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

  Demo DC 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01111     	0.01939     	0.75466     	0           
  10          	0.01105     	0.01926     	0.76042     	0           
  100         	0.01052     	0.01815     	0.80345     	0           
---> try to load best state
              	0.01052     	0.01815     	0.80345     	0                eval state : test_on_restore;
              	0.01052     	0.01815     	0.80345     	0                eval state : test_on_last_epoch;
---> final result
              	0.01052     	0.01815     	0.80345     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	202.85687   	443.80963   	-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           	202.64182   	436.31494   	-15270744.92479	0           
  10          	202.64177   	436.31494   	-15154361.45003	0           
  100         	202.64175   	436.31488   	-15058035.26314	0           
---> try to load best state
              	202.64175   	436.31488   	-15058035.26314	0                eval state : test_on_restore;
              	202.64175   	436.31488   	-15058035.26314	0                eval state : test_on_last_epoch;
---> final result
              	202.64175   	436.31488   	-15058035.26314	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           	202.28215   	435.67212   	-2517568.63535	0           
  10          	202.28198   	435.67200   	-2498371.06513	0           
  100         	202.28185   	435.67184   	-2482471.43046	0           
---> try to load best state
              	202.28185   	435.67184   	-2482471.43046	0                eval state : test_on_restore;
              	202.28185   	435.67184   	-2482471.43046	0                eval state : test_on_last_epoch;
---> final result
              	202.28185   	435.67184   	-2482471.43046	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           	202.09187   	434.76050   	-555691.78615	0           
  10          	199.16168   	430.96185   	-644.94727  	0           
  100         	97.46906    	251.32300   	0.05726     	0           
---> try to load best state
              	97.46906    	251.32300   	0.05726     	0                eval state : test_on_restore;
              	97.46906    	251.32300   	0.05726     	0                eval state : test_on_last_epoch;
---> final result
              	97.46906    	251.32300   	0.05726     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01580     	0.02743     	0.02256     	0           
  10          	0.01566     	0.02720     	0.05470     	0           
  100         	0.01436     	0.02516     	0.30027     	0           
---> try to load best state
              	0.01436     	0.02516     	0.30027     	0                eval state : test_on_restore;
              	0.01436     	0.02516     	0.30027     	0                eval state : test_on_last_epoch;
---> final result
              	0.01436     	0.02516     	0.30027     	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   	-5162545025437.53418	0           
  10          	201.49957   	436.30704   	-5162544763230.88672	0           
  100         	201.49957   	436.30704   	-5162544763230.88672	0           
---> try to load best state
              	201.49957   	436.30704   	-5162544763230.88672	0                eval state : test_on_restore;
              	201.49957   	436.30704   	-5162544763230.88672	0                eval state : test_on_last_epoch;
---> final result
              	201.49957   	436.30704   	-5162544763230.88672	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           	202.64676   	442.12314   	-75447.01109	0           
  10          	200.99776   	438.50735   	-432.70489  	0           
  100         	152.76732   	363.21936   	-4.31313    	0           
---> try to load best state
              	152.76732   	363.21936   	-4.31313    	0                eval state : test_on_restore;
              	152.76732   	363.21936   	-4.31313    	0                eval state : test_on_last_epoch;
---> final result
              	152.76732   	363.21936   	-4.31313    	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           	202.36612   	440.79559   	-351248.65922	0           
  10          	202.43575   	440.94299   	-57605277955.96225	0           
  100         	202.43590   	440.94330   	-6415370403231.23340	0           
---> try to load best state
              	202.43590   	440.94330   	-6415370403231.23340	0                eval state : test_on_restore;
              	202.43590   	440.94330   	-6415370403231.23340	0                eval state : test_on_last_epoch;
---> final result
              	202.43590   	440.94330   	-6415370403231.23340	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01429     	0.02353     	0.45094     	0           
  10          	0.01417     	0.02335     	0.46457     	0           
  100         	0.01307     	0.02174     	0.57239     	0           
---> try to load best state
              	0.01307     	0.02174     	0.57239     	0                eval state : test_on_restore;
              	0.01307     	0.02174     	0.57239     	0                eval state : test_on_last_epoch;
---> final result
              	0.01307     	0.02174     	0.57239     	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   	-52730838070141.46875	0           
  10          	204.55777   	450.00845   	-52730839681649.44531	0           
  100         	204.55777   	450.00845   	-52730641536614.98438	0           
---> try to load best state
              	204.55777   	450.00845   	-52730641536614.98438	0                eval state : test_on_restore;
              	204.55777   	450.00845   	-52730641536614.98438	0                eval state : test_on_last_epoch;
---> final result
              	204.55777   	450.00845   	-52730641536614.98438	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           	203.13799   	434.51920   	-232202860839.57840	0           
  10          	203.13799   	434.51920   	-230736240155.23755	0           
  100         	203.13799   	434.51920   	-229561402714.81351	0           
---> try to load best state
              	203.13799   	434.51920   	-229561402714.81351	0                eval state : test_on_restore;
              	203.13799   	434.51920   	-229561402714.81351	0                eval state : test_on_last_epoch;
---> final result
              	203.13799   	434.51920   	-229561402714.81351	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           	202.63020   	434.24976   	-1152125.96529	0           
  10          	201.14655   	431.30539   	-909.21641  	0           
  100         	108.68735   	254.99158   	-0.23454    	0           
---> try to load best state
              	108.68735   	254.99158   	-0.23454    	0                eval state : test_on_restore;
              	108.68735   	254.99158   	-0.23454    	0                eval state : test_on_last_epoch;
---> final result
              	108.68735   	254.99158   	-0.23454    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01405     	0.02567     	0.38526     	0           
  10          	0.01394     	0.02549     	0.40172     	0           
  100         	0.01296     	0.02381     	0.52835     	0           
---> try to load best state
              	0.01296     	0.02381     	0.52835     	0                eval state : test_on_restore;
              	0.01296     	0.02381     	0.52835     	0                eval state : test_on_last_epoch;
---> final result
              	0.01296     	0.02381     	0.52835     	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           	218.42979   	498.26785   	-53382.20979	0           
  10          	218.58720   	498.44888   	-43516956.47043	0           
  100         	218.59293   	498.45560   	-13181343641070.28516	0           
---> try to load best state
              	218.59293   	498.45560   	-13181343641070.28516	0                eval state : test_on_restore;
              	218.59293   	498.45560   	-13181343641070.28516	0                eval state : test_on_last_epoch;
---> final result
              	218.59293   	498.45560   	-13181343641070.28516	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           	206.28149   	455.58029   	-83201.02495	0           
  10          	206.45200   	455.86768   	-1117443038.67393	0           
  100         	206.45329   	455.86926   	-98865341418733.18750	0           
---> try to load best state
              	206.45329   	455.86926   	-98865341418733.18750	0                eval state : test_on_restore;
              	206.45329   	455.86926   	-98865341418733.18750	0                eval state : test_on_last_epoch;
---> final result
              	206.45329   	455.86926   	-98865341418733.18750	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	202.54735   	434.11108   	-6006.80995 	0           
  10          	195.54532   	420.61145   	-76.09086   	0           
  100         	124.56037   	310.12769   	-1.21599    	0           
---> try to load best state
              	124.56037   	310.12769   	-1.21599    	0                eval state : test_on_restore;
              	124.56037   	310.12769   	-1.21599    	0                eval state : test_on_last_epoch;
---> final result
              	124.56037   	310.12769   	-1.21599    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01535     	0.02637     	0.11530     	0           
  10          	0.01520     	0.02611     	0.14561     	0           
  100         	0.01388     	0.02376     	0.37703     	0           
---> try to load best state
              	0.01388     	0.02376     	0.37703     	0                eval state : test_on_restore;
              	0.01388     	0.02376     	0.37703     	0                eval state : test_on_last_epoch;
---> final result
              	0.01388     	0.02376     	0.37703     	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           	206.30400   	457.38235   	-331282.47533	0           
  10          	206.30371   	457.38174   	-328756.02022	0           
  100         	206.30350   	457.38123   	-326665.46622	0           
---> try to load best state
              	206.30350   	457.38123   	-326665.46622	0                eval state : test_on_restore;
              	206.30350   	457.38123   	-326665.46622	0                eval state : test_on_last_epoch;
---> final result
              	206.30350   	457.38123   	-326665.46622	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           	201.62830   	439.45654   	-11115.19605	0           
  10          	201.62624   	439.45361   	-11030.39504	0           
  100         	202.16803   	440.27109   	-1250801688.72166	0           
---> try to load best state
              	202.16803   	440.27109   	-1250801688.72166	0                eval state : test_on_restore;
              	202.16803   	440.27109   	-1250801688.72166	0                eval state : test_on_last_epoch;
---> final result
              	202.16803   	440.27109   	-1250801688.72166	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           	201.43448   	438.47516   	-10044.48015	0           
  10          	198.61247   	434.10577   	-309.53414  	0           
  100         	125.93713   	321.38647   	-1.79446    	0           
---> try to load best state
              	125.93713   	321.38647   	-1.79446    	0                eval state : test_on_restore;
              	125.93713   	321.38647   	-1.79446    	0                eval state : test_on_last_epoch;
---> final result
              	125.93713   	321.38647   	-1.79446    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01084     	0.01853     	0.80442     	0           
  10          	0.01081     	0.01842     	0.80847     	0           
  100         	0.01046     	0.01746     	0.83881     	0           
---> try to load best state
              	0.01046     	0.01746     	0.83881     	0                eval state : test_on_restore;
              	0.01046     	0.01746     	0.83881     	0                eval state : test_on_last_epoch;
---> final result
              	0.01046     	0.01746     	0.83881     	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           	218.33345   	496.03683   	-5402429614427.17090	0           
  10          	218.33345   	496.03683   	-5389570595458.32910	0           
  100         	218.33345   	496.03683   	-5381201963814.76367	0           
---> try to load best state
              	218.33345   	496.03683   	-5381201963814.76367	0                eval state : test_on_restore;
              	218.33345   	496.03683   	-5381201963814.76367	0                eval state : test_on_last_epoch;
---> final result
              	218.33345   	496.03683   	-5381201963814.76367	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           	203.50314   	446.23569   	-48181388692.90234	0           
  10          	203.50314   	446.23569   	-47877318062.17394	0           
  100         	203.50313   	446.23569   	-47775227210.39258	0           
---> try to load best state
              	203.50313   	446.23569   	-47775227210.39258	0                eval state : test_on_restore;
              	203.50313   	446.23569   	-47775227210.39258	0                eval state : test_on_last_epoch;
---> final result
              	203.50313   	446.23569   	-47775227210.39258	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           	202.58575   	440.89386   	-17081092.72083	0           
  10          	201.89351   	439.85358   	-4028.27916 	0           
  100         	189.15138   	422.93643   	-53.59913   	0           
---> try to load best state
              	189.15138   	422.93643   	-53.59913   	0                eval state : test_on_restore;
              	189.15138   	422.93643   	-53.59913   	0                eval state : test_on_last_epoch;
---> final result
              	189.15138   	422.93643   	-53.59913   	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           	201.75000   	434.01270   	-1198.16165 	0           
  10          	194.24522   	421.01135   	-35.88411   	0           
  100         	95.63397    	246.09518   	0.24296     	0           
---> try to load best state
              	95.63397    	246.09518   	0.24296     	0                eval state : test_on_restore;
              	95.63397    	246.09518   	0.24296     	0                eval state : test_on_last_epoch;
---> final result
              	95.63397    	246.09518   	0.24296     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
