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Fix bin/parser/train

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This commit is contained in:
Matthew Honnibal 2016-09-05 01:44:30 +02:00
parent cf2131d649
commit 7c7a05a466
1 changed files with 96 additions and 55 deletions
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151
bin/parser/train.py
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@ -52,18 +52,6 @@ def add_noise(orig, noise_level):
return ''.join(_corrupt(c, noise_level) for c in orig)
def score_model(scorer, nlp, raw_text, annot_tuples, verbose=False):
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
else:
tokens = nlp.tokenizer(raw_text)
nlp.tagger(tokens)
nlp.entity(tokens)
nlp.parser(tokens)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=verbose)
def _merge_sents(sents):
m_deps = [[], [], [], [], [], []]
m_brackets = []
@ -80,7 +68,7 @@ def _merge_sents(sents):
return [(m_deps, m_brackets)]
def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
def train(Language, gold_tuples, model_dir, dev_loc, n_iter=15, feat_set=u'basic',
seed=0, gold_preproc=False, n_sents=0, corruption_level=0,
beam_width=1, verbose=False,
use_orig_arc_eager=False, pseudoprojective=False):
@ -101,8 +89,9 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
# preprocess training data here before ArcEager.get_labels() is called
gold_tuples = PseudoProjectivity.preprocess_training_data(gold_tuples)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
Config.write(dep_model_dir, 'config', feat_set=feat_set, seed=seed,
labels=ArcEager.get_labels(gold_tuples),
rho=0.0, eta=1.0, mu=0.9, noise=0.0,
beam_width=beam_width,projectivize=pseudoprojective)
#feat_set, slots = get_templates('neural')
#vector_widths = [10, 10, 10]
@ -121,56 +110,108 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
# rho=rho)
Config.write(ner_model_dir, 'config', features='ner', seed=seed,
Config.write(ner_model_dir, 'config', feat_set='ner', seed=seed,
labels=BiluoPushDown.get_labels(gold_tuples),
beam_width=0)
beam_width=0, rho=0.0, eta=1.0, mu=0.9, noise=0.0)
if n_sents > 0:
gold_tuples = gold_tuples[:n_sents]
micro_eval = gold_tuples[:50]
nlp = Language(data_dir=model_dir, tagger=False, parser=False, entity=False)
nlp.tagger = Tagger.blank(nlp.vocab, Tagger.default_templates())
nlp.parser = BeamParser.from_dir(dep_model_dir, nlp.vocab.strings, ArcEager)
nlp.entity = BeamParser.from_dir(ner_model_dir, nlp.vocab.strings, BiluoPushDown)
nlp.parser = Parser.from_dir(dep_model_dir, nlp.vocab.strings, ArcEager)
nlp.entity = Parser.from_dir(ner_model_dir, nlp.vocab.strings, BiluoPushDown)
print(nlp.parser.model.widths)
for raw_text, sents in gold_tuples:
for annot_tuples, ctnt in sents:
for word in annot_tuples[1]:
_ = nlp.vocab[word]
eg_seen = 0
print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
for itn in range(n_iter):
scorer = Scorer()
loss = 0
for raw_text, sents in gold_tuples:
if gold_preproc:
raw_text = None
else:
sents = _merge_sents(sents)
for annot_tuples, ctnt in sents:
if len(annot_tuples[1]) == 1:
continue
score_model(scorer, nlp, raw_text, annot_tuples,
verbose=verbose if itn >= 2 else False)
if raw_text is None:
words = add_noise(annot_tuples[1], corruption_level)
tokens = nlp.tokenizer.tokens_from_list(words)
else:
raw_text = add_noise(raw_text, corruption_level)
tokens = nlp.tokenizer(raw_text)
nlp.tagger(tokens)
gold = GoldParse(tokens, annot_tuples)
if not gold.is_projective:
raise Exception("Non-projective sentence in training: %s" % annot_tuples[1])
loss += nlp.parser.train(tokens, gold)
nlp.entity.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
random.shuffle(gold_tuples)
print('%d:\t%d\t%.3f\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.ents_f,
scorer.tags_acc,
scorer.token_acc))
print('end training')
try:
eg_seen = _train_epoch(nlp, gold_tuples, eg_seen, itn,
dev_loc, micro_eval,
gold_preproc, corruption_level)
except KeyboardInterrupt:
print("Saving model...")
break
dev_uas = score_file(nlp, dev_loc).uas
print("Dev before average", dev_uas)
nlp.end_training(model_dir)
print('done')
print("Saved. Evaluating...")
def _train_epoch(nlp, gold_tuples, eg_seen, itn, dev_loc, micro_eval,
gold_preproc, corruption_level):
random.shuffle(gold_tuples)
loss = 0
nr_trimmed = 0
for raw_text, sents in gold_tuples:
if gold_preproc:
raw_text = None
else:
sents = _merge_sents(sents)
for annot_tuples, ctnt in sents:
if len(annot_tuples[1]) == 1:
continue
if raw_text is None:
words = add_noise(annot_tuples[1], corruption_level)
tokens = nlp.tokenizer.tokens_from_list(words)
else:
raw_text = add_noise(raw_text, corruption_level)
tokens = nlp.tokenizer(raw_text)
nlp.tagger(tokens)
gold = GoldParse(tokens, annot_tuples)
if not gold.is_projective:
raise Exception("Non-projective sentence in training: %s" % annot_tuples[1])
loss += nlp.parser.train(tokens, gold)
nlp.entity.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
eg_seen += 1
if eg_seen % 1000 == 0:
scorer = score_sents(nlp, micro_eval)
print('%d:\t%d\t%.3f\t%.3f\t%.3f\t%.3f\t%d\t%d' % (itn, loss, scorer.uas, scorer.ents_f,
scorer.tags_acc,
scorer.token_acc,
nlp.parser.model.nr_active_feat,
nlp.entity.model.nr_active_feat))
loss = 0
nlp.parser.model.learn_rate *= 0.99
scorer = score_file(nlp, dev_loc)
print('D:\t%d\t%.3f\t%.3f\t%.3f\t%.3f' % (loss, scorer.uas, scorer.ents_f,
scorer.tags_acc, scorer.token_acc))
return eg_seen
def score_file(nlp, loc):
gold_sents = read_json_file(loc, verbose=False)
scorer = Scorer()
for _, sents in gold_sents:
for annot_tuples, _ in sents:
score_model(scorer, nlp, None, annot_tuples)
return scorer
def score_sents(nlp, gold_tuples):
scorer = Scorer()
for _, sents in gold_tuples:
for annot_tuples, _ in sents:
score_model(scorer, nlp, None, annot_tuples)
return scorer
def score_model(scorer, nlp, raw_text, annot_tuples, verbose=False):
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
else:
tokens = nlp.tokenizer(raw_text)
nlp.tagger(tokens)
nlp.entity(tokens)
nlp.parser(tokens)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=verbose)
def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False,
@ -201,7 +242,7 @@ def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False
def write_parses(Language, dev_loc, model_dir, out_loc):
nlp = Language(data_dir=model_dir)
gold_tuples = read_json_file(dev_loc)
gold_tuples = read_json_file(dev_loc, verbose=True)
scorer = Scorer()
out_file = io.open(out_loc, 'w', 'utf8')
for raw_text, sents in gold_tuples:
@ -245,16 +286,16 @@ def main(language, train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc=
lang = spacy.util.get_lang_class(language)
if not eval_only:
gold_train = list(read_json_file(train_loc))
train(lang, gold_train, model_dir,
feat_set='neural' if not debug else 'debug',
gold_train = list(read_json_file(train_loc, verbose=True))
train(lang, gold_train, model_dir, dev_loc,
feat_set='basic', #'neural' if not debug else 'debug',
gold_preproc=gold_preproc, n_sents=n_sents,
corruption_level=corruption_level, n_iter=n_iter,
verbose=verbose,pseudoprojective=pseudoprojective)
if out_loc:
write_parses(lang, dev_loc, model_dir, out_loc)
print(model_dir)
scorer = evaluate(lang, list(read_json_file(dev_loc)),
scorer = evaluate(lang, list(read_json_file(dev_loc, verbose=True)),
model_dir, gold_preproc=gold_preproc, verbose=verbose)
print('TOK', scorer.token_acc)
print('POS', scorer.tags_acc)