dump.py

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


def main():
    fix_seed = 2021
    random.seed(fix_seed)
    torch.manual_seed(fix_seed)
    np.random.seed(fix_seed)

    parser = argparse.ArgumentParser(description='Autoformer & Transformer family for Time Series Forecasting')

    # basic config
    parser.add_argument('--is_training', type=int, default=1, help='status')
    parser.add_argument('--task_id', type=str, default='test', help='task id')
    parser.add_argument('--model', type=str, default='FEDformer',
                        help='model name, options: [FEDformer, Autoformer, Informer, Transformer]')

    # supplementary config for FEDformer model
    parser.add_argument('--version', type=str, default='Fourier',
                        help='for FEDformer, there are two versions to choose, options: [Fourier, Wavelets]')
    parser.add_argument('--mode_select', type=str, default='random',
                        help='for FEDformer, there are two mode selection method, options: [random, low]')
    parser.add_argument('--modes', type=int, default=64, help='modes to be selected random 64')
    parser.add_argument('--L', type=int, default=3, help='ignore level')
    parser.add_argument('--base', type=str, default='legendre', help='mwt base')
    parser.add_argument('--cross_activation', type=str, default='tanh',
                        help='mwt cross atention activation function tanh or softmax')

    # data loader
    parser.add_argument('--data', type=str, default='ETTh1', help='dataset type')
    parser.add_argument('--root_path', type=str, default='./dataset/ETT/', help='root path of the data file')
    parser.add_argument('--data_path', type=str, default='ETTh1.csv', help='data file')
    parser.add_argument('--data_prefix', type=str, default='', help='data file')
    parser.add_argument('--zeros_pct', type=float, default=1.0, help='percentage of zero only input sequence included')
    parser.add_argument('--features', type=str, default='M',
                        help='forecasting task, options:[M, S, MS, MR]; M:multivariate predict multivariate, '
                             'S:univariate predict univariate, MS:multivariate predict univariate, MR:multivariate regression')
    parser.add_argument('--target', type=str, default='OT', help='target feature in S or MS task')
    parser.add_argument('--count_target', type=str, default='OT', help='target feature used to filter out zero only input sequence')
    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('--detail_freq', type=str, default='h', help='like freq, but use in predict')
    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')
    # parser.add_argument('--cross_activation', type=str, default='tanh'

    # model define
    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('--moving_avg', default=[24], 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=0.05, 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')
    parser.add_argument('--do_predict', action='store_true', help='whether to predict unseen future data')

    # optimization
    parser.add_argument('--num_workers', type=int, default=10, help='data loader num workers')
    parser.add_argument('--itr', type=int, default=3, help='experiments times')
    parser.add_argument('--train_epochs', type=int, default=10, help='train epochs')
    parser.add_argument('--batch_size', type=int, default=32, 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', help='device ids of multi gpus')

    args = parser.parse_args()

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

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

    Exp = Exp_Main

    setting = '{}_{}_{}_modes{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_fc{}_eb{}_dt{}_{}_{}'.format(
        args.task_id,
        args.model,
        args.mode_select,
        args.modes,
        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,
        0)

    exp = Exp(args)
    exp.dump_model(setting, "transformer_regression_30seq_200mil.pt")


if __name__ == "__main__":
    main()