run.py

import argparse
import torch
from exp.exp_long_term_forecasting import Exp_Long_Term_Forecast
import random
import time
import numpy as np
if __name__ == '__main__':
    fix_seed = 2023
    random.seed(fix_seed)
    torch.manual_seed(fix_seed)
    np.random.seed(fix_seed)

    parser = argparse.ArgumentParser(description='VCformer')

    # basic config
    parser.add_argument('--task_name', type=str, required=True, default='long_term_forecast')
    parser.add_argument('--is_training', type=int,
                        required=True, default=1, help='status')
    parser.add_argument('--model_id', type=str, required=True,
                        default='test', help='model id')
    parser.add_argument('--model', type=str, required=True, default='VCformer',
                        help='model name, options: [VCformer,iTransformer,PatchTST,DSformer,Crossformer,NSformer,DLinear,Autoformer,Informer]')

    # data loader
    parser.add_argument('--data', type=str, required=True,
                        default='ETTm1', help='dataset type')
    parser.add_argument('--root_path', type=str,
                        default='./data/ETT/', help='root path of the data file')
    parser.add_argument('--data_path', type=str,
                        default='ETTh1.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_dim', type=int, default=-
                        1, help="target dim for prediction")
    parser.add_argument('--target', type=str, default='OT',
                        help='target feature in S or MS task')
    parser.add_argument('--freq', type=str, default='d',
                        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('--checkpoints', type=str,
                        default='./checkpoints/', help='location of model checkpoints')

    # forecasting task
    parser.add_argument('--seq_len', type=int, default=96,
                        help='input sequence length')
    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')


    # model define
    parser.add_argument('--top_k', type=int, default=5, help='for TimesBlock')
    parser.add_argument('--num_kernels', type=int,
                        default=6, help='for Inception')
    parser.add_argument('--enc_in', type=int, default=7,
                        help='encoder input size')
    parser.add_argument('--dec_in', type=int, default=7,
                        help='decoder input size')
    parser.add_argument('--c_out', type=int, default=7, help='output size')
    parser.add_argument('--d_model', type=int, default=512,
                        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=2,
                        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=2048,
                        help='dimension of fcn')
    parser.add_argument('--snap_size', type=int, default=16,
                        help='snapshot size for Koopman Temporal Detector')
    parser.add_argument('--proj_dim', type=int, default=128,
                        help='projection dim of Koopman space')
    parser.add_argument('--hidden_dim', type=int, default=256,
                        help='hidden dim of Koopman Enc/Dec')
    parser.add_argument('--hidden_layers', type=int, default=1,
                        help='hidden layers of Koopman Temporal Detector')
    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('--dropout', type=float, default=0.1, help='dropout')
    parser.add_argument('--embed', type=str, default='timeF',
                        help='time features encoding, options:[timeF, fixed, learned]')
    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')

    # optimization
    parser.add_argument('--num_workers', type=int,
                        default=10, help='data loader num workers')
    parser.add_argument('--itr', type=int, default=1, help='experiments times')
    parser.add_argument('--train_epochs', type=int,
                        default=10, help='train epochs')
    parser.add_argument('--batch_size', type=int, default=16,
                        help='batch size of train input data')
    parser.add_argument('--patience', type=int, default=3,
                        help='early stopping patience')
    parser.add_argument('--learning_rate', type=float,
                        default=0.0001, help='optimizer learning rate')
    parser.add_argument('--des', type=str, default='test',
                        help='exp description')
    parser.add_argument('--loss', type=str, default='MSE',
                        help='loss function')
    parser.add_argument('--lradj', type=str, default='type1',
                        help='adjust learning rate')
    parser.add_argument('--use_amp', action='store_true',
                        help='use automatic mixed precision training', default=False)

    # GPU
    parser.add_argument('--use_gpu', type=bool, default=True, help='use gpu')
    parser.add_argument('--gpu', type=int, default=0, help='gpu')
    parser.add_argument('--use_multi_gpu', action='store_true',
                        help='use multiple gpus', default=False)

    
    parser.add_argument('--devices', type=str,
                        default='0,1,2,3,4,5,6,7', help='device ids of multile gpus')

    # de-stationary projector params
    parser.add_argument('--p_hidden_dims', type=int, nargs='+', default=[128, 128],
                        help='hidden layer dimensions of projector (List)')
    parser.add_argument('--p_hidden_layers', type=int,
                        default=2, help='number of hidden layers in projector')

    # DSformer params
    parser.add_argument('--sample_interval', type=int,
                        default=2, help='sample interval for DSformer')
    parser.add_argument('--weight', type=float,
                        default=0.35, help='weight_for_LOSS')
    parser.add_argument('--IF_node', action='store_true', default=True,
                        help='Whether to use node embedding. True or False')

    
    args = parser.parse_args()
    print(args)
    args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False

    if args.use_gpu and args.use_multi_gpu:
        args.devices = args.devices.replace(' ', '')
        device_ids = args.devices.split(',')
        args.device_ids = [int(id_) for id_ in device_ids]
        args.gpu = args.device_ids[0]

    print('Args in experiment:')
    print(args)

    Exp = Exp_Long_Term_Forecast

    time_start = time.time()
    if args.is_training:
        for ii in range(args.itr):
            # setting record of experiments
            setting = '{}_{}_{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_fc{}_eb{}_dt{}_{}_{}'.format(
                args.task_name,
                args.model_id,
                args.model,
                args.data,
                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,
                args.des, ii)

            exp = Exp(args)  # set experiments
            print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))
            exp.train(setting)
            print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
            exp.test(setting)
            torch.cuda.empty_cache()
    else:
        ii = 0
        setting = '{}_{}_{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_fc{}_eb{}_dt{}_{}_{}'.format(
            args.task_name,
            args.model_id,
            args.model,
            args.data,
            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,
            args.des, ii)

        exp = Exp(args)  # set experiments
        print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
        exp.test(setting, test=1)
        torch.cuda.empty_cache()
    time_end = time.time()
    print('this experiment cost time:', time_end-time_start)