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

  Demo lar cigp 
  seed: None 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, '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
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00529     	0.01201     	0.98425     	0           
  10          	0.00481     	0.01106     	0.98662     	0           
  100         	0.00442     	0.01016     	0.98884     	0           
  300         	0.00413     	0.00963     	0.99009     	1           
  500         	0.00398     	0.00931     	0.99081     	2           
  1000        	0.00376     	0.00882     	0.99186     	4           
---> try to load best state
              	0.00376     	0.00882     	0.99186     	4                eval state : test_on_restore;
              	0.00376     	0.00882     	0.99186     	4                eval state : test_on_last_epoch;
---> final result
              	0.00376     	0.00882     	0.99186     	4                eval state : final;
----------> finish x-yl training


---------->
lar cigp for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01948     	0.04182     	0.84061     	0           
  10          	0.02435     	0.04586     	0.81782     	0           
  100         	0.02642     	0.04876     	0.79972     	0           
  300         	0.02674     	0.04923     	0.79780     	0           
  500         	0.02697     	0.04959     	0.79642     	1           
  1000        	0.02737     	0.05018     	0.79403     	2           
---> try to load best state
              	0.02737     	0.05018     	0.79403     	2                eval state : test_on_restore;
              	0.02737     	0.05018     	0.79403     	2                eval state : test_on_last_epoch;
---> final result
              	0.02737     	0.05018     	0.79403     	2                eval state : final;
              	0.02737     	0.05018     	0.79403     	2                eval state : final;module_name : LarGP;cp_record_file : True;
