mirror of https://github.com/explosion/spaCy.git
876 lines
35 KiB
Cython
876 lines
35 KiB
Cython
# cython: profile=True
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# coding: utf8
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from __future__ import unicode_literals, print_function
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import re
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import random
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import numpy
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import tempfile
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import shutil
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import itertools
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from pathlib import Path
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import srsly
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from . import _align
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from .syntax import nonproj
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from .tokens import Doc, Span
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from .errors import Errors
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from .compat import path2str
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from . import util
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from .util import minibatch, itershuffle
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from libc.stdio cimport FILE, fopen, fclose, fread, fwrite, feof, fseek
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punct_re = re.compile(r"\W")
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def tags_to_entities(tags):
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entities = []
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start = None
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for i, tag in enumerate(tags):
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if tag is None:
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continue
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if tag.startswith("O"):
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# TODO: We shouldn't be getting these malformed inputs. Fix this.
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if start is not None:
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start = None
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continue
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elif tag == "-":
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continue
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elif tag.startswith("I"):
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if start is None:
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raise ValueError(Errors.E067.format(tags=tags[:i + 1]))
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continue
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if tag.startswith("U"):
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entities.append((tag[2:], i, i))
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elif tag.startswith("B"):
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start = i
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elif tag.startswith("L"):
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entities.append((tag[2:], start, i))
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start = None
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else:
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raise ValueError(Errors.E068.format(tag=tag))
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return entities
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def merge_sents(sents):
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m_deps = [[], [], [], [], [], []]
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m_brackets = []
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m_cats = sents.pop()
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i = 0
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for (ids, words, tags, heads, labels, ner), brackets in sents:
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m_deps[0].extend(id_ + i for id_ in ids)
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m_deps[1].extend(words)
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m_deps[2].extend(tags)
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m_deps[3].extend(head + i for head in heads)
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m_deps[4].extend(labels)
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m_deps[5].extend(ner)
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m_brackets.extend((b["first"] + i, b["last"] + i, b["label"])
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for b in brackets)
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i += len(ids)
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m_deps.append(m_cats)
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return [(m_deps, m_brackets)]
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def align(tokens_a, tokens_b):
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"""Calculate alignment tables between two tokenizations, using the Levenshtein
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algorithm. The alignment is case-insensitive.
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tokens_a (List[str]): The candidate tokenization.
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tokens_b (List[str]): The reference tokenization.
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RETURNS: (tuple): A 5-tuple consisting of the following information:
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* cost (int): The number of misaligned tokens.
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* a2b (List[int]): Mapping of indices in `tokens_a` to indices in `tokens_b`.
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For instance, if `a2b[4] == 6`, that means that `tokens_a[4]` aligns
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to `tokens_b[6]`. If there's no one-to-one alignment for a token,
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it has the value -1.
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* b2a (List[int]): The same as `a2b`, but mapping the other direction.
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* a2b_multi (Dict[int, int]): A dictionary mapping indices in `tokens_a`
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to indices in `tokens_b`, where multiple tokens of `tokens_a` align to
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the same token of `tokens_b`.
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* b2a_multi (Dict[int, int]): As with `a2b_multi`, but mapping the other
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direction.
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"""
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if tokens_a == tokens_b:
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alignment = numpy.arange(len(tokens_a))
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return 0, alignment, alignment, {}, {}
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tokens_a = [w.replace(" ", "").lower() for w in tokens_a]
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tokens_b = [w.replace(" ", "").lower() for w in tokens_b]
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cost, i2j, j2i, matrix = _align.align(tokens_a, tokens_b)
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i2j_multi, j2i_multi = _align.multi_align(i2j, j2i, [len(w) for w in tokens_a],
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[len(w) for w in tokens_b])
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for i, j in list(i2j_multi.items()):
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if i2j_multi.get(i+1) != j and i2j_multi.get(i-1) != j:
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i2j[i] = j
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i2j_multi.pop(i)
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for j, i in list(j2i_multi.items()):
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if j2i_multi.get(j+1) != i and j2i_multi.get(j-1) != i:
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j2i[j] = i
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j2i_multi.pop(j)
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return cost, i2j, j2i, i2j_multi, j2i_multi
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class GoldCorpus(object):
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"""An annotated corpus, using the JSON file format. Manages
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annotations for tagging, dependency parsing and NER.
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DOCS: https://spacy.io/api/goldcorpus
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"""
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def __init__(self, train, dev, gold_preproc=False, limit=None):
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"""Create a GoldCorpus.
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train_path (unicode or Path): File or directory of training data.
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dev_path (unicode or Path): File or directory of development data.
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RETURNS (GoldCorpus): The newly created object.
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"""
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self.limit = limit
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if isinstance(train, str) or isinstance(train, Path):
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train = self.read_tuples(self.walk_corpus(train))
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dev = self.read_tuples(self.walk_corpus(dev))
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# Write temp directory with one doc per file, so we can shuffle and stream
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self.tmp_dir = Path(tempfile.mkdtemp())
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self.write_msgpack(self.tmp_dir / "train", train, limit=self.limit)
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self.write_msgpack(self.tmp_dir / "dev", dev, limit=self.limit)
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def __del__(self):
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shutil.rmtree(path2str(self.tmp_dir))
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@staticmethod
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def write_msgpack(directory, doc_tuples, limit=0):
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if not directory.exists():
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directory.mkdir()
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n = 0
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for i, doc_tuple in enumerate(doc_tuples):
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srsly.write_msgpack(directory / "{}.msg".format(i), [doc_tuple])
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n += len(doc_tuple[1])
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if limit and n >= limit:
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break
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@staticmethod
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def walk_corpus(path):
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path = util.ensure_path(path)
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if not path.is_dir():
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return [path]
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paths = [path]
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locs = []
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seen = set()
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for path in paths:
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if str(path) in seen:
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continue
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seen.add(str(path))
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if path.parts[-1].startswith("."):
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continue
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elif path.is_dir():
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paths.extend(path.iterdir())
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elif path.parts[-1].endswith((".json", ".jsonl")):
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locs.append(path)
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return locs
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@staticmethod
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def read_tuples(locs, limit=0):
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i = 0
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for loc in locs:
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loc = util.ensure_path(loc)
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if loc.parts[-1].endswith("json"):
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gold_tuples = read_json_file(loc)
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elif loc.parts[-1].endswith("jsonl"):
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gold_tuples = srsly.read_jsonl(loc)
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elif loc.parts[-1].endswith("msg"):
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gold_tuples = srsly.read_msgpack(loc)
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else:
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supported = ("json", "jsonl", "msg")
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raise ValueError(Errors.E124.format(path=path2str(loc), formats=supported))
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for item in gold_tuples:
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yield item
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i += len(item[1])
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if limit and i >= limit:
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return
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@property
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def dev_tuples(self):
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locs = (self.tmp_dir / "dev").iterdir()
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yield from self.read_tuples(locs, limit=self.limit)
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@property
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def train_tuples(self):
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locs = (self.tmp_dir / "train").iterdir()
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yield from self.read_tuples(locs, limit=self.limit)
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def count_train(self):
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n = 0
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i = 0
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for raw_text, paragraph_tuples in self.train_tuples:
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cats = paragraph_tuples.pop()
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for sent_tuples, brackets in paragraph_tuples:
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n += len(sent_tuples[1])
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if self.limit and i >= self.limit:
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break
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i += 1
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return n
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def train_docs(self, nlp, gold_preproc=False, max_length=None,
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noise_level=0.0, orth_variant_level=0.0):
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locs = list((self.tmp_dir / 'train').iterdir())
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random.shuffle(locs)
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train_tuples = self.read_tuples(locs, limit=self.limit)
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gold_docs = self.iter_gold_docs(nlp, train_tuples, gold_preproc,
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max_length=max_length,
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noise_level=noise_level,
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orth_variant_level=orth_variant_level,
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make_projective=True)
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yield from gold_docs
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def train_docs_without_preprocessing(self, nlp, gold_preproc=False):
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gold_docs = self.iter_gold_docs(nlp, self.train_tuples, gold_preproc=gold_preproc)
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yield from gold_docs
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def dev_docs(self, nlp, gold_preproc=False):
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gold_docs = self.iter_gold_docs(nlp, self.dev_tuples, gold_preproc=gold_preproc)
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yield from gold_docs
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@classmethod
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def iter_gold_docs(cls, nlp, tuples, gold_preproc, max_length=None,
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noise_level=0.0, orth_variant_level=0.0, make_projective=False):
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for raw_text, paragraph_tuples in tuples:
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if gold_preproc:
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raw_text = None
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else:
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paragraph_tuples = merge_sents(paragraph_tuples)
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docs, paragraph_tuples = cls._make_docs(nlp, raw_text,
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paragraph_tuples, gold_preproc, noise_level=noise_level,
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orth_variant_level=orth_variant_level)
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golds = cls._make_golds(docs, paragraph_tuples, make_projective)
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for doc, gold in zip(docs, golds):
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if (not max_length) or len(doc) < max_length:
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yield doc, gold
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@classmethod
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def _make_docs(cls, nlp, raw_text, paragraph_tuples, gold_preproc, noise_level=0.0, orth_variant_level=0.0):
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if raw_text is not None:
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raw_text, paragraph_tuples = make_orth_variants(nlp, raw_text, paragraph_tuples, orth_variant_level=orth_variant_level)
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raw_text = add_noise(raw_text, noise_level)
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return [nlp.make_doc(raw_text)], paragraph_tuples
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else:
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docs = []
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raw_text, paragraph_tuples = make_orth_variants(nlp, None, paragraph_tuples, orth_variant_level=orth_variant_level)
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return [Doc(nlp.vocab, words=add_noise(sent_tuples[1], noise_level))
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for (sent_tuples, brackets) in paragraph_tuples], paragraph_tuples
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@classmethod
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def _make_golds(cls, docs, paragraph_tuples, make_projective):
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if len(docs) != len(paragraph_tuples):
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n_annots = len(paragraph_tuples)
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raise ValueError(Errors.E070.format(n_docs=len(docs), n_annots=n_annots))
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return [GoldParse.from_annot_tuples(doc, sent_tuples,
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make_projective=make_projective)
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for doc, (sent_tuples, brackets)
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in zip(docs, paragraph_tuples)]
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def make_orth_variants(nlp, raw, paragraph_tuples, orth_variant_level=0.0):
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if random.random() >= orth_variant_level:
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return raw, paragraph_tuples
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if random.random() >= 0.5:
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lower = True
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if raw is not None:
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raw = raw.lower()
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ndsv = nlp.Defaults.single_orth_variants
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ndpv = nlp.Defaults.paired_orth_variants
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# modify words in paragraph_tuples
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variant_paragraph_tuples = []
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for sent_tuples, brackets in paragraph_tuples:
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ids, words, tags, heads, labels, ner, cats = sent_tuples
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if lower:
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words = [w.lower() for w in words]
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# single variants
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punct_choices = [random.choice(x["variants"]) for x in ndsv]
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for word_idx in range(len(words)):
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for punct_idx in range(len(ndsv)):
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if tags[word_idx] in ndsv[punct_idx]["tags"] \
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and words[word_idx] in ndsv[punct_idx]["variants"]:
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words[word_idx] = punct_choices[punct_idx]
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# paired variants
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punct_choices = [random.choice(x["variants"]) for x in ndpv]
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for word_idx in range(len(words)):
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for punct_idx in range(len(ndpv)):
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if tags[word_idx] in ndpv[punct_idx]["tags"] \
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and words[word_idx] in itertools.chain.from_iterable(ndpv[punct_idx]["variants"]):
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# backup option: random left vs. right from pair
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pair_idx = random.choice([0, 1])
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# best option: rely on paired POS tags like `` / ''
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if len(ndpv[punct_idx]["tags"]) == 2:
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pair_idx = ndpv[punct_idx]["tags"].index(tags[word_idx])
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# next best option: rely on position in variants
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# (may not be unambiguous, so order of variants matters)
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else:
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for pair in ndpv[punct_idx]["variants"]:
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if words[word_idx] in pair:
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pair_idx = pair.index(words[word_idx])
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words[word_idx] = punct_choices[punct_idx][pair_idx]
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variant_paragraph_tuples.append(((ids, words, tags, heads, labels, ner, cats), brackets))
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# modify raw to match variant_paragraph_tuples
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if raw is not None:
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variants = []
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for single_variants in ndsv:
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variants.extend(single_variants["variants"])
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for paired_variants in ndpv:
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variants.extend(list(itertools.chain.from_iterable(paired_variants["variants"])))
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# store variants in reverse length order to be able to prioritize
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# longer matches (e.g., "---" before "--")
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variants = sorted(variants, key=lambda x: len(x))
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variants.reverse()
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variant_raw = ""
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raw_idx = 0
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# add initial whitespace
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while raw_idx < len(raw) and re.match("\s", raw[raw_idx]):
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variant_raw += raw[raw_idx]
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raw_idx += 1
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for sent_tuples, brackets in variant_paragraph_tuples:
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ids, words, tags, heads, labels, ner, cats = sent_tuples
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for word in words:
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match_found = False
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# add identical word
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if word not in variants and raw[raw_idx:].startswith(word):
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variant_raw += word
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raw_idx += len(word)
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match_found = True
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# add variant word
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else:
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for variant in variants:
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if not match_found and \
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raw[raw_idx:].startswith(variant):
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raw_idx += len(variant)
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variant_raw += word
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match_found = True
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# something went wrong, abort
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# (add a warning message?)
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if not match_found:
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return raw, paragraph_tuples
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# add following whitespace
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while raw_idx < len(raw) and re.match("\s", raw[raw_idx]):
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variant_raw += raw[raw_idx]
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raw_idx += 1
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return variant_raw, variant_paragraph_tuples
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return raw, variant_paragraph_tuples
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def add_noise(orig, noise_level):
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if random.random() >= noise_level:
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return orig
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elif type(orig) == list:
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corrupted = [_corrupt(word, noise_level) for word in orig]
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corrupted = [w for w in corrupted if w]
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return corrupted
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else:
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return "".join(_corrupt(c, noise_level) for c in orig)
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def _corrupt(c, noise_level):
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if random.random() >= noise_level:
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return c
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elif c in [".", "'", "!", "?", ","]:
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return "\n"
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else:
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return c.lower()
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def read_json_object(json_corpus_section):
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"""Take a list of JSON-formatted documents (e.g. from an already loaded
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training data file) and yield tuples in the GoldParse format.
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json_corpus_section (list): The data.
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YIELDS (tuple): The reformatted data.
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"""
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for json_doc in json_corpus_section:
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tuple_doc = json_to_tuple(json_doc)
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for tuple_paragraph in tuple_doc:
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yield tuple_paragraph
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def json_to_tuple(doc):
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"""Convert an item in the JSON-formatted training data to the tuple format
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used by GoldParse.
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doc (dict): One entry in the training data.
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YIELDS (tuple): The reformatted data.
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"""
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paragraphs = []
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for paragraph in doc["paragraphs"]:
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sents = []
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for sent in paragraph["sentences"]:
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words = []
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ids = []
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tags = []
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heads = []
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labels = []
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ner = []
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for i, token in enumerate(sent["tokens"]):
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words.append(token["orth"])
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ids.append(i)
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tags.append(token.get('tag', "-"))
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heads.append(token.get("head", 0) + i)
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labels.append(token.get("dep", ""))
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# Ensure ROOT label is case-insensitive
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if labels[-1].lower() == "root":
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labels[-1] = "ROOT"
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ner.append(token.get("ner", "-"))
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sents.append([
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[ids, words, tags, heads, labels, ner],
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sent.get("brackets", [])])
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cats = {}
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for cat in paragraph.get("cats", {}):
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cats[cat["label"]] = cat["value"]
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sents.append(cats)
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if sents:
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yield [paragraph.get("raw", None), sents]
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def read_json_file(loc, docs_filter=None, limit=None):
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loc = util.ensure_path(loc)
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if loc.is_dir():
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for filename in loc.iterdir():
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yield from read_json_file(loc / filename, limit=limit)
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else:
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for doc in _json_iterate(loc):
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if docs_filter is not None and not docs_filter(doc):
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continue
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for json_tuple in json_to_tuple(doc):
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yield json_tuple
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def _json_iterate(loc):
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# We should've made these files jsonl...But since we didn't, parse out
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# the docs one-by-one to reduce memory usage.
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# It's okay to read in the whole file -- just don't parse it into JSON.
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cdef bytes py_raw
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loc = util.ensure_path(loc)
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with loc.open("rb") as file_:
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py_raw = file_.read()
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raw = <char*>py_raw
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cdef int square_depth = 0
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cdef int curly_depth = 0
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cdef int inside_string = 0
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cdef int escape = 0
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cdef int start = -1
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cdef char c
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cdef char quote = ord('"')
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cdef char backslash = ord("\\")
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cdef char open_square = ord("[")
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cdef char close_square = ord("]")
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|
cdef char open_curly = ord("{")
|
|
cdef char close_curly = ord("}")
|
|
for i in range(len(py_raw)):
|
|
c = raw[i]
|
|
if escape:
|
|
escape = False
|
|
continue
|
|
if c == backslash:
|
|
escape = True
|
|
continue
|
|
if c == quote:
|
|
inside_string = not inside_string
|
|
continue
|
|
if inside_string:
|
|
continue
|
|
if c == open_square:
|
|
square_depth += 1
|
|
elif c == close_square:
|
|
square_depth -= 1
|
|
elif c == open_curly:
|
|
if square_depth == 1 and curly_depth == 0:
|
|
start = i
|
|
curly_depth += 1
|
|
elif c == close_curly:
|
|
curly_depth -= 1
|
|
if square_depth == 1 and curly_depth == 0:
|
|
py_str = py_raw[start : i + 1].decode("utf8")
|
|
try:
|
|
yield srsly.json_loads(py_str)
|
|
except Exception:
|
|
print(py_str)
|
|
raise
|
|
start = -1
|
|
|
|
|
|
def iob_to_biluo(tags):
|
|
out = []
|
|
curr_label = None
|
|
tags = list(tags)
|
|
while tags:
|
|
out.extend(_consume_os(tags))
|
|
out.extend(_consume_ent(tags))
|
|
return out
|
|
|
|
|
|
def _consume_os(tags):
|
|
while tags and tags[0] == "O":
|
|
yield tags.pop(0)
|
|
|
|
|
|
def _consume_ent(tags):
|
|
if not tags:
|
|
return []
|
|
tag = tags.pop(0)
|
|
target_in = "I" + tag[1:]
|
|
target_last = "L" + tag[1:]
|
|
length = 1
|
|
while tags and tags[0] in {target_in, target_last}:
|
|
length += 1
|
|
tags.pop(0)
|
|
label = tag[2:]
|
|
if length == 1:
|
|
return ["U-" + label]
|
|
else:
|
|
start = "B-" + label
|
|
end = "L-" + label
|
|
middle = ["I-%s" % label for _ in range(1, length - 1)]
|
|
return [start] + middle + [end]
|
|
|
|
|
|
cdef class GoldParse:
|
|
"""Collection for training annotations.
|
|
|
|
DOCS: https://spacy.io/api/goldparse
|
|
"""
|
|
@classmethod
|
|
def from_annot_tuples(cls, doc, annot_tuples, make_projective=False):
|
|
_, words, tags, heads, deps, entities, cats = annot_tuples
|
|
return cls(doc, words=words, tags=tags, heads=heads, deps=deps,
|
|
entities=entities, cats=cats,
|
|
make_projective=make_projective)
|
|
|
|
def __init__(self, doc, annot_tuples=None, words=None, tags=None, morphology=None,
|
|
heads=None, deps=None, entities=None, make_projective=False,
|
|
cats=None, links=None, **_):
|
|
"""Create a GoldParse.
|
|
|
|
doc (Doc): The document the annotations refer to.
|
|
words (iterable): A sequence of unicode word strings.
|
|
tags (iterable): A sequence of strings, representing tag annotations.
|
|
heads (iterable): A sequence of integers, representing syntactic
|
|
head offsets.
|
|
deps (iterable): A sequence of strings, representing the syntactic
|
|
relation types.
|
|
entities (iterable): A sequence of named entity annotations, either as
|
|
BILUO tag strings, or as `(start_char, end_char, label)` tuples,
|
|
representing the entity positions.
|
|
cats (dict): Labels for text classification. Each key in the dictionary
|
|
may be a string or an int, or a `(start_char, end_char, label)`
|
|
tuple, indicating that the label is applied to only part of the
|
|
document (usually a sentence). Unlike entity annotations, label
|
|
annotations can overlap, i.e. a single word can be covered by
|
|
multiple labelled spans. The TextCategorizer component expects
|
|
true examples of a label to have the value 1.0, and negative
|
|
examples of a label to have the value 0.0. Labels not in the
|
|
dictionary are treated as missing - the gradient for those labels
|
|
will be zero.
|
|
links (dict): A dict with `(start_char, end_char)` keys,
|
|
and the values being dicts with kb_id:value entries,
|
|
representing the external IDs in a knowledge base (KB)
|
|
mapped to either 1.0 or 0.0, indicating positive and
|
|
negative examples respectively.
|
|
RETURNS (GoldParse): The newly constructed object.
|
|
"""
|
|
if words is None:
|
|
words = [token.text for token in doc]
|
|
if tags is None:
|
|
tags = [None for _ in words]
|
|
if heads is None:
|
|
heads = [None for _ in words]
|
|
if deps is None:
|
|
deps = [None for _ in words]
|
|
if morphology is None:
|
|
morphology = [None for _ in words]
|
|
if entities is None:
|
|
entities = ["-" for _ in doc]
|
|
elif len(entities) == 0:
|
|
entities = ["O" for _ in doc]
|
|
else:
|
|
# Translate the None values to '-', to make processing easier.
|
|
# See Issue #2603
|
|
entities = [(ent if ent is not None else "-") for ent in entities]
|
|
if not isinstance(entities[0], basestring):
|
|
# Assume we have entities specified by character offset.
|
|
entities = biluo_tags_from_offsets(doc, entities)
|
|
self.mem = Pool()
|
|
self.loss = 0
|
|
self.length = len(doc)
|
|
|
|
# These are filled by the tagger/parser/entity recogniser
|
|
self.c.tags = <int*>self.mem.alloc(len(doc), sizeof(int))
|
|
self.c.heads = <int*>self.mem.alloc(len(doc), sizeof(int))
|
|
self.c.labels = <attr_t*>self.mem.alloc(len(doc), sizeof(attr_t))
|
|
self.c.has_dep = <int*>self.mem.alloc(len(doc), sizeof(int))
|
|
self.c.sent_start = <int*>self.mem.alloc(len(doc), sizeof(int))
|
|
self.c.ner = <Transition*>self.mem.alloc(len(doc), sizeof(Transition))
|
|
|
|
self.cats = {} if cats is None else dict(cats)
|
|
self.links = links
|
|
self.words = [None] * len(doc)
|
|
self.tags = [None] * len(doc)
|
|
self.heads = [None] * len(doc)
|
|
self.labels = [None] * len(doc)
|
|
self.ner = [None] * len(doc)
|
|
self.morphology = [None] * len(doc)
|
|
|
|
# This needs to be done before we align the words
|
|
if make_projective and heads is not None and deps is not None:
|
|
heads, deps = nonproj.projectivize(heads, deps)
|
|
|
|
# Do many-to-one alignment for misaligned tokens.
|
|
# If we over-segment, we'll have one gold word that covers a sequence
|
|
# of predicted words
|
|
# If we under-segment, we'll have one predicted word that covers a
|
|
# sequence of gold words.
|
|
# If we "mis-segment", we'll have a sequence of predicted words covering
|
|
# a sequence of gold words. That's many-to-many -- we don't do that.
|
|
cost, i2j, j2i, i2j_multi, j2i_multi = align([t.orth_ for t in doc], words)
|
|
|
|
self.cand_to_gold = [(j if j >= 0 else None) for j in i2j]
|
|
self.gold_to_cand = [(i if i >= 0 else None) for i in j2i]
|
|
|
|
annot_tuples = (range(len(words)), words, tags, heads, deps, entities)
|
|
self.orig_annot = list(zip(*annot_tuples))
|
|
|
|
for i, gold_i in enumerate(self.cand_to_gold):
|
|
if doc[i].text.isspace():
|
|
self.words[i] = doc[i].text
|
|
self.tags[i] = "_SP"
|
|
self.heads[i] = None
|
|
self.labels[i] = None
|
|
self.ner[i] = None
|
|
self.morphology[i] = set()
|
|
if gold_i is None:
|
|
if i in i2j_multi:
|
|
self.words[i] = words[i2j_multi[i]]
|
|
self.tags[i] = tags[i2j_multi[i]]
|
|
self.morphology[i] = morphology[i2j_multi[i]]
|
|
is_last = i2j_multi[i] != i2j_multi.get(i+1)
|
|
is_first = i2j_multi[i] != i2j_multi.get(i-1)
|
|
# Set next word in multi-token span as head, until last
|
|
if not is_last:
|
|
self.heads[i] = i+1
|
|
self.labels[i] = "subtok"
|
|
else:
|
|
self.heads[i] = self.gold_to_cand[heads[i2j_multi[i]]]
|
|
self.labels[i] = deps[i2j_multi[i]]
|
|
# Now set NER...This is annoying because if we've split
|
|
# got an entity word split into two, we need to adjust the
|
|
# BILUO tags. We can't have BB or LL etc.
|
|
# Case 1: O -- easy.
|
|
ner_tag = entities[i2j_multi[i]]
|
|
if ner_tag == "O":
|
|
self.ner[i] = "O"
|
|
# Case 2: U. This has to become a B I* L sequence.
|
|
elif ner_tag.startswith("U-"):
|
|
if is_first:
|
|
self.ner[i] = ner_tag.replace("U-", "B-", 1)
|
|
elif is_last:
|
|
self.ner[i] = ner_tag.replace("U-", "L-", 1)
|
|
else:
|
|
self.ner[i] = ner_tag.replace("U-", "I-", 1)
|
|
# Case 3: L. If not last, change to I.
|
|
elif ner_tag.startswith("L-"):
|
|
if is_last:
|
|
self.ner[i] = ner_tag
|
|
else:
|
|
self.ner[i] = ner_tag.replace("L-", "I-", 1)
|
|
# Case 4: I. Stays correct
|
|
elif ner_tag.startswith("I-"):
|
|
self.ner[i] = ner_tag
|
|
else:
|
|
self.words[i] = words[gold_i]
|
|
self.tags[i] = tags[gold_i]
|
|
self.morphology[i] = morphology[gold_i]
|
|
if heads[gold_i] is None:
|
|
self.heads[i] = None
|
|
else:
|
|
self.heads[i] = self.gold_to_cand[heads[gold_i]]
|
|
self.labels[i] = deps[gold_i]
|
|
self.ner[i] = entities[gold_i]
|
|
|
|
# Prevent whitespace that isn't within entities from being tagged as
|
|
# an entity.
|
|
for i in range(len(self.ner)):
|
|
if self.tags[i] == "_SP":
|
|
prev_ner = self.ner[i-1] if i >= 1 else None
|
|
next_ner = self.ner[i+1] if (i+1) < len(self.ner) else None
|
|
if prev_ner == "O" or next_ner == "O":
|
|
self.ner[i] = "O"
|
|
|
|
cycle = nonproj.contains_cycle(self.heads)
|
|
if cycle is not None:
|
|
raise ValueError(Errors.E069.format(cycle=cycle,
|
|
cycle_tokens=" ".join(["'{}'".format(self.words[tok_id]) for tok_id in cycle]),
|
|
doc_tokens=" ".join(words[:50])))
|
|
|
|
def __len__(self):
|
|
"""Get the number of gold-standard tokens.
|
|
|
|
RETURNS (int): The number of gold-standard tokens.
|
|
"""
|
|
return self.length
|
|
|
|
@property
|
|
def is_projective(self):
|
|
"""Whether the provided syntactic annotations form a projective
|
|
dependency tree.
|
|
"""
|
|
return not nonproj.is_nonproj_tree(self.heads)
|
|
|
|
property sent_starts:
|
|
def __get__(self):
|
|
return [self.c.sent_start[i] for i in range(self.length)]
|
|
|
|
def __set__(self, sent_starts):
|
|
for gold_i, is_sent_start in enumerate(sent_starts):
|
|
i = self.gold_to_cand[gold_i]
|
|
if i is not None:
|
|
if is_sent_start in (1, True):
|
|
self.c.sent_start[i] = 1
|
|
elif is_sent_start in (-1, False):
|
|
self.c.sent_start[i] = -1
|
|
else:
|
|
self.c.sent_start[i] = 0
|
|
|
|
|
|
def docs_to_json(docs, id=0):
|
|
"""Convert a list of Doc objects into the JSON-serializable format used by
|
|
the spacy train command.
|
|
|
|
docs (iterable / Doc): The Doc object(s) to convert.
|
|
id (int): Id for the JSON.
|
|
RETURNS (list): The data in spaCy's JSON format.
|
|
"""
|
|
if isinstance(docs, Doc):
|
|
docs = [docs]
|
|
json_doc = {"id": id, "paragraphs": []}
|
|
for i, doc in enumerate(docs):
|
|
json_para = {'raw': doc.text, "sentences": [], "cats": []}
|
|
for cat, val in doc.cats.items():
|
|
json_cat = {"label": cat, "value": val}
|
|
json_para["cats"].append(json_cat)
|
|
ent_offsets = [(e.start_char, e.end_char, e.label_) for e in doc.ents]
|
|
biluo_tags = biluo_tags_from_offsets(doc, ent_offsets)
|
|
for j, sent in enumerate(doc.sents):
|
|
json_sent = {"tokens": [], "brackets": []}
|
|
for token in sent:
|
|
json_token = {"id": token.i, "orth": token.text}
|
|
if doc.is_tagged:
|
|
json_token["tag"] = token.tag_
|
|
if doc.is_parsed:
|
|
json_token["head"] = token.head.i-token.i
|
|
json_token["dep"] = token.dep_
|
|
json_token["ner"] = biluo_tags[token.i]
|
|
json_sent["tokens"].append(json_token)
|
|
json_para["sentences"].append(json_sent)
|
|
json_doc["paragraphs"].append(json_para)
|
|
return json_doc
|
|
|
|
|
|
def biluo_tags_from_offsets(doc, entities, missing="O"):
|
|
"""Encode labelled spans into per-token tags, using the
|
|
Begin/In/Last/Unit/Out scheme (BILUO).
|
|
|
|
doc (Doc): The document that the entity offsets refer to. The output tags
|
|
will refer to the token boundaries within the document.
|
|
entities (iterable): A sequence of `(start, end, label)` triples. `start`
|
|
and `end` should be character-offset integers denoting the slice into
|
|
the original string.
|
|
RETURNS (list): A list of unicode strings, describing the tags. Each tag
|
|
string will be of the form either "", "O" or "{action}-{label}", where
|
|
action is one of "B", "I", "L", "U". The string "-" is used where the
|
|
entity offsets don't align with the tokenization in the `Doc` object.
|
|
The training algorithm will view these as missing values. "O" denotes a
|
|
non-entity token. "B" denotes the beginning of a multi-token entity,
|
|
"I" the inside of an entity of three or more tokens, and "L" the end
|
|
of an entity of two or more tokens. "U" denotes a single-token entity.
|
|
|
|
EXAMPLE:
|
|
>>> text = 'I like London.'
|
|
>>> entities = [(len('I like '), len('I like London'), 'LOC')]
|
|
>>> doc = nlp.tokenizer(text)
|
|
>>> tags = biluo_tags_from_offsets(doc, entities)
|
|
>>> assert tags == ["O", "O", 'U-LOC', "O"]
|
|
"""
|
|
# Ensure no overlapping entity labels exist
|
|
tokens_in_ents = {}
|
|
|
|
starts = {token.idx: token.i for token in doc}
|
|
ends = {token.idx + len(token): token.i for token in doc}
|
|
biluo = ["-" for _ in doc]
|
|
# Handle entity cases
|
|
for start_char, end_char, label in entities:
|
|
for token_index in range(start_char, end_char):
|
|
if token_index in tokens_in_ents.keys():
|
|
raise ValueError(Errors.E103.format(
|
|
span1=(tokens_in_ents[token_index][0],
|
|
tokens_in_ents[token_index][1],
|
|
tokens_in_ents[token_index][2]),
|
|
span2=(start_char, end_char, label)))
|
|
tokens_in_ents[token_index] = (start_char, end_char, label)
|
|
|
|
start_token = starts.get(start_char)
|
|
end_token = ends.get(end_char)
|
|
# Only interested if the tokenization is correct
|
|
if start_token is not None and end_token is not None:
|
|
if start_token == end_token:
|
|
biluo[start_token] = "U-%s" % label
|
|
else:
|
|
biluo[start_token] = "B-%s" % label
|
|
for i in range(start_token+1, end_token):
|
|
biluo[i] = "I-%s" % label
|
|
biluo[end_token] = "L-%s" % label
|
|
# Now distinguish the O cases from ones where we miss the tokenization
|
|
entity_chars = set()
|
|
for start_char, end_char, label in entities:
|
|
for i in range(start_char, end_char):
|
|
entity_chars.add(i)
|
|
for token in doc:
|
|
for i in range(token.idx, token.idx + len(token)):
|
|
if i in entity_chars:
|
|
break
|
|
else:
|
|
biluo[token.i] = missing
|
|
return biluo
|
|
|
|
|
|
def spans_from_biluo_tags(doc, tags):
|
|
"""Encode per-token tags following the BILUO scheme into Span object, e.g.
|
|
to overwrite the doc.ents.
|
|
|
|
doc (Doc): The document that the BILUO tags refer to.
|
|
entities (iterable): A sequence of BILUO tags with each tag describing one
|
|
token. Each tags string will be of the form of either "", "O" or
|
|
"{action}-{label}", where action is one of "B", "I", "L", "U".
|
|
RETURNS (list): A sequence of Span objects.
|
|
"""
|
|
token_offsets = tags_to_entities(tags)
|
|
spans = []
|
|
for label, start_idx, end_idx in token_offsets:
|
|
span = Span(doc, start_idx, end_idx + 1, label=label)
|
|
spans.append(span)
|
|
return spans
|
|
|
|
|
|
def offsets_from_biluo_tags(doc, tags):
|
|
"""Encode per-token tags following the BILUO scheme into entity offsets.
|
|
|
|
doc (Doc): The document that the BILUO tags refer to.
|
|
entities (iterable): A sequence of BILUO tags with each tag describing one
|
|
token. Each tags string will be of the form of either "", "O" or
|
|
"{action}-{label}", where action is one of "B", "I", "L", "U".
|
|
RETURNS (list): A sequence of `(start, end, label)` triples. `start` and
|
|
`end` will be character-offset integers denoting the slice into the
|
|
original string.
|
|
"""
|
|
spans = spans_from_biluo_tags(doc, tags)
|
|
return [(span.start_char, span.end_char, span.label_) for span in spans]
|
|
|
|
|
|
def is_punct_label(label):
|
|
return label == "P" or label.lower() == "punct"
|