mirror of https://github.com/explosion/spaCy.git
302 lines
12 KiB
Cython
302 lines
12 KiB
Cython
# coding: utf8
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# cython: infer_types=True
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# cython: bounds_check=False
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# cython: profile=True
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from __future__ import unicode_literals
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from libc.string cimport memcpy, memset
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from libc.stdlib cimport malloc, free
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import numpy
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from cymem.cymem cimport Pool
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from thinc.neural.util import get_array_module
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from .doc cimport Doc, set_children_from_heads, token_by_start, token_by_end
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from .span cimport Span
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from .token cimport Token
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from ..lexeme cimport Lexeme, EMPTY_LEXEME
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from ..structs cimport LexemeC, TokenC
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from ..attrs cimport TAG
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from ..attrs import intify_attrs
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from ..util import SimpleFrozenDict
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from ..errors import Errors
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cdef class Retokenizer:
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"""Helper class for doc.retokenize() context manager."""
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cdef Doc doc
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cdef list merges
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cdef list splits
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cdef set tokens_to_merge
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def __init__(self, doc):
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self.doc = doc
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self.merges = []
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self.splits = []
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self.tokens_to_merge = set()
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def merge(self, Span span, attrs=SimpleFrozenDict()):
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"""Mark a span for merging. The attrs will be applied to the resulting
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token.
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"""
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for token in span:
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if token.i in self.tokens_to_merge:
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raise ValueError(Errors.E102.format(token=repr(token)))
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self.tokens_to_merge.add(token.i)
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attrs = intify_attrs(attrs, strings_map=self.doc.vocab.strings)
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self.merges.append((span, attrs))
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def split(self, Token token, orths, attrs=SimpleFrozenDict()):
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"""Mark a Token for splitting, into the specified orths. The attrs
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will be applied to each subtoken.
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"""
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attrs = intify_attrs(attrs, strings_map=self.doc.vocab.strings)
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self.splits.append((token.start_char, orths, attrs))
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def __enter__(self):
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self.merges = []
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self.splits = []
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return self
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def __exit__(self, *args):
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# Do the actual merging here
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if len(self.merges) > 1:
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_bulk_merge(self.doc, self.merges)
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elif len(self.merges) == 1:
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(span, attrs) = self.merges[0]
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start = span.start
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end = span.end
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_merge(self.doc, start, end, attrs)
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for start_char, orths, attrs in self.splits:
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raise NotImplementedError
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def _merge(Doc doc, int start, int end, attributes):
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"""Retokenize the document, such that the span at
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`doc.text[start_idx : end_idx]` is merged into a single token. If
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`start_idx` and `end_idx `do not mark start and end token boundaries,
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the document remains unchanged.
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start_idx (int): Character index of the start of the slice to merge.
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end_idx (int): Character index after the end of the slice to merge.
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**attributes: Attributes to assign to the merged token. By default,
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attributes are inherited from the syntactic root of the span.
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RETURNS (Token): The newly merged token, or `None` if the start and end
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indices did not fall at token boundaries.
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"""
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cdef Span span = doc[start:end]
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cdef int start_char = span.start_char
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cdef int end_char = span.end_char
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# Resize the doc.tensor, if it's set. Let the last row for each token stand
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# for the merged region. To do this, we create a boolean array indicating
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# whether the row is to be deleted, then use numpy.delete
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if doc.tensor is not None and doc.tensor.size != 0:
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doc.tensor = _resize_tensor(doc.tensor, [(start, end)])
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# Get LexemeC for newly merged token
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new_orth = ''.join([t.text_with_ws for t in span])
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if span[-1].whitespace_:
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new_orth = new_orth[:-len(span[-1].whitespace_)]
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cdef const LexemeC* lex = doc.vocab.get(doc.mem, new_orth)
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# House the new merged token where it starts
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cdef TokenC* token = &doc.c[start]
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token.spacy = doc.c[end-1].spacy
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for attr_name, attr_value in attributes.items():
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if attr_name == TAG:
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doc.vocab.morphology.assign_tag(token, attr_value)
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else:
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Token.set_struct_attr(token, attr_name, attr_value)
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# Make sure ent_iob remains consistent
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if doc.c[end].ent_iob == 1 and token.ent_iob in (0, 2):
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if token.ent_type == doc.c[end].ent_type:
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token.ent_iob = 3
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else:
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# If they're not the same entity type, let them be two entities
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doc.c[end].ent_iob = 3
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# Begin by setting all the head indices to absolute token positions
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# This is easier to work with for now than the offsets
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# Before thinking of something simpler, beware the case where a
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# dependency bridges over the entity. Here the alignment of the
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# tokens changes.
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span_root = span.root.i
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token.dep = span.root.dep
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# We update token.lex after keeping span root and dep, since
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# setting token.lex will change span.start and span.end properties
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# as it modifies the character offsets in the doc
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token.lex = lex
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for i in range(doc.length):
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doc.c[i].head += i
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# Set the head of the merged token, and its dep relation, from the Span
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token.head = doc.c[span_root].head
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# Adjust deps before shrinking tokens
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# Tokens which point into the merged token should now point to it
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# Subtract the offset from all tokens which point to >= end
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offset = (end - start) - 1
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for i in range(doc.length):
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head_idx = doc.c[i].head
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if start <= head_idx < end:
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doc.c[i].head = start
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elif head_idx >= end:
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doc.c[i].head -= offset
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# Now compress the token array
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for i in range(end, doc.length):
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doc.c[i - offset] = doc.c[i]
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for i in range(doc.length - offset, doc.length):
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memset(&doc.c[i], 0, sizeof(TokenC))
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doc.c[i].lex = &EMPTY_LEXEME
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doc.length -= offset
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for i in range(doc.length):
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# ...And, set heads back to a relative position
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doc.c[i].head -= i
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# Set the left/right children, left/right edges
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set_children_from_heads(doc.c, doc.length)
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# Clear the cached Python objects
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# Return the merged Python object
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return doc[start]
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def _bulk_merge(Doc doc, merges):
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"""Retokenize the document, such that the spans described in 'merges'
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are merged into a single token. This method assumes that the merges
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are in the same order at which they appear in the doc, and that merges
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do not intersect each other in any way.
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merges: Tokens to merge, and corresponding attributes to assign to the
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merged token. By default, attributes are inherited from the
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syntactic root of the span.
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RETURNS (Token): The first newly merged token.
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"""
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cdef Span span
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cdef const LexemeC* lex
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cdef Pool mem = Pool()
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tokens = <TokenC**>mem.alloc(len(merges), sizeof(TokenC))
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spans = []
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def _get_start(merge):
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return merge[0].start
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merges.sort(key=_get_start)
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for merge_index, (span, attributes) in enumerate(merges):
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start = span.start
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end = span.end
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spans.append(span)
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# House the new merged token where it starts
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token = &doc.c[start]
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tokens[merge_index] = token
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# Assign attributes
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for attr_name, attr_value in attributes.items():
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if attr_name == TAG:
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doc.vocab.morphology.assign_tag(token, attr_value)
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else:
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Token.set_struct_attr(token, attr_name, attr_value)
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# Resize the doc.tensor, if it's set. Let the last row for each token stand
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# for the merged region. To do this, we create a boolean array indicating
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# whether the row is to be deleted, then use numpy.delete
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if doc.tensor is not None and doc.tensor.size != 0:
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doc.tensor = _resize_tensor(doc.tensor,
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[(m[1][0].start, m[1][0].end) for m in merges])
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# Memorize span roots and sets dependencies of the newly merged
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# tokens to the dependencies of their roots.
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span_roots = []
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for i, span in enumerate(spans):
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span_roots.append(span.root.i)
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tokens[i].dep = span.root.dep
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# We update token.lex after keeping span root and dep, since
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# setting token.lex will change span.start and span.end properties
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# as it modifies the character offsets in the doc
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for token_index in range(len(merges)):
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new_orth = ''.join([t.text_with_ws for t in spans[token_index]])
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if spans[token_index][-1].whitespace_:
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new_orth = new_orth[:-len(spans[token_index][-1].whitespace_)]
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lex = doc.vocab.get(doc.mem, new_orth)
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tokens[token_index].lex = lex
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# We set trailing space here too
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tokens[token_index].spacy = doc.c[spans[token_index].end-1].spacy
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# Begin by setting all the head indices to absolute token positions
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# This is easier to work with for now than the offsets
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# Before thinking of something simpler, beware the case where a
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# dependency bridges over the entity. Here the alignment of the
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# tokens changes.
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for i in range(doc.length):
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doc.c[i].head += i
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# Set the head of the merged token from the Span
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for i in range(len(merges)):
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tokens[i].head = doc.c[span_roots[i]].head
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# Adjust deps before shrinking tokens
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# Tokens which point into the merged token should now point to it
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# Subtract the offset from all tokens which point to >= end
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offsets = []
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current_span_index = 0
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current_offset = 0
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for i in range(doc.length):
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if current_span_index < len(spans) and i == spans[current_span_index].end:
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#last token was the last of the span
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current_offset += (spans[current_span_index].end - spans[current_span_index].start) -1
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current_span_index += 1
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if current_span_index < len(spans) and \
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spans[current_span_index].start <= i < spans[current_span_index].end:
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offsets.append(spans[current_span_index].start - current_offset)
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else:
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offsets.append(i - current_offset)
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for i in range(doc.length):
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doc.c[i].head = offsets[doc.c[i].head]
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# Now compress the token array
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offset = 0
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in_span = False
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span_index = 0
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for i in range(doc.length):
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if in_span and i == spans[span_index].end:
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# First token after a span
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in_span = False
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span_index += 1
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if span_index < len(spans) and i == spans[span_index].start:
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# First token in a span
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doc.c[i - offset] = doc.c[i] # move token to its place
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offset += (spans[span_index].end - spans[span_index].start) - 1
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in_span = True
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if not in_span:
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doc.c[i - offset] = doc.c[i] # move token to its place
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for i in range(doc.length - offset, doc.length):
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memset(&doc.c[i], 0, sizeof(TokenC))
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doc.c[i].lex = &EMPTY_LEXEME
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doc.length -= offset
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# ...And, set heads back to a relative position
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for i in range(doc.length):
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doc.c[i].head -= i
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# Set the left/right children, left/right edges
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set_children_from_heads(doc.c, doc.length)
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# Make sure ent_iob remains consistent
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for (span, _) in merges:
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if(span.end < len(offsets)):
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#if it's not the last span
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token_after_span_position = offsets[span.end]
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if doc.c[token_after_span_position].ent_iob == 1\
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and doc.c[token_after_span_position - 1].ent_iob in (0, 2):
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if doc.c[token_after_span_position - 1].ent_type == doc.c[token_after_span_position].ent_type:
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doc.c[token_after_span_position - 1].ent_iob = 3
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else:
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# If they're not the same entity type, let them be two entities
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doc.c[token_after_span_position].ent_iob = 3
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# Return the merged Python object
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return doc[spans[0].start]
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def _resize_tensor(tensor, ranges):
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delete = []
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for start, end in ranges:
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for i in range(start, end-1):
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delete.append(i)
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xp = get_array_module(tensor)
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return xp.delete(tensor, delete, axis=0)
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