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

  Demo NAR cigp 
  seed: None 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'poisson_v4_02', '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[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, '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           	90.77277    	124.86848   	0.12425     	0           
  10          	79.89813    	111.54422   	0.44069     	0           
  100         	35.83702    	50.76695    	0.93966     	0           
  300         	16.54195    	24.12494    	0.98774     	1           
  500         	11.20079    	16.67691    	0.99423     	2           
  1000        	6.33634     	9.64185     	0.99809     	3           
---> try to load best state
              	6.33634     	9.64185     	0.99809     	3                eval state : test_on_restore;
              	6.33634     	9.64185     	0.99809     	3                eval state : test_on_last_epoch;
---> final result
              	6.33634     	9.64185     	0.99809     	3                eval state : final;
----------> finish x-yl training


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

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

  Demo DC 
  seed: None 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'poisson_v4_02', '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[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, '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           	90.35139    	124.34155   	0.12570     	0           
  10          	79.54449    	111.04919   	0.44204     	0           
  100         	35.73194    	50.55027    	0.93982     	0           
---> try to load best state
              	35.73194    	50.55027    	0.93982     	0                eval state : test_on_restore;
              	35.73194    	50.55027    	0.93982     	0                eval state : test_on_last_epoch;
---> final result
              	35.73194    	50.55027    	0.93982     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, '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[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           	220.98445   	267.20792   	-1456.37753 	0           
  10          	220.98254   	267.20654   	-1445.25240 	0           
  100         	220.98093   	267.20538   	-1436.04588 	0           
---> try to load best state
              	220.98093   	267.20538   	-1436.04588 	0                eval state : test_on_restore;
              	220.98093   	267.20538   	-1436.04588 	0                eval state : test_on_last_epoch;
---> final result
              	220.98093   	267.20538   	-1436.04588 	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': 'poisson_v4_02', 'interp_data': True, '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[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           	200.78778   	240.07767   	-1667964205037.04834	0           
  10          	200.78778   	240.07767   	-1667964205037.04834	0           
  100         	200.78778   	240.07767   	-1667964205037.04834	0           
---> try to load best state
              	200.78778   	240.07767   	-1667964205037.04834	0                eval state : test_on_restore;
              	200.78778   	240.07767   	-1667964205037.04834	0                eval state : test_on_last_epoch;
---> final result
              	200.78778   	240.07767   	-1667964205037.04834	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': 'poisson_v4_02', 'interp_data': True, '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[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           	195.07950   	232.41582   	-1512596198442.20654	0           
  10          	195.07950   	232.41582   	-1512596198442.20654	0           
  100         	195.07950   	232.41582   	-1512596198442.20654	0           
---> try to load best state
              	195.07950   	232.41582   	-1512596198442.20654	0                eval state : test_on_restore;
              	195.07950   	232.41582   	-1512596198442.20654	0                eval state : test_on_last_epoch;
---> final result
              	195.07950   	232.41582   	-1512596198442.20654	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': 'poisson_v4_02', 'interp_data': True, '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[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           	193.40454   	230.33287   	-1370719967860.01929	0           
  10          	193.40454   	230.33287   	-1370719967860.01929	0           
  100         	193.40454   	230.33287   	-1370719967860.01929	0           
---> try to load best state
              	193.40454   	230.33287   	-1370719967860.01929	0                eval state : test_on_restore;
              	193.40454   	230.33287   	-1370719967860.01929	0                eval state : test_on_last_epoch;
---> final result
              	193.40454   	230.33287   	-1370719967860.01929	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': 'poisson_v4_02', '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[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, '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           	103.58142   	145.11736   	-0.27650    	0           
  10          	94.41745    	134.25366   	0.06753     	0           
  100         	44.70404    	66.86895    	0.87167     	0           
---> try to load best state
              	44.70404    	66.86895    	0.87167     	0                eval state : test_on_restore;
              	44.70404    	66.86895    	0.87167     	0                eval state : test_on_last_epoch;
---> final result
              	44.70404    	66.86895    	0.87167     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, '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[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           	197.49167   	238.90179   	-78487964063200.60938	0           
  10          	197.49167   	238.90179   	-78184475654357.64062	0           
  100         	197.49167   	238.90179   	-78116547816730.09375	0           
---> try to load best state
              	197.49167   	238.90179   	-78116547816730.09375	0                eval state : test_on_restore;
              	197.49167   	238.90179   	-78116547816730.09375	0                eval state : test_on_last_epoch;
---> final result
              	197.49167   	238.90179   	-78116547816730.09375	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': 'poisson_v4_02', 'interp_data': True, '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[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           	196.58022   	235.94199   	-1509983536666.24365	0           
  10          	196.58022   	235.94199   	-1509983536666.24365	0           
  100         	196.58022   	235.94199   	-1509983536666.24365	0           
---> try to load best state
              	196.58022   	235.94199   	-1509983536666.24365	0                eval state : test_on_restore;
              	196.58022   	235.94199   	-1509983536666.24365	0                eval state : test_on_last_epoch;
---> final result
              	196.58022   	235.94199   	-1509983536666.24365	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': 'poisson_v4_02', 'interp_data': True, '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[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           	195.61971   	235.03195   	-1465736235340.59570	0           
  10          	195.61971   	235.03195   	-1465736235340.59570	0           
  100         	195.61971   	235.03195   	-1465736235340.59570	0           
---> try to load best state
              	195.61971   	235.03195   	-1465736235340.59570	0                eval state : test_on_restore;
              	195.61971   	235.03195   	-1465736235340.59570	0                eval state : test_on_last_epoch;
---> final result
              	195.61971   	235.03195   	-1465736235340.59570	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': 'poisson_v4_02', 'interp_data': True, '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[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           	195.38672   	233.32817   	-1436980520578.58008	0           
  10          	195.38672   	233.32817   	-1436980520578.58008	0           
  100         	195.38672   	233.32817   	-1436980520578.58008	0           
---> try to load best state
              	195.38672   	233.32817   	-1436980520578.58008	0                eval state : test_on_restore;
              	195.38672   	233.32817   	-1436980520578.58008	0                eval state : test_on_last_epoch;
---> final result
              	195.38672   	233.32817   	-1436980520578.58008	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': 'poisson_v4_02', '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[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, '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           	114.64204   	154.29407   	-0.88474    	0           
  10          	105.08145   	143.40700   	-0.33128    	0           
  100         	53.88068    	78.58962    	0.80430     	0           
---> try to load best state
              	53.88068    	78.58962    	0.80430     	0                eval state : test_on_restore;
              	53.88068    	78.58962    	0.80430     	0                eval state : test_on_last_epoch;
---> final result
              	53.88068    	78.58962    	0.80430     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, '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[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           	206.11732   	250.84921   	-426245.59689	0           
  10          	206.11726   	250.84914   	-422996.17283	0           
  100         	206.11720   	250.84911   	-420307.78980	0           
---> try to load best state
              	206.11720   	250.84911   	-420307.78980	0                eval state : test_on_restore;
              	206.11720   	250.84911   	-420307.78980	0                eval state : test_on_last_epoch;
---> final result
              	206.11720   	250.84911   	-420307.78980	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': 'poisson_v4_02', 'interp_data': True, '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[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           	200.51712   	242.85565   	-207087807324.58054	0           
  10          	200.51712   	242.85565   	-206993666957.19061	0           
  100         	200.51712   	242.85565   	-206890756171.55243	0           
---> try to load best state
              	200.51712   	242.85565   	-206890756171.55243	0                eval state : test_on_restore;
              	200.51712   	242.85565   	-206890756171.55243	0                eval state : test_on_last_epoch;
---> final result
              	200.51712   	242.85565   	-206890756171.55243	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': 'poisson_v4_02', 'interp_data': True, '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[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           	195.25148   	235.20222   	-43208935769497.60156	0           
  10          	195.25148   	235.20222   	-43021110448428.46094	0           
  100         	195.25148   	235.20222   	-43020994499913.18750	0           
---> try to load best state
              	195.25148   	235.20222   	-43020994499913.18750	0                eval state : test_on_restore;
              	195.25148   	235.20222   	-43020994499913.18750	0                eval state : test_on_last_epoch;
---> final result
              	195.25148   	235.20222   	-43020994499913.18750	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': 'poisson_v4_02', 'interp_data': True, '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[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           	192.86310   	231.22969   	-2255200039682.20215	0           
  10          	192.86310   	231.22969   	-2451871131665.66943	0           
  100         	192.86310   	231.22969   	-2451871131665.66943	0           
---> try to load best state
              	192.86310   	231.22969   	-2451871131665.66943	0                eval state : test_on_restore;
              	192.86310   	231.22969   	-2451871131665.66943	0                eval state : test_on_last_epoch;
---> final result
              	192.86310   	231.22969   	-2451871131665.66943	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': 'poisson_v4_02', '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[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, '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           	104.94931   	146.02463   	-0.56122    	0           
  10          	95.86022    	135.70998   	-0.12719    	0           
  100         	46.69134    	71.78957    	0.83876     	0           
---> try to load best state
              	46.69134    	71.78957    	0.83876     	0                eval state : test_on_restore;
              	46.69134    	71.78957    	0.83876     	0                eval state : test_on_last_epoch;
---> final result
              	46.69134    	71.78957    	0.83876     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, '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[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           	195.10275   	234.73505   	-4083774426009.79053	0           
  10          	195.10275   	234.73505   	-4083774426009.79053	0           
  100         	195.10275   	234.73505   	-4083774426009.79053	0           
---> try to load best state
              	195.10275   	234.73505   	-4083774426009.79053	0                eval state : test_on_restore;
              	195.10275   	234.73505   	-4083774426009.79053	0                eval state : test_on_last_epoch;
---> final result
              	195.10275   	234.73505   	-4083774426009.79053	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': 'poisson_v4_02', 'interp_data': True, '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[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           	193.41399   	233.79114   	-1706963697614.12036	0           
  10          	193.41399   	233.79114   	-1706963697614.12036	0           
  100         	193.41399   	233.79114   	-1706963697614.12036	0           
---> try to load best state
              	193.41399   	233.79114   	-1706963697614.12036	0                eval state : test_on_restore;
              	193.41399   	233.79114   	-1706963697614.12036	0                eval state : test_on_last_epoch;
---> final result
              	193.41399   	233.79114   	-1706963697614.12036	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': 'poisson_v4_02', 'interp_data': True, '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[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           	192.11520   	231.80658   	-1553367885821.03979	0           
  10          	192.11520   	231.80658   	-1553367885821.03979	0           
  100         	192.11520   	231.80658   	-1553367885821.03979	0           
---> try to load best state
              	192.11520   	231.80658   	-1553367885821.03979	0                eval state : test_on_restore;
              	192.11520   	231.80658   	-1553367885821.03979	0                eval state : test_on_last_epoch;
---> final result
              	192.11520   	231.80658   	-1553367885821.03979	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': 'poisson_v4_02', 'interp_data': True, '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[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           	193.14247   	231.72490   	-1670624198120.38354	0           
  10          	193.14247   	231.72490   	-1670624198120.38354	0           
  100         	193.14247   	231.72490   	-1670624198120.38354	0           
---> try to load best state
              	193.14247   	231.72490   	-1670624198120.38354	0                eval state : test_on_restore;
              	193.14247   	231.72490   	-1670624198120.38354	0                eval state : test_on_last_epoch;
---> final result
              	193.14247   	231.72490   	-1670624198120.38354	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': 'poisson_v4_02', '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[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, '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           	101.56859   	138.47165   	-0.31479    	0           
  10          	90.11024    	124.67731   	0.13806     	0           
  100         	38.51108    	56.20092    	0.91414     	0           
---> try to load best state
              	38.51108    	56.20092    	0.91414     	0                eval state : test_on_restore;
              	38.51108    	56.20092    	0.91414     	0                eval state : test_on_last_epoch;
---> final result
              	38.51108    	56.20092    	0.91414     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, '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[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           	219.28387   	266.57730   	-3551999783097.21191	0           
  10          	219.28387   	266.57730   	-3551999783097.21191	0           
  100         	219.28387   	266.57730   	-3551999783097.21191	0           
---> try to load best state
              	219.28387   	266.57730   	-3551999783097.21191	0                eval state : test_on_restore;
              	219.28387   	266.57730   	-3551999783097.21191	0                eval state : test_on_last_epoch;
---> final result
              	219.28387   	266.57730   	-3551999783097.21191	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': 'poisson_v4_02', 'interp_data': True, '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[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           	202.91792   	243.78702   	-1824223854150.06641	0           
  10          	202.91792   	243.78702   	-1824223854150.06641	0           
  100         	202.91792   	243.78702   	-1824223854150.06641	0           
---> try to load best state
              	202.91792   	243.78702   	-1824223854150.06641	0                eval state : test_on_restore;
              	202.91792   	243.78702   	-1824223854150.06641	0                eval state : test_on_last_epoch;
---> final result
              	202.91792   	243.78702   	-1824223854150.06641	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': 'poisson_v4_02', 'interp_data': True, '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[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           	197.65767   	235.52588   	-1502335208666.48950	0           
  10          	197.65767   	235.52588   	-1502335208666.48950	0           
  100         	197.65767   	235.52588   	-1502335208666.48950	0           
---> try to load best state
              	197.65767   	235.52588   	-1502335208666.48950	0                eval state : test_on_restore;
              	197.65767   	235.52588   	-1502335208666.48950	0                eval state : test_on_last_epoch;
---> final result
              	197.65767   	235.52588   	-1502335208666.48950	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': 'poisson_v4_02', 'interp_data': True, '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[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           	196.81940   	234.42386   	-1449052346293.27295	0           
  10          	196.81940   	234.42386   	-1449052346293.27295	0           
  100         	196.81940   	234.42386   	-1449052346293.27295	0           
---> try to load best state
              	196.81940   	234.42386   	-1449052346293.27295	0                eval state : test_on_restore;
              	196.81940   	234.42386   	-1449052346293.27295	0                eval state : test_on_last_epoch;
---> final result
              	196.81940   	234.42386   	-1449052346293.27295	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': 'poisson_v4_02', '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[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, '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           	102.06522   	140.97189   	-0.37183    	0           
  10          	91.81679    	128.94196   	0.04558     	0           
  100         	43.56654    	64.31406    	0.87924     	0           
---> try to load best state
              	43.56654    	64.31406    	0.87924     	0                eval state : test_on_restore;
              	43.56654    	64.31406    	0.87924     	0                eval state : test_on_last_epoch;
---> final result
              	43.56654    	64.31406    	0.87924     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, '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[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           	195.19199   	234.15616   	-1626595856619.68481	0           
  10          	195.19199   	234.15616   	-1626595856619.68481	0           
  100         	195.19199   	234.15616   	-1626595856619.68481	0           
---> try to load best state
              	195.19199   	234.15616   	-1626595856619.68481	0                eval state : test_on_restore;
              	195.19199   	234.15616   	-1626595856619.68481	0                eval state : test_on_last_epoch;
---> final result
              	195.19199   	234.15616   	-1626595856619.68481	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': 'poisson_v4_02', 'interp_data': True, '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[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           	205.75784   	247.58897   	-2049143334074.08643	0           
  10          	205.75784   	247.58897   	-2049143334074.08643	0           
  100         	205.75784   	247.58897   	-2049143334074.08643	0           
---> try to load best state
              	205.75784   	247.58897   	-2049143334074.08643	0                eval state : test_on_restore;
              	205.75784   	247.58897   	-2049143334074.08643	0                eval state : test_on_last_epoch;
---> final result
              	205.75784   	247.58897   	-2049143334074.08643	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': 'poisson_v4_02', 'interp_data': True, '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[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           	198.67458   	238.43561   	-1614426546058.54639	0           
  10          	198.67458   	238.43561   	-1614426546058.54639	0           
  100         	198.67458   	238.43561   	-1614426546058.54639	0           
---> try to load best state
              	198.67458   	238.43561   	-1614426546058.54639	0                eval state : test_on_restore;
              	198.67458   	238.43561   	-1614426546058.54639	0                eval state : test_on_last_epoch;
---> final result
              	198.67458   	238.43561   	-1614426546058.54639	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': 'poisson_v4_02', 'interp_data': True, '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[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           	194.81171   	233.32028   	-1385323429913.41309	0           
  10          	194.81171   	233.32028   	-1385323429913.41309	0           
  100         	194.81171   	233.32028   	-1385323429913.41309	0           
---> try to load best state
              	194.81171   	233.32028   	-1385323429913.41309	0                eval state : test_on_restore;
              	194.81171   	233.32028   	-1385323429913.41309	0                eval state : test_on_last_epoch;
---> final result
              	194.81171   	233.32028   	-1385323429913.41309	0                eval state : final;module_name : DC;cp_record_file : True;
