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

  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': 256, '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           	0.04189     	0.16350     	0.94955     	0           
  10          	0.04148     	0.16241     	0.95026     	0           
  100         	0.03721     	0.15160     	0.95694     	0           
  300         	0.02928     	0.12750     	0.96946     	2           
  500         	0.02457     	0.10432     	0.97909     	3           
  1000        	0.01423     	0.05379     	0.99364     	6           
---> try to load best state
              	0.01423     	0.05379     	0.99364     	7                eval state : test_on_restore;
              	0.01423     	0.05379     	0.99364     	7                eval state : test_on_last_epoch;
---> final result
              	0.01423     	0.05379     	0.99364     	7                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           	0.16653     	0.53591     	-4095.39711 	0           
  10          	0.16651     	0.53591     	-4074.79050 	0           
  100         	0.16633     	0.53590     	-3900.95540 	0           
  300         	0.16611     	0.53590     	-3681.90797 	0           
  500         	0.16600     	0.53589     	-3566.92926 	0           
  1000        	0.16575     	0.53588     	-3327.08914 	2           
---> try to load best state
              	0.16575     	0.53588     	-3327.08914 	2                eval state : test_on_restore;
              	0.16575     	0.53588     	-3327.08914 	2                eval state : test_on_last_epoch;
---> final result
              	0.16575     	0.53588     	-3327.08914 	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': 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           	0.16731     	0.53625     	-4505.02865 	0           
  10          	0.16728     	0.53625     	-4488.80713 	0           
  100         	0.16707     	0.53624     	-4346.22535 	0           
  300         	0.16671     	0.53624     	-4119.07767 	0           
  500         	0.16646     	0.53623     	-3932.45608 	1           
  1000        	0.16605     	0.53621     	-3541.92304 	2           
---> try to load best state
              	0.16605     	0.53621     	-3541.92304 	2                eval state : test_on_restore;
              	0.16605     	0.53621     	-3541.92304 	2                eval state : test_on_last_epoch;
---> final result
              	0.16605     	0.53621     	-3541.92304 	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': '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           	0.15011     	0.52734     	-158.71358  	0           
  10          	0.14999     	0.52731     	-157.40301  	0           
  100         	0.14878     	0.52703     	-145.12189  	0           
  300         	0.14608     	0.52646     	-121.88511  	0           
  500         	0.14338     	0.52599     	-102.85739  	1           
  1000        	0.13876     	0.52534     	-72.61516   	2           
---> try to load best state
              	0.13876     	0.52534     	-72.61516   	2                eval state : test_on_restore;
              	0.13876     	0.52534     	-72.61516   	2                eval state : test_on_last_epoch;
---> final result
              	0.13876     	0.52534     	-72.61516   	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': '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           	0.12223     	0.50555     	-32.60718   	0           
  10          	0.12202     	0.50538     	-32.22269   	0           
  100         	0.11996     	0.50384     	-28.84624   	0           
  300         	0.11608     	0.50103     	-23.49988   	0           
  500         	0.11297     	0.49884     	-19.87954   	1           
  1000        	0.10821     	0.49507     	-14.96512   	1           
---> try to load best state
              	0.10821     	0.49507     	-14.96512   	1                eval state : test_on_restore;
              	0.10821     	0.49507     	-14.96512   	1                eval state : test_on_last_epoch;
---> final result
              	0.10821     	0.49507     	-14.96512   	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           	0.03621     	0.18554     	0.58224     	0           
  10          	0.03610     	0.18503     	0.58682     	0           
  100         	0.03508     	0.18077     	0.62806     	0           
  300         	0.03354     	0.17632     	0.69257     	0           
  500         	0.03270     	0.17635     	0.72662     	1           
  1000        	0.03449     	0.17635     	0.75064     	1           
---> try to load best state
              	0.03449     	0.17635     	0.75064     	1                eval state : test_on_restore;
              	0.03449     	0.17635     	0.75064     	1                eval state : test_on_last_epoch;
---> final result
              	0.03449     	0.17635     	0.75064     	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': 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           	0.03269     	0.18532     	0.44718     	0           
  10          	0.03261     	0.18450     	0.45223     	0           
  100         	0.03189     	0.17710     	0.49874     	0           
  300         	0.03075     	0.16502     	0.58158     	0           
  500         	0.02966     	0.15786     	0.64255     	1           
  1000        	0.02682     	0.15409     	0.71620     	2           
---> try to load best state
              	0.02682     	0.15409     	0.71620     	2                eval state : test_on_restore;
              	0.02682     	0.15409     	0.71620     	2                eval state : test_on_last_epoch;
---> final result
              	0.02682     	0.15409     	0.71620     	2                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 256 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 256, '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           	0.02373     	0.11284     	0.86212     	0           
  10          	0.02368     	0.11266     	0.86295     	0           
  100         	0.02324     	0.11124     	0.86927     	0           
  300         	0.02302     	0.10929     	0.87635     	2           
  500         	0.02293     	0.10795     	0.87994     	3           
  1000        	0.02244     	0.10567     	0.88488     	8           
---> try to load best state
              	0.02244     	0.10567     	0.88488     	8                eval state : test_on_restore;
              	0.02244     	0.10567     	0.88488     	8                eval state : test_on_last_epoch;
---> final result
              	0.02244     	0.10567     	0.88488     	8                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': 256, '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           	0.03410     	0.17858     	0.92076     	0           
  10          	0.03385     	0.17756     	0.92188     	0           
  100         	0.03092     	0.16723     	0.93239     	0           
  300         	0.02612     	0.14426     	0.95179     	2           
  500         	0.02367     	0.11786     	0.96841     	4           
  1000        	0.01314     	0.07236     	0.98757     	8           
---> try to load best state
              	0.01314     	0.07236     	0.98757     	8                eval state : test_on_restore;
              	0.01314     	0.07236     	0.98757     	8                eval state : test_on_last_epoch;
---> final result
              	0.01314     	0.07236     	0.98757     	8                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           	0.15980     	0.53172     	-884.84628  	0           
  10          	0.15976     	0.53172     	-882.62396  	0           
  100         	0.15936     	0.53171     	-863.05723  	0           
  300         	0.15891     	0.53169     	-838.69810  	0           
  500         	0.15878     	0.53167     	-828.57994  	0           
  1000        	0.15839     	0.53163     	-780.57913  	1           
---> try to load best state
              	0.15839     	0.53163     	-780.57913  	1                eval state : test_on_restore;
              	0.15839     	0.53163     	-780.57913  	1                eval state : test_on_last_epoch;
---> final result
              	0.15839     	0.53163     	-780.57913  	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           	0.13638     	0.51206     	-74.42717   	0           
  10          	0.13621     	0.51203     	-73.73891   	0           
  100         	0.13438     	0.51167     	-66.44288   	0           
  300         	0.13113     	0.51100     	-55.28275   	0           
  500         	0.12909     	0.51043     	-49.16256   	0           
  1000        	0.12695     	0.50907     	-42.60544   	2           
---> try to load best state
              	0.12695     	0.50907     	-42.60544   	2                eval state : test_on_restore;
              	0.12695     	0.50907     	-42.60544   	2                eval state : test_on_last_epoch;
---> final result
              	0.12695     	0.50907     	-42.60544   	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': '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           	0.10244     	0.47756     	-10.85869   	0           
  10          	0.10221     	0.47742     	-10.74916   	0           
  100         	0.10051     	0.47616     	-9.82564    	0           
  300         	0.09869     	0.47401     	-8.54202    	0           
  500         	0.09779     	0.47225     	-7.82924    	1           
  1000        	0.09619     	0.46786     	-6.97904    	1           
---> try to load best state
              	0.09619     	0.46786     	-6.97904    	1                eval state : test_on_restore;
              	0.09619     	0.46786     	-6.97904    	1                eval state : test_on_last_epoch;
---> final result
              	0.09619     	0.46786     	-6.97904    	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           	0.09258     	0.46311     	-6.96879    	0           
  10          	0.09247     	0.46299     	-6.92564    	0           
  100         	0.09149     	0.46186     	-6.55782    	0           
  300         	0.09039     	0.45967     	-6.02379    	0           
  500         	0.08974     	0.45756     	-5.68765    	0           
  1000        	0.08838     	0.45209     	-5.16420    	1           
---> try to load best state
              	0.08838     	0.45209     	-5.16420    	1                eval state : test_on_restore;
              	0.08838     	0.45209     	-5.16420    	1                eval state : test_on_last_epoch;
---> final result
              	0.08838     	0.45209     	-5.16420    	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': 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           	0.06058     	0.38687     	-1.61225    	0           
  10          	0.06035     	0.38650     	-1.59538    	0           
  100         	0.05933     	0.38306     	-1.44841    	0           
  300         	0.05763     	0.37701     	-1.22956    	0           
  500         	0.05677     	0.37225     	-1.09726    	1           
  1000        	0.05485     	0.36331     	-0.91398    	2           
---> try to load best state
              	0.05485     	0.36331     	-0.91398    	2                eval state : test_on_restore;
              	0.05485     	0.36331     	-0.91398    	2                eval state : test_on_last_epoch;
---> final result
              	0.05485     	0.36331     	-0.91398    	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           	0.04965     	0.35225     	-0.08154    	0           
  10          	0.04964     	0.35221     	-0.08135    	0           
  100         	0.04937     	0.35036     	-0.08680    	0           
  300         	0.04949     	0.34886     	-0.08811    	0           
  500         	0.05006     	0.35089     	-0.07204    	1           
  1000        	0.05119     	0.35552     	-0.04221    	2           
---> try to load best state
              	0.05119     	0.35552     	-0.04221    	2                eval state : test_on_restore;
              	0.05119     	0.35552     	-0.04221    	2                eval state : test_on_last_epoch;
---> final result
              	0.05119     	0.35552     	-0.04221    	2                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 256 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 256, '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           	0.03714     	0.21108     	0.65631     	0           
  10          	0.03702     	0.21018     	0.65932     	0           
  100         	0.03604     	0.20266     	0.68362     	0           
  300         	0.03482     	0.19361     	0.71220     	2           
  500         	0.03444     	0.19087     	0.72266     	3           
  1000        	0.03525     	0.19679     	0.71597     	6           
---> try to load best state
              	0.03525     	0.19679     	0.71597     	6                eval state : test_on_restore;
              	0.03525     	0.19679     	0.71597     	6                eval state : test_on_last_epoch;
---> final result
              	0.03525     	0.19679     	0.71597     	6                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': 256, '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           	0.04479     	0.15366     	0.95776     	0           
  10          	0.04434     	0.15262     	0.95839     	0           
  100         	0.03953     	0.14218     	0.96430     	0           
  300         	0.03167     	0.12122     	0.97428     	1           
  500         	0.02703     	0.10062     	0.98181     	3           
  1000        	0.01836     	0.06724     	0.99105     	7           
---> try to load best state
              	0.01836     	0.06724     	0.99105     	7                eval state : test_on_restore;
              	0.01836     	0.06724     	0.99105     	7                eval state : test_on_last_epoch;
---> final result
              	0.01836     	0.06724     	0.99105     	7                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           	0.23524     	0.49147     	-33.31881   	0           
  10          	0.23477     	0.49130     	-33.17106   	0           
  100         	0.23037     	0.48967     	-31.91505   	0           
  300         	0.22202     	0.48663     	-29.76131   	0           
  500         	0.21414     	0.48386     	-27.75771   	0           
  1000        	0.19797     	0.47797     	-24.69045   	1           
---> try to load best state
              	0.19797     	0.47797     	-24.69045   	1                eval state : test_on_restore;
              	0.19797     	0.47797     	-24.69045   	1                eval state : test_on_last_epoch;
---> final result
              	0.19797     	0.47797     	-24.69045   	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': 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           	0.12173     	0.48951     	-29.18893   	0           
  10          	0.12168     	0.48949     	-29.12402   	0           
  100         	0.12129     	0.48921     	-28.51714   	0           
  300         	0.12090     	0.48860     	-27.41580   	0           
  500         	0.12049     	0.48803     	-26.45571   	1           
  1000        	0.11890     	0.48661     	-23.82529   	2           
---> try to load best state
              	0.11890     	0.48661     	-23.82529   	2                eval state : test_on_restore;
              	0.11890     	0.48661     	-23.82529   	2                eval state : test_on_last_epoch;
---> final result
              	0.11890     	0.48661     	-23.82529   	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': '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           	0.09429     	0.45633     	-6.73246    	0           
  10          	0.09396     	0.45617     	-6.64810    	0           
  100         	0.09099     	0.45472     	-5.92579    	0           
  300         	0.08789     	0.45235     	-4.91068    	0           
  500         	0.08707     	0.45020     	-4.35934    	1           
  1000        	0.08798     	0.44412     	-3.71675    	2           
---> try to load best state
              	0.08798     	0.44412     	-3.71675    	2                eval state : test_on_restore;
              	0.08798     	0.44412     	-3.71675    	2                eval state : test_on_last_epoch;
---> final result
              	0.08798     	0.44412     	-3.71675    	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': '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           	0.02911     	0.13673     	0.57556     	0           
  10          	0.02890     	0.13613     	0.57869     	0           
  100         	0.02704     	0.13138     	0.60512     	0           
  300         	0.02619     	0.12663     	0.63999     	0           
  500         	0.02566     	0.12540     	0.65629     	1           
  1000        	0.02516     	0.12485     	0.66920     	2           
---> try to load best state
              	0.02516     	0.12485     	0.66920     	2                eval state : test_on_restore;
              	0.02516     	0.12485     	0.66920     	2                eval state : test_on_last_epoch;
---> final result
              	0.02516     	0.12485     	0.66920     	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': 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           	0.02432     	0.10703     	0.82733     	0           
  10          	0.02437     	0.10726     	0.82781     	0           
  100         	0.02480     	0.10961     	0.83110     	0           
  300         	0.02539     	0.11325     	0.83496     	0           
  500         	0.02572     	0.11494     	0.83800     	1           
  1000        	0.02603     	0.11627     	0.84230     	2           
---> try to load best state
              	0.02603     	0.11627     	0.84230     	2                eval state : test_on_restore;
              	0.02603     	0.11627     	0.84230     	2                eval state : test_on_last_epoch;
---> final result
              	0.02603     	0.11627     	0.84230     	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': 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           	0.02190     	0.09180     	0.91694     	0           
  10          	0.02185     	0.09155     	0.91783     	0           
  100         	0.02138     	0.08965     	0.92473     	0           
  300         	0.02102     	0.08714     	0.93301     	0           
  500         	0.02105     	0.08561     	0.93778     	1           
  1000        	0.02101     	0.08377     	0.94452     	3           
---> try to load best state
              	0.02101     	0.08377     	0.94452     	3                eval state : test_on_restore;
              	0.02101     	0.08377     	0.94452     	3                eval state : test_on_last_epoch;
---> final result
              	0.02101     	0.08377     	0.94452     	3                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 256 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 256, '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           	0.02124     	0.10532     	0.86095     	0           
  10          	0.02122     	0.10517     	0.86209     	0           
  100         	0.02110     	0.10397     	0.87116     	1           
  300         	0.02092     	0.10271     	0.88092     	2           
  500         	0.02089     	0.10283     	0.88196     	5           
  1000        	0.02118     	0.10665     	0.86186     	10          
---> try to load best state
              	0.02118     	0.10665     	0.86186     	10               eval state : test_on_restore;
              	0.02118     	0.10665     	0.86186     	10               eval state : test_on_last_epoch;
---> final result
              	0.02118     	0.10665     	0.86186     	10               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': 256, '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           	0.04185     	0.15852     	0.95421     	0           
  10          	0.04152     	0.15743     	0.95489     	0           
  100         	0.03783     	0.14664     	0.96123     	1           
  300         	0.02979     	0.12224     	0.97304     	2           
  500         	0.02535     	0.09743     	0.98231     	4           
  1000        	0.01467     	0.05414     	0.99383     	8           
---> try to load best state
              	0.01467     	0.05414     	0.99383     	8                eval state : test_on_restore;
              	0.01467     	0.05414     	0.99383     	8                eval state : test_on_last_epoch;
---> final result
              	0.01467     	0.05414     	0.99383     	8                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           	0.10503     	0.44688     	-7.69861    	0           
  10          	0.10441     	0.44668     	-7.63722    	0           
  100         	0.09905     	0.44489     	-7.11857    	0           
  300         	0.09251     	0.44182     	-6.39671    	0           
  500         	0.09186     	0.43936     	-5.91519    	0           
  1000        	0.09342     	0.43492     	-5.26428    	1           
---> try to load best state
              	0.09342     	0.43492     	-5.26428    	1                eval state : test_on_restore;
              	0.09342     	0.43492     	-5.26428    	1                eval state : test_on_last_epoch;
---> final result
              	0.09342     	0.43492     	-5.26428    	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           	0.10803     	0.46915     	-11.32067   	0           
  10          	0.10754     	0.46896     	-11.18263   	0           
  100         	0.10329     	0.46718     	-9.99554    	0           
  300         	0.09699     	0.46380     	-8.25439    	0           
  500         	0.09442     	0.46082     	-7.23438    	0           
  1000        	0.09104     	0.45396     	-6.05873    	1           
---> try to load best state
              	0.09104     	0.45396     	-6.05873    	1                eval state : test_on_restore;
              	0.09104     	0.45396     	-6.05873    	1                eval state : test_on_last_epoch;
---> final result
              	0.09104     	0.45396     	-6.05873    	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           	0.10016     	0.47661     	-10.77365   	0           
  10          	0.09988     	0.47645     	-10.64594   	0           
  100         	0.09728     	0.47503     	-9.55959    	0           
  300         	0.09473     	0.47290     	-8.04011    	0           
  500         	0.09398     	0.47131     	-7.24584    	0           
  1000        	0.09337     	0.46719     	-6.46766    	1           
---> try to load best state
              	0.09337     	0.46719     	-6.46766    	1                eval state : test_on_restore;
              	0.09337     	0.46719     	-6.46766    	1                eval state : test_on_last_epoch;
---> final result
              	0.09337     	0.46719     	-6.46766    	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           	0.02592     	0.09431     	0.93716     	0           
  10          	0.02578     	0.09349     	0.93867     	0           
  100         	0.02467     	0.08685     	0.95027     	0           
  300         	0.02295     	0.07938     	0.96220     	0           
  500         	0.02175     	0.07685     	0.96618     	1           
  1000        	0.02048     	0.07661     	0.96715     	2           
---> try to load best state
              	0.02048     	0.07661     	0.96715     	2                eval state : test_on_restore;
              	0.02048     	0.07661     	0.96715     	2                eval state : test_on_last_epoch;
---> final result
              	0.02048     	0.07661     	0.96715     	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': 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           	0.04664     	0.19723     	0.85220     	0           
  10          	0.04649     	0.19650     	0.85443     	0           
  100         	0.04503     	0.19020     	0.87226     	0           
  300         	0.04260     	0.18056     	0.89496     	0           
  500         	0.04109     	0.17397     	0.90722     	0           
  1000        	0.03778     	0.16577     	0.91883     	1           
---> try to load best state
              	0.03778     	0.16577     	0.91883     	1                eval state : test_on_restore;
              	0.03778     	0.16577     	0.91883     	1                eval state : test_on_last_epoch;
---> final result
              	0.03778     	0.16577     	0.91883     	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           	0.02991     	0.15725     	0.81709     	0           
  10          	0.02983     	0.15595     	0.82111     	0           
  100         	0.02913     	0.14513     	0.85400     	0           
  300         	0.02898     	0.13142     	0.89606     	1           
  500         	0.02863     	0.12486     	0.91797     	1           
  1000        	0.02718     	0.11881     	0.94228     	3           
---> try to load best state
              	0.02718     	0.11881     	0.94228     	3                eval state : test_on_restore;
              	0.02718     	0.11881     	0.94228     	3                eval state : test_on_last_epoch;
---> final result
              	0.02718     	0.11881     	0.94228     	3                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 256 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 256, '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           	0.02083     	0.09967     	0.94840     	0           
  10          	0.02085     	0.09965     	0.94876     	0           
  100         	0.02111     	0.09963     	0.95130     	0           
  300         	0.02193     	0.10049     	0.95324     	2           
  500         	0.02304     	0.10146     	0.95343     	3           
  1000        	0.02405     	0.10211     	0.95282     	7           
---> try to load best state
              	0.02405     	0.10211     	0.95282     	7                eval state : test_on_restore;
              	0.02405     	0.10211     	0.95282     	7                eval state : test_on_last_epoch;
---> final result
              	0.02405     	0.10211     	0.95282     	7                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': 256, '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           	0.04254     	0.14764     	0.95919     	0           
  10          	0.04215     	0.14650     	0.95987     	0           
  100         	0.03790     	0.13522     	0.96614     	0           
  300         	0.02969     	0.11099     	0.97729     	2           
  500         	0.02497     	0.08434     	0.98648     	3           
  1000        	0.01297     	0.04275     	0.99591     	8           
---> try to load best state
              	0.01297     	0.04275     	0.99591     	8                eval state : test_on_restore;
              	0.01297     	0.04275     	0.99591     	8                eval state : test_on_last_epoch;
---> final result
              	0.01297     	0.04275     	0.99591     	8                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           	0.17053     	0.53727     	-27047.75803	0           
  10          	0.17052     	0.53727     	-26968.95021	0           
  100         	0.17045     	0.53727     	-26258.57938	0           
  300         	0.17032     	0.53727     	-25060.09359	0           
  500         	0.17020     	0.53726     	-24038.32393	1           
  1000        	0.16988     	0.53726     	-20410.17484	2           
---> try to load best state
              	0.16988     	0.53726     	-20410.17484	2                eval state : test_on_restore;
              	0.16988     	0.53726     	-20410.17484	2                eval state : test_on_last_epoch;
---> final result
              	0.16988     	0.53726     	-20410.17484	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': 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           	0.10821     	0.41398     	-4.19030    	0           
  10          	0.10781     	0.41360     	-4.17079    	0           
  100         	0.10401     	0.40992     	-3.98285    	0           
  300         	0.09749     	0.40258     	-3.60842    	0           
  500         	0.09328     	0.39650     	-3.31723    	1           
  1000        	0.08585     	0.38411     	-2.76168    	2           
---> try to load best state
              	0.08585     	0.38411     	-2.76168    	2                eval state : test_on_restore;
              	0.08585     	0.38411     	-2.76168    	2                eval state : test_on_last_epoch;
---> final result
              	0.08585     	0.38411     	-2.76168    	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': '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           	0.08069     	0.40899     	-3.06182    	0           
  10          	0.08046     	0.40866     	-3.04899    	0           
  100         	0.07823     	0.40547     	-2.92877    	0           
  300         	0.07468     	0.39920     	-2.69623    	0           
  500         	0.07373     	0.39374     	-2.49727    	1           
  1000        	0.07360     	0.38177     	-2.08355    	2           
---> try to load best state
              	0.07360     	0.38177     	-2.08355    	2                eval state : test_on_restore;
              	0.07360     	0.38177     	-2.08355    	2                eval state : test_on_last_epoch;
---> final result
              	0.07360     	0.38177     	-2.08355    	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': '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           	0.04025     	0.24352     	0.37390     	0           
  10          	0.04012     	0.24215     	0.38552     	0           
  100         	0.03901     	0.23011     	0.47985     	0           
  300         	0.03760     	0.21064     	0.60313     	0           
  500         	0.03678     	0.19630     	0.67379     	1           
  1000        	0.03467     	0.17098     	0.76941     	2           
---> try to load best state
              	0.03467     	0.17098     	0.76941     	2                eval state : test_on_restore;
              	0.03467     	0.17098     	0.76941     	2                eval state : test_on_last_epoch;
---> final result
              	0.03467     	0.17098     	0.76941     	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': 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           	0.04401     	0.29785     	-0.13242    	0           
  10          	0.04358     	0.29607     	-0.11010    	0           
  100         	0.04001     	0.27960     	0.08215     	0           
  300         	0.03639     	0.25172     	0.34983     	1           
  500         	0.03562     	0.23286     	0.49193     	1           
  1000        	0.03487     	0.20314     	0.65790     	2           
---> try to load best state
              	0.03487     	0.20314     	0.65790     	2                eval state : test_on_restore;
              	0.03487     	0.20314     	0.65790     	2                eval state : test_on_last_epoch;
---> final result
              	0.03487     	0.20314     	0.65790     	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           	0.02142     	0.08285     	0.92927     	0           
  10          	0.02137     	0.08275     	0.92985     	0           
  100         	0.02087     	0.08190     	0.93454     	0           
  300         	0.02003     	0.08082     	0.94031     	1           
  500         	0.02002     	0.08054     	0.94294     	1           
  1000        	0.02028     	0.08120     	0.94448     	3           
---> try to load best state
              	0.02028     	0.08120     	0.94448     	3                eval state : test_on_restore;
              	0.02028     	0.08120     	0.94448     	3                eval state : test_on_last_epoch;
---> final result
              	0.02028     	0.08120     	0.94448     	3                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 256 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 256, '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           	0.03147     	0.16293     	0.86305     	0           
  10          	0.03140     	0.16246     	0.86355     	0           
  100         	0.03078     	0.15765     	0.86807     	0           
  300         	0.02973     	0.14865     	0.87546     	2           
  500         	0.02910     	0.14261     	0.88053     	3           
  1000        	0.02828     	0.13355     	0.88853     	6           
---> try to load best state
              	0.02828     	0.13355     	0.88853     	6                eval state : test_on_restore;
              	0.02828     	0.13355     	0.88853     	6                eval state : test_on_last_epoch;
---> final result
              	0.02828     	0.13355     	0.88853     	6                eval state : final;module_name : NAR;cp_record_file : True;
