spaCy/spacy/tokens/span.pyx

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# coding: utf8
from __future__ import unicode_literals
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cimport numpy as np
from libc.math cimport sqrt
import numpy
import numpy.linalg
from thinc.neural.util import get_array_module
from collections import defaultdict
from .doc cimport token_by_start, token_by_end, get_token_attr, _get_lca_matrix
from .token cimport TokenC
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from ..structs cimport TokenC, LexemeC
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from ..typedefs cimport flags_t, attr_t, hash_t
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from ..attrs cimport attr_id_t
from ..parts_of_speech cimport univ_pos_t
from ..attrs cimport *
from ..lexeme cimport Lexeme
from ..util import normalize_slice
from ..compat import is_config, basestring_
from ..errors import Errors, TempErrors, Warnings, user_warning, models_warning
from ..errors import deprecation_warning
from .underscore import Underscore, get_ext_args
cdef class Span:
"""A slice from a Doc object.
DOCS: https://spacy.io/api/span
"""
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@classmethod
def set_extension(cls, name, **kwargs):
"""Define a custom attribute which becomes available as `Span._`.
name (unicode): Name of the attribute to set.
default: Optional default value of the attribute.
getter (callable): Optional getter function.
setter (callable): Optional setter function.
method (callable): Optional method for method extension.
force (bool): Force overwriting existing attribute.
DOCS: https://spacy.io/api/span#set_extension
USAGE: https://spacy.io/usage/processing-pipelines#custom-components-attributes
"""
if cls.has_extension(name) and not kwargs.get("force", False):
raise ValueError(Errors.E090.format(name=name, obj="Span"))
Underscore.span_extensions[name] = get_ext_args(**kwargs)
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@classmethod
def get_extension(cls, name):
"""Look up a previously registered extension by name.
name (unicode): Name of the extension.
RETURNS (tuple): A `(default, method, getter, setter)` tuple.
DOCS: https://spacy.io/api/span#get_extension
"""
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return Underscore.span_extensions.get(name)
@classmethod
def has_extension(cls, name):
"""Check whether an extension has been registered.
name (unicode): Name of the extension.
RETURNS (bool): Whether the extension has been registered.
DOCS: https://spacy.io/api/span#has_extension
"""
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return name in Underscore.span_extensions
@classmethod
def remove_extension(cls, name):
"""Remove a previously registered extension.
name (unicode): Name of the extension.
RETURNS (tuple): A `(default, method, getter, setter)` tuple of the
removed extension.
DOCS: https://spacy.io/api/span#remove_extension
"""
if not cls.has_extension(name):
raise ValueError(Errors.E046.format(name=name))
return Underscore.span_extensions.pop(name)
def __cinit__(self, Doc doc, int start, int end, label=0, vector=None,
vector_norm=None):
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"""Create a `Span` object from the slice `doc[start : end]`.
doc (Doc): The parent document.
start (int): The index of the first token of the span.
end (int): The index of the first token after the span.
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label (uint64): A label to attach to the Span, e.g. for named entities.
vector (ndarray[ndim=1, dtype='float32']): A meaning representation
of the span.
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RETURNS (Span): The newly constructed object.
DOCS: https://spacy.io/api/span#init
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"""
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if not (0 <= start <= end <= len(doc)):
raise IndexError(Errors.E035.format(start=start, end=end, length=len(doc)))
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self.doc = doc
self.start = start
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self.start_char = self.doc[start].idx if start < self.doc.length else 0
self.end = end
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if end >= 1:
self.end_char = self.doc[end - 1].idx + len(self.doc[end - 1])
else:
self.end_char = 0
if isinstance(label, basestring_):
label = doc.vocab.strings.add(label)
if label not in doc.vocab.strings:
raise ValueError(Errors.E084.format(label=label))
self.label = label
self._vector = vector
self._vector_norm = vector_norm
def __richcmp__(self, Span other, int op):
if other is None:
if op == 0 or op == 1 or op == 2:
return False
else:
return True
# Eq
if op == 0:
return self.start_char < other.start_char
elif op == 1:
return self.start_char <= other.start_char
elif op == 2:
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return self.start_char == other.start_char and self.end_char == other.end_char
elif op == 3:
return self.start_char != other.start_char or self.end_char != other.end_char
elif op == 4:
return self.start_char > other.start_char
elif op == 5:
return self.start_char >= other.start_char
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def __hash__(self):
return hash((self.doc, self.label, self.start_char, self.end_char))
def __len__(self):
"""Get the number of tokens in the span.
RETURNS (int): The number of tokens in the span.
DOCS: https://spacy.io/api/span#len
"""
self._recalculate_indices()
if self.end < self.start:
return 0
return self.end - self.start
def __repr__(self):
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if is_config(python3=True):
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return self.text
return self.text.encode("utf-8")
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def __getitem__(self, object i):
"""Get a `Token` or a `Span` object
i (int or tuple): The index of the token within the span, or slice of
the span to get.
RETURNS (Token or Span): The token at `span[i]`.
DOCS: https://spacy.io/api/span#getitem
"""
self._recalculate_indices()
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if isinstance(i, slice):
start, end = normalize_slice(len(self), i.start, i.stop, i.step)
return Span(self.doc, start + self.start, end + self.start)
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else:
if i < 0:
return self.doc[self.end + i]
else:
return self.doc[self.start + i]
def __iter__(self):
"""Iterate over `Token` objects.
YIELDS (Token): A `Token` object.
DOCS: https://spacy.io/api/span#iter
"""
self._recalculate_indices()
for i in range(self.start, self.end):
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yield self.doc[i]
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def __reduce__(self):
raise NotImplementedError(Errors.E112)
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@property
def _(self):
"""Custom extension attributes registered via `set_extension`."""
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return Underscore(Underscore.span_extensions, self,
start=self.start_char, end=self.end_char)
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def as_doc(self):
"""Create a `Doc` object with a copy of the `Span`'s data.
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RETURNS (Doc): The `Doc` copy of the span.
DOCS: https://spacy.io/api/span#as_doc
"""
# TODO: Fix!
words = [t.text for t in self]
spaces = [bool(t.whitespace_) for t in self]
cdef Doc doc = Doc(self.doc.vocab, words=words, spaces=spaces)
array_head = [LENGTH, SPACY, LEMMA, ENT_IOB, ENT_TYPE]
if self.doc.is_tagged:
array_head.append(TAG)
# If doc parsed add head and dep attribute
if self.doc.is_parsed:
array_head.extend([HEAD, DEP])
# Otherwise add sent_start
else:
array_head.append(SENT_START)
array = self.doc.to_array(array_head)
doc.from_array(array_head, array[self.start : self.end])
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doc.noun_chunks_iterator = self.doc.noun_chunks_iterator
doc.user_hooks = self.doc.user_hooks
doc.user_span_hooks = self.doc.user_span_hooks
doc.user_token_hooks = self.doc.user_token_hooks
doc.vector = self.vector
doc.vector_norm = self.vector_norm
doc.tensor = self.doc.tensor[self.start : self.end]
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for key, value in self.doc.cats.items():
if hasattr(key, "__len__") and len(key) == 3:
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cat_start, cat_end, cat_label = key
if cat_start == self.start_char and cat_end == self.end_char:
doc.cats[cat_label] = value
return doc
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def merge(self, *args, **attributes):
"""Retokenize the document, such that the span is merged into a single
token.
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**attributes: Attributes to assign to the merged token. By default,
attributes are inherited from the syntactic root token of the span.
RETURNS (Token): The newly merged token.
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"""
deprecation_warning(Warnings.W013.format(obj="Span"))
return self.doc.merge(self.start_char, self.end_char, *args,
**attributes)
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def get_lca_matrix(self):
"""Calculates a matrix of Lowest Common Ancestors (LCA) for a given
`Span`, where LCA[i, j] is the index of the lowest common ancestor among
the tokens span[i] and span[j]. If they have no common ancestor within
the span, LCA[i, j] will be -1.
RETURNS (np.array[ndim=2, dtype=numpy.int32]): LCA matrix with shape
(n, n), where n = len(self).
DOCS: https://spacy.io/api/span#get_lca_matrix
"""
return numpy.asarray(_get_lca_matrix(self.doc, self.start, self.end))
def similarity(self, other):
"""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`,
`Span`, `Token` and `Lexeme` objects.
RETURNS (float): A scalar similarity score. Higher is more similar.
DOCS: https://spacy.io/api/span#similarity
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"""
if "similarity" in self.doc.user_span_hooks:
self.doc.user_span_hooks["similarity"](self, other)
if len(self) == 1 and hasattr(other, "orth"):
if self[0].orth == other.orth:
return 1.0
elif hasattr(other, "__len__") and len(self) == len(other):
for i in range(len(self)):
if self[i].orth != getattr(other[i], "orth", None):
break
else:
return 1.0
if self.vocab.vectors.n_keys == 0:
models_warning(Warnings.W007.format(obj="Span"))
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if self.vector_norm == 0.0 or other.vector_norm == 0.0:
user_warning(Warnings.W008.format(obj="Span"))
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return 0.0
vector = self.vector
xp = get_array_module(vector)
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return xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm)
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cpdef np.ndarray to_array(self, object py_attr_ids):
"""Given a list of M attribute IDs, export the tokens to a numpy
`ndarray` of shape `(N, M)`, where `N` is the length of the document.
The values will be 32-bit integers.
attr_ids (list[int]): A list of attribute ID ints.
RETURNS (numpy.ndarray[long, ndim=2]): A feature matrix, with one row
per word, and one column per attribute indicated in the input
`attr_ids`.
"""
cdef int i, j
cdef attr_id_t feature
cdef np.ndarray[attr_t, ndim=2] output
# Make an array from the attributes - otherwise our inner loop is Python
# dict iteration
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cdef np.ndarray[attr_t, ndim=1] attr_ids = numpy.asarray(py_attr_ids, dtype=numpy.uint64)
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cdef int length = self.end - self.start
output = numpy.ndarray(shape=(length, len(attr_ids)), dtype=numpy.uint64)
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for i in range(self.start, self.end):
for j, feature in enumerate(attr_ids):
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output[i-self.start, j] = get_token_attr(&self.doc.c[i], feature)
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return output
cpdef int _recalculate_indices(self) except -1:
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if self.end > self.doc.length \
or self.doc.c[self.start].idx != self.start_char \
or (self.doc.c[self.end-1].idx + self.doc.c[self.end-1].lex.length) != self.end_char:
start = token_by_start(self.doc.c, self.doc.length, self.start_char)
if self.start == -1:
raise IndexError(Errors.E036.format(start=self.start_char))
end = token_by_end(self.doc.c, self.doc.length, self.end_char)
if end == -1:
raise IndexError(Errors.E037.format(end=self.end_char))
self.start = start
self.end = end + 1
property vocab:
"""RETURNS (Vocab): The Span's Doc's vocab."""
def __get__(self):
return self.doc.vocab
property sent:
"""RETURNS (Span): The sentence span that the span is a part of."""
def __get__(self):
if "sent" in self.doc.user_span_hooks:
return self.doc.user_span_hooks["sent"](self)
# This should raise if not parsed / no custom sentence boundaries
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self.doc.sents
# If doc is parsed we can use the deps to find the sentence
# otherwise we use the `sent_start` token attribute
cdef int n = 0
cdef int i
if self.doc.is_parsed:
root = &self.doc.c[self.start]
while root.head != 0:
root += root.head
n += 1
if n >= self.doc.length:
raise RuntimeError(Errors.E038)
return self.doc[root.l_edge:root.r_edge + 1]
elif self.doc.is_sentenced:
# Find start of the sentence
start = self.start
while self.doc.c[start].sent_start != 1 and start > 0:
start += -1
# Find end of the sentence
end = self.end
n = 0
while end < self.doc.length and self.doc.c[end].sent_start != 1:
end += 1
n += 1
if n >= self.doc.length:
break
return self.doc[start:end]
property ents:
"""The named entities in the span. Returns a tuple of named entity
`Span` objects, if the entity recognizer has been applied.
RETURNS (tuple): Entities in the span, one `Span` per entity.
DOCS: https://spacy.io/api/span#ents
"""
def __get__(self):
ents = []
for ent in self.doc.ents:
if ent.start >= self.start and ent.end <= self.end:
ents.append(ent)
return ents
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.
DOCS: https://spacy.io/api/span#has_vector
"""
def __get__(self):
if "has_vector" in self.doc.user_span_hooks:
return self.doc.user_span_hooks["has_vector"](self)
elif self.vocab.vectors.data.size > 0:
return any(token.has_vector for token in self)
elif self.doc.tensor.size > 0:
return True
else:
return False
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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 span's semantics.
DOCS: https://spacy.io/api/span#vector
"""
def __get__(self):
if "vector" in self.doc.user_span_hooks:
return self.doc.user_span_hooks["vector"](self)
if self._vector is None:
self._vector = sum(t.vector for t in self) / len(self)
return self._vector
property vector_norm:
"""The L2 norm of the span's vector representation.
RETURNS (float): The L2 norm of the vector representation.
DOCS: https://spacy.io/api/span#vector_norm
"""
def __get__(self):
if "vector_norm" in self.doc.user_span_hooks:
return self.doc.user_span_hooks["vector"](self)
cdef float value
cdef double norm = 0
if self._vector_norm is None:
norm = 0
for value in self.vector:
norm += value * value
self._vector_norm = sqrt(norm) if norm != 0 else 0
return self._vector_norm
property sentiment:
"""RETURNS (float): A scalar value indicating the positivity or
negativity of the span.
"""
def __get__(self):
if "sentiment" in self.doc.user_span_hooks:
return self.doc.user_span_hooks["sentiment"](self)
else:
return sum([token.sentiment for token in self]) / len(self)
property text:
"""RETURNS (unicode): The original verbatim text of the span."""
def __get__(self):
text = self.text_with_ws
if self[-1].whitespace_:
text = text[:-1]
return text
property text_with_ws:
"""The text content of the span with a trailing whitespace character if
the last token has one.
RETURNS (unicode): The text content of the span (with trailing
whitespace).
"""
def __get__(self):
return "".join([t.text_with_ws for t in self])
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property noun_chunks:
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"""Yields base noun-phrase `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.
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YIELDS (Span): Base noun-phrase `Span` objects.
DOCS: https://spacy.io/api/span#noun_chunks
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"""
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def __get__(self):
if not self.doc.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
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spans = []
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cdef attr_t label
if self.doc.noun_chunks_iterator is not None:
for start, end, label in self.doc.noun_chunks_iterator(self):
spans.append(Span(self.doc, start, end, label=label))
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for span in spans:
yield span
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property root:
"""The token with the shortest path to the root of the
sentence (or the root itself). If multiple tokens are equally
high in the tree, the first token is taken.
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RETURNS (Token): The root token.
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DOCS: https://spacy.io/api/span#root
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"""
def __get__(self):
self._recalculate_indices()
if "root" in self.doc.user_span_hooks:
return self.doc.user_span_hooks["root"](self)
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# This should probably be called 'head', and the other one called
# 'gov'. But we went with 'head' elsehwhere, and now we're stuck =/
cdef int i
# First, we scan through the Span, and check whether there's a word
# with head==0, i.e. a sentence root. If so, we can return it. The
# longer the span, the more likely it contains a sentence root, and
# in this case we return in linear time.
for i in range(self.start, self.end):
if self.doc.c[i].head == 0:
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return self.doc[i]
# If we don't have a sentence root, we do something that's not so
# algorithmically clever, but I think should be quite fast,
# especially for short spans.
# For each word, we count the path length, and arg min this measure.
# We could use better tree logic to save steps here...But I
# think this should be okay.
cdef int current_best = self.doc.length
cdef int root = -1
for i in range(self.start, self.end):
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if self.start <= (i+self.doc.c[i].head) < self.end:
continue
words_to_root = _count_words_to_root(&self.doc.c[i], self.doc.length)
if words_to_root < current_best:
current_best = words_to_root
root = i
if root == -1:
return self.doc[self.start]
else:
return self.doc[root]
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property lefts:
"""Tokens that are to the left of the span, whose head is within the
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`Span`.
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YIELDS (Token):A left-child of a token of the span.
DOCS: https://spacy.io/api/span#lefts
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"""
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def __get__(self):
for token in reversed(self): # Reverse, so we get tokens in order
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for left in token.lefts:
if left.i < self.start:
yield left
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property rights:
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"""Tokens that are to the right of the Span, whose head is within the
`Span`.
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YIELDS (Token): A right-child of a token of the span.
DOCS: https://spacy.io/api/span#rights
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"""
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def __get__(self):
for token in self:
for right in token.rights:
if right.i >= self.end:
yield right
property n_lefts:
"""The number of tokens that are to the left of the span, whose
heads are within the span.
RETURNS (int): The number of leftward immediate children of the
span, in the syntactic dependency parse.
DOCS: https://spacy.io/api/span#n_lefts
"""
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def __get__(self):
return len(list(self.lefts))
property n_rights:
"""The number of tokens that are to the right of the span, whose
heads are within the span.
RETURNS (int): The number of rightward immediate children of the
span, in the syntactic dependency parse.
DOCS: https://spacy.io/api/span#n_rights
"""
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def __get__(self):
return len(list(self.rights))
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property subtree:
"""Tokens within the span and tokens which descend from them.
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YIELDS (Token): A token within the span, or a descendant from it.
DOCS: https://spacy.io/api/span#subtree
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"""
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def __get__(self):
for word in self.lefts:
yield from word.subtree
yield from self
for word in self.rights:
yield from word.subtree
property ent_id:
"""RETURNS (uint64): The entity ID."""
def __get__(self):
return self.root.ent_id
def __set__(self, hash_t key):
raise NotImplementedError(TempErrors.T007.format(attr="ent_id"))
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property ent_id_:
"""RETURNS (unicode): The (string) entity ID."""
def __get__(self):
return self.root.ent_id_
def __set__(self, hash_t key):
raise NotImplementedError(TempErrors.T007.format(attr="ent_id_"))
property orth_:
"""Verbatim text content (identical to `Span.text`). Exists mostly for
consistency with other attributes.
RETURNS (unicode): The span's text."""
def __get__(self):
return self.text
property lemma_:
"""RETURNS (unicode): The span's lemma."""
def __get__(self):
return " ".join([t.lemma_ for t in self]).strip()
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property upper_:
"""Deprecated. Use `Span.text.upper()` instead."""
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def __get__(self):
return "".join([t.text_with_ws.upper() for t in self]).strip()
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property lower_:
"""Deprecated. Use `Span.text.lower()` instead."""
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def __get__(self):
return "".join([t.text_with_ws.lower() for t in self]).strip()
property string:
"""Deprecated: Use `Span.text_with_ws` instead."""
def __get__(self):
return "".join([t.text_with_ws for t in self])
property label_:
"""RETURNS (unicode): The span's label."""
def __get__(self):
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return self.doc.vocab.strings[self.label]
def __set__(self, unicode label_):
self.label = self.doc.vocab.strings.add(label_)
cdef int _count_words_to_root(const TokenC* token, int sent_length) except -1:
# Don't allow spaces to be the root, if there are
# better candidates
if Lexeme.c_check_flag(token.lex, IS_SPACE) and token.l_kids == 0 and token.r_kids == 0:
return sent_length-1
if Lexeme.c_check_flag(token.lex, IS_PUNCT) and token.l_kids == 0 and token.r_kids == 0:
return sent_length-1
cdef int n = 0
while token.head != 0:
token += token.head
n += 1
if n >= sent_length:
raise RuntimeError(Errors.E039)
return n