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

  Demo GAR 
  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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01067     	0.86919     	0.02502     	0           
  10          	0.01112     	0.86392     	0.02455     	0           
  100         	0.01428     	0.76726     	0.02651     	0           
  300         	0.01379     	0.83402     	0.02444     	1           
  500         	0.01390     	0.85030     	0.02526     	2           
  1000        	0.01478     	0.83584     	0.02643     	5           
---> try to load best state
              	0.01478     	0.83584     	0.02643     	5                eval state : test_on_restore;
              	0.01478     	0.83584     	0.02643     	5                eval state : test_on_last_epoch;
---> final result              	0.01478     	0.83584     	0.02643     	5                eval state : final;
----------> finish x-yl training


---------->
GAR 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           	0.00722     	0.89230     	0.01363     	0           
  10          	0.00667     	0.91142     	0.01344     	0           
  100         	0.00918     	0.93314     	0.01547     	1           
  300         	0.01065     	0.90735     	0.01688     	2           
  500         	0.01106     	0.89497     	0.01731     	4           
  1000        	0.01116     	0.87038     	0.01750     	7           
---> try to load best state
              	0.01116     	0.87038     	0.01750     	7                eval state : test_on_restore;
              	0.01116     	0.87038     	0.01750     	7                eval state : test_on_last_epoch;
---> final result
              	0.01116     	0.87038     	0.01750     	8                eval state : final;module_name : GAR;subset : 1;cp_record_file : True;
---> end


---------->
GAR 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           	0.00719     	0.89554     	0.01361     	0           
  10          	0.00675     	0.90976     	0.01363     	0           
  100         	0.00954     	0.82072     	0.01572     	0           
  300         	0.01048     	0.92087     	0.01694     	2           
  500         	0.01076     	0.90649     	0.01722     	4           
  1000        	0.01112     	0.87942     	0.01749     	8           
---> try to load best state
              	0.01112     	0.87942     	0.01749     	8                eval state : test_on_restore;
              	0.01112     	0.87942     	0.01749     	8                eval state : test_on_last_epoch;
---> final result
              	0.01112     	0.87942     	0.01749     	8                eval state : final;module_name : GAR;subset : 2;cp_record_file : True;
---> end


---------->
GAR 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           	0.00733     	0.88557     	0.01464     	0           
  10          	0.00679     	0.90934     	0.01415     	0           
  100         	0.00917     	0.91960     	0.01597     	0           
  300         	0.01052     	0.90102     	0.01719     	2           
  500         	0.01076     	0.90494     	0.01747     	4           
  1000        	0.01092     	0.88782     	0.01767     	7           
---> try to load best state
              	0.01092     	0.88782     	0.01767     	7                eval state : test_on_restore;
              	0.01092     	0.88782     	0.01767     	7                eval state : test_on_last_epoch;
---> final result
              	0.01092     	0.88782     	0.01767     	7                eval state : final;module_name : GAR;subset : 4;cp_record_file : True;
---> end


---------->
GAR 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           	0.00868     	0.87333     	0.01824     	0           
  10          	0.00793     	0.89903     	0.01772     	0           
  100         	0.01020     	0.88691     	0.01820     	0           
  300         	0.01120     	0.91286     	0.01845     	2           
  500         	0.01132     	0.89491     	0.01839     	3           
  1000        	0.01155     	0.79735     	0.01847     	7           
---> try to load best state
              	0.01155     	0.79735     	0.01847     	7                eval state : test_on_restore;
              	0.01155     	0.79735     	0.01847     	7                eval state : test_on_last_epoch;
---> final result
              	0.01155     	0.79735     	0.01847     	7                eval state : final;module_name : GAR;subset : 8;cp_record_file : True;
---> end


---------->
GAR 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           	0.00917     	0.86980     	0.01832     	0           
  10          	0.00853     	0.89728     	0.01797     	0           
  100         	0.01134     	0.84146     	0.01933     	0           
  300         	0.01228     	0.87768     	0.01954     	2           
  500         	0.01252     	0.88809     	0.01970     	3           
  1000        	0.01260     	0.88934     	0.01988     	7           
---> try to load best state
              	0.01260     	0.88934     	0.01988     	7                eval state : test_on_restore;
              	0.01260     	0.88934     	0.01988     	7                eval state : test_on_last_epoch;
---> final result
              	0.01260     	0.88934     	0.01988     	7                eval state : final;module_name : GAR;subset : 16;cp_record_file : True;
---> end


---------->
GAR 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           	0.01293     	0.84252     	0.02893     	0           
  10          	0.01357     	0.87579     	0.02916     	0           
  100         	0.01473     	0.87336     	0.02919     	0           
  300         	0.01618     	0.84640     	0.03031     	2           
  500         	0.01708     	0.53006     	0.03085     	3           
  1000        	0.01739     	0.84792     	0.03104     	7           
---> try to load best state
              	0.01739     	0.84792     	0.03104     	7                eval state : test_on_restore;
              	0.01739     	0.84792     	0.03104     	7                eval state : test_on_last_epoch;
---> final result
              	0.01739     	0.84792     	0.03104     	7                eval state : final;module_name : GAR;subset : 32;cp_record_file : True;
---> end

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

  Demo GAR 
  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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01858     	0.70600     	0.04863     	0           
  10          	0.01808     	0.71010     	0.04591     	0           
  100         	0.01811     	0.51451     	0.04105     	0           
  300         	0.01691     	0.11457     	0.03786     	1           
  500         	0.01655     	0.16071     	0.03632     	2           
  1000        	0.01622     	0.24536     	0.03482     	4           
---> try to load best state
              	0.01622     	0.24536     	0.03482     	4                eval state : test_on_restore;
              	0.01622     	0.24536     	0.03482     	4                eval state : test_on_last_epoch;
---> final result              	0.01622     	0.24536     	0.03482     	4                eval state : final;
----------> finish x-yl training


---------->
GAR 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           	0.00860     	0.88564     	0.01660     	0           
  10          	0.00746     	0.92156     	0.01538     	0           
  100         	0.00958     	0.88077     	0.01719     	0           
  300         	0.01238     	0.87167     	0.01961     	2           
  500         	0.01125     	0.90528     	0.01840     	4           
  1000        	0.00937     	0.90165     	0.01607     	7           
---> try to load best state
              	0.00937     	0.90165     	0.01607     	7                eval state : test_on_restore;
              	0.00937     	0.90165     	0.01607     	7                eval state : test_on_last_epoch;
---> final result
              	0.00937     	0.90165     	0.01607     	7                eval state : final;module_name : GAR;subset : 1;cp_record_file : True;
---> end


---------->
GAR 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           	0.00882     	0.88434     	0.01657     	0           
  10          	0.00755     	0.91858     	0.01530     	0           
  100         	0.00969     	0.84989     	0.01710     	0           
  300         	0.01188     	0.88773     	0.01917     	2           
  500         	0.01100     	0.73473     	0.01806     	3           
  1000        	0.00975     	0.89497     	0.01638     	7           
---> try to load best state
              	0.00975     	0.89497     	0.01638     	7                eval state : test_on_restore;
              	0.00975     	0.89497     	0.01638     	8                eval state : test_on_last_epoch;
---> final result
              	0.00975     	0.89497     	0.01638     	8                eval state : final;module_name : GAR;subset : 2;cp_record_file : True;
---> end


---------->
GAR 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           	0.01011     	0.87052     	0.01878     	0           
  10          	0.00854     	0.90963     	0.01706     	0           
  100         	0.00992     	0.87531     	0.01778     	0           
  300         	0.01197     	0.90422     	0.01972     	2           
  500         	0.01121     	0.89656     	0.01874     	3           
  1000        	0.01066     	0.83724     	0.01753     	7           
---> try to load best state
              	0.01066     	0.83724     	0.01753     	7                eval state : test_on_restore;
              	0.01066     	0.83724     	0.01753     	7                eval state : test_on_last_epoch;
---> final result
              	0.01066     	0.83724     	0.01753     	7                eval state : final;module_name : GAR;subset : 4;cp_record_file : True;
---> end


---------->
GAR 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           	0.01056     	0.82086     	0.02031     	0           
  10          	0.00882     	0.88972     	0.01792     	0           
  100         	0.00929     	0.88813     	0.01687     	0           
  300         	0.01171     	0.90003     	0.01905     	2           
  500         	0.01192     	0.89962     	0.01896     	3           
  1000        	0.01031     	0.89925     	0.01671     	7           
---> try to load best state
              	0.01031     	0.89925     	0.01671     	7                eval state : test_on_restore;
              	0.01031     	0.89925     	0.01671     	7                eval state : test_on_last_epoch;
---> final result
              	0.01031     	0.89925     	0.01671     	7                eval state : final;module_name : GAR;subset : 8;cp_record_file : True;
---> end


---------->
GAR 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           	0.01279     	0.70112     	0.02959     	0           
  10          	0.01090     	0.81269     	0.02637     	0           
  100         	0.00976     	0.88337     	0.01980     	0           
  300         	0.01220     	0.71956     	0.02033     	2           
  500         	0.01266     	0.88410     	0.02018     	3           
  1000        	0.01167     	0.73389     	0.01864     	7           
---> try to load best state
              	0.01167     	0.73389     	0.01864     	7                eval state : test_on_restore;
              	0.01167     	0.73389     	0.01864     	7                eval state : test_on_last_epoch;
---> final result
              	0.01167     	0.73389     	0.01864     	7                eval state : final;module_name : GAR;subset : 16;cp_record_file : True;
---> end


---------->
GAR 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           	0.01504     	0.52226     	0.03355     	0           
  10          	0.01357     	0.62262     	0.03198     	0           
  100         	0.01453     	0.38690     	0.03313     	0           
  300         	0.01551     	0.18227     	0.03388     	2           
  500         	0.01605     	0.16943     	0.03423     	4           
  1000        	0.01677     	0.20740     	0.03475     	8           
---> try to load best state
              	0.01677     	0.20740     	0.03475     	8                eval state : test_on_restore;
              	0.01677     	0.20740     	0.03475     	8                eval state : test_on_last_epoch;
---> final result
              	0.01677     	0.20740     	0.03475     	8                eval state : final;module_name : GAR;subset : 32;cp_record_file : True;
---> end

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

  Demo GAR 
  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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01222     	0.79840     	0.03128     	0           
  10          	0.01237     	0.79186     	0.03112     	0           
  100         	0.01625     	0.60082     	0.03480     	0           
  300         	0.01582     	0.39953     	0.03260     	1           
  500         	0.01542     	0.52636     	0.03091     	2           
  1000        	0.01507     	0.61232     	0.02931     	4           
---> try to load best state
              	0.01507     	0.61232     	0.02931     	4                eval state : test_on_restore;
              	0.01507     	0.61232     	0.02931     	4                eval state : test_on_last_epoch;
---> final result              	0.01507     	0.61232     	0.02931     	4                eval state : final;
----------> finish x-yl training


---------->
GAR 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           	0.00790     	0.85387     	0.01767     	0           
  10          	0.00659     	0.90184     	0.01629     	0           
  100         	0.00728     	0.89565     	0.01831     	0           
  300         	0.01047     	0.86251     	0.02121     	2           
  500         	0.01115     	0.83784     	0.02203     	4           
  1000        	0.01115     	0.82131     	0.02197     	8           
---> try to load best state
              	0.01115     	0.82131     	0.02197     	8                eval state : test_on_restore;
              	0.01115     	0.82131     	0.02197     	8                eval state : test_on_last_epoch;
---> final result
              	0.01115     	0.82131     	0.02197     	8                eval state : final;module_name : GAR;subset : 1;cp_record_file : True;
---> end


---------->
GAR 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           	0.00819     	0.85496     	0.01792     	0           
  10          	0.00698     	0.90147     	0.01654     	0           
  100         	0.00809     	0.89157     	0.01875     	0           
  300         	0.01060     	0.85957     	0.02137     	2           
  500         	0.01149     	0.73625     	0.02221     	3           
  1000        	0.01178     	0.82889     	0.02253     	7           
---> try to load best state
              	0.01178     	0.82889     	0.02253     	7                eval state : test_on_restore;
              	0.01178     	0.82889     	0.02253     	7                eval state : test_on_last_epoch;
---> final result
              	0.01178     	0.82889     	0.02253     	7                eval state : final;module_name : GAR;subset : 2;cp_record_file : True;
---> end


---------->
GAR 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           	0.00858     	0.85835     	0.01821     	0           
  10          	0.00736     	0.90054     	0.01685     	0           
  100         	0.00808     	0.89448     	0.01875     	0           
  300         	0.01101     	0.79247     	0.02143     	2           
  500         	0.01166     	0.84648     	0.02222     	3           
  1000        	0.01226     	0.82065     	0.02297     	7           
---> try to load best state
              	0.01226     	0.82065     	0.02297     	7                eval state : test_on_restore;
              	0.01226     	0.82065     	0.02297     	7                eval state : test_on_last_epoch;
---> final result
              	0.01226     	0.82065     	0.02297     	7                eval state : final;module_name : GAR;subset : 4;cp_record_file : True;
---> end


---------->
GAR 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           	0.00841     	0.87277     	0.01810     	0           
  10          	0.00725     	0.90540     	0.01705     	0           
  100         	0.00855     	0.88986     	0.01926     	0           
  300         	0.01118     	0.84197     	0.02204     	2           
  500         	0.01204     	0.81013     	0.02287     	3           
  1000        	0.01244     	0.82336     	0.02338     	7           
---> try to load best state
              	0.01244     	0.82336     	0.02338     	7                eval state : test_on_restore;
              	0.01244     	0.82336     	0.02338     	7                eval state : test_on_last_epoch;
---> final result
              	0.01244     	0.82336     	0.02338     	7                eval state : final;module_name : GAR;subset : 8;cp_record_file : True;
---> end


---------->
GAR 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           	0.00942     	0.85901     	0.01938     	0           
  10          	0.00810     	0.89445     	0.01844     	0           
  100         	0.01003     	0.86633     	0.02108     	0           
  300         	0.01214     	0.82311     	0.02315     	2           
  500         	0.01287     	0.80411     	0.02366     	3           
  1000        	0.01343     	0.77083     	0.02402     	7           
---> try to load best state
              	0.01343     	0.77083     	0.02402     	7                eval state : test_on_restore;
              	0.01343     	0.77083     	0.02402     	7                eval state : test_on_last_epoch;
---> final result
              	0.01343     	0.77083     	0.02402     	7                eval state : final;module_name : GAR;subset : 16;cp_record_file : True;
---> end


---------->
GAR 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           	0.01299     	0.74201     	0.02720     	0           
  10          	0.01221     	0.78450     	0.02630     	0           
  100         	0.01383     	0.66659     	0.02794     	0           
  300         	0.01501     	0.48492     	0.02916     	2           
  500         	0.01560     	0.46292     	0.02966     	3           
  1000        	0.01600     	0.47293     	0.03005     	7           
---> try to load best state
              	0.01600     	0.47293     	0.03005     	7                eval state : test_on_restore;
              	0.01600     	0.47293     	0.03005     	7                eval state : test_on_last_epoch;
---> final result
              	0.01600     	0.47293     	0.03005     	7                eval state : final;module_name : GAR;subset : 32;cp_record_file : True;
---> end

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

  Demo GAR 
  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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01708     	0.75052     	0.04899     	0           
  10          	0.01659     	0.75343     	0.04593     	0           
  100         	0.01799     	0.67123     	0.03765     	0           
  300         	0.01673     	0.68399     	0.03182     	1           
  500         	0.01596     	0.74259     	0.02941     	2           
  1000        	0.01559     	0.76383     	0.02757     	4           
---> try to load best state
              	0.01559     	0.76383     	0.02757     	4                eval state : test_on_restore;
              	0.01559     	0.76383     	0.02757     	4                eval state : test_on_last_epoch;
---> final result              	0.01559     	0.76383     	0.02757     	4                eval state : final;
----------> finish x-yl training


---------->
GAR 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           	0.00980     	0.74213     	0.02333     	0           
  10          	0.00888     	0.83780     	0.02302     	0           
  100         	0.00852     	0.87582     	0.02048     	0           
  300         	0.01110     	0.83833     	0.02264     	2           
  500         	0.01170     	0.84018     	0.02292     	3           
  1000        	0.01175     	0.74760     	0.02227     	7           
---> try to load best state
              	0.01175     	0.74760     	0.02227     	7                eval state : test_on_restore;
              	0.01175     	0.74760     	0.02227     	7                eval state : test_on_last_epoch;
---> final result
              	0.01175     	0.74760     	0.02227     	7                eval state : final;module_name : GAR;subset : 1;cp_record_file : True;
---> end


---------->
GAR 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           	0.00975     	0.74305     	0.02328     	0           
  10          	0.00881     	0.83951     	0.02291     	0           
  100         	0.00855     	0.87321     	0.02042     	0           
  300         	0.01159     	0.69537     	0.02274     	2           
  500         	0.01155     	0.83886     	0.02276     	4           
  1000        	0.01163     	0.67901     	0.02215     	7           
---> try to load best state
              	0.01163     	0.67901     	0.02215     	7                eval state : test_on_restore;
              	0.01163     	0.67901     	0.02215     	7                eval state : test_on_last_epoch;
---> final result
              	0.01163     	0.67901     	0.02215     	7                eval state : final;module_name : GAR;subset : 2;cp_record_file : True;
---> end


---------->
GAR 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           	0.00955     	0.75305     	0.02285     	0           
  10          	0.00862     	0.84723     	0.02250     	0           
  100         	0.00844     	0.87613     	0.02023     	0           
  300         	0.01091     	0.82963     	0.02220     	2           
  500         	0.01134     	0.82867     	0.02217     	4           
  1000        	0.01149     	0.82202     	0.02181     	8           
---> try to load best state
              	0.01149     	0.82202     	0.02181     	8                eval state : test_on_restore;
              	0.01149     	0.82202     	0.02181     	8                eval state : test_on_last_epoch;
---> final result
              	0.01149     	0.82202     	0.02181     	8                eval state : final;module_name : GAR;subset : 4;cp_record_file : True;
---> end


---------->
GAR 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           	0.00916     	0.85673     	0.01991     	0           
  10          	0.00821     	0.89438     	0.01911     	0           
  100         	0.00944     	0.86462     	0.01972     	0           
  300         	0.01193     	0.83058     	0.02289     	2           
  500         	0.01271     	0.70249     	0.02359     	3           
  1000        	0.01267     	0.77924     	0.02375     	7           
---> try to load best state
              	0.01267     	0.77924     	0.02375     	7                eval state : test_on_restore;
              	0.01267     	0.77924     	0.02375     	7                eval state : test_on_last_epoch;
---> final result
              	0.01267     	0.77924     	0.02375     	7                eval state : final;module_name : GAR;subset : 8;cp_record_file : True;
---> end


---------->
GAR 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           	0.00869     	0.87811     	0.01791     	0           
  10          	0.00788     	0.90514     	0.01694     	0           
  100         	0.00945     	0.88403     	0.01907     	0           
  300         	0.01247     	0.83634     	0.02275     	2           
  500         	0.01307     	0.83444     	0.02351     	3           
  1000        	0.01344     	0.81342     	0.02411     	8           
---> try to load best state
              	0.01344     	0.81342     	0.02411     	8                eval state : test_on_restore;
              	0.01344     	0.81342     	0.02411     	8                eval state : test_on_last_epoch;
---> final result
              	0.01344     	0.81342     	0.02411     	8                eval state : final;module_name : GAR;subset : 16;cp_record_file : True;
---> end


---------->
GAR 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           	0.01381     	0.76156     	0.02706     	0           
  10          	0.01331     	0.83542     	0.02571     	0           
  100         	0.01463     	0.75213     	0.02674     	0           
  300         	0.01563     	0.66120     	0.02788     	2           
  500         	0.01620     	0.65824     	0.02834     	3           
  1000        	0.01675     	0.59418     	0.02885     	7           
---> try to load best state
              	0.01675     	0.59418     	0.02885     	7                eval state : test_on_restore;
              	0.01675     	0.59418     	0.02885     	7                eval state : test_on_last_epoch;
---> final result
              	0.01675     	0.59418     	0.02885     	7                eval state : final;module_name : GAR;subset : 32;cp_record_file : True;
---> end

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

  Demo GAR 
  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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01090     	0.83459     	0.02682     	0           
  10          	0.01102     	0.82767     	0.02678     	0           
  100         	0.01444     	0.66896     	0.03133     	0           
  300         	0.01425     	0.60131     	0.02938     	1           
  500         	0.01373     	0.68381     	0.02734     	2           
  1000        	0.01296     	0.73650     	0.02443     	4           
---> try to load best state
              	0.01296     	0.73650     	0.02443     	4                eval state : test_on_restore;
              	0.01296     	0.73650     	0.02443     	4                eval state : test_on_last_epoch;
---> final result              	0.01296     	0.73650     	0.02443     	4                eval state : final;
----------> finish x-yl training


---------->
GAR 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           	0.00836     	0.80094     	0.02287     	0           
  10          	0.00769     	0.84454     	0.02290     	0           
  100         	0.00833     	0.84367     	0.02356     	0           
  300         	0.01000     	0.76725     	0.02463     	2           
  500         	0.01003     	0.78610     	0.02381     	3           
  1000        	0.01034     	0.77450     	0.02266     	7           
---> try to load best state
              	0.01034     	0.77450     	0.02266     	7                eval state : test_on_restore;
              	0.01034     	0.77450     	0.02266     	7                eval state : test_on_last_epoch;
---> final result
              	0.01034     	0.77450     	0.02266     	7                eval state : final;module_name : GAR;subset : 1;cp_record_file : True;
---> end


---------->
GAR 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           	0.00786     	0.82985     	0.02043     	0           
  10          	0.00727     	0.86220     	0.02064     	0           
  100         	0.00827     	0.85186     	0.02244     	0           
  300         	0.00989     	0.80601     	0.02389     	2           
  500         	0.01021     	0.80364     	0.02342     	3           
  1000        	0.01036     	0.78827     	0.02241     	7           
---> try to load best state
              	0.01036     	0.78827     	0.02241     	7                eval state : test_on_restore;
              	0.01036     	0.78827     	0.02241     	7                eval state : test_on_last_epoch;
---> final result
              	0.01036     	0.78827     	0.02241     	7                eval state : final;module_name : GAR;subset : 2;cp_record_file : True;
---> end


---------->
GAR 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           	0.00794     	0.84225     	0.01950     	0           
  10          	0.00714     	0.87236     	0.01948     	0           
  100         	0.00837     	0.86240     	0.02146     	0           
  300         	0.00982     	0.82237     	0.02326     	2           
  500         	0.01027     	0.81706     	0.02310     	3           
  1000        	0.01041     	0.79902     	0.02229     	7           
---> try to load best state
              	0.01041     	0.79902     	0.02229     	7                eval state : test_on_restore;
              	0.01041     	0.79902     	0.02229     	7                eval state : test_on_last_epoch;
---> final result
              	0.01041     	0.79902     	0.02229     	7                eval state : final;module_name : GAR;subset : 4;cp_record_file : True;
---> end


---------->
GAR 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           	0.00736     	0.87128     	0.01715     	0           
  10          	0.00643     	0.89678     	0.01643     	0           
  100         	0.00866     	0.79124     	0.01983     	0           
  300         	0.00983     	0.77684     	0.02178     	2           
  500         	0.01002     	0.78538     	0.02165     	4           
  1000        	0.01022     	0.78965     	0.02139     	8           
---> try to load best state
              	0.01022     	0.78965     	0.02139     	8                eval state : test_on_restore;
              	0.01022     	0.78965     	0.02139     	8                eval state : test_on_last_epoch;
---> final result
              	0.01022     	0.78965     	0.02139     	8                eval state : final;module_name : GAR;subset : 8;cp_record_file : True;
---> end


---------->
GAR 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           	0.00918     	0.85407     	0.01805     	0           
  10          	0.00767     	0.89126     	0.01666     	0           
  100         	0.00909     	0.84437     	0.01972     	0           
  300         	0.01045     	0.80580     	0.02181     	2           
  500         	0.01061     	0.78017     	0.02180     	4           
  1000        	0.01058     	0.74270     	0.02152     	8           
---> try to load best state
              	0.01058     	0.74270     	0.02152     	8                eval state : test_on_restore;
              	0.01058     	0.74270     	0.02152     	8                eval state : test_on_last_epoch;
---> final result
              	0.01058     	0.74270     	0.02152     	8                eval state : final;module_name : GAR;subset : 16;cp_record_file : True;
---> end


---------->
GAR 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           	0.01065     	0.83003     	0.02115     	0           
  10          	0.01046     	0.85654     	0.02104     	0           
  100         	0.01186     	0.68686     	0.02313     	0           
  300         	0.01274     	0.59074     	0.02464     	2           
  500         	0.01338     	0.56890     	0.02525     	4           
  1000        	0.01416     	0.56748     	0.02596     	8           
---> try to load best state
              	0.01416     	0.56748     	0.02596     	8                eval state : test_on_restore;
              	0.01416     	0.56748     	0.02596     	8                eval state : test_on_last_epoch;
---> final result
              	0.01416     	0.56748     	0.02596     	8                eval state : final;module_name : GAR;subset : 32;cp_record_file : True;
---> end

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

  Demo GAR 
  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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01570     	0.81445     	0.03174     	0           
  10          	0.01557     	0.81416     	0.03041     	0           
  100         	0.01631     	0.71543     	0.02889     	0           
  300         	0.01493     	0.78431     	0.02519     	1           
  500         	0.01388     	0.81516     	0.02287     	2           
  1000        	0.01477     	0.81080     	0.02284     	4           
---> try to load best state
              	0.01477     	0.81080     	0.02284     	4                eval state : test_on_restore;
              	0.01477     	0.81080     	0.02284     	4                eval state : test_on_last_epoch;
---> final result              	0.01477     	0.81080     	0.02284     	4                eval state : final;
----------> finish x-yl training


---------->
GAR 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           	0.00812     	0.87158     	0.01655     	0           
  10          	0.00719     	0.91402     	0.01537     	0           
  100         	0.01035     	0.84152     	0.01673     	0           
  300         	0.01100     	0.90107     	0.01807     	2           
  500         	0.01150     	0.87824     	0.01885     	4           
  1000        	0.01208     	0.85869     	0.01966     	7           
---> try to load best state
              	0.01208     	0.85869     	0.01966     	7                eval state : test_on_restore;
              	0.01208     	0.85869     	0.01966     	7                eval state : test_on_last_epoch;
---> final result
              	0.01208     	0.85869     	0.01966     	7                eval state : final;module_name : GAR;subset : 1;cp_record_file : True;
---> end


---------->
GAR 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           	0.00787     	0.87214     	0.01583     	0           
  10          	0.00713     	0.91439     	0.01493     	0           
  100         	0.01026     	0.90041     	0.01643     	0           
  300         	0.01119     	0.90826     	0.01814     	2           
  500         	0.01188     	0.84305     	0.01910     	3           
  1000        	0.01230     	0.84734     	0.01972     	7           
---> try to load best state
              	0.01230     	0.84734     	0.01972     	7                eval state : test_on_restore;
              	0.01230     	0.84734     	0.01972     	7                eval state : test_on_last_epoch;
---> final result
              	0.01230     	0.84734     	0.01972     	7                eval state : final;module_name : GAR;subset : 2;cp_record_file : True;
---> end


---------->
GAR 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           	0.00808     	0.87419     	0.01667     	0           
  10          	0.00751     	0.91270     	0.01596     	0           
  100         	0.01044     	0.84250     	0.01657     	0           
  300         	0.01111     	0.89947     	0.01797     	2           
  500         	0.01167     	0.89760     	0.01883     	3           
  1000        	0.01218     	0.85386     	0.01956     	8           
---> try to load best state
              	0.01218     	0.85386     	0.01956     	8                eval state : test_on_restore;
              	0.01218     	0.85386     	0.01956     	8                eval state : test_on_last_epoch;
---> final result
              	0.01218     	0.85386     	0.01956     	8                eval state : final;module_name : GAR;subset : 4;cp_record_file : True;
---> end


---------->
GAR 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           	0.00842     	0.87228     	0.01627     	0           
  10          	0.00800     	0.91840     	0.01586     	0           
  100         	0.01203     	0.87806     	0.01918     	0           
  300         	0.01274     	0.87905     	0.02110     	2           
  500         	0.01334     	0.87690     	0.02220     	4           
  1000        	0.01377     	0.85261     	0.02276     	8           
---> try to load best state
              	0.01377     	0.85261     	0.02276     	8                eval state : test_on_restore;
              	0.01377     	0.85261     	0.02276     	8                eval state : test_on_last_epoch;
---> final result
              	0.01377     	0.85261     	0.02276     	8                eval state : final;module_name : GAR;subset : 8;cp_record_file : True;
---> end


---------->
GAR 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           	0.01002     	0.83385     	0.02036     	0           
  10          	0.00927     	0.87514     	0.02014     	0           
  100         	0.01027     	0.88067     	0.01995     	0           
  300         	0.01119     	0.87536     	0.02043     	2           
  500         	0.01181     	0.79151     	0.02063     	3           
  1000        	0.01208     	0.49886     	0.02069     	7           
---> try to load best state
              	0.01208     	0.49886     	0.02069     	7                eval state : test_on_restore;
              	0.01208     	0.49886     	0.02069     	7                eval state : test_on_last_epoch;
---> final result
              	0.01208     	0.49886     	0.02069     	7                eval state : final;module_name : GAR;subset : 16;cp_record_file : True;
---> end


---------->
GAR 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           	0.01295     	0.84253     	0.02160     	0           
  10          	0.01303     	0.87752     	0.02087     	0           
  100         	0.01409     	0.82693     	0.02230     	0           
  300         	0.01531     	0.76554     	0.02400     	2           
  500         	0.01581     	0.74616     	0.02466     	4           
  1000        	0.01641     	0.64223     	0.02543     	7           
---> try to load best state
              	0.01641     	0.64223     	0.02543     	7                eval state : test_on_restore;
              	0.01641     	0.64223     	0.02543     	8                eval state : test_on_last_epoch;
---> final result
              	0.01641     	0.64223     	0.02543     	8                eval state : final;module_name : GAR;subset : 32;cp_record_file : True;
