spaCy/spacy/tokens/span.pyx

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# coding: utf8
from __future__ import unicode_literals
from collections import defaultdict
cimport numpy as np
import numpy
import numpy.linalg
from libc.math cimport sqrt
from .doc cimport token_by_start, token_by_end, get_token_attr
from ..structs cimport TokenC, LexemeC
from ..typedefs cimport flags_t, attr_t, hash_t
from ..attrs cimport attr_id_t
from ..parts_of_speech cimport univ_pos_t
from ..util import normalize_slice
from ..attrs cimport IS_PUNCT, IS_SPACE
from ..lexeme cimport Lexeme
from ..compat import is_config
from ..errors import Errors, TempErrors
from .underscore import Underscore, get_ext_args
cdef class Span:
"""A slice from a Doc object."""
@classmethod
def set_extension(cls, name, **kwargs):
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)
@classmethod
def get_extension(cls, name):
return Underscore.span_extensions.get(name)
@classmethod
def has_extension(cls, name):
return name in Underscore.span_extensions
@classmethod
def remove_extension(cls, name):
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, attr_t label=0,
vector=None, vector_norm=None):
"""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.
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.
RETURNS (Span): The newly constructed object.
"""
if not (0 <= start <= end <= len(doc)):
raise IndexError(Errors.E035.format(start=start, end=end, length=len(doc)))
self.doc = doc
self.start = start
self.start_char = self.doc[start].idx if start < self.doc.length else 0
self.end = end
if end >= 1:
self.end_char = self.doc[end - 1].idx + len(self.doc[end - 1])
else:
self.end_char = 0
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:
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
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.
"""
self._recalculate_indices()
if self.end < self.start:
return 0
return self.end - self.start
def __repr__(self):
if is_config(python3=True):
return self.text
return self.text.encode('utf-8')
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]`.
EXAMPLE:
>>> span[0]
>>> span[1:3]
"""
self._recalculate_indices()
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)
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.
"""
self._recalculate_indices()
for i in range(self.start, self.end):
yield self.doc[i]
@property
def _(self):
"""User space for adding custom attribute extensions."""
return Underscore(Underscore.span_extensions, self,
start=self.start_char, end=self.end_char)
def as_doc(self):
# TODO: fix
"""Create a `Doc` object view of the Span's data. This is mostly
useful for C-typed interfaces.
RETURNS (Doc): The `Doc` view of the span.
"""
cdef Doc doc = Doc(self.doc.vocab)
doc.length = self.end-self.start
doc.c = &self.doc.c[self.start]
doc.mem = self.doc.mem
doc.is_parsed = self.doc.is_parsed
doc.is_tagged = self.doc.is_tagged
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
for key, value in self.doc.cats.items():
if hasattr(key, '__len__') and len(key) == 3:
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
def merge(self, *args, **attributes):
"""Retokenize the document, such that the span is merged into a single
token.
**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.
"""
return self.doc.merge(self.start_char, self.end_char, *args,
**attributes)
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.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.vector_norm == 0.0 or other.vector_norm == 0.0:
return 0.0
return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
def get_lca_matrix(self):
"""Calculates the lowest common ancestor matrix for a given `Span`.
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.
"""
def __pairwise_lca(token_j, token_k, lca_matrix, margins):
offset = margins[0]
token_k_head = token_k.head if token_k.head.i in range(*margins) else token_k
token_j_head = token_j.head if token_j.head.i in range(*margins) else token_j
token_j_i = token_j.i - offset
token_k_i = token_k.i - offset
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, margins)
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)
margins = [self.start, self.end]
for j in range(len(self)):
token_j = self[j]
for k in range(len(self)):
token_k = self[k]
lca_matrix[j][k] = __pairwise_lca(token_j, token_k, lca_matrix, margins)
lca_matrix[k][j] = lca_matrix[j][k]
return lca_matrix
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.
cdef np.ndarray[attr_t, ndim=1] attr_ids = numpy.asarray(py_attr_ids, dtype=numpy.uint64)
cdef int length = self.end - self.start
output = numpy.ndarray(shape=(length, len(attr_ids)), dtype=numpy.uint64)
for i in range(self.start, self.end):
for j, feature in enumerate(attr_ids):
output[i-self.start, j] = get_token_attr(&self.doc.c[i], feature)
return output
cpdef int _recalculate_indices(self) except -1:
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 we're not parsed
# or doesen't have any sbd component :)
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 has_vector:
"""RETURNS (bool): Whether a word vector is associated with the object.
"""
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
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.
"""
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:
"""RETURNS (float): The L2 norm of the vector representation."""
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 u''.join([t.text_with_ws for t in self])
property noun_chunks:
"""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.
YIELDS (Span): Base noun-phrase `Span` objects
"""
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
spans = []
cdef attr_t label
for start, end, label in self.doc.noun_chunks_iterator(self):
spans.append(Span(self.doc, start, end, label=label))
for span in spans:
yield span
property root:
"""The token within the span that's highest in the parse tree.
If there's a tie, the earliest is prefered.
RETURNS (Token): The root token.
EXAMPLE: The root token has the shortest path to the root of the
sentence (or is the root itself). If multiple words are equally
high in the tree, the first word is taken. For example:
>>> toks = nlp(u'I like New York in Autumn.')
Let's name the indices easier than writing `toks[4]` etc.
>>> i, like, new, york, in_, autumn, dot = range(len(toks))
The head of 'new' is 'York', and the head of "York" is "like"
>>> toks[new].head.text
'York'
>>> toks[york].head.text
'like'
Create a span for "New York". Its root is "York".
>>> new_york = toks[new:york+1]
>>> new_york.root.text
'York'
Here's a more complicated case, raised by issue #214:
>>> toks = nlp(u'to, north and south carolina')
>>> to, north, and_, south, carolina = toks
>>> south.head.text, carolina.head.text
('north', 'to')
Here "south" is a child of "north", which is a child of "carolina".
Carolina is the root of the span:
>>> south_carolina = toks[-2:]
>>> south_carolina.root.text
'carolina'
"""
def __get__(self):
self._recalculate_indices()
if 'root' in self.doc.user_span_hooks:
return self.doc.user_span_hooks['root'](self)
# 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:
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):
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]
property lefts:
""" Tokens that are to the left of the span, whose head is within the
`Span`.
YIELDS (Token):A left-child of a token of the span.
"""
def __get__(self):
for token in reversed(self): # Reverse, so we get tokens in order
for left in token.lefts:
if left.i < self.start:
yield left
property rights:
"""Tokens that are to the right of the Span, whose head is within the
`Span`.
YIELDS (Token): A right-child of a token of the span.
"""
def __get__(self):
for token in self:
for right in token.rights:
if right.i >= self.end:
yield right
property n_lefts:
"""RETURNS (int): The number of leftward immediate children of the
span, in the syntactic dependency parse.
"""
def __get__(self):
return len(list(self.lefts))
property n_rights:
"""RETURNS (int): The number of rightward immediate children of the
span, in the syntactic dependency parse.
"""
def __get__(self):
return len(list(self.rights))
property subtree:
"""Tokens that descend from tokens in the span, but fall outside it.
YIELDS (Token): A descendant of a token within the span.
"""
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'))
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()
property upper_:
"""Deprecated. Use Span.text.upper() instead."""
def __get__(self):
return ''.join([t.text_with_ws.upper() for t in self]).strip()
property lower_:
"""Deprecated. Use Span.text.lower() instead."""
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):
return self.doc.vocab.strings[self.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