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

  Demo DC 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'MolecularDynamic_MF', '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': 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           	5.46412     	7.79607     	-19125071.41060	0           
  10          	5.46385     	7.79565     	-6037007.14509	0           
  100         	5.47717     	7.81723     	-11568.78195	0           
---> try to load best state
              	5.47717     	7.81723     	-11568.78195	0                eval state : test_on_restore;
              	5.47717     	7.81723     	-11568.78195	0                eval state : test_on_last_epoch;
---> final result
              	5.47717     	7.81723     	-11568.78195	0                eval state : final;
----------> finish x-yl training


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

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

  Demo DC 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'MolecularDynamic_MF', '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': 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           	5.46412     	7.79607     	-19125071.41060	0           
  10          	5.46385     	7.79565     	-6037007.14509	0           
  100         	5.47717     	7.81723     	-11568.78195	0           
---> try to load best state
              	5.47717     	7.81723     	-11568.78195	0                eval state : test_on_restore;
              	5.47717     	7.81723     	-11568.78195	0                eval state : test_on_last_epoch;
---> final result
              	5.47717     	7.81723     	-11568.78195	0                eval state : final;
----------> finish x-yl training


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

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

  Demo DC 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'MolecularDynamic_MF', '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': 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           	5.46412     	7.79607     	-19125071.41060	0           
  10          	5.46385     	7.79565     	-6037007.14509	0           
  100         	5.47717     	7.81723     	-11568.78195	0           
---> try to load best state
              	5.47717     	7.81723     	-11568.78195	0                eval state : test_on_restore;
              	5.47717     	7.81723     	-11568.78195	0                eval state : test_on_last_epoch;
---> final result
              	5.47717     	7.81723     	-11568.78195	0                eval state : final;
----------> finish x-yl training


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

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

  Demo DC 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'MolecularDynamic_MF', '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': 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           	5.46412     	7.79607     	-19125071.41060	0           
  10          	5.46385     	7.79565     	-6037007.14509	0           
  100         	5.47717     	7.81723     	-11568.78195	0           
---> try to load best state
              	5.47717     	7.81723     	-11568.78195	0                eval state : test_on_restore;
              	5.47717     	7.81723     	-11568.78195	0                eval state : test_on_last_epoch;
---> final result
              	5.47717     	7.81723     	-11568.78195	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'MolecularDynamic_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	27.27506    	38.58207    	-2841504608872.42822	0           
  10          	27.27506    	38.58207    	-2841504608872.42822	0           
  100         	27.27506    	38.58207    	-2841504608872.42822	0           
---> try to load best state
              	27.27506    	38.58207    	-2841504608872.42822	0                eval state : test_on_restore;
              	27.27506    	38.58207    	-2841504608872.42822	0                eval state : test_on_last_epoch;
---> final result
              	27.27506    	38.58207    	-2841504608872.42822	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	26.96563    	38.14458    	-24996863220849.27734	0           
  10          	26.96563    	38.14458    	-24996863220849.27734	0           
  100         	26.96563    	38.14458    	-24996863220849.27734	0           
---> try to load best state
              	26.96563    	38.14458    	-24996863220849.27734	0                eval state : test_on_restore;
              	26.96563    	38.14458    	-24996863220849.27734	0                eval state : test_on_last_epoch;
---> final result
              	26.96563    	38.14458    	-24996863220849.27734	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	20.53845    	29.05811    	-1184181981753.78198	0           
  10          	20.53845    	29.05811    	-1184181981753.78198	0           
  100         	20.53845    	29.05811    	-1184181981753.78198	0           
---> try to load best state
              	20.53845    	29.05811    	-1184181981753.78198	0                eval state : test_on_restore;
              	20.53845    	29.05811    	-1184181981753.78198	0                eval state : test_on_last_epoch;
---> final result
              	20.53845    	29.05811    	-1184181981753.78198	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	13.72337    	19.42626    	-2117005548716.72095	0           
  10          	13.72337    	19.42626    	-2117005548716.72095	0           
  100         	13.72337    	19.42626    	-2117005548716.72095	0           
---> try to load best state
              	13.72337    	19.42626    	-2117005548716.72095	0                eval state : test_on_restore;
              	13.72337    	19.42626    	-2117005548716.72095	0                eval state : test_on_last_epoch;
---> final result
              	13.72337    	19.42626    	-2117005548716.72095	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': 'MolecularDynamic_MF', '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[0]'], '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           	2.19682     	3.38630     	-2.80923    	0           
  10          	1.71870     	2.66809     	-1.50944    	0           
  100         	1.99292     	3.18268     	-2.17355    	0           
---> try to load best state
              	1.99292     	3.18268     	-2.17355    	0                eval state : test_on_restore;
              	1.99292     	3.18268     	-2.17355    	0                eval state : test_on_last_epoch;
---> final result
              	1.99292     	3.18268     	-2.17355    	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	3.40752     	4.89288     	-731184551657.37659	0           
  10          	3.40752     	4.89288     	-731184551657.37659	0           
  100         	3.40752     	4.89288     	-731184551657.37659	0           
---> try to load best state
              	3.40752     	4.89288     	-731184551657.37659	0                eval state : test_on_restore;
              	3.40752     	4.89288     	-731184551657.37659	0                eval state : test_on_last_epoch;
---> final result
              	3.40752     	4.89288     	-731184551657.37659	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	3.60637     	5.17009     	-1836856853571.76660	0           
  10          	3.60637     	5.17009     	-1836856853571.76660	0           
  100         	3.60637     	5.17009     	-1836856853571.76660	0           
---> try to load best state
              	3.60637     	5.17009     	-1836856853571.76660	0                eval state : test_on_restore;
              	3.60637     	5.17009     	-1836856853571.76660	0                eval state : test_on_last_epoch;
---> final result
              	3.60637     	5.17009     	-1836856853571.76660	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	6.38312     	9.06678     	-22596736953002.16797	0           
  10          	6.38312     	9.06678     	-22596736953002.16797	0           
  100         	6.38312     	9.06678     	-22596736953002.16797	0           
---> try to load best state
              	6.38312     	9.06678     	-22596736953002.16797	0                eval state : test_on_restore;
              	6.38312     	9.06678     	-22596736953002.16797	0                eval state : test_on_last_epoch;
---> final result
              	6.38312     	9.06678     	-22596736953002.16797	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	8.62691     	12.22968    	-1644485387047.77734	0           
  10          	8.62691     	12.22968    	-1644485387047.77734	0           
  100         	8.62691     	12.22968    	-1644485387047.77734	0           
---> try to load best state
              	8.62691     	12.22968    	-1644485387047.77734	0                eval state : test_on_restore;
              	8.62691     	12.22968    	-1644485387047.77734	0                eval state : test_on_last_epoch;
---> final result
              	8.62691     	12.22968    	-1644485387047.77734	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': 'MolecularDynamic_MF', '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[0]'], '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           	1.47471     	2.17294     	-0.60739    	0           
  10          	1.30368     	2.04968     	-0.23809    	0           
  100         	1.96209     	2.99189     	-0.83603    	0           
---> try to load best state
              	1.96209     	2.99189     	-0.83603    	0                eval state : test_on_restore;
              	1.96209     	2.99189     	-0.83603    	0                eval state : test_on_last_epoch;
---> final result
              	1.96209     	2.99189     	-0.83603    	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	7.18326     	10.19393    	-7141070779914.87793	0           
  10          	7.18326     	10.19393    	-7141070779914.87793	0           
  100         	7.18326     	10.19393    	-7141070779914.87793	0           
---> try to load best state
              	7.18326     	10.19393    	-7141070779914.87793	0                eval state : test_on_restore;
              	7.18326     	10.19393    	-7141070779914.87793	0                eval state : test_on_last_epoch;
---> final result
              	7.18326     	10.19393    	-7141070779914.87793	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	9.68873     	13.72811    	-1438992439300.55688	0           
  10          	9.68873     	13.72811    	-1438992439300.55688	0           
  100         	9.68873     	13.72811    	-1438992439300.55688	0           
---> try to load best state
              	9.68873     	13.72811    	-1438992439300.55688	0                eval state : test_on_restore;
              	9.68873     	13.72811    	-1438992439300.55688	0                eval state : test_on_last_epoch;
---> final result
              	9.68873     	13.72811    	-1438992439300.55688	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	12.16151    	17.21983    	-5094217636613.18848	0           
  10          	12.16151    	17.21983    	-5094217636613.18848	0           
  100         	12.16151    	17.21983    	-5094217636613.18848	0           
---> try to load best state
              	12.16151    	17.21983    	-5094217636613.18848	0                eval state : test_on_restore;
              	12.16151    	17.21983    	-5094217636613.18848	0                eval state : test_on_last_epoch;
---> final result
              	12.16151    	17.21983    	-5094217636613.18848	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	10.09398    	14.30016    	-1147164123674.20288	0           
  10          	10.09398    	14.30016    	-1147164123674.20288	0           
  100         	10.09398    	14.30016    	-1147164123674.20288	0           
---> try to load best state
              	10.09398    	14.30016    	-1147164123674.20288	0                eval state : test_on_restore;
              	10.09398    	14.30016    	-1147164123674.20288	0                eval state : test_on_last_epoch;
---> final result
              	10.09398    	14.30016    	-1147164123674.20288	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': 'MolecularDynamic_MF', '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[0]'], '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           	1.28887     	1.95074     	-0.39441    	0           
  10          	1.23395     	1.85894     	-0.33850    	0           
  100         	1.60338     	2.51786     	-0.55230    	0           
---> try to load best state
              	1.60338     	2.51786     	-0.55230    	0                eval state : test_on_restore;
              	1.60338     	2.51786     	-0.55230    	0                eval state : test_on_last_epoch;
---> final result
              	1.60338     	2.51786     	-0.55230    	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	14.02857    	19.85748    	0.00000     	0           
  10          	14.02857    	19.85748    	0.00000     	0           
  100         	14.02857    	19.85748    	0.00000     	0           
---> try to load best state
              	14.02857    	19.85748    	0.00000     	0                eval state : test_on_restore;
              	14.02857    	19.85748    	0.00000     	0                eval state : test_on_last_epoch;
---> final result
              	14.02857    	19.85748    	0.00000     	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	9.47163     	13.42167    	-1980675186199.62085	0           
  10          	9.47163     	13.42167    	-1980675186199.62085	0           
  100         	9.47163     	13.42167    	-1980675186199.62085	0           
---> try to load best state
              	9.47163     	13.42167    	-1980675186199.62085	0                eval state : test_on_restore;
              	9.47163     	13.42167    	-1980675186199.62085	0                eval state : test_on_last_epoch;
---> final result
              	9.47163     	13.42167    	-1980675186199.62085	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	7.59204     	10.77014    	0.00000     	0           
  10          	7.59204     	10.77014    	0.00000     	0           
  100         	7.59204     	10.77014    	0.00000     	0           
---> try to load best state
              	7.59204     	10.77014    	0.00000     	0                eval state : test_on_restore;
              	7.59204     	10.77014    	0.00000     	0                eval state : test_on_last_epoch;
---> final result
              	7.59204     	10.77014    	0.00000     	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	8.44628     	11.97486    	-9854184511533.01172	0           
  10          	8.44628     	11.97486    	-9854184511533.01172	0           
  100         	8.44628     	11.97486    	-9854184511533.01172	0           
---> try to load best state
              	8.44628     	11.97486    	-9854184511533.01172	0                eval state : test_on_restore;
              	8.44628     	11.97486    	-9854184511533.01172	0                eval state : test_on_last_epoch;
---> final result
              	8.44628     	11.97486    	-9854184511533.01172	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': 'MolecularDynamic_MF', '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[0]'], '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           	0.87586     	1.48982     	-0.28394    	0           
  10          	1.01576     	1.63664     	-0.44569    	0           
  100         	0.84735     	1.42724     	-0.12192    	0           
---> try to load best state
              	0.84735     	1.42724     	-0.12192    	0                eval state : test_on_restore;
              	0.84735     	1.42724     	-0.12192    	0                eval state : test_on_last_epoch;
---> final result
              	0.84735     	1.42724     	-0.12192    	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	11.81709    	16.73336    	-3078690099894.90967	0           
  10          	11.81709    	16.73336    	-3078690099894.90967	0           
  100         	11.81709    	16.73336    	-3078690099894.90967	0           
---> try to load best state
              	11.81709    	16.73336    	-3078690099894.90967	0                eval state : test_on_restore;
              	11.81709    	16.73336    	-3078690099894.90967	0                eval state : test_on_last_epoch;
---> final result
              	11.81709    	16.73336    	-3078690099894.90967	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	8.95167     	12.68789    	-1770023084941.72217	0           
  10          	8.95167     	12.68789    	-1770023084941.72217	0           
  100         	8.95167     	12.68789    	-1770023084941.72217	0           
---> try to load best state
              	8.95167     	12.68789    	-1770023084941.72217	0                eval state : test_on_restore;
              	8.95167     	12.68789    	-1770023084941.72217	0                eval state : test_on_last_epoch;
---> final result
              	8.95167     	12.68789    	-1770023084941.72217	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	6.19664     	8.80424     	-21307068537605.18750	0           
  10          	6.19664     	8.80424     	-21307068537605.18750	0           
  100         	6.19664     	8.80424     	-21307068537605.18750	0           
---> try to load best state
              	6.19664     	8.80424     	-21307068537605.18750	0                eval state : test_on_restore;
              	6.19664     	8.80424     	-21307068537605.18750	0                eval state : test_on_last_epoch;
---> final result
              	6.19664     	8.80424     	-21307068537605.18750	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	7.33211     	10.40373    	-29752110828020.12109	0           
  10          	7.33211     	10.40373    	-29752110828020.12109	0           
  100         	7.33211     	10.40373    	-29752110828020.12109	0           
---> try to load best state
              	7.33211     	10.40373    	-29752110828020.12109	0                eval state : test_on_restore;
              	7.33211     	10.40373    	-29752110828020.12109	0                eval state : test_on_last_epoch;
---> final result
              	7.33211     	10.40373    	-29752110828020.12109	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': 'MolecularDynamic_MF', '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[0]'], '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           	0.20337     	0.35013     	0.43110     	0           
  10          	0.34280     	0.62046     	0.33431     	0           
  100         	0.64747     	1.06113     	0.00505     	0           
---> try to load best state
              	0.64747     	1.06113     	0.00505     	0                eval state : test_on_restore;
              	0.64747     	1.06113     	0.00505     	0                eval state : test_on_last_epoch;
---> final result
              	0.64747     	1.06113     	0.00505     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	3.08242     	4.44078     	-21682959545502.78906	0           
  10          	3.08242     	4.44078     	-21682959545502.78906	0           
  100         	3.08242     	4.44078     	-21682959545502.78906	0           
---> try to load best state
              	3.08242     	4.44078     	-21682959545502.78906	0                eval state : test_on_restore;
              	3.08242     	4.44078     	-21682959545502.78906	0                eval state : test_on_last_epoch;
---> final result
              	3.08242     	4.44078     	-21682959545502.78906	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	3.48589     	5.00209     	0.00000     	0           
  10          	3.48589     	5.00209     	0.00000     	0           
  100         	3.48589     	5.00209     	0.00000     	0           
---> try to load best state
              	3.48589     	5.00209     	0.00000     	0                eval state : test_on_restore;
              	3.48589     	5.00209     	0.00000     	0                eval state : test_on_last_epoch;
---> final result
              	3.48589     	5.00209     	0.00000     	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	3.43901     	4.93675     	-2977419268125.84082	0           
  10          	3.43901     	4.93675     	-2977419268125.84082	0           
  100         	3.43901     	4.93675     	-2977419268125.84082	0           
---> try to load best state
              	3.43901     	4.93675     	-2977419268125.84082	0                eval state : test_on_restore;
              	3.43901     	4.93675     	-2977419268125.84082	0                eval state : test_on_last_epoch;
---> final result
              	3.43901     	4.93675     	-2977419268125.84082	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': 'MolecularDynamic_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[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': 0.1, '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           	5.62786     	8.00397     	-70438679256905.45312	0           
  10          	5.62786     	8.00397     	-70438679256905.45312	0           
  100         	5.62786     	8.00397     	-70438679256905.45312	0           
---> try to load best state
              	5.62786     	8.00397     	-70438679256905.45312	0                eval state : test_on_restore;
              	5.62786     	8.00397     	-70438679256905.45312	0                eval state : test_on_last_epoch;
---> final result
              	5.62786     	8.00397     	-70438679256905.45312	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
