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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00176     	0.74277     	0.00406     	0           
  10          	0.00233     	0.57212     	0.00464     	0           
  100         	0.00584     	-0.14724    	0.00877     	0           
  300         	0.00435     	0.68437     	0.00891     	1           
  500         	0.00438     	0.69572     	0.00915     	2           
  1000        	0.00434     	0.69583     	0.00908     	5           
---> try to load best state
              	0.00434     	0.69583     	0.00908     	5                eval state : test_on_restore;
              	0.00434     	0.69583     	0.00908     	5                eval state : test_on_last_epoch;
---> final result              	0.00434     	0.69583     	0.00908     	5                eval state : final;
---> end


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

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00176     	0.74277     	0.00406     	0           
  10          	0.00233     	0.57212     	0.00464     	0           
  100         	0.00584     	-0.14724    	0.00877     	0           
  300         	0.00435     	0.68437     	0.00891     	1           
  500         	0.00438     	0.69572     	0.00915     	2           
  1000        	0.00434     	0.69583     	0.00908     	4           
---> try to load best state
              	0.00434     	0.69583     	0.00908     	4                eval state : test_on_restore;
              	0.00434     	0.69583     	0.00908     	4                eval state : test_on_last_epoch;
---> final result              	0.00434     	0.69583     	0.00908     	4                eval state : final;
---> end


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

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00176     	0.74277     	0.00406     	0           
  10          	0.00233     	0.57212     	0.00464     	0           
  100         	0.00584     	-0.14724    	0.00877     	0           
  300         	0.00435     	0.68437     	0.00891     	1           
  500         	0.00438     	0.69572     	0.00915     	2           
  1000        	0.00434     	0.69583     	0.00908     	5           
---> try to load best state
              	0.00434     	0.69583     	0.00908     	5                eval state : test_on_restore;
              	0.00434     	0.69583     	0.00908     	5                eval state : test_on_last_epoch;
---> final result              	0.00434     	0.69583     	0.00908     	5                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 256, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  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], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00656     	0.68219     	0.01583     	0           
  10          	0.00514     	0.73337     	0.01160     	0           
  epoch 84 reach nan state
---> try to load best state
              	0.00435     	0.75958     	0.00961     	0                eval state : test_on_restore;
              	0.00514     	0.73337     	0.01160     	0                eval state : test_on_last_epoch;
---> final result              	0.00435     	0.75958     	0.00961     	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': 'burger_v4_02', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 256, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  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], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00380     	0.80318     	0.00822     	0           
  10          	0.00400     	0.79404     	0.00848     	0           
  epoch 85 reach nan state
---> try to load best state
              	0.00409     	0.78599     	0.00893     	0                eval state : test_on_restore;
              	0.00400     	0.79404     	0.00848     	0                eval state : test_on_last_epoch;
---> final result              	0.00400     	0.79404     	0.00848     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00176     	0.74277     	0.00406     	0           
  10          	0.00233     	0.57212     	0.00464     	0           
  100         	0.00584     	-0.14724    	0.00877     	0           
  300         	0.00435     	0.68437     	0.00891     	1           
  500         	0.00438     	0.69572     	0.00915     	2           
  1000        	0.00434     	0.69583     	0.00908     	4           
---> try to load best state
              	0.00434     	0.69583     	0.00908     	4                eval state : test_on_restore;
              	0.00434     	0.69583     	0.00908     	4                eval state : test_on_last_epoch;
---> final result              	0.00434     	0.69583     	0.00908     	4                eval state : final;
---> end


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

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

  Demo DC 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00801     	0.02289     	0.96297     	0           
  10          	0.00792     	0.02275     	0.96349     	0           
  100         	0.00750     	0.02203     	0.96615     	0           
---> try to load best state
              	0.00750     	0.02203     	0.96615     	0                eval state : test_on_restore;
              	0.00750     	0.02203     	0.96615     	0                eval state : test_on_last_epoch;
---> final result
              	0.00750     	0.02203     	0.96615     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.11154     	0.15610     	-36.96767   	0           
  10          	0.11151     	0.15608     	-36.66776   	0           
  100         	0.11148     	0.15606     	-36.41986   	0           
---> try to load best state
              	0.11148     	0.15606     	-36.41986   	0                eval state : test_on_restore;
              	0.11148     	0.15606     	-36.41986   	0                eval state : test_on_last_epoch;
---> final result
              	0.11148     	0.15606     	-36.41986   	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.09219     	0.13296     	-17.70726   	0           
  10          	0.07835     	0.12024     	-5.93391    	0           
  100         	0.02498     	0.05165     	0.74445     	0           
---> try to load best state
              	0.02498     	0.05165     	0.74445     	0                eval state : test_on_restore;
              	0.02498     	0.05165     	0.74445     	0                eval state : test_on_last_epoch;
---> final result
              	0.02498     	0.05165     	0.74445     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08218     	0.12304     	-15.41592   	0           
  10          	0.05940     	0.10271     	-2.36618    	0           
  100         	0.01352     	0.03871     	0.87452     	0           
---> try to load best state
              	0.01352     	0.03871     	0.87452     	0                eval state : test_on_restore;
              	0.01352     	0.03871     	0.87452     	0                eval state : test_on_last_epoch;
---> final result
              	0.01352     	0.03871     	0.87452     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.07054     	0.11166     	-8.09094    	0           
  10          	0.04075     	0.08494     	-0.58417    	0           
  100         	0.00828     	0.02843     	0.94034     	0           
---> try to load best state
              	0.00828     	0.02843     	0.94034     	0                eval state : test_on_restore;
              	0.00828     	0.02843     	0.94034     	0                eval state : test_on_last_epoch;
---> final result
              	0.00828     	0.02843     	0.94034     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01223     	0.03472     	0.84062     	0           
  10          	0.01179     	0.03349     	0.84751     	0           
  100         	0.01002     	0.02863     	0.88424     	0           
---> try to load best state
              	0.01002     	0.02863     	0.88424     	0                eval state : test_on_restore;
              	0.01002     	0.02863     	0.88424     	0                eval state : test_on_last_epoch;
---> final result
              	0.01002     	0.02863     	0.88424     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08633     	0.12924     	-85.02872   	0           
  10          	0.08143     	0.12459     	-33.75689   	0           
  100         	0.07402     	0.11758     	-15.25751   	0           
---> try to load best state
              	0.07402     	0.11758     	-15.25751   	0                eval state : test_on_restore;
              	0.07402     	0.11758     	-15.25751   	0                eval state : test_on_last_epoch;
---> final result
              	0.07402     	0.11758     	-15.25751   	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.09047     	0.13165     	-82.53606   	0           
  10          	0.08261     	0.12474     	-18.89534   	0           
  100         	0.03255     	0.08047     	-0.55879    	0           
---> try to load best state
              	0.03255     	0.08047     	-0.55879    	0                eval state : test_on_restore;
              	0.03255     	0.08047     	-0.55879    	0                eval state : test_on_last_epoch;
---> final result
              	0.03255     	0.08047     	-0.55879    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08618     	0.12661     	-33.99552   	0           
  10          	0.06722     	0.10721     	-4.18862    	0           
  100         	0.01545     	0.03955     	0.76716     	0           
---> try to load best state
              	0.01545     	0.03955     	0.76716     	0                eval state : test_on_restore;
              	0.01545     	0.03955     	0.76716     	0                eval state : test_on_last_epoch;
---> final result
              	0.01545     	0.03955     	0.76716     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.07145     	0.11646     	-9.78234    	0           
  10          	0.05352     	0.09651     	-1.85265    	0           
  100         	0.01841     	0.05190     	0.61546     	0           
---> try to load best state
              	0.01841     	0.05190     	0.61546     	0                eval state : test_on_restore;
              	0.01841     	0.05190     	0.61546     	0                eval state : test_on_last_epoch;
---> final result
              	0.01841     	0.05190     	0.61546     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00870     	0.02579     	0.91893     	0           
  10          	0.00837     	0.02486     	0.92393     	0           
  100         	0.00771     	0.02263     	0.93558     	0           
---> try to load best state
              	0.00771     	0.02263     	0.93558     	0                eval state : test_on_restore;
              	0.00771     	0.02263     	0.93558     	0                eval state : test_on_last_epoch;
---> final result
              	0.00771     	0.02263     	0.93558     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08309     	0.13452     	-180.46190  	0           
  10          	0.07727     	0.13028     	-48.96374   	0           
  100         	0.06259     	0.11590     	-8.20777    	0           
---> try to load best state
              	0.06259     	0.11590     	-8.20777    	0                eval state : test_on_restore;
              	0.06259     	0.11590     	-8.20777    	0                eval state : test_on_last_epoch;
---> final result
              	0.06259     	0.11590     	-8.20777    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08429     	0.13515     	-196.74883  	0           
  10          	0.07643     	0.12836     	-32.81652   	0           
  100         	0.03646     	0.06667     	-0.21172    	0           
---> try to load best state
              	0.03646     	0.06667     	-0.21172    	0                eval state : test_on_restore;
              	0.03646     	0.06667     	-0.21172    	0                eval state : test_on_last_epoch;
---> final result
              	0.03646     	0.06667     	-0.21172    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.07734     	0.13165     	-42.05805   	0           
  10          	0.06401     	0.12116     	-8.40124    	0           
  100         	0.01663     	0.04903     	0.70601     	0           
---> try to load best state
              	0.01663     	0.04903     	0.70601     	0                eval state : test_on_restore;
              	0.01663     	0.04903     	0.70601     	0                eval state : test_on_last_epoch;
---> final result
              	0.01663     	0.04903     	0.70601     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.07220     	0.11628     	-11.07660   	0           
  10          	0.04731     	0.09065     	-1.29987    	0           
  100         	0.01118     	0.03556     	0.85626     	0           
---> try to load best state
              	0.01118     	0.03556     	0.85626     	0                eval state : test_on_restore;
              	0.01118     	0.03556     	0.85626     	0                eval state : test_on_last_epoch;
---> final result
              	0.01118     	0.03556     	0.85626     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01267     	0.03766     	0.86294     	0           
  10          	0.01299     	0.03825     	0.86133     	0           
  100         	0.01130     	0.03400     	0.88373     	0           
---> try to load best state
              	0.01130     	0.03400     	0.88373     	0                eval state : test_on_restore;
              	0.01130     	0.03400     	0.88373     	0                eval state : test_on_last_epoch;
---> final result
              	0.01130     	0.03400     	0.88373     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08662     	0.13434     	-6887.64295 	0           
  10          	0.08569     	0.13407     	-2429.64098 	0           
  100         	0.08358     	0.13338     	-720.24085  	0           
---> try to load best state
              	0.08358     	0.13338     	-720.24085  	0                eval state : test_on_restore;
              	0.08358     	0.13338     	-720.24085  	0                eval state : test_on_last_epoch;
---> final result
              	0.08358     	0.13338     	-720.24085  	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08347     	0.13749     	-614.82026  	0           
  10          	0.08065     	0.13649     	-211.29995  	0           
  100         	0.07253     	0.13242     	-58.72608   	0           
---> try to load best state
              	0.07253     	0.13242     	-58.72608   	0                eval state : test_on_restore;
              	0.07253     	0.13242     	-58.72608   	0                eval state : test_on_last_epoch;
---> final result
              	0.07253     	0.13242     	-58.72608   	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.07742     	0.13158     	-47.90825   	0           
  10          	0.06721     	0.12574     	-13.18354   	0           
  100         	0.04014     	0.09904     	-1.45349    	0           
---> try to load best state
              	0.04014     	0.09904     	-1.45349    	0                eval state : test_on_restore;
              	0.04014     	0.09904     	-1.45349    	0                eval state : test_on_last_epoch;
---> final result
              	0.04014     	0.09904     	-1.45349    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.07400     	0.11912     	-11.72586   	0           
  10          	0.05343     	0.10086     	-2.11398    	0           
  100         	0.01397     	0.04343     	0.80587     	0           
---> try to load best state
              	0.01397     	0.04343     	0.80587     	0                eval state : test_on_restore;
              	0.01397     	0.04343     	0.80587     	0                eval state : test_on_last_epoch;
---> final result
              	0.01397     	0.04343     	0.80587     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00783     	0.02112     	0.95413     	0           
  10          	0.00822     	0.02226     	0.95084     	0           
  100         	0.00726     	0.01973     	0.96041     	0           
---> try to load best state
              	0.00726     	0.01973     	0.96041     	0                eval state : test_on_restore;
              	0.00726     	0.01973     	0.96041     	0                eval state : test_on_last_epoch;
---> final result
              	0.00726     	0.01973     	0.96041     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08283     	0.12862     	-44.59386   	0           
  10          	0.08281     	0.12861     	-44.24089   	0           
  100         	0.08279     	0.12860     	-43.94907   	0           
---> try to load best state
              	0.08279     	0.12860     	-43.94907   	0                eval state : test_on_restore;
              	0.08279     	0.12860     	-43.94907   	0                eval state : test_on_last_epoch;
---> final result
              	0.08279     	0.12860     	-43.94907   	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08275     	0.12860     	-66.02435   	0           
  10          	0.07279     	0.12136     	-14.94103   	0           
  100         	0.03552     	0.09246     	-1.13787    	0           
---> try to load best state
              	0.03552     	0.09246     	-1.13787    	0                eval state : test_on_restore;
              	0.03552     	0.09246     	-1.13787    	0                eval state : test_on_last_epoch;
---> final result
              	0.03552     	0.09246     	-1.13787    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.07996     	0.12594     	-34.55496   	0           
  10          	0.06456     	0.11426     	-7.57995    	0           
  100         	0.02692     	0.07992     	-0.18591    	0           
---> try to load best state
              	0.02692     	0.07992     	-0.18591    	0                eval state : test_on_restore;
              	0.02692     	0.07992     	-0.18591    	0                eval state : test_on_last_epoch;
---> final result
              	0.02692     	0.07992     	-0.18591    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.07615     	0.11758     	-9.64196    	0           
  10          	0.04617     	0.08905     	-1.31656    	0           
  100         	0.01446     	0.04612     	0.74306     	0           
---> try to load best state
              	0.01446     	0.04612     	0.74306     	0                eval state : test_on_restore;
              	0.01446     	0.04612     	0.74306     	0                eval state : test_on_last_epoch;
---> final result
              	0.01446     	0.04612     	0.74306     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01212     	0.02687     	0.93685     	0           
  10          	0.01239     	0.02712     	0.93531     	0           
  100         	0.01191     	0.02613     	0.94015     	0           
---> try to load best state
              	0.01191     	0.02613     	0.94015     	0                eval state : test_on_restore;
              	0.01191     	0.02613     	0.94015     	0                eval state : test_on_last_epoch;
---> final result
              	0.01191     	0.02613     	0.94015     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.10157     	0.13661     	-307.94345  	0           
  10          	0.10155     	0.13659     	-305.58154  	0           
  100         	0.10153     	0.13658     	-303.62872  	0           
---> try to load best state
              	0.10153     	0.13658     	-303.62872  	0                eval state : test_on_restore;
              	0.10153     	0.13658     	-303.62872  	0                eval state : test_on_last_epoch;
---> final result
              	0.10153     	0.13658     	-303.62872  	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.10762     	0.14743     	-24.69958   	0           
  10          	0.09445     	0.13341     	-4.75299    	0           
  100         	0.03422     	0.05232     	0.71721     	0           
---> try to load best state
              	0.03422     	0.05232     	0.71721     	0                eval state : test_on_restore;
              	0.03422     	0.05232     	0.71721     	0                eval state : test_on_last_epoch;
---> final result
              	0.03422     	0.05232     	0.71721     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.09624     	0.13308     	-13.38785   	0           
  10          	0.07251     	0.10711     	-1.70730    	0           
  100         	0.01423     	0.02816     	0.91571     	0           
---> try to load best state
              	0.01423     	0.02816     	0.91571     	0                eval state : test_on_restore;
              	0.01423     	0.02816     	0.91571     	0                eval state : test_on_last_epoch;
---> final result
              	0.01423     	0.02816     	0.91571     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.08254     	0.11931     	-6.74573    	0           
  10          	0.05159     	0.08191     	-0.38779    	0           
  100         	0.00958     	0.02417     	0.93712     	0           
---> try to load best state
              	0.00958     	0.02417     	0.93712     	0                eval state : test_on_restore;
              	0.00958     	0.02417     	0.93712     	0                eval state : test_on_last_epoch;
---> final result
              	0.00958     	0.02417     	0.93712     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
