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

  Demo NAR cigp 
  seed: 0 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.01891     	0.04497     	0.69301     	0           
  10          	0.01887     	0.04486     	0.69398     	0           
  100         	0.01859     	0.04393     	0.70115     	0           
  300         	0.01775     	0.04181     	0.72220     	1           
  500         	0.01690     	0.03993     	0.74209     	1           
  1000        	0.01560     	0.03714     	0.76721     	3           
---> try to load best state
              	0.01560     	0.03714     	0.76721     	3                eval state : test_on_restore;
              	0.01560     	0.03714     	0.76721     	3                eval state : test_on_last_epoch;
---> final result
              	0.01560     	0.03714     	0.76721     	3                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': True, '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           	0.10006     	0.14079     	-282493473818.25165	0           
  10          	0.10006     	0.14079     	-282493473818.25165	0           
  100         	0.10006     	0.14079     	-282493473818.25165	0           
  300         	0.10006     	0.14079     	-282493473818.25165	1           
  500         	0.10006     	0.14079     	-282493473818.25165	1           
  1000        	0.10006     	0.14079     	-282493473818.25165	3           
---> try to load best state
              	0.10006     	0.14079     	-282493473818.25165	3                eval state : test_on_restore;
              	0.10006     	0.14079     	-282493473818.25165	3                eval state : test_on_last_epoch;
---> final result
              	0.10006     	0.14079     	-282493473818.25165	3                eval state : final;module_name : NAR;subset : 2.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 0, 'train_start_index': 28, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[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           	0.10228     	0.14299     	-285327118429.50745	0           
  10          	0.10228     	0.14299     	-285327118429.50745	0           
  100         	0.10228     	0.14299     	-285327118429.50745	0           
  300         	0.10228     	0.14299     	-285327118429.50745	1           
  500         	0.10228     	0.14299     	-285327118429.50745	2           
  1000        	0.10228     	0.14299     	-285327118429.50745	4           
---> try to load best state
              	0.10228     	0.14299     	-285327118429.50745	4                eval state : test_on_restore;
              	0.10228     	0.14299     	-285327118429.50745	4                eval state : test_on_last_epoch;
---> final result
              	0.10228     	0.14299     	-285327118429.50745	4                eval state : final;module_name : NAR;subset : 4.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 0, 'train_start_index': 24, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[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           	0.08984     	0.13458     	-1926458.80659	0           
  10          	0.08984     	0.13458     	-1929353.43544	0           
  100         	0.08984     	0.13458     	-1956765.86962	0           
  300         	0.08984     	0.13458     	-1986577.97208	1           
  500         	0.08984     	0.13458     	-1988198.06149	2           
  1000        	0.08984     	0.13458     	-1989038.59801	5           
---> try to load best state
              	0.08984     	0.13458     	-1989038.59801	5                eval state : test_on_restore;
              	0.08984     	0.13458     	-1989038.59801	5                eval state : test_on_last_epoch;
---> final result
              	0.08984     	0.13458     	-1989038.59801	5                eval state : final;module_name : NAR;subset : 8.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 0, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[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           	0.09013     	0.13220     	-334.66829  	0           
  10          	0.09010     	0.13217     	-320.02175  	0           
  100         	0.08979     	0.13181     	-217.94647  	1           
  300         	0.08876     	0.13071     	-96.19294   	3           
  500         	0.08756     	0.12941     	-57.93893   	5           
  1000        	0.08432     	0.12547     	-27.27727   	11          
---> try to load best state
              	0.08432     	0.12547     	-27.27727   	11               eval state : test_on_restore;
              	0.08432     	0.12547     	-27.27727   	11               eval state : test_on_last_epoch;
---> final result
              	0.08432     	0.12547     	-27.27727   	11               eval state : final;module_name : NAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 1 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.01328     	0.03315     	0.85242     	0           
  10          	0.01336     	0.03316     	0.85212     	0           
  100         	0.01413     	0.03332     	0.84908     	0           
  300         	0.01452     	0.03267     	0.85267     	1           
  500         	0.01440     	0.03184     	0.85834     	1           
  1000        	0.01399     	0.03048     	0.86724     	3           
---> try to load best state
              	0.01399     	0.03048     	0.86724     	3                eval state : test_on_restore;
              	0.01399     	0.03048     	0.86724     	3                eval state : test_on_last_epoch;
---> final result
              	0.01399     	0.03048     	0.86724     	3                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': True, '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           	0.13021     	0.17642     	-7538.84660 	0           
  10          	0.13021     	0.17642     	-7550.07501 	0           
  100         	0.13021     	0.17642     	-7672.20812 	0           
  300         	0.13021     	0.17642     	-7977.60310 	0           
  500         	0.13022     	0.17642     	-8182.41503 	1           
  1000        	0.13022     	0.17642     	-8232.08543 	2           
---> try to load best state
              	0.13022     	0.17642     	-8232.08543 	2                eval state : test_on_restore;
              	0.13022     	0.17642     	-8232.08543 	2                eval state : test_on_last_epoch;
---> final result
              	0.13022     	0.17642     	-8232.08543 	2                eval state : final;module_name : NAR;subset : 2.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 1, 'train_start_index': 28, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[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           	0.10034     	0.13850     	-4939.24664 	0           
  10          	0.10034     	0.13850     	-4946.61537 	0           
  100         	0.10034     	0.13850     	-5023.34453 	0           
  300         	0.10035     	0.13851     	-5156.14823 	1           
  500         	0.10035     	0.13851     	-5185.10632 	1           
  1000        	0.10035     	0.13851     	-5186.17367 	3           
---> try to load best state
              	0.10035     	0.13851     	-5186.17367 	3                eval state : test_on_restore;
              	0.10035     	0.13851     	-5186.17367 	3                eval state : test_on_last_epoch;
---> final result
              	0.10035     	0.13851     	-5186.17367 	3                eval state : final;module_name : NAR;subset : 4.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 1, 'train_start_index': 24, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[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           	0.09958     	0.13820     	-7539.48718 	0           
  10          	0.09958     	0.13820     	-7550.70788 	0           
  100         	0.09959     	0.13822     	-8599.98257 	0           
  300         	0.09960     	0.13823     	-9764.15050 	1           
  500         	0.09961     	0.13825     	-11576.31862	3           
  1000        	0.09962     	0.13826     	-13059.23756	6           
---> try to load best state
              	0.09962     	0.13826     	-13059.23756	6                eval state : test_on_restore;
              	0.09962     	0.13826     	-13059.23756	6                eval state : test_on_last_epoch;
---> final result
              	0.09962     	0.13826     	-13059.23756	6                eval state : final;module_name : NAR;subset : 8.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 1, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[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           	0.09234     	0.13344     	-11343.29500	0           
  10          	0.09234     	0.13344     	-10928.54061	0           
  100         	0.09231     	0.13339     	-7942.49286 	1           
  300         	0.09177     	0.13263     	-642.28186  	3           
  500         	0.09074     	0.13132     	-165.88540  	5           
  1000        	0.08768     	0.12731     	-47.59348   	11          
---> try to load best state
              	0.08768     	0.12731     	-47.59348   	11               eval state : test_on_restore;
              	0.08768     	0.12731     	-47.59348   	11               eval state : test_on_last_epoch;
---> final result
              	0.08768     	0.12731     	-47.59348   	11               eval state : final;module_name : NAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 2 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.01771     	0.04548     	0.77095     	0           
  10          	0.01771     	0.04535     	0.77179     	0           
  100         	0.01771     	0.04406     	0.78177     	0           
  300         	0.01723     	0.04107     	0.80817     	1           
  500         	0.01697     	0.03901     	0.82652     	1           
  1000        	0.01643     	0.03583     	0.85242     	3           
---> try to load best state
              	0.01643     	0.03583     	0.85242     	3                eval state : test_on_restore;
              	0.01643     	0.03583     	0.85242     	3                eval state : test_on_last_epoch;
---> final result
              	0.01643     	0.03583     	0.85242     	3                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': True, '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           	0.08900     	0.14585     	-583668356897.77295	0           
  10          	0.08900     	0.14585     	-583668356897.77295	0           
  100         	0.08900     	0.14585     	-583668356897.77295	0           
  300         	0.08900     	0.14585     	-583668356897.77295	0           
  500         	0.08900     	0.14585     	-583668356897.77295	1           
  1000        	0.08900     	0.14585     	-583668356897.77295	3           
---> try to load best state
              	0.08900     	0.14585     	-583668356897.77295	3                eval state : test_on_restore;
              	0.08900     	0.14585     	-583668356897.77295	3                eval state : test_on_last_epoch;
---> final result
              	0.08900     	0.14585     	-583668356897.77295	3                eval state : final;module_name : NAR;subset : 2.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 2, 'train_start_index': 28, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[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           	0.08890     	0.13237     	-1011.00183 	0           
  10          	0.08890     	0.13237     	-1012.51207 	0           
  100         	0.08891     	0.13238     	-1028.24186 	0           
  300         	0.08892     	0.13239     	-1055.36114 	1           
  500         	0.08892     	0.13239     	-1061.22714 	2           
  1000        	0.08892     	0.13239     	-1061.59031 	4           
---> try to load best state
              	0.08892     	0.13239     	-1061.59031 	4                eval state : test_on_restore;
              	0.08892     	0.13239     	-1061.59031 	4                eval state : test_on_last_epoch;
---> final result
              	0.08892     	0.13239     	-1061.59031 	4                eval state : final;module_name : NAR;subset : 4.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 2, 'train_start_index': 24, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[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           	0.09940     	0.13856     	-347.24920  	0           
  10          	0.09940     	0.13856     	-347.76804  	0           
  100         	0.09941     	0.13856     	-352.90571  	0           
  300         	0.09942     	0.13857     	-358.56878  	1           
  500         	0.09942     	0.13857     	-358.94256  	2           
  1000        	0.09944     	0.13859     	-384.63682  	5           
---> try to load best state
              	0.09944     	0.13859     	-384.63682  	5                eval state : test_on_restore;
              	0.09944     	0.13859     	-384.63682  	5                eval state : test_on_last_epoch;
---> final result
              	0.09944     	0.13859     	-384.63682  	5                eval state : final;module_name : NAR;subset : 8.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 2, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[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           	0.08977     	0.13096     	-62.19521   	0           
  10          	0.08975     	0.13093     	-61.87319   	0           
  100         	0.08948     	0.13066     	-54.49103   	1           
  300         	0.08885     	0.13002     	-43.22075   	3           
  500         	0.08826     	0.12940     	-35.76886   	5           
  1000        	0.08689     	0.12804     	-25.24347   	11          
---> try to load best state
              	0.08689     	0.12804     	-25.24347   	11               eval state : test_on_restore;
              	0.08689     	0.12804     	-25.24347   	11               eval state : test_on_last_epoch;
---> final result
              	0.08689     	0.12804     	-25.24347   	11               eval state : final;module_name : NAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 3 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.01171     	0.02891     	0.88768     	0           
  10          	0.01174     	0.02891     	0.88736     	0           
  100         	0.01200     	0.02871     	0.88679     	0           
  300         	0.01192     	0.02746     	0.89462     	1           
  500         	0.01185     	0.02665     	0.89975     	1           
  1000        	0.01157     	0.02541     	0.90730     	3           
---> try to load best state
              	0.01157     	0.02541     	0.90730     	3                eval state : test_on_restore;
              	0.01157     	0.02541     	0.90730     	3                eval state : test_on_last_epoch;
---> final result
              	0.01157     	0.02541     	0.90730     	3                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': True, '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           	0.13077     	0.18183     	-309092605936.93268	0           
  10          	0.13077     	0.18183     	-309092605936.93268	0           
  100         	0.13077     	0.18183     	-309092605936.93268	0           
  300         	0.13077     	0.18183     	-309092605936.93268	1           
  500         	0.13077     	0.18183     	-309092605936.93268	1           
  1000        	0.13077     	0.18183     	-309092605936.93268	3           
---> try to load best state
              	0.13077     	0.18183     	-309092605936.93268	3                eval state : test_on_restore;
              	0.13077     	0.18183     	-309092605936.93268	3                eval state : test_on_last_epoch;
---> final result
              	0.13077     	0.18183     	-309092605936.93268	3                eval state : final;module_name : NAR;subset : 2.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 3, 'train_start_index': 28, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[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           	0.11184     	0.15461     	-992713966214.56323	0           
  10          	0.11184     	0.15461     	-992713966214.56323	0           
  100         	0.11184     	0.15461     	-992713966214.56323	0           
  300         	0.11184     	0.15461     	-992713966214.56323	1           
  500         	0.11184     	0.15461     	-992713966214.56323	1           
  1000        	0.11184     	0.15461     	-992713966214.56323	3           
---> try to load best state
              	0.11184     	0.15461     	-992713966214.56323	3                eval state : test_on_restore;
              	0.11184     	0.15461     	-992713966214.56323	3                eval state : test_on_last_epoch;
---> final result
              	0.11184     	0.15461     	-992713966214.56323	3                eval state : final;module_name : NAR;subset : 4.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 3, 'train_start_index': 24, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[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           	0.11042     	0.15153     	-3646809.61030	0           
  10          	0.11042     	0.15153     	-3652496.72771	0           
  100         	0.11042     	0.15153     	-3703182.44018	0           
  300         	0.11042     	0.15153     	-4786506.31397	1           
  500         	0.11042     	0.15153     	-4791051.01994	2           
  1000        	0.11042     	0.15153     	-6833966.80751	5           
---> try to load best state
              	0.11042     	0.15153     	-6833966.80751	5                eval state : test_on_restore;
              	0.11042     	0.15153     	-6833966.80751	5                eval state : test_on_last_epoch;
---> final result
              	0.11042     	0.15153     	-6833966.80751	5                eval state : final;module_name : NAR;subset : 8.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 3, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[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           	0.10084     	0.14046     	-234173.61049	0           
  10          	0.10083     	0.14045     	-204897.37534	0           
  100         	0.10079     	0.14041     	-56038.37566	1           
  300         	0.10062     	0.14022     	-5637.21560 	4           
  500         	0.10038     	0.13998     	-1681.92026 	6           
  1000        	0.09963     	0.13929     	-416.75432  	11          
---> try to load best state
              	0.09963     	0.13929     	-416.75432  	11               eval state : test_on_restore;
              	0.09963     	0.13929     	-416.75432  	12               eval state : test_on_last_epoch;
---> final result
              	0.09963     	0.13929     	-416.75432  	12               eval state : final;module_name : NAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  seed: 4 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.01512     	0.02910     	0.90839     	0           
  10          	0.01511     	0.02900     	0.90885     	0           
  100         	0.01494     	0.02810     	0.91282     	0           
  300         	0.01456     	0.02682     	0.91869     	1           
  500         	0.01429     	0.02607     	0.92230     	2           
  1000        	0.01382     	0.02504     	0.92736     	3           
---> try to load best state
              	0.01382     	0.02504     	0.92736     	3                eval state : test_on_restore;
              	0.01382     	0.02504     	0.92736     	3                eval state : test_on_last_epoch;
---> final result
              	0.01382     	0.02504     	0.92736     	3                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': True, '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           	0.10662     	0.14654     	-297067.29823	0           
  10          	0.10662     	0.14654     	-297249.74132	0           
  100         	0.10662     	0.14654     	-299965.15907	0           
  300         	0.10662     	0.14655     	-306808.53912	0           
  500         	0.10662     	0.14655     	-311090.48235	1           
  1000        	0.10663     	0.14655     	-312256.42978	3           
---> try to load best state
              	0.10663     	0.14655     	-312256.42978	3                eval state : test_on_restore;
              	0.10663     	0.14655     	-312256.42978	3                eval state : test_on_last_epoch;
---> final result
              	0.10663     	0.14655     	-312256.42978	3                eval state : final;module_name : NAR;subset : 2.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 4, 'train_start_index': 28, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[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           	0.09963     	0.13885     	-41119.96087	0           
  10          	0.09963     	0.13885     	-41192.50203	0           
  100         	0.09963     	0.13886     	-41806.71408	0           
  300         	0.09964     	0.13886     	-42879.07033	1           
  500         	0.09964     	0.13886     	-43110.28841	1           
  1000        	0.09964     	0.13886     	-43124.88148	3           
---> try to load best state
              	0.09964     	0.13886     	-43124.88148	3                eval state : test_on_restore;
              	0.09964     	0.13886     	-43124.88148	3                eval state : test_on_last_epoch;
---> final result
              	0.09964     	0.13886     	-43124.88148	3                eval state : final;module_name : NAR;subset : 4.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 4, 'train_start_index': 24, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[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           	0.09543     	0.13556     	-1082.14208 	0           
  10          	0.09543     	0.13555     	-1058.07999 	0           
  100         	0.09534     	0.13548     	-823.94206  	0           
  300         	0.09474     	0.13495     	-368.42841  	1           
  500         	0.09392     	0.13426     	-186.67120  	2           
  1000        	0.09225     	0.13300     	-77.50067   	5           
---> try to load best state
              	0.09225     	0.13300     	-77.50067   	5                eval state : test_on_restore;
              	0.09225     	0.13300     	-77.50067   	5                eval state : test_on_last_epoch;
---> final result
              	0.09225     	0.13300     	-77.50067   	5                eval state : final;module_name : NAR;subset : 8.0;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 4, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[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           	0.09040     	0.13317     	-2139.54059 	0           
  10          	0.09039     	0.13317     	-2067.82334 	0           
  100         	0.09030     	0.13309     	-1455.29947 	1           
  300         	0.08991     	0.13275     	-735.70422  	2           
  500         	0.08934     	0.13223     	-437.31491  	5           
  1000        	0.08769     	0.13086     	-165.10075  	10          
---> try to load best state
              	0.08769     	0.13086     	-165.10075  	10               eval state : test_on_restore;
              	0.08769     	0.13086     	-165.10075  	10               eval state : test_on_last_epoch;
---> final result
              	0.08769     	0.13086     	-165.10075  	10               eval state : final;module_name : NAR;subset : 16.0;cp_record_file : True;
