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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]'], '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           	0.01067     	0.86919     	0.02502     	0           
  10          	0.01101     	0.86828     	0.02507     	0           
  100         	0.01611     	0.83397     	0.02725     	0           
  300         	0.01849     	0.75922     	0.02963     	1           
  500         	0.01817     	0.77121     	0.02906     	2           
  1000        	0.01769     	0.79640     	0.02822     	5           
---> try to load best state
              	0.01769     	0.79640     	0.02822     	5                eval state : test_on_restore;
              	0.01769     	0.79640     	0.02822     	5                eval state : test_on_last_epoch;
---> final result              	0.01769     	0.79640     	0.02822     	5                eval state : final;
---> end


---------->
SGAR for 1 subset samples
---> Training x,yl -> yh part

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]'], '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           	0.01067     	0.86919     	0.02502     	0           
  10          	0.01101     	0.86828     	0.02507     	0           
  100         	0.01611     	0.83397     	0.02725     	0           
  300         	0.01849     	0.75922     	0.02963     	1           
  500         	0.01817     	0.77121     	0.02906     	2           
  1000        	0.01769     	0.79640     	0.02822     	5           
---> try to load best state
              	0.01769     	0.79640     	0.02822     	5                eval state : test_on_restore;
              	0.01769     	0.79640     	0.02822     	5                eval state : test_on_last_epoch;
---> final result              	0.01769     	0.79640     	0.02822     	5                eval state : final;
---> end


---------->
SGAR for 1 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 31, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00670     	0.92615     	0.01546     	0           
  10          	0.00635     	0.94052     	0.01411     	0           
  100         	0.01455     	0.89285     	0.02321     	0           
  300         	0.01629     	0.87854     	0.02510     	1           
  500         	0.01632     	0.87406     	0.02523     	2           
  1000        	0.01647     	0.87127     	0.02517     	5           
---> try to load best state
              	0.01647     	0.87127     	0.02517     	5                eval state : test_on_restore;
              	0.01647     	0.87127     	0.02517     	5                eval state : test_on_last_epoch;
---> final result              	0.01647     	0.87127     	0.02517     	5                eval state : final;module_name : SGAR;subset : 1;cp_record_file : True;
---> end


---------->
SGAR for 2 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 30, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00691     	0.92326     	0.01592     	0           
  10          	0.00674     	0.93043     	0.01529     	0           
  100         	0.01430     	0.89239     	0.02285     	0           
  300         	0.01617     	0.87512     	0.02501     	1           
  500         	0.01683     	0.87057     	0.02541     	2           
  1000        	0.01670     	0.85187     	0.02565     	5           
---> try to load best state
              	0.01670     	0.85187     	0.02565     	5                eval state : test_on_restore;
              	0.01670     	0.85187     	0.02565     	5                eval state : test_on_last_epoch;
---> final result              	0.01670     	0.85187     	0.02565     	5                eval state : final;module_name : SGAR;subset : 2;cp_record_file : True;
---> end


---------->
SGAR for 4 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 28, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00760     	0.90472     	0.01859     	0           
  10          	0.00718     	0.91658     	0.01764     	0           
  100         	0.01402     	0.88365     	0.02347     	0           
  300         	0.01583     	0.86234     	0.02589     	1           
  500         	0.01622     	0.84649     	0.02612     	2           
  1000        	0.01642     	0.79789     	0.02626     	5           
---> try to load best state
              	0.01642     	0.79789     	0.02626     	5                eval state : test_on_restore;
              	0.01642     	0.79789     	0.02626     	5                eval state : test_on_last_epoch;
---> final result              	0.01642     	0.79789     	0.02626     	5                eval state : final;module_name : SGAR;subset : 4;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 24, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00971     	0.89070     	0.02154     	0           
  10          	0.00917     	0.90275     	0.02114     	0           
  100         	0.01501     	0.87686     	0.02491     	0           
  300         	0.01693     	0.86275     	0.02712     	1           
  500         	0.01720     	0.85193     	0.02731     	2           
  1000        	0.01740     	0.77171     	0.02759     	5           
---> try to load best state
              	0.01740     	0.77171     	0.02759     	5                eval state : test_on_restore;
              	0.01740     	0.77171     	0.02759     	5                eval state : test_on_last_epoch;
---> final result              	0.01740     	0.77171     	0.02759     	5                eval state : final;module_name : SGAR;subset : 8;cp_record_file : True;
---> end


---------->
SGAR for 16 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.01144     	0.88479     	0.02213     	0           
  10          	0.01051     	0.89922     	0.02173     	0           
  100         	0.01596     	0.88060     	0.02492     	0           
  300         	0.01913     	0.85852     	0.02916     	1           
  500         	0.01976     	0.84193     	0.02974     	2           
  1000        	0.02019     	0.76136     	0.03007     	5           
---> try to load best state
              	0.02019     	0.76136     	0.03007     	5                eval state : test_on_restore;
              	0.02019     	0.76136     	0.03007     	5                eval state : test_on_last_epoch;
---> final result              	0.02019     	0.76136     	0.03007     	5                eval state : final;module_name : SGAR;subset : 16;cp_record_file : True;
---> end


---------->
SGAR for 32 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.01740     	0.83384     	0.03177     	0           
  10          	0.01880     	0.82688     	0.03150     	0           
  100         	0.01829     	0.83065     	0.02954     	0           
  300         	0.01820     	0.82653     	0.02919     	1           
  500         	0.01825     	0.81510     	0.02911     	3           
  1000        	0.01846     	0.79617     	0.02925     	5           
---> try to load best state
              	0.01846     	0.79617     	0.02925     	5                eval state : test_on_restore;
              	0.01846     	0.79617     	0.02925     	5                eval state : test_on_last_epoch;
---> final result              	0.01846     	0.79617     	0.02925     	5                eval state : final;module_name : SGAR;subset : 32;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]'], '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           	0.01858     	0.70600     	0.04863     	0           
  10          	0.01782     	0.71586     	0.04540     	0           
  100         	0.01724     	0.66053     	0.03929     	0           
  300         	0.02053     	0.36042     	0.04242     	1           
  500         	0.02038     	0.32046     	0.04210     	2           
  1000        	0.01957     	0.37683     	0.04089     	4           
---> try to load best state
              	0.01957     	0.37683     	0.04089     	4                eval state : test_on_restore;
              	0.01957     	0.37683     	0.04089     	4                eval state : test_on_last_epoch;
---> final result              	0.01957     	0.37683     	0.04089     	4                eval state : final;
---> end


---------->
SGAR for 1 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 31, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00901     	0.91933     	0.01820     	0           
  10          	0.00960     	0.92628     	0.01768     	0           
  100         	0.01572     	0.89866     	0.02364     	0           
  300         	0.02205     	0.84292     	0.03126     	1           
  500         	0.02263     	0.83257     	0.03200     	2           
  1000        	0.02276     	0.80445     	0.03220     	5           
---> try to load best state
              	0.02276     	0.80445     	0.03220     	5                eval state : test_on_restore;
              	0.02276     	0.80445     	0.03220     	5                eval state : test_on_last_epoch;
---> final result              	0.02276     	0.80445     	0.03220     	5                eval state : final;module_name : SGAR;subset : 1;cp_record_file : True;
---> end


---------->
SGAR for 2 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 30, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00888     	0.92164     	0.01791     	0           
  10          	0.00944     	0.92888     	0.01743     	0           
  100         	0.01508     	0.90034     	0.02300     	0           
  300         	0.02188     	0.84526     	0.03097     	1           
  500         	0.02290     	0.83390     	0.03196     	2           
  1000        	0.02294     	0.82994     	0.03236     	6           
---> try to load best state
              	0.02294     	0.82994     	0.03236     	6                eval state : test_on_restore;
              	0.02294     	0.82994     	0.03236     	6                eval state : test_on_last_epoch;
---> final result              	0.02294     	0.82994     	0.03236     	6                eval state : final;module_name : SGAR;subset : 2;cp_record_file : True;
---> end


---------->
SGAR for 4 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 28, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.01020     	0.91055     	0.01981     	0           
  10          	0.01049     	0.91989     	0.01900     	0           
  100         	0.01458     	0.89470     	0.02300     	0           
  300         	0.02215     	0.85157     	0.03079     	1           
  500         	0.02296     	0.82977     	0.03185     	2           
  1000        	0.02352     	0.82486     	0.03256     	5           
---> try to load best state
              	0.02352     	0.82486     	0.03256     	5                eval state : test_on_restore;
              	0.02352     	0.82486     	0.03256     	5                eval state : test_on_last_epoch;
---> final result              	0.02352     	0.82486     	0.03256     	5                eval state : final;module_name : SGAR;subset : 4;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 24, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.01054     	0.88275     	0.02139     	0           
  10          	0.01034     	0.90356     	0.01930     	0           
  100         	0.01313     	0.86902     	0.02275     	0           
  300         	0.01947     	0.85114     	0.02760     	1           
  500         	0.02031     	0.83586     	0.02888     	2           
  1000        	0.02088     	0.79421     	0.02933     	5           
---> try to load best state
              	0.02088     	0.79421     	0.02933     	5                eval state : test_on_restore;
              	0.02088     	0.79421     	0.02933     	5                eval state : test_on_last_epoch;
---> final result              	0.02088     	0.79421     	0.02933     	5                eval state : final;module_name : SGAR;subset : 8;cp_record_file : True;
---> end


---------->
SGAR for 16 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.01249     	0.78460     	0.02948     	0           
  10          	0.01145     	0.82933     	0.02608     	0           
  100         	0.01373     	0.81121     	0.02750     	0           
  300         	0.01942     	0.84064     	0.02864     	1           
  500         	0.02031     	0.82029     	0.02951     	2           
  1000        	0.02102     	0.76055     	0.03048     	5           
---> try to load best state
              	0.02102     	0.76055     	0.03048     	5                eval state : test_on_restore;
              	0.02102     	0.76055     	0.03048     	5                eval state : test_on_last_epoch;
---> final result              	0.02102     	0.76055     	0.03048     	5                eval state : final;module_name : SGAR;subset : 16;cp_record_file : True;
---> end


---------->
SGAR for 32 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.01929     	0.46719     	0.03951     	0           
  10          	0.01885     	0.46207     	0.03973     	0           
  100         	0.01895     	0.44366     	0.04015     	0           
  300         	0.01901     	0.41427     	0.04037     	1           
  500         	0.01898     	0.41361     	0.04020     	3           
  1000        	0.01908     	0.39753     	0.04054     	5           
---> try to load best state
              	0.01908     	0.39753     	0.04054     	5                eval state : test_on_restore;
              	0.01908     	0.39753     	0.04054     	5                eval state : test_on_last_epoch;
---> final result              	0.01908     	0.39753     	0.04054     	5                eval state : final;module_name : SGAR;subset : 32;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]'], '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           	0.01222     	0.79840     	0.03128     	0           
  10          	0.01215     	0.79976     	0.03063     	0           
  100         	0.01587     	0.74808     	0.03288     	0           
  300         	0.02076     	0.42577     	0.03800     	1           
  500         	0.02099     	0.36221     	0.03798     	2           
  1000        	0.01960     	0.47053     	0.03627     	4           
---> try to load best state
              	0.01960     	0.47053     	0.03627     	4                eval state : test_on_restore;
              	0.01960     	0.47053     	0.03627     	4                eval state : test_on_last_epoch;
---> final result              	0.01960     	0.47053     	0.03627     	4                eval state : final;
---> end


---------->
SGAR for 1 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 31, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00770     	0.88874     	0.02089     	0           
  10          	0.00733     	0.89009     	0.02060     	0           
  100         	0.01128     	0.84099     	0.02560     	0           
  300         	0.01479     	0.82179     	0.02744     	1           
  500         	0.01474     	0.83034     	0.02708     	2           
  1000        	0.01469     	0.82579     	0.02656     	5           
---> try to load best state
              	0.01469     	0.82579     	0.02656     	5                eval state : test_on_restore;
              	0.01469     	0.82579     	0.02656     	5                eval state : test_on_last_epoch;
---> final result              	0.01469     	0.82579     	0.02656     	5                eval state : final;module_name : SGAR;subset : 1;cp_record_file : True;
---> end


---------->
SGAR for 2 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 30, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00823     	0.88691     	0.02115     	0           
  10          	0.00787     	0.88619     	0.02089     	0           
  100         	0.01170     	0.82710     	0.02604     	0           
  300         	0.01485     	0.82102     	0.02700     	1           
  500         	0.01493     	0.82597     	0.02674     	2           
  1000        	0.01482     	0.76728     	0.02653     	5           
---> try to load best state
              	0.01482     	0.76728     	0.02653     	5                eval state : test_on_restore;
              	0.01482     	0.76728     	0.02653     	5                eval state : test_on_last_epoch;
---> final result              	0.01482     	0.76728     	0.02653     	5                eval state : final;module_name : SGAR;subset : 2;cp_record_file : True;
---> end


---------->
SGAR for 4 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 28, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00916     	0.88435     	0.02132     	0           
  10          	0.00865     	0.89041     	0.02070     	0           
  100         	0.01213     	0.83194     	0.02551     	0           
  300         	0.01479     	0.81864     	0.02664     	1           
  500         	0.01480     	0.82522     	0.02632     	2           
  1000        	0.01471     	0.79637     	0.02615     	5           
---> try to load best state
              	0.01471     	0.79637     	0.02615     	5                eval state : test_on_restore;
              	0.01471     	0.79637     	0.02615     	5                eval state : test_on_last_epoch;
---> final result              	0.01471     	0.79637     	0.02615     	5                eval state : final;module_name : SGAR;subset : 4;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 24, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00944     	0.88071     	0.02157     	0           
  10          	0.00901     	0.88728     	0.02122     	0           
  100         	0.01296     	0.82025     	0.02636     	0           
  300         	0.01481     	0.81974     	0.02643     	1           
  500         	0.01478     	0.81825     	0.02639     	2           
  1000        	0.01497     	0.74730     	0.02602     	5           
---> try to load best state
              	0.01497     	0.74730     	0.02602     	5                eval state : test_on_restore;
              	0.01497     	0.74730     	0.02602     	5                eval state : test_on_last_epoch;
---> final result              	0.01497     	0.74730     	0.02602     	5                eval state : final;module_name : SGAR;subset : 8;cp_record_file : True;
---> end


---------->
SGAR for 16 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.01229     	0.83734     	0.02450     	0           
  10          	0.01162     	0.84527     	0.02418     	0           
  100         	0.01503     	0.78145     	0.02820     	0           
  300         	0.01531     	0.80892     	0.02632     	1           
  500         	0.01526     	0.80834     	0.02612     	2           
  1000        	0.01529     	0.80902     	0.02611     	5           
---> try to load best state
              	0.01529     	0.80902     	0.02611     	5                eval state : test_on_restore;
              	0.01529     	0.80902     	0.02611     	5                eval state : test_on_last_epoch;
---> final result              	0.01529     	0.80902     	0.02611     	5                eval state : final;module_name : SGAR;subset : 16;cp_record_file : True;
---> end


---------->
SGAR for 32 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.01852     	0.59839     	0.03464     	0           
  10          	0.01928     	0.52202     	0.03587     	0           
  100         	0.01892     	0.53773     	0.03564     	0           
  300         	0.01911     	0.49924     	0.03633     	1           
  500         	0.01898     	0.50957     	0.03570     	2           
  1000        	0.01897     	0.48528     	0.03585     	5           
---> try to load best state
              	0.01897     	0.48528     	0.03585     	5                eval state : test_on_restore;
              	0.01897     	0.48528     	0.03585     	5                eval state : test_on_last_epoch;
---> final result              	0.01897     	0.48528     	0.03585     	5                eval state : final;module_name : SGAR;subset : 32;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]'], '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           	0.01708     	0.75052     	0.04899     	0           
  10          	0.01634     	0.75693     	0.04554     	0           
  100         	0.01844     	0.76333     	0.03555     	0           
  300         	0.02025     	0.63884     	0.03680     	1           
  500         	0.01998     	0.63885     	0.03624     	2           
  1000        	0.01878     	0.68425     	0.03434     	4           
---> try to load best state
              	0.01878     	0.68425     	0.03434     	4                eval state : test_on_restore;
              	0.01878     	0.68425     	0.03434     	4                eval state : test_on_last_epoch;
---> final result              	0.01878     	0.68425     	0.03434     	4                eval state : final;
---> end


---------->
SGAR for 1 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 31, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00957     	0.79584     	0.02642     	0           
  10          	0.00842     	0.84962     	0.02301     	0           
  100         	0.01253     	0.80130     	0.02853     	0           
  300         	0.01521     	0.73664     	0.03109     	1           
  500         	0.01527     	0.72611     	0.03062     	2           
  1000        	0.01536     	0.70842     	0.03044     	5           
---> try to load best state
              	0.01536     	0.70842     	0.03044     	5                eval state : test_on_restore;
              	0.01536     	0.70842     	0.03044     	5                eval state : test_on_last_epoch;
---> final result              	0.01536     	0.70842     	0.03044     	5                eval state : final;module_name : SGAR;subset : 1;cp_record_file : True;
---> end


---------->
SGAR for 2 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 30, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00952     	0.79705     	0.02633     	0           
  10          	0.00834     	0.85109     	0.02285     	0           
  100         	0.01247     	0.80217     	0.02842     	0           
  300         	0.01520     	0.73937     	0.03061     	1           
  500         	0.01523     	0.72553     	0.03057     	2           
  1000        	0.01532     	0.57677     	0.03028     	5           
---> try to load best state
              	0.01532     	0.57677     	0.03028     	5                eval state : test_on_restore;
              	0.01532     	0.57677     	0.03028     	5                eval state : test_on_last_epoch;
---> final result              	0.01532     	0.57677     	0.03028     	5                eval state : final;module_name : SGAR;subset : 2;cp_record_file : True;
---> end


---------->
SGAR for 4 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 28, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00966     	0.80798     	0.02581     	0           
  10          	0.00869     	0.84531     	0.02346     	0           
  100         	0.01282     	0.78396     	0.02879     	0           
  300         	0.01552     	0.74905     	0.03025     	1           
  500         	0.01551     	0.72535     	0.03006     	2           
  1000        	0.01557     	0.57952     	0.03007     	5           
---> try to load best state
              	0.01557     	0.57952     	0.03007     	5                eval state : test_on_restore;
              	0.01557     	0.57952     	0.03007     	5                eval state : test_on_last_epoch;
---> final result              	0.01557     	0.57952     	0.03007     	5                eval state : final;module_name : SGAR;subset : 4;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 24, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00992     	0.88023     	0.02260     	0           
  10          	0.00922     	0.88761     	0.02154     	0           
  100         	0.01355     	0.84349     	0.02625     	0           
  300         	0.01602     	0.81108     	0.02889     	1           
  500         	0.01612     	0.79983     	0.02897     	2           
  1000        	0.01606     	0.77570     	0.02894     	5           
---> try to load best state
              	0.01606     	0.77570     	0.02894     	5                eval state : test_on_restore;
              	0.01606     	0.77570     	0.02894     	5                eval state : test_on_last_epoch;
---> final result              	0.01606     	0.77570     	0.02894     	5                eval state : final;module_name : SGAR;subset : 8;cp_record_file : True;
---> end


---------->
SGAR for 16 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.01022     	0.88581     	0.02163     	0           
  10          	0.00981     	0.88797     	0.02119     	0           
  100         	0.01439     	0.84865     	0.02599     	0           
  300         	0.01678     	0.82642     	0.02850     	1           
  500         	0.01672     	0.81678     	0.02865     	3           
  1000        	0.01649     	0.80944     	0.02850     	5           
---> try to load best state
              	0.01649     	0.80944     	0.02850     	5                eval state : test_on_restore;
              	0.01649     	0.80944     	0.02850     	5                eval state : test_on_last_epoch;
---> final result              	0.01649     	0.80944     	0.02850     	5                eval state : final;module_name : SGAR;subset : 16;cp_record_file : True;
---> end


---------->
SGAR for 32 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.01746     	0.74540     	0.03255     	0           
  10          	0.01859     	0.73877     	0.03355     	0           
  100         	0.01813     	0.74061     	0.03355     	0           
  300         	0.01811     	0.72820     	0.03365     	1           
  500         	0.01808     	0.71439     	0.03368     	2           
  1000        	0.01804     	0.69377     	0.03375     	5           
---> try to load best state
              	0.01804     	0.69377     	0.03375     	5                eval state : test_on_restore;
              	0.01804     	0.69377     	0.03375     	5                eval state : test_on_last_epoch;
---> final result              	0.01804     	0.69377     	0.03375     	5                eval state : final;module_name : SGAR;subset : 32;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]'], '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           	0.01090     	0.83459     	0.02682     	0           
  10          	0.01079     	0.83593     	0.02619     	0           
  100         	0.01445     	0.79281     	0.02952     	0           
  300         	0.01899     	0.58728     	0.03422     	1           
  500         	0.01899     	0.57269     	0.03399     	2           
  1000        	0.01760     	0.64023     	0.03233     	4           
---> try to load best state
              	0.01760     	0.64023     	0.03233     	4                eval state : test_on_restore;
              	0.01760     	0.64023     	0.03233     	4                eval state : test_on_last_epoch;
---> final result              	0.01760     	0.64023     	0.03233     	4                eval state : final;
---> end


---------->
SGAR for 1 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 31, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00858     	0.79458     	0.02705     	0           
  10          	0.00832     	0.82376     	0.02626     	0           
  100         	0.01200     	0.76059     	0.03041     	0           
  300         	0.01340     	0.74082     	0.03131     	1           
  500         	0.01370     	0.74253     	0.03123     	2           
  1000        	0.01358     	0.73548     	0.03137     	6           
---> try to load best state
              	0.01358     	0.73548     	0.03137     	6                eval state : test_on_restore;
              	0.01358     	0.73548     	0.03137     	6                eval state : test_on_last_epoch;
---> final result              	0.01358     	0.73548     	0.03137     	6                eval state : final;module_name : SGAR;subset : 1;cp_record_file : True;
---> end


---------->
SGAR for 2 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 30, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00856     	0.81724     	0.02588     	0           
  10          	0.00833     	0.83639     	0.02529     	0           
  100         	0.01206     	0.77016     	0.02973     	0           
  300         	0.01326     	0.74306     	0.03133     	1           
  500         	0.01343     	0.74822     	0.03123     	2           
  1000        	0.01351     	0.74468     	0.03118     	5           
---> try to load best state
              	0.01351     	0.74468     	0.03118     	5                eval state : test_on_restore;
              	0.01351     	0.74468     	0.03118     	5                eval state : test_on_last_epoch;
---> final result              	0.01351     	0.74468     	0.03118     	5                eval state : final;module_name : SGAR;subset : 2;cp_record_file : True;
---> end


---------->
SGAR for 4 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 28, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00910     	0.82072     	0.02575     	0           
  10          	0.00883     	0.83474     	0.02543     	0           
  100         	0.01216     	0.78306     	0.02916     	0           
  300         	0.01322     	0.75544     	0.03037     	1           
  500         	0.01326     	0.75653     	0.03073     	3           
  1000        	0.01346     	0.76759     	0.03012     	6           
---> try to load best state
              	0.01346     	0.76759     	0.03012     	6                eval state : test_on_restore;
              	0.01346     	0.76759     	0.03012     	6                eval state : test_on_last_epoch;
---> final result              	0.01346     	0.76759     	0.03012     	6                eval state : final;module_name : SGAR;subset : 4;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 24, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00940     	0.84920     	0.02443     	0           
  10          	0.00971     	0.83492     	0.02548     	0           
  100         	0.01266     	0.77944     	0.02881     	0           
  300         	0.01346     	0.75114     	0.02945     	1           
  500         	0.01357     	0.74994     	0.02993     	2           
  1000        	0.01371     	0.74526     	0.02978     	5           
---> try to load best state
              	0.01371     	0.74526     	0.02978     	5                eval state : test_on_restore;
              	0.01371     	0.74526     	0.02978     	5                eval state : test_on_last_epoch;
---> final result              	0.01371     	0.74526     	0.02978     	5                eval state : final;module_name : SGAR;subset : 8;cp_record_file : True;
---> end


---------->
SGAR for 16 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.01228     	0.83026     	0.02532     	0           
  10          	0.01229     	0.82019     	0.02598     	0           
  100         	0.01350     	0.78160     	0.02858     	0           
  300         	0.01409     	0.76403     	0.02830     	1           
  500         	0.01413     	0.77190     	0.02834     	2           
  1000        	0.01419     	0.77877     	0.02839     	5           
---> try to load best state
              	0.01419     	0.77877     	0.02839     	5                eval state : test_on_restore;
              	0.01419     	0.77877     	0.02839     	5                eval state : test_on_last_epoch;
---> final result              	0.01419     	0.77877     	0.02839     	5                eval state : final;module_name : SGAR;subset : 16;cp_record_file : True;
---> end


---------->
SGAR for 32 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.01620     	0.73919     	0.03022     	0           
  10          	0.01766     	0.68970     	0.03233     	0           
  100         	0.01695     	0.69053     	0.03171     	0           
  300         	0.01694     	0.67391     	0.03174     	1           
  500         	0.01690     	0.65802     	0.03196     	2           
  1000        	0.01698     	0.64857     	0.03182     	5           
---> try to load best state
              	0.01698     	0.64857     	0.03182     	5                eval state : test_on_restore;
              	0.01698     	0.64857     	0.03182     	5                eval state : test_on_last_epoch;
---> final result              	0.01698     	0.64857     	0.03182     	5                eval state : final;module_name : SGAR;subset : 32;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]'], '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           	0.01570     	0.81445     	0.03174     	0           
  10          	0.01540     	0.81900     	0.03016     	0           
  100         	0.01729     	0.81116     	0.02781     	0           
  300         	0.01942     	0.66640     	0.03130     	1           
  500         	0.01901     	0.67917     	0.03063     	2           
  1000        	0.01752     	0.74450     	0.02853     	4           
---> try to load best state
              	0.01752     	0.74450     	0.02853     	4                eval state : test_on_restore;
              	0.01752     	0.74450     	0.02853     	4                eval state : test_on_last_epoch;
---> final result              	0.01752     	0.74450     	0.02853     	4                eval state : final;
---> end


---------->
SGAR for 1 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 31, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00795     	0.92213     	0.01731     	0           
  10          	0.00761     	0.92639     	0.01621     	0           
  100         	0.01501     	0.89618     	0.02306     	0           
  300         	0.01763     	0.87901     	0.02658     	1           
  500         	0.01748     	0.87617     	0.02626     	2           
  1000        	0.01761     	0.87238     	0.02646     	5           
---> try to load best state
              	0.01761     	0.87238     	0.02646     	5                eval state : test_on_restore;
              	0.01761     	0.87238     	0.02646     	5                eval state : test_on_last_epoch;
---> final result              	0.01761     	0.87238     	0.02646     	5                eval state : final;module_name : SGAR;subset : 1;cp_record_file : True;
---> end


---------->
SGAR for 2 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 30, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00808     	0.92035     	0.01726     	0           
  10          	0.00781     	0.92959     	0.01638     	0           
  100         	0.01506     	0.89706     	0.02311     	0           
  300         	0.01737     	0.87946     	0.02628     	1           
  500         	0.01750     	0.87872     	0.02616     	2           
  1000        	0.01762     	0.85312     	0.02627     	5           
---> try to load best state
              	0.01762     	0.85312     	0.02627     	5                eval state : test_on_restore;
              	0.01762     	0.85312     	0.02627     	5                eval state : test_on_last_epoch;
---> final result              	0.01762     	0.85312     	0.02627     	5                eval state : final;module_name : SGAR;subset : 2;cp_record_file : True;
---> end


---------->
SGAR for 4 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 28, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00845     	0.91938     	0.01794     	0           
  10          	0.00823     	0.93079     	0.01720     	0           
  100         	0.01511     	0.89831     	0.02313     	0           
  300         	0.01758     	0.88024     	0.02638     	1           
  500         	0.01735     	0.87860     	0.02600     	3           
  1000        	0.01740     	0.87663     	0.02597     	5           
---> try to load best state
              	0.01740     	0.87663     	0.02597     	5                eval state : test_on_restore;
              	0.01740     	0.87663     	0.02597     	5                eval state : test_on_last_epoch;
---> final result              	0.01740     	0.87663     	0.02597     	5                eval state : final;module_name : SGAR;subset : 4;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 24, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.00927     	0.91805     	0.01855     	0           
  10          	0.00917     	0.92677     	0.01813     	0           
  100         	0.01565     	0.89292     	0.02368     	0           
  300         	0.01810     	0.87944     	0.02653     	1           
  500         	0.01783     	0.87648     	0.02623     	2           
  1000        	0.01801     	0.87114     	0.02636     	5           
---> try to load best state
              	0.01801     	0.87114     	0.02636     	5                eval state : test_on_restore;
              	0.01801     	0.87114     	0.02636     	5                eval state : test_on_last_epoch;
---> final result              	0.01801     	0.87114     	0.02636     	5                eval state : final;module_name : SGAR;subset : 8;cp_record_file : True;
---> end


---------->
SGAR for 16 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 16, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.01179     	0.83489     	0.02518     	0           
  10          	0.01154     	0.84006     	0.02588     	0           
  100         	0.01450     	0.81126     	0.02845     	0           
  300         	0.01451     	0.78579     	0.03010     	1           
  500         	0.01464     	0.77444     	0.03079     	2           
  1000        	0.01482     	0.78339     	0.03031     	5           
---> try to load best state
              	0.01482     	0.78339     	0.03031     	5                eval state : test_on_restore;
              	0.01482     	0.78339     	0.03031     	5                eval state : test_on_last_epoch;
---> final result              	0.01482     	0.78339     	0.03031     	5                eval state : final;module_name : SGAR;subset : 16;cp_record_file : True;
---> end


---------->
SGAR for 32 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], '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           	0.01629     	0.82490     	0.02713     	0           
  10          	0.01777     	0.79925     	0.02847     	0           
  100         	0.01708     	0.79320     	0.02799     	0           
  300         	0.01713     	0.78871     	0.02798     	1           
  500         	0.01697     	0.77529     	0.02818     	2           
  1000        	0.01717     	0.75495     	0.02817     	5           
---> try to load best state
              	0.01717     	0.75495     	0.02817     	5                eval state : test_on_restore;
              	0.01717     	0.75495     	0.02817     	5                eval state : test_on_last_epoch;
---> final result              	0.01717     	0.75495     	0.02817     	5                eval state : final;module_name : SGAR;subset : 32;cp_record_file : True;
