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

  Demo sGAR 
  seed: 0 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01653     	0.80817     	0.04360     	0           
  10          	0.01583     	0.82176     	0.04059     	0           
  100         	0.01533     	0.79585     	0.03515     	4           
  300         	0.01879     	0.55608     	0.03869     	12          
  500         	0.01859     	0.55578     	0.03836     	20          
  1000        	0.01786     	0.59653     	0.03728     	39          
---> try to load best state
              	0.01786     	0.59653     	0.03728     	39               eval state : test_on_restore;
              	0.01786     	0.59653     	0.03728     	39               eval state : test_on_last_epoch;
---> final result              	0.01786     	0.59653     	0.03728     	39               eval state : final;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 0, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.17150     	-0.77731    	0.24743     	0           
  10          	0.10976     	-0.65138    	0.16370     	0           
  100         	0.01963     	0.26060     	0.04238     	1           
  300         	0.01979     	0.32183     	0.04303     	2           
  500         	0.02146     	-0.33435    	0.04467     	4           
  1000        	0.02919     	-0.71791    	0.04740     	8           
---> try to load best state
              	0.02919     	-0.71791    	0.04740     	8                eval state : test_on_restore;
              	0.02919     	-0.71791    	0.04740     	8                eval state : test_on_last_epoch;
---> final result              	0.02919     	-0.71791    	0.04740     	8                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': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 0, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.16259     	-1.13543    	0.23133     	0           
  10          	0.09688     	-0.72173    	0.14569     	0           
  100         	0.01915     	0.35675     	0.04159     	1           
  300         	0.01913     	0.40531     	0.04258     	4           
  500         	0.02030     	0.31352     	0.04193     	6           
  1000        	0.01957     	-0.72078    	0.04244     	12          
---> try to load best state
              	0.01957     	-0.72078    	0.04244     	12               eval state : test_on_restore;
              	0.01957     	-0.72078    	0.04244     	12               eval state : test_on_last_epoch;
---> final result              	0.01957     	-0.72078    	0.04244     	12               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': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 0, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03714     	0.50843     	0.06215     	0           
  10          	0.02000     	0.41754     	0.04596     	0           
  100         	0.01674     	0.45188     	0.04140     	2           
  300         	0.01851     	0.43248     	0.04321     	5           
  500         	0.01899     	0.41519     	0.04392     	9           
  1000        	0.02341     	-0.32933    	0.04877     	19          
---> try to load best state
              	0.02341     	-0.32933    	0.04877     	19               eval state : test_on_restore;
              	0.02341     	-0.32933    	0.04877     	19               eval state : test_on_last_epoch;
---> final result              	0.02341     	-0.32933    	0.04877     	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': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 0, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01942     	0.74928     	0.03696     	0           
  10          	0.01271     	0.84665     	0.02584     	0           
  100         	0.01379     	0.81910     	0.02769     	3           
  300         	0.01698     	0.70737     	0.03238     	10          
  500         	0.01813     	0.66223     	0.03157     	16          
  1000        	0.01852     	0.73047     	0.03170     	32          
---> try to load best state
              	0.01852     	0.73047     	0.03170     	32               eval state : test_on_restore;
              	0.01852     	0.73047     	0.03170     	32               eval state : test_on_last_epoch;
---> final result              	0.01852     	0.73047     	0.03170     	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': 'Heat_mfGent_v5', '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[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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01062     	0.91033     	0.02728     	0           
  10          	0.01052     	0.91208     	0.02658     	0           
  100         	0.01440     	0.86574     	0.02938     	4           
  300         	0.01781     	0.66810     	0.03341     	12          
  500         	0.01754     	0.67756     	0.03301     	20          
  1000        	0.01607     	0.74950     	0.03128     	39          
---> try to load best state
              	0.01607     	0.74950     	0.03128     	39               eval state : test_on_restore;
              	0.01607     	0.74950     	0.03128     	39               eval state : test_on_last_epoch;
---> final result              	0.01607     	0.74950     	0.03128     	39               eval state : final;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 1, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05142     	0.37313     	0.07877     	0           
  10          	0.03352     	0.57870     	0.05053     	0           
  100         	0.02087     	0.79074     	0.03214     	1           
  300         	0.03168     	0.65329     	0.04522     	2           
  500         	0.03804     	0.13928     	0.05114     	4           
  1000        	0.03078     	-0.16708    	0.04249     	8           
---> try to load best state
              	0.03078     	-0.16708    	0.04249     	8                eval state : test_on_restore;
              	0.03078     	-0.16708    	0.04249     	8                eval state : test_on_last_epoch;
---> final result              	0.03078     	-0.16708    	0.04249     	8                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': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 1, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04121     	0.47966     	0.05755     	0           
  10          	0.02088     	0.72229     	0.03380     	0           
  100         	0.01923     	0.77495     	0.03095     	1           
  300         	0.01883     	0.71541     	0.03008     	3           
  500         	0.02092     	0.65422     	0.03200     	6           
  1000        	0.02295     	0.21920     	0.03417     	12          
---> try to load best state
              	0.02295     	0.21920     	0.03417     	12               eval state : test_on_restore;
              	0.02295     	0.21920     	0.03417     	12               eval state : test_on_last_epoch;
---> final result              	0.02295     	0.21920     	0.03417     	12               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': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 1, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02621     	0.66179     	0.03825     	0           
  10          	0.01690     	0.82916     	0.02818     	0           
  100         	0.01695     	0.80547     	0.02854     	2           
  300         	0.01804     	0.78093     	0.02956     	5           
  500         	0.01865     	0.80381     	0.02933     	9           
  1000        	0.02220     	0.19220     	0.03332     	18          
---> try to load best state
              	0.02220     	0.19220     	0.03332     	18               eval state : test_on_restore;
              	0.02220     	0.19220     	0.03332     	18               eval state : test_on_last_epoch;
---> final result              	0.02220     	0.19220     	0.03332     	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': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 1, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01609     	0.81997     	0.02693     	0           
  10          	0.01223     	0.86850     	0.02386     	0           
  100         	0.01451     	0.82280     	0.02739     	3           
  300         	0.01584     	0.76578     	0.02922     	10          
  500         	0.01568     	0.79651     	0.02889     	16          
  1000        	0.01546     	0.78215     	0.02733     	32          
---> try to load best state
              	0.01546     	0.78215     	0.02733     	32               eval state : test_on_restore;
              	0.01546     	0.78215     	0.02733     	32               eval state : test_on_last_epoch;
---> final result              	0.01546     	0.78215     	0.02733     	32               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': 'Heat_mfGent_v5', '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[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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01522     	0.84230     	0.04416     	0           
  10          	0.01449     	0.85235     	0.04099     	0           
  100         	0.01668     	0.86825     	0.03233     	4           
  300         	0.01806     	0.77797     	0.03269     	12          
  500         	0.01776     	0.78903     	0.03213     	20          
  1000        	0.01698     	0.82183     	0.03061     	40          
---> try to load best state
              	0.01698     	0.82183     	0.03061     	40               eval state : test_on_restore;
              	0.01698     	0.82183     	0.03061     	40               eval state : test_on_last_epoch;
---> final result              	0.01698     	0.82183     	0.03061     	40               eval state : final;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 2, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.26245     	-6.37914    	0.28738     	0           
  10          	0.13168     	-4.40816    	0.17034     	0           
  100         	0.01824     	0.68911     	0.03342     	1           
  300         	0.03267     	-1.36580    	0.05690     	2           
  500         	0.03123     	-2.91676    	0.05541     	4           
  1000        	0.05455     	-1.92509    	0.09511     	8           
---> try to load best state
              	0.05455     	-1.92509    	0.09511     	8                eval state : test_on_restore;
              	0.05455     	-1.92509    	0.09511     	8                eval state : test_on_last_epoch;
---> final result              	0.05455     	-1.92509    	0.09511     	8                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': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 2, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.10033     	-0.04055    	0.16428     	0           
  10          	0.04723     	-0.13318    	0.07907     	0           
  100         	0.02461     	0.80618     	0.04197     	1           
  300         	0.02652     	0.73554     	0.04335     	3           
  500         	0.02691     	0.79164     	0.04426     	6           
  1000        	0.02793     	0.51091     	0.04432     	12          
---> try to load best state
              	0.02793     	0.51091     	0.04432     	12               eval state : test_on_restore;
              	0.02793     	0.51091     	0.04432     	12               eval state : test_on_last_epoch;
---> final result              	0.02793     	0.51091     	0.04432     	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': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 2, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02959     	0.59242     	0.04680     	0           
  10          	0.01714     	0.77318     	0.02815     	0           
  100         	0.01535     	0.88371     	0.02577     	2           
  300         	0.01705     	0.70616     	0.02865     	5           
  500         	0.01716     	0.85200     	0.02788     	9           
  1000        	0.01845     	0.72961     	0.02793     	18          
---> try to load best state
              	0.01845     	0.72961     	0.02793     	19               eval state : test_on_restore;
              	0.01845     	0.72961     	0.02793     	19               eval state : test_on_last_epoch;
---> final result              	0.01845     	0.72961     	0.02793     	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': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 2, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01342     	0.87529     	0.02160     	0           
  10          	0.01040     	0.90109     	0.01959     	0           
  100         	0.01478     	0.88493     	0.02565     	3           
  300         	0.01956     	0.71711     	0.02962     	10          
  500         	0.01668     	0.83317     	0.02883     	16          
  1000        	0.01747     	0.80865     	0.02819     	32          
---> try to load best state
              	0.01747     	0.80865     	0.02819     	32               eval state : test_on_restore;
              	0.01747     	0.80865     	0.02819     	32               eval state : test_on_last_epoch;
---> final result              	0.01747     	0.80865     	0.02819     	32               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': 'Heat_mfGent_v5', '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[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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00961     	0.92999     	0.02314     	0           
  10          	0.00944     	0.93062     	0.02238     	0           
  100         	0.01276     	0.89108     	0.02590     	4           
  300         	0.01638     	0.74502     	0.02990     	12          
  500         	0.01609     	0.75817     	0.02948     	20          
  1000        	0.01505     	0.80299     	0.02818     	39          
---> try to load best state
              	0.01505     	0.80299     	0.02818     	39               eval state : test_on_restore;
              	0.01505     	0.80299     	0.02818     	39               eval state : test_on_last_epoch;
---> final result              	0.01505     	0.80299     	0.02818     	39               eval state : final;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 3, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.15898     	-4.47740    	0.25950     	0           
  10          	0.04768     	-0.00551    	0.06712     	0           
  100         	0.02989     	0.30625     	0.04303     	1           
  300         	0.02670     	0.09527     	0.03938     	2           
  500         	0.03156     	0.54246     	0.04591     	4           
  1000        	0.03706     	0.37483     	0.05057     	8           
---> try to load best state
              	0.03706     	0.37483     	0.05057     	8                eval state : test_on_restore;
              	0.03706     	0.37483     	0.05057     	8                eval state : test_on_last_epoch;
---> final result              	0.03706     	0.37483     	0.05057     	8                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': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 3, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05958     	-0.14425    	0.09884     	0           
  10          	0.02710     	0.54574     	0.05095     	0           
  100         	0.01641     	0.81661     	0.03083     	1           
  300         	0.01673     	0.81252     	0.03108     	3           
  500         	0.01894     	0.77998     	0.03152     	6           
  1000        	0.01745     	0.36738     	0.03088     	12          
---> try to load best state
              	0.01745     	0.36738     	0.03088     	12               eval state : test_on_restore;
              	0.01745     	0.36738     	0.03088     	12               eval state : test_on_last_epoch;
---> final result              	0.01745     	0.36738     	0.03088     	12               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': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 3, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02290     	0.71774     	0.03757     	0           
  10          	0.01551     	0.82295     	0.02743     	0           
  100         	0.01422     	0.85513     	0.02698     	2           
  300         	0.01558     	0.83061     	0.02789     	7           
  500         	0.01534     	0.64348     	0.02757     	10          
  1000        	0.01957     	0.51119     	0.03011     	16          
---> try to load best state
              	0.01957     	0.51119     	0.03011     	16               eval state : test_on_restore;
              	0.01957     	0.51119     	0.03011     	16               eval state : test_on_last_epoch;
---> final result              	0.01957     	0.51119     	0.03011     	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': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 3, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01744     	0.76934     	0.03024     	0           
  10          	0.01251     	0.83738     	0.02389     	0           
  100         	0.01338     	0.84655     	0.02727     	2           
  300         	0.01462     	0.82997     	0.02835     	8           
  500         	0.01453     	0.82003     	0.02832     	14          
  1000        	0.01497     	0.75931     	0.02916     	28          
---> try to load best state
              	0.01497     	0.75931     	0.02916     	28               eval state : test_on_restore;
              	0.01497     	0.75931     	0.02916     	28               eval state : test_on_last_epoch;
---> final result              	0.01497     	0.75931     	0.02916     	28               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': '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01431     	0.89559     	0.02904     	0           
  10          	0.01386     	0.90117     	0.02757     	0           
  100         	0.01576     	0.90876     	0.02478     	4           
  300         	0.01682     	0.82600     	0.02668     	12          
  500         	0.01662     	0.84513     	0.02618     	20          
  1000        	0.01650     	0.87506     	0.02536     	39          
---> try to load best state
              	0.01650     	0.87506     	0.02536     	39               eval state : test_on_restore;
              	0.01650     	0.87506     	0.02536     	39               eval state : test_on_last_epoch;
---> final result              	0.01650     	0.87506     	0.02536     	39               eval state : final;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.06756     	-0.24363    	0.11958     	0           
  10          	0.02586     	0.64900     	0.04449     	0           
  100         	0.01684     	0.86606     	0.02794     	1           
  300         	0.01877     	0.77133     	0.03069     	2           
  500         	0.01991     	0.71478     	0.03242     	4           
  1000        	0.02620     	0.20811     	0.03546     	8           
---> try to load best state
              	0.02620     	0.20811     	0.03546     	8                eval state : test_on_restore;
              	0.02620     	0.20811     	0.03546     	8                eval state : test_on_last_epoch;
---> final result              	0.02620     	0.20811     	0.03546     	8                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': '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04184     	0.56178     	0.06352     	0           
  10          	0.02205     	0.81984     	0.03554     	0           
  100         	0.01573     	0.85863     	0.02754     	1           
  300         	0.01645     	0.78932     	0.02944     	3           
  500         	0.01639     	0.83167     	0.02892     	6           
  1000        	0.01632     	0.84883     	0.02788     	12          
---> try to load best state
              	0.01632     	0.84883     	0.02788     	12               eval state : test_on_restore;
              	0.01632     	0.84883     	0.02788     	12               eval state : test_on_last_epoch;
---> final result              	0.01632     	0.84883     	0.02788     	12               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': '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02095     	0.74190     	0.03739     	0           
  10          	0.01363     	0.86614     	0.02626     	0           
  100         	0.01515     	0.85632     	0.02784     	2           
  300         	0.01583     	0.79720     	0.02984     	5           
  500         	0.01911     	0.81372     	0.02970     	9           
  1000        	0.01648     	0.66166     	0.02847     	19          
---> try to load best state
              	0.01648     	0.66166     	0.02847     	19               eval state : test_on_restore;
              	0.01648     	0.66166     	0.02847     	19               eval state : test_on_last_epoch;
---> final result              	0.01648     	0.66166     	0.02847     	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': '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01470     	0.84567     	0.03255     	0           
  10          	0.01333     	0.85705     	0.03014     	0           
  100         	0.01483     	0.86332     	0.02726     	3           
  300         	0.01557     	0.81125     	0.02932     	10          
  500         	0.01541     	0.76553     	0.02939     	16          
  1000        	0.01624     	0.79450     	0.02831     	31          
---> try to load best state
              	0.01624     	0.79450     	0.02831     	32               eval state : test_on_restore;
              	0.01624     	0.79450     	0.02831     	32               eval state : test_on_last_epoch;
---> final result              	0.01624     	0.79450     	0.02831     	32               eval state : final;module_name : SGAR;subset : 16.0;cp_record_file : True;
