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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': False}
  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           	0.20884     	0.24997     	-53.66147   	0           
  10          	0.07278     	0.10404     	0.63268     	0           
  100         	0.05567     	0.08400     	0.82548     	0           
  300         	0.05069     	0.07760     	0.86439     	1           
  500         	0.04804     	0.07432     	0.88172     	2           
  1000        	0.04404     	0.06981     	0.90259     	4           
---> try to load best state
              	0.04404     	0.06981     	0.90259     	4                eval state : test_on_restore;
              	0.04404     	0.06981     	0.90259     	4                eval state : test_on_last_epoch;
---> final result              	0.04404     	0.06981     	0.90259     	4                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', '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': False}
  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           	0.19389     	0.26491     	-0.22985    	0           
  10          	0.19703     	0.27140     	0.20999     	0           
  100         	0.21405     	0.29600     	0.37667     	0           
  300         	0.24285     	0.32959     	0.35816     	1           
  500         	0.27068     	0.36792     	0.34443     	2           
  1000        	0.30135     	0.41434     	0.31975     	4           
---> try to load best state
              	0.30135     	0.41434     	0.31975     	4                eval state : test_on_restore;
              	0.30135     	0.41434     	0.31975     	4                eval state : test_on_last_epoch;
---> final result              	0.30135     	0.41434     	0.31975     	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_64', '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': False}
  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           	0.19325     	0.27057     	0.08781     	0           
  10          	0.19333     	0.27127     	0.34972     	0           
  100         	0.19234     	0.25850     	0.39040     	0           
  300         	0.21407     	0.28406     	0.35997     	1           
  500         	0.23548     	0.31382     	0.31072     	2           
  1000        	0.26182     	0.34603     	0.22875     	5           
---> try to load best state
              	0.26182     	0.34603     	0.22875     	5                eval state : test_on_restore;
              	0.26182     	0.34603     	0.22875     	5                eval state : test_on_last_epoch;
---> final result              	0.26182     	0.34603     	0.22875     	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_64', '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': False}
  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           	0.17229     	0.24716     	0.20328     	0           
  10          	0.17475     	0.25048     	0.40569     	0           
  100         	0.16832     	0.23239     	0.43126     	0           
  300         	0.18028     	0.24587     	0.42028     	1           
  500         	0.19000     	0.25906     	0.41208     	2           
  1000        	0.20187     	0.27387     	0.41402     	5           
---> try to load best state
              	0.20187     	0.27387     	0.41402     	5                eval state : test_on_restore;
              	0.20187     	0.27387     	0.41402     	5                eval state : test_on_last_epoch;
---> final result              	0.20187     	0.27387     	0.41402     	5                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_64', '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': False}
  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           	0.15817     	0.24162     	0.63570     	0           
  10          	0.15603     	0.23565     	0.62826     	0           
  100         	0.15423     	0.21691     	0.53078     	1           
  300         	0.16195     	0.22475     	0.51420     	2           
  500         	0.16609     	0.23062     	0.50033     	4           
  1000        	0.17379     	0.24171     	0.47806     	9           
---> try to load best state
              	0.17379     	0.24171     	0.47806     	9                eval state : test_on_restore;
              	0.17379     	0.24171     	0.47806     	9                eval state : test_on_last_epoch;
---> final result              	0.17379     	0.24171     	0.47806     	9                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_64', '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': False}
  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           	0.14770     	0.22941     	0.63698     	0           
  10          	0.15083     	0.23285     	0.61743     	0           
  100         	0.13992     	0.21271     	0.58257     	2           
  300         	0.14453     	0.21352     	0.54095     	7           
  500         	0.14757     	0.21679     	0.53428     	12          
  1000        	0.15253     	0.22226     	0.52558     	24          
---> try to load best state
              	0.15253     	0.22226     	0.52558     	24               eval state : test_on_restore;
              	0.15253     	0.22226     	0.52558     	24               eval state : test_on_last_epoch;
---> final result              	0.15253     	0.22226     	0.52558     	24               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  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           	0.20929     	0.25268     	-61.67464   	0           
  10          	0.07344     	0.10705     	0.64180     	0           
  100         	0.05476     	0.08241     	0.85579     	0           
  300         	0.04911     	0.07437     	0.89309     	1           
  500         	0.04591     	0.06997     	0.91006     	2           
  1000        	0.04089     	0.06328     	0.93172     	3           
---> try to load best state
              	0.04089     	0.06328     	0.93172     	3                eval state : test_on_restore;
              	0.04089     	0.06328     	0.93172     	3                eval state : test_on_last_epoch;
---> final result              	0.04089     	0.06328     	0.93172     	3                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', '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': False}
  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           	0.19600     	0.26430     	0.06081     	0           
  10          	0.19380     	0.26405     	0.32577     	0           
  epoch 72 reach nan state
---> try to load best state
              	0.20343     	0.26904     	0.32551     	0                eval state : test_on_restore;
              	0.19380     	0.26405     	0.32577     	0                eval state : test_on_last_epoch;
---> final result              	0.19380     	0.26405     	0.32577     	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_64', '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': False}
  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           	0.18245     	0.25921     	0.13760     	0           
  10          	0.18144     	0.25698     	0.35923     	0           
  100         	0.18597     	0.25851     	0.46123     	0           
  300         	0.20238     	0.27978     	0.45400     	1           
  500         	0.21488     	0.29656     	0.43175     	2           
  1000        	0.21851     	0.30054     	0.41819     	5           
---> try to load best state
              	0.21851     	0.30054     	0.41819     	5                eval state : test_on_restore;
              	0.21851     	0.30054     	0.41819     	5                eval state : test_on_last_epoch;
---> final result              	0.21851     	0.30054     	0.41819     	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_64', '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': False}
  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           	0.18365     	0.27995     	0.31666     	0           
  10          	0.18258     	0.27473     	0.32463     	0           
  100         	0.18663     	0.24864     	0.22534     	0           
  epoch 122 reach nan state
---> try to load best state
              	0.18690     	0.24880     	0.22476     	0                eval state : test_on_restore;
              	0.18663     	0.24864     	0.22534     	0                eval state : test_on_last_epoch;
---> final result              	0.18663     	0.24864     	0.22534     	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_64', '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': False}
  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           	0.14992     	0.22650     	0.54735     	0           
  10          	0.15129     	0.22321     	0.56647     	0           
  100         	0.15457     	0.21772     	0.40926     	0           
  300         	0.15924     	0.22298     	0.40131     	2           
  500         	0.16044     	0.22453     	0.39408     	4           
  1000        	0.16435     	0.22955     	0.37491     	9           
---> try to load best state
              	0.16435     	0.22955     	0.37491     	9                eval state : test_on_restore;
              	0.16435     	0.22955     	0.37491     	9                eval state : test_on_last_epoch;
---> final result              	0.16435     	0.22955     	0.37491     	9                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_64', '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': False}
  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           	0.13504     	0.21157     	0.70119     	0           
  10          	0.13685     	0.21203     	0.68094     	0           
  100         	0.12950     	0.19578     	0.64287     	2           
  300         	0.13486     	0.19697     	0.60964     	7           
  500         	0.13707     	0.19920     	0.60781     	11          
  1000        	0.13976     	0.20233     	0.60412     	23          
---> try to load best state
              	0.13976     	0.20233     	0.60412     	23               eval state : test_on_restore;
              	0.13976     	0.20233     	0.60412     	23               eval state : test_on_last_epoch;
---> final result              	0.13976     	0.20233     	0.60412     	23               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  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           	0.20939     	0.25143     	-62.20529   	0           
  10          	0.07474     	0.10587     	0.65481     	0           
  100         	0.05508     	0.08042     	0.86555     	0           
  300         	0.04878     	0.07129     	0.90407     	1           
  500         	0.04531     	0.06649     	0.92087     	1           
  1000        	0.04064     	0.06052     	0.93908     	3           
---> try to load best state
              	0.04064     	0.06052     	0.93908     	3                eval state : test_on_restore;
              	0.04064     	0.06052     	0.93908     	3                eval state : test_on_last_epoch;
---> final result              	0.04064     	0.06052     	0.93908     	3                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', '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': False}
  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           	0.18736     	0.26541     	0.10881     	0           
  10          	0.20032     	0.28277     	0.36346     	0           
  100         	0.23458     	0.32545     	0.36065     	0           
  300         	0.26203     	0.35245     	0.35196     	1           
  500         	0.28537     	0.38402     	0.29311     	2           
  1000        	0.30309     	0.41579     	0.27988     	4           
---> try to load best state
              	0.30309     	0.41579     	0.27988     	4                eval state : test_on_restore;
              	0.30309     	0.41579     	0.27988     	4                eval state : test_on_last_epoch;
---> final result              	0.30309     	0.41579     	0.27988     	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_64', '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': False}
  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           	0.17787     	0.24964     	0.28517     	0           
  10          	0.17818     	0.24916     	0.43921     	0           
  epoch 77 reach nan state
---> try to load best state
              	0.17860     	0.24673     	0.41506     	0                eval state : test_on_restore;
              	0.17818     	0.24916     	0.43921     	0                eval state : test_on_last_epoch;
---> final result              	0.17860     	0.24673     	0.41506     	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_64', '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': False}
  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           	0.18375     	0.27390     	0.46256     	0           
  10          	0.17945     	0.26341     	0.49488     	0           
  100         	0.18735     	0.24382     	0.21951     	0           
  epoch 103 reach nan state
---> try to load best state
              	0.18761     	0.24405     	0.21768     	0                eval state : test_on_restore;
              	0.18735     	0.24382     	0.21951     	0                eval state : test_on_last_epoch;
---> final result              	0.18735     	0.24382     	0.21951     	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_64', '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': False}
  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           	0.17312     	0.26552     	0.58229     	0           
  10          	0.16944     	0.25601     	0.57642     	0           
  100         	0.15060     	0.21554     	0.50172     	0           
  300         	0.15392     	0.21856     	0.50101     	2           
  500         	0.15532     	0.21951     	0.49175     	4           
  1000        	0.15555     	0.22052     	0.51168     	8           
---> try to load best state
              	0.15555     	0.22052     	0.51168     	8                eval state : test_on_restore;
              	0.15555     	0.22052     	0.51168     	8                eval state : test_on_last_epoch;
---> final result              	0.15555     	0.22052     	0.51168     	8                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_64', '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': False}
  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           	0.14820     	0.23315     	0.64494     	0           
  10          	0.15179     	0.23695     	0.62734     	0           
  100         	0.14103     	0.21394     	0.54380     	2           
  300         	0.14523     	0.21328     	0.50162     	7           
  500         	0.14557     	0.21358     	0.50296     	12          
  1000        	0.14621     	0.21405     	0.49693     	24          
---> try to load best state
              	0.14621     	0.21405     	0.49693     	24               eval state : test_on_restore;
              	0.14621     	0.21405     	0.49693     	24               eval state : test_on_last_epoch;
---> final result              	0.14621     	0.21405     	0.49693     	24               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': False}
  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           	0.20896     	0.25040     	-64.02706   	0           
  10          	0.07515     	0.10879     	0.58049     	0           
  100         	0.05428     	0.08416     	0.83753     	0           
  300         	0.04839     	0.07626     	0.88221     	1           
  500         	0.04551     	0.07238     	0.90043     	2           
  1000        	0.04174     	0.06754     	0.92000     	4           
---> try to load best state
              	0.04174     	0.06754     	0.92000     	4                eval state : test_on_restore;
              	0.04174     	0.06754     	0.92000     	4                eval state : test_on_last_epoch;
---> final result              	0.04174     	0.06754     	0.92000     	4                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', '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': False}
  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           	0.20355     	0.27276     	-0.31511    	0           
  10          	0.21746     	0.29605     	0.30437     	0           
  100         	0.24363     	0.32096     	0.38035     	0           
  epoch 118 reach nan state
---> try to load best state
              	0.24445     	0.32190     	0.37888     	0                eval state : test_on_restore;
              	0.24363     	0.32096     	0.38035     	0                eval state : test_on_last_epoch;
---> final result              	0.24363     	0.32096     	0.38035     	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_64', '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': False}
  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           	0.19501     	0.27261     	0.23147     	0           
  10          	0.19989     	0.27208     	0.32771     	0           
  epoch 70 reach nan state
---> try to load best state
              	0.19699     	0.26296     	0.34471     	0                eval state : test_on_restore;
              	0.19989     	0.27208     	0.32771     	0                eval state : test_on_last_epoch;
---> final result              	0.19699     	0.26296     	0.34471     	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_64', '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': False}
  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           	0.17671     	0.26276     	0.15572     	0           
  10          	0.17397     	0.25495     	0.19727     	0           
  100         	0.17286     	0.23320     	0.17991     	0           
  300         	0.17765     	0.23892     	0.19529     	1           
  500         	0.18014     	0.24169     	0.21953     	2           
  1000        	0.18193     	0.24559     	0.22702     	6           
---> try to load best state
              	0.18193     	0.24559     	0.22702     	6                eval state : test_on_restore;
              	0.18193     	0.24559     	0.22702     	6                eval state : test_on_last_epoch;
---> final result              	0.18193     	0.24559     	0.22702     	6                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_64', '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': False}
  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           	0.16674     	0.26052     	0.59875     	0           
  10          	0.16517     	0.25527     	0.58689     	0           
  100         	0.15234     	0.21491     	0.49879     	0           
  300         	0.16181     	0.22201     	0.45204     	2           
  500         	0.16296     	0.22321     	0.46222     	4           
  1000        	0.16573     	0.22544     	0.47105     	8           
---> try to load best state
              	0.16573     	0.22544     	0.47105     	8                eval state : test_on_restore;
              	0.16573     	0.22544     	0.47105     	8                eval state : test_on_last_epoch;
---> final result              	0.16573     	0.22544     	0.47105     	9                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_64', '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': False}
  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           	0.13009     	0.20213     	0.70172     	0           
  10          	0.13642     	0.20870     	0.67730     	0           
  100         	0.12809     	0.19216     	0.62436     	2           
  300         	0.13199     	0.19382     	0.59284     	7           
  500         	0.13351     	0.19519     	0.59124     	12          
  1000        	0.13507     	0.19689     	0.59081     	24          
---> try to load best state
              	0.13507     	0.19689     	0.59081     	24               eval state : test_on_restore;
              	0.13507     	0.19689     	0.59081     	24               eval state : test_on_last_epoch;
---> final result              	0.13507     	0.19689     	0.59081     	24               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': False}
  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           	0.20847     	0.25142     	-59.50669   	0           
  10          	0.07458     	0.10709     	0.61943     	0           
  100         	0.05580     	0.08198     	0.84702     	0           
  300         	0.04906     	0.07231     	0.89518     	1           
  500         	0.04561     	0.06733     	0.91433     	2           
  1000        	0.04110     	0.06104     	0.93379     	4           
---> try to load best state
              	0.04110     	0.06104     	0.93379     	4                eval state : test_on_restore;
              	0.04110     	0.06104     	0.93379     	4                eval state : test_on_last_epoch;
---> final result              	0.04110     	0.06104     	0.93379     	4                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', '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': False}
  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           	0.21852     	0.28264     	-0.90088    	0           
  10          	0.20821     	0.27337     	0.03619     	0           
  100         	0.25773     	0.34133     	0.09848     	0           
  300         	0.30605     	0.41680     	0.10547     	1           
  500         	0.36272     	0.51301     	0.11443     	1           
  1000        	0.39524     	0.56570     	0.03679     	4           
---> try to load best state
              	0.39524     	0.56570     	0.03679     	4                eval state : test_on_restore;
              	0.39524     	0.56570     	0.03679     	4                eval state : test_on_last_epoch;
---> final result              	0.39524     	0.56570     	0.03679     	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_64', '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': False}
  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           	0.19136     	0.26650     	0.18136     	0           
  10          	0.19132     	0.26492     	0.29907     	0           
  100         	0.19587     	0.26715     	0.27706     	0           
  300         	0.20508     	0.27813     	0.29079     	1           
  500         	0.21701     	0.29656     	0.28758     	2           
  1000        	0.23615     	0.32647     	0.23609     	5           
---> try to load best state
              	0.23615     	0.32647     	0.23609     	5                eval state : test_on_restore;
              	0.23615     	0.32647     	0.23609     	5                eval state : test_on_last_epoch;
---> final result              	0.23615     	0.32647     	0.23609     	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_64', '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': False}
  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           	0.16759     	0.25576     	0.41006     	0           
  10          	0.16497     	0.24713     	0.45917     	0           
  100         	0.17321     	0.23431     	0.31995     	0           
  300         	0.17903     	0.24213     	0.31800     	1           
  500         	0.18534     	0.24979     	0.30210     	2           
  1000        	0.19078     	0.25615     	0.28204     	5           
---> try to load best state
              	0.19078     	0.25615     	0.28204     	5                eval state : test_on_restore;
              	0.19078     	0.25615     	0.28204     	5                eval state : test_on_last_epoch;
---> final result              	0.19078     	0.25615     	0.28204     	5                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_64', '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': False}
  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           	0.14680     	0.22646     	0.59842     	0           
  10          	0.14770     	0.22415     	0.59207     	0           
  100         	0.14896     	0.21120     	0.47234     	0           
  300         	0.15010     	0.21284     	0.48783     	2           
  500         	0.15104     	0.21469     	0.48838     	4           
  1000        	0.15191     	0.21766     	0.50005     	8           
---> try to load best state
              	0.15191     	0.21766     	0.50005     	8                eval state : test_on_restore;
              	0.15191     	0.21766     	0.50005     	8                eval state : test_on_last_epoch;
---> final result              	0.15191     	0.21766     	0.50005     	8                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_64', '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': False}
  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           	0.14704     	0.23262     	0.67490     	0           
  10          	0.15068     	0.23543     	0.65883     	0           
  100         	0.13909     	0.20657     	0.63008     	2           
  300         	0.14214     	0.20680     	0.61029     	7           
  500         	0.14376     	0.20864     	0.60739     	12          
  1000        	0.14463     	0.20951     	0.60908     	24          
---> try to load best state
              	0.14463     	0.20951     	0.60908     	24               eval state : test_on_restore;
              	0.14463     	0.20951     	0.60908     	24               eval state : test_on_last_epoch;
---> final result              	0.14463     	0.20951     	0.60908     	24               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': False}
  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           	0.20913     	0.25365     	-60.13519   	0           
  10          	0.07735     	0.11275     	0.60721     	0           
  100         	0.05677     	0.08570     	0.83993     	0           
  300         	0.04993     	0.07655     	0.88497     	1           
  500         	0.04644     	0.07206     	0.90301     	2           
  1000        	0.04194     	0.06643     	0.92251     	4           
---> try to load best state
              	0.04194     	0.06643     	0.92251     	4                eval state : test_on_restore;
              	0.04194     	0.06643     	0.92251     	4                eval state : test_on_last_epoch;
---> final result              	0.04194     	0.06643     	0.92251     	4                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', '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': False}
  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           	0.20059     	0.27509     	-0.27645    	0           
  10          	0.19645     	0.26920     	0.19364     	0           
  100         	0.20044     	0.27356     	0.28628     	0           
  300         	0.23328     	0.30924     	0.07072     	1           
  500         	0.27113     	0.35951     	0.05791     	2           
  1000        	0.30783     	0.42147     	0.05986     	4           
---> try to load best state
              	0.30783     	0.42147     	0.05986     	4                eval state : test_on_restore;
              	0.30783     	0.42147     	0.05986     	4                eval state : test_on_last_epoch;
---> final result              	0.30783     	0.42147     	0.05986     	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_64', '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': False}
  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           	0.19041     	0.27729     	0.30105     	0           
  10          	0.19827     	0.28923     	0.45904     	0           
  100         	0.19433     	0.27198     	0.45639     	0           
  300         	0.20853     	0.28454     	0.41320     	1           
  500         	0.22401     	0.30270     	0.34130     	2           
  1000        	0.24472     	0.32744     	0.26574     	5           
---> try to load best state
              	0.24472     	0.32744     	0.26574     	5                eval state : test_on_restore;
              	0.24472     	0.32744     	0.26574     	5                eval state : test_on_last_epoch;
---> final result              	0.24472     	0.32744     	0.26574     	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_64', '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': False}
  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           	0.17922     	0.26523     	0.39775     	0           
  10          	0.17765     	0.25857     	0.43149     	0           
  100         	0.16815     	0.23630     	0.36691     	0           
  300         	0.17151     	0.23900     	0.37423     	1           
  500         	0.17449     	0.24067     	0.36634     	3           
  1000        	0.17975     	0.24549     	0.35205     	5           
---> try to load best state
              	0.17975     	0.24549     	0.35205     	6                eval state : test_on_restore;
              	0.17975     	0.24549     	0.35205     	6                eval state : test_on_last_epoch;
---> final result              	0.17975     	0.24549     	0.35205     	6                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_64', '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': False}
  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           	0.15884     	0.24089     	0.64112     	0           
  10          	0.15641     	0.23568     	0.63337     	0           
  100         	0.15347     	0.21634     	0.47818     	0           
  300         	0.15795     	0.21970     	0.45423     	2           
  500         	0.15982     	0.22182     	0.44653     	4           
  1000        	0.16137     	0.22422     	0.44014     	8           
---> try to load best state
              	0.16137     	0.22422     	0.44014     	8                eval state : test_on_restore;
              	0.16137     	0.22422     	0.44014     	8                eval state : test_on_last_epoch;
---> final result              	0.16137     	0.22422     	0.44014     	8                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_64', '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': False}
  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           	0.13501     	0.21609     	0.70987     	0           
  10          	0.13958     	0.22018     	0.68307     	0           
  100         	0.13218     	0.20172     	0.64577     	2           
  300         	0.13420     	0.19777     	0.60790     	7           
  500         	0.13510     	0.19816     	0.60584     	12          
  1000        	0.13501     	0.19784     	0.60903     	25          
---> try to load best state
              	0.13501     	0.19784     	0.60903     	25               eval state : test_on_restore;
              	0.13501     	0.19784     	0.60903     	25               eval state : test_on_last_epoch;
---> final result              	0.13501     	0.19784     	0.60903     	25               eval state : final;module_name : SGAR;cp_record_file : True;
