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

  Demo GAR 
  seed: None 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00802     	0.80738     	0.01627     	0.38366            	0           
  10          	0.00803     	0.81914     	0.01663     	0.29096            	0           
  100         	0.00941     	0.82196     	0.01750     	1.05215            	0           
  300         	0.00914     	0.84008     	0.01698     	1.73141            	1           
  500         	0.00898     	0.83650     	0.01675     	1.33645            	2           
  1000        	0.00889     	0.82986     	0.01649     	1.81230            	5           
---> try to load best state
              	0.00889     	0.82986     	0.01649     	1.81230            	5                eval state : test_on_restore;
              	0.00889     	0.82986     	0.01649     	1.81230            	5                eval state : test_on_last_epoch;
---> final result              	0.00889     	0.82986     	0.01649     	1.81230            	5                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

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

  Demo GAR 
  seed: None 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00848     	0.89450     	0.02223     	0.38372            	0           
  10          	0.00830     	0.89367     	0.02257     	0.29139            	0           
  100         	0.00965     	0.88834     	0.02383     	0.36391            	0           
  300         	0.00965     	0.89177     	0.02381     	0.88329            	1           
  500         	0.00940     	0.89333     	0.02355     	0.78556            	2           
  1000        	0.00882     	0.89501     	0.02298     	3.30288            	4           
---> try to load best state
              	0.00882     	0.89501     	0.02298     	3.30288            	4                eval state : test_on_restore;
              	0.00882     	0.89501     	0.02298     	3.30288            	4                eval state : test_on_last_epoch;
---> final result              	0.00882     	0.89501     	0.02298     	3.30288            	4                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.02789     	0.63145     	0.05433     	0.22437            	0           
  10          	0.02335     	0.69573     	0.04645     	0.12656            	0           
  100         	0.01347     	-0.17899    	0.03030     	-2.17594           	0           
  300         	0.01093     	0.89420     	0.02838     	-2.35309           	1           
  500         	0.01066     	0.89663     	0.02833     	-2.36247           	2           
  1000        	0.01091     	0.48985     	0.02845     	-2.38281           	4           
---> try to load best state
              	0.01091     	0.48985     	0.02845     	-2.38281           	4                eval state : test_on_restore;
              	0.01091     	0.48985     	0.02845     	-2.38281           	4                eval state : test_on_last_epoch;
---> final result
              	0.01091     	0.48985     	0.02845     	-2.38281           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01674     	0.81257     	0.04270     	0.30610            	0           
  10          	0.01463     	0.84551     	0.03714     	0.19936            	0           
  100         	0.01188     	-1.36410    	0.02897     	-1.78973           	0           
  300         	0.01072     	0.88359     	0.02833     	-1.99442           	1           
  500         	0.01068     	0.81475     	0.02837     	-2.00396           	2           
  1000        	0.01050     	0.90257     	0.02834     	-2.02563           	5           
---> try to load best state
              	0.01050     	0.90257     	0.02834     	-2.02563           	5                eval state : test_on_restore;
              	0.01050     	0.90257     	0.02834     	-2.02563           	5                eval state : test_on_last_epoch;
---> final result
              	0.01050     	0.90257     	0.02834     	-2.02563           	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01112     	0.87007     	0.03289     	0.34019            	0           
  10          	0.01045     	0.88999     	0.03049     	0.23052            	0           
  100         	0.01098     	0.43018     	0.02805     	-1.28457           	0           
  300         	0.01077     	0.85548     	0.02832     	-1.41917           	1           
  500         	0.01073     	0.50575     	0.02833     	-1.49808           	4           
  1000        	0.01050     	0.84627     	0.02828     	-1.42001           	7           
---> try to load best state
              	0.01050     	0.84627     	0.02828     	-1.42001           	7                eval state : test_on_restore;
              	0.01050     	0.84627     	0.02828     	-1.42001           	7                eval state : test_on_last_epoch;
---> final result
              	0.01050     	0.84627     	0.02828     	-1.42001           	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00801     	0.90980     	0.02589     	0.37991            	0           
  10          	0.00874     	0.91007     	0.02673     	0.28273            	0           
  100         	0.01055     	0.85402     	0.02782     	2.62415            	0           
  300         	0.01075     	0.89738     	0.02805     	1.19182            	2           
  500         	0.01171     	-1.94538    	0.02848     	0.29421            	4           
  1000        	0.01047     	0.90299     	0.02804     	2.67623            	8           
---> try to load best state
              	0.01047     	0.90299     	0.02804     	2.67623            	8                eval state : test_on_restore;
              	0.01047     	0.90299     	0.02804     	2.67623            	8                eval state : test_on_last_epoch;
---> final result
              	0.01047     	0.90299     	0.02804     	2.67623            	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 0 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01335     	0.75794     	0.03710     	0.51212            	0           
  10          	0.01226     	0.76324     	0.03458     	0.47049            	0           
  100         	0.01279     	0.72316     	0.03247     	3.44228            	0           
  300         	0.01185     	0.67342     	0.03078     	3.20087            	1           
  500         	0.01170     	0.64190     	0.03059     	1.89634            	2           
  1000        	0.01152     	0.62784     	0.03013     	3.93867            	4           
---> try to load best state
              	0.01152     	0.62784     	0.03013     	3.93867            	4                eval state : test_on_restore;
              	0.01152     	0.62784     	0.03013     	3.93867            	4                eval state : test_on_last_epoch;
---> final result              	0.01152     	0.62784     	0.03013     	3.93867            	4                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.02528     	0.34905     	0.04567     	0.17860            	0           
  10          	0.02406     	0.36553     	0.04653     	0.08778            	0           
  100         	0.01238     	0.62632     	0.02702     	-1.87699           	0           
  300         	0.01501     	0.57516     	0.03006     	-1.96556           	1           
  500         	0.01173     	0.65435     	0.02890     	-1.97729           	2           
  1000        	0.01098     	0.61107     	0.02883     	-1.99966           	4           
---> try to load best state
              	0.01098     	0.61107     	0.02883     	-1.99966           	4                eval state : test_on_restore;
              	0.01098     	0.61107     	0.02883     	-1.99966           	4                eval state : test_on_last_epoch;
---> final result
              	0.01098     	0.61107     	0.02883     	-1.99966           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01795     	0.52580     	0.03850     	0.25906            	0           
  10          	0.01708     	0.62385     	0.03863     	0.16240            	0           
  100         	0.01184     	0.74051     	0.02736     	-1.63968           	0           
  300         	0.01144     	0.69632     	0.02886     	-1.80214           	1           
  500         	0.01110     	0.67240     	0.02883     	-1.81187           	2           
  1000        	0.01299     	0.55754     	0.02971     	-1.83394           	5           
---> try to load best state
              	0.01299     	0.55754     	0.02971     	-1.83394           	5                eval state : test_on_restore;
              	0.01299     	0.55754     	0.02971     	-1.83394           	5                eval state : test_on_last_epoch;
---> final result
              	0.01299     	0.55754     	0.02971     	-1.83394           	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01136     	0.77087     	0.02883     	0.33298            	0           
  10          	0.01067     	0.80328     	0.02786     	0.22616            	0           
  100         	0.01107     	0.75810     	0.02822     	-1.29208           	0           
  300         	0.01124     	0.65506     	0.02901     	-1.45493           	1           
  500         	0.01118     	0.58195     	0.02920     	-1.46488           	3           
  1000        	0.01119     	0.46295     	0.02955     	-1.48775           	6           
---> try to load best state
              	0.01119     	0.46295     	0.02955     	-1.48775           	6                eval state : test_on_restore;
              	0.01119     	0.46295     	0.02955     	-1.48775           	6                eval state : test_on_last_epoch;
---> final result
              	0.01119     	0.46295     	0.02955     	-1.48775           	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01023     	0.77495     	0.02796     	0.50932            	0           
  10          	0.00970     	0.79882     	0.02738     	0.46133            	0           
  100         	0.01119     	0.74944     	0.02839     	1.04710            	0           
  300         	0.01124     	0.66202     	0.02888     	0.53739            	2           
  500         	0.01336     	0.54488     	0.02969     	-0.24922           	4           
  1000        	0.01111     	0.47623     	0.02937     	1.85660            	8           
---> try to load best state
              	0.01111     	0.47623     	0.02937     	1.85660            	8                eval state : test_on_restore;
              	0.01111     	0.47623     	0.02937     	1.85660            	8                eval state : test_on_last_epoch;
---> final result
              	0.01111     	0.47623     	0.02937     	1.85660            	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 1 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00929     	0.84831     	0.02473     	0.40324            	0           
  10          	0.00864     	0.84897     	0.02402     	0.31982            	0           
  100         	0.01051     	0.78844     	0.02616     	0.72961            	0           
  300         	0.01015     	0.78281     	0.02549     	2.52221            	1           
  500         	0.01005     	0.79122     	0.02500     	2.35687            	2           
  1000        	0.00987     	0.81024     	0.02414     	2.43653            	4           
---> try to load best state
              	0.00987     	0.81024     	0.02414     	2.43653            	4                eval state : test_on_restore;
              	0.00987     	0.81024     	0.02414     	2.43653            	4                eval state : test_on_last_epoch;
---> final result              	0.00987     	0.81024     	0.02414     	2.43653            	4                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.02491     	0.44313     	0.04324     	0.17853            	0           
  10          	0.02254     	0.42874     	0.04290     	0.08764            	0           
  100         	0.01161     	0.12048     	0.02287     	-1.88415           	0           
  300         	0.01386     	0.47795     	0.02555     	-1.97661           	1           
  500         	0.01031     	0.68363     	0.02374     	-1.98928           	2           
  1000        	0.00977     	0.81973     	0.02365     	-2.01217           	4           
---> try to load best state
              	0.00977     	0.81973     	0.02365     	-2.01217           	4                eval state : test_on_restore;
              	0.00977     	0.81973     	0.02365     	-2.01217           	4                eval state : test_on_last_epoch;
---> final result
              	0.00977     	0.81973     	0.02365     	-2.01217           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01750     	0.60800     	0.03552     	0.25900            	0           
  10          	0.01594     	0.69057     	0.03544     	0.16232            	0           
  100         	0.01077     	0.75181     	0.02289     	-1.64203           	0           
  300         	0.01010     	0.83333     	0.02369     	-1.80526           	1           
  500         	0.00987     	0.82081     	0.02367     	-1.81504           	2           
  1000        	0.01132     	0.32507     	0.02441     	-1.83749           	5           
---> try to load best state
              	0.01132     	0.32507     	0.02441     	-1.83749           	5                eval state : test_on_restore;
              	0.01132     	0.32507     	0.02441     	-1.83749           	5                eval state : test_on_last_epoch;
---> final result
              	0.01132     	0.32507     	0.02441     	-1.83749           	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01106     	0.82328     	0.02514     	0.33293            	0           
  10          	0.00978     	0.86590     	0.02373     	0.22609            	0           
  100         	0.00990     	0.86098     	0.02307     	-1.29218           	0           
  300         	0.00994     	0.83270     	0.02371     	-1.45451           	1           
  500         	0.00989     	0.82000     	0.02382     	-1.46450           	3           
  1000        	0.00988     	0.79396     	0.02403     	-1.48747           	6           
---> try to load best state
              	0.00988     	0.79396     	0.02403     	-1.48747           	6                eval state : test_on_restore;
              	0.00988     	0.79396     	0.02403     	-1.48747           	6                eval state : test_on_last_epoch;
---> final result
              	0.00988     	0.79396     	0.02403     	-1.48747           	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00964     	0.83914     	0.02387     	0.50930            	0           
  10          	0.00872     	0.86789     	0.02321     	0.46132            	0           
  100         	0.01004     	0.84184     	0.02334     	1.04711            	0           
  300         	0.00996     	0.83106     	0.02365     	0.53737            	2           
  500         	0.01212     	0.76090     	0.02473     	-0.24921           	4           
  1000        	0.00988     	0.79550     	0.02397     	1.85659            	8           
---> try to load best state
              	0.00988     	0.79550     	0.02397     	1.85659            	8                eval state : test_on_restore;
              	0.00988     	0.79550     	0.02397     	1.85659            	8                eval state : test_on_last_epoch;
---> final result
              	0.00988     	0.79550     	0.02397     	1.85659            	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 2 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01394     	0.78858     	0.03984     	1.04162            	0           
  10          	0.01288     	0.79208     	0.03701     	0.77893            	0           
  100         	0.01199     	0.79047     	0.03075     	0.58144            	0           
  300         	0.01070     	0.82176     	0.02674     	1.68792            	1           
  500         	0.01037     	0.83219     	0.02555     	1.78379            	2           
  1000        	0.01030     	0.83733     	0.02510     	3.32302            	4           
---> try to load best state
              	0.01030     	0.83733     	0.02510     	3.32302            	4                eval state : test_on_restore;
              	0.01030     	0.83733     	0.02510     	3.32302            	4                eval state : test_on_last_epoch;
---> final result              	0.01030     	0.83733     	0.02510     	3.32302            	4                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.02496     	0.45330     	0.04308     	0.17854            	0           
  10          	0.02297     	0.41679     	0.04383     	0.08770            	0           
  100         	0.01172     	0.37379     	0.02457     	-1.88425           	0           
  300         	0.01422     	0.49396     	0.02648     	-1.97822           	1           
  500         	0.01102     	0.66220     	0.02542     	-1.99040           	2           
  1000        	0.01037     	0.85870     	0.02509     	-2.01366           	4           
---> try to load best state
              	0.01037     	0.85870     	0.02509     	-2.01366           	4                eval state : test_on_restore;
              	0.01037     	0.85870     	0.02509     	-2.01366           	4                eval state : test_on_last_epoch;
---> final result
              	0.01037     	0.85870     	0.02509     	-2.01366           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01747     	0.61873     	0.03546     	0.25902            	0           
  10          	0.01597     	0.68327     	0.03658     	0.16239            	0           
  100         	0.01080     	0.75004     	0.02443     	-1.64247           	0           
  300         	0.01074     	0.84786     	0.02519     	-1.80555           	1           
  500         	0.01042     	0.85087     	0.02504     	-1.81540           	2           
  1000        	0.01210     	0.25487     	0.02603     	-1.83755           	5           
---> try to load best state
              	0.01210     	0.25487     	0.02603     	-1.83755           	5                eval state : test_on_restore;
              	0.01210     	0.25487     	0.02603     	-1.83755           	5                eval state : test_on_last_epoch;
---> final result
              	0.01210     	0.25487     	0.02603     	-1.83755           	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01118     	0.83234     	0.02546     	0.33293            	0           
  10          	0.00994     	0.86867     	0.02423     	0.22610            	0           
  100         	0.01039     	0.86760     	0.02463     	-1.29212           	0           
  300         	0.01050     	0.86127     	0.02504     	-1.45462           	2           
  500         	0.01045     	0.86302     	0.02508     	-1.46459           	3           
  1000        	0.01042     	0.86438     	0.02519     	-1.48749           	6           
---> try to load best state
              	0.01042     	0.86438     	0.02519     	-1.48749           	6                eval state : test_on_restore;
              	0.01042     	0.86438     	0.02519     	-1.48749           	6                eval state : test_on_last_epoch;
---> final result
              	0.01042     	0.86438     	0.02519     	-1.48749           	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00992     	0.85137     	0.02428     	0.50931            	0           
  10          	0.00893     	0.87588     	0.02353     	0.46133            	0           
  100         	0.01055     	0.84517     	0.02484     	1.04711            	0           
  300         	0.01053     	0.85921     	0.02501     	0.53738            	2           
  500         	0.01243     	0.80141     	0.02563     	-0.24915           	4           
  1000        	0.01042     	0.86393     	0.02519     	1.85659            	8           
---> try to load best state
              	0.01042     	0.86393     	0.02519     	1.85659            	8                eval state : test_on_restore;
              	0.01042     	0.86393     	0.02519     	1.85659            	8                eval state : test_on_last_epoch;
---> final result
              	0.01042     	0.86393     	0.02519     	1.85659            	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 3 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00780     	0.88514     	0.02022     	0.45559            	0           
  10          	0.00733     	0.88623     	0.01905     	0.38813            	0           
  100         	0.00852     	0.84277     	0.02063     	1.84713            	0           
  300         	0.00796     	0.85250     	0.01899     	1.77410            	1           
  500         	0.00773     	0.86415     	0.01806     	1.21411            	2           
  1000        	0.00733     	0.87696     	0.01666     	0.96448            	4           
---> try to load best state
              	0.00733     	0.87696     	0.01666     	0.96448            	4                eval state : test_on_restore;
              	0.00733     	0.87696     	0.01666     	0.96448            	4                eval state : test_on_last_epoch;
---> final result              	0.00733     	0.87696     	0.01666     	0.96448            	4                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.02330     	0.49480     	0.03957     	0.17839            	0           
  10          	0.02161     	0.47200     	0.04036     	0.08752            	0           
  100         	0.00913     	0.30824     	0.01684     	-1.88638           	0           
  300         	0.01140     	0.53541     	0.01860     	-1.97967           	1           
  500         	0.00814     	0.68970     	0.01669     	-1.99164           	2           
  1000        	0.00735     	0.89376     	0.01626     	-2.01490           	4           
---> try to load best state
              	0.00735     	0.89376     	0.01626     	-2.01490           	4                eval state : test_on_restore;
              	0.00735     	0.89376     	0.01626     	-2.01490           	4                eval state : test_on_last_epoch;
---> final result
              	0.00735     	0.89376     	0.01626     	-2.01490           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01567     	0.65749     	0.03075     	0.25887            	0           
  10          	0.01443     	0.72137     	0.03172     	0.16220            	0           
  100         	0.00840     	0.75326     	0.01705     	-1.64340           	0           
  300         	0.00782     	0.88465     	0.01652     	-1.80739           	1           
  500         	0.00741     	0.88943     	0.01628     	-1.81732           	2           
  1000        	0.00928     	0.27771     	0.01753     	-1.83967           	5           
---> try to load best state
              	0.00928     	0.27771     	0.01753     	-1.83967           	5                eval state : test_on_restore;
              	0.00928     	0.27771     	0.01753     	-1.83967           	5                eval state : test_on_last_epoch;
---> final result
              	0.00928     	0.27771     	0.01753     	-1.83967           	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00888     	0.86136     	0.01994     	0.33287            	0           
  10          	0.00787     	0.89944     	0.01902     	0.22603            	0           
  100         	0.00747     	0.91065     	0.01616     	-1.29301           	0           
  300         	0.00754     	0.89930     	0.01634     	-1.45605           	1           
  500         	0.00745     	0.89841     	0.01634     	-1.46609           	3           
  1000        	0.00740     	0.89438     	0.01646     	-1.48915           	6           
---> try to load best state
              	0.00740     	0.89438     	0.01646     	-1.48915           	6                eval state : test_on_restore;
              	0.00740     	0.89438     	0.01646     	-1.48915           	6                eval state : test_on_last_epoch;
---> final result
              	0.00740     	0.89438     	0.01646     	-1.48915           	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00751     	0.87792     	0.01843     	0.50928            	0           
  10          	0.00676     	0.90656     	0.01816     	0.46129            	0           
  100         	0.00762     	0.89485     	0.01652     	1.04710            	0           
  300         	0.00756     	0.89786     	0.01628     	0.53732            	2           
  500         	0.00944     	0.84731     	0.01730     	-0.24937           	4           
  1000        	0.00738     	0.89380     	0.01642     	1.85659            	8           
---> try to load best state
              	0.00738     	0.89380     	0.01642     	1.85659            	8                eval state : test_on_restore;
              	0.00738     	0.89380     	0.01642     	1.85659            	8                eval state : test_on_last_epoch;
---> final result
              	0.00738     	0.89380     	0.01642     	1.85659            	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 4 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01251     	0.84370     	0.02610     	0.82131            	0           
  10          	0.01219     	0.84575     	0.02567     	0.79325            	0           
  100         	0.01228     	0.85697     	0.02320     	0.59266            	0           
  300         	0.01157     	0.86724     	0.02205     	1.67361            	1           
  500         	0.01102     	0.86661     	0.02209     	1.99259            	2           
  1000        	0.01138     	0.86225     	0.02306     	2.06398            	4           
---> try to load best state
              	0.01138     	0.86225     	0.02306     	2.06398            	4                eval state : test_on_restore;
              	0.01138     	0.86225     	0.02306     	2.06398            	4                eval state : test_on_last_epoch;
---> final result              	0.01138     	0.86225     	0.02306     	2.06398            	4                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.02472     	0.51414     	0.04125     	0.17847            	0           
  10          	0.02263     	0.49247     	0.04139     	0.08759            	0           
  100         	0.01214     	0.32353     	0.02283     	-1.88329           	0           
  300         	0.01466     	0.61207     	0.02498     	-1.97611           	1           
  500         	0.01163     	0.76221     	0.02349     	-1.98880           	2           
  1000        	0.01129     	0.88691     	0.02308     	-2.01196           	4           
---> try to load best state
              	0.01129     	0.88691     	0.02308     	-2.01196           	4                eval state : test_on_restore;
              	0.01129     	0.88691     	0.02308     	-2.01196           	4                eval state : test_on_last_epoch;
---> final result
              	0.01129     	0.88691     	0.02308     	-2.01196           	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01754     	0.66442     	0.03310     	0.25895            	0           
  10          	0.01554     	0.72846     	0.03361     	0.16228            	0           
  100         	0.01120     	0.78933     	0.02218     	-1.64222           	0           
  300         	0.01143     	0.87518     	0.02313     	-1.80515           	1           
  500         	0.01129     	0.88183     	0.02297     	-1.81501           	2           
  1000        	0.01231     	0.52324     	0.02394     	-1.83708           	5           
---> try to load best state
              	0.01231     	0.52324     	0.02394     	-1.83708           	5                eval state : test_on_restore;
              	0.01231     	0.52324     	0.02394     	-1.83708           	5                eval state : test_on_last_epoch;
---> final result
              	0.01231     	0.52324     	0.02394     	-1.83708           	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01167     	0.85512     	0.02358     	0.33291            	0           
  10          	0.01014     	0.89299     	0.02195     	0.22607            	0           
  100         	0.01090     	0.89356     	0.02242     	-1.29209           	0           
  300         	0.01131     	0.88854     	0.02296     	-1.45461           	1           
  500         	0.01133     	0.89070     	0.02302     	-1.46457           	3           
  1000        	0.01139     	0.89397     	0.02318     	-1.48748           	6           
---> try to load best state
              	0.01139     	0.89397     	0.02318     	-1.48748           	6                eval state : test_on_restore;
              	0.01139     	0.89397     	0.02318     	-1.48748           	6                eval state : test_on_last_epoch;
---> final result
              	0.01139     	0.89397     	0.02318     	-1.48748           	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['medium', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01065     	0.87068     	0.02237     	0.50930            	0           
  10          	0.00940     	0.90143     	0.02131     	0.46131            	0           
  100         	0.01089     	0.88318     	0.02249     	1.04711            	0           
  300         	0.01135     	0.88644     	0.02299     	0.53735            	2           
  500         	0.01302     	0.84563     	0.02383     	-0.24925           	4           
  1000        	0.01138     	0.89255     	0.02319     	1.85659            	8           
---> try to load best state
              	0.01138     	0.89255     	0.02319     	1.85659            	8                eval state : test_on_restore;
              	0.01138     	0.89255     	0.02319     	1.85659            	8                eval state : test_on_last_epoch;
---> final result
              	0.01138     	0.89255     	0.02319     	1.85659            	8                eval state : final;module_name : GAR;cp_record_file : True;
