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

  Demo sGAR 
  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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00885     	0.01875     	0.99807     	0           
  10          	0.00863     	0.01804     	0.99822     	0           
  100         	0.01200     	0.02258     	0.99723     	0           
  300         	0.01239     	0.02369     	0.99689     	1           
  500         	0.01351     	0.02498     	0.99649     	1           
  1000        	0.01462     	0.02620     	0.99607     	3           
---> try to load best state
              	0.01462     	0.02620     	0.99607     	3                eval state : test_on_restore;
              	0.01462     	0.02620     	0.99607     	3                eval state : test_on_last_epoch;
---> final result              	0.01462     	0.02620     	0.99607     	3                eval state : final;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02207     	0.03803     	0.99160     	0           
  10          	0.01649     	0.02875     	0.99491     	0           
  epoch 83 reach nan state
---> try to load best state
              	0.01505     	0.02671     	0.99542     	0                eval state : test_on_restore;
              	0.01649     	0.02875     	0.99491     	0                eval state : test_on_last_epoch;
---> final result              	0.01505     	0.02671     	0.99542     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01913     	0.03047     	0.99398     	0           
  10          	0.01548     	0.02640     	0.99555     	0           
  epoch 84 reach nan state
---> try to load best state
              	0.01480     	0.02648     	0.99549     	0                eval state : test_on_restore;
              	0.01548     	0.02640     	0.99555     	0                eval state : test_on_last_epoch;
---> final result              	0.01548     	0.02640     	0.99555     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01521     	0.02764     	0.99537     	0           
  10          	0.01407     	0.02612     	0.99577     	0           
  epoch 86 reach nan state
---> try to load best state
              	0.01450     	0.02631     	0.99557     	0                eval state : test_on_restore;
              	0.01407     	0.02612     	0.99577     	0                eval state : test_on_last_epoch;
---> final result              	0.01407     	0.02612     	0.99577     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01434     	0.02639     	0.99554     	0           
  10          	0.01530     	0.02725     	0.99522     	0           
  epoch 87 reach nan state
---> try to load best state
              	0.01468     	0.02657     	0.99546     	0                eval state : test_on_restore;
              	0.01530     	0.02725     	0.99522     	0                eval state : test_on_last_epoch;
---> final result              	0.01468     	0.02657     	0.99546     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01335     	0.03710     	0.98842     	0           
  10          	0.01212     	0.03424     	0.99126     	0           
  100         	0.01717     	0.03385     	0.99317     	0           
  300         	0.01339     	0.03301     	0.99371     	1           
  500         	0.01356     	0.03327     	0.99380     	1           
  1000        	0.01420     	0.03367     	0.99378     	3           
---> try to load best state
              	0.01420     	0.03367     	0.99378     	3                eval state : test_on_restore;
              	0.01420     	0.03367     	0.99378     	3                eval state : test_on_last_epoch;
---> final result              	0.01420     	0.03367     	0.99378     	3                eval state : final;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02043     	0.04291     	0.98986     	0           
  10          	0.01533     	0.03498     	0.99286     	0           
  100         	0.01579     	0.03185     	0.99451     	0           
  epoch 239 reach nan state
---> try to load best state
              	0.01386     	0.03268     	0.99344     	0                eval state : test_on_restore;
              	0.01579     	0.03185     	0.99451     	0                eval state : test_on_last_epoch;
---> final result              	0.01579     	0.03185     	0.99451     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01967     	0.04054     	0.99001     	0           
  10          	0.01614     	0.03550     	0.99224     	0           
  epoch 84 reach nan state
---> try to load best state
              	0.01443     	0.03378     	0.99298     	0                eval state : test_on_restore;
              	0.01614     	0.03550     	0.99224     	0                eval state : test_on_last_epoch;
---> final result              	0.01443     	0.03378     	0.99298     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01624     	0.03925     	0.99161     	0           
  10          	0.01412     	0.03468     	0.99296     	0           
  epoch 86 reach nan state
---> try to load best state
              	0.01378     	0.03312     	0.99329     	0                eval state : test_on_restore;
              	0.01412     	0.03468     	0.99296     	0                eval state : test_on_last_epoch;
---> final result              	0.01378     	0.03312     	0.99329     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01438     	0.03149     	0.99379     	0           
  10          	0.01538     	0.03602     	0.99213     	0           
  epoch 87 reach nan state
---> try to load best state
              	0.01404     	0.03367     	0.99305     	0                eval state : test_on_restore;
              	0.01538     	0.03602     	0.99213     	0                eval state : test_on_last_epoch;
---> final result              	0.01404     	0.03367     	0.99305     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00929     	0.02473     	0.99661     	0           
  10          	0.00854     	0.02377     	0.99693     	0           
  100         	0.01484     	0.02868     	0.99551     	0           
  300         	0.01247     	0.02771     	0.99584     	1           
  500         	0.01300     	0.02801     	0.99577     	1           
  1000        	0.01313     	0.02789     	0.99584     	3           
---> try to load best state
              	0.01313     	0.02789     	0.99584     	3                eval state : test_on_restore;
              	0.01313     	0.02789     	0.99584     	3                eval state : test_on_last_epoch;
---> final result              	0.01313     	0.02789     	0.99584     	3                eval state : final;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02649     	0.04226     	0.98884     	0           
  10          	0.01695     	0.02897     	0.99483     	0           
  epoch 83 reach nan state
---> try to load best state
              	0.01369     	0.02781     	0.99531     	0                eval state : test_on_restore;
              	0.01695     	0.02897     	0.99483     	0                eval state : test_on_last_epoch;
---> final result              	0.01369     	0.02781     	0.99531     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02328     	0.04211     	0.98989     	0           
  10          	0.01527     	0.03164     	0.99417     	0           
  epoch 85 reach nan state
---> try to load best state
              	0.01341     	0.02731     	0.99552     	0                eval state : test_on_restore;
              	0.01527     	0.03164     	0.99417     	0                eval state : test_on_last_epoch;
---> final result              	0.01341     	0.02731     	0.99552     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01301     	0.02597     	0.99596     	0           
  10          	0.01313     	0.02729     	0.99554     	0           
  epoch 86 reach nan state
---> try to load best state
              	0.01305     	0.02774     	0.99539     	0                eval state : test_on_restore;
              	0.01313     	0.02729     	0.99554     	0                eval state : test_on_last_epoch;
---> final result              	0.01313     	0.02729     	0.99554     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01233     	0.02507     	0.99620     	0           
  10          	0.01391     	0.02894     	0.99497     	0           
  epoch 87 reach nan state
---> try to load best state
              	0.01305     	0.02779     	0.99537     	0                eval state : test_on_restore;
              	0.01391     	0.02894     	0.99497     	0                eval state : test_on_last_epoch;
---> final result              	0.01305     	0.02779     	0.99537     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01394     	0.03984     	0.98640     	0           
  10          	0.01282     	0.03680     	0.98934     	0           
  100         	0.01390     	0.03171     	0.99385     	0           
  300         	0.01327     	0.02893     	0.99506     	0           
  500         	0.01393     	0.02839     	0.99526     	1           
  1000        	0.01432     	0.02777     	0.99546     	3           
---> try to load best state
              	0.01432     	0.02777     	0.99546     	3                eval state : test_on_restore;
              	0.01432     	0.02777     	0.99546     	3                eval state : test_on_last_epoch;
---> final result              	0.01432     	0.02777     	0.99546     	3                eval state : final;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01745     	0.03514     	0.99278     	0           
  10          	0.01425     	0.02976     	0.99448     	0           
  100         	0.01661     	0.02859     	0.99454     	0           
  300         	0.01456     	0.02794     	0.99484     	0           
  epoch 360 reach nan state
---> try to load best state
              	0.01467     	0.02796     	0.99483     	0                eval state : test_on_restore;
              	0.01456     	0.02794     	0.99484     	0                eval state : test_on_last_epoch;
---> final result              	0.01456     	0.02794     	0.99484     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01660     	0.03329     	0.99351     	0           
  10          	0.01423     	0.02953     	0.99459     	0           
  epoch 84 reach nan state
---> try to load best state
              	0.01435     	0.02786     	0.99490     	0                eval state : test_on_restore;
              	0.01423     	0.02953     	0.99459     	0                eval state : test_on_last_epoch;
---> final result              	0.01435     	0.02786     	0.99490     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01590     	0.02960     	0.99471     	0           
  10          	0.01345     	0.02624     	0.99567     	0           
  epoch 85 reach nan state
---> try to load best state
              	0.01409     	0.02738     	0.99506     	0                eval state : test_on_restore;
              	0.01345     	0.02624     	0.99567     	0                eval state : test_on_last_epoch;
---> final result              	0.01345     	0.02624     	0.99567     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01469     	0.02823     	0.99478     	0           
  10          	0.01512     	0.02858     	0.99462     	0           
  epoch 87 reach nan state
---> try to load best state
              	0.01423     	0.02747     	0.99499     	0                eval state : test_on_restore;
              	0.01512     	0.02858     	0.99462     	0                eval state : test_on_last_epoch;
---> final result              	0.01423     	0.02747     	0.99499     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00780     	0.02022     	0.99742     	0           
  10          	0.00725     	0.01880     	0.99785     	0           
  100         	0.01184     	0.02286     	0.99703     	0           
  300         	0.00987     	0.02227     	0.99726     	0           
  500         	0.01044     	0.02297     	0.99711     	1           
  1000        	0.01114     	0.02365     	0.99694     	3           
---> try to load best state
              	0.01114     	0.02365     	0.99694     	3                eval state : test_on_restore;
              	0.01114     	0.02365     	0.99694     	3                eval state : test_on_last_epoch;
---> final result              	0.01114     	0.02365     	0.99694     	3                eval state : final;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02014     	0.03855     	0.99179     	0           
  10          	0.01321     	0.02847     	0.99524     	0           
  100         	0.01304     	0.02427     	0.99648     	0           
  epoch 103 reach nan state
---> try to load best state
              	0.01740     	0.02762     	0.99549     	0                eval state : test_on_restore;
              	0.01304     	0.02427     	0.99648     	0                eval state : test_on_last_epoch;
---> final result              	0.01304     	0.02427     	0.99648     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01360     	0.02628     	0.99574     	0           
  10          	0.01144     	0.02404     	0.99646     	0           
  100         	0.01149     	0.02381     	0.99646     	0           
  epoch 126 reach nan state
---> try to load best state
              	0.01137     	0.02357     	0.99657     	0                eval state : test_on_restore;
              	0.01149     	0.02381     	0.99646     	0                eval state : test_on_last_epoch;
---> final result              	0.01137     	0.02357     	0.99657     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01100     	0.02290     	0.99684     	0           
  10          	0.01128     	0.02392     	0.99651     	0           
  epoch 86 reach nan state
---> try to load best state
              	0.01098     	0.02362     	0.99656     	0                eval state : test_on_restore;
              	0.01128     	0.02392     	0.99651     	0                eval state : test_on_last_epoch;
---> final result              	0.01098     	0.02362     	0.99656     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01127     	0.02346     	0.99665     	0           
  10          	0.01144     	0.02407     	0.99642     	0           
  epoch 87 reach nan state
---> try to load best state
              	0.01105     	0.02361     	0.99656     	0                eval state : test_on_restore;
              	0.01144     	0.02407     	0.99642     	0                eval state : test_on_last_epoch;
---> final result              	0.01105     	0.02361     	0.99656     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01251     	0.02610     	0.99566     	0           
  10          	0.01215     	0.02563     	0.99592     	0           
  100         	0.01339     	0.02334     	0.99673     	0           
  300         	0.01219     	0.02153     	0.99725     	0           
  500         	0.01208     	0.02118     	0.99735     	1           
  1000        	0.01205     	0.02111     	0.99736     	3           
---> try to load best state
              	0.01205     	0.02111     	0.99736     	3                eval state : test_on_restore;
              	0.01205     	0.02111     	0.99736     	3                eval state : test_on_last_epoch;
---> final result              	0.01205     	0.02111     	0.99736     	3                eval state : final;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03297     	0.05128     	0.98565     	0           
  10          	0.01659     	0.02886     	0.99500     	0           
  epoch 83 reach nan state
---> try to load best state
              	0.01272     	0.02233     	0.99683     	0                eval state : test_on_restore;
              	0.01659     	0.02886     	0.99500     	0                eval state : test_on_last_epoch;
---> final result              	0.01272     	0.02233     	0.99683     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01463     	0.02657     	0.99580     	0           
  10          	0.01190     	0.02160     	0.99708     	0           
  epoch 84 reach nan state
---> try to load best state
              	0.01162     	0.02082     	0.99717     	0                eval state : test_on_restore;
              	0.01190     	0.02160     	0.99708     	0                eval state : test_on_last_epoch;
---> final result              	0.01162     	0.02082     	0.99717     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01415     	0.02296     	0.99654     	0           
  10          	0.01291     	0.02206     	0.99681     	0           
  epoch 86 reach nan state
---> try to load best state
              	0.01236     	0.02140     	0.99697     	0                eval state : test_on_restore;
              	0.01291     	0.02206     	0.99681     	0                eval state : test_on_last_epoch;
---> final result              	0.01236     	0.02140     	0.99697     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01061     	0.01851     	0.99776     	0           
  10          	0.01202     	0.02094     	0.99710     	0           
  epoch 87 reach nan state
---> try to load best state
              	0.01198     	0.02090     	0.99711     	0                eval state : test_on_restore;
              	0.01202     	0.02094     	0.99710     	0                eval state : test_on_last_epoch;
---> final result              	0.01198     	0.02090     	0.99711     	0                eval state : final;module_name : SGAR;cp_record_file : True;
