mirror of
https://github.com/wassname/ETSformer.git
synced 2026-06-27 16:28:38 +08:00
added notebook
This commit is contained in:
wassname
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/notebooks/checkpoints/
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/checkpoints/
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/notebooks/results/
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/results/
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*.npy
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*.pth
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# Jupyter NB Checkpoints
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.ipynb_checkpoints/
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__pycache__/
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+1
-1
@@ -237,4 +237,4 @@ class Exp_Main(Exp_Basic):
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np.save(folder_path + 'pred.npy', preds)
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np.save(folder_path + 'true.npy', trues)
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return
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return preds, trues
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File diff suppressed because one or more lines are too long
@@ -13,112 +13,119 @@ def set_seed(seed):
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seed += 1
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torch.manual_seed(seed)
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parser = argparse.ArgumentParser(description='ETSformer: Exponential Smoothing Transformers for Time-series Forecasting')
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# basic config
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parser.add_argument('--model_id', type=str, required=True, default='test', help='model id')
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parser.add_argument('--model', type=str, required=True, default='ETSformer',
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help='model name, options: [ETSformer]')
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def get_args(argv=None):
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parser = argparse.ArgumentParser(description='ETSformer: Exponential Smoothing Transformers for Time-series Forecasting')
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# data loader
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parser.add_argument('--data', type=str, required=True, default='ETTm1', help='dataset type')
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parser.add_argument('--root_path', type=str, default='./data/ETT/', help='root path of the data file')
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parser.add_argument('--data_path', type=str, default='ETTh1.csv', help='data file')
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parser.add_argument('--features', type=str, default='M',
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help='forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate')
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parser.add_argument('--target', type=str, default='OT', help='target feature in S or MS task')
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parser.add_argument('--freq', type=str, default='h',
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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')
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parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location of model checkpoints')
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# basic config
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parser.add_argument('--model_id', type=str, required=True, default='test', help='model id')
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parser.add_argument('--model', type=str, required=True, default='ETSformer',
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help='model name, options: [ETSformer]')
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# forecasting task
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parser.add_argument('--seq_len', type=int, required=True, help='input sequence length')
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parser.add_argument('--label_len', type=int, default=0, help='start token length')
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parser.add_argument('--pred_len', type=int, required=True, help='prediction sequence length')
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# data loader
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parser.add_argument('--data', type=str, required=True, default='ETTm1', help='dataset type')
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parser.add_argument('--root_path', type=str, default='./data/ETT/', help='root path of the data file')
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parser.add_argument('--data_path', type=str, default='ETTh1.csv', help='data file')
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parser.add_argument('--features', type=str, default='M',
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help='forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate')
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parser.add_argument('--target', type=str, default='OT', help='target feature in S or MS task')
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parser.add_argument('--freq', type=str, default='h',
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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')
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parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location of model checkpoints')
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# model define
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parser.add_argument('--enc_in', type=int, default=7, help='encoder input size')
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parser.add_argument('--dec_in', type=int, default=7, help='decoder input size')
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parser.add_argument('--c_out', type=int, default=7, help='output size')
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parser.add_argument('--d_model', type=int, default=512, help='dimension of model')
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parser.add_argument('--n_heads', type=int, default=8, help='num of heads')
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parser.add_argument('--e_layers', type=int, default=2, help='num of encoder layers')
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parser.add_argument('--d_layers', type=int, default=1, help='num of decoder layers')
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parser.add_argument('--d_ff', type=int, default=2048, help='dimension of fcn')
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parser.add_argument('--K', type=int, default=1, help='Top-K Fourier bases')
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parser.add_argument('--dropout', type=float, default=0.2, help='dropout')
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parser.add_argument('--embed', type=str, default='timeF',
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help='time features encoding, options:[timeF, fixed, learned]')
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parser.add_argument('--activation', type=str, default='sigmoid', help='activation')
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# forecasting task
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parser.add_argument('--seq_len', type=int, required=True, help='input sequence length')
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parser.add_argument('--label_len', type=int, default=0, help='start token length')
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parser.add_argument('--pred_len', type=int, required=True, help='prediction sequence length')
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parser.add_argument('--min_lr', type=float, default=1e-30)
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parser.add_argument('--warmup_epochs', type=int, default=3)
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parser.add_argument('--std', type=float, default=0.2)
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# model define
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parser.add_argument('--enc_in', type=int, default=7, help='encoder input size')
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parser.add_argument('--dec_in', type=int, default=7, help='decoder input size')
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parser.add_argument('--c_out', type=int, default=7, help='output size')
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parser.add_argument('--d_model', type=int, default=512, help='dimension of model')
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parser.add_argument('--n_heads', type=int, default=8, help='num of heads')
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parser.add_argument('--e_layers', type=int, default=2, help='num of encoder layers')
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parser.add_argument('--d_layers', type=int, default=1, help='num of decoder layers')
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parser.add_argument('--d_ff', type=int, default=2048, help='dimension of fcn')
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parser.add_argument('--K', type=int, default=1, help='Top-K Fourier bases')
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parser.add_argument('--dropout', type=float, default=0.2, help='dropout')
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parser.add_argument('--embed', type=str, default='timeF',
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help='time features encoding, options:[timeF, fixed, learned]')
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parser.add_argument('--activation', type=str, default='sigmoid', help='activation')
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parser.add_argument('--smoothing_learning_rate', type=float, default=0, help='optimizer learning rate')
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parser.add_argument('--damping_learning_rate', type=float, default=0, help='optimizer learning rate')
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parser.add_argument('--output_attention', type=bool, default=False)
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parser.add_argument('--min_lr', type=float, default=1e-30)
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parser.add_argument('--warmup_epochs', type=int, default=3)
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parser.add_argument('--std', type=float, default=0.2)
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# optimization
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parser.add_argument('--optim', type=str, default='adam', help='optimizer')
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parser.add_argument('--num_workers', type=int, default=10, help='data loader num workers')
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parser.add_argument('--itr', type=int, default=1, help='experiments times')
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parser.add_argument('--train_epochs', type=int, default=15, help='train epochs')
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parser.add_argument('--batch_size', type=int, default=32, help='batch size of train input data')
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parser.add_argument('--patience', type=int, default=5, help='early stopping patience')
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parser.add_argument('--learning_rate', type=float, default=1e-4, help='optimizer learning rate')
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parser.add_argument('--des', type=str, default='test', help='exp description')
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parser.add_argument('--lradj', type=str, default='exponential_with_warmup', help='adjust learning rate')
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parser.add_argument('--smoothing_learning_rate', type=float, default=0, help='optimizer learning rate')
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parser.add_argument('--damping_learning_rate', type=float, default=0, help='optimizer learning rate')
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parser.add_argument('--output_attention', type=bool, default=False)
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# GPU
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parser.add_argument('--use_gpu', type=bool, default=True, help='use gpu')
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parser.add_argument('--gpu', type=int, default=0, help='gpu')
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parser.add_argument('--use_multi_gpu', action='store_true', help='use multiple gpus', default=False)
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parser.add_argument('--devices', type=str, default='0,1,2,3', help='device ids of multile gpus')
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# optimization
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parser.add_argument('--optim', type=str, default='adam', help='optimizer')
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parser.add_argument('--num_workers', type=int, default=10, help='data loader num workers')
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parser.add_argument('--itr', type=int, default=1, help='experiments times')
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parser.add_argument('--train_epochs', type=int, default=15, help='train epochs')
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parser.add_argument('--batch_size', type=int, default=32, help='batch size of train input data')
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parser.add_argument('--patience', type=int, default=5, help='early stopping patience')
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parser.add_argument('--learning_rate', type=float, default=1e-4, help='optimizer learning rate')
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parser.add_argument('--des', type=str, default='test', help='exp description')
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parser.add_argument('--lradj', type=str, default='exponential_with_warmup', help='adjust learning rate')
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args = parser.parse_args()
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# GPU
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parser.add_argument('--use_gpu', type=bool, default=True, help='use gpu')
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parser.add_argument('--gpu', type=int, default=0, help='gpu')
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parser.add_argument('--use_multi_gpu', action='store_true', help='use multiple gpus', default=False)
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parser.add_argument('--devices', type=str, default='0,1,2,3', help='device ids of multile gpus')
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args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False
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args = parser.parse_args(argv)
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if args.use_gpu and args.use_multi_gpu:
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args.dvices = args.devices.replace(' ', '')
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device_ids = args.devices.split(',')
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args.device_ids = [int(id_) for id_ in device_ids]
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args.gpu = args.device_ids[0]
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args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False
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print('Args in experiment:')
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print(args)
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if args.use_gpu and args.use_multi_gpu:
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args.dvices = args.devices.replace(' ', '')
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device_ids = args.devices.split(',')
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args.device_ids = [int(id_) for id_ in device_ids]
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args.gpu = args.device_ids[0]
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Exp = Exp_Main
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print('Args in experiment:')
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print(args)
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return args
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for ii in range(args.itr):
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set_seed(ii)
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# setting record of experiments
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setting = '{}_{}_{}_ft{}_sl{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_K{}_lr{}_{}_{}'.format(
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args.model_id,
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args.model,
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args.data,
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args.features,
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args.seq_len,
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args.pred_len,
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args.d_model,
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args.n_heads,
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args.e_layers,
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args.d_layers,
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args.d_ff,
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args.K,
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args.learning_rate,
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args.des, ii)
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if __name__=="__main__":
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args = get_args()
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if os.path.exists(os.path.join(args.checkpoints, setting)):
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continue
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Exp = Exp_Main
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exp = Exp(args) # set experiments
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print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))
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exp.train(setting)
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for ii in range(args.itr):
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set_seed(ii)
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# setting record of experiments
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setting = '{}_{}_{}_ft{}_sl{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_K{}_lr{}_{}_{}'.format(
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args.model_id,
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args.model,
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args.data,
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args.features,
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args.seq_len,
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args.pred_len,
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args.d_model,
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args.n_heads,
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args.e_layers,
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args.d_layers,
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args.d_ff,
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args.K,
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args.learning_rate,
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args.des, ii)
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print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
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exp.test(setting, data='val')
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exp.test(setting, data='test')
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if os.path.exists(os.path.join(args.checkpoints, setting)):
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continue
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torch.cuda.empty_cache()
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exp = Exp(args) # set experiments
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print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))
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exp.train(setting)
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print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
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exp.test(setting, data='val')
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exp.test(setting, data='test')
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torch.cuda.empty_cache()
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