class GenerateBank2Vec:
def __init__(self):
self.sp = Support()
self.lexicon = self.sp.load_file_polarity()
self.cores = multiprocessing.cpu_count()
self.data = Support.process_data(filename='bank_message', size_msg=3, clean_data=True, replace_text=True,
stemmed=None, lemmatize=None, spelling=None)
def generate_embedding(self, size=300, min_count=3, window=5, downsampling=1e-3, negative=3):
try:
full_corpus = []
corpus_ccd = Support.import_corpus_ccd()
corpus_wikipedia_web = Support.corpus_bank_xml()
corpus_bank = Support.import_dataset_bank()
dataset = self.data['msg'].values.tolist()
full_corpus.extend(corpus_ccd)
full_corpus.extend(corpus_wikipedia_web)
full_corpus.extend(corpus_bank)
full_corpus.extend(dataset)
print('Transform sentences to vectors ...')
corpus = self.sp.sentence_vec(full_corpus)
terminology = self.sp.import_terminology_embedding(filename='bank_terminology')
print('Generate Embedding...')
model = Word2Vec(corpus, cbow_mean=1, workers=self.cores, size=size,min_count=min_count,
window=window, sample=downsampling, negative=negative, iter=30)
model.train(terminology, total_examples=model.corpus_count, epochs=model.epochs)
model.init_sims(replace=True)
# Saving the model for later use. Can be loaded using Word2Vec.load()
model_name = ROOT_DIR_DATA_EMBEDDING + "bank2vec.model"
model.save(model_name)
print('Model generated sucesfull!')
except Exception as e:
print("\t ERROR generate_embedding: ", e)
return None
def __init__(self):
sp = Support()
self.lexicon = sp.load_file_polarity()