def main(alpha=None, gamma=None):
config = Config(args.config_path)
if args.mode:
config.mode = args.mode
if args.train_id:
config.train_id = args.train_id
if args.num_epochs:
config.num_epochs = args.num_epochs
if args.base_dir:
config.base_dir = args.base_dir
config.use_bayes_opt = args.use_bayes_opt
config.use_preprocess = args.use_preprocess
config.use_swa = args.use_swa
train_path = os.path.join(config.base_dir, config.train_dir, config.train_id)
result_path = os.path.join(config.base_dir, config.result_dir, config.train_id)
data_path = os.path.join(config.base_dir, config.data_dir)
if not os.path.isdir(train_path):
os.mkdir(train_path)
if not os.path.isdir(result_path):
os.mkdir(result_path)
init_logger(os.path.join(result_path, 'log.txt'))
set_seed(config)
# get data loader
tokenizer = AutoTokenizer.from_pretrained(config.bert_model_name)
param = {"root": data_path, "batch_size": config.batch_size, "tokenizer": tokenizer, "config": config}
train_dataloader = data_loader(**param, phase='train')
validate_dataloader = data_loader(**param, phase='validate')
test_dataloader = data_loader(**param, phase='test')
# create model config 확인
model = Trainer(config, train_dataloader, validate_dataloader, test_dataloader)
if config.mode == 'train':
result = model.train(alpha=alpha, gamma=gamma)
elif config.mode == 'test':
model.load_model(config.model_weight_file)
result = model.evaluate('test')
del model
return result
PATH = '../CICFlowMeter-4.0/bin/data/daily/'
target_file = sorted(glob.glob(PATH + '*.csv'))[-1]
separator = ','
reader = open(target_file, 'r')
header = reader.readline().split(separator)
count = 0
print('Number of columns: %d' % len(header))
print('Reading %s\n' % target_file)
model = Trainer()
model.load_model('./SVM_classifier.sav')
while True:
row = reader.readline()
if not row:
time.sleep(0.1)
continue
count += 1
# sys.stdout.write('\r' + str(count))
# sys.stdout.flush()
# Preprocess
row = row.split(separator)[:-1]
row = np.delete(np.array(row), [0, 1, 2, 3, 5, 6], 0)
row = row.astype(np.float32)
