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

  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 2.0 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': 4, '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           	0.09798     	0.13948     	-4009.14075 	0           
  10          	0.09797     	0.13947     	-3785.05116 	0           
  100         	0.09781     	0.13936     	-2246.71354 	0           
  300         	0.09740     	0.13908     	-949.72915  	0           
  500         	0.09697     	0.13880     	-545.46552  	0           
  1000        	0.09592     	0.13818     	-242.60677  	1           
---> try to load best state
              	0.09592     	0.13818     	-242.60677  	1                eval state : test_on_restore;
              	0.09592     	0.13818     	-242.60677  	1                eval state : test_on_last_epoch;
---> final result
              	0.09592     	0.13818     	-242.60677  	1                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': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 28, 'train_sample': 8, '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           	0.09961     	0.14132     	-1171.06091 	0           
  10          	0.09958     	0.14131     	-1130.82984 	0           
  100         	0.09931     	0.14113     	-810.58083  	0           
  300         	0.09854     	0.14065     	-429.91270  	0           
  500         	0.09765     	0.14014     	-267.31031  	0           
  1000        	0.09537     	0.13888     	-127.63121  	1           
---> try to load best state
              	0.09537     	0.13888     	-127.63121  	1                eval state : test_on_restore;
              	0.09537     	0.13888     	-127.63121  	1                eval state : test_on_last_epoch;
---> final result
              	0.09537     	0.13888     	-127.63121  	1                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': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 24, 'train_sample': 16, '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           	0.08255     	0.12858     	-55.12509   	0           
  10          	0.08246     	0.12850     	-54.12048   	0           
  100         	0.08142     	0.12760     	-45.17499   	0           
  300         	0.07875     	0.12538     	-30.46763   	1           
  500         	0.07576     	0.12304     	-21.13417   	1           
  1000        	0.06716     	0.11637     	-9.98943    	2           
---> try to load best state
              	0.06716     	0.11637     	-9.98943    	2                eval state : test_on_restore;
              	0.06716     	0.11637     	-9.98943    	2                eval state : test_on_last_epoch;
---> final result
              	0.06716     	0.11637     	-9.98943    	2                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': '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    	0           
  1000        	0.04096     	0.07888     	-0.10279    	1           
---> try to load best state
              	0.04096     	0.07888     	-0.10279    	1                eval state : test_on_restore;
              	0.04096     	0.07888     	-0.10279    	1                eval state : test_on_last_epoch;
---> final result
              	0.04096     	0.07888     	-0.10279    	1                eval state : final;module_name : NAR;subset : 16.0;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 2.0 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': 4, '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           	0.12178     	0.16735     	-38.61073   	0           
  10          	0.12179     	0.16735     	-38.67149   	0           
  100         	0.12184     	0.16740     	-39.33441   	0           
  300         	0.12197     	0.16752     	-41.04047   	0           
  500         	0.12206     	0.16761     	-42.29615   	0           
  1000        	0.12208     	0.16763     	-42.64642   	1           
---> try to load best state
              	0.12208     	0.16763     	-42.64642   	1                eval state : test_on_restore;
              	0.12208     	0.16763     	-42.64642   	1                eval state : test_on_last_epoch;
---> final result
              	0.12208     	0.16763     	-42.64642   	1                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': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 28, 'train_sample': 8, '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           	0.09054     	0.12745     	-19.75651   	0           
  10          	0.09041     	0.12729     	-19.34306   	0           
  100         	0.08853     	0.12510     	-14.83754   	0           
  300         	0.08111     	0.11626     	-6.92892    	0           
  500         	0.07277     	0.10611     	-3.63866    	0           
  1000        	0.05564     	0.08543     	-0.90760    	1           
---> try to load best state
              	0.05564     	0.08543     	-0.90760    	1                eval state : test_on_restore;
              	0.05564     	0.08543     	-0.90760    	1                eval state : test_on_last_epoch;
---> final result
              	0.05564     	0.08543     	-0.90760    	1                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': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 24, 'train_sample': 16, '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           	0.08756     	0.12439     	-16.27355   	0           
  10          	0.08738     	0.12418     	-15.89477   	0           
  100         	0.08541     	0.12182     	-12.45189   	0           
  300         	0.07949     	0.11481     	-6.86851    	0           
  500         	0.07261     	0.10648     	-3.88390    	1           
  1000        	0.05701     	0.08585     	-0.96926    	2           
---> try to load best state
              	0.05701     	0.08585     	-0.96926    	2                eval state : test_on_restore;
              	0.05701     	0.08585     	-0.96926    	2                eval state : test_on_last_epoch;
---> final result
              	0.05701     	0.08585     	-0.96926    	2                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': '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 : 16.0;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     	2           
---> try to load best state
              	0.01660     	0.03717     	0.85641     	2                eval state : test_on_restore;
              	0.01660     	0.03717     	0.85641     	2                eval state : test_on_last_epoch;
---> final result
              	0.01660     	0.03717     	0.85641     	2                eval state : final;
----------> finish x-yl training


---------->
NAR for 2.0 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': 4, '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           	0.08761     	0.14500     	-452382028630.18585	0           
  10          	0.08761     	0.14500     	-452382028630.18585	0           
  100         	0.08761     	0.14500     	-452382028630.18585	0           
  300         	0.08761     	0.14500     	-452382028630.18585	0           
  500         	0.08761     	0.14500     	-452382028630.18585	0           
  1000        	0.08761     	0.14500     	-452382028630.18585	1           
---> try to load best state
              	0.08761     	0.14500     	-452382028630.18585	1                eval state : test_on_restore;
              	0.08761     	0.14500     	-452382028630.18585	1                eval state : test_on_last_epoch;
---> final result
              	0.08761     	0.14500     	-452382028630.18585	1                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': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 28, 'train_sample': 8, '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           	0.08419     	0.12764     	-37.62053   	0           
  10          	0.08415     	0.12759     	-37.32126   	0           
  100         	0.08359     	0.12699     	-34.08180   	0           
  300         	0.08183     	0.12503     	-25.81427   	0           
  500         	0.07989     	0.12288     	-19.46442   	0           
  1000        	0.07528     	0.11826     	-11.15493   	2           
---> try to load best state
              	0.07528     	0.11826     	-11.15493   	2                eval state : test_on_restore;
              	0.07528     	0.11826     	-11.15493   	2                eval state : test_on_last_epoch;
---> final result
              	0.07528     	0.11826     	-11.15493   	2                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': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 24, 'train_sample': 16, '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           	0.08928     	0.12804     	-18.62623   	0           
  10          	0.08913     	0.12788     	-18.25532   	0           
  100         	0.08748     	0.12614     	-14.83011   	0           
  300         	0.08286     	0.12130     	-9.09763    	0           
  500         	0.07777     	0.11586     	-5.84507    	0           
  1000        	0.06578     	0.10220     	-2.36742    	1           
---> try to load best state
              	0.06578     	0.10220     	-2.36742    	1                eval state : test_on_restore;
              	0.06578     	0.10220     	-2.36742    	1                eval state : test_on_last_epoch;
---> final result
              	0.06578     	0.10220     	-2.36742    	1                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': '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 : 16.0;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 2.0 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': 4, '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           	0.12864     	0.18049     	-97735.74400	0           
  10          	0.12864     	0.18049     	-97878.89509	0           
  100         	0.12865     	0.18049     	-99464.83923	0           
  300         	0.12865     	0.18049     	-103555.19358	0           
  500         	0.12865     	0.18049     	-106570.77028	0           
  1000        	0.12865     	0.18049     	-107393.10603	1           
---> try to load best state
              	0.12865     	0.18049     	-107393.10603	1                eval state : test_on_restore;
              	0.12865     	0.18049     	-107393.10603	1                eval state : test_on_last_epoch;
---> final result
              	0.12865     	0.18049     	-107393.10603	1                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': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 28, 'train_sample': 8, '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           	0.10892     	0.15264     	-422.30348  	0           
  10          	0.10889     	0.15262     	-410.31031  	0           
  100         	0.10855     	0.15236     	-303.75416  	0           
  300         	0.10730     	0.15141     	-154.61732  	0           
  500         	0.10570     	0.15012     	-93.33434   	0           
  1000        	0.10141     	0.14637     	-41.78325   	1           
---> try to load best state
              	0.10141     	0.14637     	-41.78325   	1                eval state : test_on_restore;
              	0.10141     	0.14637     	-41.78325   	1                eval state : test_on_last_epoch;
---> final result
              	0.10141     	0.14637     	-41.78325   	1                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': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 24, 'train_sample': 16, '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           	0.10087     	0.14225     	-33.78962   	0           
  10          	0.10072     	0.14208     	-33.06496   	0           
  100         	0.09899     	0.14014     	-26.59917   	0           
  300         	0.09414     	0.13462     	-16.04706   	1           
  500         	0.08853     	0.12817     	-9.86967    	1           
  1000        	0.07425     	0.11068     	-3.50832    	2           
---> try to load best state
              	0.07425     	0.11068     	-3.50832    	2                eval state : test_on_restore;
              	0.07425     	0.11068     	-3.50832    	2                eval state : test_on_last_epoch;
---> final result
              	0.07425     	0.11068     	-3.50832    	2                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': '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 : 16.0;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     	0           
  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 2.0 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': 4, '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           	0.10221     	0.14286     	-213.94485  	0           
  10          	0.10221     	0.14287     	-214.26677  	0           
  100         	0.10224     	0.14288     	-217.77914  	0           
  300         	0.10230     	0.14292     	-226.81231  	0           
  500         	0.10234     	0.14295     	-233.45545  	0           
  1000        	0.10235     	0.14296     	-235.30826  	1           
---> try to load best state
              	0.10235     	0.14296     	-235.30826  	1                eval state : test_on_restore;
              	0.10235     	0.14296     	-235.30826  	1                eval state : test_on_last_epoch;
---> final result
              	0.10235     	0.14296     	-235.30826  	1                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': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 28, 'train_sample': 8, '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           	0.09233     	0.13229     	-49.70032   	0           
  10          	0.09234     	0.13230     	-49.77782   	0           
  100         	0.09238     	0.13233     	-50.59371   	0           
  300         	0.09247     	0.13239     	-52.12636   	0           
  500         	0.09249     	0.13241     	-52.55366   	1           
  1000        	0.09273     	0.13265     	-55.87856   	2           
---> try to load best state
              	0.09273     	0.13265     	-55.87856   	2                eval state : test_on_restore;
              	0.09273     	0.13265     	-55.87856   	2                eval state : test_on_last_epoch;
---> final result
              	0.09273     	0.13265     	-55.87856   	2                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': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 24, 'train_sample': 16, '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           	0.08497     	0.12686     	-26.02941   	0           
  10          	0.08485     	0.12675     	-25.68836   	0           
  100         	0.08360     	0.12554     	-22.59224   	0           
  300         	0.08066     	0.12276     	-17.18965   	0           
  500         	0.07750     	0.11987     	-13.15743   	1           
  1000        	0.06917     	0.11207     	-7.20085    	2           
---> try to load best state
              	0.06917     	0.11207     	-7.20085    	2                eval state : test_on_restore;
              	0.06917     	0.11207     	-7.20085    	2                eval state : test_on_last_epoch;
---> final result
              	0.06917     	0.11207     	-7.20085    	2                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': '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 : 16.0;cp_record_file : True;
