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

  Demo lar cigp 
  seed: None 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00760     	0.01946     	0.96607     	0           
  10          	0.00696     	0.01848     	0.96942     	0           
  100         	0.00644     	0.01754     	0.97293     	0           
  300         	0.00622     	0.01696     	0.97480     	1           
  500         	0.00613     	0.01665     	0.97573     	2           
  1000        	0.00603     	0.01627     	0.97682     	3           
---> try to load best state
              	0.00603     	0.01627     	0.97682     	3                eval state : test_on_restore;
              	0.00603     	0.01627     	0.97682     	4                eval state : test_on_last_epoch;
---> final result
              	0.00603     	0.01627     	0.97682     	4                eval state : final;
----------> finish x-yl training


---------->
lar cigp for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01469     	0.03743     	0.88432     	0           
  10          	0.01766     	0.03842     	0.87202     	0           
  100         	0.01715     	0.03935     	0.87818     	0           
  300         	0.01730     	0.03972     	0.87760     	0           
  500         	0.01744     	0.04003     	0.87704     	1           
  1000        	0.01774     	0.04062     	0.87563     	2           
---> try to load best state
              	0.01774     	0.04062     	0.87563     	2                eval state : test_on_restore;
              	0.01774     	0.04062     	0.87563     	2                eval state : test_on_last_epoch;
---> final result
              	0.01774     	0.04062     	0.87563     	2                eval state : final;
              	0.01774     	0.04062     	0.87563     	2                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01016     	0.03132     	0.92207     	0           
  10          	0.01086     	0.02920     	0.92542     	0           
  100         	0.01062     	0.02950     	0.92691     	0           
  300         	0.01050     	0.02946     	0.92744     	1           
  500         	0.01038     	0.02941     	0.92787     	1           
  1000        	0.01014     	0.02927     	0.92880     	2           
---> try to load best state
              	0.01014     	0.02927     	0.92880     	2                eval state : test_on_restore;
              	0.01014     	0.02927     	0.92880     	2                eval state : test_on_last_epoch;
---> final result
              	0.01014     	0.02927     	0.92880     	2                eval state : final;
              	0.01014     	0.02927     	0.92880     	3                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00795     	0.02588     	0.94289     	0           
  10          	0.00856     	0.02427     	0.94513     	0           
  100         	0.00840     	0.02473     	0.94501     	0           
  300         	0.00836     	0.02479     	0.94508     	1           
  500         	0.00833     	0.02482     	0.94515     	1           
  1000        	0.00827     	0.02484     	0.94536     	4           
---> try to load best state
              	0.00827     	0.02484     	0.94536     	4                eval state : test_on_restore;
              	0.00827     	0.02484     	0.94536     	4                eval state : test_on_last_epoch;
---> final result
              	0.00827     	0.02484     	0.94536     	4                eval state : final;
              	0.00827     	0.02484     	0.94536     	4                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00723     	0.02255     	0.95462     	0           
  10          	0.00762     	0.02201     	0.95572     	0           
  100         	0.00752     	0.02252     	0.95451     	0           
  300         	0.00748     	0.02258     	0.95443     	1           
  500         	0.00744     	0.02261     	0.95441     	3           
  1000        	0.00737     	0.02264     	0.95447     	5           
---> try to load best state
              	0.00737     	0.02264     	0.95447     	5                eval state : test_on_restore;
              	0.00737     	0.02264     	0.95447     	5                eval state : test_on_last_epoch;
---> final result
              	0.00737     	0.02264     	0.95447     	5                eval state : final;
              	0.00737     	0.02264     	0.95447     	5                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 0 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01194     	0.03246     	0.84568     	0           
  10          	0.01010     	0.02731     	0.88502     	0           
  100         	0.00863     	0.02438     	0.90780     	0           
  300         	0.00809     	0.02342     	0.91520     	1           
  500         	0.00781     	0.02298     	0.91856     	2           
  1000        	0.00745     	0.02248     	0.92250     	4           
---> try to load best state
              	0.00745     	0.02248     	0.92250     	4                eval state : test_on_restore;
              	0.00745     	0.02248     	0.92250     	4                eval state : test_on_last_epoch;
---> final result
              	0.00745     	0.02248     	0.92250     	4                eval state : final;
----------> finish x-yl training


---------->
lar cigp for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01245     	0.03088     	0.75447     	0           
  10          	0.01297     	0.03123     	0.74908     	0           
  100         	0.01340     	0.03195     	0.73739     	0           
  300         	0.01328     	0.03181     	0.74004     	0           
  500         	0.01317     	0.03166     	0.74269     	1           
  1000        	0.01295     	0.03139     	0.74769     	2           
---> try to load best state
              	0.01295     	0.03139     	0.74769     	2                eval state : test_on_restore;
              	0.01295     	0.03139     	0.74769     	2                eval state : test_on_last_epoch;
---> final result
              	0.01295     	0.03139     	0.74769     	2                eval state : final;
              	0.01295     	0.03139     	0.74769     	2                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01040     	0.02895     	0.82133     	0           
  10          	0.00947     	0.02803     	0.82581     	0           
  100         	0.00851     	0.02597     	0.86070     	0           
  300         	0.00846     	0.02587     	0.86305     	1           
  500         	0.00842     	0.02578     	0.86495     	1           
  1000        	0.00835     	0.02564     	0.86812     	3           
---> try to load best state
              	0.00835     	0.02564     	0.86812     	3                eval state : test_on_restore;
              	0.00835     	0.02564     	0.86812     	3                eval state : test_on_last_epoch;
---> final result
              	0.00835     	0.02564     	0.86812     	3                eval state : final;
              	0.00835     	0.02564     	0.86812     	3                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00836     	0.02516     	0.88910     	0           
  10          	0.00832     	0.02571     	0.86932     	0           
  100         	0.00813     	0.02483     	0.88574     	0           
  300         	0.00811     	0.02477     	0.88725     	1           
  500         	0.00809     	0.02473     	0.88818     	2           
  1000        	0.00805     	0.02466     	0.88979     	3           
---> try to load best state
              	0.00805     	0.02466     	0.88979     	3                eval state : test_on_restore;
              	0.00805     	0.02466     	0.88979     	3                eval state : test_on_last_epoch;
---> final result
              	0.00805     	0.02466     	0.88979     	4                eval state : final;
              	0.00805     	0.02466     	0.88979     	4                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00804     	0.02510     	0.88954     	0           
  10          	0.00813     	0.02531     	0.87689     	0           
  100         	0.00804     	0.02469     	0.88965     	0           
  300         	0.00804     	0.02463     	0.89092     	1           
  500         	0.00802     	0.02461     	0.89159     	2           
  1000        	0.00798     	0.02458     	0.89249     	5           
---> try to load best state
              	0.00798     	0.02458     	0.89249     	5                eval state : test_on_restore;
              	0.00798     	0.02458     	0.89249     	5                eval state : test_on_last_epoch;
---> final result
              	0.00798     	0.02458     	0.89249     	5                eval state : final;
              	0.00798     	0.02458     	0.89249     	5                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 1 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00828     	0.02351     	0.92594     	0           
  10          	0.00755     	0.02127     	0.93762     	0           
  100         	0.00699     	0.01957     	0.94677     	0           
  300         	0.00663     	0.01892     	0.95074     	1           
  500         	0.00642     	0.01862     	0.95266     	1           
  1000        	0.00614     	0.01822     	0.95521     	3           
---> try to load best state
              	0.00614     	0.01822     	0.95521     	3                eval state : test_on_restore;
              	0.00614     	0.01822     	0.95521     	3                eval state : test_on_last_epoch;
---> final result
              	0.00614     	0.01822     	0.95521     	3                eval state : final;
----------> finish x-yl training


---------->
lar cigp for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01175     	0.02768     	0.85749     	0           
  10          	0.01100     	0.02708     	0.85869     	0           
  100         	0.01229     	0.02940     	0.83835     	0           
  300         	0.01211     	0.02910     	0.84121     	0           
  500         	0.01198     	0.02889     	0.84324     	1           
  1000        	0.01177     	0.02853     	0.84666     	2           
---> try to load best state
              	0.01177     	0.02853     	0.84666     	2                eval state : test_on_restore;
              	0.01177     	0.02853     	0.84666     	2                eval state : test_on_last_epoch;
---> final result
              	0.01177     	0.02853     	0.84666     	2                eval state : final;
              	0.01177     	0.02853     	0.84666     	2                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01013     	0.02475     	0.88383     	0           
  10          	0.00989     	0.02334     	0.89022     	0           
  100         	0.00888     	0.02130     	0.91148     	0           
  300         	0.00878     	0.02113     	0.91392     	0           
  500         	0.00872     	0.02103     	0.91546     	1           
  1000        	0.00863     	0.02087     	0.91804     	2           
---> try to load best state
              	0.00863     	0.02087     	0.91804     	2                eval state : test_on_restore;
              	0.00863     	0.02087     	0.91804     	3                eval state : test_on_last_epoch;
---> final result
              	0.00863     	0.02087     	0.91804     	3                eval state : final;
              	0.00863     	0.02087     	0.91804     	3                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00709     	0.02220     	0.91952     	0           
  10          	0.00715     	0.02152     	0.91720     	0           
  100         	0.00695     	0.02078     	0.92679     	0           
  300         	0.00692     	0.02069     	0.92804     	1           
  500         	0.00687     	0.02065     	0.92893     	2           
  1000        	0.00680     	0.02057     	0.93044     	4           
---> try to load best state
              	0.00680     	0.02057     	0.93044     	4                eval state : test_on_restore;
              	0.00680     	0.02057     	0.93044     	4                eval state : test_on_last_epoch;
---> final result
              	0.00680     	0.02057     	0.93044     	4                eval state : final;
              	0.00680     	0.02057     	0.93044     	4                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00669     	0.02050     	0.93602     	0           
  10          	0.00692     	0.02079     	0.92770     	0           
  100         	0.00667     	0.02027     	0.93547     	0           
  300         	0.00665     	0.02025     	0.93597     	1           
  500         	0.00664     	0.02023     	0.93638     	3           
  1000        	0.00661     	0.02020     	0.93705     	5           
---> try to load best state
              	0.00661     	0.02020     	0.93705     	5                eval state : test_on_restore;
              	0.00661     	0.02020     	0.93705     	5                eval state : test_on_last_epoch;
---> final result
              	0.00661     	0.02020     	0.93705     	5                eval state : final;
              	0.00661     	0.02020     	0.93705     	5                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 2 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01386     	0.03915     	0.84728     	0           
  10          	0.01080     	0.03189     	0.88527     	0           
  100         	0.00917     	0.02693     	0.90861     	0           
  300         	0.00903     	0.02641     	0.91342     	1           
  500         	0.00927     	0.02699     	0.91326     	1           
  1000        	0.01002     	0.02834     	0.91154     	3           
---> try to load best state
              	0.01002     	0.02834     	0.91154     	3                eval state : test_on_restore;
              	0.01002     	0.02834     	0.91154     	3                eval state : test_on_last_epoch;
---> final result
              	0.01002     	0.02834     	0.91154     	3                eval state : final;
----------> finish x-yl training


---------->
lar cigp for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01695     	0.03776     	0.72949     	0           
  10          	0.05476     	0.09310     	-5.75106    	0           
  100         	0.05014     	0.08577     	-2.59133    	0           
  300         	0.04913     	0.08433     	-2.31909    	0           
  500         	0.04844     	0.08333     	-2.14178    	1           
  1000        	0.04719     	0.08152     	-1.84394    	2           
---> try to load best state
              	0.04719     	0.08152     	-1.84394    	2                eval state : test_on_restore;
              	0.04719     	0.08152     	-1.84394    	2                eval state : test_on_last_epoch;
---> final result
              	0.04719     	0.08152     	-1.84394    	2                eval state : final;
              	0.04719     	0.08152     	-1.84394    	2                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01471     	0.03697     	0.75015     	0           
  10          	0.01969     	0.04767     	0.54671     	0           
  100         	0.01197     	0.03349     	0.79578     	0           
  300         	0.01156     	0.03250     	0.81417     	0           
  500         	0.01141     	0.03211     	0.82225     	1           
  1000        	0.01122     	0.03157     	0.83465     	2           
---> try to load best state
              	0.01122     	0.03157     	0.83465     	2                eval state : test_on_restore;
              	0.01122     	0.03157     	0.83465     	3                eval state : test_on_last_epoch;
---> final result
              	0.01122     	0.03157     	0.83465     	3                eval state : final;
              	0.01122     	0.03157     	0.83465     	3                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01033     	0.03110     	0.87033     	0           
  10          	0.01059     	0.03035     	0.87298     	0           
  100         	0.01040     	0.02962     	0.88774     	0           
  300         	0.01039     	0.02959     	0.88913     	1           
  500         	0.01037     	0.02957     	0.89014     	1           
  1000        	0.01034     	0.02953     	0.89164     	3           
---> try to load best state
              	0.01034     	0.02953     	0.89164     	3                eval state : test_on_restore;
              	0.01034     	0.02953     	0.89164     	4                eval state : test_on_last_epoch;
---> final result
              	0.01034     	0.02953     	0.89164     	4                eval state : final;
              	0.01034     	0.02953     	0.89164     	4                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01040     	0.03024     	0.88939     	0           
  10          	0.01047     	0.02951     	0.89372     	0           
  100         	0.01036     	0.02912     	0.90230     	0           
  300         	0.01033     	0.02914     	0.90239     	1           
  500         	0.01032     	0.02915     	0.90258     	3           
  1000        	0.01029     	0.02915     	0.90290     	6           
---> try to load best state
              	0.01029     	0.02915     	0.90290     	6                eval state : test_on_restore;
              	0.01029     	0.02915     	0.90290     	6                eval state : test_on_last_epoch;
---> final result
              	0.01029     	0.02915     	0.90290     	6                eval state : final;
              	0.01029     	0.02915     	0.90290     	6                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 3 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00822     	0.02167     	0.94743     	0           
  10          	0.00696     	0.01841     	0.96023     	0           
  100         	0.00606     	0.01581     	0.96994     	0           
  300         	0.00590     	0.01550     	0.97139     	1           
  500         	0.00584     	0.01546     	0.97181     	2           
  1000        	0.00576     	0.01541     	0.97238     	4           
---> try to load best state
              	0.00576     	0.01541     	0.97238     	4                eval state : test_on_restore;
              	0.00576     	0.01541     	0.97238     	4                eval state : test_on_last_epoch;
---> final result
              	0.00576     	0.01541     	0.97238     	4                eval state : final;
----------> finish x-yl training


---------->
lar cigp for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00834     	0.02073     	0.91851     	0           
  10          	0.00847     	0.02111     	0.91002     	0           
  100         	0.00853     	0.02144     	0.90446     	0           
  300         	0.00841     	0.02125     	0.90628     	0           
  500         	0.00835     	0.02115     	0.90723     	1           
  1000        	0.00825     	0.02099     	0.90883     	2           
---> try to load best state
              	0.00825     	0.02099     	0.90883     	2                eval state : test_on_restore;
              	0.00825     	0.02099     	0.90883     	2                eval state : test_on_last_epoch;
---> final result
              	0.00825     	0.02099     	0.90883     	2                eval state : final;
              	0.00825     	0.02099     	0.90883     	2                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00766     	0.02037     	0.92699     	0           
  10          	0.00785     	0.02075     	0.91826     	0           
  100         	0.00679     	0.01817     	0.94373     	0           
  300         	0.00673     	0.01804     	0.94559     	0           
  500         	0.00669     	0.01794     	0.94689     	1           
  1000        	0.00662     	0.01779     	0.94908     	3           
---> try to load best state
              	0.00662     	0.01779     	0.94908     	3                eval state : test_on_restore;
              	0.00662     	0.01779     	0.94908     	3                eval state : test_on_last_epoch;
---> final result
              	0.00662     	0.01779     	0.94908     	3                eval state : final;
              	0.00662     	0.01779     	0.94908     	3                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00621     	0.01813     	0.95229     	0           
  10          	0.00669     	0.01874     	0.94294     	0           
  100         	0.00634     	0.01709     	0.95810     	0           
  300         	0.00633     	0.01703     	0.95898     	1           
  500         	0.00631     	0.01700     	0.95954     	2           
  1000        	0.00627     	0.01693     	0.96056     	4           
---> try to load best state
              	0.00627     	0.01693     	0.96056     	4                eval state : test_on_restore;
              	0.00627     	0.01693     	0.96056     	4                eval state : test_on_last_epoch;
---> final result
              	0.00627     	0.01693     	0.96056     	4                eval state : final;
              	0.00627     	0.01693     	0.96056     	4                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00599     	0.01622     	0.96914     	0           
  10          	0.00618     	0.01697     	0.96124     	0           
  100         	0.00612     	0.01642     	0.96683     	0           
  300         	0.00609     	0.01640     	0.96717     	1           
  500         	0.00607     	0.01638     	0.96744     	2           
  1000        	0.00604     	0.01634     	0.96795     	5           
---> try to load best state
              	0.00604     	0.01634     	0.96795     	5                eval state : test_on_restore;
              	0.00604     	0.01634     	0.96795     	5                eval state : test_on_last_epoch;
---> final result
              	0.00604     	0.01634     	0.96795     	5                eval state : final;
              	0.00604     	0.01634     	0.96795     	5                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 4 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01206     	0.02560     	0.93564     	0           
  10          	0.01121     	0.02413     	0.94282     	0           
  100         	0.01056     	0.02283     	0.94879     	0           
  300         	0.01045     	0.02247     	0.95026     	1           
  500         	0.01032     	0.02214     	0.95156     	2           
  1000        	0.01001     	0.02143     	0.95435     	3           
---> try to load best state
              	0.01001     	0.02143     	0.95435     	3                eval state : test_on_restore;
              	0.01001     	0.02143     	0.95435     	3                eval state : test_on_last_epoch;
---> final result
              	0.01001     	0.02143     	0.95435     	4                eval state : final;
----------> finish x-yl training


---------->
lar cigp for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01589     	0.02735     	0.86982     	0           
  10          	0.01809     	0.02947     	0.83106     	0           
  100         	0.01864     	0.03056     	0.81617     	0           
  300         	0.01862     	0.03057     	0.81551     	0           
  500         	0.01863     	0.03060     	0.81467     	1           
  1000        	0.01875     	0.03074     	0.81165     	2           
---> try to load best state
              	0.01875     	0.03074     	0.81165     	2                eval state : test_on_restore;
              	0.01875     	0.03074     	0.81165     	2                eval state : test_on_last_epoch;
---> final result
              	0.01875     	0.03074     	0.81165     	2                eval state : final;
              	0.01875     	0.03074     	0.81165     	2                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01181     	0.02303     	0.94134     	0           
  10          	0.01215     	0.02320     	0.92493     	0           
  100         	0.01145     	0.02267     	0.93885     	0           
  300         	0.01138     	0.02263     	0.93992     	0           
  500         	0.01134     	0.02260     	0.94067     	1           
  1000        	0.01127     	0.02257     	0.94181     	3           
---> try to load best state
              	0.01127     	0.02257     	0.94181     	3                eval state : test_on_restore;
              	0.01127     	0.02257     	0.94181     	3                eval state : test_on_last_epoch;
---> final result
              	0.01127     	0.02257     	0.94181     	3                eval state : final;
              	0.01127     	0.02257     	0.94181     	3                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01160     	0.02504     	0.92922     	0           
  10          	0.01172     	0.02328     	0.93687     	0           
  100         	0.01150     	0.02341     	0.93793     	0           
  300         	0.01134     	0.02342     	0.93846     	1           
  500         	0.01123     	0.02352     	0.93825     	1           
  1000        	0.01127     	0.02386     	0.93623     	3           
---> try to load best state
              	0.01127     	0.02386     	0.93623     	3                eval state : test_on_restore;
              	0.01127     	0.02386     	0.93623     	3                eval state : test_on_last_epoch;
---> final result
              	0.01127     	0.02386     	0.93623     	3                eval state : final;
              	0.01127     	0.02386     	0.93623     	4                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01054     	0.02308     	0.94322     	0           
  10          	0.01049     	0.02245     	0.94590     	0           
  100         	0.01045     	0.02256     	0.94717     	0           
  300         	0.01041     	0.02259     	0.94725     	1           
  500         	0.01039     	0.02263     	0.94707     	3           
  1000        	0.01039     	0.02276     	0.94602     	5           
---> try to load best state
              	0.01039     	0.02276     	0.94602     	5                eval state : test_on_restore;
              	0.01039     	0.02276     	0.94602     	5                eval state : test_on_last_epoch;
---> final result
              	0.01039     	0.02276     	0.94602     	5                eval state : final;
              	0.01039     	0.02276     	0.94602     	5                eval state : final;module_name : LarGP;cp_record_file : True;
