spaCy/spacy/gold/corpus.py
2020-07-01 15:16:43 +02:00

129 lines
4.4 KiB
Python

import random
from .. import util
from .example import Example
from ..tokens import DocBin, Doc
class Corpus:
"""An annotated corpus, reading train and dev datasets from
the DocBin (.spacy) format.
DOCS: https://spacy.io/api/goldcorpus
"""
def __init__(self, train_loc, dev_loc, limit=0):
"""Create a Corpus.
train (str / Path): File or directory of training data.
dev (str / Path): File or directory of development data.
limit (int): Max. number of examples returned
RETURNS (Corpus): The newly created object.
"""
self.train_loc = train_loc
self.dev_loc = dev_loc
self.limit = limit
@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(".spacy"):
locs.append(path)
return locs
def make_examples(self, nlp, reference_docs, max_length=0):
for reference in reference_docs:
if len(reference) == 0:
continue
elif max_length == 0 or len(reference) < max_length:
yield Example(
nlp.make_doc(reference.text),
reference
)
elif reference.is_sentenced:
for ref_sent in reference.sents:
if len(ref_sent) == 0:
continue
elif max_length == 0 or len(ref_sent) < max_length:
yield Example(
nlp.make_doc(ref_sent.text),
ref_sent.as_doc()
)
def make_examples_gold_preproc(self, nlp, reference_docs):
for reference in reference_docs:
if reference.is_sentenced:
ref_sents = [sent.as_doc() for sent in reference.sents]
else:
ref_sents = [reference]
for ref_sent in ref_sents:
eg = Example(
Doc(
nlp.vocab,
words=[w.text for w in ref_sent],
spaces=[bool(w.whitespace_) for w in ref_sent]
),
ref_sent
)
if len(eg.x):
yield eg
def read_docbin(self, vocab, locs):
""" Yield training examples as example dicts """
i = 0
for loc in locs:
loc = util.ensure_path(loc)
if loc.parts[-1].endswith(".spacy"):
with loc.open("rb") as file_:
doc_bin = DocBin().from_bytes(file_.read())
docs = doc_bin.get_docs(vocab)
for doc in docs:
if len(doc):
yield doc
i += 1
if self.limit >= 1 and i >= self.limit:
break
def count_train(self, nlp):
"""Returns count of words in train examples"""
n = 0
i = 0
for example in self.train_dataset(nlp):
n += len(example.predicted)
if self.limit >= 0 and i >= self.limit:
break
i += 1
return n
def train_dataset(self, nlp, *, shuffle=True, gold_preproc=False,
max_length=0, **kwargs):
ref_docs = self.read_docbin(nlp.vocab, self.walk_corpus(self.train_loc))
if gold_preproc:
examples = self.make_examples_gold_preproc(nlp, ref_docs)
else:
examples = self.make_examples(nlp, ref_docs, max_length)
if shuffle:
examples = list(examples)
random.shuffle(examples)
yield from examples
def dev_dataset(self, nlp, *, gold_preproc=False, **kwargs):
ref_docs = self.read_docbin(nlp.vocab, self.walk_corpus(self.dev_loc))
if gold_preproc:
examples = self.make_examples_gold_preproc(nlp, ref_docs)
else:
examples = self.make_examples(nlp, ref_docs, max_length=0)
yield from examples