diff --git a/run.py b/run.py index c163ad3..6a953ff 100644 --- a/run.py +++ b/run.py @@ -5,161 +5,162 @@ import random import numpy as np -parser = argparse.ArgumentParser(description='Model family for Time Series Forecasting') - -# random seed -parser.add_argument('--random_seed', type=int, default=2024, help='random seed') - -# basic config -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='TQNet', - help='model name, options: [TQNet, Informer, Autoformer, ...]') - -# data loader -parser.add_argument('--data', type=str, required=True, default='ETTh1', 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', 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('--label_len', type=int, default=0, help='start token length') #fixed -parser.add_argument('--pred_len', type=int, default=96, help='prediction sequence length') - -# TQNet & CycleNet -parser.add_argument('--cycle', type=int, default=24, help='cycle length') -parser.add_argument('--model_type', type=str, default='mlp', help='model type, options: [linear, mlp]') -parser.add_argument('--use_revin', type=int, default=1, help='1: use revin or 0: no revin') - -# 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=0, 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', type=int, default=0, help='individual head; True 1 False 0') - -# SegRNN -parser.add_argument('--rnn_type', default='gru', help='rnn_type') -parser.add_argument('--dec_way', default='pmf', help='decode way') -parser.add_argument('--seg_len', type=int, default=48, help='segment length') -parser.add_argument('--channel_id', type=int, default=1, help='Whether to enable channel position encoding') - -# 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 input size') # DLinear with --individual, use this hyperparameter as the number of channels -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', 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, 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=1, help='experiments times') -parser.add_argument('--train_epochs', type=int, default=30, help='train epochs') -parser.add_argument('--batch_size', type=int, default=128, help='batch size of train input data') -parser.add_argument('--patience', type=int, default=5, 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='type3', help='adjust learning rate') -parser.add_argument('--pct_start', type=float, default=0.3, help='pct_start') -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 multile gpus') -parser.add_argument('--test_flop', action='store_true', default=False, help='See utils/tools for usage') - -args = parser.parse_args() - -# random seed -fix_seed = args.random_seed -random.seed(fix_seed) -torch.manual_seed(fix_seed) -np.random.seed(fix_seed) - - -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_Main - - -if args.is_training: - for ii in range(args.itr): - - # setting record of experiments - setting = '{}_{}_{}_ft{}_sl{}_pl{}_cycle{}_seed{}'.format( - args.model_id, - args.model, - args.data, - args.features, - args.seq_len, - args.pred_len, - args.cycle, - fix_seed) - - exp = Exp(args) # set experiments - print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting)) - exp.train(setting) - - print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting)) - exp.test(setting) - - if args.do_predict: - print('>>>>>>>predicting : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting)) - exp.predict(setting, True) - - torch.cuda.empty_cache() -else: - ii = 0 - setting = '{}_{}_{}_ft{}_sl{}_pl{}_cycle{}_seed{}'.format( - args.model_id, - args.model, - args.data, - args.features, - args.seq_len, - args.pred_len, - args.cycle, - fix_seed) - - exp = Exp(args) # set experiments - print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting)) - exp.test(setting, test=1) - torch.cuda.empty_cache() +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Model family for Time Series Forecasting') + + # random seed + parser.add_argument('--random_seed', type=int, default=2024, help='random seed') + + # basic config + 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='TQNet', + help='model name, options: [TQNet, Informer, Autoformer, ...]') + + # data loader + parser.add_argument('--data', type=str, required=True, default='ETTh1', 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', 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('--label_len', type=int, default=0, help='start token length') #fixed + parser.add_argument('--pred_len', type=int, default=96, help='prediction sequence length') + + # TQNet & CycleNet + parser.add_argument('--cycle', type=int, default=24, help='cycle length') + parser.add_argument('--model_type', type=str, default='mlp', help='model type, options: [linear, mlp]') + parser.add_argument('--use_revin', type=int, default=1, help='1: use revin or 0: no revin') + + # 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=0, 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', type=int, default=0, help='individual head; True 1 False 0') + + # SegRNN + parser.add_argument('--rnn_type', default='gru', help='rnn_type') + parser.add_argument('--dec_way', default='pmf', help='decode way') + parser.add_argument('--seg_len', type=int, default=48, help='segment length') + parser.add_argument('--channel_id', type=int, default=1, help='Whether to enable channel position encoding') + + # 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 input size') # DLinear with --individual, use this hyperparameter as the number of channels + 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', 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, 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=1, help='experiments times') + parser.add_argument('--train_epochs', type=int, default=30, help='train epochs') + parser.add_argument('--batch_size', type=int, default=128, help='batch size of train input data') + parser.add_argument('--patience', type=int, default=5, 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='type3', help='adjust learning rate') + parser.add_argument('--pct_start', type=float, default=0.3, help='pct_start') + 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 multile gpus') + parser.add_argument('--test_flop', action='store_true', default=False, help='See utils/tools for usage') + + args = parser.parse_args() + + # random seed + fix_seed = args.random_seed + random.seed(fix_seed) + torch.manual_seed(fix_seed) + np.random.seed(fix_seed) + + + 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_Main + + + if args.is_training: + for ii in range(args.itr): + + # setting record of experiments + setting = '{}_{}_{}_ft{}_sl{}_pl{}_cycle{}_seed{}'.format( + args.model_id, + args.model, + args.data, + args.features, + args.seq_len, + args.pred_len, + args.cycle, + fix_seed) + + exp = Exp(args) # set experiments + print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting)) + exp.train(setting) + + print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting)) + exp.test(setting) + + if args.do_predict: + print('>>>>>>>predicting : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting)) + exp.predict(setting, True) + + torch.cuda.empty_cache() + else: + ii = 0 + setting = '{}_{}_{}_ft{}_sl{}_pl{}_cycle{}_seed{}'.format( + args.model_id, + args.model, + args.data, + args.features, + args.seq_len, + args.pred_len, + args.cycle, + fix_seed) + + exp = Exp(args) # set experiments + print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting)) + exp.test(setting, test=1) + torch.cuda.empty_cache()