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

  Demo lar cigp 
  seed: 0 
  interp_data: False 

----------------------------------------
---> 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    	1           
  1000        	2.66613     	4.39263     	-2.00922    	2           
---> try to load best state
              	2.66613     	4.39263     	-2.00922    	2                eval state : test_on_restore;
              	2.66613     	4.39263     	-2.00922    	2                eval state : test_on_last_epoch;
---> final result
              	2.66613     	4.39263     	-2.00922    	2                eval state : final;
----------> finish x-yl training


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	3.32714     	5.19764     	-4.72640    	0           
  10          	3.34531     	5.17151     	-5.63308    	0           
  100         	3.06531     	4.66458     	-11.39734   	0           
  300         	3.09286     	4.73615     	-9.18930    	0           
  500         	3.56752     	5.57501     	-4.25208    	1           
  1000        	4.98631     	7.80351     	-3.17454    	2           
---> try to load best state
              	4.98631     	7.80351     	-3.17454    	2                eval state : test_on_restore;
              	4.98631     	7.80351     	-3.17454    	2                eval state : test_on_last_epoch;
---> final result
              	4.98631     	7.80351     	-3.17454    	2                eval state : final;
              	4.98631     	7.80351     	-3.17454    	2                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	3.33841     	5.21890     	-4.57453    	0           
  10          	3.37891     	5.21799     	-5.54329    	0           
  100         	3.26025     	4.87857     	-18.51247   	0           
  300         	3.35357     	5.04702     	-14.29158   	1           
  500         	3.47504     	5.24892     	-11.77808   	1           
  1000        	3.64751     	5.53247     	-9.56514    	2           
---> try to load best state
              	3.64751     	5.53247     	-9.56514    	2                eval state : test_on_restore;
              	3.64751     	5.53247     	-9.56514    	2                eval state : test_on_last_epoch;
---> final result
              	3.64751     	5.53247     	-9.56514    	2                eval state : final;
              	3.64751     	5.53247     	-9.56514    	2                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	3.48904     	5.44412     	-4.43384    	0           
  10          	3.43710     	5.41215     	-3.72072    	0           
  100         	3.92041     	6.12716     	-3.08666    	0           
  300         	5.21704     	7.99649     	-4.37898    	0           
  500         	5.53887     	8.57660     	-3.90639    	1           
  1000        	5.35882     	8.45970     	-3.13394    	1           
---> try to load best state
              	5.35882     	8.45970     	-3.13394    	1                eval state : test_on_restore;
              	5.35882     	8.45970     	-3.13394    	1                eval state : test_on_last_epoch;
---> final result
              	5.35882     	8.45970     	-3.13394    	1                eval state : final;
              	5.35882     	8.45970     	-3.13394    	2                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	3.78431     	5.79273     	-5.38872    	0           
  10          	3.60939     	5.68445     	-3.49661    	0           
  100         	3.74676     	5.68390     	-5.48873    	0           
  300         	4.36755     	6.48834     	-9.03655    	0           
  500         	4.72594     	7.00835     	-9.87045    	0           
  1000        	4.95721     	7.35545     	-9.90899    	1           
---> try to load best state
              	4.95721     	7.35545     	-9.90899    	1                eval state : test_on_restore;
              	4.95721     	7.35545     	-9.90899    	1                eval state : test_on_last_epoch;
---> final result
              	4.95721     	7.35545     	-9.90899    	1                eval state : final;
              	4.95721     	7.35545     	-9.90899    	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 1 
  interp_data: False 

----------------------------------------
---> 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


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	2.34427     	3.95921     	-1.71157    	0           
  10          	2.66103     	4.37010     	-2.75488    	0           
  100         	5.86051     	8.38616     	-146.14696  	0           
  300         	6.39444     	9.10965     	-486.07780  	0           
  500         	6.39301     	9.10770     	-484.10895  	1           
  1000        	6.39005     	9.10366     	-480.07045  	2           
---> try to load best state
              	6.39005     	9.10366     	-480.07045  	2                eval state : test_on_restore;
              	6.39005     	9.10366     	-480.07045  	2                eval state : test_on_last_epoch;
---> final result
              	6.39005     	9.10366     	-480.07045  	2                eval state : final;
              	6.39005     	9.10366     	-480.07045  	2                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	2.42514     	4.03954     	-3.14875    	0           
  10          	2.90603     	4.60884     	-4.80697    	0           
  100         	2.64511     	4.31026     	-3.58184    	0           
  300         	2.35429     	3.95037     	-2.47601    	1           
  500         	2.13027     	3.62773     	-2.04123    	1           
  1000        	1.93696     	3.32521     	-1.74350    	2           
---> try to load best state
              	1.93696     	3.32521     	-1.74350    	2                eval state : test_on_restore;
              	1.93696     	3.32521     	-1.74350    	2                eval state : test_on_last_epoch;
---> final result
              	1.93696     	3.32521     	-1.74350    	2                eval state : final;
              	1.93696     	3.32521     	-1.74350    	2                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	2.94176     	4.72651     	-3.35187    	0           
  10          	2.21518     	3.64387     	-2.82801    	0           
  100         	1.11480     	1.81205     	-0.84208    	0           
  300         	1.13244     	1.86128     	-0.88170    	0           
  500         	1.13085     	1.85513     	-0.87665    	0           
  1000        	1.12528     	1.83614     	-0.86245    	2           
---> try to load best state
              	1.12528     	1.83614     	-0.86245    	2                eval state : test_on_restore;
              	1.12528     	1.83614     	-0.86245    	2                eval state : test_on_last_epoch;
---> final result
              	1.12528     	1.83614     	-0.86245    	2                eval state : final;
              	1.12528     	1.83614     	-0.86245    	2                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	2.67918     	4.29672     	-4.12317    	0           
  10          	2.53623     	4.08933     	-3.60866    	0           
  100         	1.25807     	2.18509     	-2.38022    	0           
  300         	1.22991     	2.14659     	-2.16513    	0           
  500         	1.23455     	2.15285     	-2.20697    	0           
  1000        	1.24776     	2.17112     	-2.31384    	1           
---> try to load best state
              	1.24776     	2.17112     	-2.31384    	1                eval state : test_on_restore;
              	1.24776     	2.17112     	-2.31384    	1                eval state : test_on_last_epoch;
---> final result
              	1.24776     	2.17112     	-2.31384    	1                eval state : final;
              	1.24776     	2.17112     	-2.31384    	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 2 
  interp_data: False 

----------------------------------------
---> 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


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	3.79374     	6.57018     	-1.43207    	0           
  10          	3.53888     	6.19996     	-1.11529    	0           
  100         	3.17496     	5.75482     	-1.29108    	0           
  300         	3.68499     	6.45162     	-1.72294    	0           
  500         	4.11917     	7.05463     	-1.89307    	0           
  1000        	4.84690     	8.09694     	-2.09916    	1           
---> try to load best state
              	4.84690     	8.09694     	-2.09916    	1                eval state : test_on_restore;
              	4.84690     	8.09694     	-2.09916    	1                eval state : test_on_last_epoch;
---> final result
              	4.84690     	8.09694     	-2.09916    	1                eval state : final;
              	4.84690     	8.09694     	-2.09916    	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	3.62342     	6.35211     	-1.46457    	0           
  10          	3.72716     	6.51587     	-1.49389    	0           
  100         	4.47816     	7.87000     	-1.44519    	0           
  300         	4.24655     	7.46503     	-1.50546    	0           
  500         	4.25072     	7.42903     	-1.56297    	0           
  1000        	4.31389     	7.49167     	-1.61814    	1           
---> try to load best state
              	4.31389     	7.49167     	-1.61814    	1                eval state : test_on_restore;
              	4.31389     	7.49167     	-1.61814    	1                eval state : test_on_last_epoch;
---> final result
              	4.31389     	7.49167     	-1.61814    	1                eval state : final;
              	4.31389     	7.49167     	-1.61814    	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	3.88703     	6.70533     	-1.75554    	0           
  10          	3.80676     	6.66464     	-1.49916    	0           
  100         	4.87787     	8.44731     	-1.54868    	0           
  300         	4.91577     	8.63043     	-1.40274    	0           
  500         	4.87010     	8.56728     	-1.39771    	0           
  1000        	4.88060     	8.56089     	-1.43432    	1           
---> try to load best state
              	4.88060     	8.56089     	-1.43432    	1                eval state : test_on_restore;
              	4.88060     	8.56089     	-1.43432    	1                eval state : test_on_last_epoch;
---> final result
              	4.88060     	8.56089     	-1.43432    	1                eval state : final;
              	4.88060     	8.56089     	-1.43432    	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	3.95596     	6.68859     	-2.01437    	0           
  10          	3.84891     	6.51273     	-2.12495    	0           
  100         	3.18889     	5.80769     	-1.20786    	0           
  300         	3.16754     	5.77678     	-1.17369    	0           
  500         	3.16622     	5.77638     	-1.17636    	0           
  1000        	3.16346     	5.77575     	-1.18223    	1           
---> try to load best state
              	3.16346     	5.77575     	-1.18223    	1                eval state : test_on_restore;
              	3.16346     	5.77575     	-1.18223    	1                eval state : test_on_last_epoch;
---> final result
              	3.16346     	5.77575     	-1.18223    	1                eval state : final;
              	3.16346     	5.77575     	-1.18223    	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 3 
  interp_data: False 

----------------------------------------
---> 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


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	1.04890     	1.76844     	-0.05286    	0           
  10          	1.04194     	1.73637     	0.01965     	0           
  100         	2.47977     	3.91798     	-0.51523    	0           
  300         	1.52768     	2.60811     	0.10610     	0           
  500         	1.31444     	2.45266     	0.12765     	0           
  1000        	1.77401     	3.18618     	-0.18870    	1           
---> try to load best state
              	1.77401     	3.18618     	-0.18870    	1                eval state : test_on_restore;
              	1.77401     	3.18618     	-0.18870    	1                eval state : test_on_last_epoch;
---> final result
              	1.77401     	3.18618     	-0.18870    	1                eval state : final;
              	1.77401     	3.18618     	-0.18870    	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	1.09153     	1.82269     	-0.04449    	0           
  10          	1.10137     	1.82968     	0.01237     	0           
  100         	2.34209     	3.82554     	-0.61588    	0           
  300         	1.72707     	2.88534     	0.01044     	0           
  500         	1.53418     	2.67831     	0.12681     	0           
  1000        	1.50327     	2.86618     	0.12169     	1           
---> try to load best state
              	1.50327     	2.86618     	0.12169     	1                eval state : test_on_restore;
              	1.50327     	2.86618     	0.12169     	1                eval state : test_on_last_epoch;
---> final result
              	1.50327     	2.86618     	0.12169     	1                eval state : final;
              	1.50327     	2.86618     	0.12169     	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	1.06874     	1.80499     	-0.09189    	0           
  10          	1.00095     	1.69023     	0.07536     	0           
  100         	1.21797     	2.13506     	0.12211     	0           
  300         	1.31884     	2.30383     	0.10786     	0           
  500         	1.33393     	2.35505     	0.12167     	0           
  1000        	1.33971     	2.44360     	0.13365     	1           
---> try to load best state
              	1.33971     	2.44360     	0.13365     	1                eval state : test_on_restore;
              	1.33971     	2.44360     	0.13365     	1                eval state : test_on_last_epoch;
---> final result
              	1.33971     	2.44360     	0.13365     	1                eval state : final;
              	1.33971     	2.44360     	0.13365     	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	1.15073     	2.02477     	-0.21087    	0           
  10          	1.41373     	2.39043     	-0.95856    	0           
  100         	1.18207     	2.18364     	0.11424     	0           
  300         	1.59548     	2.83573     	-0.32208    	0           
  500         	1.82501     	3.13275     	-0.63867    	0           
  1000        	1.81785     	3.12799     	-0.60831    	1           
---> try to load best state
              	1.81785     	3.12799     	-0.60831    	1                eval state : test_on_restore;
              	1.81785     	3.12799     	-0.60831    	1                eval state : test_on_last_epoch;
---> final result
              	1.81785     	3.12799     	-0.60831    	1                eval state : final;
              	1.81785     	3.12799     	-0.60831    	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> 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


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.94889     	1.58804     	-0.82206    	0           
  10          	0.88365     	1.50232     	-0.83743    	0           
  100         	0.96018     	1.68364     	-1.45494    	0           
  300         	0.93060     	1.58749     	-1.04665    	0           
  500         	1.00896     	1.70346     	-0.71023    	0           
  1000        	1.65805     	2.69769     	-0.26357    	1           
---> try to load best state
              	1.65805     	2.69769     	-0.26357    	1                eval state : test_on_restore;
              	1.65805     	2.69769     	-0.26357    	1                eval state : test_on_last_epoch;
---> final result
              	1.65805     	2.69769     	-0.26357    	1                eval state : final;
              	1.65805     	2.69769     	-0.26357    	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.92037     	1.53319     	-0.76850    	0           
  10          	0.80312     	1.37235     	-0.75767    	0           
  100         	1.36391     	2.39506     	-3.84666    	0           
  300         	1.07680     	1.97528     	-2.02418    	0           
  500         	0.99886     	1.82755     	-1.22173    	0           
  1000        	1.03656     	1.78076     	-0.51106    	1           
---> try to load best state
              	1.03656     	1.78076     	-0.51106    	1                eval state : test_on_restore;
              	1.03656     	1.78076     	-0.51106    	1                eval state : test_on_last_epoch;
---> final result
              	1.03656     	1.78076     	-0.51106    	1                eval state : final;
              	1.03656     	1.78076     	-0.51106    	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.81532     	1.33981     	-0.35925    	0           
  10          	0.72404     	1.23362     	-0.35042    	0           
  100         	0.73956     	1.46632     	-1.13786    	0           
  300         	0.77935     	1.36969     	-0.63256    	0           
  500         	1.09707     	1.85155     	-0.42963    	0           
  1000        	1.46170     	2.41785     	-0.37409    	1           
---> try to load best state
              	1.46170     	2.41785     	-0.37409    	1                eval state : test_on_restore;
              	1.46170     	2.41785     	-0.37409    	1                eval state : test_on_last_epoch;
---> final result
              	1.46170     	2.41785     	-0.37409    	1                eval state : final;
              	1.46170     	2.41785     	-0.37409    	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.61624     	1.05607     	-0.17808    	0           
  10          	0.67856     	1.29165     	-0.73663    	0           
  100         	0.62909     	1.20250     	-0.61442    	0           
  300         	0.94837     	1.87219     	-1.64189    	0           
  500         	1.15971     	2.26644     	-1.63232    	0           
  1000        	1.33218     	2.60057     	-1.46592    	1           
---> try to load best state
              	1.33218     	2.60057     	-1.46592    	1                eval state : test_on_restore;
              	1.33218     	2.60057     	-1.46592    	1                eval state : test_on_last_epoch;
---> final result
              	1.33218     	2.60057     	-1.46592    	1                eval state : final;
              	1.33218     	2.60057     	-1.46592    	1                eval state : final;module_name : LarGP;cp_record_file : True;
