def main():
docs = get_docs()
texts = make_texts(docs, single=False)
questions = get_questions()
texts.extend(questions)
texts = preprocess_text(texts)
texts = [t for t in texts if t]
tokens, vocab = preprocess.tokenize(texts,
7500,
tag=False,
parse=False,
entity=False)
log.info("Got tokens and vocabulary. Vocab size: %d" % len(vocab))
corpus, flat_corpus, doc_ids, clean_set = make_corpus(tokens=tokens,
min_count=50)
log.info("Got corpus")
# Model Parameters
# Number of documents
n_docs = len(texts)
log.info("number of texts: %d" % n_docs)
# Number of unique words in the vocabulary
n_words = flat_corpus.max() + 1
# Number of dimensions in a single word vector
n_hidden = 128
# Number of topics to fit
n_topics = 20
# Get the count for each key
counts = corpus.keys_counts[:n_words]
# Get the string representation for every compact key
words = corpus.word_list(vocab)[:n_words]
log.info("Words: \n %s" % words)
# Fit the model
log.info("fitting the model")
model = LDA2Vec(n_words, n_hidden, counts, dropout_ratio=0.2)
model.add_categorical_feature(n_docs, n_topics, name='document_id')
model.finalize()
if os.path.exists('model.hdf5'):
serializers.load_hdf5('model.hdf5', model)
for _ in range(200):
log.info("attempt #%d" % _)
model.top_words_per_topic('document_id', words)
log.info("TOP_WORDS_PER_TOPIC!\n => ")
log.info(model.top_words_per_topic('document_id', words))
log.info('========')
model.fit(flat_corpus,
categorical_features=[doc_ids],
fraction=1e-3,
epochs=1)
model.to_cpu()
serializers.save_hdf5('model.hdf5', model)
model.top_words_per_topic('document_id', words)
def get_tokens():
"""
:return:
"""
docs = get_docs()
texts = make_texts(docs, single=False)
questions = get_questions()
texts.extend(questions)
texts = preprocess_text(texts)
texts = [t for t in texts if t]
tokens, vocab = preprocess.tokenize(texts, 7500, tag=False, parse=False, entity=False)
return tokens, vocab
def main():
docs = get_docs()
texts = make_texts(docs, single=False)
questions = get_questions()
texts.extend(questions)
texts = preprocess_text(texts)
texts = [t for t in texts if t]
tokens, vocab = preprocess.tokenize(texts, 7500, tag=False, parse=False, entity=False)
log.info("Got tokens and vocabulary. Vocab size: %d" % len(vocab))
corpus, flat_corpus, doc_ids, clean_set = make_corpus(tokens=tokens, min_count=50)
log.info("Got corpus")
# Model Parameters
# Number of documents
n_docs = len(texts)
log.info("number of texts: %d" % n_docs)
# Number of unique words in the vocabulary
n_words = flat_corpus.max() + 1
# Number of dimensions in a single word vector
n_hidden = 128
# Number of topics to fit
n_topics = 20
# Get the count for each key
counts = corpus.keys_counts[:n_words]
# Get the string representation for every compact key
words = corpus.word_list(vocab)[:n_words]
log.info("Words: \n %s" % words)
# Fit the model
log.info("fitting the model")
model = LDA2Vec(n_words, n_hidden, counts, dropout_ratio=0.2)
model.add_categorical_feature(n_docs, n_topics, name="document_id")
model.finalize()
if os.path.exists("model.hdf5"):
serializers.load_hdf5("model.hdf5", model)
for _ in range(200):
log.info("attempt #%d" % _)
model.top_words_per_topic("document_id", words)
log.info("TOP_WORDS_PER_TOPIC!\n => ")
log.info(model.top_words_per_topic("document_id", words))
log.info("========")
model.fit(flat_corpus, categorical_features=[doc_ids], fraction=1e-3, epochs=1)
model.to_cpu()
serializers.save_hdf5("model.hdf5", model)
model.top_words_per_topic("document_id", words)
def get_tokens():
"""
:return:
"""
docs = get_docs()
texts = make_texts(docs, single=False)
questions = get_questions()
texts.extend(questions)
texts = preprocess_text(texts)
texts = [t for t in texts if t]
tokens, vocab = preprocess.tokenize(texts,
7500,
tag=False,
parse=False,
entity=False)
return tokens, vocab
