def compare_templates(self):
for i, t in enumerate([
brill.nltkdemo18(),
brill.nltkdemo18plus(),
brill.brill24(),
brill.fntbl37()
]):
print "\nTEMPLATE {}==================\n".format(i)
self.train(templates=t)
def test_brill_tagger(self):
trainer = BrillTaggerTrainer(self.default_tagger, nltkdemo18(),
deterministic=True)
tagger = trainer.train(self.corpus, max_rules=30)
encoded = self.encoder.encode(tagger)
decoded = self.decoder.decode(encoded)
self.assertEqual(repr(tagger._initial_tagger),
repr(decoded._initial_tagger))
self.assertEqual(tagger._rules, decoded._rules)
self.assertEqual(tagger._training_stats, decoded._training_stats)
def train(self, templates=None, verbose=True):
"""Train a new Brill tagger."""
if templates is None:
templates = brill.nltkdemo18()
random.seed(len(self.tagged_data_list))
random.shuffle(self.tagged_data_list)
cutoff = int(self.dev_size * self.train_size)
training_data = self.tagged_data_list[:cutoff]
test_data = self.tagged_data_list[cutoff:self.dev_size]
# very simple regular expression tagger
regex_tagger = RegexpTagger([(r'^-?[0-9]+(.[0-9]+)?$', 'PUNCT'),
(r'.*', 'N')])
if verbose == True:
print "Regular expression tagger accuracy:\n{}\n".format(
regex_tagger.evaluate(test_data))
# unigram tagger
unigram_tagger = UnigramTagger(train=training_data,
backoff=regex_tagger)
if verbose == True:
print "Unigram tagger accuracy:\n{}\n".format(
unigram_tagger.evaluate(test_data))
# bigram tagger
bigram_tagger = BigramTagger(train=training_data,
backoff=unigram_tagger)
if verbose == True:
print "Bigram tagger accuracy:\n{}\n".format(
bigram_tagger.evaluate(test_data))
# trigram tagger
trigram_tagger = TrigramTagger(train=training_data,
backoff=bigram_tagger)
if verbose == True:
print "Trigram tagger accuracy:\n{}\n".format(
trigram_tagger.evaluate(test_data))
# first iteration
trainer = BrillTaggerTrainer(initial_tagger=trigram_tagger,
templates=templates)
brill_tagger = trainer.train(train_sents=training_data,
max_rules=self.max_rules,
min_score=self.min_score)
if verbose == True:
print "Initial Brill tagger accuracy:\n{}\n".format(
brill_tagger.evaluate(test_data))
# folding
for i in range(0, self.num_groups):
# random splitting
random.seed(len(self.tagged_data_list))
random.shuffle(self.tagged_data_list)
cutoff = int(self.dev_size * self.train_size)
training_data = self.tagged_data_list[:cutoff]
test_data = self.tagged_data_list[cutoff:self.dev_size]
# note that .train method returns a BrillTagger() object
brill_tagger = trainer.train(train_sents=training_data,
max_rules=self.max_rules,
min_score=self.min_score)
if verbose == True:
print "Brill tagger accuracy, fold {}:\n{}\n".format(
i + 1, brill_tagger.evaluate(test_data))
def train(self, templates=None, verbose=True):
"""Train a new Brill tagger."""
if templates is None:
templates = brill.nltkdemo18()
random.seed(len(self.tagged_data_list))
random.shuffle(self.tagged_data_list)
cutoff = int(self.dev_size * self.train_size)
training_data = self.tagged_data_list[:cutoff]
test_data = self.tagged_data_list[cutoff:self.dev_size]
# very simple regular expression tagger
regex_tagger = RegexpTagger([
(r'^-?[0-9]+(.[0-9]+)?$', 'PUNCT'),
(r'.*', 'N')
])
if verbose == True:
print "Regular expression tagger accuracy:\n{}\n".format(
regex_tagger.evaluate(test_data))
# unigram tagger
unigram_tagger = UnigramTagger(train=training_data,
backoff=regex_tagger)
if verbose == True:
print "Unigram tagger accuracy:\n{}\n".format(
unigram_tagger.evaluate(test_data))
# bigram tagger
bigram_tagger = BigramTagger(train=training_data,
backoff=unigram_tagger)
if verbose == True:
print "Bigram tagger accuracy:\n{}\n".format(
bigram_tagger.evaluate(test_data))
# trigram tagger
trigram_tagger = TrigramTagger(train=training_data,
backoff=bigram_tagger)
if verbose == True:
print "Trigram tagger accuracy:\n{}\n".format(
trigram_tagger.evaluate(test_data))
# first iteration
trainer = BrillTaggerTrainer(initial_tagger=trigram_tagger,
templates=templates)
brill_tagger = trainer.train(train_sents=training_data,
max_rules=self.max_rules,
min_score=self.min_score)
if verbose == True:
print "Initial Brill tagger accuracy:\n{}\n".format(
brill_tagger.evaluate(test_data))
# folding
for i in range(0, self.num_groups):
# random splitting
random.seed(len(self.tagged_data_list))
random.shuffle(self.tagged_data_list)
cutoff = int(self.dev_size * self.train_size)
training_data = self.tagged_data_list[:cutoff]
test_data = self.tagged_data_list[cutoff:self.dev_size]
# note that .train method returns a BrillTagger() object
brill_tagger = trainer.train(train_sents=training_data,
max_rules=self.max_rules,
min_score=self.min_score)
if verbose == True:
print "Brill tagger accuracy, fold {}:\n{}\n".format(
i+1, brill_tagger.evaluate(test_data))
def compare_templates(self):
for i, t in enumerate([brill.nltkdemo18(), brill.nltkdemo18plus(),
brill.brill24(), brill.fntbl37()]):
print "\nTEMPLATE {}==================\n".format(i)
self.train(templates=t)