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- 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)
-
- parser = argparse.ArgumentParser(description='Autoformer & Transformer family for Time Series Forecasting')
-
- # 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='Autoformer',
- help='model name, options: [Autoformer, Informer, Transformer]')
-
- # data loader
- parser.add_argument('--data', type=str, required=True, default='ETTm1', 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')
-
- # 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', 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=0.05, 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=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 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='0,1,2,3', help='device ids of multile 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 = '{}_{}_{}_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))
- 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, test=1)
- torch.cuda.empty_cache()
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