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

  Demo NAR cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, '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           	0.06896     	0.22212     	0.91813     	0           
  10          	0.06852     	0.22091     	0.91933     	0           
  100         	0.06387     	0.20974     	0.93008     	0           
  300         	0.05695     	0.19307     	0.94460     	0           
  500         	0.05282     	0.18716     	0.94959     	1           
  1000        	0.05188     	0.18537     	0.95135     	2           
---> try to load best state
              	0.05188     	0.18537     	0.95135     	2                eval state : test_on_restore;
              	0.05188     	0.18537     	0.95135     	2                eval state : test_on_last_epoch;
---> final result
              	0.05188     	0.18537     	0.95135     	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': None, '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           	0.15596     	0.52123     	-315.85456  	0           
  10          	0.15589     	0.52121     	-312.50818  	0           
  100         	0.15529     	0.52109     	-284.31177  	0           
  300         	0.15468     	0.52089     	-243.94787  	0           
  500         	0.15433     	0.52074     	-216.64530  	1           
  1000        	0.15363     	0.52045     	-176.11806  	2           
---> try to load best state
              	0.15363     	0.52045     	-176.11806  	2                eval state : test_on_restore;
              	0.15363     	0.52045     	-176.11806  	2                eval state : test_on_last_epoch;
---> final result
              	0.15363     	0.52045     	-176.11806  	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': None, '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           	0.15737     	0.52792     	-423.90180  	0           
  10          	0.15733     	0.52791     	-420.10486  	0           
  100         	0.15686     	0.52778     	-382.77289  	0           
  300         	0.15573     	0.52751     	-318.14628  	0           
  500         	0.15472     	0.52726     	-277.90174  	1           
  1000        	0.15276     	0.52674     	-220.56069  	2           
---> try to load best state
              	0.15276     	0.52674     	-220.56069  	2                eval state : test_on_restore;
              	0.15276     	0.52674     	-220.56069  	2                eval state : test_on_last_epoch;
---> final result
              	0.15276     	0.52674     	-220.56069  	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': None, '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           	0.12953     	0.49940     	-33.39374   	0           
  10          	0.12935     	0.49925     	-33.05244   	0           
  100         	0.12760     	0.49783     	-30.03266   	0           
  300         	0.12405     	0.49507     	-25.15794   	0           
  500         	0.12081     	0.49278     	-21.72871   	0           
  1000        	0.11436     	0.48852     	-16.44297   	1           
---> try to load best state
              	0.11436     	0.48852     	-16.44297   	1                eval state : test_on_restore;
              	0.11436     	0.48852     	-16.44297   	1                eval state : test_on_last_epoch;
---> final result
              	0.11436     	0.48852     	-16.44297   	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': None, '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           	0.07278     	0.30719     	-0.43746    	0           
  10          	0.07237     	0.30531     	-0.40945    	0           
  100         	0.06820     	0.28613     	-0.15737    	0           
  300         	0.05880     	0.24340     	0.25151     	0           
  500         	0.04951     	0.20319     	0.50940     	1           
  1000        	0.03039     	0.13073     	0.77887     	1           
---> try to load best state
              	0.03039     	0.13073     	0.77887     	1                eval state : test_on_restore;
              	0.03039     	0.13073     	0.77887     	1                eval state : test_on_last_epoch;
---> final result
              	0.03039     	0.13073     	0.77887     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, '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           	0.07859     	0.41322     	-1.48425    	0           
  10          	0.07830     	0.41280     	-1.46591    	0           
  100         	0.07547     	0.40950     	-1.31289    	0           
  300         	0.07175     	0.40910     	-1.17778    	0           
  500         	0.07071     	0.41279     	-1.19060    	0           
  1000        	0.06840     	0.40601     	-0.98679    	1           
---> try to load best state
              	0.06840     	0.40601     	-0.98679    	1                eval state : test_on_restore;
              	0.06840     	0.40601     	-0.98679    	1                eval state : test_on_last_epoch;
---> final result
              	0.06840     	0.40601     	-0.98679    	1                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     	-4133.77059 	0           
  10          	0.16651     	0.53591     	-4112.05941 	0           
  100         	0.16634     	0.53591     	-3929.82287 	0           
  300         	0.16615     	0.53590     	-3704.19777 	0           
  500         	0.16606     	0.53590     	-3591.28560 	0           
  1000        	0.16584     	0.53589     	-3366.35269 	1           
---> try to load best state
              	0.16584     	0.53589     	-3366.35269 	1                eval state : test_on_restore;
              	0.16584     	0.53589     	-3366.35269 	1                eval state : test_on_last_epoch;
---> final result
              	0.16584     	0.53589     	-3366.35269 	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           	0.16730     	0.53625     	-4536.72850 	0           
  10          	0.16728     	0.53625     	-4519.54154 	0           
  100         	0.16707     	0.53624     	-4368.69836 	0           
  300         	0.16673     	0.53624     	-4131.53638 	0           
  500         	0.16649     	0.53623     	-3943.84857 	0           
  1000        	0.16610     	0.53622     	-3568.45785 	1           
---> try to load best state
              	0.16610     	0.53622     	-3568.45785 	1                eval state : test_on_restore;
              	0.16610     	0.53622     	-3568.45785 	1                eval state : test_on_last_epoch;
---> final result
              	0.16610     	0.53622     	-3568.45785 	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           	0.15059     	0.52767     	-156.37770  	0           
  10          	0.15049     	0.52764     	-154.87687  	0           
  100         	0.14951     	0.52743     	-141.49810  	0           
  300         	0.14730     	0.52698     	-119.28675  	0           
  500         	0.14537     	0.52658     	-102.97926  	0           
  1000        	0.14123     	0.52587     	-77.19343   	1           
---> try to load best state
              	0.14123     	0.52587     	-77.19343   	1                eval state : test_on_restore;
              	0.14123     	0.52587     	-77.19343   	1                eval state : test_on_last_epoch;
---> final result
              	0.14123     	0.52587     	-77.19343   	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           	0.09657     	0.42410     	-8.76442    	0           
  10          	0.09632     	0.42362     	-8.68847    	0           
  100         	0.09398     	0.41920     	-8.01570    	0           
  300         	0.08960     	0.41157     	-6.90869    	0           
  500         	0.08599     	0.40590     	-6.09766    	1           
  1000        	0.08147     	0.39717     	-4.86266    	2           
---> try to load best state
              	0.08147     	0.39717     	-4.86266    	2                eval state : test_on_restore;
              	0.08147     	0.39717     	-4.86266    	2                eval state : test_on_last_epoch;
---> final result
              	0.08147     	0.39717     	-4.86266    	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': 32, '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           	0.06860     	0.37914     	-0.43123    	0           
  10          	0.06827     	0.37859     	-0.41884    	0           
  100         	0.06474     	0.37331     	-0.30818    	0           
  300         	0.05983     	0.36574     	-0.15246    	0           
  500         	0.05790     	0.36552     	-0.11214    	0           
  1000        	0.05620     	0.36064     	-0.03904    	1           
---> try to load best state
              	0.05620     	0.36064     	-0.03904    	1                eval state : test_on_restore;
              	0.05620     	0.36064     	-0.03904    	1                eval state : test_on_last_epoch;
---> final result
              	0.05620     	0.36064     	-0.03904    	1                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           	0.16037     	0.53168     	-925.52462  	0           
  10          	0.16031     	0.53168     	-920.97351  	0           
  100         	0.15978     	0.53165     	-880.42941  	0           
  300         	0.15905     	0.53159     	-825.04121  	0           
  500         	0.15875     	0.53155     	-797.57382  	0           
  1000        	0.15815     	0.53147     	-722.93726  	1           
---> try to load best state
              	0.15815     	0.53147     	-722.93726  	1                eval state : test_on_restore;
              	0.15815     	0.53147     	-722.93726  	1                eval state : test_on_last_epoch;
---> final result
              	0.15815     	0.53147     	-722.93726  	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           	0.13650     	0.51196     	-70.07944   	0           
  10          	0.13636     	0.51193     	-69.58501   	0           
  100         	0.13484     	0.51162     	-64.06154   	0           
  300         	0.13217     	0.51100     	-55.33170   	0           
  500         	0.13033     	0.51040     	-50.23705   	0           
  1000        	0.12784     	0.50868     	-44.07264   	1           
---> try to load best state
              	0.12784     	0.50868     	-44.07264   	1                eval state : test_on_restore;
              	0.12784     	0.50868     	-44.07264   	1                eval state : test_on_last_epoch;
---> final result
              	0.12784     	0.50868     	-44.07264   	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           	0.08571     	0.41639     	-5.62274    	0           
  10          	0.08537     	0.41564     	-5.54777    	0           
  100         	0.08273     	0.40885     	-4.91406    	0           
  300         	0.07985     	0.39771     	-4.03069    	0           
  500         	0.07860     	0.39005     	-3.53425    	0           
  1000        	0.07692     	0.37738     	-2.91847    	1           
---> try to load best state
              	0.07692     	0.37738     	-2.91847    	1                eval state : test_on_restore;
              	0.07692     	0.37738     	-2.91847    	1                eval state : test_on_last_epoch;
---> final result
              	0.07692     	0.37738     	-2.91847    	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           	0.06793     	0.35220     	-2.33585    	0           
  10          	0.06778     	0.35168     	-2.31340    	0           
  100         	0.06661     	0.34736     	-2.12895    	0           
  300         	0.06556     	0.34168     	-1.88863    	0           
  500         	0.06518     	0.33865     	-1.75766    	1           
  1000        	0.06511     	0.33460     	-1.59090    	1           
---> try to load best state
              	0.06511     	0.33460     	-1.59090    	1                eval state : test_on_restore;
              	0.06511     	0.33460     	-1.59090    	1                eval state : test_on_last_epoch;
---> final result
              	0.06511     	0.33460     	-1.59090    	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, '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           	0.07705     	0.21938     	0.90030     	0           
  10          	0.07637     	0.21842     	0.90137     	0           
  100         	0.06803     	0.20627     	0.91295     	0           
  300         	0.04759     	0.16844     	0.94233     	0           
  500         	0.04571     	0.14873     	0.95734     	1           
  1000        	0.04287     	0.13765     	0.96427     	1           
---> try to load best state
              	0.04287     	0.13765     	0.96427     	1                eval state : test_on_restore;
              	0.04287     	0.13765     	0.96427     	1                eval state : test_on_last_epoch;
---> final result
              	0.04287     	0.13765     	0.96427     	1                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           	0.23774     	0.49208     	-33.61002   	0           
  10          	0.23731     	0.49192     	-33.43701   	0           
  100         	0.23326     	0.49041     	-31.97313   	0           
  300         	0.22553     	0.48757     	-29.57771   	0           
  500         	0.21814     	0.48496     	-27.51435   	0           
  1000        	0.20264     	0.47927     	-24.52481   	1           
---> try to load best state
              	0.20264     	0.47927     	-24.52481   	1                eval state : test_on_restore;
              	0.20264     	0.47927     	-24.52481   	1                eval state : test_on_last_epoch;
---> final result
              	0.20264     	0.47927     	-24.52481   	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           	0.12835     	0.49018     	-25.29685   	0           
  10          	0.12833     	0.49016     	-25.20282   	0           
  100         	0.12824     	0.48997     	-24.39144   	0           
  300         	0.12828     	0.48957     	-23.21365   	0           
  500         	0.12813     	0.48917     	-22.45884   	0           
  1000        	0.12682     	0.48811     	-20.74900   	1           
---> try to load best state
              	0.12682     	0.48811     	-20.74900   	1                eval state : test_on_restore;
              	0.12682     	0.48811     	-20.74900   	1                eval state : test_on_last_epoch;
---> final result
              	0.12682     	0.48811     	-20.74900   	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           	0.10667     	0.46446     	-7.50767    	0           
  10          	0.10639     	0.46430     	-7.43072    	0           
  100         	0.10387     	0.46286     	-6.76542    	0           
  300         	0.10072     	0.46023     	-5.79190    	0           
  500         	0.09834     	0.45795     	-5.22351    	0           
  1000        	0.09599     	0.45212     	-4.48972    	1           
---> try to load best state
              	0.09599     	0.45212     	-4.48972    	1                eval state : test_on_restore;
              	0.09599     	0.45212     	-4.48972    	1                eval state : test_on_last_epoch;
---> final result
              	0.09599     	0.45212     	-4.48972    	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           	0.03475     	0.17348     	0.35296     	0           
  10          	0.03447     	0.17186     	0.36284     	0           
  100         	0.03201     	0.15788     	0.44397     	0           
  300         	0.03022     	0.13982     	0.54237     	0           
  500         	0.02935     	0.13283     	0.58175     	0           
  1000        	0.02945     	0.13016     	0.60290     	1           
---> try to load best state
              	0.02945     	0.13016     	0.60290     	1                eval state : test_on_restore;
              	0.02945     	0.13016     	0.60290     	1                eval state : test_on_last_epoch;
---> final result
              	0.02945     	0.13016     	0.60290     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, '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           	0.08226     	0.25243     	0.89628     	0           
  10          	0.08114     	0.25047     	0.89784     	0           
  100         	0.07129     	0.23059     	0.91254     	0           
  300         	0.05863     	0.19613     	0.93510     	0           
  500         	0.05551     	0.18506     	0.94188     	0           
  1000        	0.05517     	0.18186     	0.94421     	1           
---> try to load best state
              	0.05517     	0.18186     	0.94421     	1                eval state : test_on_restore;
              	0.05517     	0.18186     	0.94421     	1                eval state : test_on_last_epoch;
---> final result
              	0.05517     	0.18186     	0.94421     	1                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           	0.11904     	0.46184     	-11.02084   	0           
  10          	0.11849     	0.46166     	-10.91425   	0           
  100         	0.11361     	0.45998     	-10.01141   	0           
  300         	0.10675     	0.45700     	-8.74662    	0           
  500         	0.10493     	0.45461     	-7.93306    	0           
  1000        	0.10418     	0.45015     	-6.85699    	1           
---> try to load best state
              	0.10418     	0.45015     	-6.85699    	1                eval state : test_on_restore;
              	0.10418     	0.45015     	-6.85699    	1                eval state : test_on_last_epoch;
---> final result
              	0.10418     	0.45015     	-6.85699    	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           	0.12132     	0.48581     	-17.71879   	0           
  10          	0.12095     	0.48565     	-17.47649   	0           
  100         	0.11765     	0.48415     	-15.41758   	0           
  300         	0.11279     	0.48124     	-12.47463   	0           
  500         	0.11133     	0.47864     	-10.76337   	0           
  1000        	0.10797     	0.47248     	-8.65363    	1           
---> try to load best state
              	0.10797     	0.47248     	-8.65363    	1                eval state : test_on_restore;
              	0.10797     	0.47248     	-8.65363    	1                eval state : test_on_last_epoch;
---> final result
              	0.10797     	0.47248     	-8.65363    	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           	0.11823     	0.49487     	-19.36133   	0           
  10          	0.11810     	0.49474     	-19.09751   	0           
  100         	0.11699     	0.49358     	-16.87313   	0           
  300         	0.11564     	0.49159     	-13.79943   	0           
  500         	0.11504     	0.48995     	-12.15124   	0           
  1000        	0.11290     	0.48599     	-10.28361   	1           
---> try to load best state
              	0.11290     	0.48599     	-10.28361   	1                eval state : test_on_restore;
              	0.11290     	0.48599     	-10.28361   	1                eval state : test_on_last_epoch;
---> final result
              	0.11290     	0.48599     	-10.28361   	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           	0.03045     	0.09093     	0.95428     	0           
  10          	0.03062     	0.09147     	0.95387     	0           
  100         	0.03213     	0.09634     	0.94998     	0           
  300         	0.03427     	0.10467     	0.94276     	0           
  500         	0.03542     	0.11086     	0.93707     	0           
  1000        	0.03719     	0.12160     	0.92675     	1           
---> try to load best state
              	0.03719     	0.12160     	0.92675     	1                eval state : test_on_restore;
              	0.03719     	0.12160     	0.92675     	1                eval state : test_on_last_epoch;
---> final result
              	0.03719     	0.12160     	0.92675     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, '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           	0.05957     	0.22893     	0.85146     	0           
  10          	0.05872     	0.22776     	0.85305     	0           
  100         	0.04965     	0.21620     	0.86844     	0           
  300         	0.04105     	0.19867     	0.89130     	0           
  500         	0.04096     	0.19303     	0.89869     	0           
  1000        	0.04043     	0.18886     	0.90464     	1           
---> try to load best state
              	0.04043     	0.18886     	0.90464     	1                eval state : test_on_restore;
              	0.04043     	0.18886     	0.90464     	1                eval state : test_on_last_epoch;
---> final result
              	0.04043     	0.18886     	0.90464     	1                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           	0.17054     	0.53727     	-27288.44718	0           
  10          	0.17054     	0.53727     	-27114.69964	0           
  100         	0.17048     	0.53727     	-25656.17018	0           
  300         	0.17036     	0.53727     	-23647.99153	0           
  500         	0.17026     	0.53726     	-22383.52342	0           
  1000        	0.17000     	0.53726     	-18974.31109	1           
---> try to load best state
              	0.17000     	0.53726     	-18974.31109	1                eval state : test_on_restore;
              	0.17000     	0.53726     	-18974.31109	1                eval state : test_on_last_epoch;
---> final result
              	0.17000     	0.53726     	-18974.31109	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           	0.10613     	0.40379     	-3.19834    	0           
  10          	0.10571     	0.40316     	-3.17622    	0           
  100         	0.10173     	0.39708     	-2.96455    	0           
  300         	0.09474     	0.38470     	-2.55165    	0           
  500         	0.09008     	0.37395     	-2.23461    	0           
  1000        	0.08168     	0.35099     	-1.65387    	1           
---> try to load best state
              	0.08168     	0.35099     	-1.65387    	1                eval state : test_on_restore;
              	0.08168     	0.35099     	-1.65387    	1                eval state : test_on_last_epoch;
---> final result
              	0.08168     	0.35099     	-1.65387    	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           	0.08404     	0.40348     	-2.40397    	0           
  10          	0.08377     	0.40287     	-2.38661    	0           
  100         	0.08113     	0.39699     	-2.22592    	0           
  300         	0.07645     	0.38529     	-1.92548    	0           
  500         	0.07419     	0.37481     	-1.67582    	0           
  1000        	0.07179     	0.35128     	-1.18781    	1           
---> try to load best state
              	0.07179     	0.35128     	-1.18781    	1                eval state : test_on_restore;
              	0.07179     	0.35128     	-1.18781    	1                eval state : test_on_last_epoch;
---> final result
              	0.07179     	0.35128     	-1.18781    	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           	0.03338     	0.16060     	0.71905     	0           
  10          	0.03314     	0.15900     	0.72511     	0           
  100         	0.03141     	0.14611     	0.77069     	0           
  300         	0.03059     	0.13159     	0.81580     	0           
  500         	0.03023     	0.12604     	0.83169     	0           
  1000        	0.02968     	0.12309     	0.84084     	1           
---> try to load best state
              	0.02968     	0.12309     	0.84084     	1                eval state : test_on_restore;
              	0.02968     	0.12309     	0.84084     	1                eval state : test_on_last_epoch;
---> final result
              	0.02968     	0.12309     	0.84084     	1                eval state : final;module_name : NAR;cp_record_file : True;
