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

  Demo GAR 
  seed: None 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, '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           	0.00921     	0.94376     	0.02163     	0           
  10          	0.00936     	0.94266     	0.02193     	0           
  100         	0.01117     	0.93535     	0.02280     	4           
  300         	0.00912     	0.94853     	0.02043     	12          
  500         	0.00723     	0.95602     	0.01828     	19          
  1000        	0.00579     	0.96777     	0.01498     	36          
---> try to load best state
              	0.00579     	0.96777     	0.01498     	36               eval state : test_on_restore;
              	0.00579     	0.96777     	0.01498     	36               eval state : test_on_last_epoch;
---> final result              	0.00579     	0.96777     	0.01498     	37               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': True, '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           	0.01805     	0.75900     	0.04746     	0           
  10          	0.01471     	0.75231     	0.02607     	0           
  100         	0.01261     	0.46644     	0.02067     	4           
  300         	0.00825     	0.87561     	0.01518     	13          
  500         	0.00818     	0.91570     	0.01454     	21          
  1000        	0.00558     	0.96979     	0.01086     	43          
---> try to load best state
              	0.00558     	0.96979     	0.01086     	43               eval state : test_on_restore;
              	0.00558     	0.96979     	0.01086     	43               eval state : test_on_last_epoch;
---> final result
              	0.00558     	0.96979     	0.01086     	43               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': True, '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           	0.01717     	0.75975     	0.04624     	0           
  10          	0.01413     	0.82272     	0.02599     	0           
  100         	0.01166     	0.66187     	0.01840     	4           
  300         	0.00922     	0.74275     	0.01451     	13          
  500         	0.00653     	0.95638     	0.01286     	21          
  1000        	0.00895     	0.77718     	0.01816     	43          
---> try to load best state
              	0.00895     	0.77718     	0.01816     	43               eval state : test_on_restore;
              	0.00895     	0.77718     	0.01816     	43               eval state : test_on_last_epoch;
---> final result
              	0.00895     	0.77718     	0.01816     	43               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': True, '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           	0.01581     	0.76121     	0.04380     	0           
  10          	0.01333     	0.80140     	0.02584     	0           
  100         	0.01550     	0.56090     	0.02646     	5           
  300         	0.00847     	0.91058     	0.01502     	15          
  500         	0.00683     	0.94451     	0.01387     	24          
  1000        	0.00696     	0.95032     	0.01343     	47          
---> try to load best state
              	0.00696     	0.95032     	0.01343     	47               eval state : test_on_restore;
              	0.00696     	0.95032     	0.01343     	47               eval state : test_on_last_epoch;
---> final result
              	0.00696     	0.95032     	0.01343     	47               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': True, '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           	0.01481     	0.78088     	0.03886     	0           
  10          	0.01414     	0.78120     	0.02704     	0           
  100         	0.01167     	0.74554     	0.02006     	5           
  300         	0.00938     	0.89604     	0.01627     	14          
  500         	0.00640     	0.96403     	0.01285     	23          
  1000        	0.00595     	0.97008     	0.01230     	45          
---> try to load best state
              	0.00595     	0.97008     	0.01230     	45               eval state : test_on_restore;
              	0.00595     	0.97008     	0.01230     	46               eval state : test_on_last_epoch;
---> final result
              	0.00595     	0.97008     	0.01230     	46               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': True, '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           	0.00990     	0.89213     	0.02208     	0           
  10          	0.01495     	0.66546     	0.02732     	0           
  100         	0.01326     	0.74427     	0.02219     	5           
  300         	0.01876     	0.73478     	0.02746     	14          
  500         	0.00829     	0.94746     	0.01467     	23          
  1000        	0.00902     	0.93529     	0.01574     	46          
---> try to load best state
              	0.00902     	0.93529     	0.01574     	46               eval state : test_on_restore;
              	0.00902     	0.93529     	0.01574     	46               eval state : test_on_last_epoch;
---> final result
              	0.00902     	0.93529     	0.01574     	46               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': True, '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           	0.01362     	0.85982     	0.02788     	0           
  10          	0.01473     	0.69279     	0.02877     	0           
  100         	0.01194     	0.80483     	0.02447     	4           
  300         	0.01107     	0.83632     	0.02378     	14          
  500         	0.00888     	-0.18400    	0.02170     	23          
  1000        	0.00737     	0.94182     	0.02110     	46          
---> try to load best state
              	0.00737     	0.94182     	0.02110     	46               eval state : test_on_restore;
              	0.00737     	0.94182     	0.02110     	46               eval state : test_on_last_epoch;
---> final result
              	0.00737     	0.94182     	0.02110     	46               eval state : final;module_name : GAR;subset : 32;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 0 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.01653     	0.80817     	0.04360     	0           
  10          	0.01584     	0.82140     	0.04064     	0           
  100         	0.01359     	0.83942     	0.03250     	4           
  300         	0.01144     	0.84909     	0.02855     	12          
  500         	0.01028     	0.86478     	0.02634     	20          
  1000        	0.00894     	0.88075     	0.02370     	39          
---> try to load best state
              	0.00894     	0.88075     	0.02370     	39               eval state : test_on_restore;
              	0.00894     	0.88075     	0.02370     	39               eval state : test_on_last_epoch;
---> final result              	0.00894     	0.88075     	0.02370     	39               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': True, '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           	0.01159     	0.86508     	0.02177     	0           
  10          	0.01971     	0.52703     	0.03083     	0           
  100         	0.01460     	0.82230     	0.02153     	5           
  300         	0.01083     	0.90072     	0.01776     	14          
  500         	0.01134     	0.93357     	0.01815     	23          
  1000        	0.00858     	0.82509     	0.01423     	45          
---> try to load best state
              	0.00858     	0.82509     	0.01423     	45               eval state : test_on_restore;
              	0.00858     	0.82509     	0.01423     	45               eval state : test_on_last_epoch;
---> final result
              	0.00858     	0.82509     	0.01423     	45               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': True, '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           	0.01186     	0.86131     	0.02224     	0           
  10          	0.01961     	0.59539     	0.03144     	0           
  100         	0.01267     	0.75027     	0.01895     	4           
  300         	0.01117     	0.93883     	0.01812     	13          
  500         	0.01048     	0.72685     	0.01690     	20          
  1000        	0.00680     	0.96112     	0.01313     	40          
---> try to load best state
              	0.00680     	0.96112     	0.01313     	40               eval state : test_on_restore;
              	0.00680     	0.96112     	0.01313     	40               eval state : test_on_last_epoch;
---> final result
              	0.00680     	0.96112     	0.01313     	40               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': True, '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           	0.01260     	0.84623     	0.02307     	0           
  10          	0.02124     	0.58055     	0.03286     	0           
  100         	0.01202     	0.91702     	0.01888     	4           
  300         	0.01143     	0.92863     	0.01893     	12          
  500         	0.01071     	0.94189     	0.01761     	20          
  1000        	0.00793     	0.95287     	0.01471     	41          
---> try to load best state
              	0.00793     	0.95287     	0.01471     	41               eval state : test_on_restore;
              	0.00793     	0.95287     	0.01471     	41               eval state : test_on_last_epoch;
---> final result
              	0.00793     	0.95287     	0.01471     	41               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': True, '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           	0.01330     	0.83776     	0.02679     	0           
  10          	0.01898     	0.63070     	0.03183     	0           
  100         	0.01187     	0.80834     	0.01825     	5           
  300         	0.01101     	0.79430     	0.01688     	15          
  500         	0.00953     	0.95022     	0.01576     	24          
  1000        	0.00953     	0.93191     	0.01563     	46          
---> try to load best state
              	0.00953     	0.93191     	0.01563     	46               eval state : test_on_restore;
              	0.00953     	0.93191     	0.01563     	47               eval state : test_on_last_epoch;
---> final result
              	0.00953     	0.93191     	0.01563     	47               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': True, '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           	0.01737     	0.77315     	0.04303     	0           
  10          	0.02282     	0.59550     	0.05037     	0           
  100         	0.01247     	0.80743     	0.02090     	5           
  300         	0.01188     	0.67009     	0.01874     	14          
  500         	0.08098     	-4.44163    	0.10627     	24          
  1000        	0.00935     	0.93711     	0.01662     	46          
---> try to load best state
              	0.00935     	0.93711     	0.01662     	46               eval state : test_on_restore;
              	0.00935     	0.93711     	0.01662     	46               eval state : test_on_last_epoch;
---> final result
              	0.00935     	0.93711     	0.01662     	46               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': True, '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           	0.01414     	0.79630     	0.03223     	0           
  10          	0.01993     	0.67210     	0.04352     	0           
  100         	0.01444     	0.62904     	0.02778     	5           
  300         	0.01200     	0.82077     	0.02839     	14          
  500         	0.01416     	0.62529     	0.03161     	22          
  1000        	0.01318     	0.73405     	0.03262     	45          
---> try to load best state
              	0.01318     	0.73405     	0.03262     	45               eval state : test_on_restore;
              	0.01318     	0.73405     	0.03262     	46               eval state : test_on_last_epoch;
---> final result
              	0.01318     	0.73405     	0.03262     	46               eval state : final;module_name : GAR;subset : 32;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 1 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.01062     	0.91033     	0.02728     	0           
  10          	0.01052     	0.91162     	0.02660     	0           
  100         	0.01211     	0.89313     	0.02651     	3           
  300         	0.01061     	0.90402     	0.02379     	11          
  500         	0.00868     	0.91819     	0.02133     	18          
  1000        	0.00708     	0.92540     	0.01882     	37          
---> try to load best state
              	0.00708     	0.92540     	0.01882     	37               eval state : test_on_restore;
              	0.00708     	0.92540     	0.01882     	37               eval state : test_on_last_epoch;
---> final result              	0.00708     	0.92540     	0.01882     	37               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': True, '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           	0.01176     	0.84748     	0.02388     	0           
  10          	0.01705     	0.68516     	0.02954     	0           
  100         	0.01075     	0.80636     	0.01918     	4           
  300         	0.01051     	0.92031     	0.01808     	13          
  500         	0.01183     	0.84908     	0.02155     	21          
  1000        	0.00820     	0.92511     	0.01723     	41          
---> try to load best state
              	0.00820     	0.92511     	0.01723     	41               eval state : test_on_restore;
              	0.00820     	0.92511     	0.01723     	41               eval state : test_on_last_epoch;
---> final result
              	0.00820     	0.92511     	0.01723     	41               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': True, '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           	0.01204     	0.84452     	0.02464     	0           
  10          	0.01714     	0.68869     	0.02950     	0           
  100         	0.01092     	0.67501     	0.01933     	4           
  300         	0.00891     	0.93350     	0.01730     	13          
  500         	0.01170     	0.45932     	0.02174     	21          
  1000        	0.00771     	0.92633     	0.01626     	42          
---> try to load best state
              	0.00771     	0.92633     	0.01626     	42               eval state : test_on_restore;
              	0.00771     	0.92633     	0.01626     	42               eval state : test_on_last_epoch;
---> final result
              	0.00771     	0.92633     	0.01626     	42               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': True, '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           	0.01182     	0.85112     	0.02246     	0           
  10          	0.01699     	0.62694     	0.02836     	0           
  100         	0.01065     	0.83283     	0.01879     	4           
  300         	0.00865     	0.92492     	0.01688     	14          
  500         	0.00870     	0.92885     	0.01731     	22          
  1000        	0.00755     	0.90843     	0.01574     	44          
---> try to load best state
              	0.00755     	0.90843     	0.01574     	44               eval state : test_on_restore;
              	0.00755     	0.90843     	0.01574     	44               eval state : test_on_last_epoch;
---> final result
              	0.00755     	0.90843     	0.01574     	44               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': True, '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           	0.00934     	0.86336     	0.01773     	0           
  10          	0.01561     	0.61017     	0.02627     	0           
  100         	0.00943     	0.90381     	0.01780     	5           
  300         	0.00938     	0.93410     	0.01764     	15          
  500         	0.00858     	0.87719     	0.01757     	23          
  1000        	0.00733     	0.94335     	0.01660     	46          
---> try to load best state
              	0.00733     	0.94335     	0.01660     	46               eval state : test_on_restore;
              	0.00733     	0.94335     	0.01660     	46               eval state : test_on_last_epoch;
---> final result
              	0.00733     	0.94335     	0.01660     	46               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': True, '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           	0.00970     	0.87619     	0.01795     	0           
  10          	0.01532     	0.60739     	0.02599     	0           
  100         	0.01051     	0.87911     	0.01936     	5           
  300         	0.01222     	0.74044     	0.02348     	13          
  500         	0.00947     	0.89696     	0.01875     	21          
  1000        	0.00756     	0.94068     	0.01685     	42          
---> try to load best state
              	0.00756     	0.94068     	0.01685     	42               eval state : test_on_restore;
              	0.00756     	0.94068     	0.01685     	42               eval state : test_on_last_epoch;
---> final result
              	0.00756     	0.94068     	0.01685     	42               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': True, '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           	0.00964     	0.85098     	0.02163     	0           
  10          	0.01543     	0.75191     	0.02993     	0           
  100         	0.01681     	0.57024     	0.02678     	4           
  300         	0.01215     	0.85757     	0.02534     	13          
  500         	0.01156     	0.84640     	0.02699     	22          
  1000        	0.01136     	0.79229     	0.02889     	43          
---> try to load best state
              	0.01136     	0.79229     	0.02889     	43               eval state : test_on_restore;
              	0.01136     	0.79229     	0.02889     	43               eval state : test_on_last_epoch;
---> final result
              	0.01136     	0.79229     	0.02889     	43               eval state : final;module_name : GAR;subset : 32;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 2 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.01522     	0.84230     	0.04416     	0           
  10          	0.01450     	0.85200     	0.04103     	0           
  100         	0.01344     	0.88245     	0.03078     	3           
  300         	0.01066     	0.91003     	0.02464     	11          
  500         	0.00954     	0.91095     	0.02357     	18          
  1000        	0.00949     	0.91724     	0.02394     	38          
---> try to load best state
              	0.00949     	0.91724     	0.02394     	38               eval state : test_on_restore;
              	0.00949     	0.91724     	0.02394     	38               eval state : test_on_last_epoch;
---> final result              	0.00949     	0.91724     	0.02394     	38               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': True, '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           	0.02245     	0.69019     	0.06485     	0           
  10          	0.02161     	0.60611     	0.04951     	0           
  100         	0.01559     	0.58570     	0.03370     	4           
  300         	0.01232     	0.71700     	0.02765     	13          
  500         	0.00858     	0.86202     	0.02012     	22          
  1000        	0.00804     	0.88706     	0.02102     	47          
---> try to load best state
              	0.00804     	0.88706     	0.02102     	48               eval state : test_on_restore;
              	0.00804     	0.88706     	0.02102     	48               eval state : test_on_last_epoch;
---> final result
              	0.00804     	0.88706     	0.02102     	48               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': True, '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           	0.02271     	0.68762     	0.06563     	0           
  10          	0.02111     	0.62904     	0.04909     	0           
  100         	0.01609     	0.64016     	0.03524     	5           
  300         	0.01710     	0.68242     	0.04001     	15          
  500         	0.00816     	0.86724     	0.01991     	24          
  1000        	0.00683     	0.90596     	0.01700     	46          
---> try to load best state
              	0.00683     	0.90596     	0.01700     	46               eval state : test_on_restore;
              	0.00683     	0.90596     	0.01700     	46               eval state : test_on_last_epoch;
---> final result
              	0.00683     	0.90596     	0.01700     	46               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': True, '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           	0.02012     	0.71564     	0.05623     	0           
  10          	0.02172     	0.45911     	0.05004     	0           
  100         	0.01300     	0.83606     	0.02701     	4           
  300         	0.01257     	0.76183     	0.02866     	15          
  500         	0.01035     	0.13055     	0.02410     	25          
  1000        	0.00681     	0.90475     	0.01711     	47          
---> try to load best state
              	0.00681     	0.90475     	0.01711     	47               eval state : test_on_restore;
              	0.00681     	0.90475     	0.01711     	47               eval state : test_on_last_epoch;
---> final result
              	0.00681     	0.90475     	0.01711     	47               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': True, '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           	0.01103     	0.88185     	0.02287     	0           
  10          	0.01602     	0.64592     	0.02858     	0           
  100         	0.01266     	0.84881     	0.02166     	4           
  300         	0.00763     	0.92339     	0.01616     	12          
  500         	0.00772     	0.87981     	0.01565     	20          
  1000        	0.00592     	0.95507     	0.01408     	40          
---> try to load best state
              	0.00592     	0.95507     	0.01408     	41               eval state : test_on_restore;
              	0.00592     	0.95507     	0.01408     	41               eval state : test_on_last_epoch;
---> final result
              	0.00592     	0.95507     	0.01408     	41               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': True, '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           	0.01188     	0.88864     	0.02106     	0           
  10          	0.01500     	0.75293     	0.02607     	0           
  100         	0.01560     	0.69685     	0.02384     	5           
  300         	0.00730     	0.89087     	0.01575     	14          
  500         	0.00687     	0.94985     	0.01534     	23          
  1000        	0.00592     	0.95721     	0.01380     	45          
---> try to load best state
              	0.00592     	0.95721     	0.01380     	46               eval state : test_on_restore;
              	0.00592     	0.95721     	0.01380     	46               eval state : test_on_last_epoch;
---> final result
              	0.00592     	0.95721     	0.01380     	46               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': True, '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           	0.01759     	0.77570     	0.03624     	0           
  10          	0.01922     	0.58943     	0.04252     	0           
  100         	0.01350     	0.79321     	0.02835     	5           
  300         	0.01634     	0.65644     	0.03011     	15          
  500         	0.01102     	0.88209     	0.02568     	24          
  1000        	0.01174     	0.83045     	0.02690     	45          
---> try to load best state
              	0.01174     	0.83045     	0.02690     	45               eval state : test_on_restore;
              	0.01174     	0.83045     	0.02690     	45               eval state : test_on_last_epoch;
---> final result
              	0.01174     	0.83045     	0.02690     	45               eval state : final;module_name : GAR;subset : 32;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 3 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.00961     	0.92999     	0.02314     	0           
  10          	0.00944     	0.93026     	0.02241     	0           
  100         	0.00987     	0.92069     	0.02171     	3           
  300         	0.00802     	0.93850     	0.01766     	11          
  500         	0.00652     	0.95101     	0.01462     	18          
  1000        	0.00549     	0.96012     	0.01352     	38          
---> try to load best state
              	0.00549     	0.96012     	0.01352     	38               eval state : test_on_restore;
              	0.00549     	0.96012     	0.01352     	38               eval state : test_on_last_epoch;
---> final result              	0.00549     	0.96012     	0.01352     	38               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': True, '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           	0.01035     	0.87569     	0.02464     	0           
  10          	0.01570     	0.36902     	0.03066     	0           
  100         	0.01558     	0.59503     	0.03167     	5           
  300         	0.01413     	0.73815     	0.03166     	14          
  500         	0.01081     	0.72692     	0.02742     	23          
  1000        	0.01063     	0.79055     	0.02620     	43          
---> try to load best state
              	0.01063     	0.79055     	0.02620     	43               eval state : test_on_restore;
              	0.01063     	0.79055     	0.02620     	43               eval state : test_on_last_epoch;
---> final result
              	0.01063     	0.79055     	0.02620     	43               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': True, '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           	0.01060     	0.88340     	0.02538     	0           
  10          	0.01757     	0.29754     	0.03120     	0           
  100         	0.01460     	0.49909     	0.02871     	4           
  300         	0.01183     	0.52571     	0.02859     	13          
  500         	0.00919     	0.88237     	0.02511     	24          
  1000        	0.00758     	0.89544     	0.02169     	46          
---> try to load best state
              	0.00758     	0.89544     	0.02169     	46               eval state : test_on_restore;
              	0.00758     	0.89544     	0.02169     	46               eval state : test_on_last_epoch;
---> final result
              	0.00758     	0.89544     	0.02169     	46               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': True, '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           	0.01062     	0.88147     	0.02406     	0           
  10          	0.01719     	0.57977     	0.03061     	0           
  100         	0.01003     	0.84906     	0.02563     	4           
  300         	0.01082     	0.89112     	0.02530     	13          
  500         	0.00911     	0.89109     	0.02442     	22          
  1000        	0.00847     	0.88905     	0.02359     	46          
---> try to load best state
              	0.00847     	0.88905     	0.02359     	46               eval state : test_on_restore;
              	0.00847     	0.88905     	0.02359     	46               eval state : test_on_last_epoch;
---> final result
              	0.00847     	0.88905     	0.02359     	46               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': True, '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           	0.01128     	0.86942     	0.02022     	0           
  10          	0.01281     	0.75017     	0.02177     	0           
  100         	0.01410     	0.87465     	0.02169     	5           
  300         	0.00629     	0.93294     	0.01285     	15          
  500         	0.00566     	0.94515     	0.01226     	24          
  1000        	0.00719     	0.92104     	0.01517     	46          
---> try to load best state
              	0.00719     	0.92104     	0.01517     	46               eval state : test_on_restore;
              	0.00719     	0.92104     	0.01517     	46               eval state : test_on_last_epoch;
---> final result
              	0.00719     	0.92104     	0.01517     	46               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': True, '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           	0.01465     	0.80104     	0.02772     	0           
  10          	0.01625     	0.65316     	0.02886     	1           
  100         	0.01709     	0.12276     	0.02883     	5           
  300         	0.01224     	0.81043     	0.01962     	15          
  500         	0.00615     	0.95407     	0.01259     	23          
  1000        	0.00835     	0.88285     	0.01496     	43          
---> try to load best state
              	0.00835     	0.88285     	0.01496     	43               eval state : test_on_restore;
              	0.00835     	0.88285     	0.01496     	43               eval state : test_on_last_epoch;
---> final result
              	0.00835     	0.88285     	0.01496     	43               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': True, '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           	0.01184     	0.85328     	0.02292     	0           
  10          	0.01533     	0.64542     	0.02747     	0           
  100         	0.00986     	0.52768     	0.01813     	4           
  300         	0.00648     	0.89344     	0.01417     	13          
  500         	0.00969     	0.90873     	0.01811     	20          
  1000        	0.00665     	0.93263     	0.01455     	43          
---> try to load best state
              	0.00665     	0.93263     	0.01455     	43               eval state : test_on_restore;
              	0.00665     	0.93263     	0.01455     	43               eval state : test_on_last_epoch;
---> final result
              	0.00665     	0.93263     	0.01455     	43               eval state : final;module_name : GAR;subset : 32;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 4 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.01431     	0.89559     	0.02904     	0           
  10          	0.01386     	0.90085     	0.02759     	0           
  100         	0.01231     	0.92419     	0.02178     	3           
  300         	0.01142     	0.93316     	0.02044     	11          
  500         	0.01006     	0.93432     	0.02077     	18          
  1000        	0.00865     	0.94472     	0.01772     	35          
---> try to load best state
              	0.00865     	0.94472     	0.01772     	35               eval state : test_on_restore;
              	0.00865     	0.94472     	0.01772     	35               eval state : test_on_last_epoch;
---> final result              	0.00865     	0.94472     	0.01772     	35               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': True, '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           	0.01070     	0.90691     	0.02195     	0           
  10          	0.01475     	0.74537     	0.02445     	0           
  100         	0.01512     	0.61414     	0.02367     	4           
  300         	0.00822     	0.93212     	0.01421     	12          
  500         	0.00828     	0.94374     	0.01424     	19          
  1000        	0.00693     	0.95120     	0.01349     	38          
---> try to load best state
              	0.00693     	0.95120     	0.01349     	38               eval state : test_on_restore;
              	0.00693     	0.95120     	0.01349     	38               eval state : test_on_last_epoch;
---> final result
              	0.00693     	0.95120     	0.01349     	39               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': True, '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           	0.01022     	0.90054     	0.02074     	0           
  10          	0.01623     	0.74231     	0.02478     	0           
  100         	0.01261     	0.88719     	0.01939     	4           
  300         	0.00810     	0.94467     	0.01409     	12          
  500         	0.00748     	0.95574     	0.01415     	19          
  1000        	0.00767     	0.95515     	0.01464     	38          
---> try to load best state
              	0.00767     	0.95515     	0.01464     	38               eval state : test_on_restore;
              	0.00767     	0.95515     	0.01464     	38               eval state : test_on_last_epoch;
---> final result
              	0.00767     	0.95515     	0.01464     	38               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': True, '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           	0.01079     	0.90270     	0.02245     	0           
  10          	0.01720     	0.11784     	0.02610     	0           
  100         	0.01163     	0.56739     	0.01845     	4           
  300         	0.00854     	0.94718     	0.01509     	12          
  500         	0.00763     	0.95173     	0.01433     	20          
  1000        	0.00706     	0.95704     	0.01406     	42          
---> try to load best state
              	0.00706     	0.95704     	0.01406     	42               eval state : test_on_restore;
              	0.00706     	0.95704     	0.01406     	42               eval state : test_on_last_epoch;
---> final result
              	0.00706     	0.95704     	0.01406     	42               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': True, '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           	0.01137     	0.86715     	0.02218     	0           
  10          	0.01398     	0.65991     	0.02507     	0           
  100         	0.01015     	0.87274     	0.01704     	5           
  300         	0.01027     	0.90810     	0.01721     	14          
  500         	0.00803     	0.95079     	0.01470     	23          
  1000        	0.00817     	0.92249     	0.01521     	42          
---> try to load best state
              	0.00817     	0.92249     	0.01521     	42               eval state : test_on_restore;
              	0.00817     	0.92249     	0.01521     	42               eval state : test_on_last_epoch;
---> final result
              	0.00817     	0.92249     	0.01521     	42               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': True, '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           	0.01151     	0.88083     	0.02864     	0           
  10          	0.01529     	0.77410     	0.03081     	0           
  100         	0.01060     	0.84649     	0.02593     	4           
  300         	0.01388     	0.80946     	0.02980     	12          
  500         	0.00973     	0.89701     	0.02289     	20          
  1000        	0.01020     	0.23566     	0.02381     	42          
---> try to load best state
              	0.01020     	0.23566     	0.02381     	42               eval state : test_on_restore;
              	0.01020     	0.23566     	0.02381     	42               eval state : test_on_last_epoch;
---> final result
              	0.01020     	0.23566     	0.02381     	42               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': True, '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           	0.01783     	0.82246     	0.02893     	0           
  10          	0.01763     	0.69241     	0.03031     	0           
  100         	0.01351     	0.59846     	0.02312     	4           
  300         	0.01448     	0.86394     	0.02502     	13          
  500         	0.01272     	0.86994     	0.02090     	21          
  1000        	0.00948     	0.87249     	0.01922     	44          
---> try to load best state
              	0.00948     	0.87249     	0.01922     	44               eval state : test_on_restore;
              	0.00948     	0.87249     	0.01922     	44               eval state : test_on_last_epoch;
---> final result
              	0.00948     	0.87249     	0.01922     	44               eval state : final;module_name : GAR;subset : 32;cp_record_file : True;
