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

  Demo sGAR 
  seed: 0 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, '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_normalize: True
  output_normalize: True
  noise_init: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	103.13474   	-0.21354    	142.59787   	0           
  10          	93.43330    	0.12922     	131.16420   	0           
  100         	26.84498    	0.95784     	40.67867    	4           
  300         	13.26678    	0.99074     	20.40741    	13          
  500         	9.67460     	0.99526     	14.89304    	20          
  1000        	5.37317     	0.99864     	8.20057     	37          
---> try to load best state
              	5.37317     	0.99864     	8.20057     	37               eval state : test_on_restore;
              	5.37317     	0.99864     	8.20057     	37               eval state : test_on_last_epoch;
---> final result              	5.37317     	0.99864     	8.20057     	37               eval state : final;
---> end


---------->
SGAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 30, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	193.42258   	0.55690     	258.61453   	0           
  10          	71.87094    	0.64485     	94.09754    	0           
  100         	23.83834    	0.97454     	31.62068    	0           
  300         	19.76587    	0.98924     	24.94554    	2           
  500         	14.01630    	0.99317     	18.90005    	3           
  1000        	18.84093    	0.98666     	24.03232    	6           
---> try to load best state
              	18.84093    	0.98666     	24.03232    	6                eval state : test_on_restore;
              	18.84093    	0.98666     	24.03232    	6                eval state : test_on_last_epoch;
---> final result              	18.84093    	0.98666     	24.03232    	7                eval state : final;module_name : SGAR;subset : 2.0;cp_record_file : True;
---> end


---------->
SGAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 28, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	199.44740   	0.61610     	259.76038   	0           
  10          	109.65701   	-0.13689    	139.63144   	0           
  100         	10.14706    	0.99606     	14.76532    	1           
  300         	8.06334     	0.99731     	11.45467    	3           
  500         	7.48993     	0.99776     	10.48962    	4           
  1000        	13.25831    	0.99497     	16.39308    	10          
---> try to load best state
              	13.25831    	0.99497     	16.39308    	10               eval state : test_on_restore;
              	13.25831    	0.99497     	16.39308    	10               eval state : test_on_last_epoch;
---> final result              	13.25831    	0.99497     	16.39308    	10               eval state : final;module_name : SGAR;subset : 4.0;cp_record_file : True;
---> end


---------->
SGAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 24, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	134.53220   	0.68123     	189.38052   	0           
  10          	56.82586    	0.88702     	80.42194    	0           
  100         	5.51220     	0.99861     	8.32884     	1           
  300         	10.31134    	0.99572     	13.99360    	4           
  500         	6.60043     	0.99820     	9.40312     	7           
  1000        	11.73980    	0.99628     	14.31338    	14          
---> try to load best state
              	11.73980    	0.99628     	14.31338    	14               eval state : test_on_restore;
              	11.73980    	0.99628     	14.31338    	14               eval state : test_on_last_epoch;
---> final result              	11.73980    	0.99628     	14.31338    	14               eval state : final;module_name : SGAR;subset : 8.0;cp_record_file : True;
---> end


---------->
SGAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 16, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	115.97365   	0.73572     	174.07744   	0           
  10          	49.95011    	0.86329     	71.49821    	0           
  100         	4.55348     	0.99900     	7.05162     	2           
  300         	5.85004     	0.99899     	7.31169     	8           
  500         	17.24182    	0.98822     	22.05483    	14          
  1000        	5.77520     	0.99859     	8.33069     	26          
---> try to load best state
              	5.77520     	0.99859     	8.33069     	26               eval state : test_on_restore;
              	5.77520     	0.99859     	8.33069     	26               eval state : test_on_last_epoch;
---> final result              	5.77520     	0.99859     	8.33069     	26               eval state : final;module_name : SGAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, '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_normalize: True
  output_normalize: True
  noise_init: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	115.21420   	-0.90647    	156.24170   	0           
  10          	105.05499   	-0.31965    	144.58244   	0           
  100         	36.43618    	0.91844     	54.68832    	3           
  300         	16.48247    	0.98515     	25.57528    	11          
  500         	11.51421    	0.99306     	17.93617    	19          
  1000        	6.08190     	0.99820     	9.41179     	36          
---> try to load best state
              	6.08190     	0.99820     	9.41179     	36               eval state : test_on_restore;
              	6.08190     	0.99820     	9.41179     	36               eval state : test_on_last_epoch;
---> final result              	6.08190     	0.99820     	9.41179     	36               eval state : final;
---> end


---------->
SGAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 30, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	207.86287   	0.54460     	273.72046   	0           
  10          	93.99653    	-0.11486    	134.90373   	0           
  100         	22.58626    	0.97799     	30.76923    	0           
  300         	11.73557    	0.99522     	16.47233    	2           
  500         	13.94074    	0.99197     	19.48070    	3           
  1000        	10.15506    	0.99614     	14.02522    	6           
---> try to load best state
              	10.15506    	0.99614     	14.02522    	6                eval state : test_on_restore;
              	10.15506    	0.99614     	14.02522    	6                eval state : test_on_last_epoch;
---> final result              	10.15506    	0.99614     	14.02522    	6                eval state : final;module_name : SGAR;subset : 2.0;cp_record_file : True;
---> end


---------->
SGAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 28, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	169.91989   	0.60063     	225.50243   	0           
  10          	77.89516    	0.83687     	105.71974   	0           
  100         	6.22135     	0.99823     	9.52219     	1           
  300         	18.25526    	0.99168     	21.99428    	2           
  500         	6.66600     	0.99812     	9.87079     	4           
  1000        	7.21767     	0.99779     	10.53890    	9           
---> try to load best state
              	7.21767     	0.99779     	10.53890    	9                eval state : test_on_restore;
              	7.21767     	0.99779     	10.53890    	9                eval state : test_on_last_epoch;
---> final result              	7.21767     	0.99779     	10.53890    	9                eval state : final;module_name : SGAR;subset : 4.0;cp_record_file : True;
---> end


---------->
SGAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 24, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	139.30565   	0.73816     	195.43091   	0           
  10          	61.94810    	0.75437     	84.11837    	0           
  100         	5.52683     	0.99860     	8.28961     	1           
  300         	5.99121     	0.99847     	8.84542     	4           
  500         	9.67586     	0.99614     	13.41442    	7           
  1000        	26.85026    	0.96851     	33.92992    	14          
---> try to load best state
              	26.85026    	0.96851     	33.92992    	14               eval state : test_on_restore;
              	26.85026    	0.96851     	33.92992    	14               eval state : test_on_last_epoch;
---> final result              	26.85026    	0.96851     	33.92992    	15               eval state : final;module_name : SGAR;subset : 8.0;cp_record_file : True;
---> end


---------->
SGAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 16, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	157.47740   	0.69552     	230.24393   	0           
  10          	90.67735    	0.10777     	128.62166   	0           
  100         	4.51895     	0.99899     	7.13462     	2           
  300         	11.58054    	0.99433     	15.93476    	7           
  500         	5.37308     	0.99881     	7.86206     	13          
  1000        	5.70156     	0.99868     	8.25314     	29          
---> try to load best state
              	5.70156     	0.99868     	8.25314     	29               eval state : test_on_restore;
              	5.70156     	0.99868     	8.25314     	29               eval state : test_on_last_epoch;
---> final result              	5.70156     	0.99868     	8.25314     	29               eval state : final;module_name : SGAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, '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_normalize: True
  output_normalize: True
  noise_init: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	105.28571   	-0.55604    	145.36787   	0           
  10          	95.39771    	-0.10414    	134.25525   	0           
  100         	28.36408    	0.94765     	43.82598    	3           
  300         	13.18277    	0.99055     	20.16253    	10          
  500         	9.20666     	0.99563     	14.01418    	17          
  1000        	5.04153     	0.99879     	7.53598     	33          
---> try to load best state
              	5.04153     	0.99879     	7.53598     	33               eval state : test_on_restore;
              	5.04153     	0.99879     	7.53598     	33               eval state : test_on_last_epoch;
---> final result              	5.04153     	0.99879     	7.53598     	33               eval state : final;
---> end


---------->
SGAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 30, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	131.62489   	-3.35891    	171.23119   	0           
  10          	61.27364    	0.80100     	79.90677    	0           
  100         	43.35892    	0.89436     	56.01909    	0           
  300         	24.38641    	0.97315     	32.56149    	2           
  500         	27.94163    	0.96987     	36.72525    	3           
  1000        	52.58825    	0.87439     	65.89608    	6           
---> try to load best state
              	52.58825    	0.87439     	65.89608    	6                eval state : test_on_restore;
              	52.58825    	0.87439     	65.89608    	6                eval state : test_on_last_epoch;
---> final result              	52.58825    	0.87439     	65.89608    	6                eval state : final;module_name : SGAR;subset : 2.0;cp_record_file : True;
---> end


---------->
SGAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 28, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	298.36566   	0.55213     	376.26941   	0           
  10          	212.74100   	-1.37671    	282.00647   	0           
  100         	18.31612    	0.98360     	29.10934    	1           
  300         	9.97754     	0.99564     	14.71587    	2           
  500         	9.09270     	0.99664     	12.78450    	4           
  1000        	13.55025    	0.99311     	17.47853    	9           
---> try to load best state
              	13.55025    	0.99311     	17.47853    	9                eval state : test_on_restore;
              	13.55025    	0.99311     	17.47853    	9                eval state : test_on_last_epoch;
---> final result              	13.55025    	0.99311     	17.47853    	9                eval state : final;module_name : SGAR;subset : 4.0;cp_record_file : True;
---> end


---------->
SGAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 24, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	228.21017   	0.60174     	299.86600   	0           
  10          	149.48500   	-3.17279    	192.34061   	0           
  100         	8.23121     	0.99675     	12.81076    	1           
  300         	6.41857     	0.99833     	9.21525     	4           
  500         	12.66246    	0.99571     	15.44586    	7           
  1000        	15.94974    	0.99348     	19.26582    	14          
---> try to load best state
              	15.94974    	0.99348     	19.26582    	14               eval state : test_on_restore;
              	15.94974    	0.99348     	19.26582    	14               eval state : test_on_last_epoch;
---> final result              	15.94974    	0.99348     	19.26582    	14               eval state : final;module_name : SGAR;subset : 8.0;cp_record_file : True;
---> end


---------->
SGAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 16, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	146.33667   	0.72917     	204.25777   	0           
  10          	87.62835    	0.15218     	123.30683   	0           
  100         	3.64641     	0.99940     	5.53518     	2           
  300         	4.05743     	0.99938     	5.70970     	8           
  500         	4.59958     	0.99917     	6.58194     	13          
  1000        	5.40269     	0.99884     	7.64584     	26          
---> try to load best state
              	5.40269     	0.99884     	7.64584     	26               eval state : test_on_restore;
              	5.40269     	0.99884     	7.64584     	26               eval state : test_on_last_epoch;
---> final result              	5.40269     	0.99884     	7.64584     	26               eval state : final;module_name : SGAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, '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_normalize: True
  output_normalize: True
  noise_init: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	101.81847   	-0.32375    	138.39980   	0           
  10          	89.72098    	0.14904     	123.88775   	0           
  100         	23.36989    	0.96975     	35.04122    	3           
  300         	11.23876    	0.99361     	17.07111    	10          
  500         	7.96564     	0.99690     	12.09827    	17          
  1000        	4.35067     	0.99914     	6.52704     	34          
---> try to load best state
              	4.35067     	0.99914     	6.52704     	34               eval state : test_on_restore;
              	4.35067     	0.99914     	6.52704     	34               eval state : test_on_last_epoch;
---> final result              	4.35067     	0.99914     	6.52704     	34               eval state : final;
---> end


---------->
SGAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 30, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	190.99278   	0.29636     	245.61441   	0           
  10          	64.68389    	0.88599     	88.19160    	0           
  100         	14.86213    	0.99223     	21.86316    	0           
  300         	11.68948    	0.99503     	16.40204    	2           
  500         	20.38994    	0.98414     	26.50032    	3           
  1000        	17.98008    	0.98809     	23.48388    	7           
---> try to load best state
              	17.98008    	0.98809     	23.48388    	7                eval state : test_on_restore;
              	17.98008    	0.98809     	23.48388    	7                eval state : test_on_last_epoch;
---> final result              	17.98008    	0.98809     	23.48388    	7                eval state : final;module_name : SGAR;subset : 2.0;cp_record_file : True;
---> end


---------->
SGAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 28, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	182.41513   	0.34073     	241.06235   	0           
  10          	65.86358    	0.89246     	91.07941    	0           
  100         	6.74321     	0.99799     	10.34559    	1           
  300         	7.48416     	0.99785     	10.82450    	3           
  500         	16.48901    	0.98941     	21.09081    	5           
  1000        	8.32366     	0.99782     	11.00210    	10          
---> try to load best state
              	8.32366     	0.99782     	11.00210    	10               eval state : test_on_restore;
              	8.32366     	0.99782     	11.00210    	10               eval state : test_on_last_epoch;
---> final result              	8.32366     	0.99782     	11.00210    	10               eval state : final;module_name : SGAR;subset : 4.0;cp_record_file : True;
---> end


---------->
SGAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 24, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	199.53947   	0.65962     	270.32053   	0           
  10          	132.62114   	-2.44333    	173.69148   	0           
  100         	4.31875     	0.99916     	6.62583     	1           
  300         	4.94688     	0.99901     	7.22249     	4           
  500         	5.18440     	0.99896     	7.47809     	8           
  1000        	11.23969    	0.99516     	14.70337    	17          
---> try to load best state
              	11.23969    	0.99516     	14.70337    	17               eval state : test_on_restore;
              	11.23969    	0.99516     	14.70337    	18               eval state : test_on_last_epoch;
---> final result              	11.23969    	0.99516     	14.70337    	18               eval state : final;module_name : SGAR;subset : 8.0;cp_record_file : True;
---> end


---------->
SGAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 16, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	159.33440   	0.71052     	221.07248   	0           
  10          	99.27705    	0.07731     	135.57570   	0           
  100         	5.04857     	0.99866     	8.26092     	3           
  300         	4.66768     	0.99930     	6.08198     	10          
  500         	8.99740     	0.99777     	10.97587    	17          
  1000        	8.77046     	0.99786     	10.72194    	30          
---> try to load best state
              	8.77046     	0.99786     	10.72194    	30               eval state : test_on_restore;
              	8.77046     	0.99786     	10.72194    	30               eval state : test_on_last_epoch;
---> final result              	8.77046     	0.99786     	10.72194    	30               eval state : final;module_name : SGAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, '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_normalize: True
  output_normalize: True
  noise_init: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	102.69917   	-0.38722    	140.91411   	0           
  10          	91.92400    	0.05080     	128.19656   	0           
  100         	26.33698    	0.95912     	39.61972    	3           
  300         	12.17729    	0.99226     	18.47873    	11          
  500         	8.54191     	0.99634     	12.96821    	18          
  1000        	4.60568     	0.99901     	6.91411     	36          
---> try to load best state
              	4.60568     	0.99901     	6.91411     	37               eval state : test_on_restore;
              	4.60568     	0.99901     	6.91411     	37               eval state : test_on_last_epoch;
---> final result              	4.60568     	0.99901     	6.91411     	37               eval state : final;
---> end


---------->
SGAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 30, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	159.47609   	0.09857     	206.40021   	0           
  10          	60.52040    	0.83039     	80.03796    	0           
  100         	18.17963    	0.98905     	23.71148    	1           
  300         	24.19613    	0.97762     	29.81143    	2           
  500         	24.15167    	0.97862     	30.36391    	5           
  1000        	44.22793    	0.93215     	57.48761    	9           
---> try to load best state
              	44.22793    	0.93215     	57.48761    	9                eval state : test_on_restore;
              	44.22793    	0.93215     	57.48761    	9                eval state : test_on_last_epoch;
---> final result              	44.22793    	0.93215     	57.48761    	9                eval state : final;module_name : SGAR;subset : 2.0;cp_record_file : True;
---> end


---------->
SGAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 28, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	265.08109   	0.57502     	347.83929   	0           
  10          	211.41545   	-0.97732    	287.41299   	0           
  100         	16.78441    	0.98574     	26.74274    	1           
  300         	11.60395    	0.99571     	15.79032    	3           
  500         	15.31090    	0.99080     	19.77503    	4           
  1000        	21.05134    	0.98224     	26.47124    	9           
---> try to load best state
              	21.05134    	0.98224     	26.47124    	10               eval state : test_on_restore;
              	21.05134    	0.98224     	26.47124    	10               eval state : test_on_last_epoch;
---> final result              	21.05134    	0.98224     	26.47124    	10               eval state : final;module_name : SGAR;subset : 4.0;cp_record_file : True;
---> end


---------->
SGAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 24, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	220.75980   	0.61221     	301.24500   	0           
  10          	150.81982   	-1.69044    	201.77025   	0           
  100         	7.44592     	0.99734     	12.00178    	1           
  300         	6.49799     	0.99832     	9.17813     	4           
  500         	9.34342     	0.99667     	12.44088    	7           
  1000        	5.38595     	0.99889     	7.63009     	15          
---> try to load best state
              	5.38595     	0.99889     	7.63009     	15               eval state : test_on_restore;
              	5.38595     	0.99889     	7.63009     	15               eval state : test_on_last_epoch;
---> final result              	5.38595     	0.99889     	7.63009     	15               eval state : final;module_name : SGAR;subset : 8.0;cp_record_file : True;
---> end


---------->
SGAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 16, '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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	149.77008   	0.70980     	222.65741   	0           
  10          	99.05750    	0.03904     	141.48706   	0           
  100         	5.08054     	0.99855     	8.58082     	2           
  300         	5.80325     	0.99862     	8.19076     	8           
  500         	14.60357    	0.99181     	18.66057    	13          
  1000        	14.65676    	0.99167     	18.73697    	26          
---> try to load best state
              	14.65676    	0.99167     	18.73697    	26               eval state : test_on_restore;
              	14.65676    	0.99167     	18.73697    	26               eval state : test_on_last_epoch;
---> final result              	14.65676    	0.99167     	18.73697    	26               eval state : final;module_name : SGAR;subset : 16.0;cp_record_file : True;
