def setUp(self):
model = Model(20)
model.load('../testdata/lda_model')
vocabulary = Vocabulary()
vocabulary.load('../testdata/vocabulary.dat')
self.multi_chain_gibbs_sampler = \
MultiChainGibbsSampler(model, vocabulary, 10, 10, 5)
def setUp(self):
self.model = Model(20)
self.model.load('../testdata/lda_model')
self.vocabulary = Vocabulary()
self.vocabulary.load('../testdata/vocabulary.dat')
self.topic_words_stat = TopicWordsStat(self.model, self.vocabulary)
class SparseLDATrainGibbsSamplerTest(unittest.TestCase):
def setUp(self):
self.model = Model(20)
self.vocabulary = Vocabulary()
self.vocabulary.load('../testdata/vocabulary.dat')
self.sparselda_train_gibbs_sampler = \
SparseLDATrainGibbsSampler(self.model, self.vocabulary)
def test_load_corpus(self):
self.sparselda_train_gibbs_sampler.load_corpus('../testdata/corpus')
self.assertEqual(4, len(self.sparselda_train_gibbs_sampler.documents))
def test_gibbs_sampling(self):
self.sparselda_train_gibbs_sampler.load_corpus('../testdata/corpus')
rand = random.Random()
for i in xrange(100):
self.sparselda_train_gibbs_sampler.gibbs_sampling(rand)
if (i + 1) % 10 == 0:
self.sparselda_train_gibbs_sampler.save_checkpoint(
'../testdata/checkpoint', i + 1)
self.sparselda_train_gibbs_sampler.save_model(
'../testdata/train_model', 100)
def test_load_checkpoint(self):
cur_iteration = self.sparselda_train_gibbs_sampler.load_checkpoint(
'../testdata/checkpoint')
rand = random.Random()
for i in xrange(cur_iteration, 200):
self.sparselda_train_gibbs_sampler.gibbs_sampling(rand)
if (i + 1) % 10 == 0:
self.sparselda_train_gibbs_sampler.save_checkpoint(
'../testdata/checkpoint', i + 1)
def setUp(self):
model = Model(20)
model.load('../testdata/lda_model')
vocabulary = Vocabulary()
vocabulary.load('../testdata/vocabulary.dat')
self.sparselda_gibbs_sampler = \
SparseLDAGibbsSampler(model, vocabulary, 10, 5)
class SparseLDATrainGibbsSamplerTest(unittest.TestCase):
def setUp(self):
self.model = Model(20)
self.vocabulary = Vocabulary()
self.vocabulary.load('../testdata/vocabulary.dat')
self.sparselda_train_gibbs_sampler = \
SparseLDATrainGibbsSampler(self.model, self.vocabulary)
def test_load_corpus(self):
self.sparselda_train_gibbs_sampler.load_corpus('../testdata/corpus')
self.assertEqual(4, len(self.sparselda_train_gibbs_sampler.documents))
def test_gibbs_sampling(self):
self.sparselda_train_gibbs_sampler.load_corpus('../testdata/corpus')
rand = random.Random()
for i in xrange(100):
self.sparselda_train_gibbs_sampler.gibbs_sampling(rand)
if (i + 1) % 10 == 0:
self.sparselda_train_gibbs_sampler.save_checkpoint(
'../testdata/checkpoint', i + 1)
self.sparselda_train_gibbs_sampler.save_model(
'../testdata/train_model', 100)
def test_load_checkpoint(self):
cur_iteration = self.sparselda_train_gibbs_sampler.load_checkpoint(
'../testdata/checkpoint')
rand = random.Random()
for i in xrange(cur_iteration, 200):
self.sparselda_train_gibbs_sampler.gibbs_sampling(rand)
if (i + 1) % 10 == 0:
self.sparselda_train_gibbs_sampler.save_checkpoint(
'../testdata/checkpoint', i + 1)
class ModelEvaluatorTest(unittest.TestCase):
def setUp(self):
self.model = Model(20)
self.model.load('../testdata/lda_model')
self.vocabulary = Vocabulary()
self.vocabulary.load('../testdata/vocabulary.dat')
def test_compute_loglikelihood(self):
doc_tokens = [
'macbook',
'ipad', # exist in vocabulary and model
'mac os x',
'chrome', # only exist in vocabulary
'nokia',
'null'
] # inexistent
document = Document(self.model.num_topics)
rand = random.Random()
rand.seed(0)
document.parse_from_tokens(doc_tokens, rand, self.vocabulary,
self.model)
documents = [document, document]
self.assertEqual(
-14.113955684239654,
model_evaluator.compute_loglikelihood(self.model, self.vocabulary,
documents))
def setUp(self):
self.model = Model(20)
self.model.load('../testdata/lda_model')
self.vocabulary = Vocabulary()
self.vocabulary.load('../testdata/vocabulary.dat')
self.model_evaluator = ModelEvaluator(self.model, self.vocabulary)
def setUp(self):
model = Model(20)
model.load('../testdata/lda_model')
vocabulary = Vocabulary()
vocabulary.load('../testdata/vocabulary.dat')
self.multi_chain_gibbs_sampler = \
MultiChainGibbsSampler(model, vocabulary, 10, 10, 5)
def setUp(self):
model = Model(20)
model.load('../testdata/lda_model')
vocabulary = Vocabulary()
vocabulary.load('../testdata/vocabulary.dat')
self.sparselda_gibbs_sampler = \
SparseLDAGibbsSampler(model, vocabulary, 10, 5)
def setUp(self):
self.model = Model(20)
self.model.load('../testdata/lda_model')
self.vocabulary = Vocabulary()
self.vocabulary.load('../testdata/vocabulary.dat')
self.topic_words_stat = TopicWordsStat(self.model, self.vocabulary)
class TopicWordsStatTest(unittest.TestCase):
def setUp(self):
self.model = Model(20)
self.model.load("../testdata/lda_model")
self.vocabulary = Vocabulary()
self.vocabulary.load("../testdata/vocabulary.dat")
self.topic_words_stat = TopicWordsStat(self.model, self.vocabulary)
def test_save(self):
print self.topic_words_stat.save("../testdata/topic_top_words.dat", 0.8)
def test_get_topic_top_words(self):
print self.topic_words_stat.get_topic_top_words(0.8)
def test_compute_topic_word_distribution(self):
print self.topic_words_stat.compute_topic_word_distribution()
def main(args):
model = Model(0)
model.load(args.model_dir)
vocabulary = Vocabulary()
vocabulary.load(args.vocabulary)
multi_chain_gibbs_sampler = MultiChainGibbsSampler(model, vocabulary,
args.num_markov_chains, args.total_iterations,
args.burn_in_iterations)
fp = open(args.documents, 'r')
for doc_str in fp.readlines():
doc_str = doc_str.decode('gbk')
doc_tokens = doc_str.strip().split('\t')
topic_dist = multi_chain_gibbs_sampler.infer_topics(doc_tokens)
print doc_str
print topic_dist
fp.close()
def __init__(self, model_name, embed_size=200, attention_dim=300, encoder_dim=2048, decoder_dim=300, batch_size=8):
self.model_name = model_name
self.embed_size = embed_size
self.attention_dim = attention_dim
self.encoder_dim = encoder_dim
self.decoder_dim = decoder_dim
self.batch_size = batch_size
self.vocab = Vocabulary()
def main(args):
model = Model(0)
model.load(args.model_dir)
vocabulary = Vocabulary()
vocabulary.load(args.vocabulary)
multi_chain_gibbs_sampler = MultiChainGibbsSampler(model, vocabulary,
args.num_markov_chains,
args.total_iterations,
args.burn_in_iterations)
fp = open(args.documents, 'r')
for doc_str in fp.readlines():
doc_str = doc_str.decode('gbk')
doc_tokens = doc_str.strip().split('\t')
topic_dist = multi_chain_gibbs_sampler.infer_topics(doc_tokens)
print doc_str
print topic_dist
fp.close()
class TopicWordsStatTest(unittest.TestCase):
def setUp(self):
self.model = Model(20)
self.model.load('../testdata/lda_model')
self.vocabulary = Vocabulary()
self.vocabulary.load('../testdata/vocabulary.dat')
self.topic_words_stat = TopicWordsStat(self.model, self.vocabulary)
def test_save(self):
print self.topic_words_stat.save('../testdata/topic_top_words.dat',
0.8)
def test_get_topic_top_words(self):
print self.topic_words_stat.get_topic_top_words(0.8)
def test_compute_topic_word_distribution(self):
print self.topic_words_stat.compute_topic_word_distribution()
class ModelEvaluatorTest(unittest.TestCase):
def setUp(self):
self.model = Model(20)
self.model.load('../testdata/lda_model')
self.vocabulary = Vocabulary()
self.vocabulary.load('../testdata/vocabulary.dat')
def test_compute_loglikelihood(self):
doc_tokens = ['macbook', 'ipad', # exist in vocabulary and model
'mac os x', 'chrome', # only exist in vocabulary
'nokia', 'null'] # inexistent
document = Document(self.model.num_topics)
rand = random.Random()
rand.seed(0)
document.parse_from_tokens(
doc_tokens, rand, self.vocabulary, self.model)
documents = [document, document]
self.assertEqual(-14.113955684239654,
model_evaluator.compute_loglikelihood(self.model, self.vocabulary, documents))
def main(args):
model = Model(args.num_topics, args.topic_prior, args.word_prior)
vocabulary = Vocabulary()
vocabulary.load(args.vocabulary_file)
sparselda_train_gibbs_sampler = SparseLDATrainGibbsSampler(
model, vocabulary)
sparselda_train_gibbs_sampler.load_corpus(args.corpus_dir)
rand = random.Random()
for i in xrange(args.total_iterations):
logging.info('sparselda trainer, gibbs sampling iteration %d.' %
(i + 1))
sparselda_train_gibbs_sampler.gibbs_sampling(rand)
# dump lda model
if i == 0 or (i + 1) % args.save_model_interval == 0:
logging.info('iteration %d start saving lda model.' % (i + 1))
sparselda_train_gibbs_sampler.save_model(args.model_dir, i + 1)
topic_words_stat = TopicWordsStat(model, vocabulary)
topic_words_stat.save(
'%s/topic_top_words.%d' % (args.model_dir, i + 1),
args.topic_word_accumulated_prob_threshold)
logging.info('iteration %d save lda model ok.' % (i + 1))
# dump checkpoint
if i == 0 or (i + 1) % args.save_checkpoint_interval == 0:
logging.info('iteration %d start saving checkpoint.' % (i + 1))
sparselda_train_gibbs_sampler.save_checkpoint(
args.checkpoint_dir, i + 1)
logging.info('iteration %d save checkpoint ok.' % (i + 1))
# compute the loglikelihood
if i == 0 or (i + 1) % args.compute_loglikelihood_interval == 0:
logging.info('iteration %d start computing loglikelihood.' %
(i + 1))
model_evaluator = ModelEvaluator(model, vocabulary)
ll = model_evaluator.compute_loglikelihood(
sparselda_train_gibbs_sampler.documents)
logging.info('iteration %d loglikelihood is %f.' % (i + 1, ll))
def main(args):
model = Model(args.num_topics, args.topic_prior, args.word_prior)
vocabulary = Vocabulary()
vocabulary.load(args.vocabulary_file)
sparselda_train_gibbs_sampler = SparseLDATrainGibbsSampler(
model, vocabulary)
sparselda_train_gibbs_sampler.load_corpus(args.corpus_dir)
rand = random.Random()
for i in xrange(args.total_iterations):
logging.info('sparselda trainer, gibbs sampling iteration %d.'
% (i + 1))
sparselda_train_gibbs_sampler.gibbs_sampling(rand)
# dump lda model
if i == 0 or (i + 1) % args.save_model_interval == 0:
logging.info('iteration %d start saving lda model.' % (i + 1))
sparselda_train_gibbs_sampler.save_model(args.model_dir, i + 1)
topic_words_stat = TopicWordsStat(model, vocabulary)
topic_words_stat.save(
'%s/topic_top_words.%d' % (args.model_dir, i + 1),
args.topic_word_accumulated_prob_threshold)
logging.info('iteration %d save lda model ok.' % (i + 1))
# dump checkpoint
if i == 0 or (i + 1) % args.save_checkpoint_interval == 0:
logging.info('iteration %d start saving checkpoint.' % (i + 1))
sparselda_train_gibbs_sampler.save_checkpoint(
args.checkpoint_dir, i + 1)
logging.info('iteration %d save checkpoint ok.' % (i + 1))
# compute the loglikelihood
if i == 0 or (i + 1) % args.compute_loglikelihood_interval == 0:
logging.info('iteration %d start computing loglikelihood.' % (i + 1))
model_evaluator = ModelEvaluator(model, vocabulary)
ll = model_evaluator.compute_loglikelihood(
sparselda_train_gibbs_sampler.documents)
logging.info('iteration %d loglikelihood is %f.' % (i + 1, ll))
def train(self,
data,
labels,
dev_data=None,
dev_labels=None,
symbol_vocabulary_file=None,
tags_vocabulary_file=None,
lm_file=None,
model_file=None,
save_file=None):
"""
Trains the tagger on data :data: with labels :labels:
data: list of lists of sequences, a list of sentences
labels: list of lists of strs,
a list of sequences of tags, each tag is a feature-value structure
:return:
"""
if symbol_vocabulary_file is None:
self.symbols_ = Vocabulary(
character=True, min_count=self.min_char_count).train(data)
else:
self.symbols_ = vocabulary_from_json(symbol_vocabulary_file,
use_features=False)
if tags_vocabulary_file is None:
self.tags_ = FeatureVocabulary(character=False).train(labels)
else:
with open(tags_vocabulary_file, "r", encoding="utf8") as fin:
tags_info = json.load(fin)
self.tags_ = vocabulary_from_json(tags_info, use_features=True)
if self.verbose > 0:
print("{} characters, {} tags".format(self.symbols_number_,
self.tags_number_))
X_train, indexes_by_buckets = self.transform(data,
labels,
buckets_number=10)
if dev_data is not None:
X_dev, dev_indexes_by_buckets =\
self.transform(dev_data, dev_labels, bucket_size=BUCKET_SIZE)
else:
X_dev, dev_indexes_by_buckets = [None] * 2
self.build()
if save_file is not None and model_file is not None:
self.to_json(save_file, model_file, lm_file)
self._train_on_data(X_train,
indexes_by_buckets,
X_dev,
dev_indexes_by_buckets,
model_file=model_file)
return self
def __init__(self,
embed_size=200,
attention_dim=300,
encoder_dim=2048,
decoder_dim=300,
batch_size=4,
sequence_length=405):
self.embed_size = embed_size
self.attention_dim = attention_dim
self.encoder_dim = encoder_dim
self.decoder_dim = decoder_dim
self.batch_size = batch_size
self.sequence_length = sequence_length
self.vocab = Vocabulary()
def setUp(self):
self.model = Model(20)
self.vocabulary = Vocabulary()
self.vocabulary.load('../testdata/vocabulary.dat')
self.sparselda_train_gibbs_sampler = \
SparseLDATrainGibbsSampler(self.model, self.vocabulary)
def __init__(self, sequence_length=405, batch_size=4):
self.batch_size = batch_size
self.sequence_length = sequence_length
self.vocab = Vocabulary()
self.vocab_size = len(self.vocab)
def setUp(self):
self.model = Model(20)
self.vocabulary = Vocabulary()
self.vocabulary.load('../testdata/vocabulary.dat')
self.sparselda_train_gibbs_sampler = \
SparseLDATrainGibbsSampler(self.model, self.vocabulary)