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

  Demo DMFAL 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': [32, 4], 'eval_start_index': 0, 'eval_sample': [128, 128], 'seed': None, 'interp_data': False}
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04154     	-12214987109.61785	0.08286     	0           
  10          	0.04155     	-8307682.33156	0.08287     	0           
  100         	0.01248     	-0.61965    	0.03036     	0           
  300         	0.01031     	0.65089     	0.02518     	2           
  500         	0.00919     	0.75748     	0.02361     	3           
  1000        	0.00845     	0.80427     	0.02023     	6           
---> try to load best state
              	0.00845     	0.80427     	0.02023     	6                eval state : test_on_restore;
              	0.00845     	0.80427     	0.02023     	6                eval state : test_on_last_epoch;
---> final result
              	0.00845     	0.80427     	0.02023     	6                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': [32, 8], 'eval_start_index': 0, 'eval_sample': [128, 128], 'seed': None, 'interp_data': False}
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03184     	-6151444633.26522	0.07167     	0           
  10          	0.03185     	-5974543.44773	0.07168     	0           
  100         	0.02441     	0.41595     	0.04681     	0           
  300         	0.00873     	0.81380     	0.02509     	1           
----------------------------------------

  Demo DMFAL 
  seed: None 
  interp_data: False 

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', '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           	2.39786     	3.21430     	-329.52174  	0           
  10          	1.43565     	2.30529     	-0.69608    	0           
  100         	1.17115     	2.03519     	0.14541     	0           
  300         	1.07181     	1.99516     	0.32998     	1           
  500         	1.02973     	1.99120     	0.39276     	1           
  1000        	0.96969     	1.99617     	0.51908     	3           
---> try to load best state
              	0.96969     	1.99617     	0.51908     	3                eval state : test_on_restore;
              	0.96969     	1.99617     	0.51908     	3                eval state : test_on_last_epoch;
---> final result              	0.96969     	1.99617     	0.51908     	3                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', '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           	1.52376     	2.68715     	-0.43816    	0           
  10          	1.34705     	2.32654     	-0.01048    	0           
  100         	1.28232     	2.25156     	0.04325     	0           
  300         	2.19744     	3.13321     	-4.23043    	1           
  500         	2.27862     	3.25928     	-4.89799    	1           
  epoch 722 reach nan state
---> try to load best state
              	2.34954     	3.36063     	-5.44367    	2                eval state : test_on_restore;
              	2.27862     	3.25928     	-4.89799    	3                eval state : test_on_last_epoch;
---> final result              	2.27862     	3.25928     	-4.89799    	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', '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           	1.41315     	2.59885     	-0.49942    	0           
  10          	1.27732     	2.41459     	-0.24674    	0           
  100         	1.37300     	2.40517     	0.01848     	0           
  300         	1.46854     	2.62525     	0.47794     	1           
  500         	1.95773     	3.30308     	0.33904     	2           
  1000        	3.36742     	5.95319     	0.05392     	5           
---> try to load best state
              	3.36742     	5.95319     	0.05392     	5                eval state : test_on_restore;
              	3.36742     	5.95319     	0.05392     	5                eval state : test_on_last_epoch;
---> final result              	3.36742     	5.95319     	0.05392     	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': 'DoublePendu_mfGent_v01', '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           	1.08684     	2.13463     	0.46126     	0           
  10          	1.12400     	2.23723     	0.42273     	0           
  100         	1.16444     	2.36263     	0.40227     	0           
  300         	1.09652     	2.20760     	0.24871     	1           
  500         	1.09740     	2.15739     	0.25179     	2           
  epoch 673 reach nan state
---> try to load best state
              	1.13103     	2.18605     	0.26321     	3                eval state : test_on_restore;
              	1.09740     	2.15739     	0.25179     	3                eval state : test_on_last_epoch;
---> final result              	1.09740     	2.15739     	0.25179     	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', '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           	1.09520     	2.13743     	0.41621     	0           
  10          	1.02108     	1.94437     	0.42142     	0           
  100         	0.86387     	1.77073     	0.62067     	0           
  300         	0.87592     	1.84420     	0.45147     	1           
  500         	0.88253     	1.81500     	-0.04450    	3           
  1000        	0.91815     	1.84742     	0.53088     	7           
---> try to load best state
              	0.91815     	1.84742     	0.53088     	7                eval state : test_on_restore;
              	0.91815     	1.84742     	0.53088     	7                eval state : test_on_last_epoch;
---> final result              	0.91815     	1.84742     	0.53088     	7                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': 'DoublePendu_mfGent_v01', '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           	2.36578     	3.16319     	-147.02580  	0           
  10          	1.41327     	2.13519     	-0.38282    	0           
  100         	1.17857     	1.99646     	0.25794     	0           
  300         	1.06482     	1.94411     	0.38258     	2           
  500         	1.00681     	1.91727     	0.41198     	3           
  1000        	0.93133     	1.88719     	0.34143     	6           
---> try to load best state
              	0.93133     	1.88719     	0.34143     	6                eval state : test_on_restore;
              	0.93133     	1.88719     	0.34143     	6                eval state : test_on_last_epoch;
---> final result              	0.93133     	1.88719     	0.34143     	6                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', '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           	1.20799     	2.22965     	0.33101     	0           
  10          	1.22948     	2.27249     	0.31749     	0           
  100         	1.40334     	2.46104     	-0.38058    	0           
  epoch 141 reach nan state
---> try to load best state
              	1.40520     	2.46115     	-0.38831    	0                eval state : test_on_restore;
              	1.40334     	2.46104     	-0.38058    	0                eval state : test_on_last_epoch;
---> final result              	1.40334     	2.46104     	-0.38058    	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': 'DoublePendu_mfGent_v01', '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           	1.08331     	2.07279     	0.38632     	0           
  10          	1.14146     	2.15733     	0.37000     	0           
  100         	1.16391     	2.12443     	0.34959     	0           
  300         	1.19813     	2.10690     	0.36838     	1           
  500         	1.20059     	2.07492     	0.36798     	2           
  epoch 529 reach nan state
---> try to load best state
              	1.19810     	2.08221     	0.36516     	3                eval state : test_on_restore;
              	1.20059     	2.07492     	0.36798     	3                eval state : test_on_last_epoch;
---> final result              	1.20059     	2.07492     	0.36798     	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', '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           	0.99839     	1.85181     	0.28782     	0           
  10          	0.98515     	1.83666     	0.35832     	0           
  100         	1.02454     	1.92758     	0.32481     	0           
  300         	1.04578     	2.02369     	0.29110     	2           
  500         	1.07065     	2.05790     	0.22332     	3           
  1000        	1.07606     	2.05238     	0.23396     	6           
---> try to load best state
              	1.07606     	2.05238     	0.23396     	6                eval state : test_on_restore;
              	1.07606     	2.05238     	0.23396     	6                eval state : test_on_last_epoch;
---> final result              	1.07606     	2.05238     	0.23396     	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': 'DoublePendu_mfGent_v01', '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           	1.00074     	1.88477     	0.33674     	0           
  10          	0.98259     	1.83575     	0.40289     	0           
  100         	1.00478     	2.08027     	-1.16338    	0           
  300         	0.93992     	1.85673     	0.48542     	1           
  500         	0.94647     	1.84310     	0.51790     	3           
  1000        	0.96544     	1.82873     	0.51752     	6           
---> try to load best state
              	0.96544     	1.82873     	0.51752     	6                eval state : test_on_restore;
              	0.96544     	1.82873     	0.51752     	6                eval state : test_on_last_epoch;
---> final result              	0.96544     	1.82873     	0.51752     	6                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': 'DoublePendu_mfGent_v01', '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           	2.37177     	3.17134     	-214.56985  	0           
  10          	1.51447     	2.26648     	-1.31080    	0           
  100         	1.24982     	2.05923     	-0.40751    	0           
  300         	1.12436     	1.96799     	-0.01498    	1           
  500         	1.05721     	1.92637     	0.11706     	1           
  1000        	0.97194     	2.01573     	-1.01417    	3           
---> try to load best state
              	0.97194     	2.01573     	-1.01417    	3                eval state : test_on_restore;
              	0.97194     	2.01573     	-1.01417    	3                eval state : test_on_last_epoch;
---> final result              	0.97194     	2.01573     	-1.01417    	3                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', '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           	1.46530     	2.51380     	-1.20140    	0           
  10          	1.30113     	2.27646     	-0.12001    	0           
  100         	1.15688     	2.08790     	-0.71622    	0           
  300         	1.25364     	2.17870     	-0.98731    	1           
  500         	1.26340     	2.18992     	-1.09315    	2           
  1000        	1.28217     	2.19510     	-3.04309    	3           
---> try to load best state
              	1.28217     	2.19510     	-3.04309    	3                eval state : test_on_restore;
              	1.28217     	2.19510     	-3.04309    	3                eval state : test_on_last_epoch;
---> final result              	1.28217     	2.19510     	-3.04309    	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', '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           	1.11187     	2.14871     	-1.27788    	0           
  10          	1.13273     	2.18630     	-0.94380    	0           
  100         	1.40037     	2.65571     	-1.56842    	0           
  300         	1.35574     	2.44452     	-0.97005    	1           
  500         	1.41925     	2.55684     	-1.25540    	2           
  1000        	1.49681     	2.68682     	-0.86877    	3           
---> try to load best state
              	1.49681     	2.68682     	-0.86877    	4                eval state : test_on_restore;
              	1.49681     	2.68682     	-0.86877    	4                eval state : test_on_last_epoch;
---> final result              	1.49681     	2.68682     	-0.86877    	4                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', '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           	1.05394     	2.12467     	-2.02619    	0           
  10          	1.02915     	2.04033     	-4.58308    	0           
  100         	1.09105     	2.18989     	-1.20742    	0           
  300         	1.02454     	2.04192     	-4.69975    	1           
  500         	1.05436     	2.10006     	-5.76630    	2           
  1000        	1.08522     	2.09366     	-5.62148    	5           
---> try to load best state
              	1.08522     	2.09366     	-5.62148    	5                eval state : test_on_restore;
              	1.08522     	2.09366     	-5.62148    	5                eval state : test_on_last_epoch;
---> final result              	1.08522     	2.09366     	-5.62148    	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': 'DoublePendu_mfGent_v01', '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           	1.10954     	2.07224     	-1.21066    	0           
  10          	1.06928     	2.00951     	-1.27014    	0           
  epoch 92 reach nan state
---> try to load best state
              	1.02917     	1.90703     	-2.37655    	0                eval state : test_on_restore;
              	1.06928     	2.00951     	-1.27014    	0                eval state : test_on_last_epoch;
---> final result              	1.02917     	1.90703     	-2.37655    	0                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': 'DoublePendu_mfGent_v01', '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           	2.41455     	3.21986     	-277.55366  	0           
  10          	1.50151     	2.36498     	-0.91584    	0           
  100         	1.25142     	2.15157     	-0.08444    	0           
  300         	1.10186     	2.10645     	0.12544     	1           
  500         	1.04258     	2.09161     	-0.20302    	1           
  1000        	0.96077     	2.04398     	0.27723     	3           
---> try to load best state
              	0.96077     	2.04398     	0.27723     	3                eval state : test_on_restore;
              	0.96077     	2.04398     	0.27723     	3                eval state : test_on_last_epoch;
---> final result              	0.96077     	2.04398     	0.27723     	3                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', '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           	1.11703     	2.31362     	0.34241     	0           
  10          	1.08335     	2.17440     	0.15874     	0           
  epoch 84 reach nan state
---> try to load best state
              	1.07874     	2.14924     	0.35967     	0                eval state : test_on_restore;
              	1.08335     	2.17440     	0.15874     	0                eval state : test_on_last_epoch;
---> final result              	1.07874     	2.14924     	0.35967     	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': 'DoublePendu_mfGent_v01', '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           	1.21483     	2.36795     	-0.29760    	0           
  10          	1.10007     	2.11405     	0.11808     	0           
  100         	1.19097     	2.23338     	0.21566     	0           
  300         	1.34083     	2.42375     	-0.77230    	1           
  500         	1.35265     	2.46059     	-1.04211    	2           
  1000        	1.35909     	2.39727     	-0.37515    	4           
---> try to load best state
              	1.35909     	2.39727     	-0.37515    	4                eval state : test_on_restore;
              	1.35909     	2.39727     	-0.37515    	4                eval state : test_on_last_epoch;
---> final result              	1.35909     	2.39727     	-0.37515    	4                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', '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           	1.18345     	2.30355     	0.04672     	0           
  10          	1.12799     	2.18526     	0.11615     	0           
  100         	1.15029     	2.19350     	0.20461     	0           
  300         	1.14898     	2.13603     	0.20038     	1           
  500         	1.18114     	2.13912     	0.12653     	2           
  1000        	1.23440     	2.18487     	0.19235     	5           
---> try to load best state
              	1.23440     	2.18487     	0.19235     	5                eval state : test_on_restore;
              	1.23440     	2.18487     	0.19235     	5                eval state : test_on_last_epoch;
---> final result              	1.23440     	2.18487     	0.19235     	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': 'DoublePendu_mfGent_v01', '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           	1.04395     	2.05785     	-0.52032    	0           
  10          	0.97567     	1.88754     	-0.15121    	0           
  epoch 93 reach nan state
---> try to load best state
              	0.94431     	1.88758     	0.07009     	0                eval state : test_on_restore;
              	0.97567     	1.88754     	-0.15121    	0                eval state : test_on_last_epoch;
---> final result              	0.97567     	1.88754     	-0.15121    	0                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': 'DoublePendu_mfGent_v01', '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           	2.39412     	3.22959     	-297.44201  	0           
  10          	1.51924     	2.30563     	-1.01699    	0           
  100         	1.24431     	2.08861     	-0.17569    	0           
  300         	1.12577     	1.99679     	0.07886     	1           
  500         	1.06615     	1.94901     	0.18222     	1           
  1000        	0.83233     	1.70373     	0.43372     	3           
---> try to load best state
              	0.83233     	1.70373     	0.43372     	3                eval state : test_on_restore;
              	0.83233     	1.70373     	0.43372     	3                eval state : test_on_last_epoch;
---> final result              	0.83233     	1.70373     	0.43372     	3                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', '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           	1.50189     	2.75881     	-0.11172    	0           
  10          	1.16602     	2.18596     	-0.05949    	0           
  100         	1.32574     	2.49607     	0.14408     	0           
  epoch 177 reach nan state
---> try to load best state
              	1.28692     	2.39071     	0.07830     	0                eval state : test_on_restore;
              	1.32574     	2.49607     	0.14408     	0                eval state : test_on_last_epoch;
---> final result              	1.28692     	2.39071     	0.07830     	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': 'DoublePendu_mfGent_v01', '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           	0.99239     	1.99602     	-0.05209    	0           
  10          	1.04052     	2.08639     	0.16454     	0           
  100         	1.38373     	2.46469     	0.30376     	0           
  300         	1.34956     	2.24527     	0.04369     	1           
  500         	1.31470     	2.15261     	-0.00919    	2           
  1000        	1.25954     	2.09178     	-0.12444    	4           
---> try to load best state
              	1.25954     	2.09178     	-0.12444    	4                eval state : test_on_restore;
              	1.25954     	2.09178     	-0.12444    	4                eval state : test_on_last_epoch;
---> final result              	1.25954     	2.09178     	-0.12444    	4                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', '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           	0.95916     	1.82669     	-0.10072    	0           
  10          	0.95079     	1.87462     	-0.22112    	0           
  100         	0.99936     	1.94652     	0.37447     	0           
  300         	0.91101     	1.73899     	0.51729     	1           
  500         	0.97589     	1.79284     	0.48441     	2           
  1000        	1.06446     	1.88586     	0.24607     	4           
---> try to load best state
              	1.06446     	1.88586     	0.24607     	4                eval state : test_on_restore;
              	1.06446     	1.88586     	0.24607     	4                eval state : test_on_last_epoch;
---> final result              	1.06446     	1.88586     	0.24607     	4                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', '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           	0.91314     	1.73919     	0.07606     	0           
  10          	0.90375     	1.65667     	0.14237     	0           
  100         	0.87449     	1.62505     	0.27626     	0           
  epoch 161 reach nan state
---> try to load best state
              	0.90850     	1.58663     	0.42554     	1                eval state : test_on_restore;
              	0.87449     	1.62505     	0.27626     	1                eval state : test_on_last_epoch;
---> final result              	0.90850     	1.58663     	0.42554     	1                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': 'DoublePendu_mfGent_v01', '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           	2.38458     	3.22314     	-316.99611  	0           
  10          	1.58788     	2.32748     	-2.45593    	0           
  100         	1.29243     	2.17406     	-1.50740    	0           
  300         	1.12575     	2.01863     	-1.04430    	1           
  500         	1.02833     	1.92984     	-0.89974    	2           
  1000        	0.92458     	1.85029     	-0.45537    	3           
---> try to load best state
              	0.92458     	1.85029     	-0.45537    	3                eval state : test_on_restore;
              	0.92458     	1.85029     	-0.45537    	3                eval state : test_on_last_epoch;
---> final result              	0.92458     	1.85029     	-0.45537    	3                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', '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           	1.60468     	2.83409     	-0.78481    	0           
  10          	1.51463     	2.65921     	-1.06717    	0           
  epoch 82 reach nan state
---> try to load best state
              	1.44389     	2.43918     	-0.89566    	0                eval state : test_on_restore;
              	1.51463     	2.65921     	-1.06717    	0                eval state : test_on_last_epoch;
---> final result              	1.44389     	2.43918     	-0.89566    	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': 'DoublePendu_mfGent_v01', '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           	1.20865     	2.36961     	-0.72293    	0           
  10          	1.20305     	2.42073     	-0.29998    	0           
  100         	1.17694     	2.51435     	-0.37103    	0           
  300         	1.45745     	2.91618     	-0.15321    	1           
  500         	1.84159     	3.51179     	-0.03199    	2           
  1000        	1.97294     	3.48430     	-1.47544    	4           
---> try to load best state
              	1.97294     	3.48430     	-1.47544    	4                eval state : test_on_restore;
              	1.97294     	3.48430     	-1.47544    	4                eval state : test_on_last_epoch;
---> final result              	1.97294     	3.48430     	-1.47544    	4                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', '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           	1.17885     	2.22887     	-1.11799    	0           
  10          	1.11539     	2.10928     	-2.55810    	0           
  100         	1.16802     	2.26121     	-1.45645    	0           
  300         	1.05037     	1.91911     	-0.87733    	1           
  500         	1.09338     	1.97254     	-0.97567    	2           
  1000        	1.15039     	2.03271     	-1.09642    	5           
---> try to load best state
              	1.15039     	2.03271     	-1.09642    	5                eval state : test_on_restore;
              	1.15039     	2.03271     	-1.09642    	5                eval state : test_on_last_epoch;
---> final result              	1.15039     	2.03271     	-1.09642    	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': 'DoublePendu_mfGent_v01', '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           	1.00152     	1.87956     	-1.93967    	0           
  10          	0.97777     	1.78041     	-0.92698    	0           
  100         	0.90815     	1.73865     	-2.52608    	0           
  300         	0.86246     	1.70482     	-0.98050    	1           
  500         	0.87590     	1.71649     	-0.19026    	3           
  1000        	0.88240     	1.73046     	-0.27487    	6           
---> try to load best state
              	0.88240     	1.73046     	-0.27487    	6                eval state : test_on_restore;
              	0.88240     	1.73046     	-0.27487    	6                eval state : test_on_last_epoch;
---> final result              	0.88240     	1.73046     	-0.27487    	6                eval state : final;module_name : SGAR;cp_record_file : True;
