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

  Demo DC 
  seed: None 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00755     	0.01958     	0.96539     	0           
  10          	0.00745     	0.01942     	0.96599     	0           
  100         	0.00700     	0.01865     	0.96884     	0           
---> try to load best state
              	0.00700     	0.01865     	0.96884     	0                eval state : test_on_restore;
              	0.00700     	0.01865     	0.96884     	0                eval state : test_on_last_epoch;
---> final result
              	0.00700     	0.01865     	0.96884     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': None, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.12021     	0.16238     	-380.91557  	0           
  10          	0.12021     	0.16238     	-377.99305  	0           
  100         	0.12020     	0.16237     	-375.57450  	0           
---> try to load best state
              	0.12020     	0.16237     	-375.57450  	0                eval state : test_on_restore;
              	0.12020     	0.16237     	-375.57450  	0                eval state : test_on_last_epoch;
---> final result
              	0.12020     	0.16237     	-375.57450  	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': None, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.10381     	0.14209     	-3609527.35941	0           
  10          	0.10381     	0.14209     	-3603456.43067	0           
  100         	0.10381     	0.14209     	-3599912.80847	0           
---> try to load best state
              	0.10381     	0.14209     	-3599912.80847	0                eval state : test_on_restore;
              	0.10381     	0.14209     	-3599912.80847	0                eval state : test_on_last_epoch;
---> final result
              	0.10381     	0.14209     	-3599912.80847	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': None, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.09737     	0.13636     	-1677242.11728	0           
  10          	0.09737     	0.13636     	-1672445.65790	0           
  100         	0.09737     	0.13636     	-1666228.95019	0           
---> try to load best state
              	0.09737     	0.13636     	-1666228.95019	0                eval state : test_on_restore;
              	0.09737     	0.13636     	-1666228.95019	0                eval state : test_on_last_epoch;
---> final result
              	0.09737     	0.13636     	-1666228.95019	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.09475     	0.13468     	-34060.39929	0           
  10          	0.09475     	0.13468     	-33800.63777	0           
  100         	0.09475     	0.13468     	-33585.85679	0           
---> try to load best state
              	0.09475     	0.13468     	-33585.85679	0                eval state : test_on_restore;
              	0.09475     	0.13468     	-33585.85679	0                eval state : test_on_last_epoch;
---> final result
              	0.09475     	0.13468     	-33585.85679	0                eval state : final;module_name : DC;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 0 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01234     	0.03365     	0.83929     	0           
  10          	0.01172     	0.03210     	0.84937     	0           
  100         	0.00987     	0.02710     	0.88712     	0           
---> try to load best state
              	0.00987     	0.02710     	0.88712     	0                eval state : test_on_restore;
              	0.00987     	0.02710     	0.88712     	0                eval state : test_on_last_epoch;
---> final result
              	0.00987     	0.02710     	0.88712     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 0, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.09184     	0.13444     	-455196.74801	0           
  10          	0.09126     	0.13370     	-1706.40617 	0           
  100         	0.08450     	0.12725     	-41.06070   	0           
---> try to load best state
              	0.08450     	0.12725     	-41.06070   	0                eval state : test_on_restore;
              	0.08450     	0.12725     	-41.06070   	0                eval state : test_on_last_epoch;
---> final result
              	0.08450     	0.12725     	-41.06070   	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 0, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.09611     	0.13687     	-33954680740.11984	0           
  10          	0.09611     	0.13687     	-33859277414.24879	0           
  100         	0.09611     	0.13687     	-33780472904.87190	0           
---> try to load best state
              	0.09611     	0.13687     	-33780472904.87190	0                eval state : test_on_restore;
              	0.09611     	0.13687     	-33780472904.87190	0                eval state : test_on_last_epoch;
---> final result
              	0.09611     	0.13687     	-33780472904.87190	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 0, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.09631     	0.13688     	-2436659.36458	0           
  10          	0.09637     	0.13693     	-112230666.70448	0           
  100         	0.09637     	0.13693     	-296552444516.18787	0           
---> try to load best state
              	0.09637     	0.13693     	-296552444516.18787	0                eval state : test_on_restore;
              	0.09637     	0.13693     	-296552444516.18787	0                eval state : test_on_last_epoch;
---> final result
              	0.09637     	0.13693     	-296552444516.18787	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.09043     	0.13355     	-6890.54369 	0           
  10          	0.09027     	0.13345     	-2094.41306 	0           
  100         	0.05661     	0.10001     	-2.42268    	0           
---> try to load best state
              	0.05661     	0.10001     	-2.42268    	0                eval state : test_on_restore;
              	0.05661     	0.10001     	-2.42268    	0                eval state : test_on_last_epoch;
---> final result
              	0.05661     	0.10001     	-2.42268    	0                eval state : final;module_name : DC;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 1 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00862     	0.02447     	0.92092     	0           
  10          	0.00828     	0.02352     	0.92590     	0           
  100         	0.00756     	0.02132     	0.93733     	0           
---> try to load best state
              	0.00756     	0.02132     	0.93733     	0                eval state : test_on_restore;
              	0.00756     	0.02132     	0.93733     	0                eval state : test_on_last_epoch;
---> final result
              	0.00756     	0.02132     	0.93733     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 1, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.08806     	0.13757     	-41569766.76434	0           
  10          	0.08806     	0.13757     	-41387931.66592	0           
  100         	0.08806     	0.13757     	-41196159.12910	0           
---> try to load best state
              	0.08806     	0.13757     	-41196159.12910	0                eval state : test_on_restore;
              	0.08806     	0.13757     	-41196159.12910	0                eval state : test_on_last_epoch;
---> final result
              	0.08806     	0.13757     	-41196159.12910	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 1, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.08901     	0.13821     	-45868748.87471	0           
  10          	0.08901     	0.13821     	-45502831.72578	0           
  100         	0.08901     	0.13821     	-45147712.05601	0           
---> try to load best state
              	0.08901     	0.13821     	-45147712.05601	0                eval state : test_on_restore;
              	0.08901     	0.13821     	-45147712.05601	0                eval state : test_on_last_epoch;
---> final result
              	0.08901     	0.13821     	-45147712.05601	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 1, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.08737     	0.13839     	-370132.76109	0           
  10          	0.08737     	0.13839     	-367344.88869	0           
  100         	0.08737     	0.13839     	-365027.62729	0           
---> try to load best state
              	0.08737     	0.13839     	-365027.62729	0                eval state : test_on_restore;
              	0.08737     	0.13839     	-365027.62729	0                eval state : test_on_last_epoch;
---> final result
              	0.08737     	0.13839     	-365027.62729	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.09160     	0.13351     	-256445.45067	0           
  10          	0.09165     	0.13357     	-3717235321.07164	0           
  100         	0.09165     	0.13357     	-745060937865.33533	0           
---> try to load best state
              	0.09165     	0.13357     	-745060937865.33533	0                eval state : test_on_restore;
              	0.09165     	0.13357     	-745060937865.33533	0                eval state : test_on_last_epoch;
---> final result
              	0.09165     	0.13357     	-745060937865.33533	0                eval state : final;module_name : DC;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 2 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01260     	0.03655     	0.85948     	0           
  10          	0.01234     	0.03589     	0.86344     	0           
  100         	0.01084     	0.03203     	0.88371     	0           
---> try to load best state
              	0.01084     	0.03203     	0.88371     	0                eval state : test_on_restore;
              	0.01084     	0.03203     	0.88371     	0                eval state : test_on_last_epoch;
---> final result
              	0.01084     	0.03203     	0.88371     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 2, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.08901     	0.13549     	-2743988212705.85986	0           
  10          	0.08901     	0.13549     	-2732096509884.08496	0           
  100         	0.08901     	0.13549     	-2698260170537.38184	0           
---> try to load best state
              	0.08901     	0.13549     	-2698260170537.38184	0                eval state : test_on_restore;
              	0.08901     	0.13549     	-2698260170537.38184	0                eval state : test_on_last_epoch;
---> final result
              	0.08901     	0.13549     	-2698260170537.38184	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 2, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.08686     	0.13907     	-307428936.03807	0           
  10          	0.08686     	0.13907     	-306726109.34525	0           
  100         	0.08686     	0.13907     	-306068655.90236	0           
---> try to load best state
              	0.08686     	0.13907     	-306068655.90236	0                eval state : test_on_restore;
              	0.08686     	0.13907     	-306068655.90236	0                eval state : test_on_last_epoch;
---> final result
              	0.08686     	0.13907     	-306068655.90236	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 2, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.08676     	0.13759     	-2095915.22110	0           
  10          	0.08676     	0.13759     	-2078170.77837	0           
  100         	0.08679     	0.13761     	-5206001942589.66895	0           
---> try to load best state
              	0.08679     	0.13761     	-5206001942589.66895	0                eval state : test_on_restore;
              	0.08679     	0.13761     	-5206001942589.66895	0                eval state : test_on_last_epoch;
---> final result
              	0.08679     	0.13761     	-5206001942589.66895	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.09093     	0.13335     	-823.24704  	0           
  10          	0.09143     	0.13361     	-263390.81269	0           
  100         	0.09145     	0.13362     	-239391940991.41711	0           
---> try to load best state
              	0.09145     	0.13362     	-239391940991.41711	0                eval state : test_on_restore;
              	0.09145     	0.13362     	-239391940991.41711	0                eval state : test_on_last_epoch;
---> final result
              	0.09145     	0.13362     	-239391940991.41711	0                eval state : final;module_name : DC;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 3 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00769     	0.02025     	0.95198     	0           
  10          	0.00811     	0.02140     	0.94845     	0           
  100         	0.00708     	0.01879     	0.95896     	0           
---> try to load best state
              	0.00708     	0.01879     	0.95896     	0                eval state : test_on_restore;
              	0.00708     	0.01879     	0.95896     	0                eval state : test_on_last_epoch;
---> final result
              	0.00708     	0.01879     	0.95896     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 3, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.08926     	0.13337     	-989.92042  	0           
  10          	0.08925     	0.13337     	-982.36565  	0           
  100         	0.08925     	0.13337     	-976.12812  	0           
---> try to load best state
              	0.08925     	0.13337     	-976.12812  	0                eval state : test_on_restore;
              	0.08925     	0.13337     	-976.12812  	0                eval state : test_on_last_epoch;
---> final result
              	0.08925     	0.13337     	-976.12812  	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 3, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.08957     	0.13425     	-38421952.74984	0           
  10          	0.08957     	0.13425     	-38313157.06658	0           
  100         	0.08957     	0.13425     	-38224983.43614	0           
---> try to load best state
              	0.08957     	0.13425     	-38224983.43614	0                eval state : test_on_restore;
              	0.08957     	0.13425     	-38224983.43614	0                eval state : test_on_last_epoch;
---> final result
              	0.08957     	0.13425     	-38224983.43614	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 3, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.09015     	0.13395     	-6736.51523 	0           
  10          	0.09015     	0.13395     	-6685.14914 	0           
  100         	0.09015     	0.13395     	-6642.64633 	0           
---> try to load best state
              	0.09015     	0.13395     	-6642.64633 	0                eval state : test_on_restore;
              	0.09015     	0.13395     	-6642.64633 	0                eval state : test_on_last_epoch;
---> final result
              	0.09015     	0.13395     	-6642.64633 	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.09789     	0.13771     	-2185.01409 	0           
  10          	0.09789     	0.13771     	-2168.32745 	0           
  100         	0.09789     	0.13771     	-2154.52172 	0           
---> try to load best state
              	0.09789     	0.13771     	-2154.52172 	0                eval state : test_on_restore;
              	0.09789     	0.13771     	-2154.52172 	0                eval state : test_on_last_epoch;
---> final result
              	0.09789     	0.13771     	-2154.52172 	0                eval state : final;module_name : DC;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 4 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  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
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01177     	0.02520     	0.93761     	0           
  10          	0.01205     	0.02557     	0.93577     	0           
  100         	0.01147     	0.02443     	0.94132     	0           
---> try to load best state
              	0.01147     	0.02443     	0.94132     	0                eval state : test_on_restore;
              	0.01147     	0.02443     	0.94132     	0                eval state : test_on_last_epoch;
---> final result
              	0.01147     	0.02443     	0.94132     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.10800     	0.14442     	-2749605930.78873	0           
  10          	0.10800     	0.14442     	-2721544659.25924	0           
  100         	0.10800     	0.14442     	-2712576922.34208	0           
---> try to load best state
              	0.10800     	0.14442     	-2712576922.34208	0                eval state : test_on_restore;
              	0.10800     	0.14442     	-2712576922.34208	0                eval state : test_on_last_epoch;
---> final result
              	0.10800     	0.14442     	-2712576922.34208	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.11834     	0.16081     	-20756462441.56234	0           
  10          	0.11834     	0.16081     	-20650862089.11921	0           
  100         	0.11834     	0.16081     	-20591067149.57317	0           
---> try to load best state
              	0.11834     	0.16081     	-20591067149.57317	0                eval state : test_on_restore;
              	0.11834     	0.16081     	-20591067149.57317	0                eval state : test_on_last_epoch;
---> final result
              	0.11834     	0.16081     	-20591067149.57317	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 4, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.11373     	0.15464     	-1364207812.80776	0           
  10          	0.11373     	0.15464     	-1354795134.22801	0           
  100         	0.11373     	0.15464     	-1346843339.19251	0           
---> try to load best state
              	0.11373     	0.15464     	-1346843339.19251	0                eval state : test_on_restore;
              	0.11373     	0.15464     	-1346843339.19251	0                eval state : test_on_last_epoch;
---> final result
              	0.11373     	0.15464     	-1346843339.19251	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[1]'], 'outputs_format': ['y[2]'], '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           	0.10796     	0.14809     	-671.94724  	0           
  10          	0.10632     	0.14656     	-117.14202  	0           
  100         	0.04777     	0.07807     	-0.21852    	0           
---> try to load best state
              	0.04777     	0.07807     	-0.21852    	0                eval state : test_on_restore;
              	0.04777     	0.07807     	-0.21852    	0                eval state : test_on_last_epoch;
---> final result
              	0.04777     	0.07807     	-0.21852    	0                eval state : final;module_name : DC;cp_record_file : True;
