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

  Demo res 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     	4           
---> try to load best state
              	0.00603     	0.01627     	0.97682     	4                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


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01474     	0.03557     	0.89290     	0           
  10          	0.01552     	0.03546     	0.89327     	0           
  100         	0.01602     	0.03720     	0.88968     	0           
  300         	0.01613     	0.03752     	0.88890     	1           
  500         	0.01623     	0.03779     	0.88818     	1           
  1000        	0.01640     	0.03827     	0.88689     	3           
---> try to load best state
              	0.01640     	0.03827     	0.88689     	3                eval state : test_on_restore;
              	0.01640     	0.03827     	0.88689     	3                eval state : test_on_last_epoch;
---> final result
              	0.01640     	0.03827     	0.88689     	3                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00977     	0.02965     	0.92799     	0           
  10          	0.01060     	0.02844     	0.92931     	0           
  100         	0.01054     	0.02891     	0.92907     	0           
  300         	0.01044     	0.02890     	0.92939     	0           
  500         	0.01034     	0.02887     	0.92972     	1           
  1000        	0.01012     	0.02877     	0.93046     	2           
---> try to load best state
              	0.01012     	0.02877     	0.93046     	2                eval state : test_on_restore;
              	0.01012     	0.02877     	0.93046     	2                eval state : test_on_last_epoch;
---> final result
              	0.01012     	0.02877     	0.93046     	2                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00780     	0.02498     	0.94602     	0           
  10          	0.00855     	0.02412     	0.94603     	0           
  100         	0.00838     	0.02461     	0.94555     	0           
  300         	0.00834     	0.02467     	0.94559     	1           
  500         	0.00831     	0.02470     	0.94565     	1           
  1000        	0.00825     	0.02473     	0.94583     	3           
---> try to load best state
              	0.00825     	0.02473     	0.94583     	3                eval state : test_on_restore;
              	0.00825     	0.02473     	0.94583     	3                eval state : test_on_last_epoch;
---> final result
              	0.00825     	0.02473     	0.94583     	3                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00714     	0.02229     	0.95556     	0           
  10          	0.00759     	0.02203     	0.95551     	0           
  100         	0.00751     	0.02253     	0.95445     	0           
  300         	0.00747     	0.02260     	0.95436     	1           
  500         	0.00743     	0.02263     	0.95434     	2           
  1000        	0.00735     	0.02266     	0.95439     	4           
---> try to load best state
              	0.00735     	0.02266     	0.95439     	4                eval state : test_on_restore;
              	0.00735     	0.02266     	0.95439     	4                eval state : test_on_last_epoch;
---> final result
              	0.00735     	0.02266     	0.95439     	4                eval state : final;module_name : ResGP;cp_record_file : True;
---> end

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

  Demo res 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     	3           
---> try to load best state
              	0.00745     	0.02248     	0.92250     	3                eval state : test_on_restore;
              	0.00745     	0.02248     	0.92250     	3                eval state : test_on_last_epoch;
---> final result
              	0.00745     	0.02248     	0.92250     	3                eval state : final;
----------> finish x-yl training


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01203     	0.02821     	0.80413     	0           
  10          	0.01074     	0.02694     	0.82603     	0           
  100         	0.01053     	0.02682     	0.82817     	0           
  300         	0.01047     	0.02677     	0.82909     	0           
  500         	0.01041     	0.02672     	0.83007     	1           
  1000        	0.01030     	0.02663     	0.83197     	2           
---> try to load best state
              	0.01030     	0.02663     	0.83197     	2                eval state : test_on_restore;
              	0.01030     	0.02663     	0.83197     	2                eval state : test_on_last_epoch;
---> final result
              	0.01030     	0.02663     	0.83197     	2                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00928     	0.02642     	0.85020     	0           
  10          	0.00879     	0.02588     	0.85553     	0           
  100         	0.00839     	0.02542     	0.86886     	0           
  300         	0.00835     	0.02540     	0.86994     	0           
  500         	0.00833     	0.02538     	0.87082     	1           
  1000        	0.00829     	0.02534     	0.87234     	2           
---> try to load best state
              	0.00829     	0.02534     	0.87234     	2                eval state : test_on_restore;
              	0.00829     	0.02534     	0.87234     	2                eval state : test_on_last_epoch;
---> final result
              	0.00829     	0.02534     	0.87234     	2                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00800     	0.02450     	0.89369     	0           
  10          	0.00812     	0.02499     	0.87898     	0           
  100         	0.00813     	0.02474     	0.88714     	0           
  300         	0.00811     	0.02471     	0.88803     	0           
  500         	0.00809     	0.02469     	0.88875     	1           
  1000        	0.00805     	0.02464     	0.89003     	2           
---> try to load best state
              	0.00805     	0.02464     	0.89003     	3                eval state : test_on_restore;
              	0.00805     	0.02464     	0.89003     	3                eval state : test_on_last_epoch;
---> final result
              	0.00805     	0.02464     	0.89003     	3                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00778     	0.02444     	0.89449     	0           
  10          	0.00806     	0.02494     	0.88129     	0           
  100         	0.00804     	0.02469     	0.88958     	0           
  300         	0.00803     	0.02467     	0.89049     	1           
  500         	0.00802     	0.02464     	0.89108     	2           
  1000        	0.00798     	0.02460     	0.89216     	4           
---> try to load best state
              	0.00798     	0.02460     	0.89216     	4                eval state : test_on_restore;
              	0.00798     	0.02460     	0.89216     	4                eval state : test_on_last_epoch;
---> final result
              	0.00798     	0.02460     	0.89216     	4                eval state : final;module_name : ResGP;cp_record_file : True;
---> end

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

  Demo res 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     	2           
  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


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00988     	0.02388     	0.88668     	0           
  10          	0.00869     	0.02249     	0.89694     	0           
  100         	0.00839     	0.02209     	0.90099     	0           
  300         	0.00834     	0.02202     	0.90175     	0           
  500         	0.00831     	0.02198     	0.90225     	1           
  1000        	0.00825     	0.02190     	0.90313     	2           
---> try to load best state
              	0.00825     	0.02190     	0.90313     	2                eval state : test_on_restore;
              	0.00825     	0.02190     	0.90313     	2                eval state : test_on_last_epoch;
---> final result
              	0.00825     	0.02190     	0.90313     	2                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00882     	0.02231     	0.90162     	0           
  10          	0.00836     	0.02120     	0.91283     	0           
  100         	0.00846     	0.02073     	0.92049     	0           
  300         	0.00848     	0.02070     	0.92121     	0           
  500         	0.00849     	0.02069     	0.92160     	1           
  1000        	0.00850     	0.02065     	0.92230     	2           
---> try to load best state
              	0.00850     	0.02065     	0.92230     	2                eval state : test_on_restore;
              	0.00850     	0.02065     	0.92230     	2                eval state : test_on_last_epoch;
---> final result
              	0.00850     	0.02065     	0.92230     	2                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00677     	0.02082     	0.92819     	0           
  10          	0.00705     	0.02098     	0.92247     	0           
  100         	0.00695     	0.02070     	0.92750     	0           
  300         	0.00692     	0.02067     	0.92826     	0           
  500         	0.00687     	0.02064     	0.92904     	1           
  1000        	0.00680     	0.02058     	0.93040     	2           
---> try to load best state
              	0.00680     	0.02058     	0.93040     	2                eval state : test_on_restore;
              	0.00680     	0.02058     	0.93040     	2                eval state : test_on_last_epoch;
---> final result
              	0.00680     	0.02058     	0.93040     	3                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00653     	0.02005     	0.93849     	0           
  10          	0.00692     	0.02062     	0.92912     	0           
  100         	0.00668     	0.02024     	0.93574     	0           
  300         	0.00665     	0.02022     	0.93624     	1           
  500         	0.00664     	0.02020     	0.93662     	2           
  1000        	0.00662     	0.02017     	0.93724     	4           
---> try to load best state
              	0.00662     	0.02017     	0.93724     	4                eval state : test_on_restore;
              	0.00662     	0.02017     	0.93724     	4                eval state : test_on_last_epoch;
---> final result
              	0.00662     	0.02017     	0.93724     	4                eval state : final;module_name : ResGP;cp_record_file : True;
---> end

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

  Demo res 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


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01654     	0.03469     	0.78885     	0           
  10          	0.01358     	0.03242     	0.81857     	0           
  100         	0.01267     	0.03165     	0.83121     	0           
  300         	0.01258     	0.03158     	0.83240     	0           
  500         	0.01252     	0.03154     	0.83320     	1           
  1000        	0.01242     	0.03147     	0.83463     	2           
---> try to load best state
              	0.01242     	0.03147     	0.83463     	2                eval state : test_on_restore;
              	0.01242     	0.03147     	0.83463     	2                eval state : test_on_last_epoch;
---> final result
              	0.01242     	0.03147     	0.83463     	2                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01290     	0.03265     	0.81010     	0           
  10          	0.01158     	0.03122     	0.83711     	0           
  100         	0.01104     	0.03040     	0.85854     	0           
  300         	0.01106     	0.03035     	0.86128     	0           
  500         	0.01106     	0.03033     	0.86232     	1           
  1000        	0.01105     	0.03030     	0.86390     	2           
---> try to load best state
              	0.01105     	0.03030     	0.86390     	2                eval state : test_on_restore;
              	0.01105     	0.03030     	0.86390     	2                eval state : test_on_last_epoch;
---> final result
              	0.01105     	0.03030     	0.86390     	2                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01033     	0.02997     	0.88256     	0           
  10          	0.01058     	0.02982     	0.88123     	0           
  100         	0.01043     	0.02960     	0.88888     	0           
  300         	0.01041     	0.02958     	0.88986     	0           
  500         	0.01039     	0.02956     	0.89065     	1           
  1000        	0.01036     	0.02953     	0.89201     	3           
---> try to load best state
              	0.01036     	0.02953     	0.89201     	3                eval state : test_on_restore;
              	0.01036     	0.02953     	0.89201     	3                eval state : test_on_last_epoch;
---> final result
              	0.01036     	0.02953     	0.89201     	3                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01038     	0.02981     	0.89256     	0           
  10          	0.01047     	0.02947     	0.89472     	0           
  100         	0.01037     	0.02914     	0.90217     	0           
  300         	0.01034     	0.02915     	0.90237     	1           
  500         	0.01032     	0.02915     	0.90258     	2           
  1000        	0.01029     	0.02915     	0.90289     	4           
---> try to load best state
              	0.01029     	0.02915     	0.90289     	4                eval state : test_on_restore;
              	0.01029     	0.02915     	0.90289     	4                eval state : test_on_last_epoch;
---> final result
              	0.01029     	0.02915     	0.90289     	4                eval state : final;module_name : ResGP;cp_record_file : True;
---> end

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

  Demo res 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     	1           
  1000        	0.00576     	0.01541     	0.97238     	3           
---> try to load best state
              	0.00576     	0.01541     	0.97238     	3                eval state : test_on_restore;
              	0.00576     	0.01541     	0.97238     	3                eval state : test_on_last_epoch;
---> final result
              	0.00576     	0.01541     	0.97238     	3                eval state : final;
----------> finish x-yl training


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00764     	0.01843     	0.93830     	0           
  10          	0.00709     	0.01792     	0.94313     	0           
  100         	0.00698     	0.01776     	0.94610     	0           
  300         	0.00697     	0.01775     	0.94638     	0           
  500         	0.00697     	0.01775     	0.94655     	1           
  1000        	0.00698     	0.01774     	0.94681     	2           
---> try to load best state
              	0.00698     	0.01774     	0.94681     	2                eval state : test_on_restore;
              	0.00698     	0.01774     	0.94681     	2                eval state : test_on_last_epoch;
---> final result
              	0.00698     	0.01774     	0.94681     	2                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00704     	0.01821     	0.94276     	0           
  10          	0.00689     	0.01795     	0.94281     	0           
  100         	0.00669     	0.01764     	0.94934     	0           
  300         	0.00666     	0.01761     	0.95003     	0           
  500         	0.00664     	0.01759     	0.95057     	1           
  1000        	0.00659     	0.01755     	0.95156     	2           
---> try to load best state
              	0.00659     	0.01755     	0.95156     	2                eval state : test_on_restore;
              	0.00659     	0.01755     	0.95156     	2                eval state : test_on_last_epoch;
---> final result
              	0.00659     	0.01755     	0.95156     	2                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00607     	0.01705     	0.95927     	0           
  10          	0.00635     	0.01722     	0.95508     	0           
  100         	0.00634     	0.01707     	0.95831     	0           
  300         	0.00633     	0.01705     	0.95875     	1           
  500         	0.00630     	0.01703     	0.95919     	1           
  1000        	0.00625     	0.01699     	0.95993     	3           
---> try to load best state
              	0.00625     	0.01699     	0.95993     	3                eval state : test_on_restore;
              	0.00625     	0.01699     	0.95993     	3                eval state : test_on_last_epoch;
---> final result
              	0.00625     	0.01699     	0.95993     	3                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00606     	0.01648     	0.96794     	0           
  10          	0.00621     	0.01669     	0.96318     	0           
  100         	0.00611     	0.01644     	0.96658     	0           
  300         	0.00606     	0.01642     	0.96688     	1           
  500         	0.00604     	0.01641     	0.96712     	2           
  1000        	0.00601     	0.01637     	0.96756     	4           
---> try to load best state
              	0.00601     	0.01637     	0.96756     	4                eval state : test_on_restore;
              	0.00601     	0.01637     	0.96756     	4                eval state : test_on_last_epoch;
---> final result
              	0.00601     	0.01637     	0.96756     	4                eval state : final;module_name : ResGP;cp_record_file : True;
---> end

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

  Demo res 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     	1           
  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     	3                eval state : final;
----------> finish x-yl training


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01611     	0.02751     	0.88753     	0           
  10          	0.01589     	0.02697     	0.89187     	0           
  100         	0.01809     	0.02956     	0.86352     	0           
  300         	0.01818     	0.02967     	0.86237     	0           
  500         	0.01828     	0.02976     	0.86133     	1           
  1000        	0.01851     	0.02998     	0.85890     	2           
---> try to load best state
              	0.01851     	0.02998     	0.85890     	2                eval state : test_on_restore;
              	0.01851     	0.02998     	0.85890     	2                eval state : test_on_last_epoch;
---> final result
              	0.01851     	0.02998     	0.85890     	2                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01194     	0.02336     	0.93777     	0           
  10          	0.01232     	0.02335     	0.92831     	0           
  100         	0.01161     	0.02290     	0.93867     	0           
  300         	0.01155     	0.02286     	0.93954     	0           
  500         	0.01150     	0.02283     	0.94014     	1           
  1000        	0.01145     	0.02279     	0.94105     	2           
---> try to load best state
              	0.01145     	0.02279     	0.94105     	2                eval state : test_on_restore;
              	0.01145     	0.02279     	0.94105     	2                eval state : test_on_last_epoch;
---> final result
              	0.01145     	0.02279     	0.94105     	2                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01192     	0.02462     	0.93157     	0           
  10          	0.01176     	0.02330     	0.93685     	0           
  100         	0.01159     	0.02343     	0.93787     	0           
  300         	0.01144     	0.02343     	0.93844     	1           
  500         	0.01134     	0.02350     	0.93851     	1           
  1000        	0.01140     	0.02382     	0.93677     	2           
---> try to load best state
              	0.01140     	0.02382     	0.93677     	3                eval state : test_on_restore;
              	0.01140     	0.02382     	0.93677     	3                eval state : test_on_last_epoch;
---> final result
              	0.01140     	0.02382     	0.93677     	3                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res 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_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01046     	0.02316     	0.94277     	0           
  10          	0.01063     	0.02257     	0.94570     	0           
  100         	0.01037     	0.02250     	0.94732     	0           
  300         	0.01030     	0.02251     	0.94742     	1           
  500         	0.01027     	0.02256     	0.94719     	2           
  1000        	0.01028     	0.02271     	0.94597     	4           
---> try to load best state
              	0.01028     	0.02271     	0.94597     	4                eval state : test_on_restore;
              	0.01028     	0.02271     	0.94597     	4                eval state : test_on_last_epoch;
---> final result
              	0.01028     	0.02271     	0.94597     	4                eval state : final;module_name : ResGP;cp_record_file : True;
