spaCy/spacy/tokens/doc.pyx
2017-06-04 15:53:05 -05:00

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# coding: utf8
# cython: infer_types=True
# cython: bounds_check=False
from __future__ import unicode_literals
cimport cython
cimport numpy as np
import numpy
import numpy.linalg
import struct
import dill
from libc.string cimport memcpy, memset
from libc.math cimport sqrt
from .span cimport Span
from .token cimport Token
from .span cimport Span
from .token cimport Token
from .printers import parse_tree
from ..lexeme cimport Lexeme, EMPTY_LEXEME
from ..typedefs cimport attr_t, flags_t
from ..attrs import intify_attrs
from ..attrs cimport attr_id_t
from ..attrs cimport ID, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX, LENGTH, CLUSTER
from ..attrs cimport LENGTH, POS, LEMMA, TAG, DEP, HEAD, SPACY, ENT_IOB, ENT_TYPE
from ..attrs cimport SENT_START
from ..parts_of_speech cimport CCONJ, PUNCT, NOUN, univ_pos_t
from ..util import normalize_slice
from ..compat import is_config
from .. import about
from .. import util
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 == SENT_START:
return token.sent_start
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)
def _get_chunker(lang):
try:
cls = util.get_lang_class(lang)
except ImportError:
return None
except KeyError:
return None
return cls.Defaults.syntax_iterators.get(u'noun_chunks')
cdef class Doc:
"""A sequence of Token objects. Access sentences and named entities, export
annotations to numpy arrays, losslessly serialize to compressed binary strings.
The `Doc` object holds an array of `TokenC` structs. The Python-level
`Token` and `Span` objects are views of this array, i.e. they don't own
the data themselves.
EXAMPLE: Construction 1
>>> doc = nlp(u'Some text')
Construction 2
>>> from spacy.tokens import Doc
>>> doc = Doc(nlp.vocab, words=[u'hello', u'world', u'!'], spaces=[True, False, False])
"""
def __init__(self, Vocab vocab, words=None, spaces=None, orths_and_spaces=None):
"""Create a Doc object.
vocab (Vocab): A vocabulary object, which must match any models you want
to use (e.g. tokenizer, parser, entity recognizer).
words (list or None): A list of unicode strings to add to the document
as words. If `None`, defaults to empty list.
spaces (list or None): 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)`
RETURNS (Doc): The newly constructed object.
"""
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
self.c = data_start + PADDING
self.max_length = size
self.length = 0
self.is_tagged = False
self.is_parsed = False
self.sentiment = 0.0
self.user_hooks = {}
self.user_token_hooks = {}
self.user_span_hooks = {}
self.tensor = numpy.zeros((0,), dtype='float32')
self.user_data = {}
self._py_tokens = []
self._vector = None
self.noun_chunks_iterator = _get_chunker(self.vocab.lang)
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)
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)
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):
"""Get a `Token` or `Span` object.
i (int or tuple) The index of the token, or the slice of the document to get.
RETURNS (Token or Span): The token at `doc[i]]`, or the span at
`doc[start : end]`.
EXAMPLE:
>>> doc[i]
Get the `Token` object at position `i`, where `i` is an integer.
Negative indexing is supported, and follows the usual Python
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]`) 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.
"""
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:
return Token.cinit(self.vocab, &self.c[i], i, self)
def __iter__(self):
"""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 underlying C data directly from Cython.
EXAMPLE:
>>> for token in doc
"""
cdef int i
for i in range(self.length):
if self._py_tokens[i] is not None:
yield self._py_tokens[i]
else:
yield Token.cinit(self.vocab, &self.c[i], i, self)
def __len__(self):
"""The number of tokens in the document.
RETURNS (int): The number of tokens in the document.
EXAMPLE:
>>> len(doc)
"""
return self.length
def __unicode__(self):
return u''.join([t.text_with_ws for t in self])
def __bytes__(self):
return u''.join([t.text_with_ws for t in self]).encode('utf-8')
def __str__(self):
if is_config(python3=True):
return self.__unicode__()
return self.__bytes__()
def __repr__(self):
return self.__str__()
@property
def doc(self):
return self
def similarity(self, other):
"""Make a semantic similarity estimate. The default estimate is cosine
similarity using an average of word vectors.
other (object): The object to compare with. By default, accepts `Doc`,
`Span`, `Token` and `Lexeme` objects.
RETURNS (float): A scalar similarity score. Higher is more similar.
"""
if 'similarity' in self.user_hooks:
return self.user_hooks['similarity'](self, other)
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:
"""A boolean value indicating whether a word vector is associated with
the object.
RETURNS (bool): Whether a word vector is associated with the object.
"""
def __get__(self):
if 'has_vector' in self.user_hooks:
return self.user_hooks['has_vector'](self)
elif any(token.has_vector for token in self):
return True
elif self.tensor is not None:
return True
else:
return False
property vector:
"""A real-valued meaning representation. Defaults to an average of the
token vectors.
RETURNS (numpy.ndarray[ndim=1, dtype='float32']): A 1D numpy array
representing the document's semantics.
"""
def __get__(self):
if 'vector' in self.user_hooks:
return self.user_hooks['vector'](self)
if self._vector is not None:
return self._vector
elif self.has_vector and len(self):
self._vector = sum(t.vector for t in self) / len(self)
return self._vector
elif self.tensor is not None:
self._vector = self.tensor.mean(axis=0)
return self._vector
else:
return numpy.zeros((self.vocab.vectors_length,), dtype='float32')
def __set__(self, value):
self._vector = value
property vector_norm:
"""The L2 norm of the document's vector representation.
RETURNS (float): The L2 norm of the vector representation.
"""
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 text:
"""A unicode representation of the document text.
RETURNS (unicode): The original verbatim text of the document.
"""
def __get__(self):
return u''.join(t.text_with_ws for t in self)
property text_with_ws:
"""An alias of `Doc.text`, provided for duck-type compatibility with
`Span` and `Token`.
RETURNS (unicode): The original verbatim text of the document.
"""
def __get__(self):
return self.text
property ents:
"""Iterate over the entities in the document. Yields named-entity `Span`
objects, if the entity recognizer has been applied to the document.
YIELDS (Span): Entities in the document.
EXAMPLE: Iterate over the span to get individual Token objects, or access
the label:
>>> 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'
"""
def __get__(self):
cdef int i
cdef const TokenC* token
cdef int start = -1
cdef attr_t label = 0
output = []
for i in range(self.length):
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):
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
if ent_type is None or ent_type < 0:
# Mark as O
for i in range(start, end):
self.c[i].ent_type = 0
self.c[i].ent_iob = 2
else:
# Mark (inside) as I
for i in range(start, end):
self.c[i].ent_type = ent_type
self.c[i].ent_iob = 1
# Set start as B
self.c[start].ent_iob = 3
property noun_chunks:
"""Iterate over the base noun phrases in the document. 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 phrases, and no relative clauses.
YIELDS (Span): Noun chunks in the document.
"""
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__)
# 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:
"""Iterate over the sentences in the document. 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, the `sents` iterator will
be unavailable.
EXAMPLE:
>>> doc = nlp("This is a sentence. Here's another...")
>>> assert [s.root.text for s in doc.sents] == ["is", "'s"]
"""
def __get__(self):
if 'sents' in self.user_hooks:
yield from self.user_hooks['sents'](self)
return
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__)
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)
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):
"""Given a list of M attribute IDs, export the tokens to a numpy
`ndarray` of shape `(N, M)`, where `N` is the length of the document.
The values will be 32-bit integers.
attr_ids (list[int]): A list of attribute ID ints.
RETURNS (numpy.ndarray[long, ndim=2]): A feature matrix, with one row
per word, and one column per attribute indicated in the input
`attr_ids`.
EXAMPLE:
>>> from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
>>> doc = nlp(text)
>>> # All strings mapped to integers, for easy export to numpy
>>> np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
"""
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.uint64)
output = numpy.ndarray(shape=(self.length, len(attr_ids)), dtype=numpy.uint64)
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):
"""Count the frequencies of a given attribute. Produces a dict of
`{attribute (int): count (ints)}` frequencies, keyed by the values of
the given attribute ID.
attr_id (int): The attribute ID to key the counts.
RETURNS (dict): A dictionary mapping attributes to integer counts.
EXAMPLE:
>>> from spacy import attrs
>>> doc = 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]])
"""
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, array):
if SENT_START in attrs and HEAD in attrs:
raise ValueError(
"Conflicting attributes specified in doc.from_array():\n"
"(HEAD, SENT_START)\n"
"The HEAD attribute currently sets sentence boundaries implicitly,\n"
"based on the tree structure. This means the HEAD attribute would "
"potentially override the sentence boundaries set by SENT_START.\n"
"See https://github.com/spacy-io/spaCy/issues/235 for details and "
"workarounds, and to propose solutions.")
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_disk(self, path, **exclude):
"""Save the current state to a directory.
path (unicode or Path): A path to a directory, which will be created if
it doesn't exist. Paths may be either strings or `Path`-like objects.
"""
with path.open('wb') as file_:
file_.write(self.to_bytes(**exclude))
def from_disk(self, path, **exclude):
"""Loads state from a directory. Modifies the object in place and
returns it.
path (unicode or Path): A path to a directory. Paths may be either
strings or `Path`-like objects.
RETURNS (Doc): The modified `Doc` object.
"""
with path.open('rb') as file_:
bytes_data = file_.read()
self.from_bytes(bytes_data, **exclude)
def to_bytes(self, **exclude):
"""Serialize, i.e. export the document contents to a binary string.
RETURNS (bytes): A losslessly serialized copy of the `Doc`, including
all annotations.
"""
array_head = [LENGTH,SPACY,TAG,LEMMA,HEAD,DEP,ENT_IOB,ENT_TYPE]
serializers = {
'text': lambda: self.text,
'array_head': lambda: array_head,
'array_body': lambda: self.to_array(array_head),
'sentiment': lambda: self.sentiment,
'tensor': lambda: self.tensor,
'user_data': lambda: self.user_data
}
return util.to_bytes(serializers, exclude)
def from_bytes(self, bytes_data, **exclude):
"""Deserialize, i.e. import the document contents from a binary string.
data (bytes): The string to load from.
RETURNS (Doc): Itself.
"""
if self.length != 0:
raise ValueError("Cannot load into non-empty Doc")
deserializers = {
'text': lambda b: None,
'array_head': lambda b: None,
'array_body': lambda b: None,
'sentiment': lambda b: None,
'tensor': lambda b: None,
'user_data': lambda user_data: self.user_data.update(user_data)
}
msg = util.from_bytes(bytes_data, deserializers, exclude)
cdef attr_t[:, :] attrs
cdef int i, start, end, has_space
self.sentiment = msg['sentiment']
self.tensor = msg['tensor']
start = 0
cdef const LexemeC* lex
cdef unicode orth_
text = msg['text']
attrs = msg['array_body']
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(msg['array_head'][2:],
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.
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): 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] = tag
attributes[LEMMA] = lemma
attributes[ENT_TYPE] = ent_type
elif not args:
if "label" in attributes and 'ent_type' not in attributes:
if isinstance(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)))
# More deprecated attribute handling =/
if 'label' in attributes:
attributes['ent_type'] = attributes.pop('label')
attributes = intify_attrs(attributes, strings_map=self.vocab.strings)
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]
# 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
for attr_name, attr_value in attributes.items():
if attr_name == TAG:
self.vocab.morphology.assign_tag(token, attr_value)
else:
Token.set_struct_attr(token, attr_name, attr_value)
# 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]
def print_tree(self, light=False, flat=False):
"""Returns the parse trees in JSON (dict) format.
light (bool): Don't include lemmas or entities.
flat (bool): Don't include arcs or modifiers.
RETURNS (dict): Parse tree as dict.
EXAMPLE:
>>> doc = nlp('Bob brought Alice the pizza. Alice ate the pizza.')
>>> trees = doc.print_tree()
>>> trees[1]
{'modifiers': [
{'modifiers': [], 'NE': 'PERSON', 'word': 'Alice', 'arc': 'nsubj',
'POS_coarse': 'PROPN', 'POS_fine': 'NNP', 'lemma': 'Alice'},
{'modifiers': [
{'modifiers': [], 'NE': '', 'word': 'the', 'arc': 'det',
'POS_coarse': 'DET', 'POS_fine': 'DT', 'lemma': 'the'}],
'NE': '', 'word': 'pizza', 'arc': 'dobj', 'POS_coarse': 'NOUN',
'POS_fine': 'NN', 'lemma': 'pizza'},
{'modifiers': [], 'NE': '', 'word': '.', 'arc': 'punct',
'POS_coarse': 'PUNCT', 'POS_fine': '.', 'lemma': '.'}],
'NE': '', 'word': 'ate', 'arc': 'ROOT', 'POS_coarse': 'VERB',
'POS_fine': 'VBD', 'lemma': 'eat'}
"""
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