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

  Demo NAR cigp 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04260     	0.18634     	0.91863     	0           
  10          	0.04204     	0.18530     	0.91969     	0           
  100         	0.03767     	0.17490     	0.92955     	0           
  300         	0.02870     	0.15072     	0.94865     	0           
  500         	0.02559     	0.12519     	0.96486     	1           
  1000        	0.01782     	0.07587     	0.98666     	2           
---> try to load best state
              	0.01782     	0.07587     	0.98666     	2                eval state : test_on_restore;
              	0.01782     	0.07587     	0.98666     	2                eval state : test_on_last_epoch;
---> final result
              	0.01782     	0.07587     	0.98666     	2                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.16654     	0.53591     	-4260.24737 	0           
  10          	0.16651     	0.53591     	-4234.19452 	0           
  100         	0.16631     	0.53590     	-4013.59439 	0           
  300         	0.16607     	0.53589     	-3732.15391 	0           
  500         	0.16595     	0.53589     	-3586.95493 	0           
  1000        	0.16570     	0.53588     	-3315.95548 	1           
---> try to load best state
              	0.16570     	0.53588     	-3315.95548 	1                eval state : test_on_restore;
              	0.16570     	0.53588     	-3315.95548 	1                eval state : test_on_last_epoch;
---> final result
              	0.16570     	0.53588     	-3315.95548 	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': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.16736     	0.53625     	-4778.51985 	0           
  10          	0.16734     	0.53625     	-4758.06118 	0           
  100         	0.16711     	0.53624     	-4575.89136 	0           
  300         	0.16672     	0.53623     	-4278.96433 	0           
  500         	0.16645     	0.53623     	-4041.21028 	0           
  1000        	0.16599     	0.53621     	-3566.88785 	1           
---> try to load best state
              	0.16599     	0.53621     	-3566.88785 	1                eval state : test_on_restore;
              	0.16599     	0.53621     	-3566.88785 	1                eval state : test_on_last_epoch;
---> final result
              	0.16599     	0.53621     	-3566.88785 	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': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.15100     	0.52755     	-167.01697  	0           
  10          	0.15090     	0.52753     	-165.68542  	0           
  100         	0.14993     	0.52730     	-153.53494  	0           
  300         	0.14758     	0.52677     	-131.57015  	0           
  500         	0.14509     	0.52628     	-113.40231  	0           
  1000        	0.13997     	0.52543     	-81.96820   	1           
---> try to load best state
              	0.13997     	0.52543     	-81.96820   	1                eval state : test_on_restore;
              	0.13997     	0.52543     	-81.96820   	1                eval state : test_on_last_epoch;
---> final result
              	0.13997     	0.52543     	-81.96820   	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': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.12478     	0.50610     	-39.23618   	0           
  10          	0.12454     	0.50591     	-38.69093   	0           
  100         	0.12226     	0.50415     	-33.91543   	0           
  300         	0.11782     	0.50097     	-26.49123   	0           
  500         	0.11396     	0.49854     	-21.65617   	0           
  1000        	0.10802     	0.49454     	-15.47758   	1           
---> try to load best state
              	0.10802     	0.49454     	-15.47758   	1                eval state : test_on_restore;
              	0.10802     	0.49454     	-15.47758   	1                eval state : test_on_last_epoch;
---> final result
              	0.10802     	0.49454     	-15.47758   	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03845     	0.20348     	0.48923     	0           
  10          	0.03820     	0.20148     	0.49887     	0           
  100         	0.03582     	0.18390     	0.58272     	0           
  300         	0.03170     	0.16305     	0.69851     	0           
  500         	0.03087     	0.16017     	0.74910     	0           
  1000        	0.03361     	0.16289     	0.77687     	2           
---> try to load best state
              	0.03361     	0.16289     	0.77687     	2                eval state : test_on_restore;
              	0.03361     	0.16289     	0.77687     	2                eval state : test_on_last_epoch;
---> final result
              	0.03361     	0.16289     	0.77687     	2                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03739     	0.23812     	0.20247     	0           
  10          	0.03720     	0.23605     	0.21339     	0           
  100         	0.03537     	0.21597     	0.31648     	0           
  300         	0.03180     	0.17847     	0.49646     	0           
  500         	0.02891     	0.15377     	0.61436     	1           
  1000        	0.02579     	0.13963     	0.73100     	2           
---> try to load best state
              	0.02579     	0.13963     	0.73100     	2                eval state : test_on_restore;
              	0.02579     	0.13963     	0.73100     	2                eval state : test_on_last_epoch;
---> final result
              	0.02579     	0.13963     	0.73100     	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': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03838     	0.24305     	0.79337     	0           
  10          	0.03809     	0.24240     	0.79511     	0           
  100         	0.03538     	0.23665     	0.80976     	0           
  300         	0.03082     	0.22748     	0.83016     	1           
  500         	0.02894     	0.22197     	0.84107     	1           
  1000        	0.02807     	0.21652     	0.85156     	3           
---> try to load best state
              	0.02807     	0.21652     	0.85156     	3                eval state : test_on_restore;
              	0.02807     	0.21652     	0.85156     	3                eval state : test_on_last_epoch;
---> final result
              	0.02807     	0.21652     	0.85156     	3                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.16010     	0.53174     	-957.44072  	0           
  10          	0.16006     	0.53174     	-954.72705  	0           
  100         	0.15970     	0.53172     	-929.88214  	0           
  300         	0.15926     	0.53170     	-893.70301  	0           
  500         	0.15911     	0.53168     	-874.23253  	0           
  1000        	0.15864     	0.53163     	-813.14060  	1           
---> try to load best state
              	0.15864     	0.53163     	-813.14060  	1                eval state : test_on_restore;
              	0.15864     	0.53163     	-813.14060  	1                eval state : test_on_last_epoch;
---> final result
              	0.15864     	0.53163     	-813.14060  	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': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.13618     	0.51185     	-74.44784   	0           
  10          	0.13602     	0.51182     	-73.72577   	0           
  100         	0.13423     	0.51149     	-66.24248   	0           
  300         	0.13110     	0.51090     	-55.14871   	0           
  500         	0.12911     	0.51044     	-49.25772   	0           
  1000        	0.12711     	0.50951     	-43.30034   	1           
---> try to load best state
              	0.12711     	0.50951     	-43.30034   	1                eval state : test_on_restore;
              	0.12711     	0.50951     	-43.30034   	1                eval state : test_on_last_epoch;
---> final result
              	0.12711     	0.50951     	-43.30034   	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': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.10628     	0.48298     	-12.51419   	0           
  10          	0.10605     	0.48284     	-12.39395   	0           
  100         	0.10473     	0.48151     	-11.37354   	0           
  300         	0.10288     	0.47890     	-9.91901    	0           
  500         	0.10169     	0.47636     	-9.05824    	0           
  1000        	0.09898     	0.46918     	-7.84298    	1           
---> try to load best state
              	0.09898     	0.46918     	-7.84298    	1                eval state : test_on_restore;
              	0.09898     	0.46918     	-7.84298    	1                eval state : test_on_last_epoch;
---> final result
              	0.09898     	0.46918     	-7.84298    	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': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.09720     	0.46906     	-8.10444    	0           
  10          	0.09709     	0.46889     	-8.04946    	0           
  100         	0.09614     	0.46720     	-7.57101    	0           
  300         	0.09437     	0.46341     	-6.82912    	0           
  500         	0.09294     	0.45945     	-6.31841    	1           
  1000        	0.09008     	0.44896     	-5.44147    	2           
---> try to load best state
              	0.09008     	0.44896     	-5.44147    	2                eval state : test_on_restore;
              	0.09008     	0.44896     	-5.44147    	2                eval state : test_on_last_epoch;
---> final result
              	0.09008     	0.44896     	-5.44147    	2                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04498     	0.31287     	-0.46238    	0           
  10          	0.04472     	0.31198     	-0.44601    	0           
  100         	0.04278     	0.30401     	-0.30603    	0           
  300         	0.04226     	0.29110     	-0.10708    	0           
  500         	0.04214     	0.28195     	0.00697     	1           
  1000        	0.04082     	0.26519     	0.15419     	2           
---> try to load best state
              	0.04082     	0.26519     	0.15419     	2                eval state : test_on_restore;
              	0.04082     	0.26519     	0.15419     	2                eval state : test_on_last_epoch;
---> final result
              	0.04082     	0.26519     	0.15419     	2                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02614     	0.12735     	0.65122     	0           
  10          	0.02615     	0.12729     	0.65120     	0           
  100         	0.02627     	0.12722     	0.65019     	0           
  300         	0.02671     	0.12697     	0.65399     	0           
  500         	0.02655     	0.12574     	0.66053     	1           
  1000        	0.02623     	0.12368     	0.67126     	2           
---> try to load best state
              	0.02623     	0.12368     	0.67126     	2                eval state : test_on_restore;
              	0.02623     	0.12368     	0.67126     	2                eval state : test_on_last_epoch;
---> final result
              	0.02623     	0.12368     	0.67126     	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': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.05198     	0.17112     	0.94972     	0           
  10          	0.05152     	0.17007     	0.95041     	0           
  100         	0.04691     	0.15962     	0.95674     	0           
  300         	0.03654     	0.13730     	0.96809     	0           
  500         	0.03205     	0.12050     	0.97507     	1           
  1000        	0.02865     	0.10707     	0.97988     	3           
---> try to load best state
              	0.02865     	0.10707     	0.97988     	3                eval state : test_on_restore;
              	0.02865     	0.10707     	0.97988     	3                eval state : test_on_last_epoch;
---> final result
              	0.02865     	0.10707     	0.97988     	3                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.23566     	0.49146     	-32.47141   	0           
  10          	0.23520     	0.49129     	-32.32843   	0           
  100         	0.23089     	0.48969     	-31.12177   	0           
  300         	0.22269     	0.48671     	-29.08123   	0           
  500         	0.21492     	0.48401     	-27.19287   	1           
  1000        	0.19894     	0.47826     	-24.34453   	2           
---> try to load best state
              	0.19894     	0.47826     	-24.34453   	2                eval state : test_on_restore;
              	0.19894     	0.47826     	-24.34453   	2                eval state : test_on_last_epoch;
---> final result
              	0.19894     	0.47826     	-24.34453   	2                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.12199     	0.48916     	-25.44877   	0           
  10          	0.12195     	0.48914     	-25.39964   	0           
  100         	0.12172     	0.48892     	-24.96876   	0           
  300         	0.12156     	0.48845     	-24.29484   	0           
  500         	0.12129     	0.48800     	-23.74221   	1           
  1000        	0.11993     	0.48684     	-21.96986   	1           
---> try to load best state
              	0.11993     	0.48684     	-21.96986   	1                eval state : test_on_restore;
              	0.11993     	0.48684     	-21.96986   	1                eval state : test_on_last_epoch;
---> final result
              	0.11993     	0.48684     	-21.96986   	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': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.09956     	0.46191     	-7.45666    	0           
  10          	0.09927     	0.46175     	-7.37451    	0           
  100         	0.09653     	0.46028     	-6.66440    	0           
  300         	0.09302     	0.45783     	-5.63319    	0           
  500         	0.09154     	0.45585     	-5.04507    	0           
  1000        	0.09057     	0.45072     	-4.32368    	1           
---> try to load best state
              	0.09057     	0.45072     	-4.32368    	1                eval state : test_on_restore;
              	0.09057     	0.45072     	-4.32368    	1                eval state : test_on_last_epoch;
---> final result
              	0.09057     	0.45072     	-4.32368    	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': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03024     	0.14423     	0.54428     	0           
  10          	0.02997     	0.14332     	0.54905     	0           
  100         	0.02763     	0.13598     	0.58874     	0           
  300         	0.02661     	0.12826     	0.63845     	0           
  500         	0.02612     	0.12629     	0.65913     	0           
  1000        	0.02637     	0.12645     	0.67055     	1           
---> try to load best state
              	0.02637     	0.12645     	0.67055     	1                eval state : test_on_restore;
              	0.02637     	0.12645     	0.67055     	1                eval state : test_on_last_epoch;
---> final result
              	0.02637     	0.12645     	0.67055     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02230     	0.09036     	0.90281     	0           
  10          	0.02236     	0.09091     	0.90256     	0           
  100         	0.02335     	0.09600     	0.89986     	0           
  300         	0.02521     	0.10329     	0.89563     	0           
  500         	0.02621     	0.10692     	0.89392     	0           
  1000        	0.02711     	0.11032     	0.89313     	1           
---> try to load best state
              	0.02711     	0.11032     	0.89313     	1                eval state : test_on_restore;
              	0.02711     	0.11032     	0.89313     	1                eval state : test_on_last_epoch;
---> final result
              	0.02711     	0.11032     	0.89313     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02318     	0.09671     	0.92327     	0           
  10          	0.02315     	0.09648     	0.92411     	0           
  100         	0.02297     	0.09481     	0.93043     	0           
  300         	0.02304     	0.09302     	0.93742     	1           
  500         	0.02344     	0.09231     	0.94090     	1           
  1000        	0.02413     	0.09220     	0.94488     	3           
---> try to load best state
              	0.02413     	0.09220     	0.94488     	3                eval state : test_on_restore;
              	0.02413     	0.09220     	0.94488     	3                eval state : test_on_last_epoch;
---> final result
              	0.02413     	0.09220     	0.94488     	3                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': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04702     	0.18867     	0.92892     	0           
  10          	0.04649     	0.18764     	0.92977     	0           
  100         	0.04173     	0.17759     	0.93753     	0           
  300         	0.03330     	0.15707     	0.95099     	0           
  500         	0.02848     	0.13815     	0.96134     	1           
  1000        	0.02744     	0.11942     	0.97043     	2           
---> try to load best state
              	0.02744     	0.11942     	0.97043     	2                eval state : test_on_restore;
              	0.02744     	0.11942     	0.97043     	2                eval state : test_on_last_epoch;
---> final result
              	0.02744     	0.11942     	0.97043     	2                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.10728     	0.45138     	-9.15509    	0           
  10          	0.10661     	0.45118     	-9.07090    	0           
  100         	0.10088     	0.44939     	-8.35214    	0           
  300         	0.09438     	0.44628     	-7.34263    	0           
  500         	0.09363     	0.44374     	-6.69685    	0           
  1000        	0.09485     	0.43903     	-5.85845    	1           
---> try to load best state
              	0.09485     	0.43903     	-5.85845    	1                eval state : test_on_restore;
              	0.09485     	0.43903     	-5.85845    	1                eval state : test_on_last_epoch;
---> final result
              	0.09485     	0.43903     	-5.85845    	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': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.11283     	0.47510     	-13.77968   	0           
  10          	0.11232     	0.47489     	-13.61613   	0           
  100         	0.10770     	0.47292     	-12.17603   	0           
  300         	0.10038     	0.46925     	-9.95750    	0           
  500         	0.09683     	0.46617     	-8.60811    	0           
  1000        	0.09227     	0.45925     	-7.01652    	1           
---> try to load best state
              	0.09227     	0.45925     	-7.01652    	1                eval state : test_on_restore;
              	0.09227     	0.45925     	-7.01652    	1                eval state : test_on_last_epoch;
---> final result
              	0.09227     	0.45925     	-7.01652    	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': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.10483     	0.48222     	-11.60289   	0           
  10          	0.10463     	0.48208     	-11.50268   	0           
  100         	0.10261     	0.48075     	-10.63640   	0           
  300         	0.09921     	0.47840     	-9.34593    	0           
  500         	0.09718     	0.47659     	-8.58744    	0           
  1000        	0.09465     	0.47250     	-7.68106    	1           
---> try to load best state
              	0.09465     	0.47250     	-7.68106    	1                eval state : test_on_restore;
              	0.09465     	0.47250     	-7.68106    	1                eval state : test_on_last_epoch;
---> final result
              	0.09465     	0.47250     	-7.68106    	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': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02082     	0.06035     	0.97089     	0           
  10          	0.02082     	0.06052     	0.97076     	0           
  100         	0.02087     	0.06235     	0.96928     	0           
  300         	0.02095     	0.06655     	0.96577     	0           
  500         	0.02090     	0.07024     	0.96244     	0           
  1000        	0.02190     	0.07726     	0.95545     	1           
---> try to load best state
              	0.02190     	0.07726     	0.95545     	1                eval state : test_on_restore;
              	0.02190     	0.07726     	0.95545     	1                eval state : test_on_last_epoch;
---> final result
              	0.02190     	0.07726     	0.95545     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04547     	0.21531     	0.88828     	0           
  10          	0.04541     	0.21492     	0.88860     	0           
  100         	0.04462     	0.21058     	0.89201     	0           
  300         	0.04328     	0.20035     	0.89941     	0           
  500         	0.04329     	0.19080     	0.90569     	0           
  1000        	0.04254     	0.17773     	0.91229     	1           
---> try to load best state
              	0.04254     	0.17773     	0.91229     	1                eval state : test_on_restore;
              	0.04254     	0.17773     	0.91229     	1                eval state : test_on_last_epoch;
---> final result
              	0.04254     	0.17773     	0.91229     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02610     	0.11623     	0.95410     	0           
  10          	0.02614     	0.11631     	0.95407     	0           
  100         	0.02655     	0.11716     	0.95375     	0           
  300         	0.02739     	0.11868     	0.95304     	0           
  500         	0.02746     	0.11907     	0.95275     	1           
  1000        	0.02698     	0.11817     	0.95231     	2           
---> try to load best state
              	0.02698     	0.11817     	0.95231     	2                eval state : test_on_restore;
              	0.02698     	0.11817     	0.95231     	2                eval state : test_on_last_epoch;
---> final result
              	0.02698     	0.11817     	0.95231     	2                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': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04727     	0.16523     	0.95054     	0           
  10          	0.04679     	0.16386     	0.95139     	0           
  100         	0.04303     	0.15104     	0.95902     	0           
  300         	0.03371     	0.12606     	0.97140     	0           
  500         	0.02826     	0.10218     	0.98065     	1           
  1000        	0.02385     	0.07640     	0.98843     	2           
---> try to load best state
              	0.02385     	0.07640     	0.98843     	2                eval state : test_on_restore;
              	0.02385     	0.07640     	0.98843     	2                eval state : test_on_last_epoch;
---> final result
              	0.02385     	0.07640     	0.98843     	2                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.17052     	0.53727     	-26035.22904	0           
  10          	0.17052     	0.53727     	-25964.92595	0           
  100         	0.17046     	0.53727     	-25351.46943	0           
  300         	0.17035     	0.53727     	-24410.50525	0           
  500         	0.17025     	0.53727     	-23648.57132	0           
  1000        	0.16996     	0.53726     	-20541.68568	1           
---> try to load best state
              	0.16996     	0.53726     	-20541.68568	1                eval state : test_on_restore;
              	0.16996     	0.53726     	-20541.68568	1                eval state : test_on_last_epoch;
---> final result
              	0.16996     	0.53726     	-20541.68568	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': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.11137     	0.42035     	-4.85585    	0           
  10          	0.11096     	0.41995     	-4.83388    	0           
  100         	0.10704     	0.41606     	-4.61891    	0           
  300         	0.10023     	0.40826     	-4.17478    	0           
  500         	0.09579     	0.40168     	-3.81390    	0           
  1000        	0.08756     	0.38801     	-3.10895    	1           
---> try to load best state
              	0.08756     	0.38801     	-3.10895    	1                eval state : test_on_restore;
              	0.08756     	0.38801     	-3.10895    	1                eval state : test_on_last_epoch;
---> final result
              	0.08756     	0.38801     	-3.10895    	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': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08394     	0.41601     	-3.65074    	0           
  10          	0.08372     	0.41566     	-3.63720    	0           
  100         	0.08143     	0.41225     	-3.50579    	0           
  300         	0.07773     	0.40551     	-3.23017    	0           
  500         	0.07662     	0.39956     	-2.97605    	0           
  1000        	0.07582     	0.38636     	-2.42962    	1           
---> try to load best state
              	0.07582     	0.38636     	-2.42962    	1                eval state : test_on_restore;
              	0.07582     	0.38636     	-2.42962    	1                eval state : test_on_last_epoch;
---> final result
              	0.07582     	0.38636     	-2.42962    	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': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03817     	0.21200     	0.55578     	0           
  10          	0.03808     	0.21080     	0.56397     	0           
  100         	0.03734     	0.20088     	0.62841     	0           
  300         	0.03720     	0.18657     	0.70826     	0           
  500         	0.03676     	0.17650     	0.75293     	0           
  1000        	0.03511     	0.15844     	0.81328     	1           
---> try to load best state
              	0.03511     	0.15844     	0.81328     	1                eval state : test_on_restore;
              	0.03511     	0.15844     	0.81328     	1                eval state : test_on_last_epoch;
---> final result
              	0.03511     	0.15844     	0.81328     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04048     	0.26582     	0.14677     	0           
  10          	0.04005     	0.26371     	0.16753     	0           
  100         	0.03645     	0.24505     	0.33695     	0           
  300         	0.03371     	0.21745     	0.54409     	0           
  500         	0.03334     	0.20201     	0.64071     	1           
  1000        	0.03392     	0.18086     	0.74605     	2           
---> try to load best state
              	0.03392     	0.18086     	0.74605     	2                eval state : test_on_restore;
              	0.03392     	0.18086     	0.74605     	2                eval state : test_on_last_epoch;
---> final result
              	0.03392     	0.18086     	0.74605     	2                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02042     	0.08289     	0.93293     	0           
  10          	0.02035     	0.08273     	0.93364     	0           
  100         	0.01975     	0.08152     	0.93907     	0           
  300         	0.01953     	0.08023     	0.94507     	0           
  500         	0.01984     	0.08008     	0.94732     	1           
  1000        	0.02030     	0.08174     	0.94711     	2           
---> try to load best state
              	0.02030     	0.08174     	0.94711     	2                eval state : test_on_restore;
              	0.02030     	0.08174     	0.94711     	2                eval state : test_on_last_epoch;
---> final result
              	0.02030     	0.08174     	0.94711     	2                eval state : final;module_name : NAR;cp_record_file : True;
