run.py

import torch
import numpy as np
import random
from exp.exp_main import Exp_Main
import argparse
import time

fix_seed = 1024
random.seed(fix_seed)
torch.manual_seed(fix_seed)
np.random.seed(fix_seed)

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Multivariate Time Series Forecasting')

    # basic config
    parser.add_argument('--is_training', type=int, required=True, default=1, help='status')
    parser.add_argument('--model', type=str, required=True, default='Affine',
                        help='model name, options: [Affine, Linear, STD, TimeFlow]')

    # data loader
    parser.add_argument('--data', type=str, required=True, default='ETTh1', help='dataset type')
    parser.add_argument('--root_path', type=str, default='./dataset/', 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]; M:multivariate predict multivariate, S:univariate predict univariate')
    parser.add_argument('--target', type=str, default='OT', help='target feature in S or MS task')
    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('--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('--pred_len', type=int, default=96, help='prediction sequence length')
    parser.add_argument('--individual', action='store_true', default=False, help='DLinear: a linear layer for each variate(channel) individually')
    parser.add_argument('--seg', type=int, default=20, help='prediction plot segments')

    # model
    parser.add_argument('--channel', type=int, default=7, help='num of channel')
    parser.add_argument('--d_model', type=int, default=512, help='dimension of model')
    parser.add_argument('--layers', type=int, default=2, help='num of layers')
    parser.add_argument('--embed', type=str, default='timeF', help='time features encoding, options:[timeF, fixed, learned]')
    parser.add_argument('--do_predict', action='store_true', help='whether to predict unseen future data')
    parser.add_argument('--rev', action='store_true', help='whether to apply RevIN')
    parser.add_argument('--drop', type=float, default=0.1, help='dropout ratio')

    # 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=15, 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=10, help='early stopping patience')
    parser.add_argument('--learning_rate', type=float, default=0.001, help='optimizer learning rate')
    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', help='device ids of multile gpus')

    args = parser.parse_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.dvices = 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_Main

    if args.is_training:
        for ii in range(args.itr):
            # setting record of experiments
            setting = '{}_{}_ft{}_sl{}_pl{}_dm{}_eb{}_{}'.format(
                args.model,
                args.data_path[:-4],
                args.features,
                args.seq_len,
                args.pred_len,
                args.d_model,
                args.embed, ii)

            exp = Exp(args)  # set experiments
            print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))
            exp.train(setting)

            time_now = time.time()
            print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
            exp.test(setting)
            print('Inference time: ', time.time() - time_now)

            if args.do_predict:
                print('>>>>>>>predicting : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
                exp.predict(setting, True)

            torch.cuda.empty_cache()
    else:
        ii = 0
        setting = '{}_{}_ft{}_sl{}_pl{}_dm{}_eb{}_{}'.format(args.model_id,
            args.model,
            args.data_path[:-4],
            args.features,
            args.seq_len,
            args.pred_len,
            args.d_model,
            args.embed, ii)

        exp = Exp(args)  # set experiments
        print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
        exp.test(setting, test=1)
        torch.cuda.empty_cache()