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

  Demo NAR cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01176     	0.02706     	0.94026     	0           
  10          	0.01192     	0.02696     	0.94098     	0           
  100         	0.01273     	0.02688     	0.94232     	0           
  300         	0.01224     	0.02650     	0.94569     	0           
  500         	0.01183     	0.02622     	0.94783     	1           
  1000        	0.01117     	0.02572     	0.95113     	2           
---> try to load best state
              	0.01117     	0.02572     	0.95113     	2                eval state : test_on_restore;
              	0.01117     	0.02572     	0.95113     	2                eval state : test_on_last_epoch;
---> final result
              	0.01117     	0.02572     	0.95113     	2                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.11852     	0.16264     	-237.85774  	0           
  10          	0.11854     	0.16265     	-241.84453  	0           
  100         	0.11864     	0.16272     	-261.11827  	0           
  300         	0.11864     	0.16272     	-261.26795  	0           
  500         	0.11864     	0.16272     	-261.26795  	0           
  1000        	0.11864     	0.16272     	-261.26793  	1           
---> try to load best state
              	0.11864     	0.16272     	-261.26793  	1                eval state : test_on_restore;
              	0.11864     	0.16272     	-261.26793  	1                eval state : test_on_last_epoch;
---> final result
              	0.11864     	0.16272     	-261.26793  	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.09634     	0.13560     	-44.25231   	0           
  10          	0.09521     	0.13458     	-35.82560   	0           
  100         	0.07340     	0.11362     	-5.25225    	0           
  300         	0.03446     	0.06926     	0.29352     	0           
  500         	0.02485     	0.05040     	0.72067     	0           
  1000        	0.01707     	0.03692     	0.88489     	1           
---> try to load best state
              	0.01707     	0.03692     	0.88489     	1                eval state : test_on_restore;
              	0.01707     	0.03692     	0.88489     	1                eval state : test_on_last_epoch;
---> final result
              	0.01707     	0.03692     	0.88489     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08628     	0.12540     	-21.87477   	0           
  10          	0.08439     	0.12362     	-17.77615   	0           
  100         	0.05687     	0.09828     	-2.01551    	0           
  300         	0.02459     	0.06078     	0.57525     	0           
  500         	0.01678     	0.04293     	0.83030     	1           
  1000        	0.01071     	0.02659     	0.93908     	1           
---> try to load best state
              	0.01071     	0.02659     	0.93908     	1                eval state : test_on_restore;
              	0.01071     	0.02659     	0.93908     	1                eval state : test_on_last_epoch;
---> final result
              	0.01071     	0.02659     	0.93908     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.07693     	0.11613     	-11.70579   	0           
  10          	0.07401     	0.11321     	-9.32478    	0           
  100         	0.04240     	0.08554     	-0.72025    	0           
  300         	0.01892     	0.05362     	0.72499     	0           
  500         	0.01264     	0.03410     	0.90958     	1           
  1000        	0.00842     	0.02029     	0.96037     	2           
---> try to load best state
              	0.00842     	0.02029     	0.96037     	2                eval state : test_on_restore;
              	0.00842     	0.02029     	0.96037     	2                eval state : test_on_last_epoch;
---> final result
              	0.00842     	0.02029     	0.96037     	2                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01850     	0.04566     	0.70217     	0           
  10          	0.01828     	0.04483     	0.70770     	0           
  100         	0.01575     	0.03884     	0.76024     	0           
  300         	0.01400     	0.03548     	0.78814     	0           
  500         	0.01329     	0.03419     	0.80430     	1           
  1000        	0.01242     	0.03254     	0.83039     	1           
---> try to load best state
              	0.01242     	0.03254     	0.83039     	1                eval state : test_on_restore;
              	0.01242     	0.03254     	0.83039     	1                eval state : test_on_last_epoch;
---> final result
              	0.01242     	0.03254     	0.83039     	1                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08910     	0.13207     	-1039.81699 	0           
  10          	0.08881     	0.13177     	-702.21542  	0           
  100         	0.08545     	0.12818     	-102.70288  	0           
  300         	0.07747     	0.12050     	-27.37714   	0           
  500         	0.07012     	0.11387     	-14.38391   	0           
  1000        	0.05518     	0.10125     	-5.74902    	1           
---> try to load best state
              	0.05518     	0.10125     	-5.74902    	1                eval state : test_on_restore;
              	0.05518     	0.10125     	-5.74902    	1                eval state : test_on_last_epoch;
---> final result
              	0.05518     	0.10125     	-5.74902    	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.09040     	0.13155     	-77.39997   	0           
  10          	0.08968     	0.13081     	-63.35974   	0           
  100         	0.08129     	0.12305     	-16.50047   	0           
  300         	0.05870     	0.10282     	-3.31034    	0           
  500         	0.04300     	0.08686     	-1.31561    	0           
  1000        	0.02632     	0.06904     	-0.10280    	1           
---> try to load best state
              	0.02632     	0.06904     	-0.10280    	1                eval state : test_on_restore;
              	0.02632     	0.06904     	-0.10280    	1                eval state : test_on_last_epoch;
---> final result
              	0.02632     	0.06904     	-0.10280    	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08769     	0.12844     	-43.54865   	0           
  10          	0.08644     	0.12712     	-35.41727   	0           
  100         	0.06846     	0.10920     	-5.47517    	0           
  300         	0.03729     	0.06980     	0.04611     	0           
  500         	0.02223     	0.04786     	0.60861     	0           
  1000        	0.01519     	0.03628     	0.77045     	1           
---> try to load best state
              	0.01519     	0.03628     	0.77045     	1                eval state : test_on_restore;
              	0.01519     	0.03628     	0.77045     	1                eval state : test_on_last_epoch;
---> final result
              	0.01519     	0.03628     	0.77045     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.07400     	0.11763     	-12.63927   	0           
  10          	0.07210     	0.11575     	-10.26204   	0           
  100         	0.05376     	0.09528     	-2.02142    	0           
  300         	0.02953     	0.06552     	0.23042     	0           
  500         	0.02158     	0.05243     	0.53498     	0           
  1000        	0.01577     	0.04007     	0.72054     	1           
---> try to load best state
              	0.01577     	0.04007     	0.72054     	1                eval state : test_on_restore;
              	0.01577     	0.04007     	0.72054     	1                eval state : test_on_last_epoch;
---> final result
              	0.01577     	0.04007     	0.72054     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01327     	0.03429     	0.85275     	0           
  10          	0.01415     	0.03458     	0.84881     	0           
  100         	0.01431     	0.03228     	0.86265     	0           
  300         	0.01329     	0.03015     	0.87831     	0           
  500         	0.01273     	0.02917     	0.88602     	0           
  1000        	0.01179     	0.02775     	0.89727     	1           
---> try to load best state
              	0.01179     	0.02775     	0.89727     	1                eval state : test_on_restore;
              	0.01179     	0.02775     	0.89727     	1                eval state : test_on_last_epoch;
---> final result
              	0.01179     	0.02775     	0.89727     	1                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08628     	0.13651     	-2085.71836 	0           
  10          	0.08619     	0.13646     	-1545.85105 	0           
  100         	0.08341     	0.13416     	-204.09709  	0           
  300         	0.07307     	0.12468     	-32.02315   	0           
  500         	0.06389     	0.11423     	-13.99172   	0           
  1000        	0.04889     	0.09209     	-3.91547    	1           
---> try to load best state
              	0.04889     	0.09209     	-3.91547    	1                eval state : test_on_restore;
              	0.04889     	0.09209     	-3.91547    	1                eval state : test_on_last_epoch;
---> final result
              	0.04889     	0.09209     	-3.91547    	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08572     	0.13601     	-361.08043  	0           
  10          	0.08527     	0.13560     	-283.05049  	0           
  100         	0.07565     	0.12569     	-29.17264   	0           
  300         	0.05068     	0.08938     	-3.11250    	0           
  500         	0.03970     	0.06983     	-0.93135    	0           
  1000        	0.02810     	0.05028     	0.16892     	1           
---> try to load best state
              	0.02810     	0.05028     	0.16892     	1                eval state : test_on_restore;
              	0.02810     	0.05028     	0.16892     	1                eval state : test_on_last_epoch;
---> final result
              	0.02810     	0.05028     	0.16892     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08062     	0.13382     	-92.03472   	0           
  10          	0.07953     	0.13296     	-72.04251   	0           
  100         	0.06384     	0.11885     	-8.25765    	0           
  300         	0.03438     	0.07631     	-0.05423    	0           
  500         	0.02154     	0.05275     	0.60130     	0           
  1000        	0.01541     	0.03747     	0.80736     	1           
---> try to load best state
              	0.01541     	0.03747     	0.80736     	1                eval state : test_on_restore;
              	0.01541     	0.03747     	0.80736     	1                eval state : test_on_last_epoch;
---> final result
              	0.01541     	0.03747     	0.80736     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.07667     	0.11844     	-17.32652   	0           
  10          	0.07431     	0.11578     	-13.44238   	0           
  100         	0.04668     	0.08286     	-0.94791    	0           
  300         	0.01839     	0.04269     	0.71818     	0           
  500         	0.01436     	0.03601     	0.81671     	0           
  1000        	0.01274     	0.03256     	0.85543     	1           
---> try to load best state
              	0.01274     	0.03256     	0.85543     	1                eval state : test_on_restore;
              	0.01274     	0.03256     	0.85543     	1                eval state : test_on_last_epoch;
---> final result
              	0.01274     	0.03256     	0.85543     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01743     	0.04608     	0.78271     	0           
  10          	0.01765     	0.04506     	0.79031     	0           
  100         	0.01673     	0.03717     	0.85653     	0           
  300         	0.01534     	0.03333     	0.88133     	0           
  500         	0.01465     	0.03204     	0.88902     	0           
  1000        	0.01355     	0.03048     	0.89769     	1           
---> try to load best state
              	0.01355     	0.03048     	0.89769     	1                eval state : test_on_restore;
              	0.01355     	0.03048     	0.89769     	1                eval state : test_on_last_epoch;
---> final result
              	0.01355     	0.03048     	0.89769     	1                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08749     	0.13464     	-996826.91913	0           
  10          	0.08746     	0.13463     	-667955.08009	0           
  100         	0.08709     	0.13450     	-39956.65791	0           
  300         	0.08653     	0.13433     	-10586.31028	0           
  500         	0.08599     	0.13413     	-5186.87977 	0           
  1000        	0.08463     	0.13360     	-1719.43924 	1           
---> try to load best state
              	0.08463     	0.13360     	-1719.43924 	1                eval state : test_on_restore;
              	0.08463     	0.13360     	-1719.43924 	1                eval state : test_on_last_epoch;
---> final result
              	0.08463     	0.13360     	-1719.43924 	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08490     	0.13803     	-3415.19673 	0           
  10          	0.08475     	0.13797     	-2514.36137 	0           
  100         	0.08302     	0.13735     	-475.57431  	0           
  300         	0.07959     	0.13619     	-172.70545  	0           
  500         	0.07668     	0.13511     	-104.77177  	0           
  1000        	0.07028     	0.13235     	-48.51684   	1           
---> try to load best state
              	0.07028     	0.13235     	-48.51684   	1                eval state : test_on_restore;
              	0.07028     	0.13235     	-48.51684   	1                eval state : test_on_last_epoch;
---> final result
              	0.07028     	0.13235     	-48.51684   	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08151     	0.13446     	-173.53125  	0           
  10          	0.08072     	0.13399     	-134.94020  	0           
  100         	0.07163     	0.12880     	-31.51111   	0           
  300         	0.05406     	0.11555     	-6.41791    	0           
  500         	0.04222     	0.10059     	-1.85984    	0           
  1000        	0.02469     	0.06985     	0.23049     	1           
---> try to load best state
              	0.02469     	0.06985     	0.23049     	1                eval state : test_on_restore;
              	0.02469     	0.06985     	0.23049     	1                eval state : test_on_last_epoch;
---> final result
              	0.02469     	0.06985     	0.23049     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08077     	0.12553     	-33.39515   	0           
  10          	0.07907     	0.12397     	-26.95537   	0           
  100         	0.05851     	0.10402     	-3.59855    	0           
  300         	0.03127     	0.07123     	0.33874     	0           
  500         	0.02017     	0.05421     	0.68996     	0           
  1000        	0.01438     	0.03782     	0.84513     	1           
---> try to load best state
              	0.01438     	0.03782     	0.84513     	1                eval state : test_on_restore;
              	0.01438     	0.03782     	0.84513     	1                eval state : test_on_last_epoch;
---> final result
              	0.01438     	0.03782     	0.84513     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01161     	0.02967     	0.89330     	0           
  10          	0.01210     	0.02986     	0.88957     	0           
  100         	0.01201     	0.02707     	0.90579     	0           
  300         	0.01095     	0.02485     	0.91973     	0           
  500         	0.01034     	0.02373     	0.92673     	0           
  1000        	0.00936     	0.02206     	0.93669     	1           
---> try to load best state
              	0.00936     	0.02206     	0.93669     	1                eval state : test_on_restore;
              	0.00936     	0.02206     	0.93669     	1                eval state : test_on_last_epoch;
---> final result
              	0.00936     	0.02206     	0.93669     	1                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08614     	0.13077     	-153.98529  	0           
  10          	0.08616     	0.13078     	-156.56792  	0           
  100         	0.08626     	0.13085     	-169.05134  	0           
  300         	0.08626     	0.13085     	-169.14826  	0           
  500         	0.08626     	0.13085     	-169.14826  	0           
  1000        	0.08626     	0.13085     	-169.14826  	1           
---> try to load best state
              	0.08626     	0.13085     	-169.14826  	1                eval state : test_on_restore;
              	0.08626     	0.13085     	-169.14826  	1                eval state : test_on_last_epoch;
---> final result
              	0.08626     	0.13085     	-169.14826  	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08492     	0.13030     	-107.10687  	0           
  10          	0.08419     	0.12961     	-87.82675   	0           
  100         	0.07368     	0.12116     	-17.80621   	0           
  300         	0.05270     	0.10686     	-4.07234    	0           
  500         	0.04065     	0.09772     	-1.94914    	0           
  1000        	0.03197     	0.08619     	-0.75336    	1           
---> try to load best state
              	0.03197     	0.08619     	-0.75336    	1                eval state : test_on_restore;
              	0.03197     	0.08619     	-0.75336    	1                eval state : test_on_last_epoch;
---> final result
              	0.03197     	0.08619     	-0.75336    	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08303     	0.12789     	-56.37580   	0           
  10          	0.08183     	0.12691     	-43.73095   	0           
  100         	0.06606     	0.11438     	-8.75087    	0           
  300         	0.03822     	0.09093     	-1.23510    	0           
  500         	0.02884     	0.07955     	-0.18536    	0           
  1000        	0.02294     	0.06563     	0.30209     	1           
---> try to load best state
              	0.02294     	0.06563     	0.30209     	1                eval state : test_on_restore;
              	0.02294     	0.06563     	0.30209     	1                eval state : test_on_last_epoch;
---> final result
              	0.02294     	0.06563     	0.30209     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08249     	0.12283     	-18.27387   	0           
  10          	0.07974     	0.12003     	-14.14935   	0           
  100         	0.04635     	0.08712     	-1.33919    	0           
  300         	0.02158     	0.05980     	0.40391     	0           
  500         	0.01729     	0.04778     	0.66514     	0           
  1000        	0.01290     	0.03171     	0.87180     	1           
---> try to load best state
              	0.01290     	0.03171     	0.87180     	1                eval state : test_on_restore;
              	0.01290     	0.03171     	0.87180     	1                eval state : test_on_last_epoch;
---> final result
              	0.01290     	0.03171     	0.87180     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01521     	0.03018     	0.91085     	0           
  10          	0.01507     	0.02925     	0.91497     	0           
  100         	0.01401     	0.02611     	0.92983     	0           
  300         	0.01330     	0.02492     	0.93557     	0           
  500         	0.01303     	0.02459     	0.93726     	0           
  1000        	0.01279     	0.02446     	0.93851     	1           
---> try to load best state
              	0.01279     	0.02446     	0.93851     	1                eval state : test_on_restore;
              	0.01279     	0.02446     	0.93851     	1                eval state : test_on_last_epoch;
---> final result
              	0.01279     	0.02446     	0.93851     	1                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.10540     	0.14172     	-7177.86692 	0           
  10          	0.10541     	0.14174     	-7296.39303 	0           
  100         	0.10546     	0.14180     	-7869.60524 	0           
  300         	0.10546     	0.14180     	-7874.56371 	0           
  500         	0.10546     	0.14180     	-7874.56371 	0           
  1000        	0.10546     	0.14180     	-7874.52284 	1           
---> try to load best state
              	0.10546     	0.14180     	-7874.52284 	1                eval state : test_on_restore;
              	0.10546     	0.14180     	-7874.52284 	1                eval state : test_on_last_epoch;
---> final result
              	0.10546     	0.14180     	-7874.52284 	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.11049     	0.15050     	-64.51178   	0           
  10          	0.10922     	0.14881     	-46.16870   	0           
  100         	0.09048     	0.12734     	-4.07428    	0           
  300         	0.06076     	0.08793     	-0.10510    	0           
  500         	0.04190     	0.06121     	0.55591     	0           
  1000        	0.02104     	0.03382     	0.85823     	1           
---> try to load best state
              	0.02104     	0.03382     	0.85823     	1                eval state : test_on_restore;
              	0.02104     	0.03382     	0.85823     	1                eval state : test_on_last_epoch;
---> final result
              	0.02104     	0.03382     	0.85823     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.10181     	0.13925     	-33.41468   	0           
  10          	0.09950     	0.13636     	-23.45648   	0           
  100         	0.07215     	0.10424     	-1.98190    	0           
  300         	0.03593     	0.05662     	0.56652     	0           
  500         	0.02316     	0.03873     	0.84341     	0           
  1000        	0.01773     	0.03096     	0.91385     	1           
---> try to load best state
              	0.01773     	0.03096     	0.91385     	1                eval state : test_on_restore;
              	0.01773     	0.03096     	0.91385     	1                eval state : test_on_last_epoch;
---> final result
              	0.01773     	0.03096     	0.91385     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.09056     	0.12766     	-14.78664   	0           
  10          	0.08754     	0.12390     	-11.35315   	0           
  100         	0.05538     	0.08417     	-0.86540    	0           
  300         	0.02120     	0.03909     	0.79895     	0           
  500         	0.01374     	0.02731     	0.91103     	0           
  1000        	0.01171     	0.02427     	0.93824     	1           
---> try to load best state
              	0.01171     	0.02427     	0.93824     	1                eval state : test_on_restore;
              	0.01171     	0.02427     	0.93824     	1                eval state : test_on_last_epoch;
---> final result
              	0.01171     	0.02427     	0.93824     	1                eval state : final;module_name : NAR;cp_record_file : True;
