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

  Demo DC 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04260     	0.18634     	0.91863     	0           
  10          	0.04204     	0.18530     	0.91969     	0           
  100         	0.03767     	0.17490     	0.92955     	0           
---> try to load best state
              	0.03767     	0.17490     	0.92955     	0                eval state : test_on_restore;
              	0.03767     	0.17490     	0.92955     	0                eval state : test_on_last_epoch;
---> final result
              	0.03767     	0.17490     	0.92955     	0                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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.16665     	0.53591     	-4611.81173 	0           
  10          	0.16578     	0.53588     	-3110.66232 	0           
  100         	0.16423     	0.53572     	-2178.54265 	0           
---> try to load best state
              	0.16423     	0.53572     	-2178.54265 	0                eval state : test_on_restore;
              	0.16423     	0.53572     	-2178.54265 	0                eval state : test_on_last_epoch;
---> final result
              	0.16423     	0.53572     	-2178.54265 	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.16746     	0.53625     	-4912.57539 	0           
  10          	0.16614     	0.53622     	-3542.70341 	0           
  100         	0.16267     	0.53577     	-1487.48659 	0           
---> try to load best state
              	0.16267     	0.53577     	-1487.48659 	0                eval state : test_on_restore;
              	0.16267     	0.53577     	-1487.48659 	0                eval state : test_on_last_epoch;
---> final result
              	0.16267     	0.53577     	-1487.48659 	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.14974     	0.52646     	-158.13813  	0           
  10          	0.14057     	0.52471     	-72.81520   	0           
  100         	0.13619     	0.52128     	-52.82995   	0           
---> try to load best state
              	0.13619     	0.52128     	-52.82995   	0                eval state : test_on_restore;
              	0.13619     	0.52128     	-52.82995   	0                eval state : test_on_last_epoch;
---> final result
              	0.13619     	0.52128     	-52.82995   	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.12662     	0.50996     	-29.01629   	0           
  10          	0.12028     	0.50407     	-17.45060   	0           
  100         	0.10591     	0.47663     	-11.07685   	0           
---> try to load best state
              	0.10591     	0.47663     	-11.07685   	0                eval state : test_on_restore;
              	0.10591     	0.47663     	-11.07685   	0                eval state : test_on_last_epoch;
---> final result
              	0.10591     	0.47663     	-11.07685   	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04720     	0.21157     	0.91985     	0           
  10          	0.10322     	0.41008     	-0.92227    	0           
  100         	0.07036     	0.32141     	-0.11237    	0           
---> try to load best state
              	0.07036     	0.32141     	-0.11237    	0                eval state : test_on_restore;
              	0.07036     	0.32141     	-0.11237    	0                eval state : test_on_last_epoch;
---> final result
              	0.07036     	0.32141     	-0.11237    	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04525     	0.19092     	0.92327     	0           
  10          	0.09156     	0.39946     	-1.40622    	0           
  100         	0.06889     	0.35123     	-0.06213    	0           
---> try to load best state
              	0.06889     	0.35123     	-0.06213    	0                eval state : test_on_restore;
              	0.06889     	0.35123     	-0.06213    	0                eval state : test_on_last_epoch;
---> final result
              	0.06889     	0.35123     	-0.06213    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03838     	0.24305     	0.79337     	0           
  10          	0.03809     	0.24240     	0.79511     	0           
  100         	0.03538     	0.23665     	0.80976     	0           
---> try to load best state
              	0.03538     	0.23665     	0.80976     	0                eval state : test_on_restore;
              	0.03538     	0.23665     	0.80976     	0                eval state : test_on_last_epoch;
---> final result
              	0.03538     	0.23665     	0.80976     	0                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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.15951     	0.53165     	-813.52185  	0           
  10          	0.15610     	0.53122     	-445.52608  	0           
  100         	0.15194     	0.53030     	-251.89459  	0           
---> try to load best state
              	0.15194     	0.53030     	-251.89459  	0                eval state : test_on_restore;
              	0.15194     	0.53030     	-251.89459  	0                eval state : test_on_last_epoch;
---> final result
              	0.15194     	0.53030     	-251.89459  	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.13401     	0.51177     	-57.63958   	0           
  10          	0.12939     	0.50975     	-43.17036   	0           
  100         	0.11628     	0.49221     	-21.25765   	0           
---> try to load best state
              	0.11628     	0.49221     	-21.25765   	0                eval state : test_on_restore;
              	0.11628     	0.49221     	-21.25765   	0                eval state : test_on_last_epoch;
---> final result
              	0.11628     	0.49221     	-21.25765   	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.10723     	0.48070     	-9.86944    	0           
  10          	0.09842     	0.46070     	-6.37350    	0           
  100         	0.07175     	0.35380     	-1.57337    	0           
---> try to load best state
              	0.07175     	0.35380     	-1.57337    	0                eval state : test_on_restore;
              	0.07175     	0.35380     	-1.57337    	0                eval state : test_on_last_epoch;
---> final result
              	0.07175     	0.35380     	-1.57337    	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.09607     	0.46097     	-5.67022    	0           
  10          	0.08152     	0.41257     	-2.84658    	0           
  100         	0.05942     	0.29248     	-0.35677    	0           
---> try to load best state
              	0.05942     	0.29248     	-0.35677    	0                eval state : test_on_restore;
              	0.05942     	0.29248     	-0.35677    	0                eval state : test_on_last_epoch;
---> final result
              	0.05942     	0.29248     	-0.35677    	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04381     	0.26083     	0.13761     	0           
  10          	0.04197     	0.22662     	0.37901     	0           
  100         	0.04125     	0.21902     	0.41061     	0           
---> try to load best state
              	0.04125     	0.21902     	0.41061     	0                eval state : test_on_restore;
              	0.04125     	0.21902     	0.41061     	0                eval state : test_on_last_epoch;
---> final result
              	0.04125     	0.21902     	0.41061     	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03789     	0.13922     	0.63466     	0           
  10          	0.03821     	0.13513     	0.65153     	0           
  100         	0.03634     	0.13568     	0.65732     	0           
---> try to load best state
              	0.03634     	0.13568     	0.65732     	0                eval state : test_on_restore;
              	0.03634     	0.13568     	0.65732     	0                eval state : test_on_last_epoch;
---> final result
              	0.03634     	0.13568     	0.65732     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.05198     	0.17112     	0.94972     	0           
  10          	0.05152     	0.17007     	0.95041     	0           
  100         	0.04691     	0.15962     	0.95674     	0           
---> try to load best state
              	0.04691     	0.15962     	0.95674     	0                eval state : test_on_restore;
              	0.04691     	0.15962     	0.95674     	0                eval state : test_on_last_epoch;
---> final result
              	0.04691     	0.15962     	0.95674     	0                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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.22290     	0.48843     	-28.77946   	0           
  10          	0.18069     	0.47512     	-22.91048   	0           
  100         	0.14323     	0.45990     	-18.35683   	0           
---> try to load best state
              	0.14323     	0.45990     	-18.35683   	0                eval state : test_on_restore;
              	0.14323     	0.45990     	-18.35683   	0                eval state : test_on_last_epoch;
---> final result
              	0.14323     	0.45990     	-18.35683   	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.12452     	0.48983     	-34.08344   	0           
  10          	0.12214     	0.48792     	-28.74669   	0           
  100         	0.12055     	0.48514     	-23.95945   	0           
---> try to load best state
              	0.12055     	0.48514     	-23.95945   	0                eval state : test_on_restore;
              	0.12055     	0.48514     	-23.95945   	0                eval state : test_on_last_epoch;
---> final result
              	0.12055     	0.48514     	-23.95945   	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.10389     	0.46470     	-5.56895    	0           
  10          	0.10003     	0.44933     	-3.63219    	0           
  100         	0.08445     	0.39746     	-1.59331    	0           
---> try to load best state
              	0.08445     	0.39746     	-1.59331    	0                eval state : test_on_restore;
              	0.08445     	0.39746     	-1.59331    	0                eval state : test_on_last_epoch;
---> final result
              	0.08445     	0.39746     	-1.59331    	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03621     	0.13807     	0.67557     	0           
  10          	0.03623     	0.13388     	0.69538     	0           
  100         	0.03889     	0.14329     	0.66006     	0           
---> try to load best state
              	0.03889     	0.14329     	0.66006     	0                eval state : test_on_restore;
              	0.03889     	0.14329     	0.66006     	0                eval state : test_on_last_epoch;
---> final result
              	0.03889     	0.14329     	0.66006     	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03181     	0.08863     	0.94608     	0           
  10          	0.03238     	0.09024     	0.94486     	0           
  100         	0.03208     	0.08992     	0.94588     	0           
---> try to load best state
              	0.03208     	0.08992     	0.94588     	0                eval state : test_on_restore;
              	0.03208     	0.08992     	0.94588     	0                eval state : test_on_last_epoch;
---> final result
              	0.03208     	0.08992     	0.94588     	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02993     	0.08344     	0.93930     	0           
  10          	0.04477     	0.15669     	0.84790     	0           
  100         	0.04027     	0.13695     	0.89461     	0           
---> try to load best state
              	0.04027     	0.13695     	0.89461     	0                eval state : test_on_restore;
              	0.04027     	0.13695     	0.89461     	0                eval state : test_on_last_epoch;
---> final result
              	0.04027     	0.13695     	0.89461     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04702     	0.18867     	0.92892     	0           
  10          	0.04649     	0.18764     	0.92977     	0           
  100         	0.04173     	0.17759     	0.93753     	0           
---> try to load best state
              	0.04173     	0.17759     	0.93753     	0                eval state : test_on_restore;
              	0.04173     	0.17759     	0.93753     	0                eval state : test_on_last_epoch;
---> final result
              	0.04173     	0.17759     	0.93753     	0                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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.10574     	0.45271     	-6.87065    	0           
  10          	0.09263     	0.42079     	-3.75975    	0           
  100         	0.08564     	0.39373     	-2.63634    	0           
---> try to load best state
              	0.08564     	0.39373     	-2.63634    	0                eval state : test_on_restore;
              	0.08564     	0.39373     	-2.63634    	0                eval state : test_on_last_epoch;
---> final result
              	0.08564     	0.39373     	-2.63634    	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.11072     	0.47798     	-10.23474   	0           
  10          	0.10083     	0.45958     	-6.05188    	0           
  100         	0.07683     	0.37355     	-2.02113    	0           
---> try to load best state
              	0.07683     	0.37355     	-2.02113    	0                eval state : test_on_restore;
              	0.07683     	0.37355     	-2.02113    	0                eval state : test_on_last_epoch;
---> final result
              	0.07683     	0.37355     	-2.02113    	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.11018     	0.48827     	-11.06979   	0           
  10          	0.10695     	0.47721     	-7.45857    	0           
  100         	0.08346     	0.40474     	-3.06338    	0           
---> try to load best state
              	0.08346     	0.40474     	-3.06338    	0                eval state : test_on_restore;
              	0.08346     	0.40474     	-3.06338    	0                eval state : test_on_last_epoch;
---> final result
              	0.08346     	0.40474     	-3.06338    	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03298     	0.10203     	0.91457     	0           
  10          	0.03412     	0.10170     	0.91379     	0           
  100         	0.03311     	0.10012     	0.91419     	0           
---> try to load best state
              	0.03311     	0.10012     	0.91419     	0                eval state : test_on_restore;
              	0.03311     	0.10012     	0.91419     	0                eval state : test_on_last_epoch;
---> final result
              	0.03311     	0.10012     	0.91419     	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.06493     	0.25037     	0.75810     	0           
  10          	0.06280     	0.25169     	0.75568     	0           
  100         	0.06723     	0.26171     	0.74720     	0           
---> try to load best state
              	0.06723     	0.26171     	0.74720     	0                eval state : test_on_restore;
              	0.06723     	0.26171     	0.74720     	0                eval state : test_on_last_epoch;
---> final result
              	0.06723     	0.26171     	0.74720     	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03851     	0.14845     	0.83493     	0           
  10          	0.06781     	0.37734     	-0.98257    	0           
  100         	0.09433     	0.46135     	0.25058     	0           
---> try to load best state
              	0.09433     	0.46135     	0.25058     	0                eval state : test_on_restore;
              	0.09433     	0.46135     	0.25058     	0                eval state : test_on_last_epoch;
---> final result
              	0.09433     	0.46135     	0.25058     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04727     	0.16523     	0.95054     	0           
  10          	0.04679     	0.16386     	0.95139     	0           
  100         	0.04303     	0.15104     	0.95902     	0           
---> try to load best state
              	0.04303     	0.15104     	0.95902     	0                eval state : test_on_restore;
              	0.04303     	0.15104     	0.95902     	0                eval state : test_on_last_epoch;
---> final result
              	0.04303     	0.15104     	0.95902     	0                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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.17036     	0.53726     	-22749.35027	0           
  10          	0.16941     	0.53724     	-11188.63328	0           
  100         	0.16787     	0.53719     	-5243.93856 	0           
---> try to load best state
              	0.16787     	0.53719     	-5243.93856 	0                eval state : test_on_restore;
              	0.16787     	0.53719     	-5243.93856 	0                eval state : test_on_last_epoch;
---> final result
              	0.16787     	0.53719     	-5243.93856 	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.10023     	0.41307     	-2.95300    	0           
  10          	0.08049     	0.36156     	-1.48228    	0           
  100         	0.04889     	0.23664     	0.21221     	0           
---> try to load best state
              	0.04889     	0.23664     	0.21221     	0                eval state : test_on_restore;
              	0.04889     	0.23664     	0.21221     	0                eval state : test_on_last_epoch;
---> final result
              	0.04889     	0.23664     	0.21221     	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.09852     	0.43956     	-2.65368    	0           
  10          	0.10230     	0.45152     	-2.71336    	0           
  100         	0.10543     	0.46062     	-2.71739    	0           
---> try to load best state
              	0.10543     	0.46062     	-2.71739    	0                eval state : test_on_restore;
              	0.10543     	0.46062     	-2.71739    	0                eval state : test_on_last_epoch;
---> final result
              	0.10543     	0.46062     	-2.71739    	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03568     	0.11459     	0.88197     	0           
  10          	0.03726     	0.11473     	0.89081     	0           
  100         	0.03585     	0.10315     	0.90458     	0           
---> try to load best state
              	0.03585     	0.10315     	0.90458     	0                eval state : test_on_restore;
              	0.03585     	0.10315     	0.90458     	0                eval state : test_on_last_epoch;
---> final result
              	0.03585     	0.10315     	0.90458     	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03020     	0.10645     	0.88275     	0           
  10          	0.03484     	0.11699     	0.88674     	0           
  100         	0.03413     	0.10644     	0.90141     	0           
---> try to load best state
              	0.03413     	0.10644     	0.90141     	0                eval state : test_on_restore;
              	0.03413     	0.10644     	0.90141     	0                eval state : test_on_last_epoch;
---> final result
              	0.03413     	0.10644     	0.90141     	0                eval state : final;module_name : DC_cigp;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': ['x[0]', 'y[0]'], '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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03166     	0.09917     	0.92617     	0           
  10          	0.05302     	0.22480     	0.73507     	0           
  100         	0.03609     	0.11591     	0.92578     	0           
---> try to load best state
              	0.03609     	0.11591     	0.92578     	0                eval state : test_on_restore;
              	0.03609     	0.11591     	0.92578     	0                eval state : test_on_last_epoch;
---> final result
              	0.03609     	0.11591     	0.92578     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
