mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 11:29:13 +03:00
91 lines
3.3 KiB
Python
91 lines
3.3 KiB
Python
# coding: utf8
|
|
from __future__ import absolute_import, unicode_literals
|
|
|
|
import random
|
|
import tqdm
|
|
from cytoolz import partition_all
|
|
|
|
from thinc.neural.optimizers import Adam
|
|
from thinc.neural.ops import NumpyOps, CupyOps
|
|
|
|
from .syntax.nonproj import PseudoProjectivity
|
|
from .gold import GoldParse, merge_sents
|
|
from .scorer import Scorer
|
|
from .tokens.doc import Doc
|
|
|
|
|
|
class Trainer(object):
|
|
"""
|
|
Manage training of an NLP pipeline.
|
|
"""
|
|
def __init__(self, nlp, gold_tuples):
|
|
self.nlp = nlp
|
|
self.nr_epoch = 0
|
|
self.optimizer = Adam(NumpyOps(), 0.001)
|
|
self.gold_tuples = gold_tuples
|
|
|
|
def epochs(self, nr_epoch, augment_data=None, gold_preproc=False):
|
|
cached_golds = {}
|
|
def _epoch(indices):
|
|
all_docs = []
|
|
all_golds = []
|
|
for i in indices:
|
|
raw_text, paragraph_tuples = self.gold_tuples[i]
|
|
if gold_preproc:
|
|
raw_text = None
|
|
else:
|
|
paragraph_tuples = merge_sents(paragraph_tuples)
|
|
if augment_data is None:
|
|
docs = self.make_docs(raw_text, paragraph_tuples)
|
|
if i in cached_golds:
|
|
golds = cached_golds[i]
|
|
else:
|
|
golds = self.make_golds(docs, paragraph_tuples)
|
|
else:
|
|
raw_text, paragraph_tuples = augment_data(raw_text, paragraph_tuples)
|
|
docs = self.make_docs(raw_text, paragraph_tuples)
|
|
golds = self.make_golds(docs, paragraph_tuples)
|
|
all_docs.extend(docs)
|
|
all_golds.extend(golds)
|
|
for batch in partition_all(12, zip(tqdm.tqdm(all_docs), all_golds)):
|
|
X, y = zip(*batch)
|
|
yield X, y
|
|
|
|
indices = list(range(len(self.gold_tuples)))
|
|
for itn in range(nr_epoch):
|
|
random.shuffle(indices)
|
|
yield _epoch(indices)
|
|
self.nr_epoch += 1
|
|
|
|
def evaluate(self, dev_sents, gold_preproc=False):
|
|
scorer = Scorer()
|
|
for raw_text, paragraph_tuples in dev_sents:
|
|
if gold_preproc:
|
|
raw_text = None
|
|
else:
|
|
paragraph_tuples = merge_sents(paragraph_tuples)
|
|
docs = self.make_docs(raw_text, paragraph_tuples)
|
|
golds = self.make_golds(docs, paragraph_tuples)
|
|
for doc, gold in zip(docs, golds):
|
|
state = {}
|
|
for process in self.nlp.pipeline:
|
|
assert state is not None, process.name
|
|
state = process(doc, state=state)
|
|
scorer.score(doc, gold)
|
|
return scorer
|
|
|
|
def make_docs(self, raw_text, paragraph_tuples):
|
|
if raw_text is not None:
|
|
return [self.nlp.make_doc(raw_text)]
|
|
else:
|
|
return [Doc(self.nlp.vocab, words=sent_tuples[0][1])
|
|
for sent_tuples in paragraph_tuples]
|
|
|
|
def make_golds(self, docs, paragraph_tuples):
|
|
if len(docs) == 1:
|
|
return [GoldParse.from_annot_tuples(docs[0], sent_tuples[0])
|
|
for sent_tuples in paragraph_tuples]
|
|
else:
|
|
return [GoldParse.from_annot_tuples(doc, sent_tuples[0])
|
|
for doc, sent_tuples in zip(docs, paragraph_tuples)]
|