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

  Demo NAR cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	87.04569    	120.22952   	0.46940     	0           
  10          	39.80250    	55.61364    	0.93983     	0           
  100         	6.41353     	9.61887     	0.99839     	0           
  300         	2.73561     	4.17850     	0.99970     	0           
  500         	1.78392     	2.74108     	0.99987     	1           
  1000        	0.99677     	1.53333     	0.99996     	2           
---> try to load best state
              	0.99677     	1.53333     	0.99996     	2                eval state : test_on_restore;
              	0.99677     	1.53333     	0.99996     	2                eval state : test_on_last_epoch;
---> final result
              	0.99677     	1.53333     	0.99996     	2                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	223.01080   	269.38605   	-1646.74817 	0           
  10          	214.25040   	261.33582   	-103.37065  	0           
  100         	124.61301   	161.90964   	-0.34991    	0           
  300         	114.32790   	148.22331   	0.05758     	0           
  500         	112.60040   	145.99550   	0.11502     	1           
  1000        	110.44257   	143.26671   	0.18242     	1           
---> try to load best state
              	110.44257   	143.26671   	0.18242     	1                eval state : test_on_restore;
              	110.44257   	143.26671   	0.18242     	1                eval state : test_on_last_epoch;
---> final result
              	110.44257   	143.26671   	0.18242     	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': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	202.28629   	242.17345   	-1435.00007 	0           
  10          	189.04199   	229.33101   	-48.40811   	0           
  100         	67.03319    	92.50950    	0.79407     	0           
  300         	57.08681    	81.84132    	0.86952     	0           
  500         	54.79613    	80.02773    	0.88025     	0           
  1000        	52.94123    	78.83134    	0.88765     	1           
---> try to load best state
              	52.94123    	78.83134    	0.88765     	1                eval state : test_on_restore;
              	52.94123    	78.83134    	0.88765     	1                eval state : test_on_last_epoch;
---> final result
              	52.94123    	78.83134    	0.88765     	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': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	196.93039   	234.97198   	-2260.72774 	0           
  10          	183.00272   	222.36610   	-58.89165   	0           
  100         	25.39014    	39.82594    	0.96336     	0           
  300         	11.61356    	18.97902    	0.99228     	0           
  500         	8.19415     	13.54390    	0.99610     	1           
  1000        	4.83487     	8.05861     	0.99864     	2           
---> try to load best state
              	4.83487     	8.05861     	0.99864     	2                eval state : test_on_restore;
              	4.83487     	8.05861     	0.99864     	2                eval state : test_on_last_epoch;
---> final result
              	4.83487     	8.05861     	0.99864     	2                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	193.55109   	231.39464   	-652.00482  	0           
  10          	166.03831   	206.71378   	-18.71540   	0           
  100         	18.73928    	29.72202    	0.98156     	0           
  300         	8.45520     	13.61356    	0.99640     	0           
  500         	6.51124     	10.69675    	0.99773     	1           
  1000        	4.39300     	7.36095     	0.99890     	2           
---> try to load best state
              	4.39300     	7.36095     	0.99890     	2                eval state : test_on_restore;
              	4.39300     	7.36095     	0.99890     	2                eval state : test_on_last_epoch;
---> final result
              	4.39300     	7.36095     	0.99890     	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': 'poisson_v4_02', 'fidelity': ['low'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	103.18556   	146.21513   	0.08959     	0           
  10          	48.47777    	72.15495    	0.87631     	0           
  100         	7.88648     	12.38951    	0.99735     	0           
  300         	3.68709     	5.78767     	0.99944     	0           
  500         	2.55618     	3.99365     	0.99974     	0           
  1000        	1.54850     	2.39410     	0.99991     	1           
---> try to load best state
              	1.54850     	2.39410     	0.99991     	1                eval state : test_on_restore;
              	1.54850     	2.39410     	0.99991     	1                eval state : test_on_last_epoch;
---> final result
              	1.54850     	2.39410     	0.99991     	1                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	200.29404   	242.57309   	-5316.25593 	0           
  10          	200.29488   	242.57373   	-5331.88033 	0           
  100         	200.29541   	242.57414   	-5341.49396 	0           
  300         	200.29541   	242.57413   	-5341.31938 	0           
  500         	200.29541   	242.57413   	-5341.31673 	0           
  1000        	200.29541   	242.57413   	-5341.30712 	1           
---> try to load best state
              	200.29541   	242.57413   	-5341.30712 	1                eval state : test_on_restore;
              	200.29541   	242.57413   	-5341.30712 	1                eval state : test_on_last_epoch;
---> final result
              	200.29541   	242.57413   	-5341.30712 	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': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	197.96053   	238.24274   	-566.06914  	0           
  10          	190.55124   	231.95836   	-65.96209   	0           
  100         	56.26284    	84.61337    	0.76127     	0           
  300         	26.64231    	40.09208    	0.96073     	0           
  500         	18.62434    	27.73429    	0.98266     	1           
  1000        	11.33082    	16.62091    	0.99420     	1           
---> try to load best state
              	11.33082    	16.62091    	0.99420     	1                eval state : test_on_restore;
              	11.33082    	16.62091    	0.99420     	1                eval state : test_on_last_epoch;
---> final result
              	11.33082    	16.62091    	0.99420     	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': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	197.00427   	237.33624   	-632.80682  	0           
  10          	182.58943   	224.55479   	-38.79345   	0           
  100         	34.64939    	56.40716    	0.91682     	0           
  300         	13.81357    	23.46682    	0.98802     	0           
  500         	9.07317     	15.30096    	0.99513     	0           
  1000        	5.23706     	8.55166     	0.99854     	1           
---> try to load best state
              	5.23706     	8.55166     	0.99854     	1                eval state : test_on_restore;
              	5.23706     	8.55166     	0.99854     	1                eval state : test_on_last_epoch;
---> final result
              	5.23706     	8.55166     	0.99854     	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': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	194.97119   	233.91678   	-290.70698  	0           
  10          	165.26454   	207.22675   	-12.17502   	0           
  100         	27.95863    	43.33970    	0.95407     	0           
  300         	12.81979    	20.91927    	0.99037     	0           
  500         	8.83665     	14.65530    	0.99543     	1           
  1000        	5.20296     	8.67797     	0.99845     	2           
---> try to load best state
              	5.20296     	8.67797     	0.99845     	2                eval state : test_on_restore;
              	5.20296     	8.67797     	0.99845     	2                eval state : test_on_last_epoch;
---> final result
              	5.20296     	8.67797     	0.99845     	2                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': 'poisson_v4_02', 'fidelity': ['low'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	116.19002   	158.06128   	-0.35168    	0           
  10          	57.82957    	84.09892    	0.81606     	0           
  100         	10.50085    	16.27119    	0.99519     	0           
  300         	4.58354     	7.17924     	0.99911     	0           
  500         	3.04235     	4.75835     	0.99962     	1           
  1000        	1.74166     	2.69609     	0.99988     	2           
---> try to load best state
              	1.74166     	2.69609     	0.99988     	2                eval state : test_on_restore;
              	1.74166     	2.69609     	0.99988     	2                eval state : test_on_last_epoch;
---> final result
              	1.74166     	2.69609     	0.99988     	2                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	209.00911   	254.34526   	-37094.20753	0           
  10          	209.00949   	254.34564   	-37203.14922	0           
  100         	209.00970   	254.34587   	-37270.19596	0           
  300         	209.00970   	254.34587   	-37268.99211	0           
  500         	209.00970   	254.34587   	-37269.00004	0           
  1000        	209.00970   	254.34587   	-37269.05533	1           
---> try to load best state
              	209.00970   	254.34587   	-37269.05533	1                eval state : test_on_restore;
              	209.00970   	254.34587   	-37269.05533	1                eval state : test_on_last_epoch;
---> final result
              	209.00970   	254.34587   	-37269.05533	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': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	201.90283   	245.07649   	-783.78400  	0           
  10          	194.75269   	238.17203   	-73.88871   	0           
  100         	52.67495    	74.57877    	0.84177     	0           
  300         	25.39820    	36.97143    	0.96909     	0           
  500         	17.80351    	25.98233    	0.98575     	0           
  1000        	10.80929    	15.72278    	0.99511     	1           
---> try to load best state
              	10.80929    	15.72278    	0.99511     	1                eval state : test_on_restore;
              	10.80929    	15.72278    	0.99511     	1                eval state : test_on_last_epoch;
---> final result
              	10.80929    	15.72278    	0.99511     	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': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	196.75990   	237.45746   	-673.21845  	0           
  10          	184.89061   	225.79384   	-50.04437   	0           
  100         	34.48185    	51.48611    	0.93486     	0           
  300         	13.83938    	21.64200    	0.99011     	0           
  500         	9.31227     	14.75947    	0.99554     	1           
  1000        	5.50407     	8.73915     	0.99848     	2           
---> try to load best state
              	5.50407     	8.73915     	0.99848     	2                eval state : test_on_restore;
              	5.50407     	8.73915     	0.99848     	2                eval state : test_on_last_epoch;
---> final result
              	5.50407     	8.73915     	0.99848     	2                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	193.70230   	232.72461   	-557.64591  	0           
  10          	169.37996   	209.52646   	-19.79446   	0           
  100         	29.88712    	46.63913    	0.94922     	0           
  300         	13.77198    	22.05053    	0.98990     	0           
  500         	9.67153     	15.84525    	0.99488     	1           
  1000        	5.89024     	9.86998     	0.99805     	2           
---> try to load best state
              	5.89024     	9.86998     	0.99805     	2                eval state : test_on_restore;
              	5.89024     	9.86998     	0.99805     	2                eval state : test_on_last_epoch;
---> final result
              	5.89024     	9.86998     	0.99805     	2                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': 'poisson_v4_02', 'fidelity': ['low'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	104.14345   	146.44722   	-0.06564    	0           
  10          	49.68819    	75.70245    	0.85373     	0           
  100         	7.38448     	11.42128    	0.99771     	0           
  300         	3.40729     	5.25279     	0.99952     	0           
  500         	2.36258     	3.61991     	0.99977     	0           
  1000        	1.43073     	2.16702     	0.99992     	2           
---> try to load best state
              	1.43073     	2.16702     	0.99992     	2                eval state : test_on_restore;
              	1.43073     	2.16702     	0.99992     	2                eval state : test_on_last_epoch;
---> final result
              	1.43073     	2.16702     	0.99992     	2                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	198.56693   	238.98830   	-487108.31628	0           
  10          	198.56700   	238.98836   	-488538.48954	0           
  100         	198.56705   	238.98839   	-489417.91697	0           
  300         	198.56705   	238.98839   	-489401.09664	0           
  500         	198.56705   	238.98839   	-489400.60573	0           
  1000        	198.56705   	238.98839   	-489398.85478	1           
---> try to load best state
              	198.56705   	238.98839   	-489398.85478	1                eval state : test_on_restore;
              	198.56705   	238.98839   	-489398.85478	1                eval state : test_on_last_epoch;
---> final result
              	198.56705   	238.98839   	-489398.85478	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': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	196.85159   	238.20163   	-9489091.61423	0           
  10          	196.86505   	238.21410   	-4141614147.75540	0           
  100         	196.86554   	238.21454   	-113637168970.15475	0           
  300         	196.86554   	238.21454   	-113667599766.27092	0           
  500         	196.86554   	238.21454   	-113667599766.27092	1           
  1000        	196.86554   	238.21454   	-113667599766.27092	2           
---> try to load best state
              	196.86554   	238.21454   	-113667599766.27092	2                eval state : test_on_restore;
              	196.86554   	238.21454   	-113667599766.27092	2                eval state : test_on_last_epoch;
---> final result
              	196.86554   	238.21454   	-113667599766.27092	2                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	194.18068   	234.63788   	-2059.85640 	0           
  10          	183.62978   	224.78578   	-82.74133   	0           
  100         	35.20558    	58.47967    	0.89424     	0           
  300         	14.03099    	24.57540    	0.98535     	0           
  500         	9.30847     	16.19921    	0.99401     	1           
  1000        	5.35121     	9.03913     	0.99823     	2           
---> try to load best state
              	5.35121     	9.03913     	0.99823     	2                eval state : test_on_restore;
              	5.35121     	9.03913     	0.99823     	2                eval state : test_on_last_epoch;
---> final result
              	5.35121     	9.03913     	0.99823     	2                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	193.08473   	232.61388   	-527.74861  	0           
  10          	165.81160   	207.77797   	-17.26457   	0           
  100         	25.44327    	41.82872    	0.95377     	0           
  300         	12.07062    	20.85038    	0.98986     	0           
  500         	8.51477     	14.96974    	0.99497     	1           
  1000        	5.11789     	9.02734     	0.99824     	2           
---> try to load best state
              	5.11789     	9.02734     	0.99824     	2                eval state : test_on_restore;
              	5.11789     	9.02734     	0.99824     	2                eval state : test_on_last_epoch;
---> final result
              	5.11789     	9.02734     	0.99824     	2                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': 'poisson_v4_02', 'fidelity': ['low'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	98.70236    	136.04253   	0.17045     	0           
  10          	41.80249    	60.67172    	0.91825     	0           
  100         	6.49822     	9.81166     	0.99834     	0           
  300         	2.96473     	4.49894     	0.99966     	0           
  500         	2.03919     	3.08646     	0.99984     	0           
  1000        	1.22949     	1.84327     	0.99994     	1           
---> try to load best state
              	1.22949     	1.84327     	0.99994     	1                eval state : test_on_restore;
              	1.22949     	1.84327     	0.99994     	1                eval state : test_on_last_epoch;
---> final result
              	1.22949     	1.84327     	0.99994     	1                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	223.43860   	272.05289   	-31180966.46652	0           
  10          	223.43871   	272.05295   	-31262577.15170	0           
  100         	223.43878   	272.05298   	-31328067.33263	0           
  300         	223.43878   	272.05298   	-31328044.26412	0           
  500         	223.43878   	272.05298   	-31328044.19976	0           
  1000        	223.43878   	272.05298   	-31328043.80383	1           
---> try to load best state
              	223.43878   	272.05298   	-31328043.80383	1                eval state : test_on_restore;
              	223.43878   	272.05298   	-31328043.80383	1                eval state : test_on_last_epoch;
---> final result
              	223.43878   	272.05298   	-31328043.80383	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': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	205.90312   	247.82043   	-6131.59713 	0           
  10          	203.38640   	245.92906   	-476.52809  	0           
  100         	74.01709    	107.72970   	0.50543     	0           
  300         	36.65070    	55.30506    	0.92066     	0           
  500         	26.91733    	40.82555    	0.96180     	0           
  1000        	17.60600    	26.76419    	0.98524     	1           
---> try to load best state
              	17.60600    	26.76419    	0.98524     	1                eval state : test_on_restore;
              	17.60600    	26.76419    	0.98524     	1                eval state : test_on_last_epoch;
---> final result
              	17.60600    	26.76419    	0.98524     	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': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	199.64513   	238.37804   	-1138.43569 	0           
  10          	188.71681   	228.27197   	-69.06511   	0           
  100         	31.87859    	49.26073    	0.94059     	0           
  300         	13.29498    	21.22223    	0.99041     	0           
  500         	8.72881     	14.04187    	0.99595     	0           
  1000        	4.88036     	7.82667     	0.99879     	1           
---> try to load best state
              	4.88036     	7.82667     	0.99879     	1                eval state : test_on_restore;
              	4.88036     	7.82667     	0.99879     	1                eval state : test_on_last_epoch;
---> final result
              	4.88036     	7.82667     	0.99879     	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': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	197.71310   	236.12476   	-734.27128  	0           
  10          	172.68521   	212.41565   	-20.83034   	0           
  100         	22.62478    	36.16017    	0.97021     	0           
  300         	10.45958    	17.52657    	0.99348     	0           
  500         	7.33520     	12.55049    	0.99671     	0           
  1000        	4.36906     	7.55263     	0.99883     	1           
---> try to load best state
              	4.36906     	7.55263     	0.99883     	1                eval state : test_on_restore;
              	4.36906     	7.55263     	0.99883     	1                eval state : test_on_last_epoch;
---> final result
              	4.36906     	7.55263     	0.99883     	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': 'poisson_v4_02', 'fidelity': ['low'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	100.84668   	139.98384   	0.09397     	0           
  10          	46.58136    	68.04853    	0.88926     	0           
  100         	7.19850     	10.95916    	0.99788     	0           
  300         	3.21467     	4.90485     	0.99959     	0           
  500         	2.18686     	3.32486     	0.99981     	0           
  1000        	1.30262     	1.95868     	0.99994     	1           
---> try to load best state
              	1.30262     	1.95868     	0.99994     	1                eval state : test_on_restore;
              	1.30262     	1.95868     	0.99994     	1                eval state : test_on_last_epoch;
---> final result
              	1.30262     	1.95868     	0.99994     	1                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	198.55135   	238.24559   	-844011261.16359	0           
  10          	198.55135   	238.24559   	-848661041.76239	0           
  100         	198.55135   	238.24559   	-849045775.93932	0           
  300         	198.55135   	238.24559   	-849026968.74999	0           
  500         	198.55135   	238.24559   	-849026968.74999	0           
  1000        	198.55135   	238.24559   	-849026863.85913	1           
---> try to load best state
              	198.55135   	238.24559   	-849026863.85913	1                eval state : test_on_restore;
              	198.55135   	238.24559   	-849026863.85913	1                eval state : test_on_last_epoch;
---> final result
              	198.55135   	238.24559   	-849026863.85913	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': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	208.08740   	250.80125   	-11880.59632	0           
  10          	204.36462   	247.93614   	-349.53905  	0           
  100         	95.17688    	138.24399   	-0.12527    	0           
  300         	46.45238    	72.42238    	0.82858     	0           
  500         	31.56125    	49.89038    	0.93126     	0           
  1000        	17.82077    	28.27701    	0.98109     	1           
---> try to load best state
              	17.82077    	28.27701    	0.98109     	1                eval state : test_on_restore;
              	17.82077    	28.27701    	0.98109     	1                eval state : test_on_last_epoch;
---> final result
              	17.82077    	28.27701    	0.98109     	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': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	200.11172   	240.85388   	-956.05330  	0           
  10          	189.23314   	231.54951   	-70.51122   	0           
  100         	40.42908    	69.08991    	0.85158     	0           
  300         	15.48925    	28.16964    	0.98136     	0           
  500         	10.05719    	18.33136    	0.99256     	0           
  1000        	5.58204     	9.95504     	0.99791     	1           
---> try to load best state
              	5.58204     	9.95504     	0.99791     	1                eval state : test_on_restore;
              	5.58204     	9.95504     	0.99791     	1                eval state : test_on_last_epoch;
---> final result
              	5.58204     	9.95504     	0.99791     	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': 'poisson_v4_02', 'fidelity': ['low', '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	193.69650   	232.98041   	-249.48108  	0           
  10          	167.14886   	208.99210   	-13.78570   	0           
  100         	26.62391    	43.99216    	0.95122     	0           
  300         	12.21497    	21.43034    	0.98966     	0           
  500         	8.35292     	14.93043    	0.99514     	0           
  1000        	4.81262     	8.66665     	0.99842     	1           
---> try to load best state
              	4.81262     	8.66665     	0.99842     	1                eval state : test_on_restore;
              	4.81262     	8.66665     	0.99842     	1                eval state : test_on_last_epoch;
---> final result
              	4.81262     	8.66665     	0.99842     	1                eval state : final;module_name : NAR;cp_record_file : True;
