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

  Demo NAR cigp 
  seed: 0 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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           	0.00314     	0.00984     	0.92984     	0           
  10          	0.00313     	0.00973     	0.93133     	0           
  100         	0.00296     	0.00867     	0.94406     	0           
  300         	0.00216     	0.00659     	0.96367     	1           
  500         	0.00191     	0.00547     	0.97045     	1           
  1000        	0.00189     	0.00539     	0.97039     	3           
---> try to load best state
              	0.00189     	0.00539     	0.97039     	3                eval state : test_on_restore;
              	0.00189     	0.00539     	0.97039     	3                eval state : test_on_last_epoch;
---> final result
              	0.00189     	0.00539     	0.97039     	3                eval state : final;
----------> finish x-yl training


---------->
NAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 30, '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           	0.03494     	0.07403     	-21.77643   	0           
  10          	0.03494     	0.07403     	-21.81124   	0           
  100         	0.03496     	0.07404     	-22.19121   	0           
  300         	0.03501     	0.07409     	-23.16739   	0           
  500         	0.03504     	0.07412     	-23.88114   	1           
  1000        	0.03505     	0.07413     	-24.07771   	2           
---> try to load best state
              	0.03505     	0.07413     	-24.07771   	2                eval state : test_on_restore;
              	0.03505     	0.07413     	-24.07771   	2                eval state : test_on_last_epoch;
---> final result
              	0.03505     	0.07413     	-24.07771   	2                eval state : final;module_name : NAR;subset : 2.0;cp_record_file : True;
---> end


---------->
NAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 28, '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           	0.02676     	0.06592     	-28.78113   	0           
  10          	0.02674     	0.06589     	-28.45952   	0           
  100         	0.02649     	0.06561     	-25.37455   	0           
  300         	0.02596     	0.06496     	-20.73877   	1           
  500         	0.02554     	0.06433     	-17.96302   	1           
  1000        	0.02451     	0.06292     	-14.17651   	3           
---> try to load best state
              	0.02451     	0.06292     	-14.17651   	3                eval state : test_on_restore;
              	0.02451     	0.06292     	-14.17651   	3                eval state : test_on_last_epoch;
---> final result
              	0.02451     	0.06292     	-14.17651   	3                eval state : final;module_name : NAR;subset : 4.0;cp_record_file : True;
---> end


---------->
NAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 24, '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           	0.02112     	0.06052     	-17.85449   	0           
  10          	0.02107     	0.06046     	-17.66095   	0           
  100         	0.02053     	0.05993     	-16.05817   	0           
  300         	0.01947     	0.05891     	-13.60257   	1           
  500         	0.01855     	0.05805     	-11.97438   	2           
  1000        	0.01676     	0.05635     	-9.61046    	4           
---> try to load best state
              	0.01676     	0.05635     	-9.61046    	4                eval state : test_on_restore;
              	0.01676     	0.05635     	-9.61046    	4                eval state : test_on_last_epoch;
---> final result
              	0.01676     	0.05635     	-9.61046    	4                eval state : final;module_name : NAR;subset : 8.0;cp_record_file : True;
---> end


---------->
NAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 16, '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           	0.01994     	0.06157     	-28.77453   	0           
  10          	0.01990     	0.06153     	-28.60255   	0           
  100         	0.01950     	0.06116     	-26.80455   	1           
  300         	0.01873     	0.06044     	-24.10403   	3           
  500         	0.01806     	0.05985     	-22.22291   	4           
  1000        	0.01663     	0.05862     	-18.92810   	10          
---> try to load best state
              	0.01663     	0.05862     	-18.92810   	10               eval state : test_on_restore;
              	0.01663     	0.05862     	-18.92810   	10               eval state : test_on_last_epoch;
---> final result
              	0.01663     	0.05862     	-18.92810   	10               eval state : final;module_name : NAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 1 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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           	0.00302     	0.01170     	0.87149     	0           
  10          	0.00299     	0.01158     	0.87432     	0           
  100         	0.00271     	0.01032     	0.90133     	0           
  300         	0.00202     	0.00753     	0.94691     	1           
  500         	0.00158     	0.00590     	0.96550     	2           
  1000        	0.00143     	0.00515     	0.97175     	4           
---> try to load best state
              	0.00143     	0.00515     	0.97175     	4                eval state : test_on_restore;
              	0.00143     	0.00515     	0.97175     	4                eval state : test_on_last_epoch;
---> final result
              	0.00143     	0.00515     	0.97175     	4                eval state : final;
----------> finish x-yl training


---------->
NAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 30, '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           	0.02609     	0.06649     	-198.11222  	0           
  10          	0.02610     	0.06651     	-200.36144  	0           
  100         	0.02615     	0.06662     	-224.72498  	0           
  300         	0.02618     	0.06669     	-243.39600  	1           
  500         	0.02620     	0.06673     	-259.31512  	1           
  1000        	0.02621     	0.06677     	-268.11459  	3           
---> try to load best state
              	0.02621     	0.06677     	-268.11459  	3                eval state : test_on_restore;
              	0.02621     	0.06677     	-268.11459  	3                eval state : test_on_last_epoch;
---> final result
              	0.02621     	0.06677     	-268.11459  	3                eval state : final;module_name : NAR;subset : 2.0;cp_record_file : True;
---> end


---------->
NAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 28, '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           	0.02299     	0.06650     	-355.50250  	0           
  10          	0.02299     	0.06650     	-354.98717  	0           
  100         	0.02299     	0.06650     	-356.11472  	0           
  300         	0.02299     	0.06650     	-355.14352  	1           
  500         	0.02299     	0.06650     	-354.57893  	2           
  1000        	0.02300     	0.06651     	-357.96017  	4           
---> try to load best state
              	0.02300     	0.06651     	-357.96017  	4                eval state : test_on_restore;
              	0.02300     	0.06651     	-357.96017  	4                eval state : test_on_last_epoch;
---> final result
              	0.02300     	0.06651     	-357.96017  	4                eval state : final;module_name : NAR;subset : 4.0;cp_record_file : True;
---> end


---------->
NAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 24, '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           	0.01997     	0.06426     	-59.38456   	0           
  10          	0.01994     	0.06424     	-58.83384   	0           
  100         	0.01970     	0.06403     	-54.40939   	0           
  300         	0.01920     	0.06356     	-47.51951   	2           
  500         	0.01872     	0.06305     	-42.46646   	3           
  1000        	0.01752     	0.06153     	-31.26504   	6           
---> try to load best state
              	0.01752     	0.06153     	-31.26504   	6                eval state : test_on_restore;
              	0.01752     	0.06153     	-31.26504   	6                eval state : test_on_last_epoch;
---> final result
              	0.01752     	0.06153     	-31.26504   	6                eval state : final;module_name : NAR;subset : 8.0;cp_record_file : True;
---> end


---------->
NAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 16, '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           	0.01796     	0.06473     	-63.44693   	0           
  10          	0.01793     	0.06471     	-62.94985   	0           
  100         	0.01767     	0.06452     	-58.70234   	1           
  300         	0.01715     	0.06413     	-52.32554   	3           
  500         	0.01671     	0.06380     	-48.11236   	6           
  1000        	0.01583     	0.06298     	-39.98418   	12          
---> try to load best state
              	0.01583     	0.06298     	-39.98418   	12               eval state : test_on_restore;
              	0.01583     	0.06298     	-39.98418   	12               eval state : test_on_last_epoch;
---> final result
              	0.01583     	0.06298     	-39.98418   	13               eval state : final;module_name : NAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 2 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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           	0.00337     	0.01033     	0.88143     	0           
  10          	0.00335     	0.01025     	0.88303     	0           
  100         	0.00321     	0.00956     	0.89704     	0           
  300         	0.00255     	0.00823     	0.92175     	1           
  500         	0.00188     	0.00711     	0.93848     	2           
  1000        	0.00158     	0.00623     	0.95015     	3           
---> try to load best state
              	0.00158     	0.00623     	0.95015     	3                eval state : test_on_restore;
              	0.00158     	0.00623     	0.95015     	3                eval state : test_on_last_epoch;
---> final result
              	0.00158     	0.00623     	0.95015     	3                eval state : final;
----------> finish x-yl training


---------->
NAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 30, '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           	0.03460     	0.07997     	-888.26526  	0           
  10          	0.03460     	0.07997     	-889.70693  	0           
  100         	0.03460     	0.07997     	-904.04154  	0           
  300         	0.03461     	0.07997     	-941.12171  	1           
  500         	0.03461     	0.07997     	-967.97142  	1           
  1000        	0.03461     	0.07997     	-975.24138  	3           
---> try to load best state
              	0.03461     	0.07997     	-975.24138  	3                eval state : test_on_restore;
              	0.03461     	0.07997     	-975.24138  	3                eval state : test_on_last_epoch;
---> final result
              	0.03461     	0.07997     	-975.24138  	3                eval state : final;module_name : NAR;subset : 2.0;cp_record_file : True;
---> end


---------->
NAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 28, '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           	0.03298     	0.07548     	-471.00869  	0           
  10          	0.03298     	0.07548     	-470.32340  	0           
  100         	0.03298     	0.07548     	-467.81950  	0           
  300         	0.03298     	0.07548     	-467.85436  	1           
  500         	0.03298     	0.07548     	-467.86111  	2           
  1000        	0.03298     	0.07548     	-467.88796  	3           
---> try to load best state
              	0.03298     	0.07548     	-467.88796  	3                eval state : test_on_restore;
              	0.03298     	0.07548     	-467.88796  	3                eval state : test_on_last_epoch;
---> final result
              	0.03298     	0.07548     	-467.88796  	3                eval state : final;module_name : NAR;subset : 4.0;cp_record_file : True;
---> end


---------->
NAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 24, '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           	0.02467     	0.06575     	-72.06028   	0           
  10          	0.02465     	0.06573     	-71.19488   	0           
  100         	0.02447     	0.06545     	-63.33326   	0           
  300         	0.02404     	0.06477     	-49.70928   	2           
  500         	0.02361     	0.06409     	-39.72400   	3           
  1000        	0.02257     	0.06258     	-24.94783   	7           
---> try to load best state
              	0.02257     	0.06258     	-24.94783   	7                eval state : test_on_restore;
              	0.02257     	0.06258     	-24.94783   	7                eval state : test_on_last_epoch;
---> final result
              	0.02257     	0.06258     	-24.94783   	7                eval state : final;module_name : NAR;subset : 8.0;cp_record_file : True;
---> end


---------->
NAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 16, '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           	0.02208     	0.06032     	-14.80852   	0           
  10          	0.02203     	0.06024     	-14.63540   	0           
  100         	0.02152     	0.05947     	-13.09654   	1           
  300         	0.02047     	0.05790     	-10.53230   	4           
  500         	0.01949     	0.05652     	-8.68854    	6           
  1000        	0.01736     	0.05382     	-5.90537    	13          
---> try to load best state
              	0.01736     	0.05382     	-5.90537    	13               eval state : test_on_restore;
              	0.01736     	0.05382     	-5.90537    	13               eval state : test_on_last_epoch;
---> final result
              	0.01736     	0.05382     	-5.90537    	13               eval state : final;module_name : NAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 3 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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           	0.00295     	0.00983     	0.90305     	0           
  10          	0.00294     	0.00973     	0.90471     	0           
  100         	0.00277     	0.00885     	0.91977     	0           
  300         	0.00205     	0.00710     	0.94566     	1           
  500         	0.00160     	0.00603     	0.95875     	1           
  1000        	0.00147     	0.00543     	0.96502     	3           
---> try to load best state
              	0.00147     	0.00543     	0.96502     	3                eval state : test_on_restore;
              	0.00147     	0.00543     	0.96502     	3                eval state : test_on_last_epoch;
---> final result
              	0.00147     	0.00543     	0.96502     	3                eval state : final;
----------> finish x-yl training


---------->
NAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 30, '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           	0.03146     	0.07129     	-46.98789   	0           
  10          	0.03146     	0.07129     	-47.06049   	0           
  100         	0.03148     	0.07130     	-47.85165   	0           
  300         	0.03150     	0.07132     	-49.87784   	1           
  500         	0.03152     	0.07134     	-51.34593   	1           
  1000        	0.03153     	0.07134     	-51.75387   	3           
---> try to load best state
              	0.03153     	0.07134     	-51.75387   	3                eval state : test_on_restore;
              	0.03153     	0.07134     	-51.75387   	3                eval state : test_on_last_epoch;
---> final result
              	0.03153     	0.07134     	-51.75387   	3                eval state : final;module_name : NAR;subset : 2.0;cp_record_file : True;
---> end


---------->
NAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 28, '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           	0.03567     	0.07469     	-7.17983    	0           
  10          	0.03562     	0.07460     	-7.06596    	0           
  100         	0.03499     	0.07357     	-5.78161    	0           
  300         	0.03308     	0.07088     	-3.30190    	1           
  500         	0.03162     	0.06945     	-2.18472    	1           
  1000        	0.02992     	0.06773     	-1.42316    	3           
---> try to load best state
              	0.02992     	0.06773     	-1.42316    	3                eval state : test_on_restore;
              	0.02992     	0.06773     	-1.42316    	3                eval state : test_on_last_epoch;
---> final result
              	0.02992     	0.06773     	-1.42316    	3                eval state : final;module_name : NAR;subset : 4.0;cp_record_file : True;
---> end


---------->
NAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 24, '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           	0.02365     	0.05903     	-5.53088    	0           
  10          	0.02360     	0.05893     	-5.44999    	0           
  100         	0.02302     	0.05792     	-4.51694    	0           
  300         	0.02183     	0.05593     	-3.15711    	1           
  500         	0.02075     	0.05425     	-2.34553    	2           
  1000        	0.01831     	0.05084     	-1.24541    	5           
---> try to load best state
              	0.01831     	0.05084     	-1.24541    	5                eval state : test_on_restore;
              	0.01831     	0.05084     	-1.24541    	5                eval state : test_on_last_epoch;
---> final result
              	0.01831     	0.05084     	-1.24541    	5                eval state : final;module_name : NAR;subset : 8.0;cp_record_file : True;
---> end


---------->
NAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 16, '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           	0.01945     	0.05585     	-6.56556    	0           
  10          	0.01937     	0.05572     	-6.37902    	0           
  100         	0.01862     	0.05458     	-5.14561    	1           
  300         	0.01712     	0.05239     	-3.32233    	3           
  500         	0.01583     	0.05065     	-2.28638    	6           
  1000        	0.01332     	0.04794     	-1.07402    	13          
---> try to load best state
              	0.01332     	0.04794     	-1.07402    	13               eval state : test_on_restore;
              	0.01332     	0.04794     	-1.07402    	13               eval state : test_on_last_epoch;
---> final result
              	0.01332     	0.04794     	-1.07402    	14               eval state : final;module_name : NAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 4 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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           	0.00317     	0.01163     	0.84062     	0           
  10          	0.00314     	0.01156     	0.84224     	0           
  100         	0.00288     	0.01095     	0.85726     	0           
  300         	0.00242     	0.00995     	0.87975     	1           
  500         	0.00224     	0.00946     	0.88958     	1           
  1000        	0.00206     	0.00883     	0.90115     	3           
---> try to load best state
              	0.00206     	0.00883     	0.90115     	3                eval state : test_on_restore;
              	0.00206     	0.00883     	0.90115     	3                eval state : test_on_last_epoch;
---> final result
              	0.00206     	0.00883     	0.90115     	3                eval state : final;
----------> finish x-yl training


---------->
NAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 30, '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           	0.02050     	0.07107     	-6656.16243 	0           
  10          	0.02050     	0.07107     	-6666.11761 	0           
  100         	0.02050     	0.07107     	-6774.60680 	0           
  300         	0.02050     	0.07107     	-7053.25637 	1           
  500         	0.02050     	0.07107     	-7257.11085 	2           
  1000        	0.02050     	0.07107     	-7311.84904 	3           
---> try to load best state
              	0.02050     	0.07107     	-7311.84904 	3                eval state : test_on_restore;
              	0.02050     	0.07107     	-7311.84904 	3                eval state : test_on_last_epoch;
---> final result
              	0.02050     	0.07107     	-7311.84904 	3                eval state : final;module_name : NAR;subset : 2.0;cp_record_file : True;
---> end


---------->
NAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 28, '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           	0.02268     	0.06641     	-478.43856  	0           
  10          	0.02268     	0.06641     	-472.68546  	0           
  100         	0.02264     	0.06638     	-431.10162  	0           
  300         	0.02254     	0.06628     	-351.02519  	1           
  500         	0.02244     	0.06618     	-300.75160  	2           
  1000        	0.02221     	0.06594     	-240.43063  	4           
---> try to load best state
              	0.02221     	0.06594     	-240.43063  	4                eval state : test_on_restore;
              	0.02221     	0.06594     	-240.43063  	4                eval state : test_on_last_epoch;
---> final result
              	0.02221     	0.06594     	-240.43063  	4                eval state : final;module_name : NAR;subset : 4.0;cp_record_file : True;
---> end


---------->
NAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 24, '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           	0.01993     	0.06407     	-37.59391   	0           
  10          	0.01990     	0.06405     	-37.20680   	0           
  100         	0.01961     	0.06386     	-34.07356   	0           
  300         	0.01902     	0.06347     	-29.76088   	2           
  500         	0.01849     	0.06309     	-27.39832   	3           
  1000        	0.01738     	0.06224     	-23.93180   	7           
---> try to load best state
              	0.01738     	0.06224     	-23.93180   	7                eval state : test_on_restore;
              	0.01738     	0.06224     	-23.93180   	7                eval state : test_on_last_epoch;
---> final result
              	0.01738     	0.06224     	-23.93180   	7                eval state : final;module_name : NAR;subset : 8.0;cp_record_file : True;
---> end


---------->
NAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 16, '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           	0.01862     	0.05992     	-10.33439   	0           
  10          	0.01859     	0.05989     	-10.27274   	0           
  100         	0.01826     	0.05964     	-9.55822    	1           
  300         	0.01761     	0.05910     	-8.54657    	3           
  500         	0.01702     	0.05857     	-7.85468    	6           
  1000        	0.01575     	0.05726     	-6.91123    	13          
---> try to load best state
              	0.01575     	0.05726     	-6.91123    	13               eval state : test_on_restore;
              	0.01575     	0.05726     	-6.91123    	13               eval state : test_on_last_epoch;
---> final result
              	0.01575     	0.05726     	-6.91123    	13               eval state : final;module_name : NAR;subset : 16.0;cp_record_file : True;
