run_longExp.py

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

fix_seed = 2021
random.seed(fix_seed)
torch.manual_seed(fix_seed)
np.random.seed(fix_seed)
torch.set_num_threads(6)

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('--model_id', type=str, default='test', help='model id')
parser.add_argument('--model', type=str, default='FEDformer',
                    help='model name, options: [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')

# non-station module / statistics prediction module config
parser.add_argument('--j', type=int, default=0)
parser.add_argument('--learnable', action='store_true', default=False)
parser.add_argument('--wavelet', type=str, default='coif3')
parser.add_argument('--dr', type=float, default=0.05)
parser.add_argument('--pre_epoch', type=int, default=5)
parser.add_argument('--twice_epoch', type=int, default=1)
parser.add_argument('--use_norm', type=str, default='sliding')
parser.add_argument('--kernel_len', type=int, default=7)
parser.add_argument('--hkernel_len', type=int, default=5)
parser.add_argument('--station_lr', type=float, default=0.0001)
parser.add_argument('--station_type', type=str, default='adaptive')
parser.add_argument('--pd_ff', type=int, default=1024, help='dimension of fcn')
parser.add_argument('--pd_model', type=int, default=512, help='dimension of model')
parser.add_argument('--pe_layers', type=int, default=2, help='num of encoder layers')


# data loader
parser.add_argument('--data', type=str, default='ETTh2', help='dataset type')
parser.add_argument('--root_path', type=str, default='./datasets/ETT-small', help='root path of the data file')
parser.add_argument('--data_path', type=str, default='ETTh2.csv', help='data file')
parser.add_argument('--features', type=str, default='MS',
                    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')
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')

# PatchTST
parser.add_argument('--fc_dropout', type=float, default=0.05, help='fully connected dropout')
parser.add_argument('--head_dropout', type=float, default=0.0, help='head dropout')
parser.add_argument('--patch_len', type=int, default=16, help='patch length')
parser.add_argument('--stride', type=int, default=8, help='stride')
parser.add_argument('--padding_patch', default='end', help='None: None; end: padding on the end')
parser.add_argument('--revin', type=int, default=1, help='RevIN; True 1 False 0')
parser.add_argument('--affine', type=int, default=0, help='RevIN-affine; True 1 False 0')
parser.add_argument('--subtract_last', type=int, default=0, help='0: subtract mean; 1: subtract last')
parser.add_argument('--decomposition', type=int, default=0, help='decomposition; True 1 False 0')
parser.add_argument('--kernel_size', type=int, default=25, help='decomposition-kernel')
parser.add_argument('--individual_head', type=int, default=0, help='individual head; True 1 False 0')

# CrossFormer
parser.add_argument('--win_size', type=int, default=2, help='window kernel for segment merge')
parser.add_argument('--cross_factor', type=int, default=10, help='num of routers in Cross-Dimension Stage of TSA (c)')
parser.add_argument('--seg_len', type=int, default=6, help='segment length (L_seg)')

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

# DLinear
parser.add_argument('--individual', action='store_true', default=False,
                    help='DLinear: a linear layer for each variate(channel) individually')

# SCNN
parser.add_argument('--cycle_len', type=int, default=None, help='SCNN')
parser.add_argument('--short_period_len', type=int, default=None, help='SCNN')
parser.add_argument('--period_len', type=int, default=24)

# Formers 
parser.add_argument('--embed_type', type=int, default=0,
                    help='0: default 1: value embedding + temporal embedding + positional embedding 2: value embedding + temporal embedding 3: value embedding + positional embedding 4: value embedding')
parser.add_argument('--enc_in', type=int, default=7,
                    help='encoder x kernel')  # DLinear with --individual, use this hyperparameter as the number of channels
parser.add_argument('--dec_in', type=int, default=7, help='decoder x kernel')
parser.add_argument('--c_out', type=int, default=7, help='output kernel')
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', type=int, default=25, help='window kernel of moving average')
parser.add_argument('--factor', type=int, default=3, 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=0, help='data loader num workers')
parser.add_argument('--itr', type=int, default=2, 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 kernel of train x 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', help='device ids of multile gpus')
parser.add_argument('--test_flop', action='store_true', default=False, help='See utils/tools for usage')

args = parser.parse_args()
args.label_len = args.seq_len // 2
if args.features == 'S':
    args.enc_in, args.dec_in, args.c_out = 1, 1, 1

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
mses = []
maes = []

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.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))
        mse, mae = exp.test(setting)
        mses.append(mse)
        maes.append(mae)

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

        torch.cuda.empty_cache()
    print('average mse:{0:.3f}±{1:.3f}, mae:{2:.3f}±{3:.3f}'.format(np.mean(mses), np.std(mses), np.mean(maes),
                                                                    np.std(maes)))
else:
    ii = 0
    setting = '{}_{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_fc{}_eb{}_dt{}_{}_{}'.format(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()