mirror of
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105 lines
3.9 KiB
Python
105 lines
3.9 KiB
Python
# coding: utf8
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from __future__ import absolute_import, unicode_literals
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import random
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import tqdm
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from cytoolz import partition_all
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from thinc.neural.optimizers import Adam
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from thinc.neural.ops import NumpyOps, CupyOps
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from thinc.neural.train import Trainer as ThincTrainer
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from .syntax.nonproj import PseudoProjectivity
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from .gold import GoldParse, merge_sents
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from .scorer import Scorer
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from .tokens.doc import Doc
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from . import util
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class Trainer(object):
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"""
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Manage training of an NLP pipeline.
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"""
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def __init__(self, nlp, gold_tuples, **cfg):
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self.nlp = nlp
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self.nr_epoch = 0
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self.optimizer = Adam(NumpyOps(), 0.001)
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self.gold_tuples = gold_tuples
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self.cfg = cfg
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self.batch_size = float(util.env_opt('min_batch_size', 4))
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self.max_batch_size = util.env_opt('max_batch_size', 64)
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self.accel_batch_size = util.env_opt('batch_accel', 1.001)
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def epochs(self, nr_epoch, augment_data=None, gold_preproc=False):
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cached_golds = {}
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cached_docs = {}
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def _epoch(indices):
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all_docs = []
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all_golds = []
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for i in indices:
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raw_text, paragraph_tuples = self.gold_tuples[i]
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if gold_preproc:
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raw_text = None
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else:
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paragraph_tuples = merge_sents(paragraph_tuples)
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if augment_data is None:
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if i not in cached_docs:
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cached_docs[i] = self.make_docs(raw_text, paragraph_tuples)
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docs = cached_docs[i]
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if i not in cached_golds:
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cached_golds[i] = self.make_golds(docs, paragraph_tuples)
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golds = cached_golds[i]
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else:
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raw_text, paragraph_tuples = augment_data(raw_text, paragraph_tuples)
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docs = self.make_docs(raw_text, paragraph_tuples)
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golds = self.make_golds(docs, paragraph_tuples)
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all_docs.extend(docs)
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all_golds.extend(golds)
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thinc_trainer = ThincTrainer(self.nlp.pipeline[0].model)
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thinc_trainer.batch_size = int(self.batch_size)
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thinc_trainer.nb_epoch = 1
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for X, y in thinc_trainer.iterate(all_docs, all_golds):
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yield X, y
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thinc_trainer.batch_size = min(int(self.batch_size), self.max_batch_size)
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self.batch_size *= self.accel_batch_size
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indices = list(range(len(self.gold_tuples)))
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for itn in range(nr_epoch):
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random.shuffle(indices)
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yield _epoch(indices)
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self.nr_epoch += 1
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def evaluate(self, dev_sents, gold_preproc=False):
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all_docs = []
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all_golds = []
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for raw_text, paragraph_tuples in dev_sents:
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if gold_preproc:
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raw_text = None
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else:
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paragraph_tuples = merge_sents(paragraph_tuples)
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docs = self.make_docs(raw_text, paragraph_tuples)
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golds = self.make_golds(docs, paragraph_tuples)
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all_docs.extend(docs)
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all_golds.extend(golds)
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scorer = Scorer()
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for doc, gold in zip(self.nlp.pipe(all_docs), all_golds):
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scorer.score(doc, gold)
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return scorer
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def make_docs(self, raw_text, paragraph_tuples):
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if raw_text is not None:
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return [self.nlp.make_doc(raw_text)]
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else:
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return [
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Doc(self.nlp.vocab, words=sent_tuples[0][1])
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for sent_tuples in paragraph_tuples]
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def make_golds(self, docs, paragraph_tuples):
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if len(docs) == 1:
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return [GoldParse.from_annot_tuples(docs[0], sent_tuples[0])
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for sent_tuples in paragraph_tuples]
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else:
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return [GoldParse.from_annot_tuples(doc, sent_tuples[0])
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for doc, sent_tuples in zip(docs, paragraph_tuples)]
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