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

  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': 64, '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           	53.46056    	136.22884   	0.86429     	0           
  10          	53.06068    	135.07552   	0.86717     	0           
  100         	49.48153    	125.29778   	0.88946     	1           
---> try to load best state
              	49.48153    	125.29778   	0.88946     	1                eval state : test_on_restore;
              	49.48153    	125.29778   	0.88946     	1                eval state : test_on_last_epoch;
---> final result
              	49.48153    	125.29778   	0.88946     	1                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   	-6644048798244.09082	0           
  10          	202.85687   	443.80963   	-6644048798244.09082	0           
  100         	202.85687   	443.80963   	-6644048798244.09082	0           
---> try to load best state
              	202.85687   	443.80963   	-6644048798244.09082	0                eval state : test_on_restore;
              	202.85687   	443.80963   	-6644048798244.09082	0                eval state : test_on_last_epoch;
---> final result
              	202.85687   	443.80963   	-6644048798244.09082	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           	202.65071   	436.32629   	-5697808924942.14258	0           
  10          	202.65071   	436.32629   	-5697805817324.41895	0           
  100         	202.65071   	436.32629   	-5697805817324.41895	0           
---> try to load best state
              	202.65071   	436.32629   	-5697805817324.41895	0                eval state : test_on_restore;
              	202.65071   	436.32629   	-5697805817324.41895	0                eval state : test_on_last_epoch;
---> final result
              	202.65071   	436.32629   	-5697805817324.41895	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           	202.32597   	435.71085   	-923319990739.06238	0           
  10          	202.32597   	435.71085   	-918284944318.35425	0           
  100         	202.32597   	435.71085   	-914242010799.01257	0           
---> try to load best state
              	202.32597   	435.71085   	-914242010799.01257	0                eval state : test_on_restore;
              	202.32597   	435.71085   	-914242010799.01257	0                eval state : test_on_last_epoch;
---> final result
              	202.32597   	435.71085   	-914242010799.01257	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           	202.16878   	434.79843   	-4607035.84661	0           
  10          	202.06749   	434.51877   	-103064.49608	0           
  100         	130.99408   	324.14661   	-2.18464    	0           
---> try to load best state
              	130.99408   	324.14661   	-2.18464    	0                eval state : test_on_restore;
              	130.99408   	324.14661   	-2.18464    	0                eval state : test_on_last_epoch;
---> final result
              	130.99408   	324.14661   	-2.18464    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 64, '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           	202.22600   	434.34375   	-3928944.64837	0           
  10          	202.24397   	434.39523   	-54606675467044.90625	0           
  100         	202.24397   	434.39523   	-5356097282266.45898	0           
---> try to load best state
              	202.24397   	434.39523   	-5356097282266.45898	0                eval state : test_on_restore;
              	202.24397   	434.39523   	-5356097282266.45898	0                eval state : test_on_last_epoch;
---> final result
              	202.24397   	434.39523   	-5356097282266.45898	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': 64, '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           	52.61123    	133.19896   	0.87289     	0           
  10          	52.22468    	132.01955   	0.87583     	0           
  100         	49.00703    	122.34389   	0.89751     	0           
---> try to load best state
              	49.00703    	122.34389   	0.89751     	0                eval state : test_on_restore;
              	49.00703    	122.34389   	0.89751     	0                eval state : test_on_last_epoch;
---> final result
              	49.00703    	122.34389   	0.89751     	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   	-4331788795114.03418	0           
  10          	219.80775   	474.28265   	-4331788795114.03418	0           
  100         	219.80775   	474.28265   	-4331788795114.03418	0           
---> try to load best state
              	219.80775   	474.28265   	-4331788795114.03418	0                eval state : test_on_restore;
              	219.80775   	474.28265   	-4331788795114.03418	0                eval state : test_on_last_epoch;
---> final result
              	219.80775   	474.28265   	-4331788795114.03418	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   	-5162792768583.13477	0           
  10          	201.49957   	436.30704   	-5162792768583.13477	0           
  100         	201.49957   	436.30704   	-5162792768583.13477	0           
---> try to load best state
              	201.49957   	436.30704   	-5162792768583.13477	0                eval state : test_on_restore;
              	201.49957   	436.30704   	-5162792768583.13477	0                eval state : test_on_last_epoch;
---> final result
              	201.49957   	436.30704   	-5162792768583.13477	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           	202.77299   	442.44836   	-6157907480166.50391	0           
  10          	202.77299   	442.44836   	-6157907480166.50391	0           
  100         	202.77299   	442.44836   	-6157907480166.50391	0           
---> try to load best state
              	202.77299   	442.44836   	-6157907480166.50391	0                eval state : test_on_restore;
              	202.77299   	442.44836   	-6157907480166.50391	0                eval state : test_on_last_epoch;
---> final result
              	202.77299   	442.44836   	-6157907480166.50391	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           	202.43590   	440.94330   	-6415305235665.05859	0           
  10          	202.43590   	440.94330   	-6415305235665.05859	0           
  100         	202.43590   	440.94330   	-6415305235665.05859	0           
---> try to load best state
              	202.43590   	440.94330   	-6415305235665.05859	0                eval state : test_on_restore;
              	202.43590   	440.94330   	-6415305235665.05859	0                eval state : test_on_last_epoch;
---> final result
              	202.43590   	440.94330   	-6415305235665.05859	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 64, '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           	202.07529   	434.45642   	-6588050882552.91211	0           
  10          	201.98004   	434.19592   	-193925.80378	0           
  100         	111.74051   	291.41010   	-0.78820    	0           
---> try to load best state
              	111.74051   	291.41010   	-0.78820    	0                eval state : test_on_restore;
              	111.74051   	291.41010   	-0.78820    	0                eval state : test_on_last_epoch;
---> final result
              	111.74051   	291.41010   	-0.78820    	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': 64, '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           	58.51264    	150.86597   	0.81905     	0           
  10          	58.01769    	149.51288   	0.82324     	0           
  100         	53.82416    	138.03043   	0.85542     	0           
---> try to load best state
              	53.82416    	138.03043   	0.85542     	0                eval state : test_on_restore;
              	53.82416    	138.03043   	0.85542     	0                eval state : test_on_last_epoch;
---> final result
              	53.82416    	138.03043   	0.85542     	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   	-7259442636211.03516	0           
  10          	204.55777   	450.00845   	-7259442636211.03516	0           
  100         	204.55777   	450.00845   	-7259442636211.03516	0           
---> try to load best state
              	204.55777   	450.00845   	-7259442636211.03516	0                eval state : test_on_restore;
              	204.55777   	450.00845   	-7259442636211.03516	0                eval state : test_on_last_epoch;
---> final result
              	204.55777   	450.00845   	-7259442636211.03516	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           	203.13808   	434.51932   	-4874032533776.05957	0           
  10          	203.13808   	434.51932   	-4874032533776.05957	0           
  100         	203.13808   	434.51932   	-4874032533776.05957	0           
---> try to load best state
              	203.13808   	434.51932   	-4874032533776.05957	0                eval state : test_on_restore;
              	203.13808   	434.51932   	-4874032533776.05957	0                eval state : test_on_last_epoch;
---> final result
              	203.13808   	434.51932   	-4874032533776.05957	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           	202.66586   	434.33292   	-3150689880905.20996	0           
  10          	202.61206   	434.24521   	-440172.16838	0           
  100         	123.00803   	301.07669   	-1.39433    	0           
---> try to load best state
              	123.00803   	301.07669   	-1.39433    	0                eval state : test_on_restore;
              	123.00803   	301.07669   	-1.39433    	0                eval state : test_on_last_epoch;
---> final result
              	123.00803   	301.07669   	-1.39433    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 64, '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           	202.35674   	432.03076   	-1468.60909 	0           
  10          	202.13431   	431.44241   	-932.45510  	0           
  100         	101.71799   	254.75427   	-0.14549    	0           
---> try to load best state
              	101.71799   	254.75427   	-0.14549    	0                eval state : test_on_restore;
              	101.71799   	254.75427   	-0.14549    	0                eval state : test_on_last_epoch;
---> final result
              	101.71799   	254.75427   	-0.14549    	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': 64, '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           	63.15240    	176.22296   	0.74257     	0           
  10          	62.63614    	174.83255   	0.74817     	0           
  100         	58.24139    	162.78555   	0.79169     	0           
---> try to load best state
              	58.24139    	162.78555   	0.79169     	0                eval state : test_on_restore;
              	58.24139    	162.78555   	0.79169     	0                eval state : test_on_last_epoch;
---> final result
              	58.24139    	162.78555   	0.79169     	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   	-11090091568378.50977	0           
  10          	218.59293   	498.45560   	-11090091568378.50977	0           
  100         	218.59293   	498.45560   	-11090091568378.50977	0           
---> try to load best state
              	218.59293   	498.45560   	-11090091568378.50977	0                eval state : test_on_restore;
              	218.59293   	498.45560   	-11090091568378.50977	0                eval state : test_on_last_epoch;
---> final result
              	218.59293   	498.45560   	-11090091568378.50977	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           	206.45316   	455.86911   	-76292798271.68719	0           
  10          	206.45329   	455.86926   	-9871045514044.31445	0           
  100         	206.45329   	455.86926   	-7573919608105.91797	0           
---> try to load best state
              	206.45329   	455.86926   	-7573919608105.91797	0                eval state : test_on_restore;
              	206.45329   	455.86926   	-7573919608105.91797	0                eval state : test_on_last_epoch;
---> final result
              	206.45329   	455.86926   	-7573919608105.91797	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           	203.13628   	435.61691   	-483809089569.91638	0           
  10          	202.86714   	435.06223   	-48568.53284	0           
  100         	149.12318   	360.67578   	-3.65070    	0           
---> try to load best state
              	149.12318   	360.67578   	-3.65070    	0                eval state : test_on_restore;
              	149.12318   	360.67578   	-3.65070    	0                eval state : test_on_last_epoch;
---> final result
              	149.12318   	360.67578   	-3.65070    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 64, '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           	202.64850   	434.29776   	-1459163075090.52734	0           
  10          	202.59302   	434.22916   	-790369.04465	0           
  100         	118.90408   	301.88705   	-1.05771    	0           
---> try to load best state
              	118.90408   	301.88705   	-1.05771    	0                eval state : test_on_restore;
              	118.90408   	301.88705   	-1.05771    	0                eval state : test_on_last_epoch;
---> final result
              	118.90408   	301.88705   	-1.05771    	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': 64, '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           	59.84759    	148.10597   	0.83536     	0           
  10          	59.39468    	146.89270   	0.83882     	0           
  100         	55.25026    	136.34229   	0.86603     	0           
---> try to load best state
              	55.25026    	136.34229   	0.86603     	0                eval state : test_on_restore;
              	55.25026    	136.34229   	0.86603     	0                eval state : test_on_last_epoch;
---> final result
              	55.25026    	136.34229   	0.86603     	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   	-7840659209759.99707	0           
  10          	206.37552   	457.54044   	-7840659209759.99707	0           
  100         	206.37552   	457.54044   	-7840659209759.99707	0           
---> try to load best state
              	206.37552   	457.54044   	-7840659209759.99707	0                eval state : test_on_restore;
              	206.37552   	457.54044   	-7840659209759.99707	0                eval state : test_on_last_epoch;
---> final result
              	206.37552   	457.54044   	-7840659209759.99707	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           	202.16954   	440.27344   	-6060591317781.51562	0           
  10          	202.16954   	440.27344   	-6060591317781.51562	0           
  100         	202.16954   	440.27344   	-6060591317781.51562	0           
---> try to load best state
              	202.16954   	440.27344   	-6060591317781.51562	0                eval state : test_on_restore;
              	202.16954   	440.27344   	-6060591317781.51562	0                eval state : test_on_last_epoch;
---> final result
              	202.16954   	440.27344   	-6060591317781.51562	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           	201.97350   	439.36829   	-11858379877.57094	0           
  10          	201.94742   	439.34784   	-3962311.14586	0           
  100         	133.40865   	347.56323   	-3.08321    	0           
---> try to load best state
              	133.40865   	347.56323   	-3.08321    	0                eval state : test_on_restore;
              	133.40865   	347.56323   	-3.08321    	0                eval state : test_on_last_epoch;
---> final result
              	133.40865   	347.56323   	-3.08321    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 64, '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           	202.06198   	435.09393   	-530746969.33970	0           
  10          	201.84103   	434.88367   	-46800.91157	0           
  100         	107.65175   	267.47549   	-0.40198    	0           
---> try to load best state
              	107.65175   	267.47549   	-0.40198    	0                eval state : test_on_restore;
              	107.65175   	267.47549   	-0.40198    	0                eval state : test_on_last_epoch;
---> final result
              	107.65175   	267.47549   	-0.40198    	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': 64, '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           	53.09381    	136.35614   	0.86075     	0           
  10          	52.68904    	135.19226   	0.86376     	0           
  100         	49.35825    	125.51224   	0.88666     	0           
---> try to load best state
              	49.35825    	125.51224   	0.88666     	0                eval state : test_on_restore;
              	49.35825    	125.51224   	0.88666     	0                eval state : test_on_last_epoch;
---> final result
              	49.35825    	125.51224   	0.88666     	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   	-6631095332638.65527	0           
  10          	202.59552   	440.91083   	-6631095332638.65527	0           
  100         	202.59552   	440.91083   	-6631095332638.65527	0           
---> try to load best state
              	202.59552   	440.91083   	-6631095332638.65527	0                eval state : test_on_restore;
              	202.59552   	440.91083   	-6631095332638.65527	0                eval state : test_on_last_epoch;
---> final result
              	202.59552   	440.91083   	-6631095332638.65527	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           	202.79953   	436.69940   	-4394681.68314	0           
  10          	201.73634   	434.70374   	-1174.50898 	0           
  100         	134.55521   	324.19354   	-1.93748    	0           
---> try to load best state
              	134.55521   	324.19354   	-1.93748    	0                eval state : test_on_restore;
              	134.55521   	324.19354   	-1.93748    	0                eval state : test_on_last_epoch;
---> final result
              	134.55521   	324.19354   	-1.93748    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 64, '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           	202.74788   	434.78598   	-26402417.59526	0           
  10          	201.67050   	432.90338   	-2275.65795 	0           
  100         	99.04030    	249.48802   	-0.07697    	0           
---> try to load best state
              	99.04030    	249.48802   	-0.07697    	0                eval state : test_on_restore;
              	99.04030    	249.48802   	-0.07697    	0                eval state : test_on_last_epoch;
---> final result
              	99.04030    	249.48802   	-0.07697    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
