mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-25 09:26:27 +03:00
642 lines
24 KiB
Cython
642 lines
24 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 cytoolz
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import itertools
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import numpy
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import tempfile
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import shutil
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from pathlib import Path
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import msgpack
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import ujson
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from . import _align
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from .syntax import nonproj
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from .tokens import Doc
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from .errors import Errors
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from . import util
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from .util import minibatch, itershuffle
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from .compat import json_dumps
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from libc.stdio cimport FILE, fopen, fclose, fread, fwrite, feof, fseek
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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]))
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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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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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return [(m_deps, m_brackets)]
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punct_re = re.compile(r'\W')
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def align(cand_words, gold_words):
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if cand_words == gold_words:
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alignment = numpy.arange(len(cand_words))
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return 0, alignment, alignment, {}, {}
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cand_words = [w.replace(' ', '') for w in cand_words]
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gold_words = [w.replace(' ', '') for w in gold_words]
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cost, i2j, j2i, matrix = _align.align(cand_words, gold_words)
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i2j_multi, j2i_multi = _align.multi_align(i2j, j2i, [len(w) for w in cand_words],
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[len(w) for w in gold_words])
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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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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
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# and stream
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self.tmp_dir = Path(tempfile.mkdtemp())
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self.write_msgpack(self.tmp_dir / 'train', train)
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self.write_msgpack(self.tmp_dir / 'dev', dev)
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def __del__(self):
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shutil.rmtree(self.tmp_dir)
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@staticmethod
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def write_msgpack(directory, doc_tuples):
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if not directory.exists():
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directory.mkdir()
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for i, doc_tuple in enumerate(doc_tuples):
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with open(directory / '{}.msg'.format(i), 'wb') as file_:
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msgpack.dump([doc_tuple], file_, use_bin_type=True, encoding='utf8')
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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'):
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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('msg'):
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with loc.open('rb') as file_:
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gold_tuples = msgpack.load(file_, encoding='utf8')
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else:
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msg = "Cannot read from file: %s. Supported formats: .json, .msg"
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raise ValueError(msg % loc)
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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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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):
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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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make_projective=True)
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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,
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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, 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 = cls._make_docs(nlp, raw_text, paragraph_tuples,
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gold_preproc, noise_level=noise_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,
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noise_level=0.0):
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if raw_text is not None:
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raw_text = add_noise(raw_text, noise_level)
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return [nlp.make_doc(raw_text)]
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else:
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return [Doc(nlp.vocab,
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words=add_noise(sent_tuples[1], noise_level))
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for (sent_tuples, brackets) in 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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raise ValueError(Errors.E070.format(n_docs=len(docs),
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n_annots=len(paragraph_tuples)))
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if len(docs) == 1:
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return [GoldParse.from_annot_tuples(docs[0],
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paragraph_tuples[0][0],
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make_projective=make_projective)]
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else:
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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 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 == ' ':
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return '\n'
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elif c == '\n':
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return ' '
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elif c in ['.', "'", "!", "?"]:
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return ''
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else:
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return c.lower()
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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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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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if sents:
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yield [paragraph.get('raw', None), sents]
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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('{')
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cdef char close_curly = ord('}')
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for i in range(len(py_raw)):
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c = raw[i]
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if c == backslash:
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escape = True
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continue
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if escape:
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escape = False
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continue
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if c == quote:
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inside_string = not inside_string
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continue
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if inside_string:
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continue
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if c == open_square:
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square_depth += 1
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elif c == close_square:
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square_depth -= 1
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elif c == open_curly:
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if square_depth == 1 and curly_depth == 0:
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start = i
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curly_depth += 1
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elif c == close_curly:
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curly_depth -= 1
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if square_depth == 1 and curly_depth == 0:
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py_str = py_raw[start : i+1].decode('utf8')
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yield ujson.loads(py_str)
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start = -1
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def iob_to_biluo(tags):
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out = []
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curr_label = None
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tags = list(tags)
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while tags:
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out.extend(_consume_os(tags))
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out.extend(_consume_ent(tags))
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return out
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def _consume_os(tags):
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while tags and tags[0] == 'O':
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yield tags.pop(0)
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def _consume_ent(tags):
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if not tags:
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return []
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tag = tags.pop(0)
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target_in = 'I' + tag[1:]
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target_last = 'L' + tag[1:]
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length = 1
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while tags and tags[0] in {target_in, target_last}:
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length += 1
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tags.pop(0)
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label = tag[2:]
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if length == 1:
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return ['U-' + label]
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else:
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start = 'B-' + label
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end = 'L-' + label
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middle = ['I-%s' % label for _ in range(1, length - 1)]
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return [start] + middle + [end]
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cdef class GoldParse:
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"""Collection for training annotations."""
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@classmethod
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def from_annot_tuples(cls, doc, annot_tuples, make_projective=False):
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_, words, tags, heads, deps, entities = annot_tuples
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return cls(doc, words=words, tags=tags, heads=heads, deps=deps,
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entities=entities, make_projective=make_projective)
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def __init__(self, doc, annot_tuples=None, words=None, tags=None,
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heads=None, deps=None, entities=None, make_projective=False,
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cats=None, **_):
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"""Create a GoldParse.
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doc (Doc): The document the annotations refer to.
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words (iterable): A sequence of unicode word strings.
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tags (iterable): A sequence of strings, representing tag annotations.
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heads (iterable): A sequence of integers, representing syntactic
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head offsets.
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deps (iterable): A sequence of strings, representing the syntactic
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relation types.
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entities (iterable): A sequence of named entity annotations, either as
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BILUO tag strings, or as `(start_char, end_char, label)` tuples,
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representing the entity positions.
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cats (dict): Labels for text classification. Each key in the dictionary
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may be a string or an int, or a `(start_char, end_char, label)`
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tuple, indicating that the label is applied to only part of the
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document (usually a sentence). Unlike entity annotations, label
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annotations can overlap, i.e. a single word can be covered by
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multiple labelled spans. The TextCategorizer component expects
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true examples of a label to have the value 1.0, and negative
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examples of a label to have the value 0.0. Labels not in the
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dictionary are treated as missing - the gradient for those labels
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will be zero.
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RETURNS (GoldParse): The newly constructed object.
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"""
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if words is None:
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words = [token.text for token in doc]
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if tags is None:
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tags = [None for _ in doc]
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if heads is None:
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heads = [None for token in doc]
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if deps is None:
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deps = [None for _ in doc]
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if entities is None:
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entities = [None for _ in doc]
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elif len(entities) == 0:
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entities = ['O' for _ in doc]
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elif not isinstance(entities[0], basestring):
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# Assume we have entities specified by character offset.
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entities = biluo_tags_from_offsets(doc, entities)
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self.mem = Pool()
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self.loss = 0
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self.length = len(doc)
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# These are filled by the tagger/parser/entity recogniser
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self.c.tags = <int*>self.mem.alloc(len(doc), sizeof(int))
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self.c.heads = <int*>self.mem.alloc(len(doc), sizeof(int))
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self.c.labels = <attr_t*>self.mem.alloc(len(doc), sizeof(attr_t))
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self.c.has_dep = <int*>self.mem.alloc(len(doc), sizeof(int))
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self.c.sent_start = <int*>self.mem.alloc(len(doc), sizeof(int))
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self.c.ner = <Transition*>self.mem.alloc(len(doc), sizeof(Transition))
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self.cats = {} if cats is None else dict(cats)
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self.words = [None] * len(doc)
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self.tags = [None] * len(doc)
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self.heads = [None] * len(doc)
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self.labels = [None] * len(doc)
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self.ner = [None] * len(doc)
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# This needs to be done before we align the words
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if make_projective and heads is not None and deps is not None:
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heads, deps = nonproj.projectivize(heads, deps)
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# Do many-to-one alignment for misaligned tokens.
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# If we over-segment, we'll have one gold word that covers a sequence
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# of predicted words
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# If we under-segment, we'll have one predicted word that covers a
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# sequence of gold words.
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# If we "mis-segment", we'll have a sequence of predicted words covering
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# a sequence of gold words. That's many-to-many -- we don't do that.
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cost, i2j, j2i, i2j_multi, j2i_multi = align([t.orth_ for t in doc], words)
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self.cand_to_gold = [(j if j >= 0 else None) for j in i2j]
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self.gold_to_cand = [(i if i >= 0 else None) for i in j2i]
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annot_tuples = (range(len(words)), words, tags, heads, deps, entities)
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self.orig_annot = list(zip(*annot_tuples))
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for i, gold_i in enumerate(self.cand_to_gold):
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if doc[i].text.isspace():
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self.words[i] = doc[i].text
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self.tags[i] = '_SP'
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self.heads[i] = None
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self.labels[i] = None
|
|
self.ner[i] = 'O'
|
|
if gold_i is None:
|
|
if i in i2j_multi:
|
|
self.words[i] = words[i2j_multi[i]]
|
|
self.tags[i] = tags[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
|
|
# BILOU 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]
|
|
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]
|
|
|
|
cycle = nonproj.contains_cycle(self.heads)
|
|
if cycle is not None:
|
|
raise ValueError(Errors.E069.format(cycle=cycle))
|
|
|
|
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 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']
|
|
"""
|
|
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:
|
|
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 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.
|
|
"""
|
|
token_offsets = tags_to_entities(tags)
|
|
offsets = []
|
|
for label, start_idx, end_idx in token_offsets:
|
|
span = doc[start_idx : end_idx + 1]
|
|
offsets.append((span.start_char, span.end_char, label))
|
|
return offsets
|
|
|
|
|
|
def is_punct_label(label):
|
|
return label == 'P' or label.lower() == 'punct'
|