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

  Demo GAR 
  seed: None 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', '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_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           	0.00802     	0.80738     	0.01627     	0.38366            	0           
  10          	0.00803     	0.81914     	0.01663     	0.29096            	0           
  100         	0.00941     	0.82196     	0.01750     	1.05215            	0           
  300         	0.00914     	0.84008     	0.01698     	1.73141            	0           
  500         	0.00898     	0.83650     	0.01675     	1.33645            	1           
  1000        	0.00889     	0.82986     	0.01649     	1.81230            	3           
---> try to load best state
              	0.00889     	0.82986     	0.01649     	1.81230            	3                eval state : test_on_restore;
              	0.00889     	0.82986     	0.01649     	1.81230            	3                eval state : test_on_last_epoch;
---> final result              	0.00889     	0.82986     	0.01649     	1.81230            	3                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_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           	0.03826     	0.52731     	0.07535     	0.22525            	0           
  10          	0.03546     	0.52302     	0.07167     	0.12766            	0           
  100         	0.02006     	0.78404     	0.03809     	-2.00279           	0           
  300         	0.01691     	0.84495     	0.03345     	-2.33525           	1           
  500         	0.01644     	0.87226     	0.03308     	-2.34600           	2           
  1000        	0.01594     	0.85101     	0.03264     	-2.36781           	4           
---> try to load best state
              	0.01594     	0.85101     	0.03264     	-2.36781           	4                eval state : test_on_restore;
              	0.01594     	0.85101     	0.03264     	-2.36781           	4                eval state : test_on_last_epoch;
---> final result
              	0.01594     	0.85101     	0.03264     	-2.36781           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_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           	0.02294     	0.77517     	0.05603     	0.30646            	0           
  10          	0.02161     	0.78103     	0.05210     	0.19976            	0           
  100         	0.01652     	0.85696     	0.03335     	-1.65965           	0           
  300         	0.01621     	0.87592     	0.03216     	-1.98128           	1           
  500         	0.01601     	0.81335     	0.03193     	-1.99614           	2           
  1000        	0.01535     	0.75426     	0.03151     	-2.01907           	5           
---> try to load best state
              	0.01535     	0.75426     	0.03151     	-2.01907           	5                eval state : test_on_restore;
              	0.01535     	0.75426     	0.03151     	-2.01907           	5                eval state : test_on_last_epoch;
---> final result
              	0.01535     	0.75426     	0.03151     	-2.01907           	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_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           	0.01463     	0.85842     	0.03821     	0.34029            	0           
  10          	0.01361     	0.85874     	0.03612     	0.23067            	0           
  100         	0.01487     	0.78893     	0.03118     	-1.18534           	0           
  300         	0.01502     	0.87101     	0.03046     	-1.37170           	1           
  500         	0.01484     	0.78370     	0.03064     	-1.42139           	3           
----------------------------------------

  Demo GAR 
  seed: None 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', '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_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           	0.00802     	0.80738     	0.01627     	0.38366            	0           
  10          	0.00803     	0.81914     	0.01663     	0.29096            	0           
  100         	0.00941     	0.82196     	0.01750     	1.05215            	0           
  300         	0.00914     	0.84008     	0.01698     	1.73141            	1           
  500         	0.00898     	0.83650     	0.01675     	1.33645            	1           
  1000        	0.00889     	0.82986     	0.01649     	1.81230            	3           
---> try to load best state
              	0.00889     	0.82986     	0.01649     	1.81230            	3                eval state : test_on_restore;
              	0.00889     	0.82986     	0.01649     	1.81230            	3                eval state : test_on_last_epoch;
---> final result              	0.00889     	0.82986     	0.01649     	1.81230            	3                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.03361     	0.57803     	0.06278     	0.22472            	0           
  10          	0.03751     	0.39592     	0.06037     	0.12701            	0           
  100         	0.01610     	0.75489     	0.02879     	-2.16200           	0           
  300         	0.01337     	0.87908     	0.02657     	-2.36064           	1           
  500         	0.01319     	0.87952     	0.02643     	-2.37077           	1           
  1000        	0.01301     	0.58658     	0.02628     	-2.39243           	3           
---> try to load best state
              	0.01301     	0.58658     	0.02628     	-2.39243           	3                eval state : test_on_restore;
              	0.01301     	0.58658     	0.02628     	-2.39243           	3                eval state : test_on_last_epoch;
---> final result
              	0.01301     	0.58658     	0.02628     	-2.39243           	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.02022     	0.77470     	0.04623     	0.30619            	0           
  10          	0.01879     	0.79765     	0.03959     	0.19924            	0           
  100         	0.01402     	0.84039     	0.02653     	-1.78796           	0           
  300         	0.01298     	0.88204     	0.02589     	-2.00178           	1           
  500         	0.01280     	0.88193     	0.02581     	-2.01160           	2           
  1000        	0.01256     	0.87482     	0.02575     	-2.03373           	4           
---> try to load best state
              	0.01256     	0.87482     	0.02575     	-2.03373           	4                eval state : test_on_restore;
              	0.01256     	0.87482     	0.02575     	-2.03373           	4                eval state : test_on_last_epoch;
---> final result
              	0.01256     	0.87482     	0.02575     	-2.03373           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.01283     	0.85059     	0.03231     	0.34018            	0           
  10          	0.01251     	0.85688     	0.02947     	0.23043            	0           
  100         	0.01322     	0.76276     	0.02552     	-1.29601           	0           
  300         	0.01455     	-2.59546    	0.02614     	-1.46398           	1           
  500         	0.01242     	0.88427     	0.02517     	-1.43942           	2           
  1000        	0.01229     	0.87721     	0.02548     	-1.52794           	4           
---> try to load best state
              	0.01229     	0.87721     	0.02548     	-1.52794           	4                eval state : test_on_restore;
              	0.01229     	0.87721     	0.02548     	-1.52794           	4                eval state : test_on_last_epoch;
---> final result
              	0.01229     	0.87721     	0.02548     	-1.52794           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.00776     	0.89926     	0.02177     	0.37987            	0           
  10          	0.00940     	0.89180     	0.02294     	0.28692            	0           
  100         	0.01251     	0.61507     	0.02442     	0.58234            	0           
  300         	0.01228     	-0.73322    	0.02414     	0.90772            	1           
  500         	0.01220     	0.09793     	0.02437     	1.42564            	2           
  1000        	0.01209     	-0.35677    	0.02475     	-0.11673           	5           
---> try to load best state
              	0.01209     	-0.35677    	0.02475     	-0.11673           	5                eval state : test_on_restore;
              	0.01209     	-0.35677    	0.02475     	-0.11673           	5                eval state : test_on_last_epoch;
---> final result
              	0.01209     	-0.35677    	0.02475     	-0.11673           	5                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': 'Heat_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', '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_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           	0.01547     	0.63419     	0.04097     	0.51214            	0           
  10          	0.01466     	0.63123     	0.03873     	0.46554            	0           
  100         	0.01497     	0.65592     	0.03468     	0.55035            	0           
  300         	0.01352     	0.68463     	0.03121     	0.96806            	1           
  500         	0.01322     	0.69074     	0.03044     	1.59234            	2           
  1000        	0.01300     	0.69482     	0.02980     	3.34693            	4           
---> try to load best state
              	0.01300     	0.69482     	0.02980     	3.34693            	4                eval state : test_on_restore;
              	0.01300     	0.69482     	0.02980     	3.34693            	4                eval state : test_on_last_epoch;
---> final result              	0.01300     	0.69482     	0.02980     	3.34693            	4                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.03213     	0.01804     	0.05656     	0.17909            	0           
  10          	0.02319     	0.30907     	0.04427     	0.08749            	0           
  100         	0.01483     	0.67169     	0.03095     	-1.85961           	0           
  epoch 274 reach nan state
---> try to load best state
              	0.01355     	0.70228     	0.03179     	-1.94381           	1                eval state : test_on_restore;
              	0.01483     	0.67169     	0.03095     	-1.85961           	1                eval state : test_on_last_epoch;
---> final result
              	0.01483     	0.67169     	0.03095     	-1.85961           	1                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.02238     	0.41415     	0.04657     	0.25933            	0           
  10          	0.01571     	0.58807     	0.03776     	0.16226            	0           
  100         	0.01410     	0.68262     	0.03078     	-1.62831           	0           
  300         	0.01350     	0.65662     	0.03226     	-1.80355           	1           
  500         	0.01417     	0.59454     	0.03244     	-1.81308           	2           
  1000        	0.01348     	0.62456     	0.03245     	-1.83597           	4           
---> try to load best state
              	0.01348     	0.62456     	0.03245     	-1.83597           	4                eval state : test_on_restore;
              	0.01348     	0.62456     	0.03245     	-1.83597           	4                eval state : test_on_last_epoch;
---> final result
              	0.01348     	0.62456     	0.03245     	-1.83597           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.01313     	0.76006     	0.03116     	0.33302            	0           
  10          	0.01156     	0.77062     	0.02885     	0.22609            	0           
  100         	0.01348     	0.67087     	0.03119     	-1.29454           	0           
  300         	0.01356     	0.67123     	0.03240     	-1.45609           	1           
  500         	0.01357     	0.65807     	0.03241     	-1.46579           	2           
  1000        	0.01359     	0.63408     	0.03257     	-1.48892           	4           
---> try to load best state
              	0.01359     	0.63408     	0.03257     	-1.48892           	4                eval state : test_on_restore;
              	0.01359     	0.63408     	0.03257     	-1.48892           	4                eval state : test_on_last_epoch;
---> final result
              	0.01359     	0.63408     	0.03257     	-1.48892           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.01088     	0.77087     	0.02912     	0.50932            	0           
  10          	0.01084     	0.75739     	0.02868     	0.45780            	0           
  100         	0.01298     	0.68312     	0.03108     	0.20278            	0           
  300         	0.01345     	0.67569     	0.03211     	0.50262            	1           
  epoch 337 reach nan state
---> try to load best state
              	0.01355     	0.67014     	0.03218     	3.64333            	1                eval state : test_on_restore;
              	0.01345     	0.67569     	0.03211     	0.50262            	1                eval state : test_on_last_epoch;
---> final result
              	0.01345     	0.67569     	0.03211     	0.50262            	1                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': 'Heat_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', '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_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           	0.01039     	0.76961     	0.02555     	0.40325            	0           
  10          	0.01035     	0.76382     	0.02565     	0.31797            	0           
  100         	0.01195     	0.75313     	0.02681     	2.00837            	0           
  300         	0.01155     	0.78528     	0.02494     	1.23636            	1           
  500         	0.01157     	0.79038     	0.02441     	1.87587            	2           
  1000        	0.01158     	0.79397     	0.02383     	2.23117            	4           
---> try to load best state
              	0.01158     	0.79397     	0.02383     	2.23117            	4                eval state : test_on_restore;
              	0.01158     	0.79397     	0.02383     	2.23117            	4                eval state : test_on_last_epoch;
---> final result              	0.01158     	0.79397     	0.02383     	2.23117            	4                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.03118     	0.08223     	0.05438     	0.17900            	0           
  10          	0.02301     	0.41258     	0.04264     	0.08744            	0           
  100         	0.01435     	0.68599     	0.02650     	-1.86801           	0           
  epoch 274 reach nan state
---> try to load best state
              	0.01273     	0.82100     	0.02635     	-1.95669           	1                eval state : test_on_restore;
              	0.01435     	0.68599     	0.02650     	-1.86801           	1                eval state : test_on_last_epoch;
---> final result
              	0.01273     	0.82100     	0.02635     	-1.95669           	1                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.02187     	0.47689     	0.04452     	0.25928            	0           
  10          	0.01532     	0.67383     	0.03541     	0.16221            	0           
  100         	0.01355     	0.78296     	0.02594     	-1.63100           	0           
  300         	0.01249     	0.80395     	0.02666     	-1.80693           	1           
  500         	0.01328     	0.76090     	0.02702     	-1.81644           	2           
  1000        	0.01241     	0.79991     	0.02682     	-1.83977           	5           
---> try to load best state
              	0.01241     	0.79991     	0.02682     	-1.83977           	5                eval state : test_on_restore;
              	0.01241     	0.79991     	0.02682     	-1.83977           	5                eval state : test_on_last_epoch;
---> final result
              	0.01241     	0.79991     	0.02682     	-1.83977           	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.01277     	0.80587     	0.02845     	0.33297            	0           
  10          	0.01137     	0.83213     	0.02546     	0.22604            	0           
  100         	0.01283     	0.79433     	0.02592     	-1.29448           	0           
  300         	0.01245     	0.81585     	0.02666     	-1.45518           	1           
  500         	0.01252     	0.81032     	0.02669     	-1.46487           	2           
  1000        	0.01243     	0.80714     	0.02676     	-1.48803           	4           
---> try to load best state
              	0.01243     	0.80714     	0.02676     	-1.48803           	4                eval state : test_on_restore;
              	0.01243     	0.80714     	0.02676     	-1.48803           	4                eval state : test_on_last_epoch;
---> final result
              	0.01243     	0.80714     	0.02676     	-1.48803           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.01044     	0.81965     	0.02615     	0.50931            	0           
  10          	0.01038     	0.82948     	0.02508     	0.45779            	0           
  100         	0.01242     	0.80061     	0.02583     	0.20281            	0           
  300         	0.01255     	0.78786     	0.02651     	0.50261            	1           
  epoch 337 reach nan state
---> try to load best state
              	0.01263     	0.80522     	0.02657     	3.64332            	1                eval state : test_on_restore;
              	0.01255     	0.78786     	0.02651     	0.50261            	1                eval state : test_on_last_epoch;
---> final result
              	0.01255     	0.78786     	0.02651     	0.50261            	1                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': 'Heat_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', '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_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           	0.01516     	0.67006     	0.04435     	1.04162            	0           
  10          	0.01462     	0.66760     	0.04153     	0.70840            	0           
  100         	0.01315     	0.70362     	0.03233     	1.31559            	0           
  300         	0.01207     	0.77666     	0.02683     	5.62714            	1           
  500         	0.01210     	0.79211     	0.02547     	1.52066            	1           
  1000        	0.01202     	0.80225     	0.02420     	2.36364            	3           
---> try to load best state
              	0.01202     	0.80225     	0.02420     	2.36364            	3                eval state : test_on_restore;
              	0.01202     	0.80225     	0.02420     	2.36364            	3                eval state : test_on_last_epoch;
---> final result              	0.01202     	0.80225     	0.02420     	2.36364            	3                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.03127     	0.08260     	0.05444     	0.17902            	0           
  10          	0.02333     	0.42160     	0.04257     	0.08744            	0           
  100         	0.01523     	0.70488     	0.02728     	-1.86835           	0           
  epoch 274 reach nan state
---> try to load best state
              	0.01375     	0.83270     	0.02662     	-1.95782           	1                eval state : test_on_restore;
              	0.01523     	0.70488     	0.02728     	-1.86835           	1                eval state : test_on_last_epoch;
---> final result
              	0.01375     	0.83270     	0.02662     	-1.95782           	1                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.02196     	0.48893     	0.04483     	0.25930            	0           
  10          	0.01559     	0.69299     	0.03525     	0.16221            	0           
  100         	0.01435     	0.79326     	0.02661     	-1.63042           	0           
  300         	0.01338     	0.81736     	0.02653     	-1.80573           	1           
  500         	0.01398     	0.76146     	0.02668     	-1.81542           	2           
  1000        	0.01318     	0.82595     	0.02652     	-1.83870           	4           
---> try to load best state
              	0.01318     	0.82595     	0.02652     	-1.83870           	4                eval state : test_on_restore;
              	0.01318     	0.82595     	0.02652     	-1.83870           	4                eval state : test_on_last_epoch;
---> final result
              	0.01318     	0.82595     	0.02652     	-1.83870           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.01293     	0.81777     	0.02836     	0.33297            	0           
  10          	0.01182     	0.84554     	0.02551     	0.22604            	0           
  100         	0.01371     	0.80353     	0.02632     	-1.29374           	0           
  300         	0.01336     	0.83510     	0.02652     	-1.45376           	1           
  500         	0.01330     	0.83221     	0.02639     	-1.46349           	2           
  1000        	0.01323     	0.83321     	0.02643     	-1.48655           	4           
---> try to load best state
              	0.01323     	0.83321     	0.02643     	-1.48655           	4                eval state : test_on_restore;
              	0.01323     	0.83321     	0.02643     	-1.48655           	4                eval state : test_on_last_epoch;
---> final result
              	0.01323     	0.83321     	0.02643     	-1.48655           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.01062     	0.83475     	0.02591     	0.50931            	0           
  10          	0.01087     	0.84310     	0.02512     	0.45780            	0           
  100         	0.01325     	0.82877     	0.02616     	0.20286            	0           
  300         	0.01338     	0.77784     	0.02638     	0.50264            	1           
  epoch 337 reach nan state
---> try to load best state
              	0.01346     	0.81951     	0.02641     	3.64332            	1                eval state : test_on_restore;
              	0.01338     	0.77784     	0.02638     	0.50264            	1                eval state : test_on_last_epoch;
---> final result
              	0.01338     	0.77784     	0.02638     	0.50264            	1                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': 'Heat_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', '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_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           	0.00848     	0.80498     	0.02207     	0.45559            	0           
  10          	0.00846     	0.80038     	0.02196     	0.39952            	0           
  100         	0.00993     	0.79020     	0.02253     	4.63222            	0           
  300         	0.00919     	0.81265     	0.02045     	1.83037            	1           
  500         	0.00907     	0.81614     	0.01986     	3.22887            	1           
  1000        	0.00889     	0.82099     	0.01900     	1.84189            	4           
---> try to load best state
              	0.00889     	0.82099     	0.01900     	1.84189            	4                eval state : test_on_restore;
              	0.00889     	0.82099     	0.01900     	1.84189            	4                eval state : test_on_last_epoch;
---> final result              	0.00889     	0.82099     	0.01900     	1.84189            	4                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.03049     	0.11235     	0.05320     	0.17894            	0           
  10          	0.02197     	0.44122     	0.04123     	0.08737            	0           
  100         	0.01201     	0.66685     	0.02231     	-1.86927           	0           
  epoch 274 reach nan state
---> try to load best state
              	0.01009     	0.86233     	0.02121     	-1.95786           	1                eval state : test_on_restore;
              	0.01201     	0.66685     	0.02231     	-1.86927           	1                eval state : test_on_last_epoch;
---> final result
              	0.01009     	0.86233     	0.02121     	-1.95786           	1                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.02070     	0.49815     	0.04267     	0.25920            	0           
  10          	0.01402     	0.69734     	0.03351     	0.16214            	0           
  100         	0.01113     	0.80581     	0.02157     	-1.63229           	0           
  300         	0.00987     	0.82795     	0.02149     	-1.80928           	1           
  500         	0.01060     	0.72425     	0.02191     	-1.81886           	2           
  1000        	0.00981     	0.84803     	0.02171     	-1.84216           	4           
---> try to load best state
              	0.00981     	0.84803     	0.02171     	-1.84216           	4                eval state : test_on_restore;
              	0.00981     	0.84803     	0.02171     	-1.84216           	4                eval state : test_on_last_epoch;
---> final result
              	0.00981     	0.84803     	0.02171     	-1.84216           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.01145     	0.81925     	0.02670     	0.33295            	0           
  10          	0.00949     	0.85503     	0.02295     	0.22600            	0           
  100         	0.01031     	0.77159     	0.02110     	-1.29549           	0           
  300         	0.00990     	0.86260     	0.02141     	-1.45724           	1           
  500         	0.00986     	0.85598     	0.02141     	-1.46695           	2           
  1000        	0.00981     	0.85758     	0.02149     	-1.49019           	4           
---> try to load best state
              	0.00981     	0.85758     	0.02149     	-1.49019           	4                eval state : test_on_restore;
              	0.00981     	0.85758     	0.02149     	-1.49019           	4                eval state : test_on_last_epoch;
---> final result
              	0.00981     	0.85758     	0.02149     	-1.49019           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.00900     	0.83203     	0.02439     	0.50930            	0           
  10          	0.00839     	0.85407     	0.02237     	0.45777            	0           
  100         	0.00985     	0.84084     	0.02108     	0.20275            	0           
  300         	0.00988     	0.77036     	0.02128     	0.50256            	1           
  epoch 337 reach nan state
---> try to load best state
              	0.00999     	0.84386     	0.02135     	3.64333            	1                eval state : test_on_restore;
              	0.00988     	0.77036     	0.02128     	0.50256            	1                eval state : test_on_last_epoch;
---> final result
              	0.00988     	0.77036     	0.02128     	0.50256            	1                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': 'Heat_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', '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_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           	0.01272     	0.74447     	0.02672     	0.82130            	0           
  10          	0.01268     	0.73943     	0.02576     	0.81751            	0           
  100         	0.01230     	0.79206     	0.02181     	1.29220            	0           
  300         	0.01068     	0.82374     	0.01873     	1.55246            	1           
  500         	0.01030     	0.82794     	0.01785     	3.29713            	2           
  1000        	0.01032     	0.82961     	0.01744     	3.74667            	5           
---> try to load best state
              	0.01032     	0.82961     	0.01744     	3.74667            	5                eval state : test_on_restore;
              	0.01032     	0.82961     	0.01744     	3.74667            	5                eval state : test_on_last_epoch;
---> final result              	0.01032     	0.82961     	0.01744     	3.74667            	5                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.03019     	0.14595     	0.05190     	0.17890            	0           
  10          	0.02230     	0.48892     	0.04023     	0.08734            	0           
  100         	0.01373     	0.76585     	0.02176     	-1.87145           	0           
  epoch 274 reach nan state
---> try to load best state
              	0.01238     	0.87236     	0.02050     	-1.96137           	1                eval state : test_on_restore;
              	0.01373     	0.76585     	0.02176     	-1.87145           	1                eval state : test_on_last_epoch;
---> final result
              	0.01238     	0.87236     	0.02050     	-1.96137           	1                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.02071     	0.53546     	0.04160     	0.25919            	0           
  10          	0.01448     	0.73223     	0.03209     	0.16211            	0           
  100         	0.01275     	0.82016     	0.02062     	-1.63231           	0           
  300         	0.01185     	0.82790     	0.02015     	-1.80883           	1           
  500         	0.01236     	0.63379     	0.02050     	-1.81847           	2           
  1000        	0.01159     	0.87222     	0.02008     	-1.84196           	4           
---> try to load best state
              	0.01159     	0.87222     	0.02008     	-1.84196           	4                eval state : test_on_restore;
              	0.01159     	0.87222     	0.02008     	-1.84196           	4                eval state : test_on_last_epoch;
---> final result
              	0.01159     	0.87222     	0.02008     	-1.84196           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.01173     	0.83520     	0.02509     	0.33293            	0           
  10          	0.01040     	0.87197     	0.02113     	0.22598            	0           
  100         	0.01216     	0.85815     	0.02000     	-1.29473           	0           
  300         	0.01178     	0.87903     	0.02000     	-1.45593           	1           
  500         	0.01173     	0.85646     	0.01994     	-1.46562           	2           
  1000        	0.01163     	0.86609     	0.01990     	-1.48882           	4           
---> try to load best state
              	0.01163     	0.86609     	0.01990     	-1.48882           	4                eval state : test_on_restore;
              	0.01163     	0.86609     	0.01990     	-1.48882           	4                eval state : test_on_last_epoch;
---> final result
              	0.01163     	0.86609     	0.01990     	-1.48882           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], '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           	0.00967     	0.84885     	0.02257     	0.50929            	0           
  10          	0.00962     	0.87394     	0.02052     	0.45778            	0           
  100         	0.01174     	0.86343     	0.01989     	0.20281            	0           
  300         	0.01185     	0.55291     	0.01997     	0.50261            	1           
  epoch 337 reach nan state
---> try to load best state
              	0.01192     	0.83696     	0.02001     	3.64332            	1                eval state : test_on_restore;
              	0.01185     	0.55291     	0.01997     	0.50261            	1                eval state : test_on_last_epoch;
---> final result
              	0.01185     	0.55291     	0.01997     	0.50261            	1                eval state : final;module_name : GAR;cp_record_file : True;
