-
Notifications
You must be signed in to change notification settings - Fork 124
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
ed4ae0e
commit 306add5
Showing
1 changed file
with
158 additions
and
152 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,164 +1,170 @@ | ||
| import argparse | ||
| import os | ||
| import sys | ||
| 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) | ||
|
|
||
| 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='./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=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 | ||
|
|
||
| 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 = '{}_{}_{}_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, | ||
| ii) | ||
|
|
||
| exp = Exp(args) # set experiments | ||
| print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting)) | ||
| exp.train(setting) | ||
| 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('--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=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 | ||
|
|
||
| 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 = '{}_{}_{}_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, | ||
| ii) | ||
|
|
||
| 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{}_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) | ||
| exp.test(setting, test=1) | ||
| torch.cuda.empty_cache() | ||
|
|
||
| if args.do_predict: | ||
| print('>>>>>>>predicting : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting)) | ||
| exp.predict(setting, True) | ||
|
|
||
| torch.cuda.empty_cache() | ||
| 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() | ||
| if __name__ == "__main__": | ||
| main() |