def main(_):
# init
we = WordEmbedding()
dc = Document()
cf = Classifier()
# load data
docs = dc.getDocs(labeled_only=True)
# load word embedding model
if FLAGS.we_model == 'devblog':
we_model = we.loadDevblogModel(embedding_dim = FLAGS.we_dim,
epochs = FLAGS.we_epoch,
window = FLAGS.we_window,
min_count = FLAGS.we_min_count)
# han2jamo
docs.text = docs.text.apply(han2Jamo)
elif FLAGS.we_model == 'wiki':
we_model = we.loadWikiModel()
# word embedding
docs.vector = docs.text.apply(lambda x: we.embedding(we_model, x))
# training
cf_model = cf.train(docs, './checkpoint')
cf.saveModel(cf_model, FLAGS.cf_model)
def main(_):
# init
we = WordEmbedding()
dc = Document()
cf = Classifier()
# load word embedding model
if FLAGS.we_model == 'devblog':
we_model = we.loadDevblogModel(embedding_dim=FLAGS.we_dim,
epochs=FLAGS.we_epoch,
window=FLAGS.we_window,
min_count=FLAGS.we_min_count)
elif FLAGS.we_model == 'wiki':
we_model = we.loadWikiModel()
# load classifier model
cf_model = cf.loadModel(FLAGS.cf_model)
results = [{'text': r} for r in FLAGS.predict]
is_devblog = FLAGS.we_model == 'devblog'
for i, r in enumerate(FLAGS.predict):
# preprocessing
text = han2Jamo(r) if is_devblog else r
# word embedding
df = dc.preprocessing(text, devblog=is_devblog)
vector = df.text.apply(
lambda x: we.embedding(we_model, x, FLAGS.we_dim)).tolist()
if len(vector) == 0:
print('🐈 text is not valid :', r)
return
else:
# predict
results[i]['predict'] = cf.predict(cf_model, np.array(vector),
FLAGS.criterion)
return results
