explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2024-12-26 09:56:28 +03:00
Code Issues Packages Projects Releases Wiki Activity

Remove train.py -- functions now in GoldCorpus and Language

Browse Source
This commit is contained in:
Matthew Honnibal 2017-05-21 09:08:27 -05:00
parent 8904814c0e
commit 59fbfb3829
1 changed files with 0 additions and 106 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

106
spacy/train.py
View File

@ -1,106 +0,0 @@
# 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 thinc.neural.train import Trainer as ThincTrainer
from .syntax.nonproj import PseudoProjectivity
from .gold import GoldParse, merge_sents
from .scorer import Scorer
from .tokens.doc import Doc
from . import util
class Trainer(object):
"""
Manage training of an NLP pipeline.
"""
def __init__(self, nlp, gold_tuples, **cfg):
self.nlp = nlp
self.nr_epoch = 0
self.optimizer = Adam(NumpyOps(), 0.001)
self.gold_tuples = gold_tuples
self.cfg = cfg
self.batch_size = float(util.env_opt('min_batch_size', 4))
self.max_batch_size = util.env_opt('max_batch_size', 64)
self.accel_batch_size = util.env_opt('batch_accel', 1.001)
def epochs(self, nr_epoch, augment_data=None, gold_preproc=False):
cached_golds = {}
cached_docs = {}
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)
golds = self.make_golds(docs, paragraph_tuples)
#if i not in cached_docs:
# cached_docs[i] = self.make_docs(raw_text, paragraph_tuples)
#docs = cached_docs[i]
#if i not in cached_golds:
# cached_golds[i] = self.make_golds(docs, paragraph_tuples)
#golds = cached_golds[i]
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)
thinc_trainer = ThincTrainer(self.nlp.pipeline[0].model)
thinc_trainer.batch_size = int(self.batch_size)
thinc_trainer.nb_epoch = 1
for X, y in thinc_trainer.iterate(all_docs, all_golds):
yield X, y
thinc_trainer.batch_size = min(int(self.batch_size), self.max_batch_size)
self.batch_size *= self.accel_batch_size
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):
all_docs = []
all_golds = []
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)
all_docs.extend(docs)
all_golds.extend(golds)
scorer = Scorer()
for doc, gold in zip(self.nlp.pipe(all_docs, batch_size=16), all_golds):
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)]