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

  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': 128, '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           	50.23768    	129.70575   	0.87994     	0           
  10          	49.83587    	128.59578   	0.88239     	0           
  100         	46.42620    	119.34004   	0.90125     	1           
---> try to load best state
              	46.42620    	119.34004   	0.90125     	1                eval state : test_on_restore;
              	46.42620    	119.34004   	0.90125     	1                eval state : test_on_last_epoch;
---> final result
              	46.42620    	119.34004   	0.90125     	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   	-5697831723781.62695	0           
  10          	202.65071   	436.32629   	-5697831723781.62695	0           
  100         	202.65071   	436.32629   	-5697831723781.62695	0           
---> try to load best state
              	202.65071   	436.32629   	-5697831723781.62695	0                eval state : test_on_restore;
              	202.65071   	436.32629   	-5697831723781.62695	0                eval state : test_on_last_epoch;
---> final result
              	202.65071   	436.32629   	-5697831723781.62695	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           	202.32600   	435.71091   	-6184671510148.80273	0           
  10          	202.32600   	435.71091   	-6184671510148.80273	0           
  100         	202.32600   	435.71091   	-6184671510148.80273	0           
---> try to load best state
              	202.32600   	435.71091   	-6184671510148.80273	0                eval state : test_on_restore;
              	202.32600   	435.71091   	-6184671510148.80273	0                eval state : test_on_last_epoch;
---> final result
              	202.32600   	435.71091   	-6184671510148.80273	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           	202.18303   	434.84033   	-253580311.99810	0           
  10          	202.15884   	434.78577   	-1714296.80979	0           
  100         	127.11838   	316.36905   	-1.74285    	0           
---> try to load best state
              	127.11838   	316.36905   	-1.74285    	0                eval state : test_on_restore;
              	127.11838   	316.36905   	-1.74285    	0                eval state : test_on_last_epoch;
---> final result
              	127.11838   	316.36905   	-1.74285    	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           	202.24161   	434.39017   	-266291535.34853	0           
  10          	202.24397   	434.39523   	-7983896484867.29590	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


---------->
NAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 128, '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           	202.40303   	434.20493   	-422221404.03157	0           
  10          	201.76825   	432.93674   	-5819.40080 	0           
  100         	105.64317   	272.21518   	-0.42300    	0           
---> try to load best state
              	105.64317   	272.21518   	-0.42300    	0                eval state : test_on_restore;
              	105.64317   	272.21518   	-0.42300    	0                eval state : test_on_last_epoch;
---> final result
              	105.64317   	272.21518   	-0.42300    	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': 128, '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           	50.23768    	129.70575   	0.87994     	0           
  10          	49.83587    	128.59576   	0.88239     	0           
  100         	46.42620    	119.34003   	0.90125     	0           
---> try to load best state
              	46.42620    	119.34003   	0.90125     	0                eval state : test_on_restore;
              	46.42620    	119.34003   	0.90125     	0                eval state : test_on_last_epoch;
---> final result
              	46.42620    	119.34003   	0.90125     	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': 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   	-5162798429427.67090	0           
  10          	201.49957   	436.30704   	-5162798429427.67090	0           
  100         	201.49957   	436.30704   	-5162798429427.67090	0           
---> try to load best state
              	201.49957   	436.30704   	-5162798429427.67090	0                eval state : test_on_restore;
              	201.49957   	436.30704   	-5162798429427.67090	0                eval state : test_on_last_epoch;
---> final result
              	201.49957   	436.30704   	-5162798429427.67090	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           	202.43581   	440.94312   	-104849645132.85025	0           
  10          	202.43581   	440.94312   	-104086125675.80154	0           
  100         	202.43581   	440.94312   	-103462795578.39337	0           
---> try to load best state
              	202.43581   	440.94312   	-103462795578.39337	0                eval state : test_on_restore;
              	202.43581   	440.94312   	-103462795578.39337	0                eval state : test_on_last_epoch;
---> final result
              	202.43581   	440.94312   	-103462795578.39337	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           	202.07304   	434.45157   	-286883671.43271	0           
  10          	201.41702   	433.16269   	-5325.83135 	0           
  100         	110.51275   	287.08441   	-0.69607    	0           
---> try to load best state
              	110.51275   	287.08441   	-0.69607    	0                eval state : test_on_restore;
              	110.51275   	287.08441   	-0.69607    	0                eval state : test_on_last_epoch;
---> final result
              	110.51275   	287.08441   	-0.69607    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 128, '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           	202.40303   	434.20493   	-420586738.54342	0           
  10          	201.76852   	432.93732   	-5827.26020 	0           
  100         	105.63939   	272.20639   	-0.42283    	0           
---> try to load best state
              	105.63939   	272.20639   	-0.42283    	0                eval state : test_on_restore;
              	105.63939   	272.20639   	-0.42283    	0                eval state : test_on_last_epoch;
---> final result
              	105.63939   	272.20639   	-0.42283    	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': 128, '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           	50.23769    	129.70576   	0.87994     	0           
  10          	49.83586    	128.59578   	0.88239     	0           
  100         	46.42620    	119.34004   	0.90125     	0           
---> try to load best state
              	46.42620    	119.34004   	0.90125     	0                eval state : test_on_restore;
              	46.42620    	119.34004   	0.90125     	1                eval state : test_on_last_epoch;
---> final result
              	46.42620    	119.34004   	0.90125     	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': 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           	202.66589   	434.33292   	-23870368686711.69141	0           
  10          	202.63179   	434.24954   	-2107267.89725	0           
  100         	120.94175   	302.06546   	-1.13437    	0           
---> try to load best state
              	120.94175   	302.06546   	-1.13437    	0                eval state : test_on_restore;
              	120.94175   	302.06546   	-1.13437    	0                eval state : test_on_last_epoch;
---> final result
              	120.94175   	302.06546   	-1.13437    	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           	203.23575   	434.53049   	-1586700497682.60767	0           
  10          	203.23569   	434.53036   	-201049327469.76181	0           
  100         	106.08807   	272.35275   	-0.40024    	0           
---> try to load best state
              	106.08807   	272.35275   	-0.40024    	0                eval state : test_on_restore;
              	106.08807   	272.35275   	-0.40024    	0                eval state : test_on_last_epoch;
---> final result
              	106.08807   	272.35275   	-0.40024    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 128, '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           	202.40303   	434.20493   	-421387299.76287	0           
  10          	201.78770   	432.97485   	-6147.89000 	0           
  100         	105.62508   	272.17120   	-0.42206    	0           
---> try to load best state
              	105.62508   	272.17120   	-0.42206    	0                eval state : test_on_restore;
              	105.62508   	272.17120   	-0.42206    	0                eval state : test_on_last_epoch;
---> final result
              	105.62508   	272.17120   	-0.42206    	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': 128, '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           	50.23770    	129.70576   	0.87994     	0           
  10          	49.83587    	128.59576   	0.88239     	0           
  100         	46.42619    	119.34001   	0.90125     	1           
---> try to load best state
              	46.42619    	119.34001   	0.90125     	1                eval state : test_on_restore;
              	46.42619    	119.34001   	0.90125     	1                eval state : test_on_last_epoch;
---> final result
              	46.42619    	119.34001   	0.90125     	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': 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           	206.45134   	455.86588   	-327706414.26540	0           
  10          	206.45329   	455.86926   	-134331222326249.03125	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           	203.13409   	435.61197   	-229419154.97624	0           
  10          	202.56918   	434.35245   	-4777.00395 	0           
  100         	145.19598   	343.89157   	-3.02617    	0           
---> try to load best state
              	145.19598   	343.89157   	-3.02617    	0                eval state : test_on_restore;
              	145.19598   	343.89157   	-3.02617    	0                eval state : test_on_last_epoch;
---> final result
              	145.19598   	343.89157   	-3.02617    	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           	202.64847   	434.29776   	-1242624109637.48877	0           
  10          	202.56180   	434.11133   	-230349.95589	0           
  100         	113.75462   	286.39279   	-0.83988    	0           
---> try to load best state
              	113.75462   	286.39279   	-0.83988    	0                eval state : test_on_restore;
              	113.75462   	286.39279   	-0.83988    	0                eval state : test_on_last_epoch;
---> final result
              	113.75462   	286.39279   	-0.83988    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 128, '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           	202.40303   	434.20493   	-421390899.74927	0           
  10          	201.75217   	432.90533   	-5573.05468 	0           
  100         	105.65001   	272.23251   	-0.42339    	0           
---> try to load best state
              	105.65001   	272.23251   	-0.42339    	0                eval state : test_on_restore;
              	105.65001   	272.23251   	-0.42339    	0                eval state : test_on_last_epoch;
---> final result
              	105.65001   	272.23251   	-0.42339    	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': 128, '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           	50.23769    	129.70575   	0.87994     	0           
  10          	49.83586    	128.59576   	0.88239     	0           
  100         	46.42621    	119.34005   	0.90125     	0           
---> try to load best state
              	46.42621    	119.34005   	0.90125     	0                eval state : test_on_restore;
              	46.42621    	119.34005   	0.90125     	0                eval state : test_on_last_epoch;
---> final result
              	46.42621    	119.34005   	0.90125     	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           	202.16951   	440.27338   	-1601902849719.76050	0           
  10          	202.16951   	440.27338   	-1594311250648.43579	0           
  100         	202.16951   	440.27338   	-1587229474343.07080	0           
---> try to load best state
              	202.16951   	440.27338   	-1587229474343.07080	0                eval state : test_on_restore;
              	202.16951   	440.27338   	-1587229474343.07080	0                eval state : test_on_last_epoch;
---> final result
              	202.16951   	440.27338   	-1587229474343.07080	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           	201.97348   	439.36765   	-6221759844.05607	0           
  10          	201.92944   	439.28223   	-827294.14658	0           
  100         	133.58897   	351.11014   	-2.96554    	0           
---> try to load best state
              	133.58897   	351.11014   	-2.96554    	0                eval state : test_on_restore;
              	133.58897   	351.11014   	-2.96554    	0                eval state : test_on_last_epoch;
---> final result
              	133.58897   	351.11014   	-2.96554    	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           	202.06203   	435.09180   	-477372623.35555	0           
  10          	201.67323   	434.35425   	-12716.61933	0           
  100         	108.35115   	279.02844   	-0.48432    	0           
---> try to load best state
              	108.35115   	279.02844   	-0.48432    	0                eval state : test_on_restore;
              	108.35115   	279.02844   	-0.48432    	0                eval state : test_on_last_epoch;
---> final result
              	108.35115   	279.02844   	-0.48432    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 128, '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           	202.40303   	434.20493   	-419781990.13248	0           
  10          	201.79906   	432.99689   	-6347.15647 	0           
  100         	105.61741   	272.15195   	-0.42164    	0           
---> try to load best state
              	105.61741   	272.15195   	-0.42164    	0                eval state : test_on_restore;
              	105.61741   	272.15195   	-0.42164    	0                eval state : test_on_last_epoch;
---> final result
              	105.61741   	272.15195   	-0.42164    	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': 128, '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           	50.23769    	129.70576   	0.87994     	0           
  10          	49.83587    	128.59578   	0.88239     	0           
  100         	46.42621    	119.34007   	0.90125     	0           
---> try to load best state
              	46.42621    	119.34007   	0.90125     	1                eval state : test_on_restore;
              	46.42621    	119.34007   	0.90125     	1                eval state : test_on_last_epoch;
---> final result
              	46.42621    	119.34007   	0.90125     	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': 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           	202.80603   	436.71271   	-11164734.93911	0           
  10          	201.89149   	434.87668   	-1634.50238 	0           
  100         	131.90445   	321.68954   	-1.66316    	0           
---> try to load best state
              	131.90445   	321.68954   	-1.66316    	0                eval state : test_on_restore;
              	131.90445   	321.68954   	-1.66316    	0                eval state : test_on_last_epoch;
---> final result
              	131.90445   	321.68954   	-1.66316    	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           	202.75508   	434.79581   	-160512381.63812	0           
  10          	202.02992   	433.30103   	-3925.82432 	0           
  100         	97.21574    	248.17159   	-0.01944    	0           
---> try to load best state
              	97.21574    	248.17159   	-0.01944    	0                eval state : test_on_restore;
              	97.21574    	248.17159   	-0.01944    	0                eval state : test_on_last_epoch;
---> final result
              	97.21574    	248.17159   	-0.01944    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


---------->
NAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 128, '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           	202.40302   	434.20493   	-418171843.35846	0           
  10          	201.81374   	433.02576   	-6629.43261 	0           
  100         	105.60777   	272.12891   	-0.42113    	0           
---> try to load best state
              	105.60777   	272.12891   	-0.42113    	0                eval state : test_on_restore;
              	105.60777   	272.12891   	-0.42113    	0                eval state : test_on_last_epoch;
---> final result
              	105.60777   	272.12891   	-0.42113    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
