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

  Demo GAR 
  seed: None 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
----------------------------------------

  Demo GAR 
  seed: None 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
----------------------------------------

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> 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': False}
  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           	211.53770   	251.72453   	-225023.97269	0           
  10          	127.30367   	162.22102   	-62.75565   	0           
  100         	45.49870    	65.30758    	-3.19953    	5           
  300         	28.76116    	43.50648    	-1.31132    	22          
  500         	21.94339    	34.09245    	-0.76239    	39          
  1000        	14.67123    	23.44213    	-0.28088    	70          
---> try to load best state
              	14.67123    	23.44213    	-0.28088    	70               eval state : test_on_restore;
              	14.67123    	23.44213    	-0.28088    	70               eval state : test_on_last_epoch;
---> final result              	14.67123    	23.44213    	-0.28088    	70               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': False}
  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           	39.84491    	59.93818    	-1.31327    	0           
  10          	36.39268    	51.26834    	0.17409     	0           
  epoch 82 reach nan state
---> try to load best state
              	21.37714    	33.33892    	0.53970     	4                eval state : test_on_restore;
              	36.39268    	51.26834    	0.17409     	4                eval state : test_on_last_epoch;
---> final result              	21.37714    	33.33892    	0.53970     	4                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': False}
  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           	41.72703    	61.04860    	-1.96627    	0           
  10          	32.52454    	49.39393    	0.32058     	0           
  epoch 82 reach nan state
---> try to load best state
              	16.07181    	25.76642    	0.68262     	5                eval state : test_on_restore;
              	32.52454    	49.39393    	0.32058     	5                eval state : test_on_last_epoch;
---> final result              	16.07181    	25.76642    	0.68262     	5                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': False}
  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           	33.68251    	50.12173    	-1.16334    	0           
  10          	23.49915    	38.51501    	0.48425     	0           
  epoch 82 reach nan state
---> try to load best state
              	13.25215    	20.99309    	0.54832     	4                eval state : test_on_restore;
              	23.49915    	38.51501    	0.48425     	4                eval state : test_on_last_epoch;
---> final result              	13.25215    	20.99309    	0.54832     	5                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': False}
  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           	24.96875    	39.77734    	0.12075     	0           
  10          	17.75327    	28.30490    	0.49498     	0           
  100         	10.74406    	16.64250    	0.62290     	10          
  epoch 147 reach nan state
---> try to load best state
              	10.71693    	16.62410    	0.60963     	15               eval state : test_on_restore;
              	10.74406    	16.64250    	0.62290     	15               eval state : test_on_last_epoch;
---> final result              	10.71693    	16.62410    	0.60963     	15               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> 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': False}
  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   	251.55048   	-122767.71771	0           
  10          	120.42915   	155.94563   	-57.51240   	0           
  100         	45.66053    	67.19720    	-3.25724    	7           
  300         	29.24798    	45.56444    	-1.19919    	24          
  500         	22.49440    	36.05929    	-0.60087    	39          
  1000        	15.21329    	25.18618    	-0.08741    	67          
---> try to load best state
              	15.21329    	25.18618    	-0.08741    	67               eval state : test_on_restore;
              	15.21329    	25.18618    	-0.08741    	67               eval state : test_on_last_epoch;
---> final result              	15.21329    	25.18618    	-0.08741    	67               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': False}
  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           	41.19660    	62.82104    	-1.56353    	0           
  10          	46.42551    	67.21198    	0.00481     	2           
  epoch 82 reach nan state
---> try to load best state
              	35.42624    	48.75177    	0.26835     	6                eval state : test_on_restore;
              	46.42551    	67.21198    	0.00481     	6                eval state : test_on_last_epoch;
---> final result              	35.42624    	48.75177    	0.26835     	6                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': False}
  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           	38.50723    	58.53464    	-1.24014    	0           
  10          	32.14870    	47.73957    	0.20370     	0           
  epoch 82 reach nan state
---> try to load best state
              	16.23462    	25.75971    	0.67608     	8                eval state : test_on_restore;
              	32.14870    	47.73957    	0.20370     	8                eval state : test_on_last_epoch;
---> final result              	16.23462    	25.75971    	0.67608     	8                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': False}
  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           	28.88329    	44.98865    	-0.13176    	0           
  10          	23.31454    	36.99429    	0.43124     	0           
  epoch 82 reach nan state
---> try to load best state
              	12.43722    	20.29040    	0.67248     	8                eval state : test_on_restore;
              	23.31454    	36.99429    	0.43124     	8                eval state : test_on_last_epoch;
---> final result              	12.43722    	20.29040    	0.67248     	8                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': False}
  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           	26.29686    	41.75445    	0.18428     	0           
  10          	18.34252    	29.33362    	0.56233     	2           
  100         	10.61063    	17.12284    	0.70847     	9           
  epoch 137 reach nan state
---> try to load best state
              	10.61657    	17.16449    	0.71066     	13               eval state : test_on_restore;
              	10.61063    	17.12284    	0.70847     	13               eval state : test_on_last_epoch;
---> final result              	10.61063    	17.12284    	0.70847     	13               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> 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': False}
  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   	-292044.44242	0           
  10          	128.78470   	166.29515   	-74.17184   	2           
  100         	48.41975    	70.24280    	-3.46733    	11          
  300         	31.19072    	47.50534    	-1.47070    	25          
  500         	23.93498    	37.48708    	-0.87737    	39          
  1000        	16.06852    	26.00699    	-0.35027    	68          
---> try to load best state
              	16.06852    	26.00699    	-0.35027    	68               eval state : test_on_restore;
              	16.06852    	26.00699    	-0.35027    	69               eval state : test_on_last_epoch;
---> final result              	16.06852    	26.00699    	-0.35027    	69               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': False}
  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           	44.06327    	64.76658    	-2.13241    	0           
  10          	42.13671    	58.91288    	-0.08523    	0           
  epoch 82 reach nan state
---> try to load best state
              	27.35136    	38.52694    	0.37882     	5                eval state : test_on_restore;
              	42.13671    	58.91288    	-0.08523    	6                eval state : test_on_last_epoch;
---> final result              	27.35136    	38.52694    	0.37882     	7                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': False}
  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           	38.26266    	57.57529    	-0.62200    	0           
  10          	31.51951    	49.75218    	0.17958     	0           
  epoch 82 reach nan state
---> try to load best state
              	15.44465    	24.60780    	0.67001     	7                eval state : test_on_restore;
              	31.51951    	49.75218    	0.17958     	7                eval state : test_on_last_epoch;
---> final result              	15.44465    	24.60780    	0.67001     	8                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': False}
  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           	36.91357    	56.44000    	-1.14873    	0           
  10          	25.45332    	40.60452    	0.37478     	0           
  epoch 82 reach nan state
---> try to load best state
              	15.28679    	24.94093    	0.60475     	9                eval state : test_on_restore;
              	25.45332    	40.60452    	0.37478     	9                eval state : test_on_last_epoch;
---> final result              	15.28679    	24.94093    	0.60475     	9                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': False}
  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           	22.14356    	35.64460    	0.20105     	0           
  10          	18.03942    	29.25530    	0.42015     	3           
  100         	11.22706    	17.73711    	0.61312     	11          
  epoch 165 reach nan state
---> try to load best state
              	11.24701    	18.08660    	0.61146     	20               eval state : test_on_restore;
              	11.22706    	17.73711    	0.61312     	20               eval state : test_on_last_epoch;
---> final result              	11.22706    	17.73711    	0.61312     	20               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> 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': False}
  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   	-1905078.96401	0           
  10          	126.07226   	160.95959   	-97.37890   	2           
  100         	44.97102    	64.21793    	-2.91568    	11          
  300         	28.08860    	42.40435    	-1.62905    	30          
  500         	21.33769    	33.15926    	-1.27737    	48          
  1000        	14.24411    	22.83648    	-0.18485    	82          
---> try to load best state
              	14.24411    	22.83648    	-0.18485    	82               eval state : test_on_restore;
              	14.24411    	22.83648    	-0.18485    	82               eval state : test_on_last_epoch;
---> final result              	14.24411    	22.83648    	-0.18485    	82               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': False}
  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           	49.73075    	72.23319    	-1.15376    	0           
  10          	68.15590    	96.37737    	-1.89670    	1           
  epoch 82 reach nan state
---> try to load best state
              	52.21224    	68.09583    	-0.26218    	8                eval state : test_on_restore;
              	68.15590    	96.37737    	-1.89670    	8                eval state : test_on_last_epoch;
---> final result              	52.21224    	68.09583    	-0.26218    	8                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': False}
  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           	34.85770    	53.24105    	-0.78001    	0           
  10          	23.55189    	36.81804    	0.35797     	0           
  epoch 82 reach nan state
---> try to load best state
              	13.18090    	21.25288    	0.70206     	5                eval state : test_on_restore;
              	23.55189    	36.81804    	0.35797     	5                eval state : test_on_last_epoch;
---> final result              	13.18090    	21.25288    	0.70206     	5                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': False}
  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           	32.21838    	50.92308    	-0.21226    	0           
  10          	22.76719    	36.47940    	0.51678     	1           
  epoch 82 reach nan state
---> try to load best state
              	12.72284    	20.47570    	0.72628     	6                eval state : test_on_restore;
              	22.76719    	36.47940    	0.51678     	6                eval state : test_on_last_epoch;
---> final result              	12.72284    	20.47570    	0.72628     	6                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': False}
  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           	22.81475    	36.15830    	0.20045     	0           
  10          	15.07880    	24.53333    	0.64495     	2           
  epoch 83 reach nan state
---> try to load best state
              	10.90832    	16.49271    	0.67187     	8                eval state : test_on_restore;
              	15.07880    	24.53333    	0.64495     	8                eval state : test_on_last_epoch;
---> final result              	10.90832    	16.49271    	0.67187     	8                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> 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': False}
  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   	-89008.59682	0           
  10          	120.11760   	154.02092   	-47.98650   	0           
  100         	42.18934    	61.51971    	-2.84859    	6           
  300         	26.55729    	40.92216    	-1.18390    	23          
  500         	20.34007    	32.10691    	-0.67622    	40          
  1000        	13.73903    	22.16665    	-0.21352    	78          
---> try to load best state
              	13.73903    	22.16665    	-0.21352    	78               eval state : test_on_restore;
              	13.73903    	22.16665    	-0.21352    	78               eval state : test_on_last_epoch;
---> final result              	13.73903    	22.16665    	-0.21352    	78               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': False}
  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           	39.86814    	59.94321    	-1.13467    	0           
  10          	37.00465    	54.09527    	0.07228     	0           
  epoch 82 reach nan state
---> try to load best state
              	26.57316    	37.67705    	0.47829     	5                eval state : test_on_restore;
              	37.00465    	54.09527    	0.07228     	5                eval state : test_on_last_epoch;
---> final result              	26.57316    	37.67705    	0.47829     	5                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': False}
  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           	39.18200    	58.32392    	-1.97222    	0           
  10          	29.24792    	45.91527    	0.43446     	0           
  epoch 82 reach nan state
---> try to load best state
              	14.51696    	23.13383    	0.63569     	5                eval state : test_on_restore;
              	29.24792    	45.91527    	0.43446     	5                eval state : test_on_last_epoch;
---> final result              	14.51696    	23.13383    	0.63569     	5                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': False}
  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           	29.80904    	47.37788    	-0.10243    	0           
  10          	23.90211    	37.82828    	0.34452     	0           
  epoch 82 reach nan state
---> try to load best state
              	11.57660    	18.56722    	0.71162     	7                eval state : test_on_restore;
              	23.90211    	37.82828    	0.34452     	8                eval state : test_on_last_epoch;
---> final result              	11.57660    	18.56722    	0.71162     	8                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': False}
  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           	24.89661    	40.82449    	0.19904     	0           
  10          	17.85416    	29.77864    	0.60731     	0           
  epoch 83 reach nan state
---> try to load best state
              	10.42579    	16.55262    	0.69646     	9                eval state : test_on_restore;
              	17.85416    	29.77864    	0.60731     	9                eval state : test_on_last_epoch;
---> final result              	10.42579    	16.55262    	0.69646     	9                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> 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': False}
  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   	252.02652   	-118386.86966	0           
  10          	123.51045   	157.99574   	-57.11483   	3           
  100         	43.85841    	63.56157    	-2.96737    	8           
  300         	27.69118    	42.35299    	-1.14377    	24          
  500         	21.19012    	33.28389    	-0.60996    	39          
  1000        	14.29623    	23.06486    	-0.12576    	80          
---> try to load best state
              	14.29623    	23.06486    	-0.12576    	80               eval state : test_on_restore;
              	14.29623    	23.06486    	-0.12576    	80               eval state : test_on_last_epoch;
---> final result              	14.29623    	23.06486    	-0.12576    	80               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': False}
  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           	41.87212    	61.43827    	-1.20668    	0           
  10          	52.08652    	71.90506    	-0.19821    	1           
  epoch 81 reach nan state
---> try to load best state
              	30.27719    	45.98061    	0.48996     	6                eval state : test_on_restore;
              	52.08652    	71.90506    	-0.19821    	6                eval state : test_on_last_epoch;
---> final result              	30.27719    	45.98061    	0.48996     	6                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': False}
  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           	36.22432    	54.43550    	-0.55665    	0           
  10          	23.66431    	38.14644    	0.46584     	0           
  epoch 82 reach nan state
---> try to load best state
              	13.06542    	20.88235    	0.68578     	9                eval state : test_on_restore;
              	23.66431    	38.14644    	0.46584     	9                eval state : test_on_last_epoch;
---> final result              	13.06542    	20.88235    	0.68578     	9                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': False}
  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           	32.54683    	51.41591    	-0.59913    	0           
  10          	21.65900    	35.14963    	0.45142     	1           
  epoch 82 reach nan state
---> try to load best state
              	12.28882    	20.02645    	0.68405     	7                eval state : test_on_restore;
              	21.65900    	35.14963    	0.45142     	7                eval state : test_on_last_epoch;
---> final result              	12.28882    	20.02645    	0.68405     	7                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': False}
  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           	23.29782    	37.62072    	0.10224     	0           
  10          	16.09884    	26.88943    	0.49178     	0           
  epoch 82 reach nan state
---> try to load best state
              	10.63570    	16.48883    	0.62599     	8                eval state : test_on_restore;
              	16.09884    	26.88943    	0.49178     	8                eval state : test_on_last_epoch;
---> final result              	10.63570    	16.48883    	0.62599     	8                eval state : final;module_name : SGAR;cp_record_file : True;
