spaCy/spacy/gold.pyx
2020-02-18 15:17:03 +01:00

1301 lines
52 KiB
Cython

# cython: profile=True
import re
import random
import numpy
import tempfile
import shutil
import itertools
from pathlib import Path
import srsly
from .syntax import nonproj
from .tokens import Doc, Span
from .errors import Errors, AlignmentError, user_warning, Warnings
from . import util
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 _normalize_for_alignment(tokens):
tokens = [w.replace(" ", "").lower() for w in tokens]
output = []
for token in tokens:
token = token.replace(" ", "").lower()
output.append(token)
return output
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 (unicode or Path): File or directory of training data.
dev (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_examples(self.walk_corpus(train))
dev = self.read_examples(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(self.tmp_dir)
@staticmethod
def write_msgpack(directory, examples, limit=0):
if not directory.exists():
directory.mkdir()
n = 0
for i, example in enumerate(examples):
ex_dict = example.to_dict()
text = example.text
srsly.write_msgpack(directory / f"{i}.msg", (text, ex_dict))
n += 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_examples(locs, limit=0):
""" Yield training examples """
i = 0
for loc in locs:
loc = util.ensure_path(loc)
file_name = loc.parts[-1]
if file_name.endswith("json"):
examples = read_json_file(loc)
elif file_name.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):
if first_gold_tuple.get("paragraphs", None):
examples = read_json_object(gold_tuples)
elif first_gold_tuple.get("doc_annotation", None):
examples = []
for ex_dict in gold_tuples:
doc = ex_dict.get("doc", None)
if doc is None:
doc = ex_dict.get("text", None)
examples.append(Example.from_dict(ex_dict, doc=doc))
elif file_name.endswith("msg"):
text, ex_dict = srsly.read_msgpack(loc)
examples = [Example.from_dict(ex_dict, doc=text)]
else:
supported = ("json", "jsonl", "msg")
raise ValueError(Errors.E124.format(path=loc, formats=supported))
try:
for example in examples:
yield example
i += 1
if limit and i >= limit:
return
except KeyError as e:
msg = "Missing key {}".format(e)
raise KeyError(Errors.E996.format(file=file_name, msg=msg))
except UnboundLocalError as e:
msg = "Unexpected document structure"
raise ValueError(Errors.E996.format(file=file_name, msg=msg))
@property
def dev_examples(self):
locs = (self.tmp_dir / "dev").iterdir()
yield from self.read_examples(locs, limit=self.limit)
@property
def train_examples(self):
locs = (self.tmp_dir / "train").iterdir()
yield from self.read_examples(locs, limit=self.limit)
def count_train(self):
"""Returns count of words in train examples"""
n = 0
i = 0
for example in self.train_examples:
n += len(example.token_annotation.words)
if self.limit and i >= self.limit:
break
i += 1
return n
def train_dataset(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_examples = self.read_examples(locs, limit=self.limit)
gold_examples = self.iter_gold_docs(nlp, train_examples, 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_examples
def train_dataset_without_preprocessing(self, nlp, gold_preproc=False,
ignore_misaligned=False):
examples = self.iter_gold_docs(nlp, self.train_examples,
gold_preproc=gold_preproc,
ignore_misaligned=ignore_misaligned)
yield from examples
def dev_dataset(self, nlp, gold_preproc=False, ignore_misaligned=False):
examples = self.iter_gold_docs(nlp, self.dev_examples,
gold_preproc=gold_preproc,
ignore_misaligned=ignore_misaligned)
yield from examples
@classmethod
def iter_gold_docs(cls, nlp, examples, gold_preproc, max_length=None,
noise_level=0.0, orth_variant_level=0.0,
make_projective=False, ignore_misaligned=False):
""" Setting gold_preproc will result in creating a doc per sentence """
for example in examples:
if gold_preproc:
example.doc = None
split_examples = example.split_sents()
example_golds = []
for split_example in split_examples:
split_example_docs = cls._make_docs(nlp, split_example,
gold_preproc, noise_level=noise_level,
orth_variant_level=orth_variant_level)
split_example_golds = cls._make_golds(split_example_docs,
vocab=nlp.vocab, make_projective=make_projective,
ignore_misaligned=ignore_misaligned)
example_golds.extend(split_example_golds)
else:
example_docs = cls._make_docs(nlp, example,
gold_preproc, noise_level=noise_level,
orth_variant_level=orth_variant_level)
example_golds = cls._make_golds(example_docs, vocab=nlp.vocab,
make_projective=make_projective,
ignore_misaligned=ignore_misaligned)
for ex in example_golds:
if ex.goldparse is not None:
if (not max_length) or len(ex.doc) < max_length:
yield ex
@classmethod
def _make_docs(cls, nlp, example, gold_preproc, noise_level=0.0, orth_variant_level=0.0):
var_example = make_orth_variants(nlp, example, orth_variant_level=orth_variant_level)
# gold_preproc is not used ?!
if example.text is not None:
var_text = add_noise(var_example.text, noise_level)
var_doc = nlp.make_doc(var_text)
var_example.doc = var_doc
else:
var_doc = Doc(nlp.vocab, words=add_noise(var_example.token_annotation.words, noise_level))
var_example.doc = var_doc
return [var_example]
@classmethod
def _make_golds(cls, examples, vocab=None, make_projective=False,
ignore_misaligned=False):
filtered_examples = []
for example in examples:
gold_parses = example.get_gold_parses(vocab=vocab,
make_projective=make_projective,
ignore_misaligned=ignore_misaligned)
assert len(gold_parses) == 1
doc, gold = gold_parses[0]
if doc:
assert doc == example.doc
example.goldparse = gold
filtered_examples.append(example)
return filtered_examples
def make_orth_variants(nlp, example, orth_variant_level=0.0):
if random.random() >= orth_variant_level:
return example
if not example.token_annotation:
return example
raw = example.text
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_example = Example(doc=raw)
token_annotation = example.token_annotation
words = token_annotation.words
tags = token_annotation.tags
if not words or not tags:
# add the unmodified annotation
token_dict = token_annotation.to_dict()
variant_example.set_token_annotation(**token_dict)
else:
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]
token_dict = token_annotation.to_dict()
token_dict["words"] = words
token_dict["tags"] = tags
variant_example.set_token_annotation(**token_dict)
# 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 word in variant_example.token_annotation.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 example
# add following whitespace
while raw_idx < len(raw) and re.match("\s", raw[raw_idx]):
variant_raw += raw[raw_idx]
raw_idx += 1
variant_example.doc = variant_raw
return variant_example
return variant_example
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 annotations in the GoldParse format.
json_corpus_section (list): The data.
YIELDS (Example): The reformatted data - one training example per paragraph
"""
for json_doc in json_corpus_section:
examples = json_to_examples(json_doc)
for ex in examples:
yield ex
def json_to_examples(doc):
"""Convert an item in the JSON-formatted training data to the format
used by GoldParse.
doc (dict): One entry in the training data.
YIELDS (Example): The reformatted data - one training example per paragraph
"""
paragraphs = []
for paragraph in doc["paragraphs"]:
example = Example(doc=paragraph.get("raw", None))
words = []
ids = []
tags = []
pos = []
morphs = []
lemmas = []
heads = []
labels = []
ner = []
sent_starts = []
brackets = []
for sent in paragraph["sentences"]:
sent_start_i = len(words)
for i, token in enumerate(sent["tokens"]):
words.append(token["orth"])
ids.append(token.get('id', sent_start_i + i))
tags.append(token.get('tag', "-"))
pos.append(token.get("pos", ""))
morphs.append(token.get("morph", ""))
lemmas.append(token.get("lemma", ""))
heads.append(token.get("head", 0) + sent_start_i + 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", "-"))
if i == 0:
sent_starts.append(1)
else:
sent_starts.append(0)
if "brackets" in sent:
brackets.extend((b["first"] + sent_start_i,
b["last"] + sent_start_i, b["label"])
for b in sent["brackets"])
cats = {}
for cat in paragraph.get("cats", {}):
cats[cat["label"]] = cat["value"]
example.set_token_annotation(ids=ids, words=words, tags=tags,
pos=pos, morphs=morphs, lemmas=lemmas, heads=heads,
deps=labels, entities=ner, sent_starts=sent_starts,
brackets=brackets)
example.set_doc_annotation(cats=cats)
yield example
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_data in json_to_examples(doc):
yield json_data
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:
user_warning(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 = [f"I-{label}" for _ in range(1, length - 1)]
return [start] + middle + [end]
cdef class TokenAnnotation:
def __init__(self, ids=None, words=None, tags=None, pos=None, morphs=None,
lemmas=None, heads=None, deps=None, entities=None, sent_starts=None,
brackets=None):
self.ids = ids if ids else []
self.words = words if words else []
self.tags = tags if tags else []
self.pos = pos if pos else []
self.morphs = morphs if morphs else []
self.lemmas = lemmas if lemmas else []
self.heads = heads if heads else []
self.deps = deps if deps else []
self.entities = entities if entities else []
self.sent_starts = sent_starts if sent_starts else []
self.brackets = brackets if brackets else []
@classmethod
def from_dict(cls, token_dict):
return cls(ids=token_dict.get("ids", None),
words=token_dict.get("words", None),
tags=token_dict.get("tags", None),
pos=token_dict.get("pos", None),
morphs=token_dict.get("morphs", None),
lemmas=token_dict.get("lemmas", None),
heads=token_dict.get("heads", None),
deps=token_dict.get("deps", None),
entities=token_dict.get("entities", None),
sent_starts=token_dict.get("sent_starts", None),
brackets=token_dict.get("brackets", None))
def to_dict(self):
return {"ids": self.ids,
"words": self.words,
"tags": self.tags,
"pos": self.pos,
"morphs": self.morphs,
"lemmas": self.lemmas,
"heads": self.heads,
"deps": self.deps,
"entities": self.entities,
"sent_starts": self.sent_starts,
"brackets": self.brackets}
def get_id(self, i):
return self.ids[i] if i < len(self.ids) else i
def get_word(self, i):
return self.words[i] if i < len(self.words) else ""
def get_tag(self, i):
return self.tags[i] if i < len(self.tags) else "-"
def get_pos(self, i):
return self.pos[i] if i < len(self.pos) else ""
def get_morph(self, i):
return self.morphs[i] if i < len(self.morphs) else ""
def get_lemma(self, i):
return self.lemmas[i] if i < len(self.lemmas) else ""
def get_head(self, i):
return self.heads[i] if i < len(self.heads) else i
def get_dep(self, i):
return self.deps[i] if i < len(self.deps) else ""
def get_entity(self, i):
return self.entities[i] if i < len(self.entities) else "-"
def get_sent_start(self, i):
return self.sent_starts[i] if i < len(self.sent_starts) else None
cdef class DocAnnotation:
def __init__(self, cats=None, links=None):
self.cats = cats if cats else {}
self.links = links if links else {}
@classmethod
def from_dict(cls, doc_dict):
return cls(cats=doc_dict.get("cats", None), links=doc_dict.get("links", None))
def to_dict(self):
return {"cats": self.cats, "links": self.links}
cdef class Example:
def __init__(self, doc_annotation=None, token_annotation=None, doc=None,
goldparse=None):
""" Doc can either be text, or an actual Doc """
self.doc = doc
self.doc_annotation = doc_annotation if doc_annotation else DocAnnotation()
self.token_annotation = token_annotation if token_annotation else TokenAnnotation()
self.goldparse = goldparse
@classmethod
def from_gold(cls, goldparse, doc=None):
doc_annotation = DocAnnotation(cats=goldparse.cats, links=goldparse.links)
token_annotation = goldparse.get_token_annotation()
return cls(doc_annotation, token_annotation, doc)
@classmethod
def from_dict(cls, example_dict, doc=None):
token_dict = example_dict["token_annotation"]
token_annotation = TokenAnnotation.from_dict(token_dict)
doc_dict = example_dict["doc_annotation"]
doc_annotation = DocAnnotation.from_dict(doc_dict)
return cls(doc_annotation, token_annotation, doc)
def to_dict(self):
""" Note that this method does NOT export the doc, only the annotations ! """
token_dict = self.token_annotation.to_dict()
doc_dict = self.doc_annotation.to_dict()
return {"token_annotation": token_dict, "doc_annotation": doc_dict}
@property
def text(self):
if self.doc is None:
return None
if isinstance(self.doc, Doc):
return self.doc.text
return self.doc
@property
def gold(self):
if self.goldparse is None:
doc, gold = self.get_gold_parses()[0]
self.goldparse = gold
return self.goldparse
def set_token_annotation(self, ids=None, words=None, tags=None, pos=None,
morphs=None, lemmas=None, heads=None, deps=None,
entities=None, sent_starts=None, brackets=None):
self.token_annotation = TokenAnnotation(ids=ids, words=words, tags=tags,
pos=pos, morphs=morphs, lemmas=lemmas, heads=heads,
deps=deps, entities=entities,
sent_starts=sent_starts, brackets=brackets)
def set_doc_annotation(self, cats=None, links=None):
if cats:
self.doc_annotation.cats = cats
if links:
self.doc_annotation.links = links
def split_sents(self):
""" Split the token annotations into multiple Examples based on
sent_starts and return a list of the new Examples"""
s_example = Example(doc=None, doc_annotation=self.doc_annotation)
s_ids, s_words, s_tags, s_pos, s_morphs = [], [], [], [], []
s_lemmas, s_heads, s_deps, s_ents, s_sent_starts = [], [], [], [], []
s_brackets = []
sent_start_i = 0
t = self.token_annotation
split_examples = []
for i in range(len(t.words)):
if i > 0 and t.sent_starts[i] == 1:
s_example.set_token_annotation(ids=s_ids,
words=s_words, tags=s_tags, pos=s_pos, morphs=s_morphs,
lemmas=s_lemmas, heads=s_heads, deps=s_deps,
entities=s_ents, sent_starts=s_sent_starts,
brackets=s_brackets)
split_examples.append(s_example)
s_example = Example(doc=None, doc_annotation=self.doc_annotation)
s_ids, s_words, s_tags, s_pos, s_heads = [], [], [], [], []
s_deps, s_ents, s_morphs, s_lemmas = [], [], [], []
s_sent_starts, s_brackets = [], []
sent_start_i = i
s_ids.append(t.get_id(i))
s_words.append(t.get_word(i))
s_tags.append(t.get_tag(i))
s_pos.append(t.get_pos(i))
s_morphs.append(t.get_morph(i))
s_lemmas.append(t.get_lemma(i))
s_heads.append(t.get_head(i) - sent_start_i)
s_deps.append(t.get_dep(i))
s_ents.append(t.get_entity(i))
s_sent_starts.append(t.get_sent_start(i))
s_brackets.extend((b[0] - sent_start_i,
b[1] - sent_start_i, b[2])
for b in t.brackets if b[0] == i)
i += 1
s_example.set_token_annotation(ids=s_ids, words=s_words, tags=s_tags,
pos=s_pos, morphs=s_morphs, lemmas=s_lemmas, heads=s_heads,
deps=s_deps, entities=s_ents, sent_starts=s_sent_starts,
brackets=s_brackets)
split_examples.append(s_example)
return split_examples
def get_gold_parses(self, merge=True, vocab=None, make_projective=False,
ignore_misaligned=False):
"""Return a list of (doc, GoldParse) objects.
If merge is set to True, keep all Token annotations as one big list."""
d = self.doc_annotation
# merge == do not modify Example
if merge:
t = self.token_annotation
doc = self.doc
if not self.doc:
if not vocab:
raise ValueError(Errors.E998)
doc = Doc(vocab, words=t.words)
try:
gp = GoldParse.from_annotation(doc, d, t,
make_projective=make_projective)
except AlignmentError:
if ignore_misaligned:
gp = None
else:
raise
return [(doc, gp)]
# not merging: one GoldParse per sentence, defining docs with the words
# from each sentence
else:
parses = []
split_examples = self.split_sents()
for split_example in split_examples:
if not vocab:
raise ValueError(Errors.E998)
split_doc = Doc(vocab, words=split_example.token_annotation.words)
try:
gp = GoldParse.from_annotation(split_doc, d,
split_example.token_annotation,
make_projective=make_projective)
except AlignmentError:
if ignore_misaligned:
gp = None
else:
raise
if gp is not None:
parses.append((split_doc, gp))
return parses
@classmethod
def to_example_objects(cls, examples, make_doc=None, keep_raw_text=False):
"""
Return a list of Example objects, from a variety of input formats.
make_doc needs to be provided when the examples contain text strings and keep_raw_text=False
"""
if isinstance(examples, Example):
return [examples]
if isinstance(examples, tuple):
examples = [examples]
converted_examples = []
for ex in examples:
# convert string to Doc to Example
if isinstance(ex, str):
if keep_raw_text:
converted_examples.append(Example(doc=ex))
else:
doc = make_doc(ex)
converted_examples.append(Example(doc=doc))
# convert Doc to Example
elif isinstance(ex, Doc):
converted_examples.append(Example(doc=ex))
# convert tuples to Example
elif isinstance(ex, tuple) and len(ex) == 2:
doc, gold = ex
gold_dict = {}
# convert string to Doc
if isinstance(doc, str) and not keep_raw_text:
doc = make_doc(doc)
# convert dict to GoldParse
if isinstance(gold, dict):
gold_dict = gold
if doc is not None or gold.get("words", None) is not None:
gold = GoldParse(doc, **gold)
else:
gold = None
if gold is not None:
converted_examples.append(Example.from_gold(goldparse=gold, doc=doc))
else:
raise ValueError(Errors.E999.format(gold_dict=gold_dict))
else:
converted_examples.append(ex)
return converted_examples
cdef class GoldParse:
"""Collection for training annotations.
DOCS: https://spacy.io/api/goldparse
"""
@classmethod
def from_annotation(cls, doc, doc_annotation, token_annotation, make_projective=False):
return cls(doc, words=token_annotation.words,
tags=token_annotation.tags,
pos=token_annotation.pos,
morphs=token_annotation.morphs,
lemmas=token_annotation.lemmas,
heads=token_annotation.heads,
deps=token_annotation.deps,
entities=token_annotation.entities,
sent_starts=token_annotation.sent_starts,
cats=doc_annotation.cats,
links=doc_annotation.links,
make_projective=make_projective)
def get_token_annotation(self):
ids = None
if self.words:
ids = list(range(len(self.words)))
return TokenAnnotation(ids=ids, words=self.words, tags=self.tags,
pos=self.pos, morphs=self.morphs,
lemmas=self.lemmas, heads=self.heads,
deps=self.labels, entities=self.ner,
sent_starts=self.sent_starts)
def __init__(self, doc, words=None, tags=None, pos=None, morphs=None,
lemmas=None, heads=None, deps=None, entities=None,
sent_starts=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.
pos (iterable): A sequence of strings, representing UPOS annotations.
morphs (iterable): A sequence of strings, representing morph
annotations.
lemmas (iterable): A sequence of strings, representing lemma
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.
sent_starts (iterable): A sequence of sentence position tags, 1 for
the first word in a sentence, 0 for all others.
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 = {} if links is None else dict(links)
# avoid allocating memory if the doc does not contain any tokens
if self.length > 0:
if not words:
words = [token.text for token in doc]
if not tags:
tags = [None for _ in words]
if not pos:
pos = [None for _ in words]
if not morphs:
morphs = [None for _ in words]
if not lemmas:
lemmas = [None for _ in words]
if not heads:
heads = [None for _ in words]
if not deps:
deps = [None for _ in words]
if not sent_starts:
sent_starts = [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], str):
# Assume we have entities specified by character offset.
entities = biluo_tags_from_offsets(doc, entities)
# 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.pos = [None] * len(doc)
self.morphs = [None] * len(doc)
self.lemmas = [None] * len(doc)
self.heads = [None] * len(doc)
self.labels = [None] * len(doc)
self.ner = [None] * len(doc)
self.sent_starts = [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]
self.orig = TokenAnnotation(ids=list(range(len(words))),
words=words, tags=tags, pos=pos, morphs=morphs,
lemmas=lemmas, heads=heads, deps=deps, entities=entities,
sent_starts=sent_starts, brackets=[])
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.pos[i] = "SPACE"
self.morphs[i] = None
self.lemmas[i] = None
self.heads[i] = None
self.labels[i] = None
self.ner[i] = None
self.sent_starts[i] = 0
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.pos[i] = pos[i2j_multi[i]]
self.morphs[i] = morphs[i2j_multi[i]]
self.lemmas[i] = lemmas[i2j_multi[i]]
self.sent_starts[i] = sent_starts[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:
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]]
# 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.pos[i] = pos[gold_i]
self.morphs[i] = morphs[gold_i]
self.lemmas[i] = lemmas[gold_i]
self.sent_starts[i] = sent_starts[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([f"'{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)
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_
json_token["pos"] = token.pos_
json_token["morph"] = token.morph_
json_token["lemma"] = token.lemma_
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] = f"U-{label}"
else:
biluo[start_token] = f"B-{label}"
for i in range(start_token+1, end_token):
biluo[i] = f"I-{label}"
biluo[end_token] = f"L-{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"