run.py

import argparse
import os
import ast
import torch
from exp.exp_long_term_forecasting import Exp_Long_Term_Forecast
from exp.exp_long_term_forecasting_pir import Exp_Long_Term_Forecast_PIR
import random
import numpy as np


def my_list(string):
    if isinstance(string, str):
        return [int(i) for i in string.strip('[').strip(']').split(',')]
    else:
        return string


if __name__ == '__main__':
    fix_seed = 2021
    torch.set_num_threads(8)
    random.seed(fix_seed)
    torch.manual_seed(fix_seed)
    np.random.seed(fix_seed)

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

    # basic config
    parser.add_argument('--task_name', type=str, required=False, default='long_term_forecast',
                        help='task name, options:[long_term_forecast, short_term_forecast, imputation, classification, anomaly_detection]')
    parser.add_argument('--is_training', type=int, required=False, default=1, help='status')
    parser.add_argument('--model_id', type=str, required=False, default='PatchTST_96_96', help='model id')
    parser.add_argument('--model', type=str, required=False, default='PIR',
                        help='model name, options: [Autoformer, Transformer, TimesNet]')
    parser.add_argument('--checkpoints', type=str, default='./checkpoints/')

    parser.add_argument('--backbone', type=str, required=False, default='PatchTST')
    parser.add_argument('--bakcbone_checkpoints', type=str, required=False, default='./checkpoints/backbone/')
    parser.add_argument('--load_pretrained_backbone', type=int, default=2)
    parser.add_argument('--output_index', type=int, default=1)

    parser.add_argument('--refine_epochs', type=int, required=False, default=10)
    parser.add_argument('--refine_d_model', type=int, default=128)
    parser.add_argument('--refine_d_ff', type=int, default=128)
    parser.add_argument('--refine_lr', type=float, default=1e-4)
    parser.add_argument('--refine_layers', type=int, default=1)
    parser.add_argument('--retrieval_num', type=int, default=10)
    parser.add_argument('--retrieval_stride', type=int, default=1)
    parser.add_argument('--including_time_features', type=int, default=1)

    parser.add_argument('--batch_size', type=int, default=32)
    parser.add_argument('--percent', type=int, default=100)

    # data loader
    parser.add_argument('--data', type=str, required=False, default='ETTh1', help='dataset type')
    parser.add_argument('--root_path', type=str, default='dataset/ETT-small/', 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', 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('--seq_len', type=int, default=96, help='input sequence length')
    parser.add_argument('--label_len', type=int, default=0, help='start token length')
    parser.add_argument('--pred_len', type=int, default=96, help='prediction sequence length')

    parser.add_argument('--seasonal_patterns', type=str, default='Monthly', help='subset for M4')
    parser.add_argument('--inverse', action='store_true', help='inverse output data', default=False)
    # 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=16, help='dimension of model')
    parser.add_argument('--n_heads', type=int, default=4, help='num of heads')
    parser.add_argument('--e_layers', type=int, default=3, 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=128, 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('--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')

    # TimeMixer
    parser.add_argument('--down_sampling_layers', type=int, default=3)
    parser.add_argument('--down_sampling_window', type=int, default=2)
    parser.add_argument('--down_sampling_method', type=str, default='avg')
    parser.add_argument('--channel_independence', type=int, default=1)
    parser.add_argument('--decomp_method', type=str, default='moving_avg',
                        help='method of series decompsition, only support moving_avg or dft_decomp')
    parser.add_argument('--use_norm', type=int, default=1, help='whether to use normalize; True 1 False 0')

    # SparseTSF
    parser.add_argument('--period_len',type=int, default=1, help='period length')

    # optimization
    parser.add_argument('--num_workers', type=int, default=0, 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('--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='Exp', 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')

    # 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')

    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.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)

    if args.model == 'PIR':
        Exp = Exp_Long_Term_Forecast_PIR
        args.output_index = 1
    else:
        Exp = Exp_Long_Term_Forecast
        args.output_index = 0

    if args.is_training:
        mae_list, mse_list = [], []
        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))
            torch.cuda.empty_cache()
            mae, mse = exp.test(setting)
            mae_list.append(mae)
            mse_list.append(mse)
            torch.cuda.empty_cache()
        print('mse:{0:.3f},mae:{1:.3f}'.format(np.mean(mse_list), np.mean(mae_list)))
    else:
        mae_list, mse_list = [], []
        for ii in range(args.itr):
            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))
            torch.cuda.empty_cache()
            mae, mse = exp.test(setting, test=1)
            mae_list.append(mae)
            mse_list.append(mse)
            torch.cuda.empty_cache()
        print('mse:{0:.3f},mae:{1:.3f}'.format(np.mean(mse_list), np.mean(mae_list)))