i in range(length):
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tokens[i].ent_type = values[i]
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else:
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raise ValueError("Unknown attribute ID: %d" % attr_id)
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set_children_from_heads(self.c, self.length)
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return self
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def to_bytes(self):
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byte_string = self.vocab.serializer.pack(self)
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cdef uint32_t length = len(byte_string)
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return struct.pack('I', length) + byte_string
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def from_bytes(self, data):
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self.vocab.serializer.unpack_into(data[4:], self)
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return self
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@staticmethod
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def read_bytes(file_):
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keep_reading = True
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while keep_reading:
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try:
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n_bytes_str = file_.read(4)
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if len(n_bytes_str) < 4:
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break
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n_bytes = struct.unpack('I', n_bytes_str)[0]
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data = file_.read(n_bytes)
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except StopIteration:
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keep_reading = False
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yield n_bytes_str + data
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def merge(self, int start_idx, int end_idx, unicode tag, unicode lemma,
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unicode ent_type):
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"""Merge a multi-word expression into a single token. Currently
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experimental; API is likely to change."""
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cdef int start = token_by_start(self.c, self.length, start_idx)
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if start == -1:
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return None
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cdef int end = token_by_end(self.c, self.length, end_idx)
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if end == -1:
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return None
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# Currently we have the token index, we want the range-end index
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end += 1
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cdef Span span = self[start:end]
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# Get LexemeC for newly merged token
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new_orth = ''.join([t.text_with_ws for t in span])
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if span[-1].whitespace_:
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new_orth = new_orth[:-len(span[-1].whitespace_)]
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cdef const LexemeC* lex = self.vocab.get(self.mem, new_orth)
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# House the new merged token where it starts
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cdef TokenC* token = &self.c[start]
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token.spacy = self.c[end-1].spacy
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if tag in self.vocab.morphology.tag_map:
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self.vocab.morphology.assign_tag(token, tag)
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else:
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token.tag = self.vocab.strings[tag]
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token.lemma = self.vocab.strings[lemma]
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if ent_type == 'O':
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token.ent_iob = 2
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token.ent_type = 0
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else:
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token.ent_iob = 3
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token.ent_type = self.vocab.strings[ent_type]
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# Begin by setting all the head indices to absolute token positions
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# This is easier to work with for now than the offsets
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# Before thinking of something simpler, beware the case where a dependency
|
|
# bridges over the entity. Here the alignment of the tokens changes.
|
|
span_root = span.root.i
|
|
token.dep = span.root.dep
|
|
# We update token.lex after keeping span root and dep, since
|
|
# setting token.lex will change span.start and span.end properties
|
|
# as it modifies the character offsets in the doc
|
|
token.lex = lex
|
|
for i in range(self.length):
|
|
self.c[i].head += i
|
|
# Set the head of the merged token, and its dep relation, from the Span
|
|
token.head = self.c[span_root].head
|
|
# Adjust deps before shrinking tokens
|
|
# Tokens which point into the merged token should now point to it
|
|
# Subtract the offset from all tokens which point to >= end
|
|
offset = (end - start) - 1
|
|
for i in range(self.length):
|
|
head_idx = self.c[i].head
|
|
if start <= head_idx < end:
|
|
self.c[i].head = start
|
|
elif head_idx >= end:
|
|
self.c[i].head -= offset
|
|
# Now compress the token array
|
|
for i in range(end, self.length):
|
|
self.c[i - offset] = self.c[i]
|
|
for i in range(self.length - offset, self.length):
|
|
memset(&self.c[i], 0, sizeof(TokenC))
|
|
self.c[i].lex = &EMPTY_LEXEME
|
|
self.length -= offset
|
|
for i in range(self.length):
|
|
# ...And, set heads back to a relative position
|
|
self.c[i].head -= i
|
|
# Set the left/right children, left/right edges
|
|
set_children_from_heads(self.c, self.length)
|
|
# Clear the cached Python objects
|
|
self._py_tokens = [None] * self.length
|
|
# Return the merged Python object
|
|
return self[start]
|
|
|
|
|
|
cdef int token_by_start(const TokenC* tokens, int length, int start_char) except -2:
|
|
cdef int i
|
|
for i in range(length):
|
|
if tokens[i].idx == start_char:
|
|
return i
|
|
else:
|
|
return -1
|
|
|
|
|
|
cdef int token_by_end(const TokenC* tokens, int length, int end_char) except -2:
|
|
cdef int i
|
|
for i in range(length):
|
|
if tokens[i].idx + tokens[i].lex.length == end_char:
|
|
return i
|
|
else:
|
|
return -1
|
|
|
|
|
|
cdef int set_children_from_heads(TokenC* tokens, int length) except -1:
|
|
cdef TokenC* head
|
|
cdef TokenC* child
|
|
cdef int i
|
|
# Set number of left/right children to 0. We'll increment it in the loops.
|
|
for i in range(length):
|
|
tokens[i].l_kids = 0
|
|
tokens[i].r_kids = 0
|
|
tokens[i].l_edge = i
|
|
tokens[i].r_edge = i
|
|
# Set left edges
|
|
for i in range(length):
|
|
child = &tokens[i]
|
|
head = &tokens[i + child.head]
|
|
if child < head:
|
|
if child.l_edge < head.l_edge:
|
|
head.l_edge = child.l_edge
|
|
head.l_kids += 1
|
|
|
|
# Set right edges --- same as above, but iterate in reverse
|
|
for i in range(length-1, -1, -1):
|
|
child = &tokens[i]
|
|
head = &tokens[i + child.head]
|
|
if child > head:
|
|
if child.r_edge > head.r_edge:
|
|
head.r_edge = child.r_edge
|
|
head.r_kids += 1
|
|
|
|
# Set sentence starts
|
|
for i in range(length):
|
|
if tokens[i].head == 0 and tokens[i].dep != 0:
|
|
tokens[tokens[i].l_edge].sent_start = True
|
|
|