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

  Demo lar cigp 
  seed: None 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low'], '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           	0.00529     	0.01201     	0.98425     	0           
  10          	0.00481     	0.01106     	0.98662     	0           
  100         	0.00442     	0.01016     	0.98884     	0           
  300         	0.00413     	0.00963     	0.99009     	1           
  500         	0.00398     	0.00931     	0.99081     	2           
  1000        	0.00376     	0.00882     	0.99186     	4           
---> try to load best state
              	0.00376     	0.00882     	0.99186     	4                eval state : test_on_restore;
              	0.00376     	0.00882     	0.99186     	4                eval state : test_on_last_epoch;
---> final result
              	0.00376     	0.00882     	0.99186     	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': ['low', 'medium'], '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           	0.01948     	0.04182     	0.84061     	0           
  10          	0.02435     	0.04586     	0.81782     	0           
  100         	0.02642     	0.04876     	0.79972     	0           
  300         	0.02674     	0.04923     	0.79780     	0           
  500         	0.02697     	0.04959     	0.79642     	1           
  1000        	0.02737     	0.05018     	0.79403     	2           
---> try to load best state
              	0.02737     	0.05018     	0.79403     	2                eval state : test_on_restore;
              	0.02737     	0.05018     	0.79403     	2                eval state : test_on_last_epoch;
---> final result
              	0.02737     	0.05018     	0.79403     	2                eval state : final;
              	0.02737     	0.05018     	0.79403     	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': ['low', 'medium'], '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           	0.01223     	0.03192     	0.91306     	0           
  10          	0.01209     	0.03058     	0.91656     	0           
  100         	0.01119     	0.02965     	0.92255     	0           
  300         	0.01099     	0.02947     	0.92369     	1           
  500         	0.01090     	0.02936     	0.92435     	1           
  1000        	0.01085     	0.02921     	0.92528     	3           
---> try to load best state
              	0.01085     	0.02921     	0.92528     	3                eval state : test_on_restore;
              	0.01085     	0.02921     	0.92528     	3                eval state : test_on_last_epoch;
---> final result
              	0.01085     	0.02921     	0.92528     	3                eval state : final;
              	0.01085     	0.02921     	0.92528     	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': ['low', 'medium'], '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           	0.00812     	0.02323     	0.95154     	0           
  10          	0.00903     	0.02311     	0.94893     	0           
  100         	0.00866     	0.02262     	0.95237     	0           
  300         	0.00851     	0.02250     	0.95317     	1           
  500         	0.00838     	0.02240     	0.95381     	2           
  1000        	0.00812     	0.02220     	0.95498     	3           
---> try to load best state
              	0.00812     	0.02220     	0.95498     	3                eval state : test_on_restore;
              	0.00812     	0.02220     	0.95498     	3                eval state : test_on_last_epoch;
---> final result
              	0.00812     	0.02220     	0.95498     	3                eval state : final;
              	0.00812     	0.02220     	0.95498     	3                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': ['low', 'medium'], '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           	0.00625     	0.01786     	0.97136     	0           
  10          	0.00690     	0.01862     	0.96934     	0           
  100         	0.00674     	0.01844     	0.97037     	0           
  300         	0.00660     	0.01833     	0.97083     	1           
  500         	0.00651     	0.01823     	0.97119     	3           
  1000        	0.00637     	0.01804     	0.97183     	5           
---> try to load best state
              	0.00637     	0.01804     	0.97183     	5                eval state : test_on_restore;
              	0.00637     	0.01804     	0.97183     	5                eval state : test_on_last_epoch;
---> final result
              	0.00637     	0.01804     	0.97183     	5                eval state : final;
              	0.00637     	0.01804     	0.97183     	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': ['low'], '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           	0.01120     	0.02957     	0.89706     	0           
  10          	0.00913     	0.02337     	0.92935     	0           
  100         	0.00752     	0.01979     	0.94638     	0           
  300         	0.00700     	0.01867     	0.95149     	1           
  500         	0.00671     	0.01814     	0.95399     	2           
  1000        	0.00626     	0.01743     	0.95734     	4           
---> try to load best state
              	0.00626     	0.01743     	0.95734     	4                eval state : test_on_restore;
              	0.00626     	0.01743     	0.95734     	4                eval state : test_on_last_epoch;
---> final result
              	0.00626     	0.01743     	0.95734     	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': ['low', 'medium'], '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           	0.01639     	0.03462     	0.61218     	0           
  10          	0.01626     	0.03464     	0.58772     	0           
  100         	0.01730     	0.03606     	0.55785     	0           
  300         	0.01714     	0.03583     	0.56401     	0           
  500         	0.01697     	0.03559     	0.57015     	1           
  1000        	0.01668     	0.03516     	0.58131     	2           
---> try to load best state
              	0.01668     	0.03516     	0.58131     	2                eval state : test_on_restore;
              	0.01668     	0.03516     	0.58131     	2                eval state : test_on_last_epoch;
---> final result
              	0.01668     	0.03516     	0.58131     	2                eval state : final;
              	0.01668     	0.03516     	0.58131     	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': ['low', 'medium'], '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           	0.01221     	0.03085     	0.75572     	0           
  10          	0.01034     	0.02857     	0.77958     	0           
  100         	0.00936     	0.02685     	0.81665     	0           
  300         	0.00933     	0.02675     	0.81986     	1           
  500         	0.00929     	0.02664     	0.82276     	1           
  1000        	0.00923     	0.02646     	0.82757     	3           
---> try to load best state
              	0.00923     	0.02646     	0.82757     	3                eval state : test_on_restore;
              	0.00923     	0.02646     	0.82757     	3                eval state : test_on_last_epoch;
---> final result
              	0.00923     	0.02646     	0.82757     	3                eval state : final;
              	0.00923     	0.02646     	0.82757     	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': ['low', 'medium'], '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           	0.00857     	0.02400     	0.89860     	0           
  10          	0.00851     	0.02478     	0.87562     	0           
  100         	0.00829     	0.02352     	0.89478     	0           
  300         	0.00826     	0.02342     	0.89732     	1           
  500         	0.00821     	0.02336     	0.89890     	2           
  1000        	0.00812     	0.02323     	0.90162     	3           
---> try to load best state
              	0.00812     	0.02323     	0.90162     	3                eval state : test_on_restore;
              	0.00812     	0.02323     	0.90162     	3                eval state : test_on_last_epoch;
---> final result
              	0.00812     	0.02323     	0.90162     	3                eval state : final;
              	0.00812     	0.02323     	0.90162     	3                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': ['low', 'medium'], '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           	0.00801     	0.02391     	0.89906     	0           
  10          	0.00823     	0.02432     	0.88515     	0           
  100         	0.00807     	0.02335     	0.90054     	0           
  300         	0.00802     	0.02325     	0.90307     	1           
  500         	0.00793     	0.02316     	0.90511     	3           
  1000        	0.00783     	0.02301     	0.90877     	5           
---> try to load best state
              	0.00783     	0.02301     	0.90877     	5                eval state : test_on_restore;
              	0.00783     	0.02301     	0.90877     	5                eval state : test_on_last_epoch;
---> final result
              	0.00783     	0.02301     	0.90877     	5                eval state : final;
              	0.00783     	0.02301     	0.90877     	6                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': ['low'], '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           	0.00742     	0.02005     	0.95349     	0           
  10          	0.00666     	0.01731     	0.96124     	0           
  100         	0.00598     	0.01526     	0.96835     	0           
  300         	0.00561     	0.01445     	0.97167     	1           
  500         	0.00536     	0.01402     	0.97369     	2           
  1000        	0.00494     	0.01342     	0.97668     	4           
---> try to load best state
              	0.00494     	0.01342     	0.97668     	4                eval state : test_on_restore;
              	0.00494     	0.01342     	0.97668     	4                eval state : test_on_last_epoch;
---> final result
              	0.00494     	0.01342     	0.97668     	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': ['low', 'medium'], '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           	0.01541     	0.03247     	0.76172     	0           
  10          	0.02922     	0.05993     	0.16810     	0           
  100         	0.02524     	0.05202     	0.44854     	0           
  300         	0.02526     	0.05206     	0.44990     	0           
  500         	0.02527     	0.05209     	0.45099     	1           
  1000        	0.02530     	0.05214     	0.45248     	2           
---> try to load best state
              	0.02530     	0.05214     	0.45248     	2                eval state : test_on_restore;
              	0.02530     	0.05214     	0.45248     	2                eval state : test_on_last_epoch;
---> final result
              	0.02530     	0.05214     	0.45248     	2                eval state : final;
              	0.02530     	0.05214     	0.45248     	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': ['low', 'medium'], '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           	0.01290     	0.02749     	0.80926     	0           
  10          	0.01293     	0.02568     	0.80698     	0           
  100         	0.01176     	0.02289     	0.84567     	0           
  300         	0.01159     	0.02270     	0.85195     	1           
  500         	0.01145     	0.02255     	0.85661     	1           
  1000        	0.01120     	0.02231     	0.86448     	2           
---> try to load best state
              	0.01120     	0.02231     	0.86448     	2                eval state : test_on_restore;
              	0.01120     	0.02231     	0.86448     	2                eval state : test_on_last_epoch;
---> final result
              	0.01120     	0.02231     	0.86448     	3                eval state : final;
              	0.01120     	0.02231     	0.86448     	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': ['low', 'medium'], '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           	0.00766     	0.02259     	0.90575     	0           
  10          	0.00741     	0.02159     	0.90896     	0           
  100         	0.00716     	0.02034     	0.92444     	0           
  300         	0.00714     	0.02028     	0.92607     	1           
  500         	0.00712     	0.02024     	0.92714     	2           
  1000        	0.00710     	0.02018     	0.92879     	3           
---> try to load best state
              	0.00710     	0.02018     	0.92879     	4                eval state : test_on_restore;
              	0.00710     	0.02018     	0.92879     	4                eval state : test_on_last_epoch;
---> final result
              	0.00710     	0.02018     	0.92879     	4                eval state : final;
              	0.00710     	0.02018     	0.92879     	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': ['low', 'medium'], '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           	0.00666     	0.01948     	0.94212     	0           
  10          	0.00715     	0.01978     	0.93461     	0           
  100         	0.00704     	0.01930     	0.94219     	0           
  300         	0.00696     	0.01922     	0.94332     	1           
  500         	0.00689     	0.01915     	0.94431     	3           
  1000        	0.00675     	0.01901     	0.94619     	5           
---> try to load best state
              	0.00675     	0.01901     	0.94619     	6                eval state : test_on_restore;
              	0.00675     	0.01901     	0.94619     	6                eval state : test_on_last_epoch;
---> final result
              	0.00675     	0.01901     	0.94619     	6                eval state : final;
              	0.00675     	0.01901     	0.94619     	6                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': ['low'], '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           	0.01016     	0.03130     	0.90580     	0           
  10          	0.00811     	0.02458     	0.92401     	0           
  100         	0.00709     	0.02076     	0.93947     	0           
  300         	0.00671     	0.01949     	0.94572     	1           
  500         	0.00648     	0.01892     	0.94902     	2           
  1000        	0.00610     	0.01818     	0.95409     	3           
---> try to load best state
              	0.00610     	0.01818     	0.95409     	3                eval state : test_on_restore;
              	0.00610     	0.01818     	0.95409     	3                eval state : test_on_last_epoch;
---> final result
              	0.00610     	0.01818     	0.95409     	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': ['low', 'medium'], '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           	0.02178     	0.04106     	0.53543     	0           
  10          	0.06049     	0.10146     	-10.12236   	0           
  100         	0.06073     	0.10163     	-9.16075    	0           
  300         	0.06537     	0.10822     	-13.23284   	0           
  500         	0.06486     	0.10747     	-12.49850   	1           
  1000        	0.06400     	0.10618     	-11.31412   	2           
---> try to load best state
              	0.06400     	0.10618     	-11.31412   	2                eval state : test_on_restore;
              	0.06400     	0.10618     	-11.31412   	3                eval state : test_on_last_epoch;
---> final result
              	0.06400     	0.10618     	-11.31412   	3                eval state : final;
              	0.06400     	0.10618     	-11.31412   	3                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': ['low', 'medium'], '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           	0.01595     	0.03733     	0.55510     	0           
  10          	0.02926     	0.06664     	-0.36820    	0           
  100         	0.01156     	0.03262     	0.65211     	0           
  300         	0.01069     	0.03073     	0.69286     	1           
  500         	0.01039     	0.03008     	0.71014     	1           
  1000        	0.01001     	0.02923     	0.73517     	2           
---> try to load best state
              	0.01001     	0.02923     	0.73517     	3                eval state : test_on_restore;
              	0.01001     	0.02923     	0.73517     	3                eval state : test_on_last_epoch;
---> final result
              	0.01001     	0.02923     	0.73517     	3                eval state : final;
              	0.01001     	0.02923     	0.73517     	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': ['low', 'medium'], '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           	0.00871     	0.02667     	0.86240     	0           
  10          	0.00841     	0.02521     	0.87598     	0           
  100         	0.00806     	0.02373     	0.89925     	0           
  300         	0.00803     	0.02368     	0.90110     	1           
  500         	0.00801     	0.02365     	0.90212     	1           
  1000        	0.00797     	0.02361     	0.90378     	3           
---> try to load best state
              	0.00797     	0.02361     	0.90378     	3                eval state : test_on_restore;
              	0.00797     	0.02361     	0.90378     	3                eval state : test_on_last_epoch;
---> final result
              	0.00797     	0.02361     	0.90378     	3                eval state : final;
              	0.00797     	0.02361     	0.90378     	3                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': ['low', 'medium'], '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           	0.00840     	0.02488     	0.90748     	0           
  10          	0.00794     	0.02277     	0.91966     	0           
  100         	0.00783     	0.02220     	0.92681     	0           
  300         	0.00781     	0.02223     	0.92676     	1           
  500         	0.00781     	0.02223     	0.92676     	2           
  1000        	0.00781     	0.02223     	0.92678     	5           
---> try to load best state
              	0.00781     	0.02223     	0.92678     	5                eval state : test_on_restore;
              	0.00781     	0.02223     	0.92678     	5                eval state : test_on_last_epoch;
---> final result
              	0.00781     	0.02223     	0.92678     	6                eval state : final;
              	0.00781     	0.02223     	0.92678     	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': ['low'], '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           	0.00603     	0.01658     	0.96828     	0           
  10          	0.00522     	0.01423     	0.97483     	0           
  100         	0.00452     	0.01163     	0.98142     	0           
  300         	0.00431     	0.01087     	0.98373     	1           
  500         	0.00421     	0.01058     	0.98472     	2           
  1000        	0.00402     	0.01025     	0.98596     	3           
---> try to load best state
              	0.00402     	0.01025     	0.98596     	3                eval state : test_on_restore;
              	0.00402     	0.01025     	0.98596     	3                eval state : test_on_last_epoch;
---> final result
              	0.00402     	0.01025     	0.98596     	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': ['low', 'medium'], '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           	0.01003     	0.02424     	0.83217     	0           
  10          	0.01147     	0.02774     	0.74960     	0           
  100         	0.01068     	0.02638     	0.76412     	0           
  300         	0.01060     	0.02624     	0.76611     	0           
  500         	0.01055     	0.02615     	0.76767     	1           
  1000        	0.01047     	0.02600     	0.77019     	2           
---> try to load best state
              	0.01047     	0.02600     	0.77019     	2                eval state : test_on_restore;
              	0.01047     	0.02600     	0.77019     	2                eval state : test_on_last_epoch;
---> final result
              	0.01047     	0.02600     	0.77019     	2                eval state : final;
              	0.01047     	0.02600     	0.77019     	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': ['low', 'medium'], '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           	0.00879     	0.02369     	0.85314     	0           
  10          	0.00802     	0.02211     	0.85879     	0           
  100         	0.00711     	0.02025     	0.88972     	0           
  300         	0.00704     	0.02006     	0.89381     	1           
  500         	0.00699     	0.01991     	0.89701     	1           
  1000        	0.00691     	0.01965     	0.90238     	3           
---> try to load best state
              	0.00691     	0.01965     	0.90238     	3                eval state : test_on_restore;
              	0.00691     	0.01965     	0.90238     	3                eval state : test_on_last_epoch;
---> final result
              	0.00691     	0.01965     	0.90238     	3                eval state : final;
              	0.00691     	0.01965     	0.90238     	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': ['low', 'medium'], '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           	0.00630     	0.01908     	0.92566     	0           
  10          	0.00631     	0.01839     	0.92449     	0           
  100         	0.00616     	0.01736     	0.93795     	0           
  300         	0.00613     	0.01721     	0.94023     	1           
  500         	0.00611     	0.01714     	0.94136     	2           
  1000        	0.00609     	0.01703     	0.94319     	3           
---> try to load best state
              	0.00609     	0.01703     	0.94319     	3                eval state : test_on_restore;
              	0.00609     	0.01703     	0.94319     	4                eval state : test_on_last_epoch;
---> final result
              	0.00609     	0.01703     	0.94319     	4                eval state : final;
              	0.00609     	0.01703     	0.94319     	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': ['low', 'medium'], '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           	0.00579     	0.01444     	0.97085     	0           
  10          	0.00534     	0.01400     	0.97027     	0           
  100         	0.00532     	0.01378     	0.97216     	0           
  300         	0.00538     	0.01396     	0.97042     	1           
  500         	0.00541     	0.01407     	0.96949     	3           
  1000        	0.00542     	0.01409     	0.96933     	5           
---> try to load best state
              	0.00542     	0.01409     	0.96933     	5                eval state : test_on_restore;
              	0.00542     	0.01409     	0.96933     	6                eval state : test_on_last_epoch;
---> final result
              	0.00542     	0.01409     	0.96933     	6                eval state : final;
              	0.00542     	0.01409     	0.96933     	6                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': ['low'], '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           	0.00909     	0.01980     	0.96227     	0           
  10          	0.00800     	0.01754     	0.96856     	0           
  100         	0.00694     	0.01514     	0.97480     	0           
  300         	0.00658     	0.01457     	0.97728     	1           
  500         	0.00649     	0.01450     	0.97831     	2           
  1000        	0.00648     	0.01459     	0.97924     	4           
---> try to load best state
              	0.00648     	0.01459     	0.97924     	4                eval state : test_on_restore;
              	0.00648     	0.01459     	0.97924     	4                eval state : test_on_last_epoch;
---> final result
              	0.00648     	0.01459     	0.97924     	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': ['low', 'medium'], '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           	0.02205     	0.03496     	0.74510     	0           
  10          	0.03430     	0.04878     	0.17429     	0           
  100         	0.04357     	0.06016     	-0.38469    	0           
  300         	0.04368     	0.06029     	-0.39265    	0           
  500         	0.04368     	0.06029     	-0.39266    	1           
  1000        	0.04368     	0.06029     	-0.39266    	2           
---> try to load best state
              	0.04368     	0.06029     	-0.39266    	2                eval state : test_on_restore;
              	0.04368     	0.06029     	-0.39266    	2                eval state : test_on_last_epoch;
---> final result
              	0.04368     	0.06029     	-0.39266    	2                eval state : final;
              	0.04368     	0.06029     	-0.39266    	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': ['low', 'medium'], '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           	0.01387     	0.02348     	0.93526     	0           
  10          	0.01173     	0.02063     	0.94570     	0           
  100         	0.01267     	0.02173     	0.94235     	0           
  300         	0.01273     	0.02179     	0.94242     	1           
  500         	0.01277     	0.02184     	0.94238     	1           
  1000        	0.01282     	0.02188     	0.94227     	3           
---> try to load best state
              	0.01282     	0.02188     	0.94227     	3                eval state : test_on_restore;
              	0.01282     	0.02188     	0.94227     	3                eval state : test_on_last_epoch;
---> final result
              	0.01282     	0.02188     	0.94227     	3                eval state : final;
              	0.01282     	0.02188     	0.94227     	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': ['low', 'medium'], '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           	0.01364     	0.02657     	0.92634     	0           
  10          	0.01210     	0.02392     	0.93853     	0           
  100         	0.01256     	0.02489     	0.92959     	0           
  300         	0.01268     	0.02508     	0.92870     	1           
  500         	0.01276     	0.02520     	0.92809     	1           
  1000        	0.01288     	0.02538     	0.92703     	4           
---> try to load best state
              	0.01288     	0.02538     	0.92703     	4                eval state : test_on_restore;
              	0.01288     	0.02538     	0.92703     	4                eval state : test_on_last_epoch;
---> final result
              	0.01288     	0.02538     	0.92703     	4                eval state : final;
              	0.01288     	0.02538     	0.92703     	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': ['low', 'medium'], '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           	0.00914     	0.02076     	0.95351     	0           
  10          	0.00889     	0.01983     	0.95701     	0           
  100         	0.00891     	0.02032     	0.95427     	0           
  300         	0.00898     	0.02049     	0.95358     	1           
  500         	0.00905     	0.02058     	0.95309     	3           
  1000        	0.00912     	0.02063     	0.95268     	6           
---> try to load best state
              	0.00912     	0.02063     	0.95268     	6                eval state : test_on_restore;
              	0.00912     	0.02063     	0.95268     	6                eval state : test_on_last_epoch;
---> final result
              	0.00912     	0.02063     	0.95268     	6                eval state : final;
              	0.00912     	0.02063     	0.95268     	6                eval state : final;module_name : LarGP;cp_record_file : True;
