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

  Demo GAR 
  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': 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', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.02819     	0.88067     	0.05604     	0.34234            	0           
  10          	0.02718     	0.88718     	0.05454     	0.23162            	0           
  100         	0.02701     	0.88911     	0.05366     	-0.10283           	0           
  300         	0.02752     	0.88171     	0.05434     	-0.02399           	2           
  500         	0.02774     	0.87922     	0.05459     	0.05833            	3           
  1000        	0.02792     	0.87743     	0.05479     	0.15117            	6           
---> try to load best state
              	0.02792     	0.87743     	0.05479     	0.15117            	6                eval state : test_on_restore;
              	0.02792     	0.87743     	0.05479     	0.15117            	6                eval state : test_on_last_epoch;
---> final result              	0.02792     	0.87743     	0.05479     	0.15117            	6                eval state : final;
----------> finish x-yl training


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.19017     	-0.24851    	0.26120     	0.14370            	0           
  10          	0.19070     	-0.06834    	0.26061     	0.06123            	0           
  100         	0.20370     	-0.02166    	0.27591     	-0.60432           	0           
  300         	0.21710     	-0.10595    	0.29050     	-0.59147           	2           
  epoch 328 reach nan state
---> try to load best state
              	0.21713     	-0.10588    	0.29053     	-0.59120           	2                eval state : test_on_restore;
              	0.21710     	-0.10595    	0.29050     	-0.59147           	2                eval state : test_on_last_epoch;
---> final result
              	0.21710     	-0.10595    	0.29050     	-0.59147           	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.17205     	-0.03920    	0.24931     	0.20653            	0           
  10          	0.17366     	-0.02131    	0.25229     	0.12423            	0           
  100         	0.17111     	-0.01473    	0.24751     	-0.53526           	1           
  300         	0.17162     	-0.00375    	0.23999     	-0.59980           	2           
  epoch 396 reach nan state
---> try to load best state
              	0.17325     	-0.01736    	0.24085     	-0.58835           	3                eval state : test_on_restore;
              	0.17162     	-0.00375    	0.23999     	-0.59980           	3                eval state : test_on_last_epoch;
---> final result
              	0.17162     	-0.00375    	0.23999     	-0.59980           	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.15033     	0.13433     	0.23035     	0.28372            	0           
  10          	0.15596     	0.06856     	0.23715     	0.18951            	0           
  100         	0.14169     	0.17950     	0.21469     	-0.58504           	1           
  300         	0.14728     	0.14436     	0.21278     	-0.58933           	3           
  500         	0.15059     	0.11771     	0.21442     	-0.53491           	6           
  1000        	0.15466     	0.07874     	0.21685     	-0.47541           	12          
---> try to load best state
              	0.15466     	0.07874     	0.21685     	-0.47541           	12               eval state : test_on_restore;
              	0.15466     	0.07874     	0.21685     	-0.47541           	12               eval state : test_on_last_epoch;
---> final result
              	0.15466     	0.07874     	0.21685     	-0.47541           	12               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.14804     	0.22259     	0.24008     	0.33237            	0           
  10          	0.15192     	0.20117     	0.24644     	0.22955            	0           
  100         	0.12524     	0.29317     	0.19592     	-0.62639           	1           
  300         	0.12801     	0.26961     	0.19460     	-0.58045           	5           
  500         	0.12952     	0.26217     	0.19485     	-0.52394           	9           
  1000        	0.13053     	0.25417     	0.19514     	-0.45769           	18          
---> try to load best state
              	0.13053     	0.25417     	0.19514     	-0.45769           	18               eval state : test_on_restore;
              	0.13053     	0.25417     	0.19514     	-0.45769           	18               eval state : test_on_last_epoch;
---> final result
              	0.13053     	0.25417     	0.19514     	-0.45769           	18               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.14064     	0.19405     	0.22813     	0.34717            	0           
  10          	0.13502     	0.24889     	0.21879     	0.22533            	0           
  100         	0.10344     	0.44730     	0.16886     	-0.64312           	3           
  300         	0.10493     	0.43865     	0.16860     	-0.57420           	10          
  500         	0.10580     	0.43564     	0.16877     	-0.51469           	16          
  1000        	0.10635     	0.43253     	0.16893     	-0.44509           	32          
---> try to load best state
              	0.10635     	0.43253     	0.16893     	-0.44509           	32               eval state : test_on_restore;
              	0.10635     	0.43253     	0.16893     	-0.44509           	32               eval state : test_on_last_epoch;
---> final result
              	0.10635     	0.43253     	0.16893     	-0.44509           	32               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  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': 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', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.02866     	0.93184     	0.05013     	0.34632            	0           
  10          	0.02700     	0.93761     	0.04806     	0.24447            	0           
  100         	0.02650     	0.94148     	0.04673     	-0.03590           	0           
  300         	0.02650     	0.94138     	0.04650     	0.05020            	1           
  500         	0.02653     	0.94127     	0.04649     	0.12947            	2           
  1000        	0.02657     	0.94109     	0.04654     	0.22031            	5           
---> try to load best state
              	0.02657     	0.94109     	0.04654     	0.22031            	5                eval state : test_on_restore;
              	0.02657     	0.94109     	0.04654     	0.22031            	5                eval state : test_on_last_epoch;
---> final result              	0.02657     	0.94109     	0.04654     	0.22031            	5                eval state : final;
----------> finish x-yl training


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.18740     	-0.24255    	0.25697     	0.11267            	0           
  10          	0.18210     	-0.02772    	0.25114     	0.02887            	0           
  100         	0.19559     	0.02267     	0.26977     	-0.61963           	0           
  300         	0.19570     	0.02112     	0.26733     	-0.60159           	2           
  500         	0.19558     	0.01782     	0.26669     	-0.54949           	3           
  1000        	0.19650     	-0.00866    	0.26727     	-0.49219           	7           
---> try to load best state
              	0.19650     	-0.00866    	0.26727     	-0.49219           	7                eval state : test_on_restore;
              	0.19650     	-0.00866    	0.26727     	-0.49219           	7                eval state : test_on_last_epoch;
---> final result
              	0.19650     	-0.00866    	0.26727     	-0.49219           	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.17049     	0.00137     	0.24603     	0.17889            	0           
  10          	0.16847     	0.08805     	0.24373     	0.09634            	0           
  100         	0.16816     	0.16624     	0.23934     	-0.59153           	0           
  300         	0.17328     	0.16366     	0.23875     	-0.61718           	2           
  500         	0.17450     	0.15644     	0.23946     	-0.56562           	4           
  1000        	0.17576     	0.14668     	0.24058     	-0.49675           	8           
---> try to load best state
              	0.17576     	0.14668     	0.24058     	-0.49675           	8                eval state : test_on_restore;
              	0.17576     	0.14668     	0.24058     	-0.49675           	8                eval state : test_on_last_epoch;
---> final result
              	0.17576     	0.14668     	0.24058     	-0.49675           	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.17516     	0.14482     	0.26374     	0.23609            	0           
  10          	0.18191     	0.13775     	0.27335     	0.15781            	0           
  100         	0.16836     	0.18440     	0.23421     	-0.65197           	1           
  300         	0.17270     	0.15674     	0.23534     	-0.60845           	3           
  500         	0.17394     	0.15195     	0.23629     	-0.55338           	6           
  1000        	0.17473     	0.15154     	0.23695     	-0.48699           	12          
---> try to load best state
              	0.17473     	0.15154     	0.23695     	-0.48699           	12               eval state : test_on_restore;
              	0.17473     	0.15154     	0.23695     	-0.48699           	12               eval state : test_on_last_epoch;
---> final result
              	0.17473     	0.15154     	0.23695     	-0.48699           	12               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.15985     	0.23207     	0.25357     	0.32148            	0           
  10          	0.15855     	0.23151     	0.25115     	0.21391            	0           
  100         	0.13683     	0.35114     	0.20411     	-0.64762           	1           
  300         	0.14021     	0.34117     	0.20476     	-0.59504           	5           
  500         	0.14167     	0.33576     	0.20554     	-0.53464           	9           
  1000        	0.14277     	0.33157     	0.20619     	-0.46459           	18          
---> try to load best state
              	0.14277     	0.33157     	0.20619     	-0.46459           	18               eval state : test_on_restore;
              	0.14277     	0.33157     	0.20619     	-0.46459           	18               eval state : test_on_last_epoch;
---> final result
              	0.14277     	0.33157     	0.20619     	-0.46459           	18               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.15466     	0.33190     	0.26004     	0.37840            	0           
  10          	0.14934     	0.34573     	0.25098     	0.30725            	0           
  100         	0.10743     	0.55671     	0.16753     	-0.64913           	3           
  300         	0.11278     	0.54381     	0.17108     	-0.59449           	9           
  500         	0.11490     	0.53266     	0.17291     	-0.53432           	16          
  epoch 881 reach nan state
---> try to load best state
              	0.11641     	0.52410     	0.17430     	-0.47539           	28               eval state : test_on_restore;
              	0.11490     	0.53266     	0.17291     	-0.53432           	28               eval state : test_on_last_epoch;
---> final result
              	0.11490     	0.53266     	0.17291     	-0.53432           	28               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  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': 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', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.03870     	0.88605     	0.06376     	0.34700            	0           
  10          	0.03679     	0.89560     	0.06136     	0.25057            	0           
  100         	0.03916     	0.87777     	0.06423     	-0.12581           	0           
  300         	0.04134     	0.87009     	0.06658     	-0.01276           	1           
  500         	0.04175     	0.86933     	0.06692     	0.08202            	2           
  1000        	0.04197     	0.86923     	0.06710     	0.17420            	5           
---> try to load best state
              	0.04197     	0.86923     	0.06710     	0.17420            	5                eval state : test_on_restore;
              	0.04197     	0.86923     	0.06710     	0.17420            	5                eval state : test_on_last_epoch;
---> final result              	0.04197     	0.86923     	0.06710     	0.17420            	5                eval state : final;
----------> finish x-yl training


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.19283     	-0.26629    	0.26107     	0.11372            	0           
  10          	0.18852     	-0.03220    	0.25636     	0.03041            	0           
  100         	0.20095     	0.02687     	0.27486     	-0.61219           	0           
  300         	0.20017     	0.03197     	0.27199     	-0.59537           	2           
  500         	0.20018     	0.03232     	0.27138     	-0.54414           	3           
  1000        	0.20107     	0.01319     	0.27166     	-0.48793           	6           
---> try to load best state
              	0.20107     	0.01319     	0.27166     	-0.48793           	6                eval state : test_on_restore;
              	0.20107     	0.01319     	0.27166     	-0.48793           	6                eval state : test_on_last_epoch;
---> final result
              	0.20107     	0.01319     	0.27166     	-0.48793           	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.17742     	-0.03281    	0.25069     	0.17988            	0           
  10          	0.17637     	0.05170     	0.24906     	0.09763            	0           
  100         	0.17681     	0.14066     	0.24658     	-0.58359           	0           
  300         	0.18110     	0.15903     	0.24620     	-0.60866           	2           
  500         	0.18220     	0.15638     	0.24663     	-0.55846           	4           
  1000        	0.18309     	0.15065     	0.24706     	-0.49128           	8           
---> try to load best state
              	0.18309     	0.15065     	0.24706     	-0.49128           	8                eval state : test_on_restore;
              	0.18309     	0.15065     	0.24706     	-0.49128           	8                eval state : test_on_last_epoch;
---> final result
              	0.18309     	0.15065     	0.24706     	-0.49128           	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.18274     	0.10859     	0.26797     	0.23701            	0           
  10          	0.19092     	0.09697     	0.27895     	0.15928            	0           
  100         	0.17632     	0.16625     	0.24130     	-0.64334           	1           
  300         	0.18115     	0.15713     	0.24339     	-0.59983           	3           
  500         	0.18245     	0.15372     	0.24434     	-0.54587           	6           
  1000        	0.18326     	0.15266     	0.24487     	-0.48070           	12          
---> try to load best state
              	0.18326     	0.15266     	0.24487     	-0.48070           	12               eval state : test_on_restore;
              	0.18326     	0.15266     	0.24487     	-0.48070           	12               eval state : test_on_last_epoch;
---> final result
              	0.18326     	0.15266     	0.24487     	-0.48070           	12               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.16738     	0.26452     	0.25611     	0.32190            	0           
  10          	0.16642     	0.26223     	0.25375     	0.21443            	0           
  100         	0.14754     	0.33684     	0.21117     	-0.64086           	1           
  300         	0.15104     	0.32641     	0.21256     	-0.58855           	5           
  500         	0.15254     	0.32023     	0.21347     	-0.52891           	9           
  1000        	0.15373     	0.31484     	0.21422     	-0.45966           	19          
---> try to load best state
              	0.15373     	0.31484     	0.21422     	-0.45966           	19               eval state : test_on_restore;
              	0.15373     	0.31484     	0.21422     	-0.45966           	19               eval state : test_on_last_epoch;
---> final result
              	0.15373     	0.31484     	0.21422     	-0.45966           	19               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.16404     	0.46541     	0.26457     	0.37898            	0           
  10          	0.15948     	0.46231     	0.25622     	0.30800            	0           
  100         	0.12252     	0.52663     	0.17888     	-0.64087           	3           
  300         	0.12765     	0.50887     	0.18299     	-0.58665           	10          
  500         	0.12960     	0.49845     	0.18481     	-0.52738           	16          
  epoch 881 reach nan state
---> try to load best state
              	0.13097     	0.49082     	0.18611     	-0.46929           	27               eval state : test_on_restore;
              	0.12960     	0.49845     	0.18481     	-0.52738           	27               eval state : test_on_last_epoch;
---> final result
              	0.12960     	0.49845     	0.18481     	-0.52738           	27               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  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': 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', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.02744     	0.87710     	0.05250     	0.34138            	0           
  10          	0.02623     	0.88023     	0.05091     	0.23330            	0           
  100         	0.02585     	0.88119     	0.04984     	-0.10013           	0           
  300         	0.02614     	0.88223     	0.04981     	-0.01693           	1           
  500         	0.02631     	0.88170     	0.04992     	0.06698            	2           
  1000        	0.02648     	0.88147     	0.05010     	0.16370            	4           
---> try to load best state
              	0.02648     	0.88147     	0.05010     	0.16370            	4                eval state : test_on_restore;
              	0.02648     	0.88147     	0.05010     	0.16370            	4                eval state : test_on_last_epoch;
---> final result              	0.02648     	0.88147     	0.05010     	0.16370            	4                eval state : final;
----------> finish x-yl training


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.18658     	-0.28918    	0.25592     	0.11243            	0           
  10          	0.18226     	-0.08949    	0.25146     	0.02901            	0           
  100         	0.19349     	-0.01952    	0.26877     	-0.62073           	0           
  300         	0.19291     	-0.03252    	0.26560     	-0.60374           	2           
  500         	0.19303     	-0.03684    	0.26508     	-0.55123           	3           
  1000        	0.19428     	-0.05562    	0.26581     	-0.49360           	6           
---> try to load best state
              	0.19428     	-0.05562    	0.26581     	-0.49360           	6                eval state : test_on_restore;
              	0.19428     	-0.05562    	0.26581     	-0.49360           	6                eval state : test_on_last_epoch;
---> final result
              	0.19428     	-0.05562    	0.26581     	-0.49360           	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.16981     	-0.04867    	0.24513     	0.17867            	0           
  10          	0.16831     	0.02429     	0.24387     	0.09633            	0           
  100         	0.16821     	0.12520     	0.24041     	-0.59053           	0           
  300         	0.17303     	0.12296     	0.23966     	-0.61637           	2           
  500         	0.17447     	0.11561     	0.24054     	-0.56477           	3           
  1000        	0.17614     	0.10712     	0.24208     	-0.49571           	7           
---> try to load best state
              	0.17614     	0.10712     	0.24208     	-0.49571           	7                eval state : test_on_restore;
              	0.17614     	0.10712     	0.24208     	-0.49571           	7                eval state : test_on_last_epoch;
---> final result
              	0.17614     	0.10712     	0.24208     	-0.49571           	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.17483     	0.10363     	0.26291     	0.23587            	0           
  10          	0.18223     	0.07373     	0.27350     	0.15781            	0           
  100         	0.16925     	0.12826     	0.23528     	-0.65080           	1           
  300         	0.17366     	0.10503     	0.23712     	-0.60668           	3           
  500         	0.17509     	0.10160     	0.23830     	-0.55164           	5           
  1000        	0.17624     	0.10197     	0.23933     	-0.48523           	12          
---> try to load best state
              	0.17624     	0.10197     	0.23933     	-0.48523           	12               eval state : test_on_restore;
              	0.17624     	0.10197     	0.23933     	-0.48523           	12               eval state : test_on_last_epoch;
---> final result
              	0.17624     	0.10197     	0.23933     	-0.48523           	12               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.16420     	0.26470     	0.25556     	0.32172            	0           
  10          	0.16337     	0.26385     	0.25330     	0.21425            	0           
  100         	0.14316     	0.32603     	0.20885     	-0.64373           	1           
  300         	0.14679     	0.31328     	0.21027     	-0.59104           	5           
  500         	0.14816     	0.30722     	0.21110     	-0.53114           	9           
  1000        	0.14929     	0.30274     	0.21188     	-0.46156           	18          
---> try to load best state
              	0.14929     	0.30274     	0.21188     	-0.46156           	18               eval state : test_on_restore;
              	0.14929     	0.30274     	0.21188     	-0.46156           	18               eval state : test_on_last_epoch;
---> final result
              	0.14929     	0.30274     	0.21188     	-0.46156           	18               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.15966     	0.45406     	0.26385     	0.37889            	0           
  10          	0.15473     	0.45105     	0.25475     	0.30780            	0           
  100         	0.11350     	0.52351     	0.17224     	-0.64601           	3           
  300         	0.11897     	0.50523     	0.17634     	-0.59137           	10          
  500         	0.12097     	0.49495     	0.17813     	-0.53161           	16          
  epoch 881 reach nan state
---> try to load best state
              	0.12245     	0.48727     	0.17952     	-0.47302           	28               eval state : test_on_restore;
              	0.12097     	0.49495     	0.17813     	-0.53161           	28               eval state : test_on_last_epoch;
---> final result
              	0.12097     	0.49495     	0.17813     	-0.53161           	28               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  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': 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', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.03102     	0.93458     	0.05122     	0.34531            	0           
  10          	0.03020     	0.93676     	0.05044     	0.24107            	0           
  100         	0.03143     	0.93086     	0.05125     	-0.15075           	0           
  300         	0.03257     	0.92786     	0.05246     	-0.03223           	1           
  500         	0.03303     	0.92637     	0.05295     	0.06328            	2           
  1000        	0.03327     	0.92567     	0.05322     	0.16485            	5           
---> try to load best state
              	0.03327     	0.92567     	0.05322     	0.16485            	5                eval state : test_on_restore;
              	0.03327     	0.92567     	0.05322     	0.16485            	5                eval state : test_on_last_epoch;
---> final result              	0.03327     	0.92567     	0.05322     	0.16485            	5                eval state : final;
----------> finish x-yl training


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.19201     	-0.26773    	0.26070     	0.11363            	0           
  10          	0.18700     	-0.05743    	0.25516     	0.03004            	0           
  100         	0.19734     	-0.00132    	0.26992     	-0.61951           	0           
  300         	0.19649     	-0.00125    	0.26653     	-0.60278           	2           
  500         	0.19649     	-0.00430    	0.26587     	-0.55054           	3           
  1000        	0.19771     	-0.02843    	0.26666     	-0.49293           	7           
---> try to load best state
              	0.19771     	-0.02843    	0.26666     	-0.49293           	7                eval state : test_on_restore;
              	0.19771     	-0.02843    	0.26666     	-0.49293           	7                eval state : test_on_last_epoch;
---> final result
              	0.19771     	-0.02843    	0.26666     	-0.49293           	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.17596     	-0.03496    	0.24961     	0.17968            	0           
  10          	0.17365     	0.04579     	0.24725     	0.09721            	0           
  100         	0.17268     	0.12275     	0.24275     	-0.58773           	0           
  300         	0.17604     	0.12908     	0.24104     	-0.61454           	2           
  500         	0.17719     	0.12202     	0.24153     	-0.56352           	4           
  1000        	0.17843     	0.11214     	0.24245     	-0.49513           	8           
---> try to load best state
              	0.17843     	0.11214     	0.24245     	-0.49513           	8                eval state : test_on_restore;
              	0.17843     	0.11214     	0.24245     	-0.49513           	8                eval state : test_on_last_epoch;
---> final result
              	0.17843     	0.11214     	0.24245     	-0.49513           	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.17985     	0.10017     	0.26640     	0.23669            	0           
  10          	0.18599     	0.08250     	0.27564     	0.15843            	0           
  100         	0.17231     	0.14718     	0.23759     	-0.64795           	1           
  300         	0.17618     	0.12307     	0.23829     	-0.60539           	3           
  500         	0.17739     	0.11644     	0.23905     	-0.55089           	6           
  1000        	0.17816     	0.11391     	0.23950     	-0.48504           	12          
---> try to load best state
              	0.17816     	0.11391     	0.23950     	-0.48504           	12               eval state : test_on_restore;
              	0.17816     	0.11391     	0.23950     	-0.48504           	12               eval state : test_on_last_epoch;
---> final result
              	0.17816     	0.11391     	0.23950     	-0.48504           	12               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.16459     	0.24694     	0.25602     	0.32193            	0           
  10          	0.16294     	0.24717     	0.25288     	0.21433            	0           
  100         	0.14257     	0.32089     	0.20877     	-0.64301           	2           
  300         	0.14537     	0.30969     	0.20912     	-0.59135           	5           
  500         	0.14658     	0.30368     	0.20971     	-0.53157           	9           
  1000        	0.14752     	0.29880     	0.21021     	-0.46209           	19          
---> try to load best state
              	0.14752     	0.29880     	0.21021     	-0.46209           	19               eval state : test_on_restore;
              	0.14752     	0.29880     	0.21021     	-0.46209           	19               eval state : test_on_last_epoch;
---> final result
              	0.14752     	0.29880     	0.21021     	-0.46209           	19               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.16099     	0.43614     	0.26344     	0.37883            	0           
  10          	0.15549     	0.44263     	0.25379     	0.30766            	0           
  100         	0.11568     	0.52096     	0.17306     	-0.64516           	3           
  300         	0.12056     	0.50559     	0.17645     	-0.59104           	10          
  500         	0.12250     	0.49380     	0.17822     	-0.53131           	15          
  epoch 881 reach nan state
---> try to load best state
              	0.12389     	0.48450     	0.17958     	-0.47274           	27               eval state : test_on_restore;
              	0.12250     	0.49380     	0.17822     	-0.53131           	27               eval state : test_on_last_epoch;
---> final result
              	0.12250     	0.49380     	0.17822     	-0.53131           	27               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  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': 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', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.03187     	0.89269     	0.05827     	0.35045            	0           
  10          	0.03024     	0.89981     	0.05568     	0.24985            	0           
  100         	0.03034     	0.89615     	0.05561     	-0.07839           	0           
  300         	0.03172     	0.89106     	0.05779     	-0.00456           	1           
  500         	0.03189     	0.89077     	0.05799     	0.08123            	2           
  1000        	0.03202     	0.89056     	0.05813     	0.16063            	4           
---> try to load best state
              	0.03202     	0.89056     	0.05813     	0.16063            	4                eval state : test_on_restore;
              	0.03202     	0.89056     	0.05813     	0.16063            	4                eval state : test_on_last_epoch;
---> final result              	0.03202     	0.89056     	0.05813     	0.16063            	4                eval state : final;
----------> finish x-yl training


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.19164     	-0.34137    	0.25984     	0.11342            	0           
  10          	0.18484     	-0.11965    	0.25306     	0.02947            	0           
  100         	0.19398     	-0.07174    	0.26749     	-0.62278           	0           
  300         	0.19337     	-0.08555    	0.26417     	-0.60583           	2           
  500         	0.19334     	-0.09368    	0.26358     	-0.55310           	3           
  1000        	0.19453     	-0.11714    	0.26435     	-0.49515           	5           
---> try to load best state
              	0.19453     	-0.11714    	0.26435     	-0.49515           	5                eval state : test_on_restore;
              	0.19453     	-0.11714    	0.26435     	-0.49515           	5                eval state : test_on_last_epoch;
---> final result
              	0.19453     	-0.11714    	0.26435     	-0.49515           	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.17538     	-0.09141    	0.24915     	0.17955            	0           
  10          	0.17304     	-0.01301    	0.24700     	0.09711            	0           
  100         	0.17169     	0.03772     	0.24256     	-0.58819           	0           
  300         	0.17497     	0.04291     	0.24080     	-0.61495           	2           
  500         	0.17605     	0.03359     	0.24137     	-0.56381           	4           
  1000        	0.17711     	0.02172     	0.24232     	-0.49537           	7           
---> try to load best state
              	0.17711     	0.02172     	0.24232     	-0.49537           	7                eval state : test_on_restore;
              	0.17711     	0.02172     	0.24232     	-0.49537           	7                eval state : test_on_last_epoch;
---> final result
              	0.17711     	0.02172     	0.24232     	-0.49537           	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.18026     	0.05920     	0.26725     	0.23680            	0           
  10          	0.18694     	0.04511     	0.27683     	0.15866            	0           
  100         	0.17241     	0.09036     	0.23805     	-0.64775           	1           
  300         	0.17665     	0.05980     	0.23941     	-0.60454           	3           
  500         	0.17792     	0.05266     	0.24033     	-0.55000           	5           
  1000        	0.17876     	0.04993     	0.24099     	-0.48413           	10          
---> try to load best state
              	0.17876     	0.04993     	0.24099     	-0.48413           	10               eval state : test_on_restore;
              	0.17876     	0.04993     	0.24099     	-0.48413           	10               eval state : test_on_last_epoch;
---> final result
              	0.17876     	0.04993     	0.24099     	-0.48413           	10               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.16443     	0.21695     	0.25680     	0.32201            	0           
  10          	0.16337     	0.22451     	0.25406     	0.21449            	0           
  100         	0.14356     	0.27215     	0.20991     	-0.64221           	1           
  300         	0.14661     	0.25300     	0.21078     	-0.59020           	4           
  500         	0.14797     	0.24532     	0.21154     	-0.53046           	8           
  1000        	0.14906     	0.23987     	0.21222     	-0.46103           	17          
---> try to load best state
              	0.14906     	0.23987     	0.21222     	-0.46103           	17               eval state : test_on_restore;
              	0.14906     	0.23987     	0.21222     	-0.46103           	17               eval state : test_on_last_epoch;
---> final result
              	0.14906     	0.23987     	0.21222     	-0.46103           	17               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR 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', 'gaussian_loss']
  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         	r2          	rmse        	gaussian_loss      	time        
  1           	0.16096     	0.42809     	0.26472     	0.37902            	0           
  10          	0.15597     	0.43608     	0.25538     	0.30793            	0           
  100         	0.11553     	0.48031     	0.17501     	-0.64360           	3           
  300         	0.12065     	0.45650     	0.17867     	-0.58955           	10          
  500         	0.12272     	0.44372     	0.18049     	-0.52998           	16          
  epoch 881 reach nan state
---> try to load best state
              	0.12420     	0.43424     	0.18188     	-0.47156           	28               eval state : test_on_restore;
              	0.12272     	0.44372     	0.18049     	-0.52998           	28               eval state : test_on_last_epoch;
---> final result
              	0.12272     	0.44372     	0.18049     	-0.52998           	28               eval state : final;module_name : GAR;cp_record_file : True;
