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* Fix gold_preproc flag in train.py

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This commit is contained in:
Matthew Honnibal 2015-05-30 05:23:02 +02:00
parent 76300bbb1b
commit 6bbdcc5db5
1 changed files with 28 additions and 8 deletions
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36
bin/parser/train.py
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@ -51,6 +51,22 @@ def score_model(scorer, nlp, raw_text, annot_tuples):
scorer.score(tokens, gold, verbose=False)
def _merge_sents(sents):
m_deps = [[], [], [], [], [], []]
m_brackets = []
i = 0
for (ids, words, tags, heads, labels, ner), brackets in sents:
m_deps[0].extend(id_ + i for id_ in ids)
m_deps[1].extend(words)
m_deps[2].extend(tags)
m_deps[3].extend(head + i for head in heads)
m_deps[4].extend(labels)
m_deps[5].extend(ner)
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):
dep_model_dir = path.join(model_dir, 'deps')
@ -82,11 +98,13 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0
scorer = Scorer()
loss = 0
for raw_text, sents in gold_tuples:
if not gold_preproc:
if gold_preproc:
raw_text = None
else:
sents = _merge_sents(sents)
for annot_tuples, ctnt in sents:
score_model(scorer, nlp, raw_text, annot_tuples)
if raw_text is None or gold_preproc:
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
else:
tokens = nlp.tokenizer(raw_text)
@ -106,12 +124,16 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0
nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))
def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=True):
def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False):
nlp = Language(data_dir=model_dir)
scorer = Scorer()
for raw_text, sents in gold_tuples:
if gold_preproc:
raw_text = None
else:
sents = _merge_sents(sents)
for annot_tuples, brackets in sents:
if raw_text is None or gold_preproc:
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger(tokens)
nlp.entity(tokens)
@ -120,8 +142,6 @@ def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=True)
tokens = nlp(raw_text, merge_mwes=False)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=verbose)
for t in tokens:
print t.orth_, t.dep_, t.head.orth_, t.ent_type_
return scorer
@ -158,8 +178,8 @@ def main(train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbos
feat_set='basic' if not debug else 'debug',
gold_preproc=gold_preproc, n_sents=n_sents,
corruption_level=corruption_level, n_iter=n_iter)
#if out_loc:
# write_parses(English, dev_loc, model_dir, out_loc)
if out_loc:
write_parses(English, dev_loc, model_dir, out_loc)
scorer = evaluate(English, list(read_json_file(dev_loc)),
model_dir, gold_preproc=gold_preproc, verbose=verbose)
print 'TOK', 100-scorer.token_acc