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

  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  	0           
  1000        	0.15363     	0.52045     	-176.11806  	1           
---> try to load best state
              	0.15363     	0.52045     	-176.11806  	1                eval state : test_on_restore;
              	0.15363     	0.52045     	-176.11806  	1                eval state : test_on_last_epoch;
---> final result
              	0.15363     	0.52045     	-176.11806  	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': 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           
----------------------------------------

  Demo NAR cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', '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           	0.00289     	0.00871     	0.94499     	0           
  10          	0.00287     	0.00862     	0.94596     	0           
  100         	0.00261     	0.00777     	0.95490     	0           
  300         	0.00199     	0.00620     	0.96898     	0           
  500         	0.00172     	0.00552     	0.97395     	0           
  1000        	0.00156     	0.00494     	0.97758     	1           
---> try to load best state
              	0.00156     	0.00494     	0.97758     	1                eval state : test_on_restore;
              	0.00156     	0.00494     	0.97758     	1                eval state : test_on_last_epoch;
---> final result
              	0.00156     	0.00494     	0.97758     	1                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'burger_v4_02', '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           	0.04153     	0.08285     	-126309800751.20163	0           
  10          	0.04153     	0.08285     	-126309800751.20163	0           
  100         	0.04153     	0.08285     	-126309800751.20163	0           
  300         	0.04153     	0.08285     	-126309800751.20163	0           
  500         	0.04153     	0.08285     	-126309800751.20163	0           
  1000        	0.04153     	0.08285     	-126309800751.20163	1           
---> try to load best state
              	0.04153     	0.08285     	-126309800751.20163	1                eval state : test_on_restore;
              	0.04153     	0.08285     	-126309800751.20163	1                eval state : test_on_last_epoch;
---> final result
              	0.04153     	0.08285     	-126309800751.20163	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': 'burger_v4_02', '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           	0.03184     	0.07167     	-103824970023.30617	0           
  10          	0.03184     	0.07167     	-103824970023.30617	0           
  100         	0.03184     	0.07167     	-103824970023.30617	0           
  300         	0.03184     	0.07167     	-103824970023.30617	0           
  500         	0.03184     	0.07167     	-103824970023.30617	1           
  1000        	0.03184     	0.07167     	-103824970023.30617	2           
---> try to load best state
              	0.03184     	0.07167     	-103824970023.30617	2                eval state : test_on_restore;
              	0.03184     	0.07167     	-103824970023.30617	2                eval state : test_on_last_epoch;
---> final result
              	0.03184     	0.07167     	-103824970023.30617	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': 'burger_v4_02', '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           	0.02811     	0.06880     	-75744708983.59859	0           
  10          	0.02811     	0.06880     	-75744708983.59859	0           
  100         	0.02811     	0.06880     	-75744708983.59859	0           
  300         	0.02811     	0.06880     	-75744708983.59859	0           
  500         	0.02811     	0.06880     	-75744708983.59859	1           
  1000        	0.02811     	0.06880     	-75744708983.59859	2           
---> try to load best state
              	0.02811     	0.06880     	-75744708983.59859	2                eval state : test_on_restore;
              	0.02811     	0.06880     	-75744708983.59859	2                eval state : test_on_last_epoch;
---> final result
              	0.02811     	0.06880     	-75744708983.59859	2                eval state : final;module_name : NAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', '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           	0.02657     	0.06797     	-81164699623.28491	0           
  10          	0.02657     	0.06797     	-81164699623.28491	0           
  100         	0.02657     	0.06797     	-81164699623.28491	0           
  300         	0.02657     	0.06797     	-81164699623.28491	0           
  500         	0.02657     	0.06797     	-81164699623.28491	1           
  1000        	0.02657     	0.06797     	-81164699623.28491	3           
---> try to load best state
              	0.02657     	0.06797     	-81164699623.28491	3                eval state : test_on_restore;
              	0.02657     	0.06797     	-81164699623.28491	3                eval state : test_on_last_epoch;
---> final result
              	0.02657     	0.06797     	-81164699623.28491	3                eval state : final;module_name : NAR;cp_record_file : True;
