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Add doc.retokenize() context manager (#2172)

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This patch takes a step towards #1487 by introducing the
doc.retokenize() context manager, to handle merging spans, and soon
splitting tokens.

The idea is to do merging and splitting like this:

with doc.retokenize() as retokenizer:
    for start, end, label in matches:
        retokenizer.merge(doc[start : end], attrs={'ent_type': label})

The retokenizer accumulates the merge requests, and applies them
together at the end of the block. This will allow retokenization to be
more efficient, and much less error prone.

A retokenizer.split() function will then be added, to handle splitting a
single token into multiple tokens. These methods take `Span` and `Token`
objects; if the user wants to go directly from offsets, they can append
to the .merges and .splits lists on the retokenizer.

The doc.merge() method's behaviour remains unchanged, so this patch
should be 100% backwards incompatible (modulo bugs). Internally,
doc.merge() fixes up the arguments (to handle the various deprecated styles),
opens the retokenizer, and makes the single merge.

We can later start making deprecation warnings on direct calls to doc.merge(),
to migrate people to use of the retokenize context manager.
This commit is contained in:
Matthew Honnibal 2018-04-03 14:10:35 +02:00 committed by GitHub
parent 638068ec6c
commit abf8b16d71
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 177 additions and 84 deletions
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1
setup.py
View File

@ -37,6 +37,7 @@ MOD_NAMES = [
'spacy.tokens.doc',
'spacy.tokens.span',
'spacy.tokens.token',
'spacy.tokens._retokenize',
'spacy.matcher',
'spacy.syntax.ner',
'spacy.symbols',

129
spacy/tokens/_retokenize.pyx Normal file
View File

@ -0,0 +1,129 @@
# coding: utf8
# cython: infer_types=True
# cython: bounds_check=False
# cython: profile=True
from __future__ import unicode_literals
from libc.string cimport memcpy, memset
from .doc cimport Doc, set_children_from_heads, token_by_start, token_by_end
from .span cimport Span
from .token cimport Token
from ..lexeme cimport Lexeme, EMPTY_LEXEME
from ..structs cimport LexemeC, TokenC
from ..attrs cimport *
cdef class Retokenizer:
'''Helper class for doc.retokenize() context manager.'''
cdef Doc doc
cdef list merges
cdef list splits
def __init__(self, doc):
self.doc = doc
self.merges = []
self.splits = []
def merge(self, Span span, attrs=None):
'''Mark a span for merging. The attrs will be applied to the resulting
token.'''
self.merges.append((span.start_char, span.end_char, attrs))
def split(self, Token token, orths, attrs=None):
'''Mark a Token for splitting, into the specified orths. The attrs
will be applied to each subtoken.'''
self.splits.append((token.start_char, orths, attrs))
def __enter__(self):
self.merges = []
self.splits = []
return self
def __exit__(self, *args):
# Do the actual merging here
for start_char, end_char, attrs in self.merges:
start = token_by_start(self.doc.c, self.doc.length, start_char)
end = token_by_end(self.doc.c, self.doc.length, end_char)
_merge(self.doc, start, end+1, attrs)
for start_char, orths, attrs in self.splits:
raise NotImplementedError
def _merge(Doc doc, int start, int end, 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 Span span = doc[start:end]
cdef int start_char = span.start_char
cdef int end_char = span.end_char
# 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 = doc.vocab.get(doc.mem, new_orth)
# House the new merged token where it starts
cdef TokenC* token = &doc.c[start]
token.spacy = doc.c[end-1].spacy
for attr_name, attr_value in attributes.items():
if attr_name == TAG:
doc.vocab.morphology.assign_tag(token, attr_value)
else:
Token.set_struct_attr(token, attr_name, attr_value)
# Make sure ent_iob remains consistent
if doc.c[end].ent_iob == 1 and token.ent_iob in (0, 2):
if token.ent_type == doc.c[end].ent_type:
token.ent_iob = 3
else:
# If they're not the same entity type, let them be two entities
doc.c[end].ent_iob = 3
# 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(doc.length):
doc.c[i].head += i
# Set the head of the merged token, and its dep relation, from the Span
token.head = doc.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(doc.length):
head_idx = doc.c[i].head
if start <= head_idx < end:
doc.c[i].head = start
elif head_idx >= end:
doc.c[i].head -= offset
# Now compress the token array
for i in range(end, doc.length):
doc.c[i - offset] = doc.c[i]
for i in range(doc.length - offset, doc.length):
memset(&doc.c[i], 0, sizeof(TokenC))
doc.c[i].lex = &EMPTY_LEXEME
doc.length -= offset
for i in range(doc.length):
# ...And, set heads back to a relative position
doc.c[i].head -= i
# Set the left/right children, left/right edges
set_children_from_heads(doc.c, doc.length)
# Clear the cached Python objects
# Return the merged Python object
return doc[start]

2
spacy/tokens/doc.pxd
View File

@ -25,6 +25,8 @@ cdef int token_by_start(const TokenC* tokens, int length, int start_char) except
cdef int token_by_end(const TokenC* tokens, int length, int end_char) except -2
cdef int set_children_from_heads(TokenC* tokens, int length) except -1
cdef class Doc:
cdef readonly Pool mem
cdef readonly Vocab vocab

129
spacy/tokens/doc.pyx
View File

@ -34,6 +34,7 @@ from ..compat import is_config, copy_reg, pickle, basestring_
from .. import about
from .. import util
from .underscore import Underscore
from ._retokenize import Retokenizer
DEF PADDING = 5
@ -186,6 +187,20 @@ cdef class Doc:
def _(self):
return Underscore(Underscore.doc_extensions, self)
@property
def is_sentenced(self):
# Check if the document has sentence boundaries,
# i.e at least one tok has the sent_start in (-1, 1)
if 'sents' in self.user_hooks:
return True
if self.is_parsed:
return True
for i in range(self.length):
if self.c[i].sent_start == -1 or self.c[i].sent_start == 1:
return True
else:
return False
def __getitem__(self, object i):
"""Get a `Token` or `Span` object.
@ -305,7 +320,7 @@ cdef class Doc:
break
else:
return 1.0
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)
@ -517,29 +532,23 @@ cdef class Doc:
>>> assert [s.root.text for s in doc.sents] == ["is", "'s"]
"""
def __get__(self):
if not self.is_sentenced:
raise ValueError(
"Sentence boundaries unset. You can add the 'sentencizer' "
"component to the pipeline with: "
"nlp.add_pipe(nlp.create_pipe('sentencizer')) "
"Alternatively, add the dependency parser, or set "
"sentence boundaries by setting doc[i].sent_start")
if 'sents' in self.user_hooks:
yield from self.user_hooks['sents'](self)
return
cdef int i
if not self.is_parsed:
else:
start = 0
for i in range(1, self.length):
if self.c[i].sent_start != 0:
break
else:
raise ValueError(
"Sentence boundaries unset. You can add the 'sentencizer' "
"component to the pipeline with: "
"nlp.add_pipe(nlp.create_pipe('sentencizer')) "
"Alternatively, add the dependency parser, or set "
"sentence boundaries by setting doc[i].sent_start")
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)
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:
@ -559,9 +568,7 @@ cdef class Doc:
t.idx = (t-1).idx + (t-1).lex.length + (t-1).spacy
t.l_edge = self.length
t.r_edge = self.length
if t.lex.orth == 0:
raise ValueError("Invalid token: empty string ('') at position {}"
.format(self.length))
assert t.lex.orth != 0
t.spacy = has_space
self.length += 1
return t.idx + t.lex.length + t.spacy
@ -882,6 +889,18 @@ cdef class Doc:
else:
self.tensor = xp.hstack((self.tensor, tensor))
def retokenize(self):
'''Context manager to handle retokenization of the Doc.
Modifications to the Doc's tokenization are stored, and then
made all at once when the context manager exits. This is
much more efficient, and less error-prone.
All views of the Doc (Span and Token) created before the
retokenization are invalidated, although they may accidentally
continue to work.
'''
return Retokenizer(self)
def 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
@ -935,66 +954,8 @@ cdef class Doc:
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)
# Make sure ent_iob remains consistent
if self.c[end].ent_iob == 1 and token.ent_iob in (0, 2):
if token.ent_type == self.c[end].ent_type:
token.ent_iob = 3
else:
# If they're not the same entity type, let them be two entities
self.c[end].ent_iob = 3
# 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
with self.retokenize() as retokenizer:
retokenizer.merge(self[start:end], attrs=attributes)
return self[start]
def print_tree(self, light=False, flat=False):