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

  Demo sGAR 
  seed: None 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 0.005
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.18197     	0.22295     	-15.84745   	0           
  10          	0.06370     	0.09577     	0.72810     	0           
  100         	0.04787     	0.07485     	0.88266     	0           
  300         	0.04169     	0.06662     	0.91865     	3           
  500         	0.03849     	0.06247     	0.93315     	4           
  1000        	0.03451     	0.05725     	0.94788     	8           
---> try to load best state
              	0.03451     	0.05725     	0.94788     	8                eval state : test_on_restore;
              	0.03451     	0.05725     	0.94788     	8                eval state : test_on_last_epoch;
---> final result              	0.03451     	0.05725     	0.94788     	8                eval state : final;
---> end


---------->
SGAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.20019     	0.26434     	-0.97572    	0           
  10          	0.19206     	0.25884     	-0.10892    	0           
  100         	0.33316     	0.45094     	-0.08572    	0           
  epoch 149 reach nan state
---> try to load best state
              	0.33498     	0.45348     	-0.08569    	0                eval state : test_on_restore;
              	0.33316     	0.45094     	-0.08572    	0                eval state : test_on_last_epoch;
---> final result              	0.33316     	0.45094     	-0.08572    	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.20451     	0.28028     	-0.05465    	0           
  10          	0.19806     	0.27039     	0.25087     	0           
  100         	0.20432     	0.27074     	0.34144     	0           
  epoch 167 reach nan state
---> try to load best state
              	0.21368     	0.28247     	0.35468     	0                eval state : test_on_restore;
              	0.20432     	0.27074     	0.34144     	0                eval state : test_on_last_epoch;
---> final result              	0.20432     	0.27074     	0.34144     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.15582     	0.23196     	0.55144     	0           
  10          	0.15708     	0.22970     	0.55808     	0           
  100         	0.15782     	0.22128     	0.50573     	0           
  300         	0.17506     	0.24182     	0.51095     	2           
  500         	0.17817     	0.24605     	0.49341     	3           
  1000        	0.18146     	0.24954     	0.48966     	7           
---> try to load best state
              	0.18146     	0.24954     	0.48966     	7                eval state : test_on_restore;
              	0.18146     	0.24954     	0.48966     	7                eval state : test_on_last_epoch;
---> final result              	0.18146     	0.24954     	0.48966     	7                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.15823     	0.24561     	0.58655     	0           
  10          	0.15329     	0.23592     	0.59712     	0           
  100         	0.14174     	0.20978     	0.55343     	1           
  300         	0.14664     	0.21407     	0.55193     	3           
  500         	0.15095     	0.21884     	0.54530     	5           
  1000        	0.15286     	0.22125     	0.54134     	11          
---> try to load best state
              	0.15286     	0.22125     	0.54134     	11               eval state : test_on_restore;
              	0.15286     	0.22125     	0.54134     	11               eval state : test_on_last_epoch;
---> final result              	0.15286     	0.22125     	0.54134     	11               eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.13036     	0.20441     	0.72523     	0           
  10          	0.13157     	0.20734     	0.71665     	0           
  100         	0.12664     	0.19784     	0.69141     	2           
  300         	0.12619     	0.19330     	0.67125     	6           
  500         	0.12703     	0.19384     	0.66629     	10          
  1000        	0.12757     	0.19422     	0.66833     	19          
---> try to load best state
              	0.12757     	0.19422     	0.66833     	19               eval state : test_on_restore;
              	0.12757     	0.19422     	0.66833     	19               eval state : test_on_last_epoch;
---> final result              	0.12757     	0.19422     	0.66833     	19               eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.11319     	0.18622     	0.78122     	0           
  10          	0.11441     	0.18845     	0.77559     	0           
  100         	0.11625     	0.18687     	0.74383     	5           
  300         	0.11248     	0.17645     	0.72121     	17          
  500         	0.11331     	0.17704     	0.72149     	29          
  1000        	0.11354     	0.17708     	0.72469     	55          
---> try to load best state
              	0.11354     	0.17708     	0.72469     	55               eval state : test_on_restore;
              	0.11354     	0.17708     	0.72469     	55               eval state : test_on_last_epoch;
---> final result              	0.11354     	0.17708     	0.72469     	55               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 0.005
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.18008     	0.21930     	-14.13081   	0           
  10          	0.06157     	0.09080     	0.76118     	0           
  100         	0.04662     	0.07033     	0.90161     	0           
  300         	0.04070     	0.06241     	0.93179     	1           
  500         	0.03753     	0.05836     	0.94425     	3           
  1000        	0.03321     	0.05292     	0.95795     	6           
---> try to load best state
              	0.03321     	0.05292     	0.95795     	6                eval state : test_on_restore;
              	0.03321     	0.05292     	0.95795     	6                eval state : test_on_last_epoch;
---> final result              	0.03321     	0.05292     	0.95795     	6                eval state : final;
---> end


---------->
SGAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.18309     	0.25875     	0.30272     	0           
  10          	0.18881     	0.26243     	0.45851     	0           
  100         	0.21347     	0.29539     	0.48960     	0           
  300         	0.25047     	0.34116     	0.44407     	1           
  500         	0.26712     	0.36321     	0.42459     	1           
  1000        	0.28997     	0.38930     	0.38012     	3           
---> try to load best state
              	0.28997     	0.38930     	0.38012     	3                eval state : test_on_restore;
              	0.28997     	0.38930     	0.38012     	3                eval state : test_on_last_epoch;
---> final result              	0.28997     	0.38930     	0.38012     	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.19842     	0.27932     	-0.01909    	0           
  10          	0.19996     	0.27983     	0.22985     	0           
  100         	0.19381     	0.26233     	0.02665     	0           
  300         	0.21001     	0.27936     	0.07677     	1           
  500         	0.21274     	0.28375     	0.12608     	1           
  1000        	0.21899     	0.29283     	0.12194     	3           
---> try to load best state
              	0.21899     	0.29283     	0.12194     	3                eval state : test_on_restore;
              	0.21899     	0.29283     	0.12194     	3                eval state : test_on_last_epoch;
---> final result              	0.21899     	0.29283     	0.12194     	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.17656     	0.26312     	0.34838     	0           
  10          	0.17167     	0.25065     	0.40697     	0           
  100         	0.16338     	0.22909     	0.39696     	0           
  300         	0.16921     	0.23321     	0.39923     	2           
  500         	0.17223     	0.23649     	0.40472     	3           
  1000        	0.18022     	0.24904     	0.40777     	7           
---> try to load best state
              	0.18022     	0.24904     	0.40777     	7                eval state : test_on_restore;
              	0.18022     	0.24904     	0.40777     	7                eval state : test_on_last_epoch;
---> final result              	0.18022     	0.24904     	0.40777     	7                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.14633     	0.23397     	0.66203     	0           
  10          	0.14331     	0.22526     	0.66362     	0           
  100         	0.14085     	0.20495     	0.58615     	1           
  300         	0.14309     	0.20754     	0.58338     	3           
  500         	0.14669     	0.21187     	0.58317     	5           
  1000        	0.15264     	0.21946     	0.57812     	11          
---> try to load best state
              	0.15264     	0.21946     	0.57812     	11               eval state : test_on_restore;
              	0.15264     	0.21946     	0.57812     	11               eval state : test_on_last_epoch;
---> final result              	0.15264     	0.21946     	0.57812     	11               eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.12882     	0.20341     	0.73401     	0           
  10          	0.13237     	0.20771     	0.72364     	0           
  100         	0.12504     	0.19405     	0.70789     	2           
  300         	0.12804     	0.19071     	0.68431     	6           
  500         	0.12945     	0.19187     	0.68112     	10          
  1000        	0.13248     	0.19443     	0.67553     	19          
---> try to load best state
              	0.13248     	0.19443     	0.67553     	19               eval state : test_on_restore;
              	0.13248     	0.19443     	0.67553     	19               eval state : test_on_last_epoch;
---> final result              	0.13248     	0.19443     	0.67553     	19               eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.11213     	0.18311     	0.79178     	0           
  10          	0.11355     	0.18507     	0.78289     	0           
  100         	0.11208     	0.17979     	0.76334     	5           
  300         	0.11070     	0.17340     	0.73256     	16          
  500         	0.11155     	0.17425     	0.72941     	26          
  1000        	0.11283     	0.17531     	0.72835     	52          
---> try to load best state
              	0.11283     	0.17531     	0.72835     	52               eval state : test_on_restore;
              	0.11283     	0.17531     	0.72835     	52               eval state : test_on_last_epoch;
---> final result              	0.11283     	0.17531     	0.72835     	52               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 0.005
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.18069     	0.22062     	-13.97109   	0           
  10          	0.06070     	0.09094     	0.78292     	0           
  100         	0.04553     	0.07050     	0.90650     	0           
  300         	0.03940     	0.06232     	0.93561     	1           
  500         	0.03621     	0.05812     	0.94780     	3           
  1000        	0.03229     	0.05266     	0.96068     	6           
---> try to load best state
              	0.03229     	0.05266     	0.96068     	6                eval state : test_on_restore;
              	0.03229     	0.05266     	0.96068     	6                eval state : test_on_last_epoch;
---> final result              	0.03229     	0.05266     	0.96068     	6                eval state : final;
---> end


---------->
SGAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.19564     	0.26828     	-0.14511    	0           
  10          	0.19795     	0.27087     	0.29178     	0           
  100         	0.21764     	0.30048     	0.45438     	0           
  300         	0.28699     	0.39604     	0.31838     	1           
  500         	0.32652     	0.45187     	0.23128     	1           
  1000        	0.35647     	0.49388     	0.19482     	3           
---> try to load best state
              	0.35647     	0.49388     	0.19482     	3                eval state : test_on_restore;
              	0.35647     	0.49388     	0.19482     	3                eval state : test_on_last_epoch;
---> final result              	0.35647     	0.49388     	0.19482     	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.17557     	0.25513     	0.27236     	0           
  10          	0.17891     	0.26186     	0.42436     	0           
  100         	0.19062     	0.25725     	0.42434     	0           
  epoch 174 reach nan state
---> try to load best state
              	0.19881     	0.26452     	0.42198     	0                eval state : test_on_restore;
              	0.19062     	0.25725     	0.42434     	0                eval state : test_on_last_epoch;
---> final result              	0.19062     	0.25725     	0.42434     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.17605     	0.25887     	0.42919     	0           
  10          	0.17418     	0.25148     	0.47852     	0           
  100         	0.17506     	0.23171     	0.35584     	0           
  epoch 126 reach nan state
---> try to load best state
              	0.17524     	0.23189     	0.35561     	0                eval state : test_on_restore;
              	0.17506     	0.23171     	0.35584     	0                eval state : test_on_last_epoch;
---> final result              	0.17506     	0.23171     	0.35584     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.16382     	0.25391     	0.60510     	0           
  10          	0.16191     	0.24718     	0.62080     	0           
  100         	0.14721     	0.21255     	0.59509     	1           
  300         	0.15299     	0.21848     	0.58679     	2           
  500         	0.15813     	0.22425     	0.57935     	5           
  1000        	0.17208     	0.24250     	0.54729     	10          
---> try to load best state
              	0.17208     	0.24250     	0.54729     	10               eval state : test_on_restore;
              	0.17208     	0.24250     	0.54729     	10               eval state : test_on_last_epoch;
---> final result              	0.17208     	0.24250     	0.54729     	10               eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.14414     	0.23197     	0.66292     	0           
  10          	0.14201     	0.22716     	0.66479     	0           
  100         	0.12708     	0.19770     	0.67328     	1           
  300         	0.12989     	0.19859     	0.65753     	6           
  500         	0.13081     	0.19953     	0.65468     	10          
  1000        	0.13094     	0.19986     	0.65939     	19          
---> try to load best state
              	0.13094     	0.19986     	0.65939     	19               eval state : test_on_restore;
              	0.13094     	0.19986     	0.65939     	19               eval state : test_on_last_epoch;
---> final result              	0.13094     	0.19986     	0.65939     	19               eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.11099     	0.18217     	0.77451     	0           
  10          	0.11367     	0.18783     	0.76405     	0           
  100         	0.11608     	0.18943     	0.74686     	5           
  300         	0.11198     	0.17854     	0.73560     	16          
  500         	0.11287     	0.17923     	0.73316     	27          
  1000        	0.11363     	0.17989     	0.73174     	53          
---> try to load best state
              	0.11363     	0.17989     	0.73174     	53               eval state : test_on_restore;
              	0.11363     	0.17989     	0.73174     	53               eval state : test_on_last_epoch;
---> final result              	0.11363     	0.17989     	0.73174     	53               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 0.005
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.18003     	0.21913     	-14.38386   	0           
  10          	0.06284     	0.09269     	0.75776     	0           
  100         	0.04675     	0.07057     	0.90231     	0           
  300         	0.04068     	0.06229     	0.93239     	1           
  500         	0.03749     	0.05802     	0.94502     	3           
  1000        	0.03337     	0.05241     	0.95871     	6           
---> try to load best state
              	0.03337     	0.05241     	0.95871     	6                eval state : test_on_restore;
              	0.03337     	0.05241     	0.95871     	6                eval state : test_on_last_epoch;
---> final result              	0.03337     	0.05241     	0.95871     	6                eval state : final;
---> end


---------->
SGAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.20268     	0.27862     	-0.20478    	0           
  10          	0.21335     	0.29342     	0.25455     	0           
  100         	0.25344     	0.34308     	0.32798     	0           
  300         	0.26234     	0.35162     	0.27542     	1           
  500         	0.28031     	0.37392     	0.26772     	1           
  1000        	0.33709     	0.45115     	0.21389     	3           
---> try to load best state
              	0.33709     	0.45115     	0.21389     	3                eval state : test_on_restore;
              	0.33709     	0.45115     	0.21389     	3                eval state : test_on_last_epoch;
---> final result              	0.33709     	0.45115     	0.21389     	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.19343     	0.27288     	-0.17966    	0           
  10          	0.19556     	0.27819     	0.19271     	0           
  100         	0.19713     	0.26301     	0.35356     	0           
  300         	0.20747     	0.27848     	0.35629     	1           
  500         	0.21187     	0.28500     	0.32286     	2           
  1000        	0.22214     	0.29947     	0.30656     	3           
---> try to load best state
              	0.22214     	0.29947     	0.30656     	3                eval state : test_on_restore;
              	0.22214     	0.29947     	0.30656     	4                eval state : test_on_last_epoch;
---> final result              	0.22214     	0.29947     	0.30656     	4                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.18217     	0.27217     	0.44533     	0           
  10          	0.18065     	0.26318     	0.51635     	0           
  100         	0.17761     	0.24103     	0.51794     	0           
  epoch 256 reach nan state
---> try to load best state
              	0.18763     	0.25295     	0.51628     	1                eval state : test_on_restore;
              	0.17761     	0.24103     	0.51794     	1                eval state : test_on_last_epoch;
---> final result              	0.17761     	0.24103     	0.51794     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.15527     	0.24302     	0.63284     	0           
  10          	0.15270     	0.23650     	0.63365     	0           
  100         	0.14554     	0.21085     	0.56830     	1           
  300         	0.14748     	0.21242     	0.56352     	3           
  500         	0.14901     	0.21378     	0.56338     	5           
  1000        	0.15112     	0.21669     	0.55636     	11          
---> try to load best state
              	0.15112     	0.21669     	0.55636     	11               eval state : test_on_restore;
              	0.15112     	0.21669     	0.55636     	11               eval state : test_on_last_epoch;
---> final result              	0.15112     	0.21669     	0.55636     	11               eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.14787     	0.24220     	0.71874     	0           
  10          	0.15025     	0.24308     	0.70746     	0           
  100         	0.12525     	0.19031     	0.70676     	2           
  300         	0.12642     	0.18827     	0.68180     	6           
  500         	0.12694     	0.18872     	0.68376     	10          
  1000        	0.12804     	0.18968     	0.68372     	19          
---> try to load best state
              	0.12804     	0.18968     	0.68372     	19               eval state : test_on_restore;
              	0.12804     	0.18968     	0.68372     	19               eval state : test_on_last_epoch;
---> final result              	0.12804     	0.18968     	0.68372     	19               eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.10503     	0.17135     	0.80395     	0           
  10          	0.10504     	0.17265     	0.80459     	0           
  100         	0.10550     	0.16816     	0.79034     	5           
  300         	0.10496     	0.16232     	0.76397     	17          
  500         	0.10552     	0.16283     	0.76159     	27          
  1000        	0.10555     	0.16283     	0.76107     	58          
---> try to load best state
              	0.10555     	0.16283     	0.76107     	58               eval state : test_on_restore;
              	0.10555     	0.16283     	0.76107     	58               eval state : test_on_last_epoch;
---> final result              	0.10555     	0.16283     	0.76107     	58               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 0.005
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.18073     	0.22061     	-14.45079   	0           
  10          	0.06143     	0.08999     	0.79214     	0           
  100         	0.04647     	0.06848     	0.91455     	0           
  300         	0.04026     	0.05986     	0.94117     	2           
  500         	0.03702     	0.05574     	0.95165     	3           
  1000        	0.03288     	0.05070     	0.96263     	7           
---> try to load best state
              	0.03288     	0.05070     	0.96263     	7                eval state : test_on_restore;
              	0.03288     	0.05070     	0.96263     	7                eval state : test_on_last_epoch;
---> final result              	0.03288     	0.05070     	0.96263     	7                eval state : final;
---> end


---------->
SGAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.19168     	0.26089     	-0.22796    	0           
  10          	0.19569     	0.26733     	0.31000     	0           
  100         	0.26453     	0.36055     	0.16146     	0           
  300         	0.31163     	0.42983     	0.06035     	1           
  500         	0.32760     	0.45842     	0.09916     	2           
  1000        	0.37319     	0.53097     	0.08015     	4           
---> try to load best state
              	0.37319     	0.53097     	0.08015     	4                eval state : test_on_restore;
              	0.37319     	0.53097     	0.08015     	4                eval state : test_on_last_epoch;
---> final result              	0.37319     	0.53097     	0.08015     	4                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.18765     	0.25570     	-0.30696    	0           
  10          	0.19013     	0.26172     	0.41033     	0           
  100         	0.21838     	0.30276     	0.48293     	0           
  300         	0.23050     	0.31550     	0.44815     	1           
  500         	0.23380     	0.31843     	0.43678     	2           
  1000        	0.25425     	0.34270     	0.40416     	5           
---> try to load best state
              	0.25425     	0.34270     	0.40416     	5                eval state : test_on_restore;
              	0.25425     	0.34270     	0.40416     	5                eval state : test_on_last_epoch;
---> final result              	0.25425     	0.34270     	0.40416     	5                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.17045     	0.25475     	0.39511     	0           
  10          	0.17336     	0.25679     	0.49087     	0           
  100         	0.16213     	0.23053     	0.54640     	0           
  300         	0.17042     	0.24181     	0.52886     	2           
  500         	0.17575     	0.24832     	0.51344     	4           
  1000        	0.18843     	0.26414     	0.49291     	8           
---> try to load best state
              	0.18843     	0.26414     	0.49291     	8                eval state : test_on_restore;
              	0.18843     	0.26414     	0.49291     	8                eval state : test_on_last_epoch;
---> final result              	0.18843     	0.26414     	0.49291     	8                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.15470     	0.23718     	0.64241     	0           
  10          	0.15190     	0.23166     	0.64597     	0           
  100         	0.14273     	0.20926     	0.61508     	1           
  300         	0.14732     	0.21350     	0.60961     	4           
  500         	0.14937     	0.21526     	0.60683     	7           
  1000        	0.15139     	0.21679     	0.60023     	13          
---> try to load best state
              	0.15139     	0.21679     	0.60023     	13               eval state : test_on_restore;
              	0.15139     	0.21679     	0.60023     	13               eval state : test_on_last_epoch;
---> final result              	0.15139     	0.21679     	0.60023     	13               eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.12985     	0.20191     	0.73838     	0           
  10          	0.13254     	0.20489     	0.72709     	0           
  100         	0.12036     	0.18450     	0.69166     	2           
  300         	0.12289     	0.18487     	0.66753     	7           
  500         	0.12292     	0.18505     	0.66927     	12          
  1000        	0.12307     	0.18515     	0.67182     	24          
---> try to load best state
              	0.12307     	0.18515     	0.67182     	24               eval state : test_on_restore;
              	0.12307     	0.18515     	0.67182     	24               eval state : test_on_last_epoch;
---> final result              	0.12307     	0.18515     	0.67182     	24               eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.10171     	0.16447     	0.82894     	0           
  10          	0.10413     	0.16901     	0.82078     	0           
  100         	0.10593     	0.16960     	0.78655     	6           
  300         	0.10322     	0.16090     	0.76100     	19          
  500         	0.10414     	0.16182     	0.75766     	34          
  1000        	0.10479     	0.16244     	0.75615     	62          
---> try to load best state
              	0.10479     	0.16244     	0.75615     	62               eval state : test_on_restore;
              	0.10479     	0.16244     	0.75615     	62               eval state : test_on_last_epoch;
---> final result              	0.10479     	0.16244     	0.75615     	62               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 0.005
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.18214     	0.22298     	-15.43400   	0           
  10          	0.06385     	0.09571     	0.74986     	0           
  100         	0.04662     	0.07294     	0.89738     	0           
  300         	0.04056     	0.06465     	0.92747     	1           
  500         	0.03734     	0.06019     	0.94055     	3           
  1000        	0.03302     	0.05391     	0.95571     	6           
---> try to load best state
              	0.03302     	0.05391     	0.95571     	6                eval state : test_on_restore;
              	0.03302     	0.05391     	0.95571     	6                eval state : test_on_last_epoch;
---> final result              	0.03302     	0.05391     	0.95571     	6                eval state : final;
---> end


---------->
SGAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.19616     	0.26875     	0.01745     	0           
  10          	0.20095     	0.27169     	0.34910     	0           
  100         	0.23038     	0.30506     	0.34208     	0           
  300         	0.28204     	0.38147     	0.15808     	1           
  500         	0.32726     	0.44803     	0.08641     	1           
  1000        	0.35982     	0.49057     	0.03321     	3           
---> try to load best state
              	0.35982     	0.49057     	0.03321     	3                eval state : test_on_restore;
              	0.35982     	0.49057     	0.03321     	3                eval state : test_on_last_epoch;
---> final result              	0.35982     	0.49057     	0.03321     	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.17737     	0.24565     	0.10708     	0           
  10          	0.17930     	0.25119     	0.46250     	0           
  100         	0.19666     	0.26944     	0.51549     	0           
  300         	0.20961     	0.28392     	0.54805     	1           
  500         	0.21282     	0.28604     	0.54824     	2           
  1000        	0.23041     	0.30770     	0.51011     	4           
---> try to load best state
              	0.23041     	0.30770     	0.51011     	4                eval state : test_on_restore;
              	0.23041     	0.30770     	0.51011     	4                eval state : test_on_last_epoch;
---> final result              	0.23041     	0.30770     	0.51011     	4                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.15989     	0.24038     	0.30101     	0           
  10          	0.16408     	0.24350     	0.41998     	0           
  100         	0.15563     	0.22271     	0.39288     	0           
  300         	0.15959     	0.22491     	0.41360     	2           
  500         	0.16174     	0.22733     	0.39253     	3           
  1000        	0.16683     	0.23275     	0.37817     	7           
---> try to load best state
              	0.16683     	0.23275     	0.37817     	7                eval state : test_on_restore;
              	0.16683     	0.23275     	0.37817     	7                eval state : test_on_last_epoch;
---> final result              	0.16683     	0.23275     	0.37817     	7                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.17197     	0.26400     	0.44218     	0           
  10          	0.17005     	0.25929     	0.46048     	0           
  100         	0.15579     	0.21713     	0.42552     	1           
  300         	0.15922     	0.21996     	0.43562     	3           
  500         	0.16096     	0.22159     	0.43478     	5           
  1000        	0.16180     	0.22234     	0.43730     	11          
---> try to load best state
              	0.16180     	0.22234     	0.43730     	11               eval state : test_on_restore;
              	0.16180     	0.22234     	0.43730     	11               eval state : test_on_last_epoch;
---> final result              	0.16180     	0.22234     	0.43730     	11               eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.12886     	0.20839     	0.74882     	0           
  10          	0.12961     	0.20767     	0.73806     	0           
  100         	0.11744     	0.18448     	0.74376     	2           
  300         	0.11912     	0.18119     	0.72201     	6           
  500         	0.11998     	0.18203     	0.72043     	10          
  1000        	0.12029     	0.18208     	0.71990     	20          
---> try to load best state
              	0.12029     	0.18208     	0.71990     	20               eval state : test_on_restore;
              	0.12029     	0.18208     	0.71990     	20               eval state : test_on_last_epoch;
---> final result              	0.12029     	0.18208     	0.71990     	20               eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  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, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.11112     	0.19109     	0.77684     	0           
  10          	0.11087     	0.19171     	0.77663     	0           
  100         	0.10709     	0.17520     	0.76589     	5           
  300         	0.10630     	0.16712     	0.74889     	18          
  500         	0.10700     	0.16737     	0.75019     	31          
  1000        	0.10691     	0.16693     	0.75293     	63          
---> try to load best state
              	0.10691     	0.16693     	0.75293     	63               eval state : test_on_restore;
              	0.10691     	0.16693     	0.75293     	63               eval state : test_on_last_epoch;
---> final result              	0.10691     	0.16693     	0.75293     	63               eval state : final;module_name : SGAR;cp_record_file : True;
