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

  Demo sGAR 
  seed: 0 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], '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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00205     	0.80146     	0.00500     	0           
  10          	0.00216     	0.79727     	0.00524     	0           
  100         	0.00417     	0.54358     	0.00803     	4           
  300         	0.00442     	0.53348     	0.00914     	13          
  500         	0.00418     	0.66984     	0.00884     	22          
  1000        	0.00413     	0.68496     	0.00866     	43          
---> try to load best state
              	0.00413     	0.68496     	0.00866     	43               eval state : test_on_restore;
              	0.00413     	0.68496     	0.00866     	44               eval state : test_on_last_epoch;
---> final result              	0.00413     	0.68496     	0.00866     	44               eval state : final;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 30, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.02114     	-0.58496    	0.02656     	0           
  10          	0.01085     	0.41049     	0.01620     	0           
  100         	0.00617     	0.74609     	0.01164     	1           
  300         	0.00577     	0.74455     	0.01064     	3           
  500         	0.01398     	-0.46385    	0.01885     	5           
  1000        	0.00634     	0.60606     	0.01098     	9           
---> try to load best state
              	0.00634     	0.60606     	0.01098     	9                eval state : test_on_restore;
              	0.00634     	0.60606     	0.01098     	9                eval state : test_on_last_epoch;
---> final result              	0.00634     	0.60606     	0.01098     	9                eval state : final;module_name : SGAR;subset : 2.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 28, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.00952     	0.30970     	0.01398     	0           
  10          	0.00700     	0.57811     	0.01092     	0           
  100         	0.00513     	0.76721     	0.00958     	1           
  300         	0.00503     	0.73684     	0.00914     	4           
  500         	0.00844     	0.19437     	0.01239     	7           
  1000        	0.00546     	0.67791     	0.00977     	14          
---> try to load best state
              	0.00546     	0.67791     	0.00977     	14               eval state : test_on_restore;
              	0.00546     	0.67791     	0.00977     	14               eval state : test_on_last_epoch;
---> final result              	0.00546     	0.67791     	0.00977     	14               eval state : final;module_name : SGAR;subset : 4.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 24, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.00519     	0.61093     	0.00909     	0           
  10          	0.00389     	0.65290     	0.00714     	0           
  100         	0.00432     	0.76318     	0.00826     	2           
  300         	0.00435     	0.74171     	0.00829     	5           
  500         	0.00476     	0.71362     	0.00858     	9           
  1000        	0.00751     	0.18111     	0.01108     	19          
---> try to load best state
              	0.00751     	0.18111     	0.01108     	19               eval state : test_on_restore;
              	0.00751     	0.18111     	0.01108     	19               eval state : test_on_last_epoch;
---> final result              	0.00751     	0.18111     	0.01108     	19               eval state : final;module_name : SGAR;subset : 8.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.00342     	0.64658     	0.00712     	0           
  10          	0.00253     	0.58858     	0.00567     	0           
  100         	0.00429     	0.75497     	0.00823     	3           
  300         	0.00462     	0.74901     	0.00845     	9           
  500         	0.00445     	0.67955     	0.00849     	15          
  1000        	0.00435     	0.70597     	0.00826     	31          
---> try to load best state
              	0.00435     	0.70597     	0.00826     	32               eval state : test_on_restore;
              	0.00435     	0.70597     	0.00826     	32               eval state : test_on_last_epoch;
---> final result              	0.00435     	0.70597     	0.00826     	32               eval state : final;module_name : SGAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], '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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00162     	0.80942     	0.00368     	0           
  10          	0.00179     	0.79911     	0.00398     	0           
  100         	0.00370     	0.50924     	0.00916     	4           
  300         	0.00440     	0.38034     	0.01187     	12          
  500         	0.00410     	0.58357     	0.01143     	20          
  1000        	0.00403     	0.60757     	0.01116     	40          
---> try to load best state
              	0.00403     	0.60757     	0.01116     	40               eval state : test_on_restore;
              	0.00403     	0.60757     	0.01116     	40               eval state : test_on_last_epoch;
---> final result              	0.00403     	0.60757     	0.01116     	40               eval state : final;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 30, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.02542     	-0.17482    	0.04462     	0           
  10          	0.01369     	0.12244     	0.02159     	0           
  100         	0.00542     	0.65846     	0.01240     	1           
  300         	0.00518     	0.41937     	0.01199     	2           
  500         	0.00629     	-0.01869    	0.01290     	4           
  1000        	0.00548     	0.55282     	0.01185     	9           
---> try to load best state
              	0.00548     	0.55282     	0.01185     	9                eval state : test_on_restore;
              	0.00548     	0.55282     	0.01185     	9                eval state : test_on_last_epoch;
---> final result              	0.00548     	0.55282     	0.01185     	9                eval state : final;module_name : SGAR;subset : 2.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 28, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.00708     	0.34772     	0.01205     	0           
  10          	0.00612     	0.45361     	0.01136     	0           
  100         	0.00440     	0.72634     	0.00979     	1           
  300         	0.00611     	0.73978     	0.01156     	4           
  500         	0.01136     	-1.42586    	0.02186     	6           
  1000        	0.00577     	0.56368     	0.01065     	13          
---> try to load best state
              	0.00577     	0.56368     	0.01065     	13               eval state : test_on_restore;
              	0.00577     	0.56368     	0.01065     	13               eval state : test_on_last_epoch;
---> final result              	0.00577     	0.56368     	0.01065     	13               eval state : final;module_name : SGAR;subset : 4.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 24, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.00369     	0.72825     	0.00744     	0           
  10          	0.00343     	0.48972     	0.00908     	0           
  100         	0.00377     	0.71765     	0.00988     	2           
  300         	0.00382     	0.59373     	0.01055     	6           
  500         	0.00392     	0.63629     	0.01007     	10          
  1000        	0.00399     	0.46842     	0.01013     	19          
---> try to load best state
              	0.00399     	0.46842     	0.01013     	19               eval state : test_on_restore;
              	0.00399     	0.46842     	0.01013     	19               eval state : test_on_last_epoch;
---> final result              	0.00399     	0.46842     	0.01013     	19               eval state : final;module_name : SGAR;subset : 8.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.00326     	0.47486     	0.00945     	0           
  10          	0.00321     	0.46461     	0.00870     	0           
  100         	0.00368     	0.71189     	0.00994     	3           
  300         	0.00382     	0.72436     	0.01013     	10          
  500         	0.00386     	0.53530     	0.01101     	17          
  1000        	0.00386     	0.58418     	0.01015     	33          
---> try to load best state
              	0.00386     	0.58418     	0.01015     	33               eval state : test_on_restore;
              	0.00386     	0.58418     	0.01015     	33               eval state : test_on_last_epoch;
---> final result              	0.00386     	0.58418     	0.01015     	33               eval state : final;module_name : SGAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], '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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00158     	0.79228     	0.00386     	0           
  10          	0.00169     	0.78598     	0.00435     	0           
  100         	0.00398     	0.35730     	0.00869     	4           
  300         	0.00416     	0.48177     	0.00939     	12          
  500         	0.00384     	0.67143     	0.00908     	21          
  1000        	0.00378     	0.68328     	0.00892     	41          
---> try to load best state
              	0.00378     	0.68328     	0.00892     	41               eval state : test_on_restore;
              	0.00378     	0.68328     	0.00892     	41               eval state : test_on_last_epoch;
---> final result              	0.00378     	0.68328     	0.00892     	41               eval state : final;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 30, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.12140     	-0.63779    	0.23529     	0           
  10          	0.04874     	-0.34007    	0.08898     	0           
  100         	0.00864     	0.19176     	0.02026     	1           
  300         	0.01000     	0.17030     	0.02489     	2           
  500         	0.01333     	-0.54402    	0.02194     	4           
  1000        	0.01346     	-0.72818    	0.02720     	9           
---> try to load best state
              	0.01346     	-0.72818    	0.02720     	9                eval state : test_on_restore;
              	0.01346     	-0.72818    	0.02720     	9                eval state : test_on_last_epoch;
---> final result              	0.01346     	-0.72818    	0.02720     	9                eval state : final;module_name : SGAR;subset : 2.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 28, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.01072     	0.39295     	0.01964     	0           
  10          	0.00595     	0.52264     	0.01082     	0           
  100         	0.00374     	0.70460     	0.00883     	1           
  300         	0.00409     	0.64279     	0.00908     	4           
  500         	0.00556     	-0.06248    	0.01096     	6           
  1000        	0.00397     	0.63154     	0.00896     	13          
---> try to load best state
              	0.00397     	0.63154     	0.00896     	13               eval state : test_on_restore;
              	0.00397     	0.63154     	0.00896     	13               eval state : test_on_last_epoch;
---> final result              	0.00397     	0.63154     	0.00896     	13               eval state : final;module_name : SGAR;subset : 4.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 24, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.00741     	0.39646     	0.01646     	0           
  10          	0.00411     	0.54668     	0.00934     	0           
  100         	0.00358     	0.69062     	0.00900     	2           
  300         	0.00379     	0.71796     	0.00886     	5           
  500         	0.00374     	0.62781     	0.00902     	10          
  1000        	0.00377     	0.54935     	0.00909     	19          
---> try to load best state
              	0.00377     	0.54935     	0.00909     	19               eval state : test_on_restore;
              	0.00377     	0.54935     	0.00909     	20               eval state : test_on_last_epoch;
---> final result              	0.00377     	0.54935     	0.00909     	20               eval state : final;module_name : SGAR;subset : 8.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.00268     	0.76681     	0.00613     	0           
  10          	0.00274     	0.74085     	0.00761     	0           
  100         	0.00369     	0.64226     	0.00949     	3           
  300         	0.00386     	0.67110     	0.00914     	10          
  500         	0.00382     	0.65805     	0.00918     	17          
  1000        	0.00423     	0.64929     	0.00915     	33          
---> try to load best state
              	0.00423     	0.64929     	0.00915     	33               eval state : test_on_restore;
              	0.00423     	0.64929     	0.00915     	33               eval state : test_on_last_epoch;
---> final result              	0.00423     	0.64929     	0.00915     	33               eval state : final;module_name : SGAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], '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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00143     	0.82551     	0.00352     	0           
  10          	0.00160     	0.81542     	0.00398     	0           
  100         	0.00369     	0.55843     	0.00847     	4           
  300         	0.00412     	0.53507     	0.00963     	13          
  500         	0.00388     	0.68498     	0.00937     	22          
  1000        	0.00382     	0.69496     	0.00922     	42          
---> try to load best state
              	0.00382     	0.69496     	0.00922     	42               eval state : test_on_restore;
              	0.00382     	0.69496     	0.00922     	42               eval state : test_on_last_epoch;
---> final result              	0.00382     	0.69496     	0.00922     	42               eval state : final;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 30, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.01618     	0.14698     	0.02494     	0           
  10          	0.01133     	0.15119     	0.01873     	0           
  100         	0.00484     	0.30145     	0.01033     	1           
  300         	0.00568     	-0.22037    	0.01028     	2           
  500         	0.00703     	-2.63431    	0.01108     	4           
  1000        	0.00907     	-5.33739    	0.01254     	9           
---> try to load best state
              	0.00907     	-5.33739    	0.01254     	9                eval state : test_on_restore;
              	0.00907     	-5.33739    	0.01254     	9                eval state : test_on_last_epoch;
---> final result              	0.00907     	-5.33739    	0.01254     	9                eval state : final;module_name : SGAR;subset : 2.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 28, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.00599     	0.63949     	0.00931     	0           
  10          	0.00516     	0.73541     	0.00904     	0           
  100         	0.00931     	0.69631     	0.01730     	1           
  300         	0.00747     	0.64529     	0.01506     	3           
  500         	0.00791     	0.56040     	0.01497     	7           
  1000        	0.00723     	0.61399     	0.01402     	15          
---> try to load best state
              	0.00723     	0.61399     	0.01402     	15               eval state : test_on_restore;
              	0.00723     	0.61399     	0.01402     	16               eval state : test_on_last_epoch;
---> final result              	0.00723     	0.61399     	0.01402     	16               eval state : final;module_name : SGAR;subset : 4.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 24, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.00378     	0.76485     	0.00689     	0           
  10          	0.00321     	0.78974     	0.00721     	0           
  100         	0.00529     	0.76790     	0.01039     	1           
  300         	0.00584     	0.74723     	0.01096     	5           
  500         	0.00543     	0.70951     	0.01014     	11          
  1000        	0.00562     	0.68301     	0.01007     	19          
---> try to load best state
              	0.00562     	0.68301     	0.01007     	19               eval state : test_on_restore;
              	0.00562     	0.68301     	0.01007     	19               eval state : test_on_last_epoch;
---> final result              	0.00562     	0.68301     	0.01007     	19               eval state : final;module_name : SGAR;subset : 8.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.00224     	0.81541     	0.00494     	0           
  10          	0.00228     	0.83129     	0.00556     	0           
  100         	0.00449     	0.78099     	0.00899     	3           
  300         	0.00474     	0.79014     	0.00910     	10          
  500         	0.00469     	0.75406     	0.00891     	16          
  1000        	0.00488     	0.77855     	0.00908     	31          
---> try to load best state
              	0.00488     	0.77855     	0.00908     	32               eval state : test_on_restore;
              	0.00488     	0.77855     	0.00908     	32               eval state : test_on_last_epoch;
---> final result              	0.00488     	0.77855     	0.00908     	32               eval state : final;module_name : SGAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', '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[0]'], '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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00143     	0.75100     	0.00369     	0           
  10          	0.00155     	0.73582     	0.00420     	0           
  100         	0.00355     	0.58613     	0.00948     	3           
  300         	0.00465     	0.43842     	0.01186     	11          
  500         	0.00434     	0.59381     	0.01151     	19          
  1000        	0.00425     	0.61670     	0.01130     	37          
---> try to load best state
              	0.00425     	0.61670     	0.01130     	37               eval state : test_on_restore;
              	0.00425     	0.61670     	0.01130     	37               eval state : test_on_last_epoch;
---> final result              	0.00425     	0.61670     	0.01130     	37               eval state : final;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 30, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.02421     	-0.20848    	0.05370     	0           
  10          	0.01333     	-0.00297    	0.03021     	0           
  100         	0.00492     	0.22379     	0.01598     	0           
  300         	0.00499     	0.21879     	0.01365     	2           
  500         	0.00752     	-1.00990    	0.01681     	3           
  1000        	0.00618     	-1.40228    	0.01738     	7           
---> try to load best state
              	0.00618     	-1.40228    	0.01738     	7                eval state : test_on_restore;
              	0.00618     	-1.40228    	0.01738     	7                eval state : test_on_last_epoch;
---> final result              	0.00618     	-1.40228    	0.01738     	7                eval state : final;module_name : SGAR;subset : 2.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 28, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.01038     	-0.20826    	0.02187     	0           
  10          	0.00529     	0.27989     	0.01443     	0           
  100         	0.00480     	-0.65875    	0.01650     	1           
  300         	0.00471     	-0.52475    	0.01576     	3           
  500         	0.00586     	0.27314     	0.01592     	4           
  1000        	0.01023     	-0.88214    	0.02253     	9           
---> try to load best state
              	0.01023     	-0.88214    	0.02253     	9                eval state : test_on_restore;
              	0.01023     	-0.88214    	0.02253     	10               eval state : test_on_last_epoch;
---> final result              	0.01023     	-0.88214    	0.02253     	10               eval state : final;module_name : SGAR;subset : 4.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 24, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.00350     	0.69922     	0.00752     	0           
  10          	0.00354     	0.53449     	0.00994     	0           
  100         	0.00396     	0.56155     	0.01195     	1           
  300         	0.00421     	0.62290     	0.01167     	4           
  500         	0.00465     	0.52412     	0.01275     	8           
  1000        	0.01655     	-2.91107    	0.02164     	16          
---> try to load best state
              	0.01655     	-2.91107    	0.02164     	16               eval state : test_on_restore;
              	0.01655     	-2.91107    	0.02164     	16               eval state : test_on_last_epoch;
---> final result              	0.01655     	-2.91107    	0.02164     	16               eval state : final;module_name : SGAR;subset : 8.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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]}
  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           	0.00240     	0.69730     	0.00623     	0           
  10          	0.00289     	0.70215     	0.00858     	0           
  100         	0.00392     	0.63462     	0.01127     	3           
  300         	0.00401     	0.60365     	0.01134     	9           
  500         	0.00405     	0.56386     	0.01169     	16          
  1000        	0.00463     	0.30859     	0.01212     	33          
---> try to load best state
              	0.00463     	0.30859     	0.01212     	33               eval state : test_on_restore;
              	0.00463     	0.30859     	0.01212     	33               eval state : test_on_last_epoch;
---> final result              	0.00463     	0.30859     	0.01212     	33               eval state : final;module_name : SGAR;subset : 16.0;cp_record_file : True;
