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spaCy/spacy/tokens/doc.pyx

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# cython: infer_types=True, bounds_check=False, profile=True
from typing import Set
cimport cython
cimport numpy as np
from libc.string cimport memcpy
from libc.math cimport sqrt
from libc.stdint cimport int32_t, uint64_t
import copy
from collections import Counter, defaultdict
from enum import Enum
import itertools
import numpy
import srsly
from thinc.api import get_array_module, get_current_ops
from thinc.util import copy_array
import warnings
from .span cimport Span
from .token cimport MISSING_DEP
from .span_groups import SpanGroups
from .token cimport Token
from ..lexeme cimport Lexeme, EMPTY_LEXEME
from ..typedefs cimport attr_t, flags_t
from ..attrs cimport attr_id_t
from ..attrs cimport LENGTH, POS, LEMMA, TAG, MORPH, DEP, HEAD, SPACY, ENT_IOB
from ..attrs cimport ENT_TYPE, ENT_ID, ENT_KB_ID, SENT_START, IDX, NORM
from ..attrs import intify_attr, IDS
from ..compat import copy_reg, pickle
from ..errors import Errors, Warnings
from ..morphology import Morphology
from .. import util
from .. import parts_of_speech
from .. import schemas
from .underscore import Underscore, get_ext_args
from .retokenizer import Retokenizer
from .doc_bin import ALL_ATTRS as DOCBIN_ALL_ATTRS
from ..util import get_words_and_spaces
DEF PADDING = 5
cdef int bounds_check(int i, int length, int padding) except -1:
if (i + padding) < 0:
raise IndexError(Errors.E026.format(i=i, length=length))
if (i - padding) >= length:
raise IndexError(Errors.E026.format(i=i, length=length))
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 == NORM:
if not token.norm:
return token.lex.norm
return token.norm
elif feat_name == POS:
return token.pos
elif feat_name == TAG:
return token.tag
elif feat_name == MORPH:
return token.morph
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
elif feat_name == ENT_ID:
return token.ent_id
elif feat_name == ENT_KB_ID:
return token.ent_kb_id
elif feat_name == IDX:
return token.idx
else:
return Lexeme.get_struct_attr(token.lex, feat_name)
cdef attr_t get_token_attr_for_matcher(const TokenC* token, attr_id_t feat_name) nogil:
if feat_name == SENT_START:
if token.sent_start == 1:
return True
else:
return False
else:
return get_token_attr(token, feat_name)
class SetEntsDefault(str, Enum):
blocked = "blocked"
missing = "missing"
outside = "outside"
unmodified = "unmodified"
@classmethod
def values(cls):
return list(cls.__members__.keys())
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=["hello", "world", "!"], spaces=[True, False, False])
DOCS: https://spacy.io/api/doc
"""
@classmethod
def set_extension(cls, name, **kwargs):
"""Define a custom attribute which becomes available as `Doc._`.
name (str): Name of the attribute to set.
default: Optional default value of the attribute.
getter (callable): Optional getter function.
setter (callable): Optional setter function.
method (callable): Optional method for method extension.
force (bool): Force overwriting existing attribute.
DOCS: https://spacy.io/api/doc#set_extension
USAGE: https://spacy.io/usage/processing-pipelines#custom-components-attributes
"""
if cls.has_extension(name) and not kwargs.get("force", False):
raise ValueError(Errors.E090.format(name=name, obj="Doc"))
Underscore.doc_extensions[name] = get_ext_args(**kwargs)
@classmethod
def get_extension(cls, name):
"""Look up a previously registered extension by name.
name (str): Name of the extension.
RETURNS (tuple): A `(default, method, getter, setter)` tuple.
DOCS: https://spacy.io/api/doc#get_extension
"""
return Underscore.doc_extensions.get(name)
@classmethod
def has_extension(cls, name):
"""Check whether an extension has been registered.
name (str): Name of the extension.
RETURNS (bool): Whether the extension has been registered.
DOCS: https://spacy.io/api/doc#has_extension
"""
return name in Underscore.doc_extensions
@classmethod
def remove_extension(cls, name):
"""Remove a previously registered extension.
name (str): Name of the extension.
RETURNS (tuple): A `(default, method, getter, setter)` tuple of the
removed extension.
DOCS: https://spacy.io/api/doc#remove_extension
"""
if not cls.has_extension(name):
raise ValueError(Errors.E046.format(name=name))
return Underscore.doc_extensions.pop(name)
def __init__(
self,
Vocab vocab,
words=None,
spaces=None,
*,
user_data=None,
tags=None,
pos=None,
morphs=None,
lemmas=None,
heads=None,
deps=None,
sent_starts=None,
ents=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 (Optional[List[Union[str, int]]]): A list of unicode strings or
hash values to add to the document as words. If `None`, defaults to
empty list.
spaces (Optional[List[bool]]): 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)`
user_data (dict or None): Optional extra data to attach to the Doc.
tags (Optional[List[str]]): A list of unicode strings, of the same
length as words, to assign as token.tag. Defaults to None.
pos (Optional[List[str]]): A list of unicode strings, of the same
length as words, to assign as token.pos. Defaults to None.
morphs (Optional[List[str]]): A list of unicode strings, of the same
length as words, to assign as token.morph. Defaults to None.
lemmas (Optional[List[str]]): A list of unicode strings, of the same
length as words, to assign as token.lemma. Defaults to None.
heads (Optional[List[int]]): A list of values, of the same length as
words, to assign as heads. Head indices are the position of the
head in the doc. Defaults to None.
deps (Optional[List[str]]): A list of unicode strings, of the same
length as words, to assign as token.dep. Defaults to None.
sent_starts (Optional[List[Union[bool, int, None]]]): A list of values,
of the same length as words, to assign as token.is_sent_start. Will
be overridden by heads if heads is provided. Defaults to None.
ents (Optional[List[str]]): A list of unicode strings, of the same
length as words, as IOB tags to assign as token.ent_iob and
token.ent_type. Defaults to None.
DOCS: https://spacy.io/api/doc#init
"""
self.vocab = vocab
size = max(20, (len(words) if words is not None else 0))
self.mem = Pool()
self.spans = SpanGroups(self)
# 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.
assert size + (PADDING*2) > 0
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.cats = {}
self.activations = {}
self.user_hooks = {}
self.user_token_hooks = {}
self.user_span_hooks = {}
self.tensor = numpy.zeros((0,), dtype="float32")
self.user_data = {} if user_data is None else user_data
self._vector = None
self.noun_chunks_iterator = self.vocab.get_noun_chunks
cdef bint has_space
if words is None and spaces is not None:
raise ValueError(Errors.E908)
elif spaces is None and words is not None:
self.has_unknown_spaces = True
else:
self.has_unknown_spaces = False
words = words if words is not None else []
spaces = spaces if spaces is not None else ([True] * len(words))
if len(spaces) != len(words):
raise ValueError(Errors.E027)
cdef const LexemeC* lexeme
for word, has_space in zip(words, spaces):
if isinstance(word, str):
lexeme = self.vocab.get(word)
elif isinstance(word, bytes):
raise ValueError(Errors.E028.format(value=word))
else:
try:
lexeme = self.vocab.get_by_orth(word)
except TypeError:
raise TypeError(Errors.E1022.format(wtype=type(word)))
self.push_back(lexeme, has_space)
if heads is not None:
heads = [head - i if head is not None else 0 for i, head in enumerate(heads)]
if deps is not None:
MISSING_DEP_ = self.vocab.strings[MISSING_DEP]
deps = [dep if dep is not None else MISSING_DEP_ for dep in deps]
if deps and not heads:
heads = [0] * len(deps)
if heads and not deps:
raise ValueError(Errors.E1017)
sent_starts = list(sent_starts) if sent_starts is not None else None
if sent_starts is not None:
for i in range(len(sent_starts)):
if sent_starts[i] is True:
sent_starts[i] = 1
elif sent_starts[i] is False:
sent_starts[i] = -1
elif sent_starts[i] is None or sent_starts[i] not in [-1, 0, 1]:
sent_starts[i] = 0
if pos is not None:
for pp in set(pos):
if pp not in parts_of_speech.IDS:
raise ValueError(Errors.E1021.format(pp=pp))
ent_iobs = None
ent_types = None
if ents is not None:
ents = [ent if ent != "" else None for ent in ents]
iob_strings = Token.iob_strings()
# make valid IOB2 out of IOB1 or IOB2
for i, ent in enumerate(ents):
if ent is not None and not isinstance(ent, str):
raise ValueError(Errors.E177.format(tag=ent))
if i < len(ents) - 1:
# OI -> OB
if (ent is None or ent.startswith("O")) and \
(ents[i+1] is not None and ents[i+1].startswith("I")):
ents[i+1] = "B" + ents[i+1][1:]
# B-TYPE1 I-TYPE2 or I-TYPE1 I-TYPE2 -> B/I-TYPE1 B-TYPE2
if ent is not None and ents[i+1] is not None and \
(ent.startswith("B") or ent.startswith("I")) and \
ents[i+1].startswith("I") and \
ent[1:] != ents[i+1][1:]:
ents[i+1] = "B" + ents[i+1][1:]
ent_iobs = []
ent_types = []
for ent in ents:
if ent is None:
ent_iobs.append(iob_strings.index(""))
ent_types.append("")
elif ent == "O":
ent_iobs.append(iob_strings.index(ent))
ent_types.append("")
else:
if len(ent) < 3 or ent[1] != "-":
raise ValueError(Errors.E177.format(tag=ent))
ent_iob, ent_type = ent.split("-", 1)
if ent_iob not in iob_strings:
raise ValueError(Errors.E177.format(tag=ent))
ent_iob = iob_strings.index(ent_iob)
ent_iobs.append(ent_iob)
ent_types.append(ent_type)
headings = []
values = []
annotations = [pos, heads, deps, lemmas, tags, morphs, sent_starts, ent_iobs, ent_types]
possible_headings = [POS, HEAD, DEP, LEMMA, TAG, MORPH, SENT_START, ENT_IOB, ENT_TYPE]
for a, annot in enumerate(annotations):
if annot is not None:
if len(annot) != len(words):
raise ValueError(Errors.E189)
headings.append(possible_headings[a])
if annot is not heads and annot is not sent_starts and annot is not ent_iobs:
values.extend(annot)
for value in values:
if value is not None:
self.vocab.strings.add(value)
# if there are any other annotations, set them
if headings:
attrs = self.to_array(headings)
j = 0
for annot in annotations:
if annot:
if annot is heads or annot is sent_starts or annot is ent_iobs:
annot = numpy.array(annot, dtype=numpy.int32).astype(numpy.uint64)
for i in range(len(words)):
if attrs.ndim == 1:
attrs[i] = annot[i]
else:
attrs[i, j] = annot[i]
elif annot is morphs:
for i in range(len(words)):
morph_key = vocab.morphology.add(morphs[i])
if attrs.ndim == 1:
attrs[i] = morph_key
else:
attrs[i, j] = morph_key
else:
for i in range(len(words)):
if attrs.ndim == 1:
attrs[i] = self.vocab.strings[annot[i]]
else:
attrs[i, j] = self.vocab.strings[annot[i]]
j += 1
self.from_array(headings, attrs)
@property
def _(self):
"""Custom extension attributes registered via `set_extension`."""
return Underscore(Underscore.doc_extensions, self)
@property
def is_tagged(self):
warnings.warn(Warnings.W107.format(prop="is_tagged", attr="TAG"), DeprecationWarning)
return self.has_annotation("TAG")
@property
def is_parsed(self):
warnings.warn(Warnings.W107.format(prop="is_parsed", attr="DEP"), DeprecationWarning)
return self.has_annotation("DEP")
@property
def is_nered(self):
warnings.warn(Warnings.W107.format(prop="is_nered", attr="ENT_IOB"), DeprecationWarning)
return self.has_annotation("ENT_IOB")
@property
def is_sentenced(self):
warnings.warn(Warnings.W107.format(prop="is_sentenced", attr="SENT_START"), DeprecationWarning)
return self.has_annotation("SENT_START")
def has_annotation(self, attr, *, require_complete=False):
"""Check whether the doc contains annotation on a token attribute.
attr (Union[int, str]): The attribute string name or int ID.
require_complete (bool): Whether to check that the attribute is set on
every token in the doc.
RETURNS (bool): Whether annotation is present.
DOCS: https://spacy.io/api/doc#has_annotation
"""
# empty docs are always annotated
input_attr = attr
if self.length == 0:
return True
cdef int i
cdef int range_start = 0
if attr == "IS_SENT_START" or attr == self.vocab.strings["IS_SENT_START"]:
attr = SENT_START
elif attr == "IS_SENT_END" or attr == self.vocab.strings["IS_SENT_END"]:
attr = SENT_START
attr = intify_attr(attr)
if attr is None:
raise ValueError(
Errors.E1037.format(attr=input_attr)
)
# adjust attributes
if attr == HEAD:
# HEAD does not have an unset state, so rely on DEP
attr = DEP
# special cases for sentence boundaries
if attr == SENT_START:
if "sents" in self.user_hooks:
return True
# docs of length 1 always have sentence boundaries
if self.length == 1:
return True
range_start = 1
if require_complete:
return all(Token.get_struct_attr(&self.c[i], attr) for i in range(range_start, self.length))
else:
return any(Token.get_struct_attr(&self.c[i], attr) for i in range(range_start, self.length))
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.
DOCS: https://spacy.io/api/doc#getitem
"""
if isinstance(i, slice):
start, stop = util.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)
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.
DOCS: https://spacy.io/api/doc#iter
"""
cdef int i
for i in range(self.length):
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.
DOCS: https://spacy.io/api/doc#len
"""
return self.length
def __unicode__(self):
return "".join([t.text_with_ws for t in self])
def __bytes__(self):
return "".join([t.text_with_ws for t in self]).encode("utf-8")
def __str__(self):
return self.__unicode__()
def __repr__(self):
return self.__str__()
@property
def doc(self):
return self
def char_span(self, int start_idx, int end_idx, label=0, kb_id=0, vector=None, alignment_mode="strict", span_id=0):
"""Create a `Span` object from the slice
`doc.text[start_idx : end_idx]`. Returns None if no valid `Span` can be
created.
doc (Doc): The parent document.
start_idx (int): The index of the first character of the span.
end_idx (int): The index of the first character after the span.
label (Union[int, str]): A label to attach to the Span, e.g. for
named entities.
kb_id (Union[int, str]): An ID from a KB to capture the meaning of a
named entity.
vector (ndarray[ndim=1, dtype='float32']): A meaning representation of
the span.
alignment_mode (str): How character indices are aligned to token
boundaries. Options: "strict" (character indices must be aligned
with token boundaries), "contract" (span of all tokens completely
within the character span), "expand" (span of all tokens at least
partially covered by the character span). Defaults to "strict".
span_id (Union[int, str]): An identifier to associate with the span.
RETURNS (Span): The newly constructed object.
DOCS: https://spacy.io/api/doc#char_span
"""
if not isinstance(label, int):
label = self.vocab.strings.add(label)
if not isinstance(kb_id, int):
kb_id = self.vocab.strings.add(kb_id)
alignment_modes = ("strict", "contract", "expand")
if alignment_mode not in alignment_modes:
raise ValueError(
Errors.E202.format(
name="alignment",
mode=alignment_mode,
modes=", ".join(alignment_modes),
)
)
cdef int start = token_by_char(self.c, self.length, start_idx)
if start < 0 or (alignment_mode == "strict" and start_idx != self[start].idx):
return None
# end_idx is exclusive, so find the token at one char before
cdef int end = token_by_char(self.c, self.length, end_idx - 1)
if end < 0 or (alignment_mode == "strict" and end_idx != self[end].idx + len(self[end])):
return None
# Adjust start and end by alignment_mode
if alignment_mode == "contract":
if self[start].idx < start_idx:
start += 1
if end_idx < self[end].idx + len(self[end]):
end -= 1
# if no tokens are completely within the span, return None
if end < start:
return None
elif alignment_mode == "expand":
# Don't consider the trailing whitespace to be part of the previous
# token
if start_idx == self[start].idx + len(self[start]):
start += 1
# Currently we have the token index, we want the range-end index
end += 1
cdef Span span = Span(self, start, end, label=label, kb_id=kb_id, span_id=span_id, vector=vector)
return span
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.
DOCS: https://spacy.io/api/doc#similarity
"""
if "similarity" in self.user_hooks:
return self.user_hooks["similarity"](self, other)
if isinstance(other, (Lexeme, Token)) and self.length == 1:
if self.c[0].lex.orth == other.orth:
return 1.0
elif isinstance(other, (Span, Doc)) and len(self) == len(other):
similar = True
for i in range(self.length):
if self[i].orth != other[i].orth:
similar = False
break
if similar:
return 1.0
if self.vocab.vectors.n_keys == 0:
warnings.warn(Warnings.W007.format(obj="Doc"))
if self.vector_norm == 0 or other.vector_norm == 0:
if not self.has_vector or not other.has_vector:
warnings.warn(Warnings.W008.format(obj="Doc"))
return 0.0
vector = self.vector
xp = get_array_module(vector)
result = xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm)
# ensure we get a scalar back (numpy does this automatically but cupy doesn't)
return result.item()
@property
def has_vector(self):
"""A boolean value indicating whether a word vector is associated with
the object.
RETURNS (bool): Whether a word vector is associated with the object.
DOCS: https://spacy.io/api/doc#has_vector
"""
if "has_vector" in self.user_hooks:
return self.user_hooks["has_vector"](self)
elif self.vocab.vectors.size:
return any(token.has_vector for token in self)
elif self.tensor.size:
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.
DOCS: https://spacy.io/api/doc#vector
"""
def __get__(self):
if "vector" in self.user_hooks:
return self.user_hooks["vector"](self)
if self._vector is not None:
return self._vector
xp = get_array_module(self.vocab.vectors.data)
if not len(self):
self._vector = xp.zeros((self.vocab.vectors_length,), dtype="f")
return self._vector
elif self.vocab.vectors.size > 0:
self._vector = sum(t.vector for t in self) / len(self)
return self._vector
elif self.tensor.size > 0:
self._vector = self.tensor.mean(axis=0)
return self._vector
else:
return xp.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.
DOCS: https://spacy.io/api/doc#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 text(self):
"""A unicode representation of the document text.
RETURNS (str): The original verbatim text of the document.
"""
return "".join(t.text_with_ws for t in self)
@property
def text_with_ws(self):
"""An alias of `Doc.text`, provided for duck-type compatibility with
`Span` and `Token`.
RETURNS (str): The original verbatim text of the document.
"""
return self.text
property ents:
"""The named entities in the document. Returns a tuple of named entity
`Span` objects, if the entity recognizer has been applied.
RETURNS (tuple): Entities in the document, one `Span` per entity.
DOCS: https://spacy.io/api/doc#ents
"""
def __get__(self):
cdef int i
cdef const TokenC* token
cdef int start = -1
cdef attr_t label = 0
cdef attr_t kb_id = 0
cdef attr_t ent_id = 0
output = []
for i in range(self.length):
token = &self.c[i]
if token.ent_iob == 1:
if start == -1:
seq = [f"{t.text}|{t.ent_iob_}" for t in self[i-5:i+5]]
raise ValueError(Errors.E093.format(seq=" ".join(seq)))
elif token.ent_iob == 2 or token.ent_iob == 0 or \
(token.ent_iob == 3 and token.ent_type == 0):
if start != -1:
output.append(Span(self, start, i, label=label, kb_id=kb_id, span_id=ent_id))
start = -1
label = 0
kb_id = 0
ent_id = 0
elif token.ent_iob == 3:
if start != -1:
output.append(Span(self, start, i, label=label, kb_id=kb_id, span_id=ent_id))
start = i
label = token.ent_type
kb_id = token.ent_kb_id
ent_id = token.ent_id
if start != -1:
output.append(Span(self, start, self.length, label=label, kb_id=kb_id, span_id=ent_id))
# remove empty-label spans
output = [o for o in output if o.label_ != ""]
return tuple(output)
def __set__(self, ents):
# TODO:
# 1. Test basic data-driven ORTH gazetteer
# 2. Test more nuanced date and currency regex
cdef attr_t entity_type, kb_id, ent_id
cdef int ent_start, ent_end
ent_spans = []
for ent_info in ents:
entity_type_, kb_id, ent_start, ent_end, ent_id = get_entity_info(ent_info)
if isinstance(entity_type_, str):
self.vocab.strings.add(entity_type_)
span = Span(self, ent_start, ent_end, label=entity_type_, kb_id=kb_id, span_id=ent_id)
ent_spans.append(span)
self.set_ents(ent_spans, default=SetEntsDefault.outside)
def set_ents(self, entities, *, blocked=None, missing=None, outside=None, default=SetEntsDefault.outside):
"""Set entity annotation.
entities (List[Span]): Spans with labels to set as entities.
blocked (Optional[List[Span]]): Spans to set as 'blocked' (never an
entity) for spacy's built-in NER component. Other components may
ignore this setting.
missing (Optional[List[Span]]): Spans with missing/unknown entity
information.
outside (Optional[List[Span]]): Spans outside of entities (O in IOB).
default (str): How to set entity annotation for tokens outside of any
provided spans. Options: "blocked", "missing", "outside" and
"unmodified" (preserve current state). Defaults to "outside".
"""
if default not in SetEntsDefault.values():
raise ValueError(Errors.E1011.format(default=default, modes=", ".join(SetEntsDefault)))
# Ignore spans with missing labels
entities = [ent for ent in entities if ent.label > 0]
if blocked is None:
blocked = tuple()
if missing is None:
missing = tuple()
if outside is None:
outside = tuple()
# Find all tokens covered by spans and check that none are overlapping
cdef int i
seen_tokens = set()
for span in itertools.chain.from_iterable([entities, blocked, missing, outside]):
if not isinstance(span, Span):
raise ValueError(Errors.E1012.format(span=span))
for i in range(span.start, span.end):
if i in seen_tokens:
raise ValueError(Errors.E1010.format(i=i))
seen_tokens.add(i)
# Set all specified entity information
for span in entities:
for i in range(span.start, span.end):
if i == span.start:
self.c[i].ent_iob = 3
else:
self.c[i].ent_iob = 1
self.c[i].ent_type = span.label
self.c[i].ent_kb_id = span.kb_id
self.c[i].ent_id = span.id
for span in blocked:
for i in range(span.start, span.end):
self.c[i].ent_iob = 3
self.c[i].ent_type = 0
self.c[i].ent_kb_id = 0
self.c[i].ent_id = 0
for span in missing:
for i in range(span.start, span.end):
self.c[i].ent_iob = 0
self.c[i].ent_type = 0
self.c[i].ent_kb_id = 0
self.c[i].ent_id = 0
for span in outside:
for i in range(span.start, span.end):
self.c[i].ent_iob = 2
self.c[i].ent_type = 0
self.c[i].ent_kb_id = 0
self.c[i].ent_id = 0
# Set tokens outside of all provided spans
if default != SetEntsDefault.unmodified:
for i in range(self.length):
if i not in seen_tokens:
self.c[i].ent_type = 0
self.c[i].ent_kb_id = 0
self.c[i].ent_id = 0
if default == SetEntsDefault.outside:
self.c[i].ent_iob = 2
elif default == SetEntsDefault.missing:
self.c[i].ent_iob = 0
elif default == SetEntsDefault.blocked:
self.c[i].ent_iob = 3
# Fix any resulting inconsistent annotation
for i in range(self.length - 1):
# I must follow B or I: convert I to B
if (self.c[i].ent_iob == 0 or self.c[i].ent_iob == 2) and \
self.c[i+1].ent_iob == 1:
self.c[i+1].ent_iob = 3
# Change of type with BI or II: convert second I to B
if self.c[i].ent_type != self.c[i+1].ent_type and \
(self.c[i].ent_iob == 3 or self.c[i].ent_iob == 1) and \
self.c[i+1].ent_iob == 1:
self.c[i+1].ent_iob = 3
@property
def noun_chunks(self):
"""Iterate over the base noun phrases in the document. Yields base
noun-phrase #[code Span] objects, if the language has a noun chunk iterator.
Raises a NotImplementedError otherwise.
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.
DOCS: https://spacy.io/api/doc#noun_chunks
"""
if self.noun_chunks_iterator is None:
raise NotImplementedError(Errors.E894.format(lang=self.vocab.lang))
# Accumulate the result before beginning to iterate over it. This
# prevents the tokenization from being changed out from under us
# during the iteration. The tricky thing here is that Span accepts
# its tokenization 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
def sents(self):
"""Iterate over the sentences in the document. Yields sentence `Span`
objects. Sentence spans have no label.
YIELDS (Span): Sentences in the document.
DOCS: https://spacy.io/api/doc#sents
"""
if not self.has_annotation("SENT_START"):
raise ValueError(Errors.E030)
if "sents" in self.user_hooks:
yield from self.user_hooks["sents"](self)
else:
start = 0
for i in range(1, self.length):
if self.c[i].sent_start == 1:
yield Span(self, start, i)
start = i
if start != self.length:
yield Span(self, start, self.length)
@property
def lang(self):
"""RETURNS (uint64): ID of the language of the doc's vocabulary."""
return self.vocab.strings[self.vocab.lang]
@property
def lang_(self):
"""RETURNS (str): Language of the doc's vocabulary, e.g. 'en'."""
return self.vocab.lang
cdef int push_back(self, LexemeOrToken lex_or_tok, bint has_space) except -1:
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
if t.lex.orth == 0:
raise ValueError(Errors.E031.format(i=self.length))
t.spacy = has_space
self.length += 1
if self.length == 1:
# Set token.sent_start to 1 for first token. See issue #2869
self.c[0].sent_start = 1
return t.idx + t.lex.length + t.spacy
@cython.boundscheck(False)
cpdef np.ndarray to_array(self, object py_attr_ids):
"""Export given token attributes to a numpy `ndarray`.
If `attr_ids` is a sequence of M attributes, the output array will be
of shape `(N, M)`, where N is the length of the `Doc` (in tokens). If
`attr_ids` is a single attribute, the output shape will be (N,). You
can specify attributes by integer ID (e.g. spacy.attrs.LEMMA) or
string name (e.g. 'LEMMA' or 'lemma').
py_attr_ids (list[]): A list of attributes (int IDs or string names).
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
# Handle scalar/list inputs of strings/ints for py_attr_ids
# See also #3064
if isinstance(py_attr_ids, str):
# Handle inputs like doc.to_array('ORTH')
py_attr_ids = [py_attr_ids]
elif not hasattr(py_attr_ids, "__iter__"):
# Handle inputs like doc.to_array(ORTH)
py_attr_ids = [py_attr_ids]
# Allow strings, e.g. 'lemma' or 'LEMMA'
try:
py_attr_ids = [(IDS[id_.upper()] if hasattr(id_, "upper") else id_)
for id_ in py_attr_ids]
except KeyError as msg:
keys = list(IDS.keys())
raise KeyError(Errors.E983.format(dict="IDS", key=msg, keys=keys)) from None
# Make an array from the attributes --- otherwise our inner loop is
# Python dict iteration.
cdef Pool mem = Pool()
cdef int n_attrs = len(py_attr_ids)
cdef attr_id_t* c_attr_ids
if n_attrs > 0:
c_attr_ids = <attr_id_t*>mem.alloc(n_attrs, sizeof(attr_id_t))
for i, attr_id in enumerate(py_attr_ids):
c_attr_ids[i] = attr_id
output = numpy.ndarray(shape=(self.length, n_attrs), dtype=numpy.uint64)
c_output = <attr_t*>output.data
cdef TokenC* token
for i in range(self.length):
token = &self.c[i]
for j in range(n_attrs):
c_output[i*n_attrs + j] = get_token_attr(token, c_attr_ids[j])
# Handle 1d case
return output if n_attrs >= 2 else output.reshape((self.length,))
def count_by(self, attr_id_t attr_id, exclude=None, object 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.
DOCS: https://spacy.io/api/doc#count_by
"""
cdef int i
cdef attr_t attr
cdef size_t count
if counts is None:
counts = Counter()
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[get_token_attr(&self.c[i], attr_id)] += 1
else:
for i in range(self.length):
if not exclude(self[i]):
counts[get_token_attr(&self.c[i], attr_id)] += 1
if output_dict:
return dict(counts)
def _realloc(self, new_size):
if new_size < self.max_length:
return
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
def from_array(self, attrs, array):
"""Load attributes from a numpy array. Write to a `Doc` object, from an
`(M, N)` array of attributes.
attrs (list) A list of attribute ID ints.
array (numpy.ndarray[ndim=2, dtype='int32']): The attribute values.
RETURNS (Doc): Itself.
DOCS: https://spacy.io/api/doc#from_array
"""
# Handle scalar/list inputs of strings/ints for py_attr_ids
# See also #3064
if isinstance(attrs, str):
# Handle inputs like doc.to_array('ORTH')
attrs = [attrs]
elif not hasattr(attrs, "__iter__"):
# Handle inputs like doc.to_array(ORTH)
attrs = [attrs]
# Allow strings, e.g. 'lemma' or 'LEMMA'
attrs = [(IDS[id_.upper()] if hasattr(id_, "upper") else id_)
for id_ in attrs]
if array.dtype != numpy.uint64:
warnings.warn(Warnings.W028.format(type=array.dtype))
cdef int i, col
cdef int32_t abs_head_index
cdef attr_id_t attr_id
cdef TokenC* tokens = self.c
cdef int length = len(array)
if length != len(self):
raise ValueError(Errors.E971.format(array_length=length, doc_length=len(self)))
# Get set up for fast loading
cdef Pool mem = Pool()
cdef int n_attrs = len(attrs)
# attrs should not be empty, but make sure to avoid zero-length mem alloc
assert n_attrs > 0
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
if len(array.shape) == 1:
array = array.reshape((array.size, 1))
cdef np.ndarray transposed_array = numpy.ascontiguousarray(array.T)
values = <const uint64_t*>transposed_array.data
stride = transposed_array.shape[1]
# Check that all heads are within the document bounds
if HEAD in attrs:
col = attrs.index(HEAD)
for i in range(length):
# cast index to signed int
abs_head_index = <int32_t>values[col * stride + i]
abs_head_index += i
if abs_head_index < 0 or abs_head_index >= length:
raise ValueError(
Errors.E190.format(
index=i,
value=array[i, col],
rel_head_index=abs_head_index-i
)
)
# Verify ENT_IOB are proper integers
if ENT_IOB in attrs:
iob_strings = Token.iob_strings()
col = attrs.index(ENT_IOB)
n_iob_strings = len(iob_strings)
for i in range(length):
value = values[col * stride + i]
if value < 0 or value >= n_iob_strings:
raise ValueError(
Errors.E982.format(
values=iob_strings,
value=value
)
)
# Now load the data
for i in range(length):
token = &self.c[i]
for j in range(n_attrs):
value = values[j * stride + i]
if attr_ids[j] == MORPH:
# add morph to morphology table
self.vocab.morphology.add(self.vocab.strings[value])
Token.set_struct_attr(token, attr_ids[j], value)
# If document is parsed, set children and sentence boundaries
if HEAD in attrs and DEP in attrs:
col = attrs.index(DEP)
if array[:, col].any():
set_children_from_heads(self.c, 0, length)
return self
@staticmethod
def from_docs(docs, ensure_whitespace=True, attrs=None, *, exclude=tuple()):
"""Concatenate multiple Doc objects to form a new one. Raises an error
if the `Doc` objects do not all share the same `Vocab`.
docs (list): A list of Doc objects.
ensure_whitespace (bool): Insert a space between two adjacent docs
whenever the first doc does not end in whitespace.
attrs (list): Optional list of attribute ID ints or attribute name
strings.
exclude (Iterable[str]): Doc attributes to exclude. Supported
attributes: `spans`, `tensor`, `user_data`.
RETURNS (Doc): A doc that contains the concatenated docs, or None if no
docs were given.
DOCS: https://spacy.io/api/doc#from_docs
"""
if not docs:
return None
vocab = {doc.vocab for doc in docs}
if len(vocab) > 1:
raise ValueError(Errors.E999)
(vocab,) = vocab
if attrs is None:
attrs = list(Doc._get_array_attrs())
else:
if any(isinstance(attr, str) for attr in attrs): # resolve attribute names
attrs = [intify_attr(attr) for attr in attrs] # intify_attr returns None for invalid attrs
attrs = list(attr for attr in set(attrs) if attr) # filter duplicates, remove None if present
if SPACY not in attrs:
attrs.append(SPACY)
concat_words = []
concat_spaces = []
concat_user_data = {}
concat_spans = defaultdict(list)
char_offset = 0
for doc in docs:
concat_words.extend(t.text for t in doc)
concat_spaces.extend(bool(t.whitespace_) for t in doc)
if "user_data" not in exclude:
for key, value in doc.user_data.items():
if isinstance(key, tuple) and len(key) >= 4 and key[0] == "._.":
data_type = key[0]
name = key[1]
start = key[2]
end = key[3]
if start is not None or end is not None:
start += char_offset
if end is not None:
end += char_offset
_label = key[4]
_kb_id = key[5]
_span_id = key[6]
concat_user_data[(data_type, name, start, end, _label, _kb_id, _span_id)] = copy.copy(value)
else:
concat_user_data[(data_type, name, start, end)] = copy.copy(value)
else:
warnings.warn(Warnings.W101.format(name=name))
else:
warnings.warn(Warnings.W102.format(key=key, value=value))
if "spans" not in exclude:
for key in doc.spans:
# if a spans key is in any doc, include it in the merged doc
# even if it is empty
if key not in concat_spans:
concat_spans[key] = []
for span in doc.spans[key]:
concat_spans[key].append((
span.start_char + char_offset,
span.end_char + char_offset,
span.label,
span.kb_id,
span.id,
span.text, # included as a check
))
char_offset += len(doc.text)
if len(doc) > 0 and ensure_whitespace and not doc[-1].is_space and not bool(doc[-1].whitespace_):
char_offset += 1
arrays = [doc.to_array(attrs) for doc in docs]
if ensure_whitespace:
spacy_index = attrs.index(SPACY)
for i, array in enumerate(arrays[:-1]):
if len(array) > 0 and not docs[i][-1].is_space:
array[-1][spacy_index] = 1
if len(concat_spaces) > 0:
token_offset = -1
for doc in docs[:-1]:
token_offset += len(doc)
if len(doc) > 0 and not doc[-1].is_space:
concat_spaces[token_offset] = True
concat_array = numpy.concatenate(arrays)
concat_doc = Doc(vocab, words=concat_words, spaces=concat_spaces, user_data=concat_user_data)
concat_doc.from_array(attrs, concat_array)
for key in concat_spans:
if key not in concat_doc.spans:
concat_doc.spans[key] = []
for span_tuple in concat_spans[key]:
span = concat_doc.char_span(
span_tuple[0],
span_tuple[1],
label=span_tuple[2],
kb_id=span_tuple[3],
span_id=span_tuple[4],
)
text = span_tuple[5]
if span is not None and span.text == text:
concat_doc.spans[key].append(span)
else:
raise ValueError(Errors.E873.format(key=key, text=text))
if "tensor" not in exclude and any(len(doc) for doc in docs):
ops = get_current_ops()
concat_doc.tensor = ops.xp.vstack([ops.asarray(doc.tensor) for doc in docs if len(doc)])
return concat_doc
def get_lca_matrix(self):
"""Calculates a matrix of Lowest Common Ancestors (LCA) for a given
`Doc`, where LCA[i, j] is the index of the lowest common ancestor among
token i and j.
RETURNS (np.array[ndim=2, dtype=numpy.int32]): LCA matrix with shape
(n, n), where n = len(self).
DOCS: https://spacy.io/api/doc#get_lca_matrix
"""
return numpy.asarray(_get_lca_matrix(self, 0, len(self)))
def copy(self):
cdef Doc other = Doc(self.vocab)
other._vector = copy.deepcopy(self._vector)
other._vector_norm = copy.deepcopy(self._vector_norm)
other.tensor = copy.deepcopy(self.tensor)
other.cats = copy.deepcopy(self.cats)
other.user_data = copy.deepcopy(self.user_data)
other.has_unknown_spaces = self.has_unknown_spaces
other.user_hooks = dict(self.user_hooks)
other.user_token_hooks = dict(self.user_token_hooks)
other.user_span_hooks = dict(self.user_span_hooks)
other.length = self.length
other.max_length = self.max_length
other.spans = self.spans.copy(doc=other)
buff_size = other.max_length + (PADDING*2)
assert buff_size > 0
tokens = <TokenC*>other.mem.alloc(buff_size, sizeof(TokenC))
memcpy(tokens, self.c - PADDING, buff_size * sizeof(TokenC))
other.c = &tokens[PADDING]
return other
def to_disk(self, path, *, exclude=tuple()):
"""Save the current state to a directory.
path (str / Path): A path to a directory, which will be created if
it doesn't exist. Paths may be either strings or Path-like objects.
exclude (Iterable[str]): String names of serialization fields to exclude.
DOCS: https://spacy.io/api/doc#to_disk
"""
path = util.ensure_path(path)
with path.open("wb") as file_:
file_.write(self.to_bytes(exclude=exclude))
def from_disk(self, path, *, exclude=tuple()):
"""Loads state from a directory. Modifies the object in place and
returns it.
path (str / Path): A path to a directory. Paths may be either
strings or `Path`-like objects.
exclude (list): String names of serialization fields to exclude.
RETURNS (Doc): The modified `Doc` object.
DOCS: https://spacy.io/api/doc#from_disk
"""
path = util.ensure_path(path)
with path.open("rb") as file_:
bytes_data = file_.read()
return self.from_bytes(bytes_data, exclude=exclude)
def to_bytes(self, *, exclude=tuple()):
"""Serialize, i.e. export the document contents to a binary string.
exclude (list): String names of serialization fields to exclude.
RETURNS (bytes): A losslessly serialized copy of the `Doc`, including
all annotations.
DOCS: https://spacy.io/api/doc#to_bytes
"""
return srsly.msgpack_dumps(self.to_dict(exclude=exclude))
def from_bytes(self, bytes_data, *, exclude=tuple()):
"""Deserialize, i.e. import the document contents from a binary string.
data (bytes): The string to load from.
exclude (list): String names of serialization fields to exclude.
RETURNS (Doc): Itself.
DOCS: https://spacy.io/api/doc#from_bytes
"""
return self.from_dict(srsly.msgpack_loads(bytes_data), exclude=exclude)
def to_dict(self, *, exclude=tuple()):
"""Export the document contents to a dictionary for serialization.
exclude (list): String names of serialization fields to exclude.
RETURNS (bytes): A losslessly serialized copy of the `Doc`, including
all annotations.
DOCS: https://spacy.io/api/doc#to_bytes
"""
array_head = Doc._get_array_attrs()
strings = set()
for token in self:
strings.add(token.tag_)
strings.add(token.lemma_)
strings.add(str(token.morph))
strings.add(token.dep_)
strings.add(token.ent_type_)
strings.add(token.ent_kb_id_)
strings.add(token.ent_id_)
strings.add(token.norm_)
for group in self.spans.values():
for span in group:
strings.add(span.label_)
# Msgpack doesn't distinguish between lists and tuples, which is
# vexing for user data. As a best guess, we *know* that within
# keys, we must have tuples. In values we just have to hope
# users don't mind getting a list instead of a tuple.
serializers = {
"text": lambda: self.text,
"array_head": lambda: array_head,
"array_body": lambda: self.to_array(array_head),
"tensor": lambda: self.tensor,
"cats": lambda: self.cats,
"spans": lambda: self.spans.to_bytes(),
"strings": lambda: list(strings),
"has_unknown_spaces": lambda: self.has_unknown_spaces
}
if "user_data" not in exclude and self.user_data:
user_data_keys, user_data_values = list(zip(*self.user_data.items()))
if "user_data_keys" not in exclude:
serializers["user_data_keys"] = lambda: srsly.msgpack_dumps(user_data_keys)
if "user_data_values" not in exclude:
serializers["user_data_values"] = lambda: srsly.msgpack_dumps(user_data_values)
if "user_hooks" not in exclude and any((self.user_hooks, self.user_token_hooks, self.user_span_hooks)):
warnings.warn(Warnings.W109)
return util.to_dict(serializers, exclude)
def from_dict(self, msg, *, exclude=tuple()):
"""Deserialize, i.e. import the document contents from a binary string.
data (bytes): The string to load from.
exclude (list): String names of serialization fields to exclude.
RETURNS (Doc): Itself.
DOCS: https://spacy.io/api/doc#from_dict
"""
if self.length != 0:
raise ValueError(Errors.E033.format(length=self.length))
# Msgpack doesn't distinguish between lists and tuples, which is
# vexing for user data. As a best guess, we *know* that within
# keys, we must have tuples. In values we just have to hope
# users don't mind getting a list instead of a tuple.
if "user_data" not in exclude and "user_data_keys" in msg:
user_data_keys = srsly.msgpack_loads(msg["user_data_keys"], use_list=False)
user_data_values = srsly.msgpack_loads(msg["user_data_values"])
for key, value in zip(user_data_keys, user_data_values):
self.user_data[key] = value
cdef int i, start, end, has_space
if "tensor" not in exclude and "tensor" in msg:
self.tensor = msg["tensor"]
if "cats" not in exclude and "cats" in msg:
self.cats = msg["cats"]
if "strings" not in exclude and "strings" in msg:
for s in msg["strings"]:
self.vocab.strings.add(s)
if "has_unknown_spaces" not in exclude and "has_unknown_spaces" in msg:
self.has_unknown_spaces = msg["has_unknown_spaces"]
start = 0
cdef const LexemeC* lex
cdef str 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(orth_)
self.push_back(lex, has_space)
start = end + has_space
self.from_array(msg["array_head"][2:], attrs[:, 2:])
if "spans" in msg:
self.spans.from_bytes(msg["spans"])
else:
self.spans.clear()
return self
def extend_tensor(self, tensor):
"""Concatenate a new tensor onto the doc.tensor object.
The doc.tensor attribute holds dense feature vectors
computed by the models in the pipeline. Let's say a
document with 30 words has a tensor with 128 dimensions
per word. doc.tensor.shape will be (30, 128). After
calling doc.extend_tensor with an array of shape (30, 64),
doc.tensor == (30, 192).
"""
xp = get_array_module(self.tensor)
if self.tensor.size == 0:
self.tensor.resize(tensor.shape, refcheck=False)
copy_array(self.tensor, tensor)
else:
self.tensor = xp.hstack((self.tensor, tensor))
def retokenize(self):
"""Context manager to handle retokenization of the Doc.
Modifications to the Doc's tokenization are stored, and then
made all at once when the context manager exits. This is
much more efficient, and less error-prone.
All views of the Doc (Span and Token) created before the
retokenization are invalidated, although they may accidentally
continue to work.
DOCS: https://spacy.io/api/doc#retokenize
USAGE: https://spacy.io/usage/linguistic-features#retokenization
"""
return Retokenizer(self)
def _bulk_merge(self, spans, attributes):
"""Retokenize the document, such that the spans given as arguments
are merged into single tokens. The spans need to be in document
order, and no span intersection is allowed.
spans (Span[]): Spans to merge, in document order, with all span
intersections empty. Cannot be empty.
attributes (Dictionary[]): Attributes to assign to the merged tokens. By default,
must be the same length as spans, empty dictionaries are allowed.
attributes are inherited from the syntactic root of the span.
RETURNS (Token): The first newly merged token.
"""
cdef str tag, lemma, ent_type
attr_len = len(attributes)
span_len = len(spans)
if not attr_len == span_len:
raise ValueError(Errors.E121.format(attr_len=attr_len, span_len=span_len))
with self.retokenize() as retokenizer:
for i, span in enumerate(spans):
fix_attributes(self, attributes[i])
remove_label_if_necessary(attributes[i])
retokenizer.merge(span, attributes[i])
def from_json(self, doc_json, *, validate=False):
"""Convert a JSON document generated by Doc.to_json() to a Doc.
doc_json (Dict): JSON representation of doc object to load.
validate (bool): Whether to validate `doc_json` against the expected schema.
Defaults to False.
RETURNS (Doc): A doc instance corresponding to the specified JSON representation.
"""
if validate:
schema_validation_message = schemas.validate(schemas.DocJSONSchema, doc_json)
if schema_validation_message:
raise ValueError(Errors.E1038.format(message=schema_validation_message))
### Token-level properties ###
words = []
token_attrs_ids = (POS, HEAD, DEP, LEMMA, TAG, MORPH)
# Map annotation type IDs to their string equivalents.
token_attrs = {t: self.vocab.strings[t].lower() for t in token_attrs_ids}
token_annotations = {}
# Gather token-level properties.
for token_json in doc_json["tokens"]:
words.append(doc_json["text"][token_json["start"]:token_json["end"]])
for attr, attr_json in token_attrs.items():
if attr_json in token_json:
if token_json["id"] == 0 and attr not in token_annotations:
token_annotations[attr] = []
elif attr not in token_annotations:
raise ValueError(Errors.E1040.format(partial_attrs=attr))
token_annotations[attr].append(token_json[attr_json])
# Initialize doc instance.
start = 0
cdef const LexemeC* lex
cdef bint has_space
reconstructed_words, spaces = get_words_and_spaces(words, doc_json["text"])
assert words == reconstructed_words
for word, has_space in zip(words, spaces):
lex = self.vocab.get(word)
self.push_back(lex, has_space)
# Set remaining token-level attributes via Doc.from_array().
if HEAD in token_annotations:
token_annotations[HEAD] = [
head - i for i, head in enumerate(token_annotations[HEAD])
]
if DEP in token_annotations and HEAD not in token_annotations:
token_annotations[HEAD] = [0] * len(token_annotations[DEP])
if HEAD in token_annotations and DEP not in token_annotations:
raise ValueError(Errors.E1017)
if POS in token_annotations:
for pp in set(token_annotations[POS]):
if pp not in parts_of_speech.IDS:
raise ValueError(Errors.E1021.format(pp=pp))
# Collect token attributes, assert all tokens have exactly the same set of attributes.
attrs = []
partial_attrs: Set[str] = set()
for attr in token_attrs.keys():
if attr in token_annotations:
if len(token_annotations[attr]) != len(words):
partial_attrs.add(token_attrs[attr])
attrs.append(attr)
if len(partial_attrs):
raise ValueError(Errors.E1040.format(partial_attrs=partial_attrs))
# If there are any other annotations, set them.
if attrs:
array = self.to_array(attrs)
if array.ndim == 1:
array = numpy.reshape(array, (array.size, 1))
j = 0
for j, (attr, annot) in enumerate(token_annotations.items()):
if attr is HEAD:
annot = numpy.array(annot, dtype=numpy.int32).astype(numpy.uint64)
for i in range(len(words)):
array[i, j] = annot[i]
elif attr is MORPH:
for i in range(len(words)):
array[i, j] = self.vocab.morphology.add(annot[i])
else:
for i in range(len(words)):
array[i, j] = self.vocab.strings.add(annot[i])
self.from_array(attrs, array)
### Span/document properties ###
# Complement other document-level properties (cats, spans, ents).
self.cats = doc_json.get("cats", {})
# Set sentence boundaries, if dependency parser not available but sentences are specified in JSON.
if not self.has_annotation("DEP"):
for sent in doc_json.get("sents", {}):
char_span = self.char_span(sent["start"], sent["end"])
if char_span is None:
raise ValueError(Errors.E1039.format(obj="sentence", start=sent["start"], end=sent["end"]))
char_span[0].is_sent_start = True
for token in char_span[1:]:
token.is_sent_start = False
for span_group in doc_json.get("spans", {}):
spans = []
for span in doc_json["spans"][span_group]:
char_span = self.char_span(span["start"], span["end"], span["label"], span["kb_id"])
if char_span is None:
raise ValueError(Errors.E1039.format(obj="span", start=span["start"], end=span["end"]))
spans.append(char_span)
self.spans[span_group] = spans
if "ents" in doc_json:
ents = []
for ent in doc_json["ents"]:
char_span = self.char_span(ent["start"], ent["end"], ent["label"])
if char_span is None:
raise ValueError(Errors.E1039.format(obj="entity"), start=ent["start"], end=ent["end"])
ents.append(char_span)
self.ents = ents
# Add custom attributes for the whole Doc object.
for attr in doc_json.get("_", {}):
if not Doc.has_extension(attr):
Doc.set_extension(attr)
self._.set(attr, doc_json["_"][attr])
for token_attr in doc_json.get("underscore_token", {}):
if not Token.has_extension(token_attr):
Token.set_extension(token_attr)
for token_data in doc_json["underscore_token"][token_attr]:
start = token_by_char(self.c, self.length, token_data["start"])
value = token_data["value"]
self[start]._.set(token_attr, value)
for span_attr in doc_json.get("underscore_span", {}):
if not Span.has_extension(span_attr):
Span.set_extension(span_attr)
for span_data in doc_json["underscore_span"][span_attr]:
value = span_data["value"]
span = self.char_span(span_data["start"], span_data["end"])
span.label = span_data["label"]
span.kb_id = span_data["kb_id"]
span.id = span_data["id"]
span._.set(span_attr, value)
return self
def to_json(self, underscore=None):
"""Convert a Doc to JSON.
underscore (list): Optional list of string names of custom doc._.
attributes. Attribute values need to be JSON-serializable. Values will
be added to an "_" key in the data, e.g. "_": {"foo": "bar"}.
RETURNS (dict): The data in JSON format.
"""
data = {"text": self.text}
if self.has_annotation("ENT_IOB"):
data["ents"] = [{"start": ent.start_char, "end": ent.end_char, "label": ent.label_} for ent in self.ents]
if self.has_annotation("SENT_START"):
sents = list(self.sents)
data["sents"] = [{"start": sent.start_char, "end": sent.end_char} for sent in sents]
if self.cats:
data["cats"] = self.cats
data["tokens"] = []
attrs = ["TAG", "MORPH", "POS", "LEMMA", "DEP"]
include_annotation = {attr: self.has_annotation(attr) for attr in attrs}
for token in self:
token_data = {"id": token.i, "start": token.idx, "end": token.idx + len(token)}
if include_annotation["TAG"]:
token_data["tag"] = token.tag_
if include_annotation["POS"]:
token_data["pos"] = token.pos_
if include_annotation["MORPH"]:
token_data["morph"] = token.morph.to_json()
if include_annotation["LEMMA"]:
token_data["lemma"] = token.lemma_
if include_annotation["DEP"]:
token_data["dep"] = token.dep_
token_data["head"] = token.head.i
data["tokens"].append(token_data)
if self.spans:
data["spans"] = {}
for span_group in self.spans:
data["spans"][span_group] = []
for span in self.spans[span_group]:
span_data = {"start": span.start_char, "end": span.end_char, "label": span.label_, "kb_id": span.kb_id_}
data["spans"][span_group].append(span_data)
if underscore:
user_keys = set()
# Handle doc attributes with .get to include values from getters
# and not only values stored in user_data, for backwards
# compatibility
for attr in underscore:
if self.has_extension(attr):
if "_" not in data:
data["_"] = {}
value = self._.get(attr)
if not srsly.is_json_serializable(value):
raise ValueError(Errors.E107.format(attr=attr, value=repr(value)))
data["_"][attr] = value
user_keys.add(attr)
# Token and span attributes only include values stored in user_data
# and not values generated by getters
if self.user_data:
for data_key, value in self.user_data.copy().items():
if type(data_key) == tuple and len(data_key) >= 4 and data_key[0] == "._.":
attr = data_key[1]
start = data_key[2]
end = data_key[3]
if attr in underscore:
user_keys.add(attr)
if not srsly.is_json_serializable(value):
raise ValueError(Errors.E107.format(attr=attr, value=repr(value)))
# Token attribute
if start is not None and end is None:
if "underscore_token" not in data:
data["underscore_token"] = {}
if attr not in data["underscore_token"]:
data["underscore_token"][attr] = []
data["underscore_token"][attr].append({"start": start, "value": value})
# Else span attribute
elif end is not None:
_label = data_key[4]
_kb_id = data_key[5]
_span_id = data_key[6]
if "underscore_span" not in data:
data["underscore_span"] = {}
if attr not in data["underscore_span"]:
data["underscore_span"][attr] = []
data["underscore_span"][attr].append({"start": start, "end": end, "value": value, "label": _label, "kb_id": _kb_id, "id":_span_id})
for attr in underscore:
if attr not in user_keys:
raise ValueError(Errors.E106.format(attr=attr, opts=underscore))
return data
def to_utf8_array(self, int nr_char=-1):
"""Encode word strings to utf8, and export to a fixed-width array
of characters. Characters are placed into the array in the order:
0, -1, 1, -2, etc
For example, if the array is sliced array[:, :8], the array will
contain the first 4 characters and last 4 characters of each word ---
with the middle characters clipped out. The value 255 is used as a pad
value.
"""
byte_strings = [token.orth_.encode('utf8') for token in self]
if nr_char == -1:
nr_char = max(len(bs) for bs in byte_strings)
cdef np.ndarray output = numpy.zeros((len(byte_strings), nr_char), dtype='uint8')
output.fill(255)
cdef int i, j, start_idx, end_idx
cdef bytes byte_string
cdef unsigned char utf8_char
for i, byte_string in enumerate(byte_strings):
j = 0
start_idx = 0
end_idx = len(byte_string) - 1
while j < nr_char and start_idx <= end_idx:
output[i, j] = <unsigned char>byte_string[start_idx]
start_idx += 1
j += 1
if j < nr_char and start_idx <= end_idx:
output[i, j] = <unsigned char>byte_string[end_idx]
end_idx -= 1
j += 1
return output
@staticmethod
def _get_array_attrs():
attrs = [LENGTH, SPACY]
attrs.extend(intify_attr(x) for x in DOCBIN_ALL_ATTRS)
return tuple(attrs)
cdef int token_by_start(const TokenC* tokens, int length, int start_char) except -2:
cdef int i = token_by_char(tokens, length, start_char)
if i >= 0 and 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:
# end_char is exclusive, so find the token at one char before
cdef int i = token_by_char(tokens, length, end_char - 1)
if i >= 0 and tokens[i].idx + tokens[i].lex.length == end_char:
return i
else:
return -1
cdef int token_by_char(const TokenC* tokens, int length, int char_idx) except -2:
cdef int start = 0, mid, end = length - 1
while start <= end:
mid = (start + end) / 2
if char_idx < tokens[mid].idx:
end = mid - 1
elif char_idx >= tokens[mid].idx + tokens[mid].lex.length + tokens[mid].spacy:
start = mid + 1
else:
return mid
return -1
cdef int set_children_from_heads(TokenC* tokens, int start, int end) except -1:
# note: end is exclusive
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(start, end):
tokens[i].l_kids = 0
tokens[i].r_kids = 0
tokens[i].l_edge = i
tokens[i].r_edge = i
cdef int loop_count = 0
cdef bint heads_within_sents = False
# Try up to 10 iterations of adjusting lr_kids and lr_edges in order to
# handle non-projective dependency parses, stopping when all heads are
# within their respective sentence boundaries. We have documented cases
# that need at least 4 iterations, so this is to be on the safe side
# without risking getting stuck in an infinite loop if something is
# terribly malformed.
while not heads_within_sents:
heads_within_sents = _set_lr_kids_and_edges(tokens, start, end, loop_count)
if loop_count > 10:
util.logger.debug(Warnings.W026)
break
loop_count += 1
# Set sentence starts
for i in range(start, end):
tokens[i].sent_start = -1
for i in range(start, end):
if tokens[i].head == 0 and not Token.missing_head(&tokens[i]):
tokens[tokens[i].l_edge].sent_start = 1
cdef int _set_lr_kids_and_edges(TokenC* tokens, int start, int end, int loop_count) except -1:
# May be called multiple times due to non-projectivity. See issues #3170
# and #4688.
# Set left edges
cdef TokenC* head
cdef TokenC* child
cdef int i, j
for i in range(start, end):
child = &tokens[i]
head = &tokens[i + child.head]
if loop_count == 0 and child < head:
head.l_kids += 1
if child.l_edge < head.l_edge:
head.l_edge = child.l_edge
if child.r_edge > head.r_edge:
head.r_edge = child.r_edge
# Set right edges - same as above, but iterate in reverse
for i in range(end-1, start-1, -1):
child = &tokens[i]
head = &tokens[i + child.head]
if loop_count == 0 and child > head:
head.r_kids += 1
if child.r_edge > head.r_edge:
head.r_edge = child.r_edge
if child.l_edge < head.l_edge:
head.l_edge = child.l_edge
# Get sentence start positions according to current state
sent_starts = set()
for i in range(start, end):
if tokens[i].head == 0:
sent_starts.add(tokens[i].l_edge)
cdef int curr_sent_start = 0
cdef int curr_sent_end = 0
# Check whether any heads are not within the current sentence
for i in range(start, end):
if (i > 0 and i in sent_starts) or i == end - 1:
curr_sent_end = i
for j in range(curr_sent_start, curr_sent_end):
if tokens[j].head + j < curr_sent_start or tokens[j].head + j >= curr_sent_end + 1:
return False
curr_sent_start = i
return True
cdef int _get_tokens_lca(Token token_j, Token token_k):
"""Given two tokens, returns the index of the lowest common ancestor
(LCA) among the two. If they have no common ancestor, -1 is returned.
token_j (Token): a token.
token_k (Token): another token.
RETURNS (int): index of lowest common ancestor, or -1 if the tokens
have no common ancestor.
"""
if token_j == token_k:
return token_j.i
elif token_j.head == token_k:
return token_k.i
elif token_k.head == token_j:
return token_j.i
token_j_ancestors = set(token_j.ancestors)
if token_k in token_j_ancestors:
return token_k.i
for token_k_ancestor in token_k.ancestors:
if token_k_ancestor == token_j:
return token_j.i
if token_k_ancestor in token_j_ancestors:
return token_k_ancestor.i
return -1
cdef int [:,:] _get_lca_matrix(Doc doc, int start, int end):
"""Given a doc and a start and end position defining a set of contiguous
tokens within it, returns a matrix of Lowest Common Ancestors (LCA), where
LCA[i, j] is the index of the lowest common ancestor among token i and j.
If the tokens have no common ancestor within the specified span,
LCA[i, j] will be -1.
doc (Doc): The index of the token, or the slice of the document
start (int): First token to be included in the LCA matrix.
end (int): Position of next to last token included in the LCA matrix.
RETURNS (int [:, :]): memoryview of numpy.array[ndim=2, dtype=numpy.int32],
with shape (n, n), where n = len(doc).
"""
cdef int [:,:] lca_matrix
cdef int j, k
n_tokens= end - start
lca_mat = numpy.empty((n_tokens, n_tokens), dtype=numpy.int32)
lca_mat.fill(-1)
lca_matrix = lca_mat
for j in range(n_tokens):
token_j = doc[start + j]
# the common ancestor of token and itself is itself:
lca_matrix[j, j] = j
# we will only iterate through tokens in the same sentence
sent = token_j.sent
sent_start = sent.start
j_idx_in_sent = start + j - sent_start
n_missing_tokens_in_sent = len(sent) - j_idx_in_sent
# make sure we do not go past `end`, in cases where `end` < sent.end
max_range = min(j + n_missing_tokens_in_sent, end - start)
for k in range(j + 1, max_range):
lca = _get_tokens_lca(token_j, doc[start + k])
# if lca is outside of span, we set it to -1
if not start <= lca < end:
lca_matrix[j, k] = -1
lca_matrix[k, j] = -1
else:
lca_matrix[j, k] = lca - start
lca_matrix[k, j] = lca - start
return lca_matrix
def pickle_doc(doc):
bytes_data = doc.to_bytes(exclude=["vocab", "user_data", "user_hooks"])
hooks_and_data = (doc.user_data, doc.user_hooks, doc.user_span_hooks,
doc.user_token_hooks)
return (unpickle_doc, (doc.vocab, srsly.pickle_dumps(hooks_and_data), bytes_data))
def unpickle_doc(vocab, hooks_and_data, bytes_data):
user_data, doc_hooks, span_hooks, token_hooks = srsly.pickle_loads(hooks_and_data)
doc = Doc(vocab, user_data=user_data).from_bytes(bytes_data, exclude=["user_data"])
doc.user_hooks.update(doc_hooks)
doc.user_span_hooks.update(span_hooks)
doc.user_token_hooks.update(token_hooks)
return doc
copy_reg.pickle(Doc, pickle_doc, unpickle_doc)
def remove_label_if_necessary(attributes):
# More deprecated attribute handling =/
if "label" in attributes:
attributes["ent_type"] = attributes.pop("label")
def fix_attributes(doc, attributes):
if "label" in attributes and "ent_type" not in attributes:
if isinstance(attributes["label"], int):
attributes[ENT_TYPE] = attributes["label"]
else:
attributes[ENT_TYPE] = doc.vocab.strings[attributes["label"]]
if "ent_type" in attributes:
attributes[ENT_TYPE] = attributes["ent_type"]
def get_entity_info(ent_info):
ent_kb_id = 0
ent_id = 0
if isinstance(ent_info, Span):
ent_type = ent_info.label
ent_kb_id = ent_info.kb_id
start = ent_info.start
end = ent_info.end
ent_id = ent_info.id
elif len(ent_info) == 3:
ent_type, start, end = ent_info
elif len(ent_info) == 4:
ent_type, ent_kb_id, start, end = ent_info
else:
ent_id, ent_kb_id, ent_type, start, end = ent_info
return ent_type, ent_kb_id, start, end, ent_id
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