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

  Demo GAR 
  seed: None 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 128, '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           	0.01851     	0.94615     	0.03751     	0.38766            	0           
  10          	0.01811     	0.94752     	0.03697     	0.32445            	0           
  100         	0.01829     	0.94418     	0.03725     	-0.02207           	0           
  300         	0.01863     	0.94373     	0.03750     	0.05852            	2           
  500         	0.01877     	0.94378     	0.03749     	0.13577            	4           
  1000        	0.01889     	0.94385     	0.03749     	0.22601            	8           
---> try to load best state
              	0.01889     	0.94385     	0.03749     	0.22601            	8                eval state : test_on_restore;
              	0.01889     	0.94385     	0.03749     	0.22601            	8                eval state : test_on_last_epoch;
---> final result              	0.01889     	0.94385     	0.03749     	0.22601            	8                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.18871     	-0.17578    	0.26098     	0.14364            	0           
  10          	0.19134     	0.00790     	0.26226     	0.06174            	0           
  100         	0.20229     	0.05770     	0.27419     	-0.60670           	0           
  300         	0.21608     	-0.01129    	0.28983     	-0.59246           	2           
  epoch 328 reach nan state
---> try to load best state
              	0.21611     	-0.01128    	0.28987     	-0.59218           	2                eval state : test_on_restore;
              	0.21608     	-0.01129    	0.28983     	-0.59246           	2                eval state : test_on_last_epoch;
---> final result
              	0.21608     	-0.01129    	0.28983     	-0.59246           	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.17037     	0.03320     	0.24850     	0.20642            	0           
  10          	0.17224     	0.06474     	0.25123     	0.12407            	0           
  100         	0.16779     	0.07378     	0.24369     	-0.53883           	0           
  300         	0.16904     	0.09517     	0.23704     	-0.60291           	2           
  epoch 396 reach nan state
---> try to load best state
              	0.17077     	0.08214     	0.23804     	-0.59123           	3                eval state : test_on_restore;
              	0.16904     	0.09517     	0.23704     	-0.60291           	3                eval state : test_on_last_epoch;
---> final result
              	0.16904     	0.09517     	0.23704     	-0.60291           	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.14940     	0.23280     	0.22837     	0.28342            	0           
  10          	0.15466     	0.19596     	0.23427     	0.18899            	0           
  100         	0.13870     	0.26903     	0.21053     	-0.58872           	1           
  300         	0.14467     	0.24134     	0.20961     	-0.59214           	4           
  500         	0.14793     	0.21570     	0.21138     	-0.53729           	6           
  1000        	0.15186     	0.17906     	0.21389     	-0.47744           	12          
---> try to load best state
              	0.15186     	0.17906     	0.21389     	-0.47744           	12               eval state : test_on_restore;
              	0.15186     	0.17906     	0.21389     	-0.47744           	12               eval state : test_on_last_epoch;
---> final result
              	0.15186     	0.17906     	0.21389     	-0.47744           	12               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.14675     	0.30989     	0.23755     	0.33202            	0           
  10          	0.15025     	0.29828     	0.24256     	0.22890            	0           
  100         	0.12310     	0.36873     	0.19207     	-0.62991           	1           
  300         	0.12640     	0.35513     	0.19178     	-0.58279           	5           
  500         	0.12791     	0.34791     	0.19224     	-0.52586           	9           
  1000        	0.12890     	0.34165     	0.19270     	-0.45925           	18          
---> try to load best state
              	0.12890     	0.34165     	0.19270     	-0.45925           	18               eval state : test_on_restore;
              	0.12890     	0.34165     	0.19270     	-0.45925           	18               eval state : test_on_last_epoch;
---> final result
              	0.12890     	0.34165     	0.19270     	-0.45925           	18               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 64, '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           	0.14130     	0.37113     	0.22668     	0.34700            	0           
  10          	0.13468     	0.39024     	0.21594     	0.22491            	0           
  100         	0.10166     	0.51979     	0.16459     	-0.64640           	3           
  300         	0.10338     	0.51179     	0.16481     	-0.57672           	9           
  500         	0.10419     	0.50751     	0.16509     	-0.51685           	16          
  1000        	0.10472     	0.50334     	0.16534     	-0.44691           	31          
---> try to load best state
              	0.10472     	0.50334     	0.16534     	-0.44691           	31               eval state : test_on_restore;
              	0.10472     	0.50334     	0.16534     	-0.44691           	31               eval state : test_on_last_epoch;
---> final result
              	0.10472     	0.50334     	0.16534     	-0.44691           	31               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 128, '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           	0.10470     	0.24773     	0.18018     	0.41485            	0           
  10          	0.11019     	0.31081     	0.19066     	0.48659            	0           
  100         	0.08773     	0.53141     	0.14961     	-0.27386           	7           
  300         	0.08156     	0.63110     	0.13642     	-0.57385           	22          
  500         	0.08251     	0.63548     	0.13675     	-0.52277           	36          
  1000        	0.08303     	0.63322     	0.13699     	-0.44951           	73          
---> try to load best state
              	0.08303     	0.63322     	0.13699     	-0.44951           	73               eval state : test_on_restore;
              	0.08303     	0.63322     	0.13699     	-0.44951           	73               eval state : test_on_last_epoch;
---> final result
              	0.08303     	0.63322     	0.13699     	-0.44951           	73               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': 'TopOP_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 128, '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           	0.02135     	0.94414     	0.04551     	0.38461            	0           
  10          	0.02066     	0.94566     	0.04440     	0.30228            	0           
  100         	0.02125     	0.94268     	0.04462     	-0.06783           	0           
  300         	0.02217     	0.94091     	0.04609     	0.03835            	2           
  500         	0.02239     	0.94054     	0.04640     	0.13051            	4           
  1000        	0.02255     	0.94028     	0.04666     	0.21493            	8           
---> try to load best state
              	0.02255     	0.94028     	0.04666     	0.21493            	8                eval state : test_on_restore;
              	0.02255     	0.94028     	0.04666     	0.21493            	8                eval state : test_on_last_epoch;
---> final result              	0.02255     	0.94028     	0.04666     	0.21493            	8                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.18692     	-0.18453    	0.25936     	0.11330            	0           
  10          	0.18380     	0.01450     	0.25477     	0.02999            	0           
  100         	0.19579     	0.05759     	0.27242     	-0.61549           	0           
  300         	0.19577     	0.05150     	0.27016     	-0.59746           	2           
  500         	0.19597     	0.04640     	0.26986     	-0.54554           	3           
  1000        	0.19723     	0.02082     	0.27075     	-0.48849           	7           
---> try to load best state
              	0.19723     	0.02082     	0.27075     	-0.48849           	7                eval state : test_on_restore;
              	0.19723     	0.02082     	0.27075     	-0.48849           	7                eval state : test_on_last_epoch;
---> final result
              	0.19723     	0.02082     	0.27075     	-0.48849           	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.17129     	0.03213     	0.24887     	0.17953            	0           
  10          	0.17020     	0.11045     	0.24684     	0.09713            	0           
  100         	0.16986     	0.18831     	0.24266     	-0.58767           	0           
  300         	0.17417     	0.19543     	0.24217     	-0.61299           	2           
  500         	0.17536     	0.18946     	0.24271     	-0.56211           	4           
  1000        	0.17644     	0.17964     	0.24334     	-0.49418           	8           
---> try to load best state
              	0.17644     	0.17964     	0.24334     	-0.49418           	8                eval state : test_on_restore;
              	0.17644     	0.17964     	0.24334     	-0.49418           	8                eval state : test_on_last_epoch;
---> final result
              	0.17644     	0.17964     	0.24334     	-0.49418           	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.17615     	0.16367     	0.26604     	0.23667            	0           
  10          	0.18416     	0.14506     	0.27588     	0.15856            	0           
  100         	0.16938     	0.21980     	0.23649     	-0.64905           	1           
  300         	0.17417     	0.19847     	0.23820     	-0.60530           	3           
  500         	0.17544     	0.19333     	0.23907     	-0.55072           	6           
  1000        	0.17615     	0.19214     	0.23946     	-0.48494           	12          
---> try to load best state
              	0.17615     	0.19214     	0.23946     	-0.48494           	12               eval state : test_on_restore;
              	0.17615     	0.19214     	0.23946     	-0.48494           	12               eval state : test_on_last_epoch;
---> final result
              	0.17615     	0.19214     	0.23946     	-0.48494           	12               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.16060     	0.31051     	0.25220     	0.32128            	0           
  10          	0.15916     	0.30661     	0.24913     	0.21356            	0           
  100         	0.13769     	0.38731     	0.20281     	-0.64907           	1           
  300         	0.14107     	0.37619     	0.20375     	-0.59613           	5           
  500         	0.14257     	0.36976     	0.20466     	-0.53549           	9           
  1000        	0.14372     	0.36455     	0.20545     	-0.46525           	18          
---> try to load best state
              	0.14372     	0.36455     	0.20545     	-0.46525           	18               eval state : test_on_restore;
              	0.14372     	0.36455     	0.20545     	-0.46525           	18               eval state : test_on_last_epoch;
---> final result
              	0.14372     	0.36455     	0.20545     	-0.46525           	18               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 64, '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           	0.15745     	0.49462     	0.26108     	0.37853            	0           
  10          	0.15222     	0.49196     	0.25183     	0.30737            	0           
  100         	0.11026     	0.57539     	0.16930     	-0.64800           	3           
  300         	0.11502     	0.55912     	0.17284     	-0.59351           	9           
  500         	0.11705     	0.54714     	0.17479     	-0.53338           	16          
  epoch 881 reach nan state
---> try to load best state
              	0.11847     	0.53847     	0.17620     	-0.47455           	27               eval state : test_on_restore;
              	0.11705     	0.54714     	0.17479     	-0.53338           	27               eval state : test_on_last_epoch;
---> final result
              	0.11705     	0.54714     	0.17479     	-0.53338           	27               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 128, '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           	0.10977     	0.55223     	0.19028     	0.40810            	0           
  10          	0.11897     	0.54430     	0.20433     	0.48960            	0           
  100         	0.09412     	0.59864     	0.15901     	-0.37726           	7           
  300         	0.08820     	0.65656     	0.14637     	-0.57525           	22          
  500         	0.08962     	0.65646     	0.14709     	-0.52343           	36          
  1000        	0.09035     	0.65383     	0.14749     	-0.44850           	72          
---> try to load best state
              	0.09035     	0.65383     	0.14749     	-0.44850           	72               eval state : test_on_restore;
              	0.09035     	0.65383     	0.14749     	-0.44850           	72               eval state : test_on_last_epoch;
---> final result
              	0.09035     	0.65383     	0.14749     	-0.44850           	72               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': 'TopOP_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 128, '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           	0.02250     	0.94015     	0.04476     	0.38166            	0           
  10          	0.02172     	0.94346     	0.04331     	0.29720            	0           
  100         	0.02294     	0.93324     	0.04596     	-0.09922           	0           
  300         	0.02355     	0.92959     	0.04737     	0.03904            	2           
  500         	0.02372     	0.92865     	0.04764     	0.13457            	4           
  1000        	0.02386     	0.92800     	0.04785     	0.22308            	8           
---> try to load best state
              	0.02386     	0.92800     	0.04785     	0.22308            	8                eval state : test_on_restore;
              	0.02386     	0.92800     	0.04785     	0.22308            	8                eval state : test_on_last_epoch;
---> final result              	0.02386     	0.92800     	0.04785     	0.22308            	8                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.18489     	-0.21225    	0.25494     	0.11225            	0           
  10          	0.18106     	-0.00912    	0.24993     	0.02864            	0           
  100         	0.19306     	0.03604     	0.26712     	-0.62308           	0           
  300         	0.19312     	0.01834     	0.26476     	-0.60479           	2           
  500         	0.19336     	0.01218     	0.26443     	-0.55190           	3           
  1000        	0.19462     	-0.01347    	0.26513     	-0.49417           	6           
---> try to load best state
              	0.19462     	-0.01347    	0.26513     	-0.49417           	6                eval state : test_on_restore;
              	0.19462     	-0.01347    	0.26513     	-0.49417           	6                eval state : test_on_last_epoch;
---> final result
              	0.19462     	-0.01347    	0.26513     	-0.49417           	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.16880     	0.01580     	0.24425     	0.17855            	0           
  10          	0.16749     	0.09761     	0.24229     	0.09601            	0           
  100         	0.16667     	0.17796     	0.23759     	-0.59333           	0           
  300         	0.17137     	0.17862     	0.23668     	-0.61945           	2           
  500         	0.17277     	0.17118     	0.23744     	-0.56756           	4           
  1000        	0.17427     	0.15860     	0.23863     	-0.49834           	8           
---> try to load best state
              	0.17427     	0.15860     	0.23863     	-0.49834           	8                eval state : test_on_restore;
              	0.17427     	0.15860     	0.23863     	-0.49834           	8                eval state : test_on_last_epoch;
---> final result
              	0.17427     	0.15860     	0.23863     	-0.49834           	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.17444     	0.14759     	0.26226     	0.23583            	0           
  10          	0.18190     	0.12790     	0.27220     	0.15760            	0           
  100         	0.16713     	0.19639     	0.23253     	-0.65387           	1           
  300         	0.17188     	0.17173     	0.23429     	-0.60950           	3           
  500         	0.17329     	0.16581     	0.23542     	-0.55413           	6           
  1000        	0.17422     	0.16315     	0.23620     	-0.48754           	13          
---> try to load best state
              	0.17422     	0.16315     	0.23620     	-0.48754           	13               eval state : test_on_restore;
              	0.17422     	0.16315     	0.23620     	-0.48754           	13               eval state : test_on_last_epoch;
---> final result
              	0.17422     	0.16315     	0.23620     	-0.48754           	13               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.16195     	0.28298     	0.25317     	0.32142            	0           
  10          	0.16052     	0.28161     	0.25018     	0.21374            	0           
  100         	0.13814     	0.36907     	0.20402     	-0.64789           	2           
  300         	0.14157     	0.35616     	0.20523     	-0.59488           	5           
  500         	0.14297     	0.34979     	0.20607     	-0.53445           	9           
  1000        	0.14404     	0.34479     	0.20678     	-0.46441           	18          
---> try to load best state
              	0.14404     	0.34479     	0.20678     	-0.46441           	18               eval state : test_on_restore;
              	0.14404     	0.34479     	0.20678     	-0.46441           	18               eval state : test_on_last_epoch;
---> final result
              	0.14404     	0.34479     	0.20678     	-0.46441           	18               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 64, '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           	0.15808     	0.48148     	0.26212     	0.37866            	0           
  10          	0.15324     	0.47998     	0.25378     	0.30765            	0           
  100         	0.11028     	0.55992     	0.16957     	-0.64795           	3           
  300         	0.11492     	0.54462     	0.17261     	-0.59381           	9           
  500         	0.11693     	0.53338     	0.17438     	-0.53376           	16          
  epoch 881 reach nan state
---> try to load best state
              	0.11834     	0.52537     	0.17566     	-0.47496           	27               eval state : test_on_restore;
              	0.11693     	0.53338     	0.17438     	-0.53376           	27               eval state : test_on_last_epoch;
---> final result
              	0.11693     	0.53338     	0.17438     	-0.53376           	27               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 128, '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           	0.11455     	0.59192     	0.19516     	0.40849            	0           
  10          	0.12190     	0.57110     	0.20726     	0.48982            	0           
  100         	0.09819     	0.62431     	0.16246     	-0.37595           	7           
  300         	0.09062     	0.62793     	0.14842     	-0.57425           	22          
  500         	0.09180     	0.62565     	0.14885     	-0.52268           	36          
  1000        	0.09244     	0.62405     	0.14917     	-0.44790           	72          
---> try to load best state
              	0.09244     	0.62405     	0.14917     	-0.44790           	72               eval state : test_on_restore;
              	0.09244     	0.62405     	0.14917     	-0.44790           	72               eval state : test_on_last_epoch;
---> final result
              	0.09244     	0.62405     	0.14917     	-0.44790           	72               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': 'TopOP_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 128, '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           	0.02422     	0.93715     	0.04867     	0.37127            	0           
  10          	0.02381     	0.93966     	0.04712     	0.28115            	0           
  100         	0.02407     	0.93486     	0.04823     	-0.12088           	0           
  300         	0.02489     	0.93132     	0.05040     	-0.00034           	2           
  500         	0.02521     	0.93021     	0.05104     	0.08994            	4           
  1000        	0.02546     	0.92934     	0.05158     	0.17529            	8           
---> try to load best state
              	0.02546     	0.92934     	0.05158     	0.17529            	8                eval state : test_on_restore;
              	0.02546     	0.92934     	0.05158     	0.17529            	8                eval state : test_on_last_epoch;
---> final result              	0.02546     	0.92934     	0.05158     	0.17529            	8                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.18870     	-0.21258    	0.26092     	0.11371            	0           
  10          	0.18519     	-0.01380    	0.25646     	0.03050            	0           
  100         	0.19741     	0.03455     	0.27417     	-0.61275           	0           
  300         	0.19773     	0.03751     	0.27231     	-0.59448           	2           
  500         	0.19807     	0.03476     	0.27218     	-0.54280           	3           
  1000        	0.19949     	0.01375     	0.27317     	-0.48606           	6           
---> try to load best state
              	0.19949     	0.01375     	0.27317     	-0.48606           	6                eval state : test_on_restore;
              	0.19949     	0.01375     	0.27317     	-0.48606           	6                eval state : test_on_last_epoch;
---> final result
              	0.19949     	0.01375     	0.27317     	-0.48606           	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.17273     	0.01069     	0.25027     	0.17983            	0           
  10          	0.17149     	0.09051     	0.24840     	0.09752            	0           
  100         	0.17126     	0.17205     	0.24429     	-0.58587           	0           
  300         	0.17581     	0.18366     	0.24405     	-0.61088           	2           
  500         	0.17702     	0.17910     	0.24464     	-0.56023           	4           
  1000        	0.17815     	0.17013     	0.24532     	-0.49255           	8           
---> try to load best state
              	0.17815     	0.17013     	0.24532     	-0.49255           	8                eval state : test_on_restore;
              	0.17815     	0.17013     	0.24532     	-0.49255           	8                eval state : test_on_last_epoch;
---> final result
              	0.17815     	0.17013     	0.24532     	-0.49255           	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.17791     	0.14193     	0.26761     	0.23697            	0           
  10          	0.18549     	0.12256     	0.27738     	0.15893            	0           
  100         	0.17018     	0.20210     	0.23788     	-0.64732           	1           
  300         	0.17512     	0.18398     	0.24023     	-0.60312           	3           
  500         	0.17638     	0.17905     	0.24116     	-0.54876           	6           
  1000        	0.17716     	0.17731     	0.24166     	-0.48319           	12          
---> try to load best state
              	0.17716     	0.17731     	0.24166     	-0.48319           	12               eval state : test_on_restore;
              	0.17716     	0.17731     	0.24166     	-0.48319           	12               eval state : test_on_last_epoch;
---> final result
              	0.17716     	0.17731     	0.24166     	-0.48319           	12               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.16172     	0.27423     	0.25322     	0.32143            	0           
  10          	0.16010     	0.27846     	0.24999     	0.21371            	0           
  100         	0.13940     	0.36283     	0.20468     	-0.64739           	1           
  300         	0.14296     	0.35439     	0.20603     	-0.59432           	5           
  500         	0.14444     	0.34840     	0.20700     	-0.53387           	10          
  1000        	0.14558     	0.34337     	0.20785     	-0.46384           	19          
---> try to load best state
              	0.14558     	0.34337     	0.20785     	-0.46384           	19               eval state : test_on_restore;
              	0.14558     	0.34337     	0.20785     	-0.46384           	19               eval state : test_on_last_epoch;
---> final result
              	0.14558     	0.34337     	0.20785     	-0.46384           	19               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 64, '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           	0.15828     	0.46878     	0.26181     	0.37863            	0           
  10          	0.15281     	0.47264     	0.25201     	0.30740            	0           
  100         	0.11210     	0.55721     	0.17126     	-0.64660           	3           
  300         	0.11737     	0.54283     	0.17554     	-0.59174           	10          
  500         	0.11947     	0.53184     	0.17760     	-0.53175           	16          
  epoch 881 reach nan state
---> try to load best state
              	0.12092     	0.52368     	0.17908     	-0.47307           	27               eval state : test_on_restore;
              	0.11947     	0.53184     	0.17760     	-0.53175           	27               eval state : test_on_last_epoch;
---> final result
              	0.11947     	0.53184     	0.17760     	-0.53175           	27               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 128, '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           	0.11175     	0.52838     	0.19194     	0.40823            	0           
  10          	0.11986     	0.52610     	0.20442     	0.48961            	0           
  100         	0.09610     	0.59266     	0.15997     	-0.37689           	7           
  300         	0.09185     	0.63997     	0.14947     	-0.57357           	22          
  500         	0.09345     	0.63947     	0.15061     	-0.52171           	36          
  1000        	0.09423     	0.63792     	0.15118     	-0.44695           	72          
---> try to load best state
              	0.09423     	0.63792     	0.15118     	-0.44695           	72               eval state : test_on_restore;
              	0.09423     	0.63792     	0.15118     	-0.44695           	73               eval state : test_on_last_epoch;
---> final result
              	0.09423     	0.63792     	0.15118     	-0.44695           	73               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': 'TopOP_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 128, '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           	0.02279     	0.94325     	0.04634     	0.39005            	0           
  10          	0.02204     	0.94459     	0.04529     	0.31079            	0           
  100         	0.02263     	0.94065     	0.04607     	-0.08682           	0           
  300         	0.02311     	0.94005     	0.04679     	0.04250            	2           
  500         	0.02325     	0.93986     	0.04695     	0.13052            	4           
  1000        	0.02334     	0.93977     	0.04708     	0.21185            	8           
---> try to load best state
              	0.02334     	0.93977     	0.04708     	0.21185            	8                eval state : test_on_restore;
              	0.02334     	0.93977     	0.04708     	0.21185            	8                eval state : test_on_last_epoch;
---> final result              	0.02334     	0.93977     	0.04708     	0.21185            	8                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.18799     	-0.16896    	0.26017     	0.11350            	0           
  10          	0.18504     	0.03002     	0.25561     	0.03022            	0           
  100         	0.19714     	0.07363     	0.27258     	-0.61536           	0           
  300         	0.19672     	0.06853     	0.26979     	-0.59815           	2           
  500         	0.19686     	0.06284     	0.26938     	-0.54630           	3           
  1000        	0.19815     	0.03688     	0.27026     	-0.48918           	6           
---> try to load best state
              	0.19815     	0.03688     	0.27026     	-0.48918           	6                eval state : test_on_restore;
              	0.19815     	0.03688     	0.27026     	-0.48918           	6                eval state : test_on_last_epoch;
---> final result
              	0.19815     	0.03688     	0.27026     	-0.48918           	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.17237     	0.04111     	0.24973     	0.17971            	0           
  10          	0.17147     	0.12099     	0.24781     	0.09737            	0           
  100         	0.17068     	0.20217     	0.24281     	-0.58757           	0           
  300         	0.17469     	0.20636     	0.24152     	-0.61382           	2           
  500         	0.17575     	0.19936     	0.24180     	-0.56312           	4           
  1000        	0.17664     	0.18909     	0.24210     	-0.49533           	8           
---> try to load best state
              	0.17664     	0.18909     	0.24210     	-0.49533           	8                eval state : test_on_restore;
              	0.17664     	0.18909     	0.24210     	-0.49533           	8                eval state : test_on_last_epoch;
---> final result
              	0.17664     	0.18909     	0.24210     	-0.49533           	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.17700     	0.17085     	0.26642     	0.23673            	0           
  10          	0.18496     	0.15469     	0.27628     	0.15866            	0           
  100         	0.17016     	0.22880     	0.23636     	-0.64926           	1           
  300         	0.17432     	0.20502     	0.23733     	-0.60632           	3           
  500         	0.17540     	0.19903     	0.23792     	-0.55186           	6           
  1000        	0.17590     	0.19784     	0.23797     	-0.48618           	12          
---> try to load best state
              	0.17590     	0.19784     	0.23797     	-0.48618           	12               eval state : test_on_restore;
              	0.17590     	0.19784     	0.23797     	-0.48618           	12               eval state : test_on_last_epoch;
---> final result
              	0.17590     	0.19784     	0.23797     	-0.48618           	12               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.16048     	0.31369     	0.25195     	0.32125            	0           
  10          	0.15903     	0.31241     	0.24876     	0.21350            	0           
  100         	0.13808     	0.39536     	0.20253     	-0.64932           	1           
  300         	0.14127     	0.38418     	0.20305     	-0.59673           	5           
  500         	0.14262     	0.37763     	0.20380     	-0.53614           	9           
  1000        	0.14358     	0.37245     	0.20437     	-0.46595           	18          
---> try to load best state
              	0.14358     	0.37245     	0.20437     	-0.46595           	18               eval state : test_on_restore;
              	0.14358     	0.37245     	0.20437     	-0.46595           	18               eval state : test_on_last_epoch;
---> final result
              	0.14358     	0.37245     	0.20437     	-0.46595           	18               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 64, '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           	0.15765     	0.49560     	0.26021     	0.37842            	0           
  10          	0.15219     	0.49397     	0.25074     	0.30721            	0           
  100         	0.11082     	0.58268     	0.16851     	-0.64865           	3           
  300         	0.11554     	0.56675     	0.17198     	-0.59417           	9           
  500         	0.11750     	0.55460     	0.17389     	-0.53400           	16          
  epoch 881 reach nan state
---> try to load best state
              	0.11883     	0.54562     	0.17523     	-0.47514           	28               eval state : test_on_restore;
              	0.11750     	0.55460     	0.17389     	-0.53400           	28               eval state : test_on_last_epoch;
---> final result
              	0.11750     	0.55460     	0.17389     	-0.53400           	28               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 128, '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           	0.11151     	0.59227     	0.19030     	0.40811            	0           
  10          	0.11966     	0.57116     	0.20373     	0.48956            	0           
  100         	0.09482     	0.62547     	0.15815     	-0.37758           	7           
  300         	0.08937     	0.66320     	0.14601     	-0.57550           	22          
  500         	0.09080     	0.66236     	0.14673     	-0.52368           	36          
  1000        	0.09143     	0.65912     	0.14703     	-0.44876           	72          
---> try to load best state
              	0.09143     	0.65912     	0.14703     	-0.44876           	72               eval state : test_on_restore;
              	0.09143     	0.65912     	0.14703     	-0.44876           	72               eval state : test_on_last_epoch;
---> final result
              	0.09143     	0.65912     	0.14703     	-0.44876           	72               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': 'TopOP_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 128, '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           	0.01862     	0.97083     	0.03314     	0.41680            	0           
  10          	0.01818     	0.97156     	0.03263     	0.34739            	0           
  100         	0.01825     	0.97019     	0.03221     	0.04770            	0           
  300         	0.01846     	0.97043     	0.03221     	0.10467            	2           
  500         	0.01853     	0.97042     	0.03226     	0.17727            	4           
  1000        	0.01861     	0.97030     	0.03238     	0.24003            	8           
---> try to load best state
              	0.01861     	0.97030     	0.03238     	0.24003            	8                eval state : test_on_restore;
              	0.01861     	0.97030     	0.03238     	0.24003            	8                eval state : test_on_last_epoch;
---> final result              	0.01861     	0.97030     	0.03238     	0.24003            	8                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.18485     	-0.22838    	0.25496     	0.11222            	0           
  10          	0.18017     	-0.01643    	0.24918     	0.02840            	0           
  100         	0.19107     	0.03694     	0.26527     	-0.62571           	0           
  300         	0.19076     	0.02153     	0.26206     	-0.60841           	2           
  500         	0.19088     	0.01438     	0.26157     	-0.55518           	3           
  1000        	0.19217     	-0.00973    	0.26246     	-0.49682           	7           
---> try to load best state
              	0.19217     	-0.00973    	0.26246     	-0.49682           	7                eval state : test_on_restore;
              	0.19217     	-0.00973    	0.26246     	-0.49682           	7                eval state : test_on_last_epoch;
---> final result
              	0.19217     	-0.00973    	0.26246     	-0.49682           	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.16826     	0.00474     	0.24425     	0.17853            	0           
  10          	0.16649     	0.09152     	0.24191     	0.09591            	0           
  100         	0.16536     	0.17059     	0.23644     	-0.59457           	0           
  300         	0.16966     	0.16354     	0.23481     	-0.62156           	2           
  500         	0.17093     	0.15380     	0.23546     	-0.56953           	4           
  1000        	0.17227     	0.14120     	0.23657     	-0.50007           	8           
---> try to load best state
              	0.17227     	0.14120     	0.23657     	-0.50007           	8                eval state : test_on_restore;
              	0.17227     	0.14120     	0.23657     	-0.50007           	8                eval state : test_on_last_epoch;
---> final result
              	0.17227     	0.14120     	0.23657     	-0.50007           	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.17306     	0.14246     	0.26195     	0.23576            	0           
  10          	0.18007     	0.12516     	0.27155     	0.15743            	0           
  100         	0.16584     	0.18406     	0.23147     	-0.65514           	1           
  300         	0.17019     	0.15131     	0.23250     	-0.61138           	3           
  500         	0.17153     	0.14412     	0.23348     	-0.55591           	6           
  1000        	0.17238     	0.14186     	0.23412     	-0.48917           	12          
---> try to load best state
              	0.17238     	0.14186     	0.23412     	-0.48917           	12               eval state : test_on_restore;
              	0.17238     	0.14186     	0.23412     	-0.48917           	12               eval state : test_on_last_epoch;
---> final result
              	0.17238     	0.14186     	0.23412     	-0.48917           	12               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', '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           	0.15950     	0.27988     	0.25227     	0.32130            	0           
  10          	0.15812     	0.27715     	0.24905     	0.21356            	0           
  100         	0.13524     	0.35561     	0.20114     	-0.65044           	1           
  300         	0.13828     	0.34366     	0.20159     	-0.59776           	5           
  500         	0.13966     	0.33769     	0.20232     	-0.53704           	9           
  1000        	0.14073     	0.33359     	0.20295     	-0.46668           	18          
---> try to load best state
              	0.14073     	0.33359     	0.20295     	-0.46668           	18               eval state : test_on_restore;
              	0.14073     	0.33359     	0.20295     	-0.46668           	18               eval state : test_on_last_epoch;
---> final result
              	0.14073     	0.33359     	0.20295     	-0.46668           	18               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 64, '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           	0.15611     	0.46213     	0.26037     	0.37844            	0           
  10          	0.15083     	0.45759     	0.25113     	0.30728            	0           
  100         	0.10657     	0.53862     	0.16528     	-0.65092           	3           
  300         	0.11114     	0.52829     	0.16801     	-0.59674           	9           
  500         	0.11315     	0.51679     	0.16979     	-0.53637           	15          
  epoch 881 reach nan state
---> try to load best state
              	0.11459     	0.50814     	0.17112     	-0.47725           	27               eval state : test_on_restore;
              	0.11315     	0.51679     	0.16979     	-0.53637           	27               eval state : test_on_last_epoch;
---> final result
              	0.11315     	0.51679     	0.16979     	-0.53637           	27               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 128, '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           	0.11353     	0.56808     	0.19346     	0.40836            	0           
  10          	0.12087     	0.55578     	0.20553     	0.48970            	0           
  100         	0.09685     	0.60624     	0.16081     	-0.37657           	7           
  300         	0.08898     	0.62290     	0.14673     	-0.57510           	22          
  500         	0.09001     	0.62388     	0.14698     	-0.52354           	37          
  1000        	0.09067     	0.62164     	0.14726     	-0.44865           	73          
---> try to load best state
              	0.09067     	0.62164     	0.14726     	-0.44865           	73               eval state : test_on_restore;
              	0.09067     	0.62164     	0.14726     	-0.44865           	73               eval state : test_on_last_epoch;
---> final result
              	0.09067     	0.62164     	0.14726     	-0.44865           	73               eval state : final;module_name : GAR;cp_record_file : True;
