# 预测时间必须是整数，且不等于0
if not isinstance(args.predict_time, int) and args.predict_time == 0:
return 'error predict time'
if not os.path.exists(args.data_dir):
return 'the data file is not exist'
else:
return ''
if __name__ == "__main__":
parser = ArgumentParser(
description='Periodic prediction of the time series.')
parser.add_argument('--model_name',
default='lr',
choices=models.names(),
help='Name of the model to use.')
parser.add_argument(
'--data_dir',
default='./aiopstools/timeseries_predict/data/timeseries_data.csv',
help='Dir of the data to train')
parser.add_argument('--predict_time',
type=int,
help='The prediction time.')
args = parser.parse_args()
check_result = check_param(args)
if check_result == '':
# 预测时间必须是整数，且不等于0
if not isinstance(args.predict_time, int) and args.predict_time == 0:
return 'error predict time'
if not os.path.exists(args.data_dir):
return 'the data file is not exist'
else:
return ''
if __name__ == "__main__":
parser = ArgumentParser(description='Prediction of the time series.')
parser.add_argument(
'--model_name', default='lr',
choices=models.names(), help='Name of the model to use.')
parser.add_argument(
'--data_dir', default='./timeseries_predict/data/timeseries_data.csv',
help='Dir of the data to train')
parser.add_argument(
'--predict_time', type=int,
help='The prediction time.')
args = parser.parse_args()
check_result = check_param(args)
if check_result == '':
ori_data, timestamp_list, value_list = handle_data.get_train_data(args.data_dir, args.predict_time)
if len(value_list) < args.predict_time: