mirror of https://github.com/explosion/spaCy.git
373 lines
13 KiB
Cython
373 lines
13 KiB
Cython
cimport cython
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from libc.string cimport memcpy, memset
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import numpy
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from ..lexeme cimport EMPTY_LEXEME
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from ..serialize import BitArray
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from ..strings cimport slice_unicode
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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 import UNIV_POS_NAMES
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from ..parts_of_speech cimport CONJ, PUNCT
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from ..lexeme cimport check_flag
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from .spans import Span
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from ..structs cimport UniStr
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from .token cimport Token
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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 get_lex_attr(token.lex, feat_name)
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cdef attr_t get_lex_attr(const LexemeC* lex, attr_id_t feat_name) nogil:
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if feat_name < (sizeof(flags_t) * 8):
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return check_flag(lex, feat_name)
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elif feat_name == ID:
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return lex.id
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elif feat_name == ORTH:
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return lex.orth
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elif feat_name == LOWER:
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return lex.lower
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elif feat_name == NORM:
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return lex.norm
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elif feat_name == SHAPE:
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return lex.shape
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elif feat_name == PREFIX:
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return lex.prefix
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elif feat_name == SUFFIX:
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return lex.suffix
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elif feat_name == LENGTH:
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return lex.length
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elif feat_name == CLUSTER:
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return lex.cluster
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else:
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return 0
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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):
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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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self.data = 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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def __getitem__(self, object i):
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"""Get a token.
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Returns:
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token (Token):
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"""
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if isinstance(i, slice):
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if i.step is not None:
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raise ValueError("Stepped slices not supported in Span objects."
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"Try: list(doc)[start:stop:step] instead.")
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return Span(self, i.start, i.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.data[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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for i in range(self.length):
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yield Token.cinit(self.vocab, &self.data[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.string for t in self])
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@property
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def string(self):
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return unicode(self)
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@property
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def ents(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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for i in range(self.length):
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token = &self.data[i]
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if token.ent_iob == 1:
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assert start != -1
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pass
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elif token.ent_iob == 2:
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if start != -1:
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yield 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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yield 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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yield Span(self, start, self.length, label=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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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.data[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.data[self.length]
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if LexemeOrToken is 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.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[long, 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[long, ndim=1] attr_ids = numpy.asarray(py_attr_ids)
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output = numpy.ndarray(shape=(self.length, len(attr_ids)), dtype=numpy.int)
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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.data[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(self.length)
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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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attr = get_token_attr(&self.data[i], attr_id)
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counts.inc(attr, 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.data[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.data - PADDING
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data_start = <TokenC*>self.mem.realloc(data_start, n * sizeof(TokenC))
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self.data = 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.data[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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self.is_parsed = True
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for i in range(self.length):
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self.data[i] = parsed[i]
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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 i
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cdef int start = -1
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cdef int end = -1
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for i in range(self.length):
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if self.data[i].idx == start_idx:
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start = i
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if (self.data[i].idx + self.data[i].lex.length) == end_idx:
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if start == -1:
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return None
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end = i + 1
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break
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else:
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return None
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cdef unicode string = self.string
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# Get LexemeC for newly merged token
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cdef UniStr new_orth_c
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slice_unicode(&new_orth_c, string, start_idx, end_idx)
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cdef const LexemeC* lex = self.vocab.get(self.mem, &new_orth_c)
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# House the new merged token where it starts
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cdef TokenC* token = &self.data[start]
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# Update fields
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token.lex = lex
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# What to do about morphology??
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# TODO: token.morph = ???
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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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# Fix dependencies
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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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for i in range(self.length):
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self.data[i].head += i
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# Find the head of the merged token, and its dep relation
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outer_heads = {}
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for i in range(start, end):
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head_idx = self.data[i].head
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if head_idx == i or head_idx < start or head_idx >= end:
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# Don't consider "heads" which are actually dominated by a word
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# in the region we're merging
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gp = head_idx
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while self.data[gp].head != gp:
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if start <= gp < end:
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break
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gp = self.data[gp].head
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else:
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# If we have multiple words attaching to the same head,
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# but with different dep labels, we're preferring the last
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# occurring dep label. Shrug. What else could we do, I guess?
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outer_heads[head_idx] = self.data[i].dep
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token.head, token.dep = max(outer_heads.items())
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# Adjust deps before shrinking tokens
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# Tokens which point into the merged token should now point to it
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# Subtract the offset from all tokens which point to >= end
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offset = (end - start) - 1
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for i in range(self.length):
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head_idx = self.data[i].head
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if start <= head_idx < end:
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self.data[i].head = start
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elif head_idx >= end:
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self.data[i].head -= offset
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# TODO: Fix left and right deps
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# Now compress the token array
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for i in range(end, self.length):
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self.data[i - offset] = self.data[i]
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for i in range(self.length - offset, self.length):
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memset(&self.data[i], 0, sizeof(TokenC))
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self.data[i].lex = &EMPTY_LEXEME
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self.length -= offset
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for i in range(self.length):
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# ...And, set heads back to a relative position
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self.data[i].head -= i
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# Return the merged Python object
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return self[start]
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