mirror of https://github.com/explosion/spaCy.git
813 lines
30 KiB
Cython
813 lines
30 KiB
Cython
# coding: utf8
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# cython: infer_types=True
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# cython: bounds_check=False
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from __future__ import unicode_literals
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cimport cython
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cimport numpy as np
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import numpy
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import numpy.linalg
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import struct
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import dill
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from libc.string cimport memcpy, memset
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from libc.stdint cimport uint32_t
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from libc.math cimport sqrt
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from .span cimport Span
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from .token cimport Token
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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 LENGTH, POS, LEMMA, TAG, DEP, HEAD, SPACY, ENT_IOB, ENT_TYPE
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from ..parts_of_speech cimport CCONJ, 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 ..util import normalize_slice
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from ..syntax.iterators import CHUNKERS
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from ..compat import is_config
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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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A sequence of `Token` objects. Access sentences and named entities,
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export annotations to numpy arrays, losslessly serialize to compressed
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binary strings.
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Aside: Internals
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The `Doc` object holds an array of `TokenC` structs.
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The Python-level `Token` and `Span` objects are views of this
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array, i.e. they don't own the data themselves.
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Code: Construction 1
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doc = nlp.tokenizer(u'Some text')
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Code: Construction 2
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doc = Doc(nlp.vocab, orths_and_spaces=[(u'Some', True), (u'text', True)])
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"""
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def __init__(self, Vocab vocab, words=None, spaces=None, orths_and_spaces=None):
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"""
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Create a Doc object.
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Arguments:
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vocab:
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A Vocabulary object, which must match any models you want to
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use (e.g. tokenizer, parser, entity recognizer).
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words:
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A list of unicode strings to add to the document as words. If None,
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defaults to empty list.
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spaces:
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A list of boolean values, of the same length as words. True
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means that the word is followed by a space, False means it is not.
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If None, defaults to [True]*len(words)
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"""
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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.sentiment = 0.0
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self.user_hooks = {}
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self.user_token_hooks = {}
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self.user_span_hooks = {}
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self.tensor = numpy.zeros((0,), dtype='float32')
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self.user_data = {}
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self._py_tokens = []
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self._vector = None
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self.noun_chunks_iterator = CHUNKERS.get(self.vocab.lang)
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cdef unicode orth
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cdef bint has_space
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if orths_and_spaces is None and words is not None:
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if spaces is None:
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spaces = [True] * len(words)
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elif len(spaces) != len(words):
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raise ValueError(
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"Arguments 'words' and 'spaces' should be sequences of the "
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"same length, or 'spaces' should be left default at None. "
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"spaces should be a sequence of booleans, with True meaning "
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"that the word owns a ' ' character following it.")
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orths_and_spaces = zip(words, spaces)
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if orths_and_spaces is not None:
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for orth_space in orths_and_spaces:
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if isinstance(orth_space, unicode):
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orth = orth_space
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has_space = True
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elif isinstance(orth_space, bytes):
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raise ValueError(
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"orths_and_spaces expects either List(unicode) or "
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"List((unicode, bool)). Got bytes instance: %s" % (str(orth_space)))
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else:
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orth, has_space = orth_space
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# Note that we pass self.mem here --- we have ownership, if LexemeC
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# must be created.
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self.push_back(
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<const LexemeC*>self.vocab.get(self.mem, orth), has_space)
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# Tough to decide on policy for this. Is an empty doc tagged and parsed?
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# There's no information we'd like to add to it, so I guess so?
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if self.length == 0:
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self.is_tagged = True
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self.is_parsed = True
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def __getitem__(self, object i):
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"""
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doc[i]
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Get the Token object at position i, where i is an integer.
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Negative indexing is supported, and follows the usual Python
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semantics, i.e. doc[-2] is doc[len(doc) - 2].
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doc[start : end]]
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Get a `Span` object, starting at position `start`
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and ending at position `end`, where `start` and
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`end` are token indices. For instance,
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`doc[2:5]` produces a span consisting of
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tokens 2, 3 and 4. Stepped slices (e.g. `doc[start : end : step]`)
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are not supported, as `Span` objects must be contiguous (cannot have gaps).
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You can use negative indices and open-ended ranges, which have their
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normal Python semantics.
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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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"""
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for token in doc
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Iterate over `Token` objects, from which the annotations can
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be easily accessed. This is the main way of accessing Token
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objects, which are the main way annotations are accessed from
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Python. If faster-than-Python speeds are required, you can
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instead access the annotations as a numpy array, or access the
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underlying C data directly from Cython.
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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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"""
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len(doc)
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The number of tokens in the document.
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"""
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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 is_config(python3=True):
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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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@property
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def doc(self):
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return self
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def similarity(self, other):
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"""
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Make a semantic similarity estimate. The default estimate is cosine
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similarity using an average of word vectors.
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Arguments:
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other (object): The object to compare with. By default, accepts Doc,
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Span, Token and Lexeme objects.
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Return:
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score (float): A scalar similarity score. Higher is more similar.
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"""
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if 'similarity' in self.user_hooks:
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return self.user_hooks['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 has_vector:
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"""
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A boolean value indicating whether a word vector is associated with the object.
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"""
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def __get__(self):
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if 'has_vector' in self.user_hooks:
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return self.user_hooks['has_vector'](self)
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return any(token.has_vector for token in self)
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property vector:
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"""
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A real-valued meaning representation. Defaults to an average of the token vectors.
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Type: numpy.ndarray[ndim=1, dtype='float32']
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"""
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def __get__(self):
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if 'vector' in self.user_hooks:
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return self.user_hooks['vector'](self)
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if self._vector is None:
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if len(self):
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self._vector = sum(t.vector for t in self) / len(self)
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else:
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return numpy.zeros((self.vocab.vectors_length,), dtype='float32')
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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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if 'vector_norm' in self.user_hooks:
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return self.user_hooks['vector_norm'](self)
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cdef float value
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cdef double norm = 0
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if self._vector_norm is None:
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norm = 0.0
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for value in self.vector:
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norm += value * value
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self._vector_norm = sqrt(norm) if norm != 0 else 0
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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
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property text:
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"""
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A unicode representation of the document text.
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"""
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def __get__(self):
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return u''.join(t.text_with_ws for t in self)
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property text_with_ws:
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"""
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An alias of Doc.text, provided for duck-type compatibility with Span and Token.
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"""
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def __get__(self):
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return self.text
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property ents:
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"""
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Yields named-entity `Span` objects, if the entity recognizer
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has been applied to the document. Iterate over the span to get
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individual Token objects, or access the label:
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Example:
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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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assert ents[0].label == 346
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assert ents[0].label_ == 'PERSON'
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assert ents[0].orth_ == 'Best'
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assert ents[0].text == 'Mr. Best'
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"""
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def __get__(self):
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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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# At this point we don't know whether the NER has run over the
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# Doc. If the ent_iob is missing, leave it missing.
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if self.c[i].ent_iob != 0:
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self.c[i].ent_iob = 2 # Means O. Non-O are set from ents.
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cdef attr_t ent_type
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cdef int start, end
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for ent_info in ents:
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if isinstance(ent_info, Span):
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ent_id = ent_info.ent_id
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ent_type = ent_info.label
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start = ent_info.start
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end = ent_info.end
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elif len(ent_info) == 3:
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ent_type, start, end = ent_info
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else:
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ent_id, ent_type, start, end = ent_info
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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 noun_chunks:
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"""
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Yields base noun-phrase #[code Span] objects, if the document
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has been syntactically parsed. A base noun phrase, or
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'NP chunk', is a noun phrase that does not permit other NPs to
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be nested within it – so no NP-level coordination, no prepositional
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phrases, and no relative clauses.
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"""
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def __get__(self):
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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 download %s\n"
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"to install the data" % self.vocab.lang)
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# Accumulate the result before beginning to iterate over it. This prevents
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# the tokenisation from being changed out from under us during the iteration.
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# The tricky thing here is that Span accepts its tokenisation changing,
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# so it's okay once we have the Span objects. See Issue #375
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spans = []
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for start, end, label in self.noun_chunks_iterator(self):
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spans.append(Span(self, start, end, label=label))
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for span in spans:
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yield span
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property sents:
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"""
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Yields sentence `Span` objects. Sentence spans have no label.
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To improve accuracy on informal texts, spaCy calculates sentence
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boundaries from the syntactic dependency parse. If the parser is disabled,
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`sents` iterator will be unavailable.
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Example:
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from spacy.en import English
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nlp = English()
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doc = nlp("This is a sentence. Here's another...")
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assert [s.root.orth_ for s in doc.sents] == ["is", "'s"]
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"""
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def __get__(self):
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if 'sents' in self.user_hooks:
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return self.user_hooks['sents'](self)
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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 download %s\n"
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"to install the data" % self.vocab.lang)
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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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if start != self.length:
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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 == 0:
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# Flip these to false when we see the first token.
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self.is_tagged = False
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self.is_parsed = False
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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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"""
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Given a list of M attribute IDs, export the tokens to a numpy
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`ndarray` of shape (N, M), where `N` is the length
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of the document. The values will be 32-bit integers.
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Example:
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from spacy import attrs
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doc = nlp(text)
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# All strings mapped to integers, for easy export to numpy
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np_array = doc.to_array([attrs.LOWER, attrs.POS, attrs.ENT_TYPE, attrs.IS_ALPHA])
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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)
|
||
for i in range(self.length):
|
||
for j, feature in enumerate(attr_ids):
|
||
output[i, j] = get_token_attr(&self.c[i], feature)
|
||
return output
|
||
|
||
def count_by(self, attr_id_t attr_id, exclude=None, PreshCounter counts=None):
|
||
"""
|
||
Produce a dict of {attribute (int): count (ints)} frequencies, keyed
|
||
by the values of the given attribute ID.
|
||
|
||
Example:
|
||
from spacy.en import English
|
||
from spacy import attrs
|
||
nlp = English()
|
||
tokens = nlp(u'apple apple orange banana')
|
||
tokens.count_by(attrs.ORTH)
|
||
# {12800L: 1, 11880L: 2, 7561L: 1}
|
||
tokens.to_array([attrs.ORTH])
|
||
# array([[11880],
|
||
# [11880],
|
||
# [ 7561],
|
||
# [12800]])
|
||
|
||
Arguments:
|
||
attr_id
|
||
int
|
||
The attribute ID to key the counts.
|
||
"""
|
||
cdef int i
|
||
cdef attr_t attr
|
||
cdef size_t count
|
||
|
||
if counts is None:
|
||
counts = PreshCounter()
|
||
output_dict = True
|
||
else:
|
||
output_dict = False
|
||
# Take this check out of the loop, for a bit of extra speed
|
||
if exclude is None:
|
||
for i in range(self.length):
|
||
counts.inc(get_token_attr(&self.c[i], attr_id), 1)
|
||
else:
|
||
for i in range(self.length):
|
||
if not exclude(self[i]):
|
||
attr = get_token_attr(&self.c[i], attr_id)
|
||
counts.inc(attr, 1)
|
||
if output_dict:
|
||
return dict(counts)
|
||
|
||
def _realloc(self, new_size):
|
||
self.max_length = new_size
|
||
n = new_size + (PADDING * 2)
|
||
# What we're storing is a "padded" array. We've jumped forward PADDING
|
||
# places, and are storing the pointer to that. This way, we can access
|
||
# words out-of-bounds, and get out-of-bounds markers.
|
||
# Now that we want to realloc, we need the address of the true start,
|
||
# so we jump the pointer back PADDING places.
|
||
cdef TokenC* data_start = self.c - PADDING
|
||
data_start = <TokenC*>self.mem.realloc(data_start, n * sizeof(TokenC))
|
||
self.c = data_start + PADDING
|
||
cdef int i
|
||
for i in range(self.length, self.max_length + PADDING):
|
||
self.c[i].lex = &EMPTY_LEXEME
|
||
|
||
cdef void set_parse(self, const TokenC* parsed) nogil:
|
||
# TODO: This method is fairly misleading atm. It's used by Parser
|
||
# to actually apply the parse calculated. Need to rethink this.
|
||
|
||
# Probably we should use from_array?
|
||
self.is_parsed = True
|
||
for i in range(self.length):
|
||
self.c[i] = parsed[i]
|
||
|
||
def from_array(self, attrs, int[:, :] array):
|
||
"""
|
||
Write to a `Doc` object, from an `(M, N)` array of attributes.
|
||
"""
|
||
cdef int i, col
|
||
cdef attr_id_t attr_id
|
||
cdef TokenC* tokens = self.c
|
||
cdef int length = len(array)
|
||
# Get set up for fast loading
|
||
cdef Pool mem = Pool()
|
||
cdef int n_attrs = len(attrs)
|
||
attr_ids = <attr_id_t*>mem.alloc(n_attrs, sizeof(attr_id_t))
|
||
for i, attr_id in enumerate(attrs):
|
||
attr_ids[i] = attr_id
|
||
# Now load the data
|
||
for i in range(self.length):
|
||
token = &self.c[i]
|
||
for j in range(n_attrs):
|
||
Token.set_struct_attr(token, attr_ids[j], array[i, j])
|
||
# Auxiliary loading logic
|
||
for col, attr_id in enumerate(attrs):
|
||
if attr_id == TAG:
|
||
for i in range(length):
|
||
if array[i, col] != 0:
|
||
self.vocab.morphology.assign_tag(&tokens[i], array[i, col])
|
||
set_children_from_heads(self.c, self.length)
|
||
self.is_parsed = bool(HEAD in attrs or DEP in attrs)
|
||
self.is_tagged = bool(TAG in attrs or POS in attrs)
|
||
return self
|
||
|
||
def to_bytes(self):
|
||
"""
|
||
Serialize, producing a byte string.
|
||
"""
|
||
return dill.dumps(
|
||
(self.text,
|
||
self.to_array([LENGTH,SPACY,TAG,LEMMA,HEAD,DEP,ENT_IOB,ENT_TYPE]),
|
||
self.sentiment,
|
||
self.tensor,
|
||
self.noun_chunks_iterator,
|
||
self.user_data,
|
||
(self.user_hooks, self.user_token_hooks, self.user_span_hooks)),
|
||
protocol=-1)
|
||
|
||
def from_bytes(self, data):
|
||
"""
|
||
Deserialize, loading from bytes.
|
||
"""
|
||
if self.length != 0:
|
||
raise ValueError("Cannot load into non-empty Doc")
|
||
cdef int[:, :] attrs
|
||
cdef int i, start, end, has_space
|
||
fields = dill.loads(data)
|
||
text, attrs = fields[:2]
|
||
self.sentiment, self.tensor = fields[2:4]
|
||
self.noun_chunks_iterator, self.user_data = fields[4:6]
|
||
self.user_hooks, self.user_token_hooks, self.user_span_hooks = fields[6]
|
||
|
||
start = 0
|
||
cdef const LexemeC* lex
|
||
cdef unicode orth_
|
||
for i in range(attrs.shape[0]):
|
||
end = start + attrs[i, 0]
|
||
has_space = attrs[i, 1]
|
||
orth_ = text[start:end]
|
||
lex = self.vocab.get(self.mem, orth_)
|
||
self.push_back(lex, has_space)
|
||
|
||
start = end + has_space
|
||
self.from_array([TAG,LEMMA,HEAD,DEP,ENT_IOB,ENT_TYPE],
|
||
attrs[:, 2:])
|
||
return self
|
||
|
||
def merge(self, int start_idx, int end_idx, *args, **attributes):
|
||
"""
|
||
Retokenize the document, such that the span at doc.text[start_idx : end_idx]
|
||
is merged into a single token. If start_idx and end_idx do not mark start
|
||
and end token boundaries, the document remains unchanged.
|
||
|
||
Arguments:
|
||
start_idx (int): The character index of the start of the slice to merge.
|
||
end_idx (int): The character index after the end of the slice to merge.
|
||
**attributes:
|
||
Attributes to assign to the merged token. By default, attributes
|
||
are inherited from the syntactic root token of the span.
|
||
Returns:
|
||
token (Token):
|
||
The newly merged token, or None if the start and end indices did
|
||
not fall at token boundaries.
|
||
"""
|
||
cdef unicode tag, lemma, ent_type
|
||
if len(args) == 3:
|
||
# TODO: Warn deprecation
|
||
tag, lemma, ent_type = args
|
||
attributes[TAG] = self.vocab.strings[tag]
|
||
attributes[LEMMA] = self.vocab.strings[lemma]
|
||
attributes[ENT_TYPE] = self.vocab.strings[ent_type]
|
||
elif not args:
|
||
# TODO: This code makes little sense overall. We're still
|
||
# ignoring most of the attributes?
|
||
if "label" in attributes and 'ent_type' not in attributes:
|
||
if type(attributes["label"]) == int:
|
||
attributes[ENT_TYPE] = attributes["label"]
|
||
else:
|
||
attributes[ENT_TYPE] = self.vocab.strings[attributes["label"]]
|
||
if 'ent_type' in attributes:
|
||
attributes[ENT_TYPE] = attributes['ent_type']
|
||
elif args:
|
||
raise ValueError(
|
||
"Doc.merge received %d non-keyword arguments. "
|
||
"Expected either 3 arguments (deprecated), or 0 (use keyword arguments). "
|
||
"Arguments supplied:\n%s\n"
|
||
"Keyword arguments:%s\n" % (len(args), repr(args), repr(attributes)))
|
||
|
||
cdef int start = token_by_start(self.c, self.length, start_idx)
|
||
if start == -1:
|
||
return None
|
||
cdef int end = token_by_end(self.c, self.length, end_idx)
|
||
if end == -1:
|
||
return None
|
||
# Currently we have the token index, we want the range-end index
|
||
end += 1
|
||
cdef Span span = self[start:end]
|
||
tag = self.vocab.strings[attributes.get(TAG, span.root.tag)]
|
||
lemma = self.vocab.strings[attributes.get(LEMMA, span.root.lemma)]
|
||
ent_type = self.vocab.strings[attributes.get(ENT_TYPE, span.root.ent_type)]
|
||
ent_id = attributes.get('ent_id', span.root.ent_id)
|
||
if isinstance(ent_id, basestring):
|
||
ent_id = self.vocab.strings[ent_id]
|
||
|
||
# Get LexemeC for newly merged token
|
||
new_orth = ''.join([t.text_with_ws for t in span])
|
||
if span[-1].whitespace_:
|
||
new_orth = new_orth[:-len(span[-1].whitespace_)]
|
||
cdef const LexemeC* lex = self.vocab.get(self.mem, new_orth)
|
||
# House the new merged token where it starts
|
||
cdef TokenC* token = &self.c[start]
|
||
token.spacy = self.c[end-1].spacy
|
||
if tag in self.vocab.morphology.tag_map:
|
||
self.vocab.morphology.assign_tag(token, tag)
|
||
else:
|
||
token.tag = self.vocab.strings[tag]
|
||
token.lemma = self.vocab.strings[lemma]
|
||
if ent_type == 'O':
|
||
token.ent_iob = 2
|
||
token.ent_type = 0
|
||
else:
|
||
token.ent_iob = 3
|
||
token.ent_type = self.vocab.strings[ent_type]
|
||
token.ent_id = ent_id
|
||
# Begin by setting all the head indices to absolute token positions
|
||
# This is easier to work with for now than the offsets
|
||
# 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
|
||
|