def evaluate(Language, dev_loc, model_dir, gold_preproc=False, verbose=True):
assert not gold_preproc
nlp = Language(data_dir=model_dir)
gold_tuples = read_docparse_file(dev_loc)
scorer = Scorer()
for raw_text, segmented_text, annot_tuples in gold_tuples:
tokens = nlp(raw_text, merge_mwes=False)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=verbose)
return scorer
def evaluate(Language, dev_loc, model_dir, gold_preproc=False, verbose=True):
assert not gold_preproc
nlp = Language(data_dir=model_dir)
gold_tuples = read_docparse_file(dev_loc)
scorer = Scorer()
for raw_text, segmented_text, annot_tuples in gold_tuples:
tokens = nlp(raw_text, merge_mwes=False)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=verbose)
return scorer
def train(Language, train_loc, model_dir, n_iter=15, feat_set=u'basic', seed=0,
gold_preproc=False, n_sents=0):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
ner_model_dir = path.join(model_dir, 'ner')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
if path.exists(ner_model_dir):
shutil.rmtree(ner_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
os.mkdir(ner_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir)
gold_tuples = read_docparse_file(train_loc)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
labels=Language.ParserTransitionSystem.get_labels(gold_tuples))
Config.write(ner_model_dir, 'config', features='ner', seed=seed,
labels=Language.EntityTransitionSystem.get_labels(gold_tuples))
if n_sents > 0:
gold_tuples = gold_tuples[:n_sents]
nlp = Language(data_dir=model_dir)
print "Itn.\tUAS\tNER F.\tTag %"
for itn in range(n_iter):
scorer = Scorer()
for raw_text, segmented_text, annot_tuples in gold_tuples:
# Eval before train
tokens = nlp(raw_text, merge_mwes=False)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=False)
if gold_preproc:
sents = [nlp.tokenizer.tokens_from_list(s) for s in segmented_text]
else:
sents = [nlp.tokenizer(raw_text)]
for tokens in sents:
gold = GoldParse(tokens, annot_tuples)
nlp.tagger(tokens)
nlp.parser.train(tokens, gold)
if gold.ents:
nlp.entity.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
print '%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.ents_f, scorer.tags_acc)
random.shuffle(gold_tuples)
nlp.parser.model.end_training()
nlp.entity.model.end_training()
nlp.tagger.model.end_training()
nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))
def write_parses(Language, dev_loc, model_dir, out_loc):
nlp = Language()
gold_tuples = read_docparse_file(dev_loc)
scorer = Scorer()
out_file = codecs.open(out_loc, 'w', 'utf8')
for raw_text, segmented_text, annot_tuples in gold_tuples:
tokens = nlp(raw_text)
for t in tokens:
out_file.write('%s\t%s\t%s\t%s\n' %
(t.orth_, t.tag_, t.head.orth_, t.dep_))
return scorer
def write_parses(Language, dev_loc, model_dir, out_loc):
nlp = Language()
gold_tuples = read_docparse_file(dev_loc)
scorer = Scorer()
out_file = codecs.open(out_loc, 'w', 'utf8')
for raw_text, segmented_text, annot_tuples in gold_tuples:
tokens = nlp(raw_text)
for t in tokens:
out_file.write(
'%s\t%s\t%s\t%s\n' % (t.orth_, t.tag_, t.head.orth_, t.dep_)
)
return scorer
def train(Language,
train_loc,
model_dir,
n_iter=15,
feat_set=u'basic',
seed=0,
gold_preproc=False,
n_sents=0):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
ner_model_dir = path.join(model_dir, 'ner')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
if path.exists(ner_model_dir):
shutil.rmtree(ner_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
os.mkdir(ner_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES,
pos_model_dir)
gold_tuples = read_docparse_file(train_loc)
Config.write(
dep_model_dir,
'config',
features=feat_set,
seed=seed,
labels=Language.ParserTransitionSystem.get_labels(gold_tuples))
Config.write(
ner_model_dir,
'config',
features='ner',
seed=seed,
labels=Language.EntityTransitionSystem.get_labels(gold_tuples))
if n_sents > 0:
gold_tuples = gold_tuples[:n_sents]
nlp = Language(data_dir=model_dir)
print "Itn.\tUAS\tNER F.\tTag %"
for itn in range(n_iter):
scorer = Scorer()
for raw_text, segmented_text, annot_tuples in gold_tuples:
# Eval before train
tokens = nlp(raw_text, merge_mwes=False)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=False)
if gold_preproc:
sents = [
nlp.tokenizer.tokens_from_list(s) for s in segmented_text
]
else:
sents = [nlp.tokenizer(raw_text)]
for tokens in sents:
gold = GoldParse(tokens, annot_tuples)
nlp.tagger(tokens)
nlp.parser.train(tokens, gold)
if gold.ents:
nlp.entity.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
print '%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.ents_f,
scorer.tags_acc)
random.shuffle(gold_tuples)
nlp.parser.model.end_training()
nlp.entity.model.end_training()
nlp.tagger.model.end_training()
nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))