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* Clean up train.py, removing unused tag jackknifing code

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This commit is contained in:
Matthew Honnibal 2015-06-05 15:01:28 +02:00
parent e772b48dcd
commit 8466600add
1 changed files with 2 additions and 76 deletions
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78
bin/parser/train.py
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@ -70,77 +70,9 @@ def _merge_sents(sents):
return [(m_deps, m_brackets)]
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):
random.shuffle(docs)
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, beam_width=1):
beam_width=1):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
ner_model_dir = path.join(model_dir, 'ner')
@ -185,11 +117,7 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
else:
tokens = nlp.tokenizer(raw_text)
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)
nlp.tagger(tokens)
gold = GoldParse(tokens, annot_tuples, make_projective=True)
loss += nlp.parser.train(tokens, gold)
@ -256,8 +184,6 @@ 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, beam_width=1):
gold_train = list(read_json_file(train_loc))
#taggings = get_train_tags(English, model_dir, gold_train, gold_preproc)
taggings = None
train(English, gold_train, model_dir,
feat_set='basic' if not debug else 'debug',
gold_preproc=gold_preproc, n_sents=n_sents,