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

  Demo sGAR 
  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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	5.46404     	-21641494.44532	5.75401     	0           
  10          	5.46336     	-4843707.28676	5.75321     	0           
  100         	5.46852     	-74797.17871	5.75734     	0           
  300         	5.51204     	-19539.54965	5.80236     	0           
  500         	5.57182     	-2198.11074 	5.86714     	1           
  1000        	5.74658     	-275.26584  	6.06402     	2           
---> try to load best state
              	5.74658     	-275.26584  	6.06402     	2                eval state : test_on_restore;
              	5.74658     	-275.26584  	6.06402     	2                eval state : test_on_last_epoch;
---> final result              	5.74658     	-275.26584  	6.06402     	2                eval state : final;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	6.32500     	-289.80583  	6.42365     	0           
  10          	8.24245     	-420.50339  	8.35178     	0           
  100         	27.19607    	-11372.70215	33.80622    	0           
  300         	27.59338    	-10208.43824	34.36196    	0           
  500         	27.46251    	-10451.84707	34.17891    	1           
  1000        	26.92277    	-11519.08963	33.42417    	1           
---> try to load best state
              	26.92277    	-11519.08963	33.42417    	1                eval state : test_on_restore;
              	26.92277    	-11519.08963	33.42417    	1                eval state : test_on_last_epoch;
---> final result              	26.92277    	-11519.08963	33.42417    	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	6.11420     	-281.46383  	6.24741     	0           
  10          	6.13420     	-278.57436  	6.26469     	0           
  100         	6.05732     	-262.27263  	6.21057     	0           
  300         	6.16189     	-264.29758  	6.29529     	0           
  500         	6.16190     	-264.29785  	6.29530     	1           
  1000        	6.16196     	-264.29901  	6.29534     	2           
---> try to load best state
              	6.16196     	-264.29901  	6.29534     	2                eval state : test_on_restore;
              	6.16196     	-264.29901  	6.29534     	2                eval state : test_on_last_epoch;
---> final result              	6.16196     	-264.29901  	6.29534     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	6.25763     	-287.59357  	6.36634     	0           
  10          	7.54725     	-371.18920  	7.59802     	0           
  100         	9.70510     	-447.50444  	10.07345    	0           
  300         	10.06557    	-495.82502  	10.51565    	0           
  500         	10.06558    	-495.82645  	10.51566    	0           
  1000        	10.06558    	-495.82696  	10.51566    	1           
---> try to load best state
              	10.06558    	-495.82696  	10.51566    	1                eval state : test_on_restore;
              	10.06558    	-495.82696  	10.51566    	1                eval state : test_on_last_epoch;
---> final result              	10.06558    	-495.82696  	10.51566    	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	6.18952     	-285.39874  	6.30933     	0           
  10          	6.85501     	-330.17599  	6.90504     	0           
  100         	8.74801     	-413.64473  	8.93616     	0           
  300         	8.63977     	-407.82012  	8.81073     	0           
  500         	8.64006     	-407.89783  	8.81107     	0           
  1000        	8.64114     	-408.18732  	8.81230     	1           
---> try to load best state
              	8.64114     	-408.18732  	8.81230     	1                eval state : test_on_restore;
              	8.64114     	-408.18732  	8.81230     	1                eval state : test_on_last_epoch;
---> final result              	8.64114     	-408.18732  	8.81230     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	2.14881     	-4.34234    	2.54434     	0           
  10          	1.68545     	-2.61141    	2.13142     	0           
  100         	1.61400     	-0.93208    	2.13548     	0           
  300         	1.50777     	-0.91401    	1.89666     	0           
  500         	1.52542     	-1.02696    	1.85756     	1           
  1000        	1.66837     	-0.88931    	1.99988     	2           
---> try to load best state
              	1.66837     	-0.88931    	1.99988     	2                eval state : test_on_restore;
              	1.66837     	-0.88931    	1.99988     	2                eval state : test_on_last_epoch;
---> final result              	1.66837     	-0.88931    	1.99988     	2                eval state : final;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.53929     	-0.48921    	1.81221     	0           
  10          	1.61568     	-0.91918    	1.94179     	0           
  100         	2.53547     	-0.50464    	3.47952     	0           
  epoch 153 reach nan state
---> try to load best state
              	3.50653     	-0.65483    	5.19950     	0                eval state : test_on_restore;
              	2.53547     	-0.50464    	3.47952     	0                eval state : test_on_last_epoch;
---> final result              	2.53547     	-0.50464    	3.47952     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': '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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.59328     	-0.40557    	1.86850     	0           
  10          	1.66684     	-0.80079    	2.00489     	0           
  100         	2.70261     	-0.79016    	3.77980     	0           
  epoch 208 reach nan state
---> try to load best state
              	3.70806     	-0.47696    	5.60349     	0                eval state : test_on_restore;
              	2.70261     	-0.79016    	3.77980     	0                eval state : test_on_last_epoch;
---> final result              	2.70261     	-0.79016    	3.77980     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.64379     	-0.52010    	1.99742     	0           
  10          	1.75257     	-0.57483    	2.16195     	0           
  100         	3.15334     	-1.95003    	4.18649     	0           
  300         	3.98360     	-3.04889    	5.28693     	0           
  500         	3.97233     	-2.88238    	5.29688     	0           
  1000        	3.94735     	-2.56004    	5.31233     	1           
---> try to load best state
              	3.94735     	-2.56004    	5.31233     	1                eval state : test_on_restore;
              	3.94735     	-2.56004    	5.31233     	1                eval state : test_on_last_epoch;
---> final result              	3.94735     	-2.56004    	5.31233     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.74399     	-0.87682    	2.15525     	0           
  10          	1.89671     	-0.72790    	2.32733     	0           
  100         	3.10181     	-6.20270    	3.82599     	0           
  300         	3.32754     	-7.78050    	4.10925     	0           
  500         	3.35159     	-8.17894    	4.12709     	0           
  1000        	3.41044     	-8.76029    	4.19627     	1           
---> try to load best state
              	3.41044     	-8.76029    	4.19627     	1                eval state : test_on_restore;
              	3.41044     	-8.76029    	4.19627     	1                eval state : test_on_last_epoch;
---> final result              	3.41044     	-8.76029    	4.19627     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.27202     	-0.91044    	1.68116     	0           
  10          	1.39983     	-0.51142    	1.94715     	0           
  100         	2.49900     	-4.33699    	2.98403     	0           
  300         	2.36818     	-3.61009    	2.86645     	0           
  500         	2.30206     	-3.24488    	2.81914     	0           
  1000        	2.20694     	-2.64846    	2.78100     	1           
---> try to load best state
              	2.20694     	-2.64846    	2.78100     	1                eval state : test_on_restore;
              	2.20694     	-2.64846    	2.78100     	1                eval state : test_on_last_epoch;
---> final result              	2.20694     	-2.64846    	2.78100     	1                eval state : final;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	2.87817     	-4.78168    	3.26304     	0           
  10          	3.06365     	-7.64496    	3.45084     	0           
  100         	4.91453     	-119.85245  	5.27547     	0           
  epoch 193 reach nan state
---> try to load best state
              	3.91112     	-41.91219   	4.17607     	0                eval state : test_on_restore;
              	4.91453     	-119.85245  	5.27547     	0                eval state : test_on_last_epoch;
---> final result              	3.91112     	-41.91219   	4.17607     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': '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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	2.83637     	-5.26298    	3.23998     	0           
  10          	2.97529     	-5.98629    	3.33169     	0           
  100         	4.64214     	-20.84505   	5.13133     	0           
  300         	5.14904     	-70.08731   	5.64197     	0           
  500         	5.20935     	-88.59273   	5.70371     	0           
  1000        	5.20436     	-86.60026   	5.69862     	1           
---> try to load best state
              	5.20436     	-86.60026   	5.69862     	1                eval state : test_on_restore;
              	5.20436     	-86.60026   	5.69862     	1                eval state : test_on_last_epoch;
---> final result              	5.20436     	-86.60026   	5.69862     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	3.42903     	-6.02116    	3.79132     	0           
  10          	3.13036     	-4.57550    	3.51496     	0           
  100         	4.19005     	-22.00041   	4.54200     	0           
  300         	4.05774     	-18.46447   	4.41021     	0           
  500         	4.05556     	-18.41559   	4.40804     	0           
  1000        	4.04982     	-18.28849   	4.40234     	1           
---> try to load best state
              	4.04982     	-18.28849   	4.40234     	1                eval state : test_on_restore;
              	4.04982     	-18.28849   	4.40234     	1                eval state : test_on_last_epoch;
---> final result              	4.04982     	-18.28849   	4.40234     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	3.09715     	-6.34338    	3.43453     	0           
  10          	2.93086     	-10.30916   	3.29137     	0           
  100         	4.35769     	-23.45336   	4.73385     	0           
  300         	4.30174     	-22.18350   	4.67512     	0           
  500         	4.30127     	-22.15639   	4.67472     	0           
  1000        	4.29946     	-22.05893   	4.67319     	1           
---> try to load best state
              	4.29946     	-22.05893   	4.67319     	1                eval state : test_on_restore;
              	4.29946     	-22.05893   	4.67319     	1                eval state : test_on_last_epoch;
---> final result              	4.29946     	-22.05893   	4.67319     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.27895     	-1.09016    	1.65296     	0           
  10          	1.18874     	-0.61883    	1.62602     	0           
  100         	1.40021     	-1.00477    	1.98573     	0           
  300         	1.15431     	-0.37016    	1.51673     	0           
  500         	1.09742     	-0.15783    	1.38262     	1           
  1000        	1.14510     	-0.06175    	1.42477     	1           
---> try to load best state
              	1.14510     	-0.06175    	1.42477     	1                eval state : test_on_restore;
              	1.14510     	-0.06175    	1.42477     	1                eval state : test_on_last_epoch;
---> final result              	1.14510     	-0.06175    	1.42477     	1                eval state : final;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.46220     	-2.36976    	1.69986     	0           
  10          	1.54000     	-3.04553    	1.79610     	0           
  100         	3.82445     	-9.54606    	4.75701     	0           
  epoch 266 reach nan state
---> try to load best state
              	4.31967     	-22.80244   	5.40957     	0                eval state : test_on_restore;
              	3.82445     	-9.54606    	4.75701     	0                eval state : test_on_last_epoch;
---> final result              	3.82445     	-9.54606    	4.75701     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': '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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.26930     	-0.65645    	1.54629     	0           
  10          	1.57485     	-1.99355    	1.83919     	0           
  100         	3.25238     	-13.98618   	4.03553     	0           
  300         	3.47926     	-12.16214   	4.38267     	0           
  500         	3.76085     	-11.68324   	4.78278     	0           
  1000        	3.86546     	-11.48333   	4.93243     	1           
---> try to load best state
              	3.86546     	-11.48333   	4.93243     	1                eval state : test_on_restore;
              	3.86546     	-11.48333   	4.93243     	1                eval state : test_on_last_epoch;
---> final result              	3.86546     	-11.48333   	4.93243     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.53740     	-1.59461    	1.79847     	0           
  10          	1.55203     	-2.53274    	1.82210     	0           
  100         	2.96621     	-10.83016   	3.71536     	0           
  300         	3.23448     	-9.80574    	4.10212     	0           
  500         	3.45917     	-9.67501    	4.40953     	1           
  1000        	3.67317     	-10.09537   	4.69260     	1           
---> try to load best state
              	3.67317     	-10.09537   	4.69260     	1                eval state : test_on_restore;
              	3.67317     	-10.09537   	4.69260     	1                eval state : test_on_last_epoch;
---> final result              	3.67317     	-10.09537   	4.69260     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.62186     	-0.37949    	2.07067     	0           
  10          	1.68753     	-0.35842    	2.13704     	0           
  100         	4.24489     	-11.17785   	5.44367     	0           
  300         	4.10626     	-11.94867   	5.23744     	0           
  500         	4.10628     	-11.94854   	5.23747     	0           
  1000        	4.10633     	-11.94834   	5.23754     	2           
---> try to load best state
              	4.10633     	-11.94834   	5.23754     	2                eval state : test_on_restore;
              	4.10633     	-11.94834   	5.23754     	2                eval state : test_on_last_epoch;
---> final result              	4.10633     	-11.94834   	5.23754     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.47505     	-1.77306    	1.78554     	0           
  10          	1.58841     	-2.67299    	1.95489     	0           
  100         	1.86591     	-6.11063    	2.23933     	0           
  300         	1.66105     	-3.07901    	2.05298     	0           
  500         	1.59996     	-2.41282    	2.00154     	1           
  1000        	1.59345     	-1.97511    	2.03278     	2           
---> try to load best state
              	1.59345     	-1.97511    	2.03278     	2                eval state : test_on_restore;
              	1.59345     	-1.97511    	2.03278     	2                eval state : test_on_last_epoch;
---> final result              	1.59345     	-1.97511    	2.03278     	2                eval state : final;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.91109     	-6.88909    	2.21320     	0           
  10          	1.88895     	-5.58877    	2.19639     	0           
  epoch 82 reach nan state
---> try to load best state
              	1.81186     	-2.34817    	2.15216     	0                eval state : test_on_restore;
              	1.88895     	-5.58877    	2.19639     	0                eval state : test_on_last_epoch;
---> final result              	1.81186     	-2.34817    	2.15216     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': '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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.93763     	-5.01194    	2.22442     	0           
  10          	1.90700     	-4.90260    	2.20190     	0           
  100         	3.06482     	-7.96922    	3.36362     	0           
  epoch 113 reach nan state
---> try to load best state
              	3.04369     	-7.19547    	3.34702     	0                eval state : test_on_restore;
              	3.06482     	-7.96922    	3.36362     	0                eval state : test_on_last_epoch;
---> final result              	3.04369     	-7.19547    	3.34702     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.85797     	-9.62723    	2.20036     	0           
  10          	1.82818     	-6.81859    	2.18413     	0           
  100         	2.20974     	-2.82367    	2.51650     	0           
  300         	2.40188     	-3.19373    	2.71847     	0           
  500         	2.52499     	-3.72437    	2.85143     	1           
  1000        	2.60666     	-4.12952    	2.93600     	2           
---> try to load best state
              	2.60666     	-4.12952    	2.93600     	2                eval state : test_on_restore;
              	2.60666     	-4.12952    	2.93600     	2                eval state : test_on_last_epoch;
---> final result              	2.60666     	-4.12952    	2.93600     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.97997     	-7.94604    	2.27705     	0           
  10          	2.29360     	-23.73893   	2.56311     	0           
  100         	2.68747     	-4.69421    	3.05641     	0           
  300         	2.70137     	-4.16497    	3.09769     	1           
  500         	2.59774     	-3.99186    	2.98473     	1           
  1000        	2.47951     	-3.66935    	2.85800     	2           
---> try to load best state
              	2.47951     	-3.66935    	2.85800     	2                eval state : test_on_restore;
              	2.47951     	-3.66935    	2.85800     	2                eval state : test_on_last_epoch;
---> final result              	2.47951     	-3.66935    	2.85800     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.28690     	-5.32889    	1.62524     	0           
  10          	1.40584     	-3.67798    	1.74337     	0           
  100         	1.68465     	-8.94777    	1.99133     	0           
  300         	1.46293     	-4.60733    	1.83293     	0           
  500         	1.35191     	-3.43146    	1.72987     	1           
  1000        	1.35639     	-3.15883    	1.68229     	2           
---> try to load best state
              	1.35639     	-3.15883    	1.68229     	2                eval state : test_on_restore;
              	1.35639     	-3.15883    	1.68229     	2                eval state : test_on_last_epoch;
---> final result              	1.35639     	-3.15883    	1.68229     	2                eval state : final;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.52535     	-9.65880    	1.76073     	0           
  10          	1.54189     	-10.35259   	1.77388     	0           
  100         	1.59105     	-2.74213    	1.78685     	0           
  epoch 151 reach nan state
---> try to load best state
              	1.59517     	-2.73579    	1.78973     	0                eval state : test_on_restore;
              	1.59105     	-2.74213    	1.78685     	0                eval state : test_on_last_epoch;
---> final result              	1.59105     	-2.74213    	1.78685     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': '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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.50183     	-6.78977    	1.73963     	0           
  10          	1.58691     	-8.04439    	1.82309     	0           
  100         	1.74086     	-5.85750    	1.94683     	0           
  300         	1.59939     	-2.46794    	1.78065     	0           
  500         	1.53013     	-2.13386    	1.72196     	1           
  epoch 968 reach nan state
---> try to load best state
              	1.84949     	-3.12198    	2.01220     	2                eval state : test_on_restore;
              	1.53013     	-2.13386    	1.72196     	2                eval state : test_on_last_epoch;
---> final result              	1.53013     	-2.13386    	1.72196     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.45338     	-2.56077    	1.70260     	0           
  10          	1.48353     	-2.52534    	1.71877     	0           
  100         	1.64150     	-3.11263    	1.82883     	0           
  epoch 188 reach nan state
---> try to load best state
              	1.77339     	-2.88482    	1.93936     	0                eval state : test_on_restore;
              	1.64150     	-3.11263    	1.82883     	0                eval state : test_on_last_epoch;
---> final result              	1.64150     	-3.11263    	1.82883     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	1.70638     	-2.55622    	1.90628     	0           
  10          	1.90820     	-4.44398    	2.12052     	0           
  100         	2.22178     	-7.20878    	2.50495     	0           
  300         	2.14363     	-7.17602    	2.43048     	0           
  500         	2.07886     	-7.98218    	2.37134     	1           
  1000        	1.97459     	-8.65488    	2.27841     	2           
---> try to load best state
              	1.97459     	-8.65488    	2.27841     	2                eval state : test_on_restore;
              	1.97459     	-8.65488    	2.27841     	2                eval state : test_on_last_epoch;
---> final result              	1.97459     	-8.65488    	2.27841     	2                eval state : final;module_name : SGAR;cp_record_file : True;
