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

  Demo NAR cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[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           	0.01175     	0.02708     	0.94014     	0           
  10          	0.01176     	0.02706     	0.94026     	0           
  100         	0.01190     	0.02696     	0.94105     	0           
  300         	0.01221     	0.02690     	0.94167     	0           
  500         	0.01245     	0.02689     	0.94188     	1           
  1000        	0.01267     	0.02686     	0.94248     	2           
---> try to load best state
              	0.01267     	0.02686     	0.94248     	2                eval state : test_on_restore;
              	0.01267     	0.02686     	0.94248     	2                eval state : test_on_last_epoch;
---> final result
              	0.01267     	0.02686     	0.94248     	2                eval state : final;
----------> finish x-yl training


---------->
NAR for 1 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 31, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.06844     	0.10921     	-5.40839    	0           
  10          	0.06815     	0.10891     	-5.27944    	0           
  100         	0.06507     	0.10581     	-4.11332    	0           
  300         	0.05721     	0.09798     	-2.17852    	0           
  500         	0.04942     	0.09036     	-1.05593    	1           
  1000        	0.03538     	0.07566     	0.10113     	2           
---> try to load best state
              	0.03538     	0.07566     	0.10113     	2                eval state : test_on_restore;
              	0.03538     	0.07566     	0.10113     	2                eval state : test_on_last_epoch;
---> final result
              	0.03538     	0.07566     	0.10113     	2                eval state : final;module_name : NAR;subset : 1;cp_record_file : True;
---> end


---------->
NAR for 2 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 30, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.06862     	0.10780     	-4.67871    	0           
  10          	0.06832     	0.10749     	-4.56529    	0           
  100         	0.06510     	0.10418     	-3.53951    	0           
  300         	0.05705     	0.09599     	-1.84714    	0           
  500         	0.04914     	0.08811     	-0.85169    	1           
  1000        	0.03496     	0.07299     	0.19600     	2           
---> try to load best state
              	0.03496     	0.07299     	0.19600     	2                eval state : test_on_restore;
              	0.03496     	0.07299     	0.19600     	2                eval state : test_on_last_epoch;
---> final result
              	0.03496     	0.07299     	0.19600     	2                eval state : final;module_name : NAR;subset : 2;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 28, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.06740     	0.10962     	-6.13208    	0           
  10          	0.06710     	0.10935     	-5.99136    	0           
  100         	0.06404     	0.10653     	-4.72264    	0           
  300         	0.05655     	0.09967     	-2.64281    	0           
  500         	0.04927     	0.09300     	-1.40883    	1           
  1000        	0.03632     	0.07918     	-0.05812    	2           
---> try to load best state
              	0.03632     	0.07918     	-0.05812    	2                eval state : test_on_restore;
              	0.03632     	0.07918     	-0.05812    	2                eval state : test_on_last_epoch;
---> final result
              	0.03632     	0.07918     	-0.05812    	2                eval state : final;module_name : NAR;subset : 4;cp_record_file : True;
---> end


---------->
NAR for 8 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 24, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07153     	0.11420     	-8.37348    	0           
  10          	0.07127     	0.11397     	-8.19098    	0           
  100         	0.06858     	0.11154     	-6.55961    	0           
  300         	0.06203     	0.10561     	-3.93976    	0           
  500         	0.05562     	0.09969     	-2.37734    	1           
  1000        	0.04267     	0.08575     	-0.51993    	2           
---> try to load best state
              	0.04267     	0.08575     	-0.51993    	2                eval state : test_on_restore;
              	0.04267     	0.08575     	-0.51993    	2                eval state : test_on_last_epoch;
---> final result
              	0.04267     	0.08575     	-0.51993    	2                eval state : final;module_name : NAR;subset : 8;cp_record_file : True;
---> end


---------->
NAR for 16 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07242     	0.11356     	-9.67514    	0           
  10          	0.07215     	0.11329     	-9.45694    	0           
  100         	0.06917     	0.11038     	-7.44987    	0           
  300         	0.06141     	0.10316     	-4.09130    	0           
  500         	0.05369     	0.09644     	-2.22333    	1           
  1000        	0.03962     	0.08302     	-0.37561    	1           
---> try to load best state
              	0.03962     	0.08302     	-0.37561    	1                eval state : test_on_restore;
              	0.03962     	0.08302     	-0.37561    	1                eval state : test_on_last_epoch;
---> final result
              	0.03962     	0.08302     	-0.37561    	1                eval state : final;module_name : NAR;subset : 16;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]], 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           	0.07751     	0.11685     	-18.67071   	0           
  10          	0.07720     	0.11654     	-18.07153   	0           
  100         	0.07396     	0.11326     	-12.99501   	0           
  300         	0.06549     	0.10520     	-6.12683    	0           
  500         	0.05696     	0.09780     	-3.10597    	1           
  1000        	0.04096     	0.08475     	-0.68281    	2           
---> try to load best state
              	0.04096     	0.08475     	-0.68281    	2                eval state : test_on_restore;
              	0.04096     	0.08475     	-0.68281    	2                eval state : test_on_last_epoch;
---> final result
              	0.04096     	0.08475     	-0.68281    	2                eval state : final;module_name : NAR;subset : 32;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.01853     	0.04577     	0.70127     	0           
  10          	0.01850     	0.04566     	0.70217     	0           
  100         	0.01827     	0.04482     	0.70821     	0           
  300         	0.01772     	0.04323     	0.72105     	0           
  500         	0.01699     	0.04155     	0.73788     	0           
  1000        	0.01575     	0.03887     	0.76164     	1           
---> try to load best state
              	0.01575     	0.03887     	0.76164     	1                eval state : test_on_restore;
              	0.01575     	0.03887     	0.76164     	1                eval state : test_on_last_epoch;
---> final result
              	0.01575     	0.03887     	0.76164     	1                eval state : final;
----------> finish x-yl training


---------->
NAR for 1 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 31, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.06855     	0.10391     	-2.32771    	0           
  10          	0.06822     	0.10354     	-2.25231    	0           
  100         	0.06479     	0.09978     	-1.59587    	0           
  300         	0.05674     	0.09066     	-0.59564    	0           
  500         	0.04905     	0.08130     	-0.02334    	1           
  1000        	0.03455     	0.06173     	0.57662     	2           
---> try to load best state
              	0.03455     	0.06173     	0.57662     	2                eval state : test_on_restore;
              	0.03455     	0.06173     	0.57662     	2                eval state : test_on_last_epoch;
---> final result
              	0.03455     	0.06173     	0.57662     	2                eval state : final;module_name : NAR;subset : 1;cp_record_file : True;
---> end


---------->
NAR for 2 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 30, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.06907     	0.10469     	-2.39169    	0           
  10          	0.06874     	0.10434     	-2.31667    	0           
  100         	0.06542     	0.10070     	-1.66359    	0           
  300         	0.05763     	0.09183     	-0.65964    	0           
  500         	0.05006     	0.08252     	-0.07069    	1           
  1000        	0.03535     	0.06243     	0.56345     	2           
---> try to load best state
              	0.03535     	0.06243     	0.56345     	2                eval state : test_on_restore;
              	0.03535     	0.06243     	0.56345     	2                eval state : test_on_last_epoch;
---> final result
              	0.03535     	0.06243     	0.56345     	2                eval state : final;module_name : NAR;subset : 2;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 28, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07079     	0.10706     	-3.14856    	0           
  10          	0.07048     	0.10673     	-3.05715    	0           
  100         	0.06734     	0.10334     	-2.25879    	0           
  300         	0.05985     	0.09494     	-1.03371    	0           
  500         	0.05241     	0.08592     	-0.31440    	1           
  1000        	0.03716     	0.06572     	0.46576     	2           
---> try to load best state
              	0.03716     	0.06572     	0.46576     	2                eval state : test_on_restore;
              	0.03716     	0.06572     	0.46576     	2                eval state : test_on_last_epoch;
---> final result
              	0.03716     	0.06572     	0.46576     	2                eval state : final;module_name : NAR;subset : 4;cp_record_file : True;
---> end


---------->
NAR for 8 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 24, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07053     	0.11228     	-5.99658    	0           
  10          	0.07027     	0.11203     	-5.86885    	0           
  100         	0.06770     	0.10955     	-4.74983    	0           
  300         	0.06155     	0.10351     	-2.90773    	0           
  500         	0.05496     	0.09675     	-1.65340    	1           
  1000        	0.03947     	0.07920     	-0.08898    	2           
---> try to load best state
              	0.03947     	0.07920     	-0.08898    	2                eval state : test_on_restore;
              	0.03947     	0.07920     	-0.08898    	2                eval state : test_on_last_epoch;
---> final result
              	0.03947     	0.07920     	-0.08898    	2                eval state : final;module_name : NAR;subset : 8;cp_record_file : True;
---> end


---------->
NAR for 16 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.06815     	0.10741     	-4.55454    	0           
  10          	0.06789     	0.10713     	-4.44920    	0           
  100         	0.06534     	0.10433     	-3.52976    	0           
  300         	0.05950     	0.09810     	-2.07616    	0           
  500         	0.05357     	0.09194     	-1.15502    	1           
  1000        	0.04096     	0.07888     	-0.10279    	2           
---> try to load best state
              	0.04096     	0.07888     	-0.10279    	2                eval state : test_on_restore;
              	0.04096     	0.07888     	-0.10279    	2                eval state : test_on_last_epoch;
---> final result
              	0.04096     	0.07888     	-0.10279    	2                eval state : final;module_name : NAR;subset : 16;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07261     	0.11369     	-9.14613    	0           
  10          	0.07239     	0.11346     	-8.94413    	0           
  100         	0.07025     	0.11119     	-7.23134    	0           
  300         	0.06567     	0.10632     	-4.80054    	0           
  500         	0.06138     	0.10157     	-3.35551    	1           
  1000        	0.05164     	0.09014     	-1.47165    	2           
---> try to load best state
              	0.05164     	0.09014     	-1.47165    	2                eval state : test_on_restore;
              	0.05164     	0.09014     	-1.47165    	2                eval state : test_on_last_epoch;
---> final result
              	0.05164     	0.09014     	-1.47165    	2                eval state : final;module_name : NAR;subset : 32;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.01320     	0.03428     	0.85301     	0           
  10          	0.01327     	0.03429     	0.85275     	0           
  100         	0.01406     	0.03450     	0.84955     	0           
  300         	0.01472     	0.03428     	0.84961     	0           
  500         	0.01464     	0.03354     	0.85471     	0           
  1000        	0.01425     	0.03220     	0.86356     	1           
---> try to load best state
              	0.01425     	0.03220     	0.86356     	1                eval state : test_on_restore;
              	0.01425     	0.03220     	0.86356     	1                eval state : test_on_last_epoch;
---> final result
              	0.01425     	0.03220     	0.86356     	1                eval state : final;
----------> finish x-yl training


---------->
NAR for 1 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 31, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07479     	0.11812     	-11.42549   	0           
  10          	0.07457     	0.11788     	-11.16376   	0           
  100         	0.07240     	0.11546     	-8.90210    	0           
  300         	0.06707     	0.10969     	-5.35584    	0           
  500         	0.06132     	0.10359     	-3.20537    	1           
  1000        	0.04756     	0.08863     	-0.82032    	2           
---> try to load best state
              	0.04756     	0.08863     	-0.82032    	2                eval state : test_on_restore;
              	0.04756     	0.08863     	-0.82032    	2                eval state : test_on_last_epoch;
---> final result
              	0.04756     	0.08863     	-0.82032    	2                eval state : final;module_name : NAR;subset : 1;cp_record_file : True;
---> end


---------->
NAR for 2 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 30, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07363     	0.11783     	-10.43321   	0           
  10          	0.07341     	0.11759     	-10.18481   	0           
  100         	0.07116     	0.11512     	-8.02329    	0           
  300         	0.06577     	0.10940     	-4.76556    	0           
  500         	0.06021     	0.10365     	-2.88026    	1           
  1000        	0.04739     	0.09001     	-0.79885    	2           
---> try to load best state
              	0.04739     	0.09001     	-0.79885    	2                eval state : test_on_restore;
              	0.04739     	0.09001     	-0.79885    	2                eval state : test_on_last_epoch;
---> final result
              	0.04739     	0.09001     	-0.79885    	2                eval state : final;module_name : NAR;subset : 2;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 28, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07387     	0.11871     	-11.14789   	0           
  10          	0.07366     	0.11848     	-10.88025   	0           
  100         	0.07153     	0.11615     	-8.57694    	0           
  300         	0.06655     	0.11086     	-5.18831    	0           
  500         	0.06151     	0.10568     	-3.25156    	1           
  1000        	0.04967     	0.09310     	-1.04197    	2           
---> try to load best state
              	0.04967     	0.09310     	-1.04197    	2                eval state : test_on_restore;
              	0.04967     	0.09310     	-1.04197    	2                eval state : test_on_last_epoch;
---> final result
              	0.04967     	0.09310     	-1.04197    	2                eval state : final;module_name : NAR;subset : 4;cp_record_file : True;
---> end


---------->
NAR for 8 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 24, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07496     	0.11628     	-9.99062    	0           
  10          	0.07473     	0.11602     	-9.74102    	0           
  100         	0.07238     	0.11339     	-7.61661    	0           
  300         	0.06677     	0.10720     	-4.50690    	0           
  500         	0.06088     	0.10071     	-2.69396    	1           
  1000        	0.04659     	0.08412     	-0.58101    	2           
---> try to load best state
              	0.04659     	0.08412     	-0.58101    	2                eval state : test_on_restore;
              	0.04659     	0.08412     	-0.58101    	2                eval state : test_on_last_epoch;
---> final result
              	0.04659     	0.08412     	-0.58101    	2                eval state : final;module_name : NAR;subset : 8;cp_record_file : True;
---> end


---------->
NAR for 16 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07307     	0.10953     	-6.42094    	0           
  10          	0.07280     	0.10923     	-6.24889    	0           
  100         	0.07014     	0.10629     	-4.81887    	0           
  300         	0.06416     	0.09975     	-2.83522    	0           
  500         	0.05830     	0.09325     	-1.69504    	1           
  1000        	0.04529     	0.07769     	-0.30877    	2           
---> try to load best state
              	0.04529     	0.07769     	-0.30877    	2                eval state : test_on_restore;
              	0.04529     	0.07769     	-0.30877    	2                eval state : test_on_last_epoch;
---> final result
              	0.04529     	0.07769     	-0.30877    	2                eval state : final;module_name : NAR;subset : 16;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07633     	0.11516     	-13.97683   	0           
  10          	0.07608     	0.11488     	-13.57544   	0           
  100         	0.07351     	0.11182     	-10.21492   	0           
  300         	0.06697     	0.10387     	-5.50974    	0           
  500         	0.06000     	0.09530     	-2.98784    	1           
  1000        	0.04441     	0.07640     	-0.51927    	2           
---> try to load best state
              	0.04441     	0.07640     	-0.51927    	2                eval state : test_on_restore;
              	0.04441     	0.07640     	-0.51927    	2                eval state : test_on_last_epoch;
---> final result
              	0.04441     	0.07640     	-0.51927    	2                eval state : final;module_name : NAR;subset : 32;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.01743     	0.04621     	0.78190     	0           
  10          	0.01743     	0.04608     	0.78271     	0           
  100         	0.01754     	0.04499     	0.79095     	0           
  300         	0.01732     	0.04235     	0.81401     	0           
  500         	0.01710     	0.04034     	0.83147     	0           
  1000        	0.01660     	0.03717     	0.85641     	1           
---> try to load best state
              	0.01660     	0.03717     	0.85641     	1                eval state : test_on_restore;
              	0.01660     	0.03717     	0.85641     	1                eval state : test_on_last_epoch;
---> final result
              	0.01660     	0.03717     	0.85641     	1                eval state : final;
----------> finish x-yl training


---------->
NAR for 1 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 31, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.06863     	0.12296     	-7.08650    	0           
  10          	0.06846     	0.12283     	-7.00708    	0           
  100         	0.06674     	0.12153     	-6.28620    	0           
  300         	0.06307     	0.11841     	-5.02830    	0           
  500         	0.05970     	0.11529     	-4.06532    	1           
  1000        	0.05313     	0.10904     	-2.53116    	2           
---> try to load best state
              	0.05313     	0.10904     	-2.53116    	2                eval state : test_on_restore;
              	0.05313     	0.10904     	-2.53116    	2                eval state : test_on_last_epoch;
---> final result
              	0.05313     	0.10904     	-2.53116    	2                eval state : final;module_name : NAR;subset : 1;cp_record_file : True;
---> end


---------->
NAR for 2 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 30, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.06876     	0.12233     	-7.17324    	0           
  10          	0.06857     	0.12220     	-7.08766    	0           
  100         	0.06676     	0.12082     	-6.31457    	0           
  300         	0.06285     	0.11758     	-4.95060    	0           
  500         	0.05927     	0.11437     	-3.93450    	1           
  1000        	0.05263     	0.10804     	-2.41374    	2           
---> try to load best state
              	0.05263     	0.10804     	-2.41374    	2                eval state : test_on_restore;
              	0.05263     	0.10804     	-2.41374    	2                eval state : test_on_last_epoch;
---> final result
              	0.05263     	0.10804     	-2.41374    	2                eval state : final;module_name : NAR;subset : 2;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 28, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.06894     	0.12001     	-6.86913    	0           
  10          	0.06876     	0.11987     	-6.78606    	0           
  100         	0.06696     	0.11842     	-6.03544    	0           
  300         	0.06308     	0.11508     	-4.73385    	0           
  500         	0.05962     	0.11188     	-3.78092    	1           
  1000        	0.05297     	0.10563     	-2.32748    	2           
---> try to load best state
              	0.05297     	0.10563     	-2.32748    	2                eval state : test_on_restore;
              	0.05297     	0.10563     	-2.32748    	2                eval state : test_on_last_epoch;
---> final result
              	0.05297     	0.10563     	-2.32748    	2                eval state : final;module_name : NAR;subset : 4;cp_record_file : True;
---> end


---------->
NAR for 8 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 24, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.06926     	0.11314     	-5.17167    	0           
  10          	0.06904     	0.11293     	-5.09386    	0           
  100         	0.06692     	0.11082     	-4.38745    	0           
  300         	0.06232     	0.10610     	-3.16451    	0           
  500         	0.05818     	0.10169     	-2.29927    	1           
  1000        	0.04969     	0.09274     	-1.08607    	2           
---> try to load best state
              	0.04969     	0.09274     	-1.08607    	2                eval state : test_on_restore;
              	0.04969     	0.09274     	-1.08607    	2                eval state : test_on_last_epoch;
---> final result
              	0.04969     	0.09274     	-1.08607    	2                eval state : final;module_name : NAR;subset : 8;cp_record_file : True;
---> end


---------->
NAR for 16 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.06972     	0.11171     	-4.92942    	0           
  10          	0.06947     	0.11145     	-4.84591    	0           
  100         	0.06699     	0.10878     	-4.07228    	0           
  300         	0.06111     	0.10242     	-2.64061    	0           
  500         	0.05518     	0.09602     	-1.60568    	1           
  1000        	0.04264     	0.08161     	-0.26321    	2           
---> try to load best state
              	0.04264     	0.08161     	-0.26321    	2                eval state : test_on_restore;
              	0.04264     	0.08161     	-0.26321    	2                eval state : test_on_last_epoch;
---> final result
              	0.04264     	0.08161     	-0.26321    	2                eval state : final;module_name : NAR;subset : 16;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.08050     	0.12501     	-35.57295   	0           
  10          	0.08032     	0.12485     	-34.71850   	0           
  100         	0.07843     	0.12315     	-27.43269   	0           
  300         	0.07385     	0.11905     	-16.75304   	0           
  500         	0.06897     	0.11470     	-10.56724   	1           
  1000        	0.05714     	0.10366     	-3.75110    	2           
---> try to load best state
              	0.05714     	0.10366     	-3.75110    	2                eval state : test_on_restore;
              	0.05714     	0.10366     	-3.75110    	2                eval state : test_on_last_epoch;
---> final result
              	0.05714     	0.10366     	-3.75110    	2                eval state : final;module_name : NAR;subset : 32;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.01158     	0.02967     	0.89363     	0           
  10          	0.01161     	0.02967     	0.89330     	0           
  100         	0.01200     	0.02973     	0.89070     	0           
  300         	0.01217     	0.02890     	0.89513     	0           
  500         	0.01213     	0.02813     	0.89987     	0           
  1000        	0.01188     	0.02691     	0.90708     	1           
---> try to load best state
              	0.01188     	0.02691     	0.90708     	1                eval state : test_on_restore;
              	0.01188     	0.02691     	0.90708     	1                eval state : test_on_last_epoch;
---> final result
              	0.01188     	0.02691     	0.90708     	1                eval state : final;
----------> finish x-yl training


---------->
NAR for 1 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 31, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07461     	0.12155     	-18.12189   	0           
  10          	0.07442     	0.12138     	-17.77279   	0           
  100         	0.07238     	0.11969     	-14.69865   	0           
  300         	0.06727     	0.11538     	-9.64972    	0           
  500         	0.06163     	0.11050     	-6.34151    	1           
  1000        	0.04831     	0.09884     	-2.40204    	2           
---> try to load best state
              	0.04831     	0.09884     	-2.40204    	2                eval state : test_on_restore;
              	0.04831     	0.09884     	-2.40204    	2                eval state : test_on_last_epoch;
---> final result
              	0.04831     	0.09884     	-2.40204    	2                eval state : final;module_name : NAR;subset : 1;cp_record_file : True;
---> end


---------->
NAR for 2 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 30, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07474     	0.12117     	-17.19243   	0           
  10          	0.07454     	0.12101     	-16.86324   	0           
  100         	0.07253     	0.11938     	-13.98163   	0           
  300         	0.06757     	0.11528     	-9.29865    	0           
  500         	0.06212     	0.11059     	-6.23951    	1           
  1000        	0.04930     	0.09876     	-2.51369    	2           
---> try to load best state
              	0.04930     	0.09876     	-2.51369    	2                eval state : test_on_restore;
              	0.04930     	0.09876     	-2.51369    	2                eval state : test_on_last_epoch;
---> final result
              	0.04930     	0.09876     	-2.51369    	2                eval state : final;module_name : NAR;subset : 2;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 28, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07699     	0.12033     	-16.07613   	0           
  10          	0.07679     	0.12015     	-15.78870   	0           
  100         	0.07475     	0.11830     	-13.23090   	0           
  300         	0.06961     	0.11359     	-8.87933    	0           
  500         	0.06371     	0.10801     	-5.82484    	1           
  1000        	0.04933     	0.09336     	-2.04988    	2           
---> try to load best state
              	0.04933     	0.09336     	-2.04988    	2                eval state : test_on_restore;
              	0.04933     	0.09336     	-2.04988    	2                eval state : test_on_last_epoch;
---> final result
              	0.04933     	0.09336     	-2.04988    	2                eval state : final;module_name : NAR;subset : 4;cp_record_file : True;
---> end


---------->
NAR for 8 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 24, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07573     	0.11692     	-10.33790   	0           
  10          	0.07548     	0.11666     	-10.14896   	0           
  100         	0.07293     	0.11402     	-8.43431    	0           
  300         	0.06647     	0.10726     	-5.36412    	0           
  500         	0.05946     	0.09993     	-3.24199    	1           
  1000        	0.04365     	0.08361     	-0.83938    	2           
---> try to load best state
              	0.04365     	0.08361     	-0.83938    	2                eval state : test_on_restore;
              	0.04365     	0.08361     	-0.83938    	2                eval state : test_on_last_epoch;
---> final result
              	0.04365     	0.08361     	-0.83938    	2                eval state : final;module_name : NAR;subset : 8;cp_record_file : True;
---> end


---------->
NAR for 16 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.08869     	0.13006     	-24.35208   	0           
  10          	0.08849     	0.12987     	-23.75493   	0           
  100         	0.08639     	0.12784     	-18.59780   	0           
  300         	0.08057     	0.12200     	-10.69024   	0           
  500         	0.07386     	0.11512     	-6.25255    	1           
  1000        	0.05834     	0.09832     	-2.02072    	2           
---> try to load best state
              	0.05834     	0.09832     	-2.02072    	2                eval state : test_on_restore;
              	0.05834     	0.09832     	-2.02072    	2                eval state : test_on_last_epoch;
---> final result
              	0.05834     	0.09832     	-2.02072    	2                eval state : final;module_name : NAR;subset : 16;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.08252     	0.12269     	-18.94851   	0           
  10          	0.08225     	0.12240     	-18.39940   	0           
  100         	0.07922     	0.11926     	-13.71928   	0           
  300         	0.07092     	0.11049     	-7.06558    	0           
  500         	0.06188     	0.10095     	-3.78030    	1           
  1000        	0.04323     	0.08206     	-0.90118    	2           
---> try to load best state
              	0.04323     	0.08206     	-0.90118    	2                eval state : test_on_restore;
              	0.04323     	0.08206     	-0.90118    	2                eval state : test_on_last_epoch;
---> final result
              	0.04323     	0.08206     	-0.90118    	2                eval state : final;module_name : NAR;subset : 32;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.01522     	0.03028     	0.91039     	0           
  10          	0.01521     	0.03018     	0.91085     	0           
  100         	0.01507     	0.02928     	0.91485     	0           
  300         	0.01472     	0.02797     	0.92087     	0           
  500         	0.01447     	0.02720     	0.92458     	1           
  1000        	0.01403     	0.02617     	0.92955     	1           
---> try to load best state
              	0.01403     	0.02617     	0.92955     	1                eval state : test_on_restore;
              	0.01403     	0.02617     	0.92955     	1                eval state : test_on_last_epoch;
---> final result
              	0.01403     	0.02617     	0.92955     	1                eval state : final;
----------> finish x-yl training


---------->
NAR for 1 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 31, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07288     	0.12173     	-20.00097   	0           
  10          	0.07269     	0.12156     	-19.62591   	0           
  100         	0.07080     	0.11983     	-16.26225   	0           
  300         	0.06659     	0.11579     	-10.71775   	0           
  500         	0.06235     	0.11167     	-7.06038    	1           
  1000        	0.05232     	0.10219     	-2.73055    	2           
---> try to load best state
              	0.05232     	0.10219     	-2.73055    	2                eval state : test_on_restore;
              	0.05232     	0.10219     	-2.73055    	2                eval state : test_on_last_epoch;
---> final result
              	0.05232     	0.10219     	-2.73055    	2                eval state : final;module_name : NAR;subset : 1;cp_record_file : True;
---> end


---------->
NAR for 2 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 30, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07282     	0.12113     	-19.15182   	0           
  10          	0.07262     	0.12095     	-18.77353   	0           
  100         	0.07063     	0.11915     	-15.40484   	0           
  300         	0.06617     	0.11489     	-9.93445    	0           
  500         	0.06181     	0.11056     	-6.44661    	1           
  1000        	0.05170     	0.10088     	-2.47927    	2           
---> try to load best state
              	0.05170     	0.10088     	-2.47927    	2                eval state : test_on_restore;
              	0.05170     	0.10088     	-2.47927    	2                eval state : test_on_last_epoch;
---> final result
              	0.05170     	0.10088     	-2.47927    	2                eval state : final;module_name : NAR;subset : 2;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 28, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07275     	0.11999     	-17.20572   	0           
  10          	0.07253     	0.11980     	-16.83697   	0           
  100         	0.07038     	0.11786     	-13.64701   	0           
  300         	0.06561     	0.11326     	-8.72091    	0           
  500         	0.06082     	0.10852     	-5.62424    	1           
  1000        	0.04974     	0.09730     	-2.00547    	2           
---> try to load best state
              	0.04974     	0.09730     	-2.00547    	2                eval state : test_on_restore;
              	0.04974     	0.09730     	-2.00547    	2                eval state : test_on_last_epoch;
---> final result
              	0.04974     	0.09730     	-2.00547    	2                eval state : final;module_name : NAR;subset : 4;cp_record_file : True;
---> end


---------->
NAR for 8 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 24, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07284     	0.12087     	-16.17601   	0           
  10          	0.07264     	0.12071     	-15.91311   	0           
  100         	0.07071     	0.11909     	-13.61764   	0           
  300         	0.06651     	0.11532     	-9.85277    	0           
  500         	0.06236     	0.11154     	-7.11573    	1           
  1000        	0.05256     	0.10260     	-3.18146    	2           
---> try to load best state
              	0.05256     	0.10260     	-3.18146    	2                eval state : test_on_restore;
              	0.05256     	0.10260     	-3.18146    	2                eval state : test_on_last_epoch;
---> final result
              	0.05256     	0.10260     	-3.18146    	2                eval state : final;module_name : NAR;subset : 8;cp_record_file : True;
---> end


---------->
NAR for 16 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.07551     	0.12140     	-20.98083   	0           
  10          	0.07532     	0.12124     	-20.60603   	0           
  100         	0.07346     	0.11963     	-17.41069   	0           
  300         	0.06929     	0.11575     	-12.54774   	0           
  500         	0.06515     	0.11159     	-9.23041    	1           
  1000        	0.05513     	0.10116     	-4.33080    	2           
---> try to load best state
              	0.05513     	0.10116     	-4.33080    	2                eval state : test_on_restore;
              	0.05513     	0.10116     	-4.33080    	2                eval state : test_on_last_epoch;
---> final result
              	0.05513     	0.10116     	-4.33080    	2                eval state : final;module_name : NAR;subset : 16;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[1]], 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           	0.09080     	0.12751     	-15.34318   	0           
  10          	0.09050     	0.12713     	-14.91799   	0           
  100         	0.08739     	0.12327     	-11.36669   	0           
  300         	0.07987     	0.11397     	-6.35166    	0           
  500         	0.07198     	0.10422     	-3.58367    	1           
  1000        	0.05447     	0.08246     	-0.76102    	2           
---> try to load best state
              	0.05447     	0.08246     	-0.76102    	2                eval state : test_on_restore;
              	0.05447     	0.08246     	-0.76102    	2                eval state : test_on_last_epoch;
---> final result
              	0.05447     	0.08246     	-0.76102    	2                eval state : final;module_name : NAR;subset : 32;cp_record_file : True;
