mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 04:08:09 +03:00
a4b32b9552
* Handle missing reference values in scorer Handle missing values in reference doc during scoring where it is possible to detect an unset state for the attribute. If no reference docs contain annotation, `None` is returned instead of a score. `spacy evaluate` displays `-` for missing scores and the missing scores are saved as `None`/`null` in the metrics. Attributes without unset states: * `token.head`: relies on `token.dep` to recognize unset values * `doc.cats`: unable to handle missing annotation Additional changes: * add optional `has_annotation` check to `score_scans` to replace `doc.sents` hack * update `score_token_attr_per_feat` to handle missing and empty morph representations * fix bug in `Doc.has_annotation` for normalization of `IS_SENT_START` vs. `SENT_START` * Fix import * Update return types
1701 lines
68 KiB
Cython
1701 lines
68 KiB
Cython
# cython: infer_types=True, bounds_check=False, profile=True
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cimport cython
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cimport numpy as np
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from libc.string cimport memcpy
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from libc.math cimport sqrt
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from libc.stdint cimport int32_t, uint64_t
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import copy
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from collections import Counter
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from enum import Enum
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import itertools
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import numpy
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import srsly
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from thinc.api import get_array_module
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from thinc.util import copy_array
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import warnings
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from .span cimport Span
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from .token cimport Token
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from ..lexeme cimport Lexeme, EMPTY_LEXEME
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from ..typedefs cimport attr_t, flags_t
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from ..attrs cimport attr_id_t
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from ..attrs cimport LENGTH, POS, LEMMA, TAG, MORPH, DEP, HEAD, SPACY, ENT_IOB
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from ..attrs cimport ENT_TYPE, ENT_ID, ENT_KB_ID, SENT_START, IDX, NORM
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from ..attrs import intify_attr, IDS
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from ..compat import copy_reg, pickle
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from ..errors import Errors, Warnings
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from ..morphology import Morphology
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from .. import util
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from .underscore import Underscore, get_ext_args
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from ._retokenize import Retokenizer
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from ._serialize import ALL_ATTRS as DOCBIN_ALL_ATTRS
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DEF PADDING = 5
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cdef int bounds_check(int i, int length, int padding) except -1:
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if (i + padding) < 0:
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raise IndexError(Errors.E026.format(i=i, length=length))
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if (i - padding) >= length:
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raise IndexError(Errors.E026.format(i=i, length=length))
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cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) nogil:
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if feat_name == LEMMA:
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return token.lemma
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elif feat_name == NORM:
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if not token.norm:
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return token.lex.norm
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return token.norm
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elif feat_name == POS:
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return token.pos
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elif feat_name == TAG:
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return token.tag
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elif feat_name == MORPH:
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return token.morph
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elif feat_name == DEP:
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return token.dep
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elif feat_name == HEAD:
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return token.head
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elif feat_name == SENT_START:
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return token.sent_start
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elif feat_name == SPACY:
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return token.spacy
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elif feat_name == ENT_IOB:
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return token.ent_iob
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elif feat_name == ENT_TYPE:
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return token.ent_type
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elif feat_name == ENT_ID:
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return token.ent_id
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elif feat_name == ENT_KB_ID:
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return token.ent_kb_id
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elif feat_name == IDX:
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return token.idx
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else:
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return Lexeme.get_struct_attr(token.lex, feat_name)
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cdef attr_t get_token_attr_for_matcher(const TokenC* token, attr_id_t feat_name) nogil:
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if feat_name == SENT_START:
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if token.sent_start == 1:
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return True
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else:
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return False
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else:
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return get_token_attr(token, feat_name)
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class SetEntsDefault(str, Enum):
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blocked = "blocked"
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missing = "missing"
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outside = "outside"
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unmodified = "unmodified"
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@classmethod
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def values(cls):
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return list(cls.__members__.keys())
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cdef class Doc:
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"""A sequence of Token objects. Access sentences and named entities, export
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annotations to numpy arrays, losslessly serialize to compressed binary
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strings. The `Doc` object holds an array of `TokenC` structs. The
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Python-level `Token` and `Span` objects are views of this array, i.e.
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they don't own the data themselves.
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EXAMPLE:
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Construction 1
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>>> doc = nlp(u'Some text')
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Construction 2
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>>> from spacy.tokens import Doc
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>>> doc = Doc(nlp.vocab, words=["hello", "world", "!"], spaces=[True, False, False])
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DOCS: https://nightly.spacy.io/api/doc
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"""
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@classmethod
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def set_extension(cls, name, **kwargs):
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"""Define a custom attribute which becomes available as `Doc._`.
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name (str): Name of the attribute to set.
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default: Optional default value of the attribute.
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getter (callable): Optional getter function.
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setter (callable): Optional setter function.
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method (callable): Optional method for method extension.
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force (bool): Force overwriting existing attribute.
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DOCS: https://nightly.spacy.io/api/doc#set_extension
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USAGE: https://nightly.spacy.io/usage/processing-pipelines#custom-components-attributes
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"""
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if cls.has_extension(name) and not kwargs.get("force", False):
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raise ValueError(Errors.E090.format(name=name, obj="Doc"))
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Underscore.doc_extensions[name] = get_ext_args(**kwargs)
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@classmethod
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def get_extension(cls, name):
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"""Look up a previously registered extension by name.
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name (str): Name of the extension.
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RETURNS (tuple): A `(default, method, getter, setter)` tuple.
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DOCS: https://nightly.spacy.io/api/doc#get_extension
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"""
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return Underscore.doc_extensions.get(name)
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@classmethod
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def has_extension(cls, name):
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"""Check whether an extension has been registered.
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name (str): Name of the extension.
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RETURNS (bool): Whether the extension has been registered.
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DOCS: https://nightly.spacy.io/api/doc#has_extension
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"""
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return name in Underscore.doc_extensions
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@classmethod
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def remove_extension(cls, name):
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"""Remove a previously registered extension.
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name (str): Name of the extension.
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RETURNS (tuple): A `(default, method, getter, setter)` tuple of the
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removed extension.
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DOCS: https://nightly.spacy.io/api/doc#remove_extension
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"""
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if not cls.has_extension(name):
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raise ValueError(Errors.E046.format(name=name))
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return Underscore.doc_extensions.pop(name)
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def __init__(
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self,
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Vocab vocab,
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words=None,
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spaces=None,
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*,
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user_data=None,
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tags=None,
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pos=None,
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morphs=None,
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lemmas=None,
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heads=None,
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deps=None,
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sent_starts=None,
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ents=None,
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):
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"""Create a Doc object.
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vocab (Vocab): A vocabulary object, which must match any models you
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want to use (e.g. tokenizer, parser, entity recognizer).
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words (Optional[List[str]]): A list of unicode strings to add to the document
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as words. If `None`, defaults to empty list.
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spaces (Optional[List[bool]]): A list of boolean values, of the same length as
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words. True means that the word is followed by a space, False means
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it is not. If `None`, defaults to `[True]*len(words)`
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user_data (dict or None): Optional extra data to attach to the Doc.
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tags (Optional[List[str]]): A list of unicode strings, of the same
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length as words, to assign as token.tag. Defaults to None.
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pos (Optional[List[str]]): A list of unicode strings, of the same
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length as words, to assign as token.pos. Defaults to None.
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morphs (Optional[List[str]]): A list of unicode strings, of the same
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length as words, to assign as token.morph. Defaults to None.
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lemmas (Optional[List[str]]): A list of unicode strings, of the same
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length as words, to assign as token.lemma. Defaults to None.
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heads (Optional[List[int]]): A list of values, of the same length as
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words, to assign as heads. Head indices are the position of the
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head in the doc. Defaults to None.
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deps (Optional[List[str]]): A list of unicode strings, of the same
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length as words, to assign as token.dep. Defaults to None.
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sent_starts (Optional[List[Union[bool, None]]]): A list of values, of
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the same length as words, to assign as token.is_sent_start. Will be
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overridden by heads if heads is provided. Defaults to None.
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ents (Optional[List[str]]): A list of unicode strings, of the same
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length as words, as IOB tags to assign as token.ent_iob and
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token.ent_type. Defaults to None.
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DOCS: https://nightly.spacy.io/api/doc#init
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"""
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self.vocab = vocab
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size = max(20, (len(words) if words is not None else 0))
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self.mem = Pool()
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# Guarantee self.lex[i-x], for any i >= 0 and x < padding is in bounds
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# However, we need to remember the true starting places, so that we can
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# realloc.
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data_start = <TokenC*>self.mem.alloc(size + (PADDING*2), sizeof(TokenC))
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cdef int i
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for i in range(size + (PADDING*2)):
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data_start[i].lex = &EMPTY_LEXEME
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data_start[i].l_edge = i
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data_start[i].r_edge = i
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self.c = data_start + PADDING
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self.max_length = size
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self.length = 0
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self.sentiment = 0.0
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self.cats = {}
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self.user_hooks = {}
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self.user_token_hooks = {}
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self.user_span_hooks = {}
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self.tensor = numpy.zeros((0,), dtype="float32")
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self.user_data = {} if user_data is None else user_data
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self._vector = None
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self.noun_chunks_iterator = self.vocab.get_noun_chunks
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cdef bint has_space
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if words is None and spaces is not None:
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raise ValueError(Errors.E908)
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elif spaces is None and words is not None:
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self.has_unknown_spaces = True
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else:
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self.has_unknown_spaces = False
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words = words if words is not None else []
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spaces = spaces if spaces is not None else ([True] * len(words))
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if len(spaces) != len(words):
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raise ValueError(Errors.E027)
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cdef const LexemeC* lexeme
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for word, has_space in zip(words, spaces):
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if isinstance(word, unicode):
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lexeme = self.vocab.get(self.mem, word)
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elif isinstance(word, bytes):
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raise ValueError(Errors.E028.format(value=word))
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else:
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lexeme = self.vocab.get_by_orth(self.mem, word)
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self.push_back(lexeme, has_space)
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if heads is not None:
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heads = [head - i for i, head in enumerate(heads)]
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if deps and not heads:
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heads = [0] * len(deps)
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if sent_starts is not None:
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for i in range(len(sent_starts)):
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if sent_starts[i] is True:
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sent_starts[i] = 1
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elif sent_starts[i] is False:
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sent_starts[i] = -1
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elif sent_starts[i] is None or sent_starts[i] not in [-1, 0, 1]:
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sent_starts[i] = 0
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ent_iobs = None
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ent_types = None
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if ents is not None:
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iob_strings = Token.iob_strings()
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# make valid IOB2 out of IOB1 or IOB2
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for i, ent in enumerate(ents):
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if ent is "":
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ents[i] = None
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elif ent is not None and not isinstance(ent, str):
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raise ValueError(Errors.E177.format(tag=ent))
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if i < len(ents) - 1:
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# OI -> OB
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if (ent is None or ent.startswith("O")) and \
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(ents[i+1] is not None and ents[i+1].startswith("I")):
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ents[i+1] = "B" + ents[i+1][1:]
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# B-TYPE1 I-TYPE2 or I-TYPE1 I-TYPE2 -> B/I-TYPE1 B-TYPE2
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if ent is not None and ents[i+1] is not None and \
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(ent.startswith("B") or ent.startswith("I")) and \
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ents[i+1].startswith("I") and \
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ent[1:] != ents[i+1][1:]:
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ents[i+1] = "B" + ents[i+1][1:]
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ent_iobs = []
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ent_types = []
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for ent in ents:
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if ent is None:
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ent_iobs.append(iob_strings.index(""))
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ent_types.append("")
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elif ent == "O":
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ent_iobs.append(iob_strings.index(ent))
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ent_types.append("")
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else:
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if len(ent) < 3 or ent[1] != "-":
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raise ValueError(Errors.E177.format(tag=ent))
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ent_iob, ent_type = ent.split("-", 1)
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if ent_iob not in iob_strings:
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raise ValueError(Errors.E177.format(tag=ent))
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ent_iob = iob_strings.index(ent_iob)
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ent_iobs.append(ent_iob)
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ent_types.append(ent_type)
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headings = []
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values = []
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annotations = [pos, heads, deps, lemmas, tags, morphs, sent_starts, ent_iobs, ent_types]
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possible_headings = [POS, HEAD, DEP, LEMMA, TAG, MORPH, SENT_START, ENT_IOB, ENT_TYPE]
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for a, annot in enumerate(annotations):
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if annot is not None:
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if len(annot) != len(words):
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raise ValueError(Errors.E189)
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headings.append(possible_headings[a])
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if annot is not heads and annot is not sent_starts and annot is not ent_iobs:
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values.extend(annot)
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for value in values:
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self.vocab.strings.add(value)
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# if there are any other annotations, set them
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if headings:
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attrs = self.to_array(headings)
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j = 0
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for annot in annotations:
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if annot:
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if annot is heads or annot is sent_starts or annot is ent_iobs:
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for i in range(len(words)):
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if attrs.ndim == 1:
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attrs[i] = annot[i]
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else:
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attrs[i, j] = annot[i]
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elif annot is morphs:
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for i in range(len(words)):
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morph_key = vocab.morphology.add(morphs[i])
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if attrs.ndim == 1:
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attrs[i] = morph_key
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else:
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attrs[i, j] = morph_key
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else:
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for i in range(len(words)):
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if attrs.ndim == 1:
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attrs[i] = self.vocab.strings[annot[i]]
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else:
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attrs[i, j] = self.vocab.strings[annot[i]]
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j += 1
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self.from_array(headings, attrs)
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@property
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def _(self):
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"""Custom extension attributes registered via `set_extension`."""
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return Underscore(Underscore.doc_extensions, self)
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@property
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def is_tagged(self):
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warnings.warn(Warnings.W107.format(prop="is_tagged", attr="TAG"), DeprecationWarning)
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return self.has_annotation("TAG")
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@property
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def is_parsed(self):
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warnings.warn(Warnings.W107.format(prop="is_parsed", attr="DEP"), DeprecationWarning)
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return self.has_annotation("DEP")
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@property
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def is_nered(self):
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warnings.warn(Warnings.W107.format(prop="is_nered", attr="ENT_IOB"), DeprecationWarning)
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return self.has_annotation("ENT_IOB")
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@property
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def is_sentenced(self):
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warnings.warn(Warnings.W107.format(prop="is_sentenced", attr="SENT_START"), DeprecationWarning)
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return self.has_annotation("SENT_START")
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def has_annotation(self, attr, *, require_complete=False):
|
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"""Check whether the doc contains annotation on a token attribute.
|
||
|
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attr (Union[int, str]): The attribute string name or int ID.
|
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require_complete (bool): Whether to check that the attribute is set on
|
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every token in the doc.
|
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RETURNS (bool): Whether annotation is present.
|
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|
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DOCS: https://nightly.spacy.io/api/doc#has_annotation
|
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"""
|
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|
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# empty docs are always annotated
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if self.length == 0:
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return True
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cdef int i
|
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cdef int range_start = 0
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if attr == "IS_SENT_START" or attr == self.vocab.strings["IS_SENT_START"]:
|
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attr = SENT_START
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attr = intify_attr(attr)
|
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# adjust attributes
|
||
if attr == HEAD:
|
||
# HEAD does not have an unset state, so rely on DEP
|
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attr = DEP
|
||
# special cases for sentence boundaries
|
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if attr == SENT_START:
|
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if "sents" in self.user_hooks:
|
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return True
|
||
# docs of length 1 always have sentence boundaries
|
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if self.length == 1:
|
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return True
|
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range_start = 1
|
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if require_complete:
|
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return all(Token.get_struct_attr(&self.c[i], attr) for i in range(range_start, self.length))
|
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else:
|
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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
|
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`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://nightly.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://nightly.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://nightly.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"):
|
||
"""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 (uint64 or string): A label to attach to the Span, e.g. for
|
||
named entities.
|
||
kb_id (uint64 or string): 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".
|
||
RETURNS (Span): The newly constructed object.
|
||
|
||
DOCS: https://nightly.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)
|
||
if alignment_mode not in ("strict", "contract", "expand"):
|
||
alignment_mode = "strict"
|
||
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, 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://nightly.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:
|
||
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://nightly.spacy.io/api/doc#has_vector
|
||
"""
|
||
if "has_vector" in self.user_hooks:
|
||
return self.user_hooks["has_vector"](self)
|
||
elif self.vocab.vectors.data.size:
|
||
return True
|
||
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://nightly.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.data.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://nightly.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://nightly.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
|
||
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))
|
||
start = -1
|
||
label = 0
|
||
kb_id = 0
|
||
elif token.ent_iob == 3:
|
||
if start != -1:
|
||
output.append(Span(self, start, i, label=label, kb_id=kb_id))
|
||
start = i
|
||
label = token.ent_type
|
||
kb_id = token.ent_kb_id
|
||
if start != -1:
|
||
output.append(Span(self, start, self.length, label=label, kb_id=kb_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
|
||
cdef int ent_start, ent_end
|
||
ent_spans = []
|
||
for ent_info in ents:
|
||
entity_type_, kb_id, ent_start, ent_end = 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)
|
||
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
|
||
for span in blocked:
|
||
for i in range(span.start, span.end):
|
||
self.c[i].ent_iob = 3
|
||
self.c[i].ent_type = 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
|
||
for span in outside:
|
||
for i in range(span.start, span.end):
|
||
self.c[i].ent_iob = 2
|
||
self.c[i].ent_type = 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
|
||
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 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.
|
||
|
||
DOCS: https://nightly.spacy.io/api/doc#noun_chunks
|
||
"""
|
||
|
||
# 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 = []
|
||
if self.noun_chunks_iterator is not None:
|
||
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://nightly.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').
|
||
|
||
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 = [k for k in IDS.keys() if not k.startswith("FLAG")]
|
||
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 np.ndarray attr_ids = numpy.asarray(py_attr_ids, dtype="i")
|
||
output = numpy.ndarray(shape=(self.length, len(attr_ids)), dtype=numpy.uint64)
|
||
c_output = <attr_t*>output.data
|
||
c_attr_ids = <attr_id_t*>attr_ids.data
|
||
cdef TokenC* token
|
||
cdef int nr_attr = attr_ids.shape[0]
|
||
for i in range(self.length):
|
||
token = &self.c[i]
|
||
for j in range(nr_attr):
|
||
c_output[i*nr_attr + j] = get_token_attr(token, c_attr_ids[j])
|
||
# Handle 1d case
|
||
return output if len(attr_ids) >= 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://nightly.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://nightly.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):
|
||
"""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.
|
||
RETURNS (Doc): A doc that contains the concatenated docs, or None if no docs were given.
|
||
|
||
DOCS: https://nightly.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 = 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 = {}
|
||
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)
|
||
|
||
for key, value in doc.user_data.items():
|
||
if isinstance(key, tuple) and len(key) == 4:
|
||
data_type, name, start, end = key
|
||
if start is not None or end is not None:
|
||
start += char_offset
|
||
if end is not None:
|
||
end += char_offset
|
||
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))
|
||
char_offset += len(doc.text)
|
||
if ensure_whitespace and not (len(doc) > 0 and doc[-1].is_space):
|
||
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
|
||
token_offset = -1
|
||
for doc in docs[:-1]:
|
||
token_offset += len(doc)
|
||
if not (len(doc) > 0 and 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)
|
||
|
||
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://nightly.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.sentiment = self.sentiment
|
||
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
|
||
buff_size = other.max_length + (PADDING*2)
|
||
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://nightly.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://nightly.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://nightly.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://nightly.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://nightly.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_)
|
||
# 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),
|
||
"sentiment": lambda: self.sentiment,
|
||
"tensor": lambda: self.tensor,
|
||
"cats": lambda: self.cats,
|
||
"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)
|
||
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://nightly.spacy.io/api/doc#from_dict
|
||
"""
|
||
if self.length != 0:
|
||
raise ValueError(Errors.E033.format(length=self.length))
|
||
deserializers = {
|
||
"text": lambda b: None,
|
||
"array_head": lambda b: None,
|
||
"array_body": lambda b: None,
|
||
"sentiment": lambda b: None,
|
||
"tensor": lambda b: None,
|
||
"cats": lambda b: None,
|
||
"strings": lambda b: None,
|
||
"user_data_keys": lambda b: None,
|
||
"user_data_values": lambda b: None,
|
||
"has_unknown_spaces": lambda b: None
|
||
}
|
||
# 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 "sentiment" not in exclude and "sentiment" in msg:
|
||
self.sentiment = msg["sentiment"]
|
||
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 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 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://nightly.spacy.io/api/doc#retokenize
|
||
USAGE: https://nightly.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 unicode 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 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 spaCy's 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 underscore:
|
||
data["_"] = {}
|
||
for attr in underscore:
|
||
if not self.has_extension(attr):
|
||
raise ValueError(Errors.E106.format(attr=attr, opts=underscore))
|
||
value = self._.get(attr)
|
||
if not srsly.is_json_serializable(value):
|
||
raise ValueError(Errors.E107.format(attr=attr, value=repr(value)))
|
||
data["_"][attr] = value
|
||
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:
|
||
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)
|
||
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"])
|
||
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):
|
||
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
|
||
elif len(ent_info) == 3:
|
||
ent_type, start, end = ent_info
|
||
ent_kb_id = 0
|
||
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
|