mirror of https://github.com/explosion/spaCy.git
985 lines
39 KiB
Cython
985 lines
39 KiB
Cython
# cython: profile=True
|
|
# coding: utf8
|
|
from __future__ import unicode_literals, print_function
|
|
|
|
import re
|
|
import random
|
|
import numpy
|
|
import tempfile
|
|
import shutil
|
|
import itertools
|
|
from pathlib import Path
|
|
import srsly
|
|
import warnings
|
|
|
|
from .syntax import nonproj
|
|
from .tokens import Doc, Span
|
|
from .errors import Errors, AlignmentError, Warnings
|
|
from .compat import path2str
|
|
from . import util
|
|
from .util import minibatch, itershuffle
|
|
|
|
from libc.stdio cimport FILE, fopen, fclose, fread, fwrite, feof, fseek
|
|
|
|
|
|
punct_re = re.compile(r"\W")
|
|
|
|
|
|
def tags_to_entities(tags):
|
|
entities = []
|
|
start = None
|
|
for i, tag in enumerate(tags):
|
|
if tag is None:
|
|
continue
|
|
if tag.startswith("O"):
|
|
# TODO: We shouldn't be getting these malformed inputs. Fix this.
|
|
if start is not None:
|
|
start = None
|
|
continue
|
|
elif tag == "-":
|
|
continue
|
|
elif tag.startswith("I"):
|
|
if start is None:
|
|
raise ValueError(Errors.E067.format(tags=tags[:i + 1]))
|
|
continue
|
|
if tag.startswith("U"):
|
|
entities.append((tag[2:], i, i))
|
|
elif tag.startswith("B"):
|
|
start = i
|
|
elif tag.startswith("L"):
|
|
entities.append((tag[2:], start, i))
|
|
start = None
|
|
else:
|
|
raise ValueError(Errors.E068.format(tag=tag))
|
|
return entities
|
|
|
|
|
|
def merge_sents(sents):
|
|
m_deps = [[], [], [], [], [], []]
|
|
m_cats = {}
|
|
m_brackets = []
|
|
i = 0
|
|
for (ids, words, tags, heads, labels, ner), (cats, brackets) in sents:
|
|
m_deps[0].extend(id_ + i for id_ in ids)
|
|
m_deps[1].extend(words)
|
|
m_deps[2].extend(tags)
|
|
m_deps[3].extend(head + i for head in heads)
|
|
m_deps[4].extend(labels)
|
|
m_deps[5].extend(ner)
|
|
m_brackets.extend((b["first"] + i, b["last"] + i, b["label"])
|
|
for b in brackets)
|
|
m_cats.update(cats)
|
|
i += len(ids)
|
|
return [(m_deps, (m_cats, m_brackets))]
|
|
|
|
|
|
def _normalize_for_alignment(tokens):
|
|
return [w.replace(" ", "").lower() for w in tokens]
|
|
|
|
|
|
def align(tokens_a, tokens_b):
|
|
"""Calculate alignment tables between two tokenizations.
|
|
|
|
tokens_a (List[str]): The candidate tokenization.
|
|
tokens_b (List[str]): The reference tokenization.
|
|
RETURNS: (tuple): A 5-tuple consisting of the following information:
|
|
* cost (int): The number of misaligned tokens.
|
|
* a2b (List[int]): Mapping of indices in `tokens_a` to indices in `tokens_b`.
|
|
For instance, if `a2b[4] == 6`, that means that `tokens_a[4]` aligns
|
|
to `tokens_b[6]`. If there's no one-to-one alignment for a token,
|
|
it has the value -1.
|
|
* b2a (List[int]): The same as `a2b`, but mapping the other direction.
|
|
* a2b_multi (Dict[int, int]): A dictionary mapping indices in `tokens_a`
|
|
to indices in `tokens_b`, where multiple tokens of `tokens_a` align to
|
|
the same token of `tokens_b`.
|
|
* b2a_multi (Dict[int, int]): As with `a2b_multi`, but mapping the other
|
|
direction.
|
|
"""
|
|
tokens_a = _normalize_for_alignment(tokens_a)
|
|
tokens_b = _normalize_for_alignment(tokens_b)
|
|
cost = 0
|
|
a2b = numpy.empty(len(tokens_a), dtype="i")
|
|
b2a = numpy.empty(len(tokens_b), dtype="i")
|
|
a2b.fill(-1)
|
|
b2a.fill(-1)
|
|
a2b_multi = {}
|
|
b2a_multi = {}
|
|
i = 0
|
|
j = 0
|
|
offset_a = 0
|
|
offset_b = 0
|
|
while i < len(tokens_a) and j < len(tokens_b):
|
|
a = tokens_a[i][offset_a:]
|
|
b = tokens_b[j][offset_b:]
|
|
if a == b:
|
|
if offset_a == offset_b == 0:
|
|
a2b[i] = j
|
|
b2a[j] = i
|
|
elif offset_a == 0:
|
|
cost += 2
|
|
a2b_multi[i] = j
|
|
elif offset_b == 0:
|
|
cost += 2
|
|
b2a_multi[j] = i
|
|
offset_a = offset_b = 0
|
|
i += 1
|
|
j += 1
|
|
elif a == "":
|
|
assert offset_a == 0
|
|
cost += 1
|
|
i += 1
|
|
elif b == "":
|
|
assert offset_b == 0
|
|
cost += 1
|
|
j += 1
|
|
elif b.startswith(a):
|
|
cost += 1
|
|
if offset_a == 0:
|
|
a2b_multi[i] = j
|
|
i += 1
|
|
offset_a = 0
|
|
offset_b += len(a)
|
|
elif a.startswith(b):
|
|
cost += 1
|
|
if offset_b == 0:
|
|
b2a_multi[j] = i
|
|
j += 1
|
|
offset_b = 0
|
|
offset_a += len(b)
|
|
else:
|
|
assert "".join(tokens_a) != "".join(tokens_b)
|
|
raise AlignmentError(Errors.E186.format(tok_a=tokens_a, tok_b=tokens_b))
|
|
return cost, a2b, b2a, a2b_multi, b2a_multi
|
|
|
|
|
|
class GoldCorpus(object):
|
|
"""An annotated corpus, using the JSON file format. Manages
|
|
annotations for tagging, dependency parsing and NER.
|
|
|
|
DOCS: https://spacy.io/api/goldcorpus
|
|
"""
|
|
def __init__(self, train, dev, gold_preproc=False, limit=None):
|
|
"""Create a GoldCorpus.
|
|
|
|
train_path (unicode or Path): File or directory of training data.
|
|
dev_path (unicode or Path): File or directory of development data.
|
|
RETURNS (GoldCorpus): The newly created object.
|
|
"""
|
|
self.limit = limit
|
|
if isinstance(train, str) or isinstance(train, Path):
|
|
train = self.read_tuples(self.walk_corpus(train))
|
|
dev = self.read_tuples(self.walk_corpus(dev))
|
|
# Write temp directory with one doc per file, so we can shuffle and stream
|
|
self.tmp_dir = Path(tempfile.mkdtemp())
|
|
self.write_msgpack(self.tmp_dir / "train", train, limit=self.limit)
|
|
self.write_msgpack(self.tmp_dir / "dev", dev, limit=self.limit)
|
|
|
|
def __del__(self):
|
|
shutil.rmtree(path2str(self.tmp_dir))
|
|
|
|
@staticmethod
|
|
def write_msgpack(directory, doc_tuples, limit=0):
|
|
if not directory.exists():
|
|
directory.mkdir()
|
|
n = 0
|
|
for i, doc_tuple in enumerate(doc_tuples):
|
|
srsly.write_msgpack(directory / "{}.msg".format(i), [doc_tuple])
|
|
n += len(doc_tuple[1])
|
|
if limit and n >= limit:
|
|
break
|
|
|
|
@staticmethod
|
|
def walk_corpus(path):
|
|
path = util.ensure_path(path)
|
|
if not path.is_dir():
|
|
return [path]
|
|
paths = [path]
|
|
locs = []
|
|
seen = set()
|
|
for path in paths:
|
|
if str(path) in seen:
|
|
continue
|
|
seen.add(str(path))
|
|
if path.parts[-1].startswith("."):
|
|
continue
|
|
elif path.is_dir():
|
|
paths.extend(path.iterdir())
|
|
elif path.parts[-1].endswith((".json", ".jsonl")):
|
|
locs.append(path)
|
|
return locs
|
|
|
|
@staticmethod
|
|
def read_tuples(locs, limit=0):
|
|
i = 0
|
|
for loc in locs:
|
|
loc = util.ensure_path(loc)
|
|
if loc.parts[-1].endswith("json"):
|
|
gold_tuples = read_json_file(loc)
|
|
elif loc.parts[-1].endswith("jsonl"):
|
|
gold_tuples = srsly.read_jsonl(loc)
|
|
first_gold_tuple = next(gold_tuples)
|
|
gold_tuples = itertools.chain([first_gold_tuple], gold_tuples)
|
|
# TODO: proper format checks with schemas
|
|
if isinstance(first_gold_tuple, dict):
|
|
gold_tuples = read_json_object(gold_tuples)
|
|
elif loc.parts[-1].endswith("msg"):
|
|
gold_tuples = srsly.read_msgpack(loc)
|
|
else:
|
|
supported = ("json", "jsonl", "msg")
|
|
raise ValueError(Errors.E124.format(path=path2str(loc), formats=supported))
|
|
for item in gold_tuples:
|
|
yield item
|
|
i += len(item[1])
|
|
if limit and i >= limit:
|
|
return
|
|
|
|
@property
|
|
def dev_tuples(self):
|
|
locs = (self.tmp_dir / "dev").iterdir()
|
|
yield from self.read_tuples(locs, limit=self.limit)
|
|
|
|
@property
|
|
def train_tuples(self):
|
|
locs = (self.tmp_dir / "train").iterdir()
|
|
yield from self.read_tuples(locs, limit=self.limit)
|
|
|
|
def count_train(self):
|
|
n = 0
|
|
i = 0
|
|
for raw_text, paragraph_tuples in self.train_tuples:
|
|
for sent_tuples, brackets in paragraph_tuples:
|
|
n += len(sent_tuples[1])
|
|
if self.limit and i >= self.limit:
|
|
break
|
|
i += 1
|
|
return n
|
|
|
|
def train_docs(self, nlp, gold_preproc=False, max_length=None,
|
|
noise_level=0.0, orth_variant_level=0.0,
|
|
ignore_misaligned=False):
|
|
locs = list((self.tmp_dir / 'train').iterdir())
|
|
random.shuffle(locs)
|
|
train_tuples = self.read_tuples(locs, limit=self.limit)
|
|
gold_docs = self.iter_gold_docs(nlp, train_tuples, gold_preproc,
|
|
max_length=max_length,
|
|
noise_level=noise_level,
|
|
orth_variant_level=orth_variant_level,
|
|
make_projective=True,
|
|
ignore_misaligned=ignore_misaligned)
|
|
yield from gold_docs
|
|
|
|
def train_docs_without_preprocessing(self, nlp, gold_preproc=False):
|
|
gold_docs = self.iter_gold_docs(nlp, self.train_tuples, gold_preproc=gold_preproc)
|
|
yield from gold_docs
|
|
|
|
def dev_docs(self, nlp, gold_preproc=False, ignore_misaligned=False):
|
|
gold_docs = self.iter_gold_docs(nlp, self.dev_tuples, gold_preproc=gold_preproc,
|
|
ignore_misaligned=ignore_misaligned)
|
|
yield from gold_docs
|
|
|
|
@classmethod
|
|
def iter_gold_docs(cls, nlp, tuples, gold_preproc, max_length=None,
|
|
noise_level=0.0, orth_variant_level=0.0, make_projective=False,
|
|
ignore_misaligned=False):
|
|
for raw_text, paragraph_tuples in tuples:
|
|
if gold_preproc:
|
|
raw_text = None
|
|
else:
|
|
paragraph_tuples = merge_sents(paragraph_tuples)
|
|
docs, paragraph_tuples = cls._make_docs(nlp, raw_text,
|
|
paragraph_tuples, gold_preproc, noise_level=noise_level,
|
|
orth_variant_level=orth_variant_level)
|
|
golds = cls._make_golds(docs, paragraph_tuples, make_projective,
|
|
ignore_misaligned=ignore_misaligned)
|
|
for doc, gold in zip(docs, golds):
|
|
if gold is not None:
|
|
if (not max_length) or len(doc) < max_length:
|
|
yield doc, gold
|
|
|
|
@classmethod
|
|
def _make_docs(cls, nlp, raw_text, paragraph_tuples, gold_preproc, noise_level=0.0, orth_variant_level=0.0):
|
|
if raw_text is not None:
|
|
raw_text, paragraph_tuples = make_orth_variants(nlp, raw_text, paragraph_tuples, orth_variant_level=orth_variant_level)
|
|
raw_text = add_noise(raw_text, noise_level)
|
|
return [nlp.make_doc(raw_text)], paragraph_tuples
|
|
else:
|
|
docs = []
|
|
raw_text, paragraph_tuples = make_orth_variants(nlp, None, paragraph_tuples, orth_variant_level=orth_variant_level)
|
|
return [Doc(nlp.vocab, words=add_noise(sent_tuples[1], noise_level))
|
|
for (sent_tuples, brackets) in paragraph_tuples], paragraph_tuples
|
|
|
|
|
|
@classmethod
|
|
def _make_golds(cls, docs, paragraph_tuples, make_projective, ignore_misaligned=False):
|
|
if len(docs) != len(paragraph_tuples):
|
|
n_annots = len(paragraph_tuples)
|
|
raise ValueError(Errors.E070.format(n_docs=len(docs), n_annots=n_annots))
|
|
golds = []
|
|
for doc, (sent_tuples, (cats, brackets)) in zip(docs, paragraph_tuples):
|
|
try:
|
|
gold = GoldParse.from_annot_tuples(doc, sent_tuples, cats=cats,
|
|
make_projective=make_projective)
|
|
except AlignmentError:
|
|
if ignore_misaligned:
|
|
gold = None
|
|
else:
|
|
raise
|
|
golds.append(gold)
|
|
return golds
|
|
|
|
|
|
def make_orth_variants(nlp, raw, paragraph_tuples, orth_variant_level=0.0):
|
|
if random.random() >= orth_variant_level:
|
|
return raw, paragraph_tuples
|
|
if random.random() >= 0.5:
|
|
lower = True
|
|
if raw is not None:
|
|
raw = raw.lower()
|
|
ndsv = nlp.Defaults.single_orth_variants
|
|
ndpv = nlp.Defaults.paired_orth_variants
|
|
# modify words in paragraph_tuples
|
|
variant_paragraph_tuples = []
|
|
for sent_tuples, brackets in paragraph_tuples:
|
|
ids, words, tags, heads, labels, ner = sent_tuples
|
|
if lower:
|
|
words = [w.lower() for w in words]
|
|
# single variants
|
|
punct_choices = [random.choice(x["variants"]) for x in ndsv]
|
|
for word_idx in range(len(words)):
|
|
for punct_idx in range(len(ndsv)):
|
|
if tags[word_idx] in ndsv[punct_idx]["tags"] \
|
|
and words[word_idx] in ndsv[punct_idx]["variants"]:
|
|
words[word_idx] = punct_choices[punct_idx]
|
|
# paired variants
|
|
punct_choices = [random.choice(x["variants"]) for x in ndpv]
|
|
for word_idx in range(len(words)):
|
|
for punct_idx in range(len(ndpv)):
|
|
if tags[word_idx] in ndpv[punct_idx]["tags"] \
|
|
and words[word_idx] in itertools.chain.from_iterable(ndpv[punct_idx]["variants"]):
|
|
# backup option: random left vs. right from pair
|
|
pair_idx = random.choice([0, 1])
|
|
# best option: rely on paired POS tags like `` / ''
|
|
if len(ndpv[punct_idx]["tags"]) == 2:
|
|
pair_idx = ndpv[punct_idx]["tags"].index(tags[word_idx])
|
|
# next best option: rely on position in variants
|
|
# (may not be unambiguous, so order of variants matters)
|
|
else:
|
|
for pair in ndpv[punct_idx]["variants"]:
|
|
if words[word_idx] in pair:
|
|
pair_idx = pair.index(words[word_idx])
|
|
words[word_idx] = punct_choices[punct_idx][pair_idx]
|
|
|
|
variant_paragraph_tuples.append(((ids, words, tags, heads, labels, ner), brackets))
|
|
# modify raw to match variant_paragraph_tuples
|
|
if raw is not None:
|
|
variants = []
|
|
for single_variants in ndsv:
|
|
variants.extend(single_variants["variants"])
|
|
for paired_variants in ndpv:
|
|
variants.extend(list(itertools.chain.from_iterable(paired_variants["variants"])))
|
|
# store variants in reverse length order to be able to prioritize
|
|
# longer matches (e.g., "---" before "--")
|
|
variants = sorted(variants, key=lambda x: len(x))
|
|
variants.reverse()
|
|
variant_raw = ""
|
|
raw_idx = 0
|
|
# add initial whitespace
|
|
while raw_idx < len(raw) and re.match("\s", raw[raw_idx]):
|
|
variant_raw += raw[raw_idx]
|
|
raw_idx += 1
|
|
for sent_tuples, brackets in variant_paragraph_tuples:
|
|
ids, words, tags, heads, labels, ner = sent_tuples
|
|
for word in words:
|
|
match_found = False
|
|
# add identical word
|
|
if word not in variants and raw[raw_idx:].startswith(word):
|
|
variant_raw += word
|
|
raw_idx += len(word)
|
|
match_found = True
|
|
# add variant word
|
|
else:
|
|
for variant in variants:
|
|
if not match_found and \
|
|
raw[raw_idx:].startswith(variant):
|
|
raw_idx += len(variant)
|
|
variant_raw += word
|
|
match_found = True
|
|
# something went wrong, abort
|
|
# (add a warning message?)
|
|
if not match_found:
|
|
return raw, paragraph_tuples
|
|
# add following whitespace
|
|
while raw_idx < len(raw) and re.match("\s", raw[raw_idx]):
|
|
variant_raw += raw[raw_idx]
|
|
raw_idx += 1
|
|
return variant_raw, variant_paragraph_tuples
|
|
return raw, variant_paragraph_tuples
|
|
|
|
|
|
def add_noise(orig, noise_level):
|
|
if random.random() >= noise_level:
|
|
return orig
|
|
elif type(orig) == list:
|
|
corrupted = [_corrupt(word, noise_level) for word in orig]
|
|
corrupted = [w for w in corrupted if w]
|
|
return corrupted
|
|
else:
|
|
return "".join(_corrupt(c, noise_level) for c in orig)
|
|
|
|
|
|
def _corrupt(c, noise_level):
|
|
if random.random() >= noise_level:
|
|
return c
|
|
elif c in [".", "'", "!", "?", ","]:
|
|
return "\n"
|
|
else:
|
|
return c.lower()
|
|
|
|
|
|
def read_json_object(json_corpus_section):
|
|
"""Take a list of JSON-formatted documents (e.g. from an already loaded
|
|
training data file) and yield tuples in the GoldParse format.
|
|
|
|
json_corpus_section (list): The data.
|
|
YIELDS (tuple): The reformatted data.
|
|
"""
|
|
for json_doc in json_corpus_section:
|
|
tuple_doc = json_to_tuple(json_doc)
|
|
for tuple_paragraph in tuple_doc:
|
|
yield tuple_paragraph
|
|
|
|
|
|
def json_to_tuple(doc):
|
|
"""Convert an item in the JSON-formatted training data to the tuple format
|
|
used by GoldParse.
|
|
|
|
doc (dict): One entry in the training data.
|
|
YIELDS (tuple): The reformatted data.
|
|
"""
|
|
paragraphs = []
|
|
for paragraph in doc["paragraphs"]:
|
|
sents = []
|
|
cats = {}
|
|
for cat in paragraph.get("cats", {}):
|
|
cats[cat["label"]] = cat["value"]
|
|
for sent in paragraph["sentences"]:
|
|
words = []
|
|
ids = []
|
|
tags = []
|
|
heads = []
|
|
labels = []
|
|
ner = []
|
|
for i, token in enumerate(sent["tokens"]):
|
|
words.append(token["orth"])
|
|
ids.append(i)
|
|
tags.append(token.get('tag', "-"))
|
|
heads.append(token.get("head", 0) + i)
|
|
labels.append(token.get("dep", ""))
|
|
# Ensure ROOT label is case-insensitive
|
|
if labels[-1].lower() == "root":
|
|
labels[-1] = "ROOT"
|
|
ner.append(token.get("ner", "-"))
|
|
sents.append([
|
|
[ids, words, tags, heads, labels, ner],
|
|
[cats, sent.get("brackets", [])]])
|
|
if sents:
|
|
yield [paragraph.get("raw", None), sents]
|
|
|
|
|
|
def read_json_file(loc, docs_filter=None, limit=None):
|
|
loc = util.ensure_path(loc)
|
|
if loc.is_dir():
|
|
for filename in loc.iterdir():
|
|
yield from read_json_file(loc / filename, limit=limit)
|
|
else:
|
|
for doc in _json_iterate(loc):
|
|
if docs_filter is not None and not docs_filter(doc):
|
|
continue
|
|
for json_tuple in json_to_tuple(doc):
|
|
yield json_tuple
|
|
|
|
|
|
def _json_iterate(loc):
|
|
# We should've made these files jsonl...But since we didn't, parse out
|
|
# the docs one-by-one to reduce memory usage.
|
|
# It's okay to read in the whole file -- just don't parse it into JSON.
|
|
cdef bytes py_raw
|
|
loc = util.ensure_path(loc)
|
|
with loc.open("rb") as file_:
|
|
py_raw = file_.read()
|
|
cdef long file_length = len(py_raw)
|
|
if file_length > 2 ** 30:
|
|
warnings.warn(Warnings.W027.format(size=file_length))
|
|
|
|
raw = <char*>py_raw
|
|
cdef int square_depth = 0
|
|
cdef int curly_depth = 0
|
|
cdef int inside_string = 0
|
|
cdef int escape = 0
|
|
cdef long start = -1
|
|
cdef char c
|
|
cdef char quote = ord('"')
|
|
cdef char backslash = ord("\\")
|
|
cdef char open_square = ord("[")
|
|
cdef char close_square = ord("]")
|
|
cdef char open_curly = ord("{")
|
|
cdef char close_curly = ord("}")
|
|
for i in range(file_length):
|
|
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 = []
|
|
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:
|
|
if len(label) == 0:
|
|
raise ValueError(Errors.E177.format(tag=tag))
|
|
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, cats=None, make_projective=False):
|
|
_, words, tags, heads, deps, entities = 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. The fields will not be initialized if len(doc) is zero.
|
|
|
|
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.
|
|
"""
|
|
self.mem = Pool()
|
|
self.loss = 0
|
|
self.length = len(doc)
|
|
|
|
self.cats = {} if cats is None else dict(cats)
|
|
self.links = links
|
|
|
|
# orig_annot is used as an iterator in `nlp.evalate` even if self.length == 0,
|
|
# so set a empty list to avoid error.
|
|
# if self.lenght > 0, this is modified latter.
|
|
self.orig_annot = []
|
|
|
|
# temporary doc for aligning entity annotation
|
|
entdoc = None
|
|
|
|
# avoid allocating memory if the doc does not contain any tokens
|
|
if self.length > 0:
|
|
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 words]
|
|
elif len(entities) == 0:
|
|
entities = ["O" for _ in words]
|
|
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.
|
|
# Create a temporary Doc corresponding to provided words
|
|
# (to preserve gold tokenization) and text (to preserve
|
|
# character offsets).
|
|
entdoc_words, entdoc_spaces = util.get_words_and_spaces(words, doc.text)
|
|
entdoc = Doc(doc.vocab, words=entdoc_words, spaces=entdoc_spaces)
|
|
entdoc_entities = biluo_tags_from_offsets(entdoc, entities)
|
|
# There may be some additional whitespace tokens in the
|
|
# temporary doc, so check that the annotations align with
|
|
# the provided words while building a list of BILUO labels.
|
|
entities = []
|
|
words_offset = 0
|
|
for i in range(len(entdoc_words)):
|
|
if words[i + words_offset] == entdoc_words[i]:
|
|
entities.append(entdoc_entities[i])
|
|
else:
|
|
words_offset -= 1
|
|
if len(entities) != len(words):
|
|
warnings.warn(Warnings.W029.format(text=doc.text))
|
|
entities = ["-" for _ in words]
|
|
|
|
# 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.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
|
|
# except for NER spans where the start and end can be aligned.
|
|
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)
|
|
# 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:
|
|
head_i = heads[i2j_multi[i]]
|
|
if head_i:
|
|
self.heads[i] = self.gold_to_cand[head_i]
|
|
self.labels[i] = deps[i2j_multi[i]]
|
|
ner_tag = entities[i2j_multi[i]]
|
|
# Assign O/- for many-to-one O/- NER tags
|
|
if ner_tag in ("O", "-"):
|
|
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]
|
|
# Assign O/- for one-to-many O/- NER tags
|
|
for j, cand_j in enumerate(self.gold_to_cand):
|
|
if cand_j is None:
|
|
if j in j2i_multi:
|
|
i = j2i_multi[j]
|
|
ner_tag = entities[j]
|
|
if ner_tag in ("O", "-"):
|
|
self.ner[i] = ner_tag
|
|
|
|
# If there is entity annotation and some tokens remain unaligned,
|
|
# align all entities at the character level to account for all
|
|
# possible token misalignments within the entity spans
|
|
if any([e not in ("O", "-") for e in entities]) and None in self.ner:
|
|
# If the temporary entdoc wasn't created above, initialize it
|
|
if not entdoc:
|
|
entdoc_words, entdoc_spaces = util.get_words_and_spaces(words, doc.text)
|
|
entdoc = Doc(doc.vocab, words=entdoc_words, spaces=entdoc_spaces)
|
|
# Get offsets based on gold words and BILUO entities
|
|
entdoc_offsets = offsets_from_biluo_tags(entdoc, entities)
|
|
aligned_offsets = []
|
|
aligned_spans = []
|
|
# Filter offsets to identify those that align with doc tokens
|
|
for offset in entdoc_offsets:
|
|
span = doc.char_span(offset[0], offset[1])
|
|
if span and not span.text.isspace():
|
|
aligned_offsets.append(offset)
|
|
aligned_spans.append(span)
|
|
# Convert back to BILUO for doc tokens and assign NER for all
|
|
# aligned spans
|
|
biluo_tags = biluo_tags_from_offsets(doc, aligned_offsets, missing=None)
|
|
for span in aligned_spans:
|
|
for i in range(span.start, span.end):
|
|
self.ner[i] = biluo_tags[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, ner_missing_tag="O"):
|
|
"""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 (dict): The data in spaCy's JSON format
|
|
- each input doc will be treated as a paragraph in the output doc
|
|
"""
|
|
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, missing=ner_missing_tag)
|
|
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"
|