spaCy/spacy/tokens/doc.pyx

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# coding: utf8
from __future__ import unicode_literals
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cimport cython
cimport numpy as np
import numpy
import numpy.linalg
import struct
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from libc.string cimport memcpy, memset
from libc.stdint cimport uint32_t
from libc.math cimport sqrt
from .span cimport Span
from .token cimport Token
from ..lexeme cimport Lexeme
from ..lexeme cimport EMPTY_LEXEME
from ..typedefs cimport attr_t, flags_t
from ..attrs cimport attr_id_t
from ..attrs cimport ID, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX, LENGTH, CLUSTER
from ..attrs cimport POS, LEMMA, TAG, DEP, HEAD, SPACY, ENT_IOB, ENT_TYPE
from ..parts_of_speech cimport CCONJ, PUNCT, NOUN
from ..parts_of_speech cimport univ_pos_t
from ..lexeme cimport Lexeme
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from .span cimport Span
from .token cimport Token
from .printers import parse_tree
from ..serialize.bits cimport BitArray
from ..util import normalize_slice
from ..syntax.iterators import CHUNKERS
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from ..compat import is_config
from .. import about
DEF PADDING = 5
cdef int bounds_check(int i, int length, int padding) except -1:
if (i + padding) < 0:
raise IndexError
if (i - padding) >= length:
raise IndexError
cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) nogil:
if feat_name == LEMMA:
return token.lemma
elif feat_name == POS:
return token.pos
elif feat_name == TAG:
return token.tag
elif feat_name == DEP:
return token.dep
elif feat_name == HEAD:
return token.head
elif feat_name == SPACY:
return token.spacy
elif feat_name == ENT_IOB:
return token.ent_iob
elif feat_name == ENT_TYPE:
return token.ent_type
else:
return Lexeme.get_struct_attr(token.lex, feat_name)
cdef class Doc:
"""
A sequence of `Token` objects. Access sentences and named entities,
export annotations to numpy arrays, losslessly serialize to compressed
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binary strings.
Aside: Internals
The `Doc` object holds an array of `TokenC` structs.
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.
Code: Construction 1
doc = nlp.tokenizer(u'Some text')
Code: Construction 2
doc = Doc(nlp.vocab, orths_and_spaces=[(u'Some', True), (u'text', True)])
"""
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.
Aside: Implementation
This method of constructing a `Doc` object is usually only used
for deserialization. Standard usage is to construct the document via
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a call to the language object.
Arguments:
vocab:
A Vocabulary object, which must match any models you want to
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use (e.g. tokenizer, parser, entity recognizer).
words:
A list of unicode strings to add to the document as words. If None,
defaults to empty list.
spaces:
A list of boolean values, of the same length as words. True
means that the word is followed by a space, False means it is not.
If None, defaults to [True]*len(words)
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"""
self.vocab = vocab
size = 20
self.mem = Pool()
# Guarantee self.lex[i-x], for any i >= 0 and x < padding is in bounds
# However, we need to remember the true starting places, so that we can
# realloc.
data_start = <TokenC*>self.mem.alloc(size + (PADDING*2), sizeof(TokenC))
cdef int i
for i in range(size + (PADDING*2)):
data_start[i].lex = &EMPTY_LEXEME
data_start[i].l_edge = i
data_start[i].r_edge = i
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self.c = data_start + PADDING
self.max_length = size
self.length = 0
self.is_tagged = False
self.is_parsed = False
self.sentiment = 0.0
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self.user_hooks = {}
self.user_token_hooks = {}
self.user_span_hooks = {}
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self.tensor = numpy.zeros((0,), dtype='float32')
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self.user_data = {}
self._py_tokens = []
self._vector = None
self.noun_chunks_iterator = CHUNKERS.get(self.vocab.lang)
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cdef unicode orth
cdef bint has_space
if orths_and_spaces is None and words is not None:
if spaces is None:
spaces = [True] * len(words)
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elif len(spaces) != len(words):
raise ValueError(
"Arguments 'words' and 'spaces' should be sequences of the "
"same length, or 'spaces' should be left default at None. "
"spaces should be a sequence of booleans, with True meaning "
"that the word owns a ' ' character following it.")
orths_and_spaces = zip(words, spaces)
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if orths_and_spaces is not None:
for orth_space in orths_and_spaces:
if isinstance(orth_space, unicode):
orth = orth_space
has_space = True
elif isinstance(orth_space, bytes):
raise ValueError(
"orths_and_spaces expects either List(unicode) or "
"List((unicode, bool)). Got bytes instance: %s" % (str(orth_space)))
else:
orth, has_space = orth_space
# Note that we pass self.mem here --- we have ownership, if LexemeC
# must be created.
self.push_back(
<const LexemeC*>self.vocab.get(self.mem, orth), has_space)
# Tough to decide on policy for this. Is an empty doc tagged and parsed?
# There's no information we'd like to add to it, so I guess so?
if self.length == 0:
self.is_tagged = True
self.is_parsed = True
def __getitem__(self, object i):
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"""
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doc[i]
Get the Token object at position i, where i is an integer.
Negative indexing is supported, and follows the usual Python
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semantics, i.e. doc[-2] is doc[len(doc) - 2].
doc[start : end]]
Get a `Span` object, starting at position `start`
and ending at position `end`, where `start` and
`end` are token indices. For instance,
`doc[2:5]` produces a span consisting of
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).
You can use negative indices and open-ended ranges, which have their
normal Python semantics.
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"""
if isinstance(i, slice):
start, stop = normalize_slice(len(self), i.start, i.stop, i.step)
return Span(self, start, stop, label=0)
if i < 0:
i = self.length + i
bounds_check(i, self.length, PADDING)
if self._py_tokens[i] is not None:
return self._py_tokens[i]
else:
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return Token.cinit(self.vocab, &self.c[i], i, self)
def __iter__(self):
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"""
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for token in doc
Iterate over `Token` objects, from which the annotations can
be easily accessed. This is the main way of accessing Token
objects, which are the main way annotations are accessed from
Python. If faster-than-Python speeds are required, you can
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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"""
cdef int i
for i in range(self.length):
if self._py_tokens[i] is not None:
yield self._py_tokens[i]
else:
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yield Token.cinit(self.vocab, &self.c[i], i, self)
def __len__(self):
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"""
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len(doc)
The number of tokens in the document.
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"""
return self.length
def __unicode__(self):
return u''.join([t.text_with_ws for t in self])
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def __bytes__(self):
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__()
return self.__bytes__()
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def __repr__(self):
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return self.__str__()
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@property
def doc(self):
return self
def similarity(self, other):
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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.
Arguments:
other (object): The object to compare with. By default, accepts Doc,
Span, Token and Lexeme objects.
Return:
score (float): A scalar similarity score. Higher is more similar.
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"""
if 'similarity' in self.user_hooks:
return self.user_hooks['similarity'](self, other)
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if self.vector_norm == 0 or other.vector_norm == 0:
return 0.0
return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
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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"""
def __get__(self):
if 'has_vector' in self.user_hooks:
return self.user_hooks['has_vector'](self)
return any(token.has_vector for token in self)
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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"""
def __get__(self):
if 'vector' in self.user_hooks:
return self.user_hooks['vector'](self)
if self._vector is None:
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if len(self):
self._vector = sum(t.vector for t in self) / len(self)
else:
return numpy.zeros((self.vocab.vectors_length,), dtype='float32')
return self._vector
def __set__(self, value):
self._vector = value
property vector_norm:
def __get__(self):
if 'vector_norm' in self.user_hooks:
return self.user_hooks['vector_norm'](self)
cdef float value
cdef double norm = 0
if self._vector_norm is None:
norm = 0.0
for value in self.vector:
norm += value * value
self._vector_norm = sqrt(norm) if norm != 0 else 0
return self._vector_norm
def __set__(self, value):
self._vector_norm = value
@property
def string(self):
return self.text
property text:
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"""
A unicode representation of the document text.
"""
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def __get__(self):
return u''.join(t.text_with_ws for t in self)
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property text_with_ws:
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"""
An alias of Doc.text, provided for duck-type compatibility with Span and Token.
"""
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def __get__(self):
return self.text
property ents:
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"""
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Yields named-entity `Span` objects, if the entity recognizer
has been applied to the document. Iterate over the span to get
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individual Token objects, or access the label:
Example:
from spacy.en import English
nlp = English()
tokens = nlp(u'Mr. Best flew to New York on Saturday morning.')
ents = list(tokens.ents)
assert ents[0].label == 346
assert ents[0].label_ == 'PERSON'
assert ents[0].orth_ == 'Best'
assert ents[0].text == 'Mr. Best'
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"""
def __get__(self):
cdef int i
cdef const TokenC* token
cdef int start = -1
cdef int label = 0
output = []
for i in range(self.length):
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token = &self.c[i]
if token.ent_iob == 1:
assert start != -1
elif token.ent_iob == 2 or token.ent_iob == 0:
if start != -1:
output.append(Span(self, start, i, label=label))
start = -1
label = 0
elif token.ent_iob == 3:
if start != -1:
output.append(Span(self, start, i, label=label))
start = i
label = token.ent_type
if start != -1:
output.append(Span(self, start, self.length, label=label))
return tuple(output)
def __set__(self, ents):
# TODO:
# 1. Allow negative matches
# 2. Ensure pre-set NERs are not over-written during statistical prediction
# 3. Test basic data-driven ORTH gazetteer
# 4. Test more nuanced date and currency regex
cdef int i
for i in range(self.length):
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self.c[i].ent_type = 0
# At this point we don't know whether the NER has run over the
# Doc. If the ent_iob is missing, leave it missing.
if self.c[i].ent_iob != 0:
self.c[i].ent_iob = 2 # Means O. Non-O are set from ents.
cdef attr_t ent_type
cdef int start, end
for ent_info in ents:
if isinstance(ent_info, Span):
ent_id = ent_info.ent_id
ent_type = ent_info.label
start = ent_info.start
end = ent_info.end
elif len(ent_info) == 3:
ent_type, start, end = ent_info
else:
ent_id, ent_type, start, end = ent_info
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if ent_type is None or ent_type < 0:
# Mark as O
for i in range(start, end):
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self.c[i].ent_type = 0
self.c[i].ent_iob = 2
else:
# Mark (inside) as I
for i in range(start, end):
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self.c[i].ent_type = ent_type
self.c[i].ent_iob = 1
# 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
has been syntactically parsed. A base noun phrase, or
'NP chunk', is a noun phrase that does not permit other NPs to
be nested within it so no NP-level coordination, no prepositional
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phrases, and no relative clauses.
"""
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def __get__(self):
if not self.is_parsed:
raise ValueError(
"noun_chunks requires the dependency parse, which "
"requires data to be installed. For more info, see the "
"documentation: \n%s\n" % about.__docs_models__)
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# Accumulate the result before beginning to iterate over it. This prevents
# the tokenisation from being changed out from under us during the iteration.
# The tricky thing here is that Span accepts its tokenisation changing,
# so it's okay once we have the Span objects. See Issue #375
spans = []
for start, end, label in self.noun_chunks_iterator(self):
spans.append(Span(self, start, end, label=label))
for span in spans:
yield span
property sents:
"""
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Yields sentence `Span` objects. Sentence spans have no label.
To improve accuracy on informal texts, spaCy calculates sentence
boundaries from the syntactic dependency parse. If the parser is disabled,
`sents` iterator will be unavailable.
Example:
from spacy.en import English
nlp = English()
doc = nlp("This is a sentence. Here's another...")
assert [s.root.orth_ for s in doc.sents] == ["is", "'s"]
"""
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def __get__(self):
if 'sents' in self.user_hooks:
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yield from self.user_hooks['sents'](self)
return
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if not self.is_parsed:
raise ValueError(
"Sentence boundary detection requires the dependency parse, which "
"requires data to be installed. For more info, see the "
"documentation: \n%s\n" % about.__docs_models__)
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cdef int i
start = 0
for i in range(1, self.length):
if self.c[i].sent_start:
yield Span(self, start, i)
start = i
if start != self.length:
yield Span(self, start, self.length)
cdef int push_back(self, LexemeOrToken lex_or_tok, bint has_space) except -1:
if self.length == 0:
# Flip these to false when we see the first token.
self.is_tagged = False
self.is_parsed = False
if self.length == self.max_length:
self._realloc(self.length * 2)
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cdef TokenC* t = &self.c[self.length]
if LexemeOrToken is const_TokenC_ptr:
t[0] = lex_or_tok[0]
else:
t.lex = lex_or_tok
if self.length == 0:
t.idx = 0
else:
t.idx = (t-1).idx + (t-1).lex.length + (t-1).spacy
t.l_edge = self.length
t.r_edge = self.length
assert t.lex.orth != 0
t.spacy = has_space
self.length += 1
self._py_tokens.append(None)
return t.idx + t.lex.length + t.spacy
@cython.boundscheck(False)
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` of shape (N, M), where `N` is the length
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of the document. The values will be 32-bit integers.
Example:
from spacy import attrs
doc = nlp(text)
# All strings mapped to integers, for easy export to numpy
np_array = doc.to_array([attrs.LOWER, attrs.POS, attrs.ENT_TYPE, attrs.IS_ALPHA])
Arguments:
attr_ids (list[int]): A list of attribute ID ints.
Returns:
feat_array (numpy.ndarray[long, ndim=2]):
A feature matrix, with one row per word, and one column per attribute
indicated in the input attr_ids.
"""
cdef int i, j
cdef attr_id_t feature
cdef np.ndarray[attr_t, ndim=2] output
# Make an array from the attributes --- otherwise our inner loop is Python
# dict iteration.
cdef np.ndarray[attr_t, ndim=1] attr_ids = numpy.asarray(py_attr_ids, dtype=numpy.int32)
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):
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output[i, j] = get_token_attr(&self.c[i], feature)
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
by the values of the given attribute ID.
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Example:
from spacy.en import English
from spacy import attrs
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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
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if counts is None:
counts = PreshCounter()
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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):
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counts.inc(get_token_attr(&self.c[i], attr_id), 1)
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else:
for i in range(self.length):
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)
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.
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cdef TokenC* data_start = self.c - PADDING
data_start = <TokenC*>self.mem.realloc(data_start, n * sizeof(TokenC))
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self.c = data_start + PADDING
cdef int i
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 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):
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self.c[i] = parsed[i]
def from_array(self, attrs, array):
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"""
Write to a `Doc` object, from an `(M, N)` array of attributes.
"""
cdef int i, col
cdef attr_id_t attr_id
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cdef TokenC* tokens = self.c
cdef int length = len(array)
cdef attr_t[:] values
for col, attr_id in enumerate(attrs):
values = array[:, col]
if attr_id == HEAD:
for i in range(length):
tokens[i].head = values[i]
if values[i] >= 1:
tokens[i + values[i]].l_kids += 1
elif values[i] < 0:
tokens[i + values[i]].r_kids += 1
elif attr_id == TAG:
for i in range(length):
if values[i] != 0:
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self.vocab.morphology.assign_tag(&tokens[i], values[i])
elif attr_id == POS:
for i in range(length):
tokens[i].pos = <univ_pos_t>values[i]
elif attr_id == DEP:
for i in range(length):
tokens[i].dep = values[i]
elif attr_id == ENT_IOB:
for i in range(length):
tokens[i].ent_iob = values[i]
elif attr_id == ENT_TYPE:
for i in range(length):
tokens[i].ent_type = values[i]
else:
raise ValueError("Unknown attribute ID: %d" % attr_id)
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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 get_lca_matrix(self):
'''
Calculates the lowest common ancestor matrix
for a given Spacy doc.
Returns LCA matrix containing the integer index
of the ancestor, or -1 if no common ancestor is
found (ex if span excludes a necessary ancestor).
Apologies about the recursion, but the
impact on performance is negligible given
the natural limitations on the depth of a typical human sentence.
'''
# Efficiency notes:
#
# We can easily improve the performance here by iterating in Cython.
# To loop over the tokens in Cython, the easiest way is:
# for token in doc.c[:doc.c.length]:
# head = token + token.head
# Both token and head will be TokenC* here. The token.head attribute
# is an integer offset.
def __pairwise_lca(token_j, token_k, lca_matrix):
if lca_matrix[token_j.i][token_k.i] != -2:
return lca_matrix[token_j.i][token_k.i]
elif token_j == token_k:
lca_index = token_j.i
elif token_k.head == token_j:
lca_index = token_j.i
elif token_j.head == token_k:
lca_index = token_k.i
elif (token_j.head == token_j) and (token_k.head == token_k):
lca_index = -1
else:
lca_index = __pairwise_lca(token_j.head, token_k.head, lca_matrix)
lca_matrix[token_j.i][token_k.i] = lca_index
lca_matrix[token_k.i][token_j.i] = lca_index
return lca_index
lca_matrix = numpy.empty((len(self), len(self)), dtype=numpy.int32)
lca_matrix.fill(-2)
for j in range(len(self)):
token_j = self[j]
for k in range(j, len(self)):
token_k = self[k]
lca_matrix[j][k] = __pairwise_lca(token_j, token_k, lca_matrix)
lca_matrix[k][j] = lca_matrix[j][k]
return lca_matrix
def to_bytes(self):
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"""
Serialize, producing a byte string.
"""
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byte_string = self.vocab.serializer.pack(self)
cdef uint32_t length = len(byte_string)
return struct.pack('I', length) + byte_string
def from_bytes(self, data):
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"""
Deserialize, loading from bytes.
"""
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self.vocab.serializer.unpack_into(data[4:], self)
return self
@staticmethod
def read_bytes(file_):
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"""
A static method, used to read serialized #[code Doc] objects from
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a file. For example:
Example:
from spacy.tokens.doc import Doc
loc = 'test_serialize.bin'
with open(loc, 'wb') as file_:
file_.write(nlp(u'This is a document.').to_bytes())
file_.write(nlp(u'This is another.').to_bytes())
docs = []
with open(loc, 'rb') as file_:
for byte_string in Doc.read_bytes(file_):
docs.append(Doc(nlp.vocab).from_bytes(byte_string))
assert len(docs) == 2
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"""
keep_reading = True
while keep_reading:
try:
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n_bytes_str = file_.read(4)
if len(n_bytes_str) < 4:
break
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n_bytes = struct.unpack('I', n_bytes_str)[0]
data = file_.read(n_bytes)
except StopIteration:
keep_reading = False
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yield n_bytes_str + data
def merge(self, int start_idx, int end_idx, *args, **attributes):
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"""
Retokenize the document, such that the span at doc.text[start_idx : end_idx]
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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
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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]
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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)
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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
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cdef TokenC* token = &self.c[start]
token.spacy = self.c[end-1].spacy
if tag in self.vocab.morphology.tag_map:
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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):
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self.c[i].head += i
# Set the head of the merged token, and its dep relation, from the Span
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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):
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head_idx = self.c[i].head
if start <= head_idx < end:
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self.c[i].head = start
elif head_idx >= end:
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self.c[i].head -= offset
# Now compress the token array
for i in range(end, self.length):
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self.c[i - offset] = self.c[i]
for i in range(self.length - offset, self.length):
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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
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self.c[i].head -= i
# Set the left/right children, left/right edges
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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]
def print_tree(self, light=False, flat=False):
"""Returns the parse trees in the JSON (Dict) format."""
return parse_tree(self, light=light, flat=flat)
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