262 lines
14 KiB
Python
262 lines
14 KiB
Python
import argparse
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import os
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import torch
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import torch.backends
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from exp.exp_long_term_forecasting import Exp_Long_Term_Forecast
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from exp.exp_imputation import Exp_Imputation
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from exp.exp_short_term_forecasting import Exp_Short_Term_Forecast
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from exp.exp_anomaly_detection import Exp_Anomaly_Detection
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from exp.exp_classification import Exp_Classification
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from utils.print_args import print_args
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import random
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import numpy as np
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if __name__ == '__main__':
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fix_seed = 2021
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random.seed(fix_seed)
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torch.manual_seed(fix_seed)
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np.random.seed(fix_seed)
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parser = argparse.ArgumentParser(description='TimesNet')
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# basic config
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parser.add_argument('--task_name', type=str, required=True, default='long_term_forecast',
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help='task name, options:[long_term_forecast, short_term_forecast, imputation, classification, anomaly_detection]')
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parser.add_argument('--is_training', type=int, required=True, default=1, help='status')
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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='Autoformer',
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help='model name, options: [Autoformer, Transformer, TimesNet]')
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# data loader
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parser.add_argument('--data', type=str, required=True, default='ETTh1', 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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# forecasting task
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parser.add_argument('--seq_len', type=int, default=96, help='input sequence length')
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parser.add_argument('--label_len', type=int, default=48, help='start token length')
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parser.add_argument('--pred_len', type=int, default=96, help='prediction sequence length')
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parser.add_argument('--seasonal_patterns', type=str, default='Monthly', help='subset for M4')
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parser.add_argument('--inverse', action='store_true', help='inverse output data', default=False)
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# inputation task
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parser.add_argument('--mask_rate', type=float, default=0.25, help='mask ratio')
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# anomaly detection task
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parser.add_argument('--anomaly_ratio', type=float, default=0.25, help='prior anomaly ratio (%%)')
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# model define
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parser.add_argument('--expand', type=int, default=2, help='expansion factor for Mamba')
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parser.add_argument('--d_conv', type=int, default=4, help='conv kernel size for Mamba')
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parser.add_argument('--top_k', type=int, default=5, help='for TimesBlock')
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parser.add_argument('--num_kernels', type=int, default=6, help='for Inception')
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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('--moving_avg', type=int, default=25, help='window size of moving average')
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parser.add_argument('--factor', type=int, default=1, help='attn factor')
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parser.add_argument('--distil', action='store_false',
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help='whether to use distilling in encoder, using this argument means not using distilling',
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default=True)
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parser.add_argument('--dropout', type=float, default=0.1, 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='gelu', help='activation')
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parser.add_argument('--channel_independence', type=int, default=1,
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help='0: channel dependence 1: channel independence for FreTS model')
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parser.add_argument('--decomp_method', type=str, default='moving_avg',
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help='method of series decompsition, only support moving_avg or dft_decomp')
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parser.add_argument('--use_norm', type=int, default=1, help='whether to use normalize; True 1 False 0')
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parser.add_argument('--down_sampling_layers', type=int, default=0, help='num of down sampling layers')
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parser.add_argument('--down_sampling_window', type=int, default=1, help='down sampling window size')
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parser.add_argument('--down_sampling_method', type=str, default=None,
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help='down sampling method, only support avg, max, conv')
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parser.add_argument('--seg_len', type=int, default=96,
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help='the length of segmen-wise iteration of SegRNN')
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# optimization
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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=10, 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=3, help='early stopping patience')
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parser.add_argument('--learning_rate', type=float, default=0.0001, 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('--loss', type=str, default='MSE', help='loss function')
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parser.add_argument('--lradj', type=str, default='type1', help='adjust learning rate')
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parser.add_argument('--use_amp', action='store_true', help='use automatic mixed precision training', 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('--gpu_type', type=str, default='cuda', help='gpu type') # cuda or mps
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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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# de-stationary projector params
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parser.add_argument('--p_hidden_dims', type=int, nargs='+', default=[128, 128],
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help='hidden layer dimensions of projector (List)')
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parser.add_argument('--p_hidden_layers', type=int, default=2, help='number of hidden layers in projector')
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# metrics (dtw)
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parser.add_argument('--use_dtw', type=bool, default=False,
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help='the controller of using dtw metric (dtw is time consuming, not suggested unless necessary)')
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# Augmentation
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parser.add_argument('--augmentation_ratio', type=int, default=0, help="How many times to augment")
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parser.add_argument('--seed', type=int, default=2, help="Randomization seed")
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parser.add_argument('--jitter', default=False, action="store_true", help="Jitter preset augmentation")
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parser.add_argument('--scaling', default=False, action="store_true", help="Scaling preset augmentation")
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parser.add_argument('--permutation', default=False, action="store_true",
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help="Equal Length Permutation preset augmentation")
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parser.add_argument('--randompermutation', default=False, action="store_true",
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help="Random Length Permutation preset augmentation")
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parser.add_argument('--magwarp', default=False, action="store_true", help="Magnitude warp preset augmentation")
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parser.add_argument('--timewarp', default=False, action="store_true", help="Time warp preset augmentation")
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parser.add_argument('--windowslice', default=False, action="store_true", help="Window slice preset augmentation")
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parser.add_argument('--windowwarp', default=False, action="store_true", help="Window warp preset augmentation")
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parser.add_argument('--rotation', default=False, action="store_true", help="Rotation preset augmentation")
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parser.add_argument('--spawner', default=False, action="store_true", help="SPAWNER preset augmentation")
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parser.add_argument('--dtwwarp', default=False, action="store_true", help="DTW warp preset augmentation")
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parser.add_argument('--shapedtwwarp', default=False, action="store_true", help="Shape DTW warp preset augmentation")
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parser.add_argument('--wdba', default=False, action="store_true", help="Weighted DBA preset augmentation")
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parser.add_argument('--discdtw', default=False, action="store_true",
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help="Discrimitive DTW warp preset augmentation")
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parser.add_argument('--discsdtw', default=False, action="store_true",
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help="Discrimitive shapeDTW warp preset augmentation")
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parser.add_argument('--extra_tag', type=str, default="", help="Anything extra")
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# TimeXer
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parser.add_argument('--patch_len', type=int, default=16, help='patch length')
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args, unknown = parser.parse_known_args()
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# Parse unknown arguments dynamically
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for i in range(0, len(unknown), 2):
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if i + 1 < len(unknown) and unknown[i].startswith('--'):
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param_name = unknown[i][2:] # Remove '--' prefix
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param_value = unknown[i + 1]
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# Smart type conversion
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if param_value.isdigit() or (param_value.startswith('-') and param_value[1:].isdigit()):
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param_value = int(param_value)
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elif param_value.replace('.', '', 1).replace('-', '', 1).isdigit():
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param_value = float(param_value)
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elif param_value.lower() in ['true', 'yes', '1']:
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param_value = True
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elif param_value.lower() in ['false', 'no', '0']:
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param_value = False
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setattr(args, param_name, param_value)
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print(f"Dynamic parameter: --{param_name} = {param_value} ({type(param_value).__name__})")
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if unknown:
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print(f"Parsed {len(unknown)//2} dynamic parameters")
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if torch.cuda.is_available() and args.use_gpu:
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args.device = torch.device('cuda:{}'.format(args.gpu))
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print('Using GPU')
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else:
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if hasattr(torch.backends, "mps"):
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args.device = torch.device("mps") if torch.backends.mps.is_available() else torch.device("cpu")
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else:
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args.device = torch.device("cpu")
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print('Using cpu or mps')
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if args.use_gpu and args.use_multi_gpu:
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args.devices = 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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print('Args in experiment:')
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print_args(args)
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if args.task_name == 'long_term_forecast':
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Exp = Exp_Long_Term_Forecast
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elif args.task_name == 'short_term_forecast':
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Exp = Exp_Short_Term_Forecast
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elif args.task_name == 'imputation':
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Exp = Exp_Imputation
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elif args.task_name == 'anomaly_detection':
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Exp = Exp_Anomaly_Detection
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elif args.task_name == 'classification':
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Exp = Exp_Classification
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else:
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Exp = Exp_Long_Term_Forecast
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if args.is_training:
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for ii in range(args.itr):
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# setting record of experiments
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exp = Exp(args) # set experiments
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setting = '{}_{}_{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_expand{}_dc{}_fc{}_eb{}_dt{}_{}_{}'.format(
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args.task_name,
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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.label_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.expand,
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args.d_conv,
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args.factor,
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args.embed,
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args.distil,
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args.des, ii)
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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)
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if args.gpu_type == 'mps':
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torch.backends.mps.empty_cache()
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elif args.gpu_type == 'cuda':
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torch.cuda.empty_cache()
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else:
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exp = Exp(args) # set experiments
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ii = 0
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setting = '{}_{}_{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_expand{}_dc{}_fc{}_eb{}_dt{}_{}_{}'.format(
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args.task_name,
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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.label_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.expand,
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args.d_conv,
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args.factor,
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args.embed,
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args.distil,
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args.des, ii)
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print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
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exp.test(setting, test=1)
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if args.gpu_type == 'mps':
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torch.backends.mps.empty_cache()
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elif args.gpu_type == 'cuda':
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torch.cuda.empty_cache()
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