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

  Demo DC 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	138.77553   	250.73946   	0.74367     	0           
  10          	138.07928   	249.10338   	0.74967     	0           
  100         	131.43468   	234.79115   	0.79442     	0           
---> try to load best state
              	131.43468   	234.79115   	0.79442     	0                eval state : test_on_restore;
              	131.43468   	234.79115   	0.79442     	0                eval state : test_on_last_epoch;
---> final result
              	131.43468   	234.79115   	0.79442     	0                eval state : final;
----------> finish x-yl training


---------->
NAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	202.85687   	443.80963   	-12261415884170.92383	0           
  10          	202.85687   	443.80963   	-12261413097767.61523	0           
  100         	202.85687   	443.80963   	-12261413097767.61523	0           
---> try to load best state
              	202.85687   	443.80963   	-12261413097767.61523	0                eval state : test_on_restore;
              	202.85687   	443.80963   	-12261413097767.61523	0                eval state : test_on_last_epoch;
---> final result
              	202.85687   	443.80963   	-12261413097767.61523	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	202.65068   	436.32623   	-940757730209.81274	0           
  10          	202.65068   	436.32623   	-935248382965.53479	0           
  100         	202.65068   	436.32623   	-930834955694.91223	0           
---> try to load best state
              	202.65068   	436.32623   	-930834955694.91223	0                eval state : test_on_restore;
              	202.65068   	436.32623   	-930834955694.91223	0                eval state : test_on_last_epoch;
---> final result
              	202.65068   	436.32623   	-930834955694.91223	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	202.32483   	435.70883   	-1832094890.03347	0           
  10          	202.32483   	435.70883   	-1818136435.72593	0           
  100         	202.32481   	435.70880   	-1806585262.99746	0           
---> try to load best state
              	202.32481   	435.70880   	-1806585262.99746	0                eval state : test_on_restore;
              	202.32481   	435.70880   	-1806585262.99746	0                eval state : test_on_last_epoch;
---> final result
              	202.32481   	435.70880   	-1806585262.99746	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	202.18491   	434.84476   	-45221184680.56544	0           
  10          	201.85606   	434.14117   	-35764.80764	0           
  100         	120.55385   	304.09946   	-1.34937    	0           
---> try to load best state
              	120.55385   	304.09946   	-1.34937    	0                eval state : test_on_restore;
              	120.55385   	304.09946   	-1.34937    	0                eval state : test_on_last_epoch;
---> final result
              	120.55385   	304.09946   	-1.34937    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	197.50685   	358.35605   	-0.09550    	0           
  10          	195.69127   	355.58310   	-0.05976    	0           
  100         	179.63792   	330.06586   	0.21359     	0           
---> try to load best state
              	179.63792   	330.06586   	0.21359     	0                eval state : test_on_restore;
              	179.63792   	330.06586   	0.21359     	0                eval state : test_on_last_epoch;
---> final result
              	179.63792   	330.06586   	0.21359     	0                eval state : final;
----------> finish x-yl training


---------->
NAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	219.80775   	474.28265   	-10737877206707.90625	0           
  10          	219.80775   	474.28265   	-10737877206707.90625	0           
  100         	219.80775   	474.28265   	-10737872918965.46680	0           
---> try to load best state
              	219.80775   	474.28265   	-10737872918965.46680	0                eval state : test_on_restore;
              	219.80775   	474.28265   	-10737872918965.46680	0                eval state : test_on_last_epoch;
---> final result
              	219.80775   	474.28265   	-10737872918965.46680	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	201.49957   	436.30704   	-95757250210015.32812	0           
  10          	201.49957   	436.30704   	-95223012886799.59375	0           
  100         	201.49957   	436.30704   	-95554073084998.03125	0           
---> try to load best state
              	201.49957   	436.30704   	-95554073084998.03125	0                eval state : test_on_restore;
              	201.49957   	436.30704   	-95554073084998.03125	0                eval state : test_on_last_epoch;
---> final result
              	201.49957   	436.30704   	-95554073084998.03125	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	202.77173   	442.44513   	-797608476.92214	0           
  10          	202.77173   	442.44513   	-791533853.62871	0           
  100         	202.77173   	442.44510   	-786507176.98877	0           
---> try to load best state
              	202.77173   	442.44510   	-786507176.98877	0                eval state : test_on_restore;
              	202.77173   	442.44510   	-786507176.98877	0                eval state : test_on_last_epoch;
---> final result
              	202.77173   	442.44510   	-786507176.98877	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	202.40048   	440.87057   	-1092413.37119	0           
  10          	202.43579   	440.94305   	-102181616510.89050	0           
  100         	202.43590   	440.94330   	-10885925519952.66797	0           
---> try to load best state
              	202.43590   	440.94330   	-10885925519952.66797	0                eval state : test_on_restore;
              	202.43590   	440.94330   	-10885925519952.66797	0                eval state : test_on_last_epoch;
---> final result
              	202.43590   	440.94330   	-10885925519952.66797	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	178.59821   	302.61151   	0.42124     	0           
  10          	177.11867   	300.35889   	0.43536     	0           
  100         	163.57587   	279.92032   	0.54745     	0           
---> try to load best state
              	163.57587   	279.92032   	0.54745     	0                eval state : test_on_restore;
              	163.57587   	279.92032   	0.54745     	0                eval state : test_on_last_epoch;
---> final result
              	163.57587   	279.92032   	0.54745     	0                eval state : final;
----------> finish x-yl training


---------->
NAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	204.55777   	450.00845   	-7259428285706.14355	0           
  10          	204.55777   	450.00845   	-7259428285706.14355	0           
  100         	204.55777   	450.00845   	-7259428285706.14355	0           
---> try to load best state
              	204.55777   	450.00845   	-7259428285706.14355	0                eval state : test_on_restore;
              	204.55777   	450.00845   	-7259428285706.14355	0                eval state : test_on_last_epoch;
---> final result
              	204.55777   	450.00845   	-7259428285706.14355	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	206.00169   	456.79968   	-7722805689195.99316	0           
  10          	206.00169   	456.79968   	-7722805689195.99316	0           
  100         	206.00169   	456.79968   	-7722805689195.99316	0           
---> try to load best state
              	206.00169   	456.79968   	-7722805689195.99316	0                eval state : test_on_restore;
              	206.00169   	456.79968   	-7722805689195.99316	0                eval state : test_on_last_epoch;
---> final result
              	206.00169   	456.79968   	-7722805689195.99316	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	203.13805   	434.51926   	-941364135662.79419	0           
  10          	203.13805   	434.51926   	-936180924022.08081	0           
  100         	203.13805   	434.51926   	-932005790008.43237	0           
---> try to load best state
              	203.13805   	434.51926   	-932005790008.43237	0                eval state : test_on_restore;
              	203.13805   	434.51926   	-932005790008.43237	0                eval state : test_on_last_epoch;
---> final result
              	203.13805   	434.51926   	-932005790008.43237	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	202.66437   	434.33109   	-3134528331.73113	0           
  10          	202.31445   	433.85675   	-78230.38935	0           
  100         	118.66882   	282.23712   	-0.86153    	0           
---> try to load best state
              	118.66882   	282.23712   	-0.86153    	0                eval state : test_on_restore;
              	118.66882   	282.23712   	-0.86153    	0                eval state : test_on_last_epoch;
---> final result
              	118.66882   	282.23712   	-0.86153    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	173.74155   	334.46317   	0.34726     	0           
  10          	172.35533   	332.10190   	0.36470     	0           
  100         	160.53120   	310.73581   	0.49882     	0           
---> try to load best state
              	160.53120   	310.73581   	0.49882     	0                eval state : test_on_restore;
              	160.53120   	310.73581   	0.49882     	0                eval state : test_on_last_epoch;
---> final result
              	160.53120   	310.73581   	0.49882     	0                eval state : final;
----------> finish x-yl training


---------->
NAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	206.47003   	438.12555   	-4850997334524.51758	0           
  10          	206.47003   	438.12555   	-4850997334524.51758	0           
  100         	206.47003   	438.12555   	-4850997334524.51758	0           
---> try to load best state
              	206.47003   	438.12555   	-4850997334524.51758	0                eval state : test_on_restore;
              	206.47003   	438.12555   	-4850997334524.51758	0                eval state : test_on_last_epoch;
---> final result
              	206.47003   	438.12555   	-4850997334524.51758	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	218.59293   	498.45560   	-18419882081983.28906	0           
  10          	218.58760   	498.45108   	-80857650.29855	0           
  100         	216.97285   	496.25864   	-3444.41838 	0           
---> try to load best state
              	216.97285   	496.25864   	-3444.41838 	0                eval state : test_on_restore;
              	216.97285   	496.25864   	-3444.41838 	0                eval state : test_on_last_epoch;
---> final result
              	216.97285   	496.25864   	-3444.41838 	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	206.22015   	455.33661   	-23195.77170	0           
  10          	204.20503   	451.57874   	-272.24140  	0           
  100         	181.70508   	425.01370   	-20.41322   	0           
---> try to load best state
              	181.70508   	425.01370   	-20.41322   	0                eval state : test_on_restore;
              	181.70508   	425.01370   	-20.41322   	0                eval state : test_on_last_epoch;
---> final result
              	181.70508   	425.01370   	-20.41322   	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	202.89639   	434.94720   	-20597.83900	0           
  10          	200.24373   	429.33041   	-189.95624  	0           
  100         	148.11839   	349.69330   	-4.58953    	0           
---> try to load best state
              	148.11839   	349.69330   	-4.58953    	0                eval state : test_on_restore;
              	148.11839   	349.69330   	-4.58953    	0                eval state : test_on_last_epoch;
---> final result
              	148.11839   	349.69330   	-4.58953    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	190.19421   	346.10031   	0.05073     	0           
  10          	188.35249   	342.78317   	0.08269     	0           
  100         	171.96010   	312.76389   	0.32758     	0           
---> try to load best state
              	171.96010   	312.76389   	0.32758     	0                eval state : test_on_restore;
              	171.96010   	312.76389   	0.32758     	0                eval state : test_on_last_epoch;
---> final result
              	171.96010   	312.76389   	0.32758     	0                eval state : final;
----------> finish x-yl training


---------->
NAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	216.01331   	478.52234   	-10651421687006.06055	0           
  10          	216.01331   	478.52234   	-10651421687006.06055	0           
  100         	216.01331   	478.52234   	-10651421687006.06055	0           
---> try to load best state
              	216.01331   	478.52234   	-10651421687006.06055	0                eval state : test_on_restore;
              	216.01331   	478.52234   	-10651421687006.06055	0                eval state : test_on_last_epoch;
---> final result
              	216.01331   	478.52234   	-10651421687006.06055	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	206.37552   	457.54044   	-13082786502778.51172	0           
  10          	206.37552   	457.54044   	-13009440185479.34570	0           
  100         	206.37552   	457.54044   	-12984421503805.12891	0           
---> try to load best state
              	206.37552   	457.54044   	-12984421503805.12891	0                eval state : test_on_restore;
              	206.37552   	457.54044   	-12984421503805.12891	0                eval state : test_on_last_epoch;
---> final result
              	206.37552   	457.54044   	-12984421503805.12891	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	202.15005   	440.24350   	-7540882.63750	0           
  10          	202.14998   	440.24338   	-7483409.75463	0           
  100         	202.14990   	440.24329   	-7435857.19224	0           
---> try to load best state
              	202.14990   	440.24329   	-7435857.19224	0                eval state : test_on_restore;
              	202.14990   	440.24329   	-7435857.19224	0                eval state : test_on_last_epoch;
---> final result
              	202.14990   	440.24329   	-7435857.19224	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	201.90771   	439.26596   	-512755.06776	0           
  10          	200.99080   	437.86115   	-3376.13407 	0           
  100         	127.08425   	326.58939   	-2.41597    	0           
---> try to load best state
              	127.08425   	326.58939   	-2.41597    	0                eval state : test_on_restore;
              	127.08425   	326.58939   	-2.41597    	0                eval state : test_on_last_epoch;
---> final result
              	127.08425   	326.58939   	-2.41597    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	135.78708   	241.20798   	0.78973     	0           
  10          	135.16093   	239.71599   	0.79409     	0           
  100         	129.75902   	226.75250   	0.82699     	0           
---> try to load best state
              	129.75902   	226.75250   	0.82699     	0                eval state : test_on_restore;
              	129.75902   	226.75250   	0.82699     	0                eval state : test_on_last_epoch;
---> final result
              	129.75902   	226.75250   	0.82699     	0                eval state : final;
----------> finish x-yl training


---------->
NAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	218.33350   	496.03687   	-11687652487726.33984	0           
  10          	218.33350   	496.03687   	-11687652487726.33984	0           
  100         	218.33350   	496.03687   	-11687652487726.33984	0           
---> try to load best state
              	218.33350   	496.03687   	-11687652487726.33984	0                eval state : test_on_restore;
              	218.33350   	496.03687   	-11687652487726.33984	0                eval state : test_on_last_epoch;
---> final result
              	218.33350   	496.03687   	-11687652487726.33984	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	203.50552   	446.23688   	-7060656204142.36230	0           
  10          	203.50552   	446.23688   	-7060656204142.36230	0           
  100         	203.50552   	446.23688   	-7060656204142.36230	0           
---> try to load best state
              	203.50552   	446.23688   	-7060656204142.36230	0                eval state : test_on_restore;
              	203.50552   	446.23688   	-7060656204142.36230	0                eval state : test_on_last_epoch;
---> final result
              	203.50552   	446.23688   	-7060656204142.36230	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	202.59552   	440.91083   	-6631099370842.76270	0           
  10          	202.59438   	440.90887   	-2750935107.92056	0           
  100         	147.86024   	363.66495   	-4.48156    	0           
---> try to load best state
              	147.86024   	363.66495   	-4.48156    	0                eval state : test_on_restore;
              	147.86024   	363.66495   	-4.48156    	0                eval state : test_on_last_epoch;
---> final result
              	147.86024   	363.66495   	-4.48156    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	202.79115   	436.66486   	-1864466.40095	0           
  10          	200.80006   	431.84030   	-477.07674  	0           
  100         	120.00501   	296.79993   	-0.94585    	0           
---> try to load best state
              	120.00501   	296.79993   	-0.94585    	0                eval state : test_on_restore;
              	120.00501   	296.79993   	-0.94585    	0                eval state : test_on_last_epoch;
---> final result
              	120.00501   	296.79993   	-0.94585    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
