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

  Demo sGAR 
  seed: None 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': 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']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 0.005
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	210.63278   	252.37054   	-257869.95252	0           
  10          	125.01874   	160.18661   	-85.09506   	0           
  100         	45.40002    	65.47220    	-3.77484    	0           
  300         	28.94412    	44.14979    	-1.47776    	1           
  500         	22.20938    	34.89003    	-0.81269    	2           
  1000        	14.96899    	24.31342    	-0.24140    	5           
---> try to load best state
              	14.96899    	24.31342    	-0.24140    	5                eval state : test_on_restore;
              	14.96899    	24.31342    	-0.24140    	5                eval state : test_on_last_epoch;
---> final result              	14.96899    	24.31342    	-0.24140    	5                eval state : final;
---> end


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

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

  Demo sGAR 
  seed: None 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': 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']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 0.005
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	210.38322   	251.42592   	-82842.63684	0           
  10          	118.71664   	153.03433   	-38.22472   	0           
  100         	41.84155    	61.46597    	-2.00078    	0           
  300         	26.73495    	41.57050    	-0.73400    	1           
  500         	20.60879    	32.96233    	-0.34582    	1           
  1000        	14.00757    	23.09778    	0.01218     	3           
---> try to load best state
              	14.00757    	23.09778    	0.01218     	3                eval state : test_on_restore;
              	14.00757    	23.09778    	0.01218     	3                eval state : test_on_last_epoch;
---> final result              	14.00757    	23.09778    	0.01218     	3                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	40.07047    	61.84052    	-1.42250    	0           
  10          	37.77575    	54.73055    	-0.12503    	0           
  epoch 82 reach nan state
---> try to load best state
              	24.12865    	38.15859    	0.49404     	0                eval state : test_on_restore;
              	37.77575    	54.73055    	-0.12503    	0                eval state : test_on_last_epoch;
---> final result              	24.12865    	38.15859    	0.49404     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	32.99833    	50.47533    	-0.60779    	0           
  10          	27.58201    	41.71680    	0.25310     	0           
  epoch 82 reach nan state
---> try to load best state
              	13.21131    	21.77273    	0.72066     	0                eval state : test_on_restore;
              	27.58201    	41.71680    	0.25310     	0                eval state : test_on_last_epoch;
---> final result              	13.21131    	21.77273    	0.72066     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	28.22544    	45.21492    	-0.08325    	0           
  10          	20.42904    	33.10627    	0.53217     	0           
  epoch 82 reach nan state
---> try to load best state
              	10.69432    	17.61243    	0.76180     	0                eval state : test_on_restore;
              	20.42904    	33.10627    	0.53217     	0                eval state : test_on_last_epoch;
---> final result              	10.69432    	17.61243    	0.76180     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	24.13500    	40.00422    	0.22947     	0           
  10          	15.42554    	26.07618    	0.54195     	0           
  epoch 83 reach nan state
---> try to load best state
              	10.23314    	16.33258    	0.71893     	0                eval state : test_on_restore;
              	15.42554    	26.07618    	0.54195     	0                eval state : test_on_last_epoch;
---> final result              	10.23314    	16.33258    	0.71893     	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': 'poisson_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': 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']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 0.005
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	211.82242   	251.55046   	-122767.59910	0           
  10          	120.42916   	155.94565   	-57.51232   	0           
  100         	45.66042    	67.19712    	-3.25720    	0           
  300         	29.24797    	45.56447    	-1.19921    	1           
  500         	22.49438    	36.05932    	-0.60086    	1           
  1000        	15.21324    	25.18613    	-0.08744    	3           
---> try to load best state
              	15.21324    	25.18613    	-0.08744    	3                eval state : test_on_restore;
              	15.21324    	25.18613    	-0.08744    	3                eval state : test_on_last_epoch;
---> final result              	15.21324    	25.18613    	-0.08744    	3                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	43.19938    	62.65221    	-1.91122    	0           
  10          	45.80243    	64.73147    	-0.08359    	0           
  epoch 82 reach nan state
---> try to load best state
              	25.99998    	42.80903    	0.53360     	0                eval state : test_on_restore;
              	45.80243    	64.73147    	-0.08359    	0                eval state : test_on_last_epoch;
---> final result              	25.99998    	42.80903    	0.53360     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	35.00186    	53.77617    	-0.33207    	0           
  10          	26.21719    	40.92827    	0.43204     	0           
  epoch 82 reach nan state
---> try to load best state
              	13.07904    	21.03485    	0.69160     	0                eval state : test_on_restore;
              	26.21719    	40.92827    	0.43204     	0                eval state : test_on_last_epoch;
---> final result              	13.07904    	21.03485    	0.69160     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	33.73537    	51.21088    	-0.13807    	0           
  10          	23.60112    	38.11234    	-0.10104    	0           
  epoch 82 reach nan state
---> try to load best state
              	12.64054    	20.50479    	0.70956     	0                eval state : test_on_restore;
              	23.60112    	38.11234    	-0.10104    	0                eval state : test_on_last_epoch;
---> final result              	12.64054    	20.50479    	0.70956     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	24.26661    	39.30286    	0.19946     	0           
  10          	15.73061    	26.04340    	0.65040     	0           
  epoch 83 reach nan state
---> try to load best state
              	11.26296    	17.65188    	0.61944     	0                eval state : test_on_restore;
              	15.73061    	26.04340    	0.65040     	0                eval state : test_on_last_epoch;
---> final result              	11.26296    	17.65188    	0.61944     	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': 'poisson_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': 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']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 0.005
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	211.18616   	253.80969   	-292044.11510	0           
  10          	128.78453   	166.29498   	-74.17139   	0           
  100         	48.41963    	70.24269    	-3.46730    	0           
  300         	31.19073    	47.50535    	-1.47071    	1           
  500         	23.93488    	37.48701    	-0.87736    	1           
  1000        	16.06837    	26.00690    	-0.35026    	3           
---> try to load best state
              	16.06837    	26.00690    	-0.35026    	3                eval state : test_on_restore;
              	16.06837    	26.00690    	-0.35026    	3                eval state : test_on_last_epoch;
---> final result              	16.06837    	26.00690    	-0.35026    	3                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	39.99251    	59.48953    	-1.19044    	0           
  10          	35.83870    	51.55094    	0.17957     	0           
  epoch 82 reach nan state
---> try to load best state
              	20.77824    	30.94133    	0.64281     	0                eval state : test_on_restore;
              	35.83870    	51.55094    	0.17957     	0                eval state : test_on_last_epoch;
---> final result              	20.77824    	30.94133    	0.64281     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	36.99292    	55.82146    	-0.86266    	0           
  10          	31.93897    	48.33100    	0.30558     	0           
  epoch 82 reach nan state
---> try to load best state
              	15.69792    	25.30143    	0.69742     	0                eval state : test_on_restore;
              	31.93897    	48.33100    	0.30558     	0                eval state : test_on_last_epoch;
---> final result              	15.69792    	25.30143    	0.69742     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	28.27905    	44.76816    	-0.15325    	0           
  10          	21.90781    	35.64798    	0.50526     	0           
  epoch 82 reach nan state
---> try to load best state
              	12.83122    	20.44952    	0.64645     	0                eval state : test_on_restore;
              	21.90781    	35.64798    	0.50526     	0                eval state : test_on_last_epoch;
---> final result              	12.83122    	20.44952    	0.64645     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	23.69431    	38.17680    	0.03561     	0           
  10          	17.13125    	27.48287    	0.54881     	0           
  100         	10.97697    	17.51964    	0.67732     	0           
  epoch 110 reach nan state
---> try to load best state
              	11.57482    	18.25300    	0.65790     	0                eval state : test_on_restore;
              	10.97697    	17.51964    	0.67732     	0                eval state : test_on_last_epoch;
---> final result              	10.97697    	17.51964    	0.67732     	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': 'poisson_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': 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']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 0.005
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	213.38510   	253.16353   	-1905079.30876	0           
  10          	126.07220   	160.95953   	-97.37874   	0           
  100         	44.97092    	64.21784    	-2.91566    	0           
  300         	28.08861    	42.40437    	-1.62910    	1           
  500         	21.33759    	33.15918    	-1.27765    	2           
  1000        	14.24414    	22.83652    	-0.18482    	3           
---> try to load best state
              	14.24414    	22.83652    	-0.18482    	3                eval state : test_on_restore;
              	14.24414    	22.83652    	-0.18482    	3                eval state : test_on_last_epoch;
---> final result              	14.24414    	22.83652    	-0.18482    	3                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	43.27825    	64.08122    	-1.86862    	0           
  10          	38.42925    	55.19318    	-0.51937    	0           
  epoch 82 reach nan state
---> try to load best state
              	24.75971    	36.82383    	0.42962     	0                eval state : test_on_restore;
              	38.42925    	55.19318    	-0.51937    	0                eval state : test_on_last_epoch;
---> final result              	24.75971    	36.82383    	0.42962     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	36.97389    	56.69377    	-0.91326    	0           
  10          	29.24150    	45.50004    	0.46463     	0           
  epoch 82 reach nan state
---> try to load best state
              	14.95373    	24.07704    	0.70151     	0                eval state : test_on_restore;
              	29.24150    	45.50004    	0.46463     	0                eval state : test_on_last_epoch;
---> final result              	14.95373    	24.07704    	0.70151     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	34.58321    	53.41492    	-0.58155    	0           
  10          	25.88948    	40.19734    	0.27852     	0           
  epoch 82 reach nan state
---> try to load best state
              	12.94362    	20.96330    	0.69580     	0                eval state : test_on_restore;
              	25.88948    	40.19734    	0.27852     	0                eval state : test_on_last_epoch;
---> final result              	12.94362    	20.96330    	0.69580     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	23.30516    	37.64498    	0.18042     	0           
  10          	17.69924    	28.32675    	0.39581     	0           
  100         	12.30259    	18.56029    	0.65512     	0           
  300         	13.03591    	21.12193    	0.69279     	1           
  epoch 366 reach nan state
---> try to load best state
              	12.58088    	21.02877    	0.71298     	2                eval state : test_on_restore;
              	13.03591    	21.12193    	0.69279     	2                eval state : test_on_last_epoch;
---> final result              	12.58088    	21.02877    	0.71298     	2                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': 'poisson_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': 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']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 0.005
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	213.65561   	253.34459   	-89008.53889	0           
  10          	120.11757   	154.02089   	-47.98645   	0           
  100         	42.18929    	61.51967    	-2.84857    	0           
  300         	26.55733    	40.92224    	-1.18389    	1           
  500         	20.34006    	32.10691    	-0.67622    	2           
  1000        	13.73893    	22.16656    	-0.21351    	4           
---> try to load best state
              	13.73893    	22.16656    	-0.21351    	4                eval state : test_on_restore;
              	13.73893    	22.16656    	-0.21351    	4                eval state : test_on_last_epoch;
---> final result              	13.73893    	22.16656    	-0.21351    	4                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	46.96030    	67.73339    	-4.70032    	0           
  10          	40.78803    	58.70024    	-0.01480    	0           
  epoch 82 reach nan state
---> try to load best state
              	28.05376    	39.53801    	0.54074     	0                eval state : test_on_restore;
              	40.78803    	58.70024    	-0.01480    	0                eval state : test_on_last_epoch;
---> final result              	28.05376    	39.53801    	0.54074     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	34.64425    	55.42404    	-1.02917    	0           
  10          	25.13391    	42.00705    	0.18460     	0           
  epoch 82 reach nan state
---> try to load best state
              	19.51708    	28.34675    	0.55762     	0                eval state : test_on_restore;
              	25.13391    	42.00705    	0.18460     	0                eval state : test_on_last_epoch;
---> final result              	19.51708    	28.34675    	0.55762     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	30.60405    	47.38974    	-0.95734    	0           
  10          	19.76567    	31.63984    	0.51218     	0           
  epoch 82 reach nan state
---> try to load best state
              	11.07808    	17.84256    	0.65327     	0                eval state : test_on_restore;
              	19.76567    	31.63984    	0.51218     	0                eval state : test_on_last_epoch;
---> final result              	11.07808    	17.84256    	0.65327     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	22.58355    	36.33956    	0.13201     	0           
  10          	15.58774    	25.84729    	0.56951     	0           
  epoch 82 reach nan state
---> try to load best state
              	10.03827    	15.69639    	0.67558     	0                eval state : test_on_restore;
              	15.58774    	25.84729    	0.56951     	0                eval state : test_on_last_epoch;
---> final result              	10.03827    	15.69639    	0.67558     	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': 'poisson_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': 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']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 0.005
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	212.27139   	252.02650   	-118386.69990	0           
  10          	123.51038   	157.99567   	-57.11473   	0           
  100         	43.85837    	63.56153    	-2.96737    	0           
  300         	27.69105    	42.35290    	-1.14376    	1           
  500         	21.19018    	33.28394    	-0.60999    	1           
  1000        	14.29619    	23.06484    	-0.12572    	3           
---> try to load best state
              	14.29619    	23.06484    	-0.12572    	3                eval state : test_on_restore;
              	14.29619    	23.06484    	-0.12572    	3                eval state : test_on_last_epoch;
---> final result              	14.29619    	23.06484    	-0.12572    	3                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	46.24546    	66.93858    	-1.64180    	0           
  10          	37.46307    	54.30498    	-0.00307    	0           
  epoch 82 reach nan state
---> try to load best state
              	35.10723    	52.58788    	0.39858     	0                eval state : test_on_restore;
              	37.46307    	54.30498    	-0.00307    	0                eval state : test_on_last_epoch;
---> final result              	35.10723    	52.58788    	0.39858     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	33.56465    	51.81995    	-0.60865    	0           
  10          	24.48106    	37.26200    	0.41496     	0           
  epoch 82 reach nan state
---> try to load best state
              	12.47912    	20.62396    	0.71227     	0                eval state : test_on_restore;
              	24.48106    	37.26200    	0.41496     	0                eval state : test_on_last_epoch;
---> final result              	12.47912    	20.62396    	0.71227     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	34.39931    	54.64715    	-0.45458    	0           
  10          	30.34690    	48.78139    	0.41150     	0           
  epoch 82 reach nan state
---> try to load best state
              	14.54065    	24.80184    	0.68981     	0                eval state : test_on_restore;
              	30.34690    	48.78139    	0.41150     	0                eval state : test_on_last_epoch;
---> final result              	14.54065    	24.80184    	0.68981     	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': 'poisson_v4_02', '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']
  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         	rmse        	r2          	time        
  1           	21.81980    	34.95700    	0.20645     	0           
  10          	16.07029    	26.04330    	0.59542     	0           
  100         	10.81293    	16.60798    	0.66473     	0           
  epoch 107 reach nan state
---> try to load best state
              	10.88964    	16.72396    	0.65025     	0                eval state : test_on_restore;
              	10.81293    	16.60798    	0.66473     	0                eval state : test_on_last_epoch;
---> final result              	10.81293    	16.60798    	0.66473     	0                eval state : final;module_name : SGAR;cp_record_file : True;
