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

  Demo NAR cigp 
  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           
  300         	5.49882     	7.85535     	-835.75470  	0           
  500         	5.49728     	7.85919     	-283.13618  	1           
  1000        	5.47152     	7.84088     	-74.54732   	2           
---> try to load best state
              	5.47152     	7.84088     	-74.54732   	2                eval state : test_on_restore;
              	5.47152     	7.84088     	-74.54732   	2                eval state : test_on_last_epoch;
---> final result
              	5.47152     	7.84088     	-74.54732   	2                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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	27.27506    	38.58207    	-2841504608872.42822	0           
  10          	27.27506    	38.58207    	-2841504608872.42822	0           
  100         	27.27506    	38.58207    	-2841504608872.42822	0           
  300         	27.27506    	38.58207    	-2841504608872.42822	0           
  500         	27.27506    	38.58207    	-2841504608872.42822	1           
  1000        	27.27506    	38.58207    	-2841504608872.42822	2           
---> try to load best state
              	27.27506    	38.58207    	-2841504608872.42822	2                eval state : test_on_restore;
              	27.27506    	38.58207    	-2841504608872.42822	2                eval state : test_on_last_epoch;
---> final result
              	27.27506    	38.58207    	-2841504608872.42822	2                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	26.96564    	38.14458    	-99987467237238.96875	0           
  10          	26.96564    	38.14458    	-99987467237238.96875	0           
  100         	26.96564    	38.14458    	-99987467237238.96875	0           
  300         	26.96564    	38.14458    	-99987467237238.96875	0           
  500         	26.96564    	38.14458    	-99987467237238.96875	1           
  1000        	26.96564    	38.14458    	-99987467237238.96875	2           
---> try to load best state
              	26.96564    	38.14458    	-99987467237238.96875	2                eval state : test_on_restore;
              	26.96564    	38.14458    	-99987467237238.96875	2                eval state : test_on_last_epoch;
---> final result
              	26.96564    	38.14458    	-99987467237238.96875	2                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	20.53845    	29.05811    	-1184181981753.78198	0           
  10          	20.53845    	29.05811    	-1184181981753.78198	0           
  100         	20.53845    	29.05811    	-1184181981753.78198	0           
  300         	20.53845    	29.05811    	-1184181981753.78198	0           
  500         	20.53845    	29.05811    	-1184181981753.78198	1           
  1000        	20.53845    	29.05811    	-1184181981753.78198	2           
---> try to load best state
              	20.53845    	29.05811    	-1184181981753.78198	2                eval state : test_on_restore;
              	20.53845    	29.05811    	-1184181981753.78198	2                eval state : test_on_last_epoch;
---> final result
              	20.53845    	29.05811    	-1184181981753.78198	2                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	13.72327    	19.42612    	-1052192391.87435	0           
  10          	13.72299    	19.42575    	-84501951.35056	0           
  100         	13.72334    	19.42622    	-9744426107.56440	0           
  300         	13.72334    	19.42622    	-11844511902.86366	0           
  500         	13.72334    	19.42622    	-13421803214.41783	0           
  1000        	13.72335    	19.42623    	-17974941697.81854	1           
---> try to load best state
              	13.72335    	19.42623    	-17974941697.81854	1                eval state : test_on_restore;
              	13.72335    	19.42623    	-17974941697.81854	1                eval state : test_on_last_epoch;
---> final result
              	13.72335    	19.42623    	-17974941697.81854	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  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           
  300         	2.36800     	3.84937     	-2.21041    	0           
  500         	2.55390     	4.17186     	-2.13907    	0           
  1000        	2.66613     	4.39263     	-2.00922    	1           
---> try to load best state
              	2.66613     	4.39263     	-2.00922    	1                eval state : test_on_restore;
              	2.66613     	4.39263     	-2.00922    	1                eval state : test_on_last_epoch;
---> final result
              	2.66613     	4.39263     	-2.00922    	1                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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	3.29937     	4.80874     	-75.06977   	0           
  10          	3.28401     	4.79486     	-64.99495   	0           
  100         	3.20170     	4.70947     	-37.71091   	0           
  300         	3.17887     	4.68767     	-29.10899   	0           
  500         	3.17665     	4.68646     	-27.79820   	0           
  1000        	3.17538     	4.68553     	-27.36512   	1           
---> try to load best state
              	3.17538     	4.68553     	-27.36512   	1                eval state : test_on_restore;
              	3.17538     	4.68553     	-27.36512   	1                eval state : test_on_last_epoch;
---> final result
              	3.17538     	4.68553     	-27.36512   	1                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	3.50253     	5.08849     	-80.48396   	0           
  10          	3.48591     	5.07402     	-68.03064   	0           
  100         	3.36195     	4.94861     	-34.43919   	0           
  300         	3.24845     	4.81723     	-22.33803   	0           
  500         	3.20588     	4.76843     	-18.16037   	0           
  1000        	3.17558     	4.73540     	-15.37564   	1           
---> try to load best state
              	3.17558     	4.73540     	-15.37564   	1                eval state : test_on_restore;
              	3.17558     	4.73540     	-15.37564   	1                eval state : test_on_last_epoch;
---> final result
              	3.17558     	4.73540     	-15.37564   	1                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	5.02370     	7.63795     	-6.95794    	0           
  10          	4.83330     	7.39395     	-6.40838    	0           
  100         	3.33827     	5.23689     	-5.97556    	0           
  300         	3.28646     	4.94616     	-15.14939   	0           
  500         	3.80149     	5.70416     	-11.99021   	0           
  1000        	4.46232     	6.73941     	-7.99571    	1           
---> try to load best state
              	4.46232     	6.73941     	-7.99571    	1                eval state : test_on_restore;
              	4.46232     	6.73941     	-7.99571    	1                eval state : test_on_last_epoch;
---> final result
              	4.46232     	6.73941     	-7.99571    	1                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	5.61239     	8.71993     	-4.28343    	0           
  10          	5.22077     	8.19284     	-3.89570    	0           
  100         	3.38365     	5.37875     	-4.53826    	0           
  300         	2.76836     	4.23677     	-9.07142    	0           
  500         	2.73573     	4.11960     	-12.21017   	0           
  1000        	2.75988     	4.11906     	-14.61844   	1           
---> try to load best state
              	2.75988     	4.11906     	-14.61844   	1                eval state : test_on_restore;
              	2.75988     	4.11906     	-14.61844   	1                eval state : test_on_last_epoch;
---> final result
              	2.75988     	4.11906     	-14.61844   	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  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           
  300         	1.85907     	2.85564     	-0.67231    	0           
  500         	1.79672     	2.77500     	-0.58679    	0           
  1000        	1.69614     	2.64695     	-0.46883    	1           
---> try to load best state
              	1.69614     	2.64695     	-0.46883    	1                eval state : test_on_restore;
              	1.69614     	2.64695     	-0.46883    	1                eval state : test_on_last_epoch;
---> final result
              	1.69614     	2.64695     	-0.46883    	1                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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	7.18326     	10.19393    	-3173810071789.16895	0           
  10          	7.18326     	10.19393    	-3173810071789.16895	0           
  100         	7.18326     	10.19393    	-3173810071789.16895	0           
  300         	7.18326     	10.19393    	-3173810071789.16895	0           
  500         	7.18326     	10.19393    	-3173810071789.16895	0           
  1000        	7.18326     	10.19393    	-3173810071789.16895	1           
---> try to load best state
              	7.18326     	10.19393    	-3173810071789.16895	1                eval state : test_on_restore;
              	7.18326     	10.19393    	-3173810071789.16895	1                eval state : test_on_last_epoch;
---> final result
              	7.18326     	10.19393    	-3173810071789.16895	1                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	8.87018     	12.79627    	-26.83467   	0           
  10          	8.71738     	12.60740    	-23.25236   	0           
  100         	7.35030     	10.83107    	-11.55577   	0           
  300         	5.99416     	9.01771     	-7.42676    	0           
  500         	5.39347     	8.20834     	-6.19953    	0           
  1000        	4.90721     	7.55012     	-5.36797    	1           
---> try to load best state
              	4.90721     	7.55012     	-5.36797    	1                eval state : test_on_restore;
              	4.90721     	7.55012     	-5.36797    	1                eval state : test_on_last_epoch;
---> final result
              	4.90721     	7.55012     	-5.36797    	1                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	6.40577     	10.47809    	-2.14011    	0           
  10          	5.74997     	9.60482     	-1.86673    	0           
  100         	6.26756     	10.19762    	-2.24408    	0           
  300         	6.21670     	10.13486    	-2.21146    	0           
  500         	6.22185     	10.14420    	-2.20930    	0           
  1000        	6.23252     	10.16424    	-2.20363    	1           
---> try to load best state
              	6.23252     	10.16424    	-2.20363    	1                eval state : test_on_restore;
              	6.23252     	10.16424    	-2.20363    	1                eval state : test_on_last_epoch;
---> final result
              	6.23252     	10.16424    	-2.20363    	1                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	5.41467     	8.81440     	-2.32417    	0           
  10          	5.04563     	8.28775     	-2.20595    	0           
  100         	4.89173     	8.07309     	-2.17369    	0           
  300         	4.94869     	8.15150     	-2.19737    	0           
  500         	4.97469     	8.18743     	-2.20724    	0           
  1000        	5.03018     	8.26386     	-2.22854    	1           
---> try to load best state
              	5.03018     	8.26386     	-2.22854    	1                eval state : test_on_restore;
              	5.03018     	8.26386     	-2.22854    	1                eval state : test_on_last_epoch;
---> final result
              	5.03018     	8.26386     	-2.22854    	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  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           
  300         	2.12581     	3.48400     	-0.58290    	0           
  500         	2.45607     	4.10761     	-0.56947    	0           
  1000        	2.93281     	5.03568     	-0.53690    	1           
---> try to load best state
              	2.93281     	5.03568     	-0.53690    	1                eval state : test_on_restore;
              	2.93281     	5.03568     	-0.53690    	1                eval state : test_on_last_epoch;
---> final result
              	2.93281     	5.03568     	-0.53690    	1                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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	9.90732     	14.75573    	-5.40720    	0           
  10          	9.14415     	13.75362    	-4.58372    	0           
  100         	5.26137     	8.43243     	-2.55275    	0           
  300         	4.62706     	7.52948     	-2.33897    	0           
  500         	4.60281     	7.49579     	-2.32729    	0           
  1000        	4.58199     	7.46828     	-2.31216    	2           
---> try to load best state
              	4.58199     	7.46828     	-2.31216    	2                eval state : test_on_restore;
              	4.58199     	7.46828     	-2.31216    	2                eval state : test_on_last_epoch;
---> final result
              	4.58199     	7.46828     	-2.31216    	2                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	6.50698     	9.90658     	-5.09332    	0           
  10          	6.13110     	9.41932     	-4.55244    	0           
  100         	4.01475     	6.49104     	-3.12908    	0           
  300         	3.62613     	6.02352     	-2.49670    	0           
  500         	3.61040     	6.06293     	-2.20708    	0           
  1000        	3.61381     	6.14528     	-1.93629    	1           
---> try to load best state
              	3.61381     	6.14528     	-1.93629    	1                eval state : test_on_restore;
              	3.61381     	6.14528     	-1.93629    	1                eval state : test_on_last_epoch;
---> final result
              	3.61381     	6.14528     	-1.93629    	1                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	5.22970     	8.14594     	-4.21723    	0           
  10          	4.96995     	7.80297     	-3.90516    	0           
  100         	3.84319     	6.14523     	-3.68684    	0           
  300         	3.10264     	5.15928     	-3.01582    	0           
  500         	3.13546     	5.31166     	-2.53694    	0           
  1000        	3.48033     	5.99416     	-2.04331    	1           
---> try to load best state
              	3.48033     	5.99416     	-2.04331    	1                eval state : test_on_restore;
              	3.48033     	5.99416     	-2.04331    	1                eval state : test_on_last_epoch;
---> final result
              	3.48033     	5.99416     	-2.04331    	1                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	4.76010     	7.75401     	-2.39139    	0           
  10          	4.40506     	7.23348     	-2.29936    	0           
  100         	4.47090     	7.28880     	-2.45642    	0           
  300         	4.47768     	7.30143     	-2.44915    	0           
  500         	4.48861     	7.31964     	-2.44464    	1           
  1000        	4.51172     	7.35834     	-2.43439    	1           
---> try to load best state
              	4.51172     	7.35834     	-2.43439    	1                eval state : test_on_restore;
              	4.51172     	7.35834     	-2.43439    	1                eval state : test_on_last_epoch;
---> final result
              	4.51172     	7.35834     	-2.43439    	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  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           
  300         	0.70120     	1.27763     	0.10973     	0           
  500         	0.78256     	1.38233     	0.10773     	0           
  1000        	0.99984     	1.73038     	0.02361     	1           
---> try to load best state
              	0.99984     	1.73038     	0.02361     	1                eval state : test_on_restore;
              	0.99984     	1.73038     	0.02361     	1                eval state : test_on_last_epoch;
---> final result
              	0.99984     	1.73038     	0.02361     	1                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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	9.99607     	14.47669    	-15.22747   	0           
  10          	9.59177     	13.94831    	-12.71743   	0           
  100         	7.49125     	11.06061    	-7.30776    	0           
  300         	6.24773     	9.40906     	-4.09144    	0           
  500         	6.01763     	9.12240     	-3.54214    	0           
  1000        	5.95856     	9.05031     	-3.40735    	1           
---> try to load best state
              	5.95856     	9.05031     	-3.40735    	1                eval state : test_on_restore;
              	5.95856     	9.05031     	-3.40735    	1                eval state : test_on_last_epoch;
---> final result
              	5.95856     	9.05031     	-3.40735    	1                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	7.97786     	11.51437    	-22.93108   	0           
  10          	7.69854     	11.16575    	-17.69609   	0           
  100         	6.20038     	9.10034     	-10.94622   	0           
  300         	4.48721     	6.74179     	-5.20058    	0           
  500         	3.49101     	5.39653     	-2.83621    	0           
  1000        	2.29970     	3.90609     	-0.90052    	1           
---> try to load best state
              	2.29970     	3.90609     	-0.90052    	1                eval state : test_on_restore;
              	2.29970     	3.90609     	-0.90052    	1                eval state : test_on_last_epoch;
---> final result
              	2.29970     	3.90609     	-0.90052    	1                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	4.82790     	7.26066     	-7.30744    	0           
  10          	4.55794     	6.91433     	-6.27569    	0           
  100         	2.97181     	4.63733     	-5.03821    	0           
  300         	2.31566     	3.65217     	-4.30773    	0           
  500         	1.91164     	3.07828     	-3.00590    	0           
  1000        	1.30394     	2.24391     	-0.92701    	1           
---> try to load best state
              	1.30394     	2.24391     	-0.92701    	1                eval state : test_on_restore;
              	1.30394     	2.24391     	-0.92701    	1                eval state : test_on_last_epoch;
---> final result
              	1.30394     	2.24391     	-0.92701    	1                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	5.31017     	8.11053     	-5.54346    	0           
  10          	5.04553     	7.77400     	-4.91823    	0           
  100         	3.03669     	4.85174     	-4.24346    	0           
  300         	2.64533     	4.10592     	-5.15567    	0           
  500         	2.57419     	4.01707     	-4.01208    	0           
  1000        	2.45469     	3.86748     	-3.39493    	1           
---> try to load best state
              	2.45469     	3.86748     	-3.39493    	1                eval state : test_on_restore;
              	2.45469     	3.86748     	-3.39493    	1                eval state : test_on_last_epoch;
---> final result
              	2.45469     	3.86748     	-3.39493    	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  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           
  300         	0.24875     	0.47388     	0.41139     	0           
  500         	0.44969     	0.71910     	0.28726     	0           
  1000        	1.01962     	1.64246     	-0.77506    	1           
---> try to load best state
              	1.01962     	1.64246     	-0.77506    	1                eval state : test_on_restore;
              	1.01962     	1.64246     	-0.77506    	1                eval state : test_on_last_epoch;
---> final result
              	1.01962     	1.64246     	-0.77506    	1                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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	2.47404     	3.77300     	-8.88050    	0           
  10          	2.36281     	3.63499     	-7.75152    	0           
  100         	1.42736     	2.38788     	-5.16707    	0           
  300         	0.99694     	1.78159     	-3.80153    	0           
  500         	0.94769     	1.70983     	-3.50967    	0           
  1000        	0.94006     	1.69850     	-3.45417    	1           
---> try to load best state
              	0.94006     	1.69850     	-3.45417    	1                eval state : test_on_restore;
              	0.94006     	1.69850     	-3.45417    	1                eval state : test_on_last_epoch;
---> final result
              	0.94006     	1.69850     	-3.45417    	1                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	3.03232     	4.54040     	-11.66028   	0           
  10          	2.94818     	4.44667     	-9.93011    	0           
  100         	2.12146     	3.38679     	-5.58743    	0           
  300         	1.38585     	2.36402     	-4.68556    	0           
  500         	1.13779     	2.02150     	-3.87747    	0           
  1000        	0.95262     	1.75777     	-2.83782    	1           
---> try to load best state
              	0.95262     	1.75777     	-2.83782    	1                eval state : test_on_restore;
              	0.95262     	1.75777     	-2.83782    	1                eval state : test_on_last_epoch;
---> final result
              	0.95262     	1.75777     	-2.83782    	1                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	2.75244     	4.19314     	-6.56745    	0           
  10          	2.62649     	4.03902     	-5.55761    	0           
  100         	1.86993     	3.00085     	-3.81044    	0           
  300         	1.16368     	1.99591     	-3.15901    	0           
  500         	0.80233     	1.50443     	-2.44809    	0           
  1000        	0.64940     	1.20625     	-1.00887    	1           
---> try to load best state
              	0.64940     	1.20625     	-1.00887    	1                eval state : test_on_restore;
              	0.64940     	1.20625     	-1.00887    	1                eval state : test_on_last_epoch;
---> final result
              	0.64940     	1.20625     	-1.00887    	1                eval state : final;module_name : NAR;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': ['np.concatenate([x[0],y[0]], axis=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': 0.01, '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           	3.19688     	5.22068     	-2.65883    	0           
  10          	3.00238     	4.92388     	-2.63370    	0           
  100         	1.89836     	3.18130     	-2.73261    	0           
  300         	1.27557     	2.20673     	-1.73090    	0           
  500         	1.17278     	2.06116     	-2.42465    	0           
  1000        	1.13080     	2.08255     	-2.78045    	1           
---> try to load best state
              	1.13080     	2.08255     	-2.78045    	1                eval state : test_on_restore;
              	1.13080     	2.08255     	-2.78045    	1                eval state : test_on_last_epoch;
---> final result
              	1.13080     	2.08255     	-2.78045    	1                eval state : final;module_name : NAR;cp_record_file : True;
