----------------------------------------

  Demo GAR 
  seed: 4 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01115     	0.93094     	0.02265     	0           
  10          	0.01119     	0.93284     	0.02276     	0           
  100         	0.00983     	0.94096     	0.02051     	4           
  300         	0.00908     	0.94631     	0.01914     	11          
  500         	0.00805     	0.95017     	0.01653     	18          
  1000        	0.00870     	0.94826     	0.01632     	37          
---> try to load best state
              	0.00870     	0.94826     	0.01632     	37               eval state : test_on_restore;
              	0.00870     	0.94826     	0.01632     	37               eval state : test_on_last_epoch;
---> final result              	0.00870     	0.94826     	0.01632     	37               eval state : final;
----------> finish x-yl training


---------->
GAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 4, 'train_start_index': 30, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05939     	0.11766     	0.09406     	0           
  10          	0.06983     	-0.00523    	0.12168     	0           
  100         	0.03595     	0.41285     	0.05342     	1           
  300         	0.02245     	0.76207     	0.04500     	4           
  500         	0.02578     	0.69836     	0.05182     	6           
  1000        	0.02310     	0.72374     	0.04606     	12          
---> try to load best state
              	0.02310     	0.72374     	0.04606     	12               eval state : test_on_restore;
              	0.02310     	0.72374     	0.04606     	12               eval state : test_on_last_epoch;
---> final result
              	0.02310     	0.72374     	0.04606     	12               eval state : final;module_name : GAR;subset : 2.0;cp_record_file : True;
---> end


---------->
GAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 4, 'train_start_index': 28, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04204     	0.56994     	0.06854     	0           
  10          	0.04642     	0.48536     	0.07838     	0           
  100         	0.02557     	0.74599     	0.04225     	2           
  300         	0.01443     	0.89733     	0.02381     	6           
  500         	0.01257     	0.87871     	0.02242     	10          
  1000        	0.00973     	0.92888     	0.01718     	19          
---> try to load best state
              	0.00973     	0.92888     	0.01718     	19               eval state : test_on_restore;
              	0.00973     	0.92888     	0.01718     	19               eval state : test_on_last_epoch;
---> final result
              	0.00973     	0.92888     	0.01718     	19               eval state : final;module_name : GAR;subset : 4.0;cp_record_file : True;
---> end


---------->
GAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 4, 'train_start_index': 24, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02164     	0.73312     	0.04988     	0           
  10          	0.02023     	0.73162     	0.04600     	0           
  100         	0.01265     	0.82621     	0.02409     	3           
  300         	0.00822     	0.93437     	0.01793     	8           
  500         	0.01233     	0.76716     	0.03052     	13          
  1000        	0.00743     	0.92665     	0.01858     	26          
---> try to load best state
              	0.00743     	0.92665     	0.01858     	26               eval state : test_on_restore;
              	0.00743     	0.92665     	0.01858     	26               eval state : test_on_last_epoch;
---> final result
              	0.00743     	0.92665     	0.01858     	26               eval state : final;module_name : GAR;subset : 8.0;cp_record_file : True;
---> end


---------->
GAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 4, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00965     	0.88171     	0.02663     	0           
  10          	0.01069     	0.83146     	0.02427     	0           
  100         	0.00851     	0.90823     	0.02207     	4           
  300         	0.00949     	0.90362     	0.01913     	14          
  500         	0.00612     	0.95048     	0.01549     	23          
  epoch 817 reach nan state
---> try to load best state
              	0.05416     	-2.75451    	0.07973     	37               eval state : test_on_restore;
              	0.00612     	0.95048     	0.01549     	37               eval state : test_on_last_epoch;
---> final result
              	0.00612     	0.95048     	0.01549     	37               eval state : final;module_name : GAR;subset : 16.0;cp_record_file : True;
