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* Allow parser to jackknife POS tags before training.

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This commit is contained in:
Matthew Honnibal 2015-05-31 01:11:11 +02:00
parent c4f0914b4e
commit d512d20d81
1 changed files with 99 additions and 13 deletions
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112
bin/parser/train.py
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@ -39,14 +39,19 @@ def add_noise(c, noise_level):
return c.lower()
def score_model(scorer, nlp, raw_text, annot_tuples):
def score_model(scorer, nlp, raw_text, annot_tuples, train_tags=None):
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger(tokens)
nlp.entity(tokens)
nlp.parser(tokens)
else:
tokens = nlp(raw_text, merge_mwes=False)
tokens = nlp.tokenizer(raw_text, merge_mwes=False)
if train_tags is not None:
key = hash(tokens.string)
nlp.tagger.tag_from_strings(tokens, train_tags[key])
else:
nlp.tagger(tokens)
nlp.entity(tokens)
nlp.parser(tokens)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=False)
@ -65,10 +70,78 @@ def _merge_sents(sents):
m_brackets.extend((b['first'] + i, b['last'] + i, b['label']) for b in brackets)
i += len(ids)
return [(m_deps, m_brackets)]
def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0,
gold_preproc=False, n_sents=0, corruption_level=0):
def get_train_tags(Language, model_dir, docs, gold_preproc):
taggings = {}
for train_part, test_part in get_partitions(docs, 5):
nlp = _train_tagger(Language, model_dir, train_part, gold_preproc)
for tokens in _tag_partition(nlp, test_part):
taggings[hash(tokens.string)] = [w.tag_ for w in tokens]
return taggings
def get_partitions(docs, n_parts):
n_test = len(docs) / n_parts
n_train = len(docs) - n_test
for part in range(n_parts):
start = int(part * n_test)
end = int(start + n_test)
yield docs[:start] + docs[end:], docs[start:end]
def _train_tagger(Language, model_dir, docs, gold_preproc=False, n_iter=5):
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(pos_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir)
nlp = Language(data_dir=model_dir)
print "Itn.\tTag %"
for itn in range(n_iter):
scorer = Scorer()
correct = 0
total = 0
for raw_text, sents in docs:
if gold_preproc:
raw_text = None
else:
sents = _merge_sents(sents)
for annot_tuples, ctnt in sents:
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
else:
tokens = nlp.tokenizer(raw_text)
gold = GoldParse(tokens, annot_tuples)
correct += nlp.tagger.train(tokens, gold.tags)
total += len(tokens)
random.shuffle(docs)
print itn, '%.3f' % (correct / total)
nlp.tagger.model.end_training()
nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))
return nlp
def _tag_partition(nlp, docs, gold_preproc=False):
for raw_text, sents in docs:
if gold_preproc:
raw_text = None
else:
sents = _merge_sents(sents)
for annot_tuples, _ in sents:
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
else:
tokens = nlp.tokenizer(raw_text)
nlp.tagger(tokens)
yield tokens
def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
seed=0, gold_preproc=False, n_sents=0, corruption_level=0,
train_tags=None):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
ner_model_dir = path.join(model_dir, 'ner')
@ -91,6 +164,7 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0
if n_sents > 0:
gold_tuples = gold_tuples[:n_sents]
nlp = Language(data_dir=model_dir)
print "Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %"
@ -103,15 +177,25 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0
else:
sents = _merge_sents(sents)
for annot_tuples, ctnt in sents:
score_model(scorer, nlp, raw_text, annot_tuples)
score_model(scorer, nlp, raw_text, annot_tuples, train_tags)
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
else:
tokens = nlp.tokenizer(raw_text)
gold = GoldParse(tokens, annot_tuples)
nlp.tagger(tokens)
if train_tags is not None:
sent_id = hash(tokens.string)
nlp.tagger.tag_from_strings(tokens, train_tags[sent_id])
else:
nlp.tagger(tokens)
gold = GoldParse(tokens, annot_tuples, make_projective=True)
if gold.is_projective:
loss += nlp.parser.train(tokens, gold)
try:
loss += nlp.parser.train(tokens, gold)
except:
for i in range(len(tokens)):
print tokens[i].orth_, gold.heads[i]
raise
nlp.entity.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
random.shuffle(gold_tuples)
@ -174,10 +258,12 @@ def write_parses(Language, dev_loc, model_dir, out_loc):
def main(train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbose=False,
debug=False, corruption_level=0.0, gold_preproc=False):
gold_train = list(read_json_file(train_loc))
taggings = get_train_tags(English, model_dir, gold_train, gold_preproc)
train(English, gold_train, model_dir,
feat_set='basic' if not debug else 'debug',
gold_preproc=gold_preproc, n_sents=n_sents,
corruption_level=corruption_level, n_iter=n_iter)
corruption_level=corruption_level, n_iter=n_iter,
train_tags=taggings)
if out_loc:
write_parses(English, dev_loc, model_dir, out_loc)
scorer = evaluate(English, list(read_json_file(dev_loc)),