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473504d837
spaCy/spacy/tokens/span.pyx
Adriane Boyd fa79a0db9f
Add AttributeRuler for token attribute exceptions (#5842) 
* Add AttributeRuler for token attribute exceptions

Add the `AttributeRuler` to handle exceptions for token-level
attributes. The `AttributeRuler` uses `Matcher` patterns to identify
target spans and applies the specified attributes to the token at the
provided index in the matched span. A negative index can be used to
index from the end of the matched span. The retokenizer is used to
"merge" the individual tokens and assign them the provided attributes.

Helper functions can import existing tag maps and morph rules to the
corresponding `Matcher` patterns.

There is an additional minor bug fix for `MORPH` attributes in the
retokenizer to correctly normalize the values and to handle `MORPH`
alongside `_` in an attrs dict.

* Fix default name

* Update name in error message

* Extend AttributeRuler functionality

* Add option to initialize with a dict of AttributeRuler patterns

* Instead of silently discarding overlapping matches (the default
behavior for the retokenizer if only the attrs differ), split the
matches into disjoint sets and retokenize each set separately. This
allows, for instance, one pattern to set the POS and another pattern to
set the lemma. (If two matches modify the same attribute, it looks like
the attrs are applied in the order they were added, but it may not be
deterministic?)

* Improve types

* Sort spans before processing

* Fix index boundaries in Span

* Refactor retokenizer to separate attrs methods

Add top-level `normalize_token_attrs` and `set_token_attrs` methods.

* Update AttributeRuler to use refactored methods

Update `AttributeRuler` to replace use of full retokenizer with only the
relevant methods for normalizing and setting attributes for a single
token.

* Update spacy/pipeline/attributeruler.py

Co-authored-by: Ines Montani <ines@ines.io>

* Make API more similar to EntityRuler

* Add `AttributeRuler.add_patterns` to add patterns from a list of dicts
* Return list of dicts as property `AttributeRuler.patterns`

* Make attrs_unnormed private

* Add test loading patterns from assets

* Revert "Fix index boundaries in Span"

This reverts commit 8f8a5c3386.

* Add Span index boundary checks (#5861)

* Add Span index boundary checks

* Return Span-specific IndexError in all cases

* Simplify and fix if/else

Co-authored-by: Ines Montani <ines@ines.io>
2020-08-04 17:02:39 +02:00

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from __future__ import unicode_literals
cimport numpy as np
from libc.math cimport sqrt
import numpy
import numpy.linalg
from thinc.api import get_array_module
from collections import defaultdict
import warnings
from .doc cimport token_by_start, token_by_end, get_token_attr, _get_lca_matrix
from .token cimport TokenC
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 ..attrs cimport *
from ..lexeme cimport Lexeme
from ..symbols cimport dep
from ..util import normalize_slice
from ..errors import Errors, TempErrors, Warnings
from .underscore import Underscore, get_ext_args
cdef class Span:
"""A slice from a Doc object.
DOCS: https://spacy.io/api/span
"""
@classmethod
def set_extension(cls, name, **kwargs):
"""Define a custom attribute which becomes available as `Span._`.
name (str): 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)
@classmethod
def get_extension(cls, name):
"""Look up a previously registered extension by name.
name (str): Name of the extension.
RETURNS (tuple): A `(default, method, getter, setter)` tuple.
DOCS: https://spacy.io/api/span#get_extension
"""
return Underscore.span_extensions.get(name)
@classmethod
def has_extension(cls, name):
"""Check whether an extension has been registered.
name (str): Name of the extension.
RETURNS (bool): Whether the extension has been registered.
DOCS: https://spacy.io/api/span#has_extension
"""
return name in Underscore.span_extensions
@classmethod
def remove_extension(cls, name):
"""Remove a previously registered extension.
name (str): 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, kb_id=0):
"""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.
kb_id (uint64): An identifier from a Knowledge Base to capture the meaning of a named entity.
vector (ndarray[ndim=1, dtype='float32']): A meaning representation
of the span.
DOCS: https://spacy.io/api/span#init
"""
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 isinstance(label, str):
label = doc.vocab.strings.add(label)
if isinstance(kb_id, str):
kb_id = doc.vocab.strings.add(kb_id)
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
self.kb_id = kb_id
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
# <
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.doc, self.start_char, self.end_char, self.label, self.kb_id) == (other.doc, other.start_char, other.end_char, other.label, other.kb_id)
# !=
elif op == 3:
return (self.doc, self.start_char, self.end_char, self.label, self.kb_id) != (other.doc, other.start_char, other.end_char, other.label, other.kb_id)
# >
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.start_char, self.end_char, self.label, self.kb_id))
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):
return self.text
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()
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:
token_i = self.end + i
else:
token_i = self.start + i
if self.start <= token_i < self.end:
return self.doc[token_i]
else:
raise IndexError(Errors.E1002)
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):
yield self.doc[i]
def __reduce__(self):
raise NotImplementedError(Errors.E112)
@property
def _(self):
"""Custom extension attributes registered via `set_extension`."""
return Underscore(Underscore.span_extensions, self,
start=self.start_char, end=self.end_char)
def as_doc(self, bint copy_user_data=False):
"""Create a `Doc` object with a copy of the `Span`'s data.
copy_user_data (bool): Whether or not to copy the original doc's user data.
RETURNS (Doc): The `Doc` copy of the span.
DOCS: https://spacy.io/api/span#as_doc
"""
# TODO: make copy_user_data a keyword-only argument (Python 3 only)
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, ENT_ID, ENT_KB_ID]
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)
array = array[self.start : self.end]
self._fix_dep_copy(array_head, array)
doc.from_array(array_head, array)
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]
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
if copy_user_data:
doc.user_data = self.doc.user_data
return doc
def _fix_dep_copy(self, attrs, array):
""" Rewire dependency links to make sure their heads fall into the span
while still keeping the correct number of sentences. """
cdef int length = len(array)
cdef attr_t value
cdef int i, head_col, ancestor_i
old_to_new_root = dict()
if HEAD in attrs:
head_col = attrs.index(HEAD)
for i in range(length):
# if the HEAD refers to a token outside this span, find a more appropriate ancestor
token = self[i]
ancestor_i = token.head.i - self.start # span offset
if ancestor_i not in range(length):
if DEP in attrs:
array[i, attrs.index(DEP)] = dep
# try finding an ancestor within this span
ancestors = token.ancestors
for ancestor in ancestors:
ancestor_i = ancestor.i - self.start
if ancestor_i in range(length):
array[i, head_col] = ancestor_i - i
# if there is no appropriate ancestor, define a new artificial root
value = array[i, head_col]
if (i+value) not in range(length):
new_root = old_to_new_root.get(ancestor_i, None)
if new_root is not None:
# take the same artificial root as a previous token from the same sentence
array[i, head_col] = new_root - i
else:
# set this token as the new artificial root
array[i, head_col] = 0
old_to_new_root[ancestor_i] = i
return array
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
similarity using an average of word vectors.
other (object): The object to compare with. By default, accepts `Doc`,
`Span`, `Token` and `Lexeme` objects.
RETURNS (float): A scalar similarity score. Higher is more similar.
DOCS: https://spacy.io/api/span#similarity
"""
if "similarity" in self.doc.user_span_hooks:
return 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 isinstance(other, (Doc, Span)) and len(self) == len(other):
similar = True
for i in range(len(self)):
if self[i].orth != getattr(other[i], "orth", None):
similar = False
break
if similar:
return 1.0
if self.vocab.vectors.n_keys == 0:
warnings.warn(Warnings.W007.format(obj="Span"))
if self.vector_norm == 0.0 or other.vector_norm == 0.0:
warnings.warn(Warnings.W008.format(obj="Span"))
return 0.0
vector = self.vector
xp = get_array_module(vector)
return xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm)
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
def vocab(self):
"""RETURNS (Vocab): The Span's Doc's vocab."""
return self.doc.vocab
@property
def sent(self):
"""RETURNS (Span): The sentence span that the span is a part of."""
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
self.doc.sents
# Use `sent_start` token attribute to find sentence boundaries
cdef int n = 0
if 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
def ents(self):
"""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
"""
ents = []
for ent in self.doc.ents:
if ent.start >= self.start and ent.end <= self.end:
ents.append(ent)
return ents
@property
def has_vector(self):
"""A boolean value indicating whether a word vector is associated with
the object.
RETURNS (bool): Whether a word vector is associated with the object.
DOCS: https://spacy.io/api/span#has_vector
"""
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
def vector(self):
"""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
"""
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
def vector_norm(self):
"""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
"""
if "vector_norm" in self.doc.user_span_hooks:
return self.doc.user_span_hooks["vector"](self)
vector = self.vector
xp = get_array_module(vector)
if self._vector_norm is None:
total = (vector*vector).sum()
self._vector_norm = xp.sqrt(total) if total != 0. else 0.
return self._vector_norm
@property
def tensor(self):
"""The span's slice of the doc's tensor.
RETURNS (ndarray[ndim=2, dtype='float32']): A 2D numpy or cupy array
representing the span's semantics.
"""
if self.doc.tensor is None:
return None
return self.doc.tensor[self.start : self.end]
@property
def sentiment(self):
"""RETURNS (float): A scalar value indicating the positivity or
negativity of the span.
"""
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
def text(self):
"""RETURNS (str): The original verbatim text of the span."""
text = self.text_with_ws
if self[-1].whitespace_:
text = text[:-1]
return text
@property
def text_with_ws(self):
"""The text content of the span with a trailing whitespace character if
the last token has one.
RETURNS (str): The text content of the span (with trailing
whitespace).
"""
return "".join([t.text_with_ws for t in self])
@property
def noun_chunks(self):
"""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.
DOCS: https://spacy.io/api/span#noun_chunks
"""
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
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))
for span in spans:
yield span
@property
def root(self):
"""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.
RETURNS (Token): The root token.
DOCS: https://spacy.io/api/span#root
"""
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' elsewhere, 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]
def char_span(self, int start_idx, int end_idx, label=0, kb_id=0, vector=None):
"""Create a `Span` object from the slice `span.text[start : end]`.
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.
kb_id (uint64 or string): An ID from a KB to capture the meaning of a named entity.
vector (ndarray[ndim=1, dtype='float32']): A meaning representation of
the span.
RETURNS (Span): The newly constructed object.
"""
start_idx += self.start_char
end_idx += self.start_char
return self.doc.char_span(start_idx, end_idx)
@property
def conjuncts(self):
"""Tokens that are conjoined to the span's root.
RETURNS (tuple): A tuple of Token objects.
DOCS: https://spacy.io/api/span#lefts
"""
return self.root.conjuncts
@property
def lefts(self):
"""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.
DOCS: https://spacy.io/api/span#lefts
"""
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
def rights(self):
"""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.
DOCS: https://spacy.io/api/span#rights
"""
for token in self:
for right in token.rights:
if right.i >= self.end:
yield right
@property
def n_lefts(self):
"""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
"""
return len(list(self.lefts))
@property
def n_rights(self):
"""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
"""
return len(list(self.rights))
@property
def subtree(self):
"""Tokens within the span and tokens which descend from them.
YIELDS (Token): A token within the span, or a descendant from it.
DOCS: https://spacy.io/api/span#subtree
"""
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 (str): 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
def orth_(self):
"""Verbatim text content (identical to `Span.text`). Exists mostly for
consistency with other attributes.
RETURNS (str): The span's text."""
return self.text
@property
def lemma_(self):
"""RETURNS (str): The span's lemma."""
return " ".join([t.lemma_ for t in self]).strip()
property label_:
"""RETURNS (str): The span's label."""
def __get__(self):
return self.doc.vocab.strings[self.label]
def __set__(self, unicode label_):
if not label_:
label_ = ''
raise NotImplementedError(Errors.E129.format(start=self.start, end=self.end, label=label_))
property kb_id_:
"""RETURNS (str): The named entity's KB ID."""
def __get__(self):
return self.doc.vocab.strings[self.kb_id]
def __set__(self, unicode kb_id_):
if not kb_id_:
kb_id_ = ''
current_label = self.label_
if not current_label:
current_label = ''
raise NotImplementedError(Errors.E131.format(start=self.start, end=self.end,
label=current_label, kb_id=kb_id_))
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
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