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