repo_description: A Library for Advanced Deep Time Series Models for General Time Series Analysis.
repo_topics: deep-learning time-series time-series-analysis
code: import argparse
  import os
  import torch
  import torch.backends
  from utils.print_args import print_args
  import random
  import numpy as np
  
  if __name__ == '__main__':
      fix_seed = 2021
      random.seed(fix_seed)
      torch.manual_seed(fix_seed)
      np.random.seed(fix_seed)
  
      parser = argparse.ArgumentParser(description='TimesNet')
  
      # basic config
      parser.add_argument('--task_name', type=str, required=True, default='long_term_forecast',
                          help='task name, options:[long_term_forecast, short_term_forecast, imputation, classification, anomaly_detection]')
      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, Transformer, TimesNet]')
  
      # 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=48, help='start token length')
      parser.add_argument('--pred_len', type=int, default=96, help='prediction sequence length')
      parser.add_argument('--seasonal_patterns', type=str, default='Monthly', help='subset for M4')
      parser.add_argument('--inverse', action='store_true', help='inverse output data', default=False)
  
      # inputation task
      parser.add_argument('--mask_rate', type=float, default=0.25, help='mask ratio')
  
      # anomaly detection task
      parser.add_argument('--anomaly_ratio', type=float, default=0.25, help='prior anomaly ratio (%%)')
  
      # model define
      parser.add_argument('--expand', type=int, default=2, help='expansion factor for Mamba')
      parser.add_argument('--d_conv', type=int, default=4, help='conv kernel size for Mamba')
      parser.add_argument('--tv_dt', type=int, default=0, help='whether to use time variant dt for MambaSL')
      parser.add_argument('--tv_B', type=int, default=0, help='whether to use time variant B for MambaSL')
      parser.add_argument('--tv_C', type=int, default=0, help='whether to use time variant C for MambaSL')
      parser.add_argument('--use_D', type=int, default=0, help='whether to use D for MambaSL')
      parser.add_argument('--top_k', type=int, default=5, help='for TimesBlock')
      parser.add_argument('--num_kernels', type=int, default=6, help='for Inception')
      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=0.1, 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('--channel_independence', type=int, default=1,
                          help='0: channel dependence 1: channel independence for FreTS model')
      parser.add_argument('--decomp_method', type=str, default='moving_avg',
                          help='method of series decompsition, only support moving_avg or dft_decomp')
      parser.add_argument('--use_norm', type=int, default=1, help='whether to use normalize; True 1 False 0')
      parser.add_argument('--down_sampling_layers', type=int, default=0, help='num of down sampling layers')
      parser.add_argument('--down_sampling_window', type=int, default=1, help='down sampling window size')
      parser.add_argument('--down_sampling_method', type=str, default=None,
                          help='down sampling method, only support avg, max, conv')
      parser.add_argument('--seg_len', type=int, default=96,
                          help='the length of segmen-wise iteration of SegRNN')
  
      # 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=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', action='store_true', default=True, help='use gpu (default: on)')
      parser.add_argument('--no_use_gpu', action='store_false', dest='use_gpu', help='disable gpu (force cpu)')
      parser.add_argument('--gpu', type=int, default=0, help='gpu')
      parser.add_argument('--gpu_type', type=str, default='cuda', help='gpu type')  # cuda or mps
      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')
  
      # de-stationary projector params
      parser.add_argument('--p_hidden_dims', type=int, nargs='+', default=[128, 128],
                          help='hidden layer dimensions of projector (List)')
      parser.add_argument('--p_hidden_layers', type=int, default=2, help='number of hidden layers in projector')
  
      # metrics (dtw)
      parser.add_argument('--use_dtw', action='store_true', default=False,
                          help='enable dtw metric (time consuming; default: off)')
  
      # Augmentation
      parser.add_argument('--augmentation_ratio', type=int, default=0, help="How many times to augment")
      parser.add_argument('--seed', type=int, default=2, help="Randomization seed")
      parser.add_argument('--jitter', default=False, action="store_true", help="Jitter preset augmentation")
      parser.add_argument('--scaling', default=False, action="store_true", help="Scaling preset augmentation")
      parser.add_argument('--permutation', default=False, action="store_true",
                          help="Equal Length Permutation preset augmentation")
      parser.add_argument('--randompermutation', default=False, action="store_true",
                          help="Random Length Permutation preset augmentation")
      parser.add_argument('--magwarp', default=False, action="store_true", help="Magnitude warp preset augmentation")
      parser.add_argument('--timewarp', default=False, action="store_true", help="Time warp preset augmentation")
      parser.add_argument('--windowslice', default=False, action="store_true", help="Window slice preset augmentation")
      parser.add_argument('--windowwarp', default=False, action="store_true", help="Window warp preset augmentation")
      parser.add_argument('--rotation', default=False, action="store_true", help="Rotation preset augmentation")
      parser.add_argument('--spawner', default=False, action="store_true", help="SPAWNER preset augmentation")
      parser.add_argument('--dtwwarp', default=False, action="store_true", help="DTW warp preset augmentation")
      parser.add_argument('--shapedtwwarp', default=False, action="store_true", help="Shape DTW warp preset augmentation")
      parser.add_argument('--wdba', default=False, action="store_true", help="Weighted DBA preset augmentation")
      parser.add_argument('--discdtw', default=False, action="store_true",
                          help="Discrimitive DTW warp preset augmentation")
      parser.add_argument('--discsdtw', default=False, action="store_true",
                          help="Discrimitive shapeDTW warp preset augmentation")
      parser.add_argument('--extra_tag', type=str, default="", help="Anything extra")
  
      # TimeXer
      parser.add_argument('--patch_len', type=int, default=16, help='patch length')
  
      # GCN
      parser.add_argument('--node_dim', type=int, default=10, help='each node embbed to dim dimentions')
      parser.add_argument('--gcn_depth', type=int, default=2, help='')
      parser.add_argument('--gcn_dropout', type=float, default=0.3, help='')
      parser.add_argument('--propalpha', type=float, default=0.3, help='')
      parser.add_argument('--conv_channel', type=int, default=32, help='')
      parser.add_argument('--skip_channel', type=int, default=32, help='')
  
      parser.add_argument('--individual', action='store_true', default=False,
                          help='DLinear: a linear layer for each variate(channel) individually')
  
      # TimeFilter
      parser.add_argument('--alpha', type=float, default=0.1, help='KNN for Graph Construction')
      parser.add_argument('--top_p', type=float, default=0.5, help='Dynamic Routing in MoE')
      parser.add_argument('--pos', type=int, choices=[0, 1], default=1, help='Positional Embedding. Set pos to 0 or 1')
  
      args = parser.parse_args()
      if torch.cuda.is_available() and args.use_gpu:
          args.device = torch.device('cuda:{}'.format(args.gpu))
          print('Using GPU')
      else:
          if hasattr(torch.backends, "mps"):
              args.device = torch.device("mps") if torch.backends.mps.is_available() else torch.device("cpu")
          else:
              args.device = torch.device("cpu")
          print('Using cpu or mps')
  
      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(args)
  
  
      if args.task_name == 'long_term_forecast':
          from exp.exp_long_term_forecasting import Exp_Long_Term_Forecast
          Exp = Exp_Long_Term_Forecast
      elif args.task_name == 'short_term_forecast':
          from exp.exp_short_term_forecasting import Exp_Short_Term_Forecast
          Exp = Exp_Short_Term_Forecast
      elif args.task_name == 'imputation':
          from exp.exp_imputation import Exp_Imputation
          Exp = Exp_Imputation
      elif args.task_name == 'anomaly_detection':
          from exp.exp_anomaly_detection import Exp_Anomaly_Detection
          Exp = Exp_Anomaly_Detection
      elif args.task_name == 'classification':
          from exp.exp_classification import Exp_Classification
          Exp = Exp_Classification
      elif args.task_name == 'zero_shot_forecast':
          from exp.exp_zero_shot_forecasting import Exp_Zero_Shot_Forecast
          Exp = Exp_Zero_Shot_Forecast
      else:
          from exp.exp_long_term_forecasting import Exp_Long_Term_Forecast
          Exp = Exp_Long_Term_Forecast
  
      if args.is_training:
          for ii in range(args.itr):
              # setting record of experiments
              exp = Exp(args)  # set experiments
              setting = '{}_{}_{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_expand{}_dc{}_fc{}_eb{}_dt{}_{}_{}'.format(
                  args.task_name,
                  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.expand,
                  args.d_conv,
                  args.factor,
                  args.embed,
                  args.distil,
                  args.des, ii)
              
              # Override setting for specific model to ensure proper checkpoint naming and logging
              if args.model == 'MambaSingleLayer' and args.task_name == 'classification':
                  setting = f'{args.task_name}_CLS_{args.model_id}_{args.model}_{args.data}_ft{args.features}' \
                          + f'_sl{args.seq_len}_ll{args.label_len}_pl{args.pred_len}_dm{args.d_model}_ds{args.d_ff}' \
                          + f'_expand{args.expand}_dc{args.d_conv}_nk{args.num_kernels}' \
                          + f'_tvdt{int(args.tv_dt)}_tvB{int(args.tv_B)}_tvC{int(args.tv_C)}_useD{int(args.use_D)}_{args.des}_{ii}'
  
              print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))
              exp.train(setting)
  
              print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
              exp.test(setting)
              if args.use_gpu:
                  if args.gpu_type == 'mps':
                      torch.backends.mps.empty_cache()
                  elif args.gpu_type == 'cuda':
                      torch.cuda.empty_cache()
      else:
          exp = Exp(args)  # set experiments
          ii = 0
          setting = '{}_{}_{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_expand{}_dc{}_fc{}_eb{}_dt{}_{}_{}'.format(
              args.task_name,
              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.expand,
              args.d_conv,
              args.factor,
              args.embed,
              args.distil,
              args.des, ii)
          
          # Override setting for specific model to ensure proper checkpoint naming and logging
          if args.model == 'MambaSingleLayer' and args.task_name == 'classification':
              setting = f'{args.task_name}_CLS_{args.model_id}_{args.model}_{args.data}_ft{args.features}' \
                      + f'_sl{args.seq_len}_ll{args.label_len}_pl{args.pred_len}_dm{args.d_model}_ds{args.d_ff}' \
                      + f'_expand{args.expand}_dc{args.d_conv}_nk{args.num_kernels}' \
                      + f'_tvdt{args.tv_dt}_tvB{args.tv_B}_tvC{args.tv_C}_useD{int(args.use_D)}_{args.des}_{ii}'
  
          print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
          exp.test(setting, test=1)
          if args.use_gpu:
              if args.gpu_type == 'mps':
                  torch.backends.mps.empty_cache()
              elif args.gpu_type == 'cuda':
                  torch.cuda.empty_cache()
  