exp.py

import argparse
from exp.exp_long_term_forecasting import Exp_Long_Term_Forecast
from exp.exp_short_term_forecasting import Exp_Short_Term_Forecast



if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Informer')
    parser.add_argument('--model', type=str, default='Etsformer',
                        help='model name, options: [Reformer, Transformer, Informer, Pyraformer, Autoformer, Etsformer]')
    parser.add_argument('--patience', type=int, default=50, help='early stop')
    parser.add_argument('--embed', type=str, default='timeF',
                        help='time features encoding, options:[timeF, fixed, learned]')
    parser.add_argument('--task_name', type=str, default='short_term_forecast',
                        help='task name, options:[short_term_forecast, short_term_forecast]')
    parser.add_argument('--batch_size', type=int, default=32, help='batch size of train input data')
    parser.add_argument('--freq', type=str, default='h',
                        help='freq for time features encoding, options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h')
    parser.add_argument('--seq_len', type=int, default=96, help='input sequence length')
    parser.add_argument('--num_workers', type=int, default=10, help='data loader num workers')
    parser.add_argument('--seasonal_patterns', type=str, default='Monthly', help='subset for M4')
    parser.add_argument('--label_len', type=int, default=48, help='start token length')
    parser.add_argument('--pred_len', type=int, default=96, help='prediction sequence length')
    parser.add_argument('--learning_rate', type=float, default=0.0001, help='optimizer learning rate')
    # parser.add_argument('--data', type=str, default='ETTm1', help='dataset type')
    # parser.add_argument('--root_path', type=str, default='./data/long_term_forecast/ETTh/', help='root path of the data file')
    # parser.add_argument('--data_path', type=str, default='ETTh1.csv', help='data file')
    parser.add_argument('--root_path', type=str, default='./data/short_term_forecast/m4/', help='root path of the data file')
    parser.add_argument('--data_path', type=str, default='Hourly-train.csv', help='data file')
    parser.add_argument('--features', type=str, default='M',
                        help='forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate')
    parser.add_argument('--target', type=str, default='OT', help='target feature in S or MS task')

    # model define
    parser.add_argument('--top_k', type=int, default=5, help='for TimesBlock')
    '''
    If features == M, (enc_in, dec_in, c_out) = (7,7,7);
    elif features == S, (enc_in, dec_in, c_out) = (1,1,1)
    elif features == MS, (enc_in, dec_in, c_out) = (7,7,1)
    '''
    parser.add_argument('--enc_in', type=int, default=1, help='encoder input size')
    parser.add_argument('--dec_in', type=int, default=1, help='decoder input size')
    parser.add_argument('--c_out', type=int, default=1, help='output size')
    parser.add_argument('--d_model', type=int, default=256, help='dimension of model')
    parser.add_argument('--n_heads', type=int, default=8, help='num of heads')
    parser.add_argument('--e_layers', type=int, default=1, help='num of encoder layers')
    parser.add_argument('--d_layers', type=int, default=1, help='num of decoder layers')
    parser.add_argument('--d_ff', type=int, default=1024, help='dimension of fcn')
    parser.add_argument('--moving_avg', type=int, default=25, help='window size of moving average')
    parser.add_argument('--factor', type=int, default=1, help='attn factor')
    parser.add_argument('--distil', action='store_false',
                        help='whether to use distilling in encoder, using this argument means not using distilling',
                        default=True)
    parser.add_argument('--train_epochs', type=int, default=1, help='train epochs')
    parser.add_argument('--dropout', type=float, default=0.1, help='dropout')
    parser.add_argument('--activation', type=str, default='gelu', help='activation')
    parser.add_argument('--output_attention', action='store_true', help='whether to output attention in ecoder')
    parser.add_argument('--loss', type=str, default='SMAPE', help='loss function')
    args = parser.parse_args()


    if args.task_name == 'long_term_forecast':
        Exp = Exp_Long_Term_Forecast
    elif args.task_name == 'short_term_forecast':
        Exp = Exp_Short_Term_Forecast
    setting = '{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_fc{}_eb{}_dt{}'.format(
        args.task_name,
        args.model,
        args.features,
        args.seq_len,
        args.label_len,
        args.pred_len,
        args.d_model,
        args.n_heads,
        args.e_layers,
        args.d_layers,
        args.d_ff,
        args.factor,
        args.embed,
        args.distil)
    exp = Exp(args)  # set experiments
    print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))
    exp.train(setting)

    print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
    exp.test(setting)