mirror of https://github.com/explosion/spaCy.git
1150 lines
45 KiB
Cython
1150 lines
45 KiB
Cython
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# coding: utf8
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# cython: infer_types=True
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# cython: bounds_check=False
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# cython: profile=True
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from __future__ import unicode_literals
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cimport cython
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cimport numpy as np
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import numpy
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import numpy.linalg
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import struct
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import srsly
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from thinc.neural.util import get_array_module, copy_array
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import srsly
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from libc.string cimport memcpy, memset
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from libc.math cimport sqrt
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from .span cimport Span
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from .token cimport Token
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from .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 import intify_attrs, IDS
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from ..attrs cimport attr_id_t
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from ..attrs cimport ID, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX, CLUSTER
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from ..attrs cimport LENGTH, POS, LEMMA, TAG, DEP, HEAD, SPACY, ENT_IOB
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from ..attrs cimport ENT_TYPE, SENT_START
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from ..parts_of_speech cimport CCONJ, PUNCT, NOUN, univ_pos_t
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from ..util import normalize_slice
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from ..compat import is_config, copy_reg, pickle, basestring_
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from ..errors import deprecation_warning, models_warning, user_warning
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from ..errors import Errors, Warnings
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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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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 == POS:
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return token.pos
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elif feat_name == TAG:
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return token.tag
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elif feat_name == DEP:
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return token.dep
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elif feat_name == HEAD:
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return token.head
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elif feat_name == 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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else:
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return Lexeme.get_struct_attr(token.lex, feat_name)
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def _get_chunker(lang):
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try:
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cls = util.get_lang_class(lang)
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except ImportError:
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return None
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except KeyError:
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return None
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return cls.Defaults.syntax_iterators.get(u'noun_chunks')
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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: 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=[u'hello', u'world', u'!'],
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spaces=[True, False, False])
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"""
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@classmethod
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def set_extension(cls, name, **kwargs):
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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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return Underscore.doc_extensions.get(name)
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@classmethod
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def has_extension(cls, name):
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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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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__(self, Vocab vocab, words=None, spaces=None, user_data=None,
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orths_and_spaces=None):
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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 (list or None): 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 (list or None): 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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RETURNS (Doc): The newly constructed object.
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"""
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self.vocab = vocab
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size = 20
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self.mem = Pool()
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# Guarantee self.lex[i-x], for any i >= 0 and x < padding is in bounds
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# However, we need to remember the true starting places, so that we can
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# realloc.
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data_start = <TokenC*>self.mem.alloc(size + (PADDING*2), sizeof(TokenC))
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cdef int i
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for i in range(size + (PADDING*2)):
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data_start[i].lex = &EMPTY_LEXEME
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data_start[i].l_edge = i
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data_start[i].r_edge = i
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self.c = data_start + PADDING
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self.max_length = size
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self.length = 0
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self.is_tagged = False
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self.is_parsed = False
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self.sentiment = 0.0
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self.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 = _get_chunker(self.vocab.lang)
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cdef unicode orth
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cdef bint has_space
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if orths_and_spaces is None and words is not None:
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if spaces is None:
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spaces = [True] * len(words)
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elif len(spaces) != len(words):
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raise ValueError(Errors.E027)
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orths_and_spaces = zip(words, spaces)
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if orths_and_spaces is not None:
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for orth_space in orths_and_spaces:
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if isinstance(orth_space, unicode):
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orth = orth_space
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has_space = True
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elif isinstance(orth_space, bytes):
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raise ValueError(Errors.E028.format(value=orth_space))
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else:
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orth, has_space = orth_space
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# Note that we pass self.mem here --- we have ownership, if LexemeC
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# must be created.
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self.push_back(
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<const LexemeC*>self.vocab.get(self.mem, orth), has_space)
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# Tough to decide on policy for this. Is an empty doc tagged and parsed?
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# There's no information we'd like to add to it, so I guess so?
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if self.length == 0:
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self.is_tagged = True
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self.is_parsed = True
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@property
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def _(self):
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return Underscore(Underscore.doc_extensions, self)
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@property
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def is_sentenced(self):
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# Check if the document has sentence boundaries,
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# i.e at least one tok has the sent_start in (-1, 1)
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if 'sents' in self.user_hooks:
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return True
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if self.is_parsed:
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return True
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for i in range(self.length):
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if self.c[i].sent_start == -1 or self.c[i].sent_start == 1:
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return True
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else:
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return False
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def __getitem__(self, object i):
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"""Get a `Token` or `Span` object.
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i (int or tuple) The index of the token, or the slice of the document
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to get.
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RETURNS (Token or Span): The token at `doc[i]]`, or the span at
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`doc[start : end]`.
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EXAMPLE:
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>>> doc[i]
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Get the `Token` object at position `i`, where `i` is an integer.
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Negative indexing is supported, and follows the usual Python
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semantics, i.e. `doc[-2]` is `doc[len(doc) - 2]`.
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>>> doc[start : end]]
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Get a `Span` object, starting at position `start` and ending at
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position `end`, where `start` and `end` are token indices. For
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instance, `doc[2:5]` produces a span consisting of tokens 2, 3 and
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4. Stepped slices (e.g. `doc[start : end : step]`) are not
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supported, as `Span` objects must be contiguous (cannot have gaps).
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You can use negative indices and open-ended ranges, which have
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their normal Python semantics.
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"""
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if isinstance(i, slice):
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start, stop = normalize_slice(len(self), i.start, i.stop, i.step)
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return Span(self, start, stop, label=0)
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if i < 0:
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i = self.length + i
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bounds_check(i, self.length, PADDING)
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return Token.cinit(self.vocab, &self.c[i], i, self)
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def __iter__(self):
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"""Iterate over `Token` objects, from which the annotations can be
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easily accessed. This is the main way of accessing `Token` objects,
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which are the main way annotations are accessed from Python. If faster-
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than-Python speeds are required, you can instead access the annotations
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as a numpy array, or access the underlying C data directly from Cython.
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EXAMPLE:
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>>> for token in doc
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"""
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cdef int i
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for i in range(self.length):
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yield Token.cinit(self.vocab, &self.c[i], i, self)
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def __len__(self):
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"""The number of tokens in the document.
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RETURNS (int): The number of tokens in the document.
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EXAMPLE:
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>>> len(doc)
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"""
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return self.length
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def __unicode__(self):
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return u''.join([t.text_with_ws for t in self])
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def __bytes__(self):
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return u''.join([t.text_with_ws for t in self]).encode('utf-8')
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def __str__(self):
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if is_config(python3=True):
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return self.__unicode__()
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return self.__bytes__()
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def __repr__(self):
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return self.__str__()
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@property
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def doc(self):
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return self
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def char_span(self, int start_idx, int end_idx, label=0, vector=None):
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"""Create a `Span` object from the slice `doc.text[start : end]`.
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doc (Doc): The parent document.
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start (int): The index of the first character of the span.
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end (int): The index of the first character after the span.
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label (uint64 or string): A label to attach to the Span, e.g. for
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named entities.
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vector (ndarray[ndim=1, dtype='float32']): A meaning representation of
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the span.
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RETURNS (Span): The newly constructed object.
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"""
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if not isinstance(label, int):
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label = self.vocab.strings.add(label)
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cdef int start = token_by_start(self.c, self.length, start_idx)
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if start == -1:
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return None
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cdef int end = token_by_end(self.c, self.length, end_idx)
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if end == -1:
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return None
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# Currently we have the token index, we want the range-end index
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end += 1
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cdef Span span = Span(self, start, end, label=label, vector=vector)
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return span
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def similarity(self, other):
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"""Make a semantic similarity estimate. The default estimate is cosine
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similarity using an average of word vectors.
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other (object): The object to compare with. By default, accepts `Doc`,
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`Span`, `Token` and `Lexeme` objects.
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RETURNS (float): A scalar similarity score. Higher is more similar.
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"""
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if 'similarity' in self.user_hooks:
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return self.user_hooks['similarity'](self, other)
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if isinstance(other, (Lexeme, Token)) and self.length == 1:
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if self.c[0].lex.orth == other.orth:
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return 1.0
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elif isinstance(other, (Span, Doc)):
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if len(self) == len(other):
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for i in range(self.length):
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if self[i].orth != other[i].orth:
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break
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else:
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return 1.0
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if self.vocab.vectors.n_keys == 0:
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models_warning(Warnings.W007.format(obj='Doc'))
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if self.vector_norm == 0 or other.vector_norm == 0:
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user_warning(Warnings.W008.format(obj='Doc'))
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return 0.0
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return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
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property has_vector:
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"""A boolean value indicating whether a word vector is associated with
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the object.
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RETURNS (bool): Whether a word vector is associated with the object.
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"""
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def __get__(self):
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if 'has_vector' in self.user_hooks:
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return self.user_hooks['has_vector'](self)
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elif self.vocab.vectors.data.size:
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return True
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elif self.tensor.size:
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return True
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else:
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return False
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property vector:
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"""A real-valued meaning representation. Defaults to an average of the
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token vectors.
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RETURNS (numpy.ndarray[ndim=1, dtype='float32']): A 1D numpy array
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representing the document's semantics.
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"""
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def __get__(self):
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if 'vector' in self.user_hooks:
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return self.user_hooks['vector'](self)
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if self._vector is not None:
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return self._vector
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elif not len(self):
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self._vector = numpy.zeros((self.vocab.vectors_length,),
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dtype='f')
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return self._vector
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elif self.vocab.vectors.data.size > 0:
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vector = numpy.zeros((self.vocab.vectors_length,), dtype='f')
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for token in self.c[:self.length]:
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vector += self.vocab.get_vector(token.lex.orth)
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self._vector = vector / len(self)
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return self._vector
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elif self.tensor.size > 0:
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self._vector = self.tensor.mean(axis=0)
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return self._vector
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else:
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return numpy.zeros((self.vocab.vectors_length,),
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dtype='float32')
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def __set__(self, value):
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self._vector = value
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property vector_norm:
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"""The L2 norm of the document's vector representation.
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RETURNS (float): The L2 norm of the vector representation.
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"""
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def __get__(self):
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if 'vector_norm' in self.user_hooks:
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return self.user_hooks['vector_norm'](self)
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cdef float value
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cdef double norm = 0
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if self._vector_norm is None:
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norm = 0.0
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for value in self.vector:
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norm += value * value
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self._vector_norm = sqrt(norm) if norm != 0 else 0
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return self._vector_norm
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def __set__(self, value):
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self._vector_norm = value
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property text:
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"""A unicode representation of the document text.
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RETURNS (unicode): The original verbatim text of the document.
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"""
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def __get__(self):
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return u''.join(t.text_with_ws for t in self)
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property text_with_ws:
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"""An alias of `Doc.text`, provided for duck-type compatibility with
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`Span` and `Token`.
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RETURNS (unicode): The original verbatim text of the document.
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"""
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def __get__(self):
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return self.text
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property ents:
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"""Iterate over the entities in the document. Yields named-entity
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`Span` objects, if the entity recognizer has been applied to the
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document.
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YIELDS (Span): Entities in the document.
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EXAMPLE: Iterate over the span to get individual Token objects,
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or access the label:
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>>> tokens = nlp(u'Mr. Best flew to New York on Saturday morning.')
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>>> ents = list(tokens.ents)
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>>> assert ents[0].label == 346
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>>> assert ents[0].label_ == 'PERSON'
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>>> assert ents[0].orth_ == 'Best'
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>>> assert ents[0].text == 'Mr. Best'
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"""
|
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def __get__(self):
|
||
cdef int i
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cdef const TokenC* token
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cdef int start = -1
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cdef attr_t label = 0
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||
output = []
|
||
for i in range(self.length):
|
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token = &self.c[i]
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if token.ent_iob == 1:
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if start == -1:
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seq = ['%s|%s' % (t.text, t.ent_iob_) for t in self[i-5:i+5]]
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raise ValueError(Errors.E093.format(seq=' '.join(seq)))
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elif token.ent_iob == 2 or token.ent_iob == 0:
|
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if start != -1:
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output.append(Span(self, start, i, label=label))
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||
start = -1
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||
label = 0
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||
elif token.ent_iob == 3:
|
||
if start != -1:
|
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output.append(Span(self, start, i, label=label))
|
||
start = i
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||
label = token.ent_type
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||
if start != -1:
|
||
output.append(Span(self, start, self.length, label=label))
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||
return tuple(output)
|
||
|
||
def __set__(self, ents):
|
||
# TODO:
|
||
# 1. Allow negative matches
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||
# 2. Ensure pre-set NERs are not over-written during statistical
|
||
# prediction
|
||
# 3. Test basic data-driven ORTH gazetteer
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||
# 4. Test more nuanced date and currency regex
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||
|
||
tokens_in_ents = {}
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||
cdef attr_t entity_type
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||
cdef int ent_start, ent_end
|
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for ent_info in ents:
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||
entity_type, ent_start, ent_end = get_entity_info(ent_info)
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||
for token_index in range(ent_start, ent_end):
|
||
if token_index in tokens_in_ents.keys():
|
||
raise ValueError(Errors.E103.format(
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span1=(tokens_in_ents[token_index][0],
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||
tokens_in_ents[token_index][1],
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||
self.vocab.strings[tokens_in_ents[token_index][2]]),
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span2=(ent_start, ent_end, self.vocab.strings[entity_type])))
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||
tokens_in_ents[token_index] = (ent_start, ent_end, entity_type)
|
||
|
||
cdef int i
|
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for i in range(self.length):
|
||
self.c[i].ent_type = 0
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||
self.c[i].ent_iob = 0 # Means missing.
|
||
cdef attr_t ent_type
|
||
cdef int start, end
|
||
for ent_info in ents:
|
||
ent_type, start, end = get_entity_info(ent_info)
|
||
if ent_type is None or ent_type < 0:
|
||
# Mark as O
|
||
for i in range(start, end):
|
||
self.c[i].ent_type = 0
|
||
self.c[i].ent_iob = 2
|
||
else:
|
||
# Mark (inside) as I
|
||
for i in range(start, end):
|
||
self.c[i].ent_type = ent_type
|
||
self.c[i].ent_iob = 1
|
||
# Set start as B
|
||
self.c[start].ent_iob = 3
|
||
|
||
property noun_chunks:
|
||
"""Iterate over the base noun phrases in the document. Yields base
|
||
noun-phrase #[code Span] objects, if the document has been
|
||
syntactically parsed. A base noun phrase, or "NP chunk", is a noun
|
||
phrase that does not permit other NPs to be nested within it – so no
|
||
NP-level coordination, no prepositional phrases, and no relative
|
||
clauses.
|
||
|
||
YIELDS (Span): Noun chunks in the document.
|
||
"""
|
||
def __get__(self):
|
||
if not self.is_parsed:
|
||
raise ValueError(Errors.E029)
|
||
# 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 sents:
|
||
"""Iterate over the sentences in the document. Yields sentence `Span`
|
||
objects. Sentence spans have no label. To improve accuracy on informal
|
||
texts, spaCy calculates sentence boundaries from the syntactic
|
||
dependency parse. If the parser is disabled, the `sents` iterator will
|
||
be unavailable.
|
||
|
||
EXAMPLE:
|
||
>>> doc = nlp("This is a sentence. Here's another...")
|
||
>>> assert [s.root.text for s in doc.sents] == ["is", "'s"]
|
||
"""
|
||
def __get__(self):
|
||
if not self.is_sentenced:
|
||
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)
|
||
|
||
cdef int push_back(self, LexemeOrToken lex_or_tok, bint has_space) except -1:
|
||
if self.length == 0:
|
||
# Flip these to false when we see the first token.
|
||
self.is_tagged = False
|
||
self.is_parsed = False
|
||
if self.length == self.max_length:
|
||
self._realloc(self.length * 2)
|
||
cdef TokenC* t = &self.c[self.length]
|
||
if LexemeOrToken is const_TokenC_ptr:
|
||
t[0] = lex_or_tok[0]
|
||
else:
|
||
t.lex = lex_or_tok
|
||
if self.length == 0:
|
||
t.idx = 0
|
||
else:
|
||
t.idx = (t-1).idx + (t-1).lex.length + (t-1).spacy
|
||
t.l_edge = self.length
|
||
t.r_edge = self.length
|
||
if t.lex.orth == 0:
|
||
raise ValueError(Errors.E031.format(i=self.length))
|
||
t.spacy = has_space
|
||
self.length += 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, basestring_):
|
||
# 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'
|
||
py_attr_ids = [(IDS[id_.upper()] if hasattr(id_, 'upper') else id_)
|
||
for id_ in py_attr_ids]
|
||
# 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,
|
||
PreshCounter counts=None):
|
||
"""Count the frequencies of a given attribute. Produces a dict of
|
||
`{attribute (int): count (ints)}` frequencies, keyed by the values of
|
||
the given attribute ID.
|
||
|
||
attr_id (int): The attribute ID to key the counts.
|
||
RETURNS (dict): A dictionary mapping attributes to integer counts.
|
||
|
||
EXAMPLE:
|
||
>>> from spacy import attrs
|
||
>>> doc = nlp(u'apple apple orange banana')
|
||
>>> tokens.count_by(attrs.ORTH)
|
||
{12800L: 1, 11880L: 2, 7561L: 1}
|
||
>>> tokens.to_array([attrs.ORTH])
|
||
array([[11880], [11880], [7561], [12800]])
|
||
"""
|
||
cdef int i
|
||
cdef attr_t attr
|
||
cdef size_t count
|
||
|
||
if counts is None:
|
||
counts = PreshCounter()
|
||
output_dict = True
|
||
else:
|
||
output_dict = False
|
||
# Take this check out of the loop, for a bit of extra speed
|
||
if exclude is None:
|
||
for i in range(self.length):
|
||
counts.inc(get_token_attr(&self.c[i], attr_id), 1)
|
||
else:
|
||
for i in range(self.length):
|
||
if not exclude(self[i]):
|
||
attr = get_token_attr(&self.c[i], attr_id)
|
||
counts.inc(attr, 1)
|
||
if output_dict:
|
||
return dict(counts)
|
||
|
||
def _realloc(self, new_size):
|
||
self.max_length = new_size
|
||
n = new_size + (PADDING * 2)
|
||
# What we're storing is a "padded" array. We've jumped forward PADDING
|
||
# places, and are storing the pointer to that. This way, we can access
|
||
# words out-of-bounds, and get out-of-bounds markers.
|
||
# Now that we want to realloc, we need the address of the true start,
|
||
# so we jump the pointer back PADDING places.
|
||
cdef TokenC* data_start = self.c - PADDING
|
||
data_start = <TokenC*>self.mem.realloc(data_start, n * sizeof(TokenC))
|
||
self.c = data_start + PADDING
|
||
cdef int i
|
||
for i in range(self.length, self.max_length + PADDING):
|
||
self.c[i].lex = &EMPTY_LEXEME
|
||
|
||
cdef void set_parse(self, const TokenC* parsed) nogil:
|
||
# TODO: This method is fairly misleading atm. It's used by Parser
|
||
# to actually apply the parse calculated. Need to rethink this.
|
||
|
||
# Probably we should use from_array?
|
||
self.is_parsed = True
|
||
for i in range(self.length):
|
||
self.c[i] = parsed[i]
|
||
|
||
def from_array(self, attrs, array):
|
||
if SENT_START in attrs and HEAD in attrs:
|
||
raise ValueError(Errors.E032)
|
||
cdef int i, col
|
||
cdef attr_id_t attr_id
|
||
cdef TokenC* tokens = self.c
|
||
cdef int length = len(array)
|
||
# Get set up for fast loading
|
||
cdef Pool mem = Pool()
|
||
cdef int n_attrs = len(attrs)
|
||
attr_ids = <attr_id_t*>mem.alloc(n_attrs, sizeof(attr_id_t))
|
||
for i, attr_id in enumerate(attrs):
|
||
attr_ids[i] = attr_id
|
||
# Now load the data
|
||
for i in range(self.length):
|
||
token = &self.c[i]
|
||
for j in range(n_attrs):
|
||
Token.set_struct_attr(token, attr_ids[j], array[i, j])
|
||
# Auxiliary loading logic
|
||
for col, attr_id in enumerate(attrs):
|
||
if attr_id == TAG:
|
||
for i in range(length):
|
||
if array[i, col] != 0:
|
||
self.vocab.morphology.assign_tag(&tokens[i], array[i, col])
|
||
# set flags
|
||
self.is_parsed = bool(self.is_parsed or HEAD in attrs or DEP in attrs)
|
||
self.is_tagged = bool(self.is_tagged or TAG in attrs or POS in attrs)
|
||
# if document is parsed, set children
|
||
if self.is_parsed:
|
||
set_children_from_heads(self.c, self.length)
|
||
return self
|
||
|
||
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).
|
||
"""
|
||
return numpy.asarray(_get_lca_matrix(self, 0, len(self)))
|
||
|
||
def to_disk(self, path, **exclude):
|
||
"""Save the current state to a directory.
|
||
|
||
path (unicode or Path): A path to a directory, which will be created if
|
||
it doesn't exist. Paths may be either strings or Path-like objects.
|
||
"""
|
||
path = util.ensure_path(path)
|
||
with path.open('wb') as file_:
|
||
file_.write(self.to_bytes(**exclude))
|
||
|
||
def from_disk(self, path, **exclude):
|
||
"""Loads state from a directory. Modifies the object in place and
|
||
returns it.
|
||
|
||
path (unicode or Path): A path to a directory. Paths may be either
|
||
strings or `Path`-like objects.
|
||
RETURNS (Doc): The modified `Doc` object.
|
||
"""
|
||
path = util.ensure_path(path)
|
||
with path.open('rb') as file_:
|
||
bytes_data = file_.read()
|
||
return self.from_bytes(bytes_data, **exclude)
|
||
|
||
def to_bytes(self, **exclude):
|
||
"""Serialize, i.e. export the document contents to a binary string.
|
||
|
||
RETURNS (bytes): A losslessly serialized copy of the `Doc`, including
|
||
all annotations.
|
||
"""
|
||
array_head = [LENGTH, SPACY, LEMMA, ENT_IOB, ENT_TYPE]
|
||
|
||
if self.is_tagged:
|
||
array_head.append(TAG)
|
||
# if doc parsed add head and dep attribute
|
||
if self.is_parsed:
|
||
array_head.extend([HEAD, DEP])
|
||
# otherwise add sent_start
|
||
else:
|
||
array_head.append(SENT_START)
|
||
# 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,
|
||
}
|
||
if 'user_data' not in exclude and self.user_data:
|
||
user_data_keys, user_data_values = list(zip(*self.user_data.items()))
|
||
serializers['user_data_keys'] = lambda: srsly.msgpack_dumps(user_data_keys)
|
||
serializers['user_data_values'] = lambda: srsly.msgpack_dumps(user_data_values)
|
||
|
||
return util.to_bytes(serializers, exclude)
|
||
|
||
def from_bytes(self, bytes_data, **exclude):
|
||
"""Deserialize, i.e. import the document contents from a binary string.
|
||
|
||
data (bytes): The string to load from.
|
||
RETURNS (Doc): Itself.
|
||
"""
|
||
if self.length != 0:
|
||
raise ValueError(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,
|
||
'user_data_keys': lambda b: None,
|
||
'user_data_values': lambda b: None,
|
||
}
|
||
|
||
msg = util.from_bytes(bytes_data, deserializers, exclude)
|
||
# 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']
|
||
|
||
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.
|
||
'''
|
||
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 emty.
|
||
attributes (Dictionary[]): Attributes to assign to the merged tokens. By default,
|
||
must be the same lenghth as spans, emty 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
|
||
|
||
assert len(attributes) == len(spans), "attribute length should be equal to span length" + str(len(attributes)) +\
|
||
str(len(spans))
|
||
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 merge(self, int start_idx, int end_idx, *args, **attributes):
|
||
"""Retokenize the document, such that the span at
|
||
`doc.text[start_idx : end_idx]` is merged into a single token. If
|
||
`start_idx` and `end_idx `do not mark start and end token boundaries,
|
||
the document remains unchanged.
|
||
|
||
start_idx (int): Character index of the start of the slice to merge.
|
||
end_idx (int): Character index after the end of the slice to merge.
|
||
**attributes: Attributes to assign to the merged token. By default,
|
||
attributes are inherited from the syntactic root of the span.
|
||
RETURNS (Token): The newly merged token, or `None` if the start and end
|
||
indices did not fall at token boundaries.
|
||
"""
|
||
cdef unicode tag, lemma, ent_type
|
||
if len(args) == 3:
|
||
deprecation_warning(Warnings.W003)
|
||
tag, lemma, ent_type = args
|
||
attributes[TAG] = tag
|
||
attributes[LEMMA] = lemma
|
||
attributes[ENT_TYPE] = ent_type
|
||
elif not args:
|
||
fix_attributes(self, attributes)
|
||
elif args:
|
||
raise ValueError(Errors.E034.format(n_args=len(args),
|
||
args=repr(args),
|
||
kwargs=repr(attributes)))
|
||
remove_label_if_necessary(attributes)
|
||
|
||
attributes = intify_attrs(attributes, strings_map=self.vocab.strings)
|
||
|
||
cdef int start = token_by_start(self.c, self.length, start_idx)
|
||
if start == -1:
|
||
return None
|
||
cdef int end = token_by_end(self.c, self.length, end_idx)
|
||
if end == -1:
|
||
return None
|
||
# Currently we have the token index, we want the range-end index
|
||
end += 1
|
||
with self.retokenize() as retokenizer:
|
||
retokenizer.merge(self[start:end], attrs=attributes)
|
||
return self[start]
|
||
|
||
def print_tree(self, light=False, flat=False):
|
||
raise ValueError(Errors.E105)
|
||
|
||
def to_json(self, underscore=None):
|
||
"""Convert a Doc to JSON. Produces the same format used by the spacy
|
||
train command.
|
||
|
||
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}
|
||
data['ents'] = [{'start': ent.start_char, 'end': ent.end_char,
|
||
'label': ent.label_} for ent in self.ents]
|
||
sents = list(self.sents)
|
||
if sents:
|
||
data['sents'] = [{'start': sent.start_char, 'end': sent.end_char}
|
||
for sent in sents]
|
||
if self.cats:
|
||
data['cats'] = self.cats
|
||
data['tokens'] = []
|
||
for token in self:
|
||
token_data = {'id': token.i, 'start': token.idx, 'end': token.idx + len(token)}
|
||
if token.pos_:
|
||
token_data['pos'] = token.pos_
|
||
if token.tag_:
|
||
token_data['tag'] = token.tag_
|
||
if token.dep_:
|
||
token_data['dep'] = token.dep_
|
||
if token.head:
|
||
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
|
||
|
||
|
||
cdef int token_by_start(const TokenC* tokens, int length, int start_char) except -2:
|
||
cdef int i
|
||
for i in range(length):
|
||
if tokens[i].idx == start_char:
|
||
return i
|
||
else:
|
||
return -1
|
||
|
||
|
||
cdef int token_by_end(const TokenC* tokens, int length, int end_char) except -2:
|
||
cdef int i
|
||
for i in range(length):
|
||
if tokens[i].idx + tokens[i].lex.length == end_char:
|
||
return i
|
||
else:
|
||
return -1
|
||
|
||
|
||
cdef int set_children_from_heads(TokenC* tokens, int length) except -1:
|
||
cdef TokenC* head
|
||
cdef TokenC* child
|
||
cdef int i
|
||
# Set number of left/right children to 0. We'll increment it in the loops.
|
||
for i in range(length):
|
||
tokens[i].l_kids = 0
|
||
tokens[i].r_kids = 0
|
||
tokens[i].l_edge = i
|
||
tokens[i].r_edge = i
|
||
# Twice, for non-projectivity
|
||
for loop_count in range(2):
|
||
# Set left edges
|
||
for i in range(length):
|
||
child = &tokens[i]
|
||
head = &tokens[i + child.head]
|
||
if child < head and loop_count == 0:
|
||
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(length-1, -1, -1):
|
||
child = &tokens[i]
|
||
head = &tokens[i + child.head]
|
||
if child > head and loop_count == 0:
|
||
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
|
||
# Set sentence starts
|
||
for i in range(length):
|
||
if tokens[i].head == 0 and tokens[i].dep != 0:
|
||
tokens[tokens[i].l_edge].sent_start = True
|
||
|
||
|
||
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
|
||
|
||
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(vocab=False, user_data=False)
|
||
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
|
||
start = ent_info.start
|
||
end = ent_info.end
|
||
elif len(ent_info) == 3:
|
||
ent_type, start, end = ent_info
|
||
else:
|
||
ent_id, ent_type, start, end = ent_info
|
||
return ent_type, start, end
|