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spaCy/spacy/tokens/doc.pyx

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# coding: utf8
# cython: infer_types=True
# cython: bounds_check=False
from __future__ import unicode_literals
cimport cython
cimport numpy as np
import numpy
import numpy.linalg
import struct
import dill
import msgpack
from thinc.neural.util import get_array_module, copy_array
from libc.string cimport memcpy, memset
from libc.math cimport sqrt
from .span cimport Span
from .token cimport Token
from .span cimport Span
from .token cimport Token
from .printers import parse_tree
from ..lexeme cimport Lexeme, EMPTY_LEXEME
from ..typedefs cimport attr_t, flags_t
from ..attrs import intify_attrs, IDS
from ..attrs cimport attr_id_t
from ..attrs cimport ID, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX, CLUSTER
from ..attrs cimport LENGTH, POS, LEMMA, TAG, DEP, HEAD, SPACY, ENT_IOB
from ..attrs cimport ENT_TYPE, SENT_START
from ..parts_of_speech cimport CCONJ, PUNCT, NOUN, univ_pos_t
from ..util import normalize_slice
from ..compat import is_config, copy_reg, pickle, basestring_
from .. import about
from .. import util
from .underscore import Underscore
DEF PADDING = 5
cdef int bounds_check(int i, int length, int padding) except -1:
if (i + padding) < 0:
raise IndexError
if (i - padding) >= length:
raise IndexError
cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) nogil:
if feat_name == LEMMA:
return token.lemma
elif feat_name == POS:
return token.pos
elif feat_name == TAG:
return token.tag
elif feat_name == DEP:
return token.dep
elif feat_name == HEAD:
return token.head
elif feat_name == SENT_START:
return token.sent_start
elif feat_name == SPACY:
return token.spacy
elif feat_name == ENT_IOB:
return token.ent_iob
elif feat_name == ENT_TYPE:
return token.ent_type
else:
return Lexeme.get_struct_attr(token.lex, feat_name)
def _get_chunker(lang):
try:
cls = util.get_lang_class(lang)
except ImportError:
return None
except KeyError:
return None
return cls.Defaults.syntax_iterators.get(u'noun_chunks')
cdef class Doc:
"""A sequence of Token objects. Access sentences and named entities, export
annotations to numpy arrays, losslessly serialize to compressed binary
strings. The `Doc` object holds an array of `TokenC` structs. The
Python-level `Token` and `Span` objects are views of this array, i.e.
they don't own the data themselves.
EXAMPLE: Construction 1
>>> doc = nlp(u'Some text')
Construction 2
>>> from spacy.tokens import Doc
>>> doc = Doc(nlp.vocab, words=[u'hello', u'world', u'!'],
spaces=[True, False, False])
"""
@classmethod
def set_extension(cls, name, default=None, method=None,
getter=None, setter=None):
nr_defined = sum(t is not None for t in (default, getter, setter, method))
assert nr_defined == 1
Underscore.doc_extensions[name] = (default, method, getter, setter)
@classmethod
def get_extension(cls, name):
return Underscore.doc_extensions.get(name)
@classmethod
def has_extension(cls, name):
return name in Underscore.doc_extensions
def __init__(self, Vocab vocab, words=None, spaces=None, user_data=None,
orths_and_spaces=None):
"""Create a Doc object.
vocab (Vocab): A vocabulary object, which must match any models you
want to use (e.g. tokenizer, parser, entity recognizer).
words (list or None): A list of unicode strings to add to the document
as words. If `None`, defaults to empty list.
spaces (list or None): A list of boolean values, of the same length as
words. True means that the word is followed by a space, False means
it is not. If `None`, defaults to `[True]*len(words)`
user_data (dict or None): Optional extra data to attach to the Doc.
RETURNS (Doc): The newly constructed object.
"""
self.vocab = vocab
size = 20
self.mem = Pool()
# Guarantee self.lex[i-x], for any i >= 0 and x < padding is in bounds
# However, we need to remember the true starting places, so that we can
# realloc.
data_start = <TokenC*>self.mem.alloc(size + (PADDING*2), sizeof(TokenC))
cdef int i
for i in range(size + (PADDING*2)):
data_start[i].lex = &EMPTY_LEXEME
data_start[i].l_edge = i
data_start[i].r_edge = i
self.c = data_start + PADDING
self.max_length = size
self.length = 0
self.is_tagged = False
self.is_parsed = False
self.sentiment = 0.0
self.cats = {}
self.user_hooks = {}
self.user_token_hooks = {}
self.user_span_hooks = {}
self.tensor = numpy.zeros((0,), dtype='float32')
self.user_data = {} if user_data is None else user_data
self._vector = None
self.noun_chunks_iterator = _get_chunker(self.vocab.lang)
cdef unicode orth
cdef bint has_space
if orths_and_spaces is None and words is not None:
if spaces is None:
spaces = [True] * len(words)
elif len(spaces) != len(words):
raise ValueError(
"Arguments 'words' and 'spaces' should be sequences of "
"the same length, or 'spaces' should be left default at "
"None. spaces should be a sequence of booleans, with True "
"meaning that the word owns a ' ' character following it.")
orths_and_spaces = zip(words, spaces)
if orths_and_spaces is not None:
for orth_space in orths_and_spaces:
if isinstance(orth_space, unicode):
orth = orth_space
has_space = True
elif isinstance(orth_space, bytes):
raise ValueError(
"orths_and_spaces expects either List(unicode) or "
"List((unicode, bool)). "
"Got bytes instance: %s" % (str(orth_space)))
else:
orth, has_space = orth_space
# Note that we pass self.mem here --- we have ownership, if LexemeC
# must be created.
self.push_back(
<const LexemeC*>self.vocab.get(self.mem, orth), has_space)
# Tough to decide on policy for this. Is an empty doc tagged and parsed?
# There's no information we'd like to add to it, so I guess so?
if self.length == 0:
self.is_tagged = True
self.is_parsed = True
@property
def _(self):
return Underscore(Underscore.doc_extensions, self)
def __getitem__(self, object i):
"""Get a `Token` or `Span` object.
i (int or tuple) The index of the token, or the slice of the document
to get.
RETURNS (Token or Span): The token at `doc[i]]`, or the span at
`doc[start : end]`.
EXAMPLE:
>>> doc[i]
Get the `Token` object at position `i`, where `i` is an integer.
Negative indexing is supported, and follows the usual Python
semantics, i.e. `doc[-2]` is `doc[len(doc) - 2]`.
>>> doc[start : end]]
Get a `Span` object, starting at position `start` and ending at
position `end`, where `start` and `end` are token indices. For
instance, `doc[2:5]` produces a span consisting of tokens 2, 3 and
4. Stepped slices (e.g. `doc[start : end : step]`) are not
supported, as `Span` objects must be contiguous (cannot have gaps).
You can use negative indices and open-ended ranges, which have
their normal Python semantics.
"""
if isinstance(i, slice):
start, stop = normalize_slice(len(self), i.start, i.stop, i.step)
return Span(self, start, stop, label=0)
if i < 0:
i = self.length + i
bounds_check(i, self.length, PADDING)
return Token.cinit(self.vocab, &self.c[i], i, self)
def __iter__(self):
"""Iterate over `Token` objects, from which the annotations can be
easily accessed. This is the main way of accessing `Token` objects,
which are the main way annotations are accessed from Python. If faster-
than-Python speeds are required, you can instead access the annotations
as a numpy array, or access the underlying C data directly from Cython.
EXAMPLE:
>>> for token in doc
"""
cdef int i
for i in range(self.length):
yield Token.cinit(self.vocab, &self.c[i], i, self)
def __len__(self):
"""The number of tokens in the document.
RETURNS (int): The number of tokens in the document.
EXAMPLE:
>>> len(doc)
"""
return self.length
def __unicode__(self):
return u''.join([t.text_with_ws for t in self])
def __bytes__(self):
return u''.join([t.text_with_ws for t in self]).encode('utf-8')
def __str__(self):
if is_config(python3=True):
return self.__unicode__()
return self.__bytes__()
def __repr__(self):
return self.__str__()
@property
def doc(self):
return self
def char_span(self, int start_idx, int end_idx, label=0, vector=None):
"""Create a `Span` object from the slice `doc.text[start : end]`.
doc (Doc): The parent document.
start (int): The index of the first character of the span.
end (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.
vector (ndarray[ndim=1, dtype='float32']): A meaning representation of
the span.
RETURNS (Span): The newly constructed object.
"""
if not isinstance(label, int):
label = self.vocab.strings.add(label)
cdef int start = token_by_start(self.c, self.length, start_idx)
if start == -1:
return None
cdef int end = token_by_end(self.c, self.length, end_idx)
if end == -1:
return None
# Currently we have the token index, we want the range-end index
end += 1
cdef Span span = Span(self, start, end, label=label, 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.
"""
if 'similarity' in self.user_hooks:
return self.user_hooks['similarity'](self, other)
if self.vector_norm == 0 or other.vector_norm == 0:
return 0.0
return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
property has_vector:
"""A boolean value indicating whether a word vector is associated with
the object.
RETURNS (bool): Whether a word vector is associated with the object.
"""
def __get__(self):
if 'has_vector' in self.user_hooks:
return self.user_hooks['has_vector'](self)
elif 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.
"""
def __get__(self):
if 'vector' in self.user_hooks:
return self.user_hooks['vector'](self)
if self._vector is not None:
return self._vector
elif not len(self):
self._vector = numpy.zeros((self.vocab.vectors_length,),
dtype='f')
return self._vector
elif self.vocab.vectors.data.size > 0:
vector = numpy.zeros((self.vocab.vectors_length,), dtype='f')
for token in self.c[:self.length]:
vector += self.vocab.get_vector(token.lex.orth)
self._vector = vector / len(self)
return self._vector
elif self.tensor.size > 0:
self._vector = self.tensor.mean(axis=0)
return self._vector
else:
return numpy.zeros((self.vocab.vectors_length,),
dtype='float32')
def __set__(self, value):
self._vector = value
property vector_norm:
"""The L2 norm of the document's vector representation.
RETURNS (float): The L2 norm of the vector representation.
"""
def __get__(self):
if 'vector_norm' in self.user_hooks:
return self.user_hooks['vector_norm'](self)
cdef float value
cdef double norm = 0
if self._vector_norm is None:
norm = 0.0
for value in self.vector:
norm += value * value
self._vector_norm = sqrt(norm) if norm != 0 else 0
return self._vector_norm
def __set__(self, value):
self._vector_norm = value
property text:
"""A unicode representation of the document text.
RETURNS (unicode): The original verbatim text of the document.
"""
def __get__(self):
return u''.join(t.text_with_ws for t in self)
property text_with_ws:
"""An alias of `Doc.text`, provided for duck-type compatibility with
`Span` and `Token`.
RETURNS (unicode): The original verbatim text of the document.
"""
def __get__(self):
return self.text
property ents:
"""Iterate over the entities in the document. Yields named-entity
`Span` objects, if the entity recognizer has been applied to the
document.
YIELDS (Span): Entities in the document.
EXAMPLE: Iterate over the span to get individual Token objects,
or access the label:
>>> tokens = nlp(u'Mr. Best flew to New York on Saturday morning.')
>>> ents = list(tokens.ents)
>>> assert ents[0].label == 346
>>> assert ents[0].label_ == 'PERSON'
>>> assert ents[0].orth_ == 'Best'
>>> assert ents[0].text == 'Mr. Best'
"""
def __get__(self):
cdef int i
cdef const TokenC* token
cdef int start = -1
cdef attr_t label = 0
output = []
for i in range(self.length):
token = &self.c[i]
if token.ent_iob == 1:
assert start != -1
elif token.ent_iob == 2 or token.ent_iob == 0:
if start != -1:
output.append(Span(self, start, i, label=label))
start = -1
label = 0
elif token.ent_iob == 3:
if start != -1:
output.append(Span(self, start, i, label=label))
start = i
label = token.ent_type
if start != -1:
output.append(Span(self, start, self.length, label=label))
return tuple(output)
def __set__(self, ents):
# TODO:
# 1. Allow negative matches
# 2. Ensure pre-set NERs are not over-written during statistical
# prediction
# 3. Test basic data-driven ORTH gazetteer
# 4. Test more nuanced date and currency regex
cdef int i
for i in range(self.length):
self.c[i].ent_type = 0
# At this point we don't know whether the NER has run over the
# Doc. If the ent_iob is missing, leave it missing.
if self.c[i].ent_iob != 0:
self.c[i].ent_iob = 2 # Means O. Non-O are set from ents.
cdef attr_t ent_type
cdef int start, end
for ent_info in ents:
if isinstance(ent_info, Span):
ent_id = ent_info.ent_id
ent_type = ent_info.label
start = ent_info.start
end = ent_info.end
elif len(ent_info) == 3:
ent_type, start, end = ent_info
else:
ent_id, ent_type, start, end = ent_info
if ent_type is None or ent_type < 0:
# Mark as O
for i in range(start, end):
self.c[i].ent_type = 0
self.c[i].ent_iob = 2
else:
# Mark (inside) as I
for i in range(start, end):
self.c[i].ent_type = ent_type
self.c[i].ent_iob = 1
# Set start as B
self.c[start].ent_iob = 3
property noun_chunks:
"""Iterate over the base noun phrases in the document. Yields base
noun-phrase #[code Span] objects, if the document has been
syntactically parsed. A base noun phrase, or "NP chunk", is a noun
phrase that does not permit other NPs to be nested within it – so no
NP-level coordination, no prepositional phrases, and no relative
clauses.
YIELDS (Span): Noun chunks in the document.
"""
def __get__(self):
if not self.is_parsed:
raise ValueError(
"noun_chunks requires the dependency parse, which "
"requires a statistical model to be installed and loaded. "
"For more info, see the "
"documentation: \n%s\n" % about.__docs_models__)
# Accumulate the result before beginning to iterate over it. This
# prevents the tokenisation from being changed out from under us
# during the iteration. The tricky thing here is that Span accepts
# its tokenisation changing, so it's okay once we have the Span
# objects. See Issue #375.
spans = []
for start, end, label in self.noun_chunks_iterator(self):
spans.append(Span(self, start, end, label=label))
for span in spans:
yield span
property sents:
"""Iterate over the sentences in the document. Yields sentence `Span`
objects. Sentence spans have no label. To improve accuracy on informal
texts, spaCy calculates sentence boundaries from the syntactic
dependency parse. If the parser is disabled, the `sents` iterator will
be unavailable.
EXAMPLE:
>>> doc = nlp("This is a sentence. Here's another...")
>>> assert [s.root.text for s in doc.sents] == ["is", "'s"]
"""
def __get__(self):
if 'sents' in self.user_hooks:
yield from self.user_hooks['sents'](self)
return
if not self.is_parsed:
raise ValueError(
"Sentence boundary detection requires the dependency "
"parse, which requires a statistical model to be "
"installed and loaded. For more info, see the "
"documentation: \n%s\n" % about.__docs_models__)
cdef int i
start = 0
for i in range(1, self.length):
if self.c[i].sent_start == 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
assert t.lex.orth != 0
t.spacy = has_space
self.length += 1
# Set morphological attributes, e.g. by lemma, if possible
self.vocab.morphology.assign_untagged(t)
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=1] attr_ids
cdef np.ndarray[attr_t, ndim=2] output
# Handle scalar/list inputs of strings/ints for py_attr_ids
if not hasattr(py_attr_ids, '__iter__') \
and not isinstance(py_attr_ids, basestring_):
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.
attr_ids = numpy.asarray(py_attr_ids, dtype=numpy.uint64)
output = numpy.ndarray(shape=(self.length, len(attr_ids)),
dtype=numpy.uint64)
for i in range(self.length):
for j, feature in enumerate(attr_ids):
output[i, j] = get_token_attr(&self.c[i], feature)
# 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(
"Conflicting attributes specified in doc.from_array(): "
"(HEAD, SENT_START)\n"
"The HEAD attribute currently sets sentence boundaries "
"implicitly, based on the tree structure. This means the HEAD "
"attribute would potentially override the sentence boundaries "
"set by SENT_START.")
cdef int i, col
cdef attr_id_t attr_id
cdef TokenC* tokens = self.c
cdef int length = len(array)
# Get set up for fast loading
cdef Pool mem = Pool()
cdef int n_attrs = len(attrs)
attr_ids = <attr_id_t*>mem.alloc(n_attrs, sizeof(attr_id_t))
for i, attr_id in enumerate(attrs):
attr_ids[i] = attr_id
# Now load the data
for i in range(self.length):
token = &self.c[i]
for j in range(n_attrs):
Token.set_struct_attr(token, attr_ids[j], array[i, j])
# Auxiliary loading logic
for col, attr_id in enumerate(attrs):
if attr_id == TAG:
for i in range(length):
if array[i, col] != 0:
self.vocab.morphology.assign_tag(&tokens[i], array[i, col])
set_children_from_heads(self.c, self.length)
self.is_parsed = bool(HEAD in attrs or DEP in attrs)
self.is_tagged = bool(TAG in attrs or POS in attrs)
return self
def get_lca_matrix(self):
"""Calculates the lowest common ancestor matrix for a given `Doc`.
Returns LCA matrix containing the integer index of the ancestor, or -1
if no common ancestor is found (ex if span excludes a necessary
ancestor). Apologies about the recursion, but the impact on
performance is negligible given the natural limitations on the depth
of a typical human sentence.
"""
# Efficiency notes:
# We can easily improve the performance here by iterating in Cython.
# To loop over the tokens in Cython, the easiest way is:
# for token in doc.c[:doc.c.length]:
# head = token + token.head
# Both token and head will be TokenC* here. The token.head attribute
# is an integer offset.
def __pairwise_lca(token_j, token_k, lca_matrix):
if lca_matrix[token_j.i][token_k.i] != -2:
return lca_matrix[token_j.i][token_k.i]
elif token_j == token_k:
lca_index = token_j.i
elif token_k.head == token_j:
lca_index = token_j.i
elif token_j.head == token_k:
lca_index = token_k.i
elif (token_j.head == token_j) and (token_k.head == token_k):
lca_index = -1
else:
lca_index = __pairwise_lca(token_j.head, token_k.head,
lca_matrix)
lca_matrix[token_j.i][token_k.i] = lca_index
lca_matrix[token_k.i][token_j.i] = lca_index
return lca_index
lca_matrix = numpy.empty((len(self), len(self)), dtype=numpy.int32)
lca_matrix.fill(-2)
for j in range(len(self)):
token_j = self[j]
for k in range(j, len(self)):
token_k = self[k]
lca_matrix[j][k] = __pairwise_lca(token_j, token_k, lca_matrix)
lca_matrix[k][j] = lca_matrix[j][k]
return lca_matrix
def to_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, TAG, LEMMA, HEAD, DEP, ENT_IOB, ENT_TYPE]
# 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: msgpack.dumps(user_data_keys)
serializers['user_data_values'] = lambda: 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("Cannot load into non-empty Doc")
deserializers = {
'text': lambda b: None,
'array_head': lambda b: None,
'array_body': lambda b: None,
'sentiment': lambda b: None,
'tensor': lambda b: None,
'user_data_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 = msgpack.loads(msg['user_data_keys'],
use_list=False)
user_data_values = msgpack.loads(msg['user_data_values'])
for key, value in zip(user_data_keys, user_data_values):
self.user_data[key] = value
cdef attr_t[:, :] attrs
cdef int i, start, end, has_space
self.sentiment = msg['sentiment']
self.tensor = msg['tensor']
start = 0
cdef const LexemeC* lex
cdef unicode orth_
text = msg['text']
attrs = msg['array_body']
for i in range(attrs.shape[0]):
end = start + attrs[i, 0]
has_space = attrs[i, 1]
orth_ = text[start:end]
lex = self.vocab.get(self.mem, orth_)
self.push_back(lex, has_space)
start = end + has_space
self.from_array(msg['array_head'][2:],
attrs[:, 2:])
return self
def 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 hape (30, 64),
doc.tensor == (30, 192).
'''
xp = get_array_module(self.tensor)
if self.tensor.size == 0:
self.tensor.resize(tensor.shape)
copy_array(self.tensor, tensor)
else:
self.tensor = xp.hstack((self.tensor, tensor))
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:
util.deprecated(
"Positional arguments to Doc.merge are deprecated. Instead, "
"use the keyword arguments, for example tag=, lemma= or "
"ent_type=.")
tag, lemma, ent_type = args
attributes[TAG] = tag
attributes[LEMMA] = lemma
attributes[ENT_TYPE] = ent_type
elif not args:
if 'label' in attributes and 'ent_type' not in attributes:
if isinstance(attributes['label'], int):
attributes[ENT_TYPE] = attributes['label']
else:
attributes[ENT_TYPE] = self.vocab.strings[attributes['label']]
if 'ent_type' in attributes:
attributes[ENT_TYPE] = attributes['ent_type']
elif args:
raise ValueError(
"Doc.merge received %d non-keyword arguments. Expected either "
"3 arguments (deprecated), or 0 (use keyword arguments). "
"Arguments supplied:\n%s\n"
"Keyword arguments: %s\n" % (len(args), repr(args),
repr(attributes)))
# More deprecated attribute handling =/
if 'label' in attributes:
attributes['ent_type'] = attributes.pop('label')
attributes = intify_attrs(attributes, strings_map=self.vocab.strings)
cdef int start = token_by_start(self.c, self.length, start_idx)
if start == -1:
return None
cdef int end = token_by_end(self.c, self.length, end_idx)
if end == -1:
return None
# Currently we have the token index, we want the range-end index
end += 1
cdef Span span = self[start:end]
# Get LexemeC for newly merged token
new_orth = ''.join([t.text_with_ws for t in span])
if span[-1].whitespace_:
new_orth = new_orth[:-len(span[-1].whitespace_)]
cdef const LexemeC* lex = self.vocab.get(self.mem, new_orth)
# House the new merged token where it starts
cdef TokenC* token = &self.c[start]
token.spacy = self.c[end-1].spacy
for attr_name, attr_value in attributes.items():
if attr_name == TAG:
self.vocab.morphology.assign_tag(token, attr_value)
else:
Token.set_struct_attr(token, attr_name, attr_value)
# Begin by setting all the head indices to absolute token positions
# This is easier to work with for now than the offsets
# Before thinking of something simpler, beware the case where a
# dependency bridges over the entity. Here the alignment of the
# tokens changes.
span_root = span.root.i
token.dep = span.root.dep
# We update token.lex after keeping span root and dep, since
# setting token.lex will change span.start and span.end properties
# as it modifies the character offsets in the doc
token.lex = lex
for i in range(self.length):
self.c[i].head += i
# Set the head of the merged token, and its dep relation, from the Span
token.head = self.c[span_root].head
# Adjust deps before shrinking tokens
# Tokens which point into the merged token should now point to it
# Subtract the offset from all tokens which point to >= end
offset = (end - start) - 1
for i in range(self.length):
head_idx = self.c[i].head
if start <= head_idx < end:
self.c[i].head = start
elif head_idx >= end:
self.c[i].head -= offset
# Now compress the token array
for i in range(end, self.length):
self.c[i - offset] = self.c[i]
for i in range(self.length - offset, self.length):
memset(&self.c[i], 0, sizeof(TokenC))
self.c[i].lex = &EMPTY_LEXEME
self.length -= offset
for i in range(self.length):
# ...And, set heads back to a relative position
self.c[i].head -= i
# Set the left/right children, left/right edges
set_children_from_heads(self.c, self.length)
# Clear the cached Python objects
# Return the merged Python object
return self[start]
def print_tree(self, light=False, flat=False):
"""Returns the parse trees in JSON (dict) format.
light (bool): Don't include lemmas or entities.
flat (bool): Don't include arcs or modifiers.
RETURNS (dict): Parse tree as dict.
EXAMPLE:
>>> doc = nlp('Bob brought Alice the pizza. Alice ate the pizza.')
>>> trees = doc.print_tree()
>>> trees[1]
{'modifiers': [
{'modifiers': [], 'NE': 'PERSON', 'word': 'Alice',
'arc': 'nsubj', 'POS_coarse': 'PROPN', 'POS_fine': 'NNP',
'lemma': 'Alice'},
{'modifiers': [
{'modifiers': [], 'NE': '', 'word': 'the', 'arc': 'det',
'POS_coarse': 'DET', 'POS_fine': 'DT', 'lemma': 'the'}],
'NE': '', 'word': 'pizza', 'arc': 'dobj', 'POS_coarse': 'NOUN',
'POS_fine': 'NN', 'lemma': 'pizza'},
{'modifiers': [], 'NE': '', 'word': '.', 'arc': 'punct',
'POS_coarse': 'PUNCT', 'POS_fine': '.', 'lemma': '.'}],
'NE': '', 'word': 'ate', 'arc': 'ROOT', 'POS_coarse': 'VERB',
'POS_fine': 'VBD', 'lemma': 'eat'}
"""
return parse_tree(self, light=light, flat=flat)
cdef int token_by_start(const TokenC* tokens, int length, int start_char) except -2:
cdef int i
for i in range(length):
if tokens[i].idx == start_char:
return i
else:
return -1
cdef int token_by_end(const TokenC* tokens, int length, int end_char) except -2:
cdef int i
for i in range(length):
if tokens[i].idx + tokens[i].lex.length == end_char:
return i
else:
return -1
cdef int set_children_from_heads(TokenC* tokens, int length) except -1:
cdef TokenC* head
cdef TokenC* child
cdef int i
# Set number of left/right children to 0. We'll increment it in the loops.
for i in range(length):
tokens[i].l_kids = 0
tokens[i].r_kids = 0
tokens[i].l_edge = i
tokens[i].r_edge = i
# Set left edges
for i in range(length):
child = &tokens[i]
head = &tokens[i + child.head]
if child < head:
if child.l_edge < head.l_edge:
head.l_edge = child.l_edge
head.l_kids += 1
# Set right edges --- same as above, but iterate in reverse
for i in range(length-1, -1, -1):
child = &tokens[i]
head = &tokens[i + child.head]
if child > head:
if child.r_edge > head.r_edge:
head.r_edge = child.r_edge
head.r_kids += 1
# Set sentence starts
for i in range(length):
if tokens[i].head == 0 and tokens[i].dep != 0:
tokens[tokens[i].l_edge].sent_start = True
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, dill.dumps(hooks_and_data), bytes_data))
def unpickle_doc(vocab, hooks_and_data, bytes_data):
user_data, doc_hooks, span_hooks, token_hooks = dill.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)
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