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

  Demo lar cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> 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           	135.78708   	241.20796   	0.78973     	0           
  10          	135.16093   	239.71599   	0.79409     	0           
  100         	129.75902   	226.75250   	0.82699     	0           
  300         	118.67701   	204.69368   	0.86559     	1           
  500         	108.11190   	187.74069   	0.88647     	1           
  1000        	89.72105    	157.15001   	0.91848     	3           
---> try to load best state
              	89.72105    	157.15001   	0.91848     	3                eval state : test_on_restore;
              	89.72105    	157.15001   	0.91848     	3                eval state : test_on_last_epoch;
---> final result
              	89.72105    	157.15001   	0.91848     	3                eval state : final;
----------> finish x-yl training

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

  Demo lar cigp 
  seed: 4 
  interp_data: False 

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

  Demo lar cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> 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           	138.77553   	250.73946   	0.74367     	0           
  10          	138.07928   	249.10339   	0.74967     	0           
  100         	131.43466   	234.79117   	0.79442     	0           
  300         	118.30173   	210.62581   	0.84537     	1           
  500         	106.90660   	191.83754   	0.87399     	2           
  1000        	86.12368    	156.51593   	0.91808     	5           
---> try to load best state
              	86.12368    	156.51593   	0.91808     	5                eval state : test_on_restore;
              	86.12368    	156.51593   	0.91808     	5                eval state : test_on_last_epoch;
---> final result
              	86.12368    	156.51593   	0.91808     	5                eval state : final;
----------> finish x-yl training

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

  Demo lar cigp 
  seed: None 
  interp_data: False 

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

  Demo lar cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> 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           	138.77553   	250.73946   	0.74367     	0           
  10          	138.07928   	249.10339   	0.74967     	0           
  100         	131.43466   	234.79117   	0.79442     	0           
  300         	118.30173   	210.62581   	0.84537     	1           
  500         	106.90660   	191.83754   	0.87399     	1           
  1000        	86.12368    	156.51593   	0.91808     	3           
---> try to load best state
              	86.12368    	156.51593   	0.91808     	3                eval state : test_on_restore;
              	86.12368    	156.51593   	0.91808     	3                eval state : test_on_last_epoch;
---> final result
              	86.12368    	156.51593   	0.91808     	3                eval state : final;
----------> finish x-yl training

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

  Demo lar cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	190.68338   	283.37970   	-0.23577    	0           
  10          	180.15802   	272.63062   	-0.19997    	0           
  100         	237.01172   	390.19446   	-1.56210    	0           
---> try to load best state
              	237.01172   	390.19446   	-1.56210    	0                eval state : test_on_restore;
              	237.01172   	390.19446   	-1.56210    	0                eval state : test_on_last_epoch;
---> final result
              	237.01172   	390.19446   	-1.56210    	0                eval state : final;
              	237.01172   	390.19446   	-1.56210    	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	154.15378   	237.67984   	0.36628     	0           
  10          	143.00072   	225.20677   	0.42997     	0           
  100         	157.51067   	256.74103   	0.54474     	0           
---> try to load best state
              	157.51067   	256.74103   	0.54474     	0                eval state : test_on_restore;
              	157.51067   	256.74103   	0.54474     	0                eval state : test_on_last_epoch;
---> final result
              	157.51067   	256.74103   	0.54474     	0                eval state : final;
              	157.51067   	256.74103   	0.54474     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	118.54272   	190.99065   	0.65248     	0           
  10          	111.19643   	184.85046   	0.69887     	0           
  100         	95.82401    	169.88229   	0.87287     	0           
---> try to load best state
              	95.82401    	169.88229   	0.87287     	0                eval state : test_on_restore;
              	95.82401    	169.88229   	0.87287     	0                eval state : test_on_last_epoch;
---> final result
              	95.82401    	169.88229   	0.87287     	0                eval state : final;
              	95.82401    	169.88229   	0.87287     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	95.38995    	163.60834   	0.76553     	0           
  10          	94.39297    	164.75269   	0.78633     	0           
  100         	83.71937    	146.44119   	0.91201     	0           
---> try to load best state
              	83.71937    	146.44119   	0.91201     	0                eval state : test_on_restore;
              	83.71937    	146.44119   	0.91201     	0                eval state : test_on_last_epoch;
---> final result
              	83.71937    	146.44119   	0.91201     	0                eval state : final;
              	83.71937    	146.44119   	0.91201     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 0 
  interp_data: False 

----------------------------------------
---> 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           	197.50684   	358.35605   	-0.09550    	0           
  10          	195.69128   	355.58313   	-0.05976    	0           
  100         	179.63795   	330.06586   	0.21359     	0           
  300         	154.71494   	286.79059   	0.51320     	1           
  500         	138.81902   	256.73804   	0.64668     	2           
  1000        	111.81945   	205.44865   	0.80404     	4           
---> try to load best state
              	111.81945   	205.44865   	0.80404     	4                eval state : test_on_restore;
              	111.81945   	205.44865   	0.80404     	4                eval state : test_on_last_epoch;
---> final result
              	111.81945   	205.44865   	0.80404     	4                eval state : final;
----------> finish x-yl training

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

  Demo lar cigp 
  seed: 0 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	188.20897   	284.23358   	-0.17060    	0           
  10          	187.24396   	283.12140   	-0.08773    	0           
  100         	284.97531   	426.47366   	-1.34687    	0           
---> try to load best state
              	284.97531   	426.47366   	-1.34687    	0                eval state : test_on_restore;
              	284.97531   	426.47366   	-1.34687    	0                eval state : test_on_last_epoch;
---> final result
              	284.97531   	426.47366   	-1.34687    	0                eval state : final;
              	284.97531   	426.47366   	-1.34687    	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 0 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	167.96712   	263.18021   	0.17228     	0           
  10          	163.49492   	262.41034   	0.23957     	0           
  100         	204.83469   	332.71817   	0.15566     	0           
---> try to load best state
              	204.83469   	332.71817   	0.15566     	0                eval state : test_on_restore;
              	204.83469   	332.71817   	0.15566     	0                eval state : test_on_last_epoch;
---> final result
              	204.83469   	332.71817   	0.15566     	0                eval state : final;
              	204.83469   	332.71817   	0.15566     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 0 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	135.93301   	232.53381   	0.55231     	0           
  10          	134.01442   	233.29749   	0.58716     	0           
  100         	121.26186   	218.09590   	0.75901     	0           
---> try to load best state
              	121.26186   	218.09590   	0.75901     	0                eval state : test_on_restore;
              	121.26186   	218.09590   	0.75901     	0                eval state : test_on_last_epoch;
---> final result
              	121.26186   	218.09590   	0.75901     	0                eval state : final;
              	121.26186   	218.09590   	0.75901     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 0 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	127.40264   	222.61844   	0.59104     	0           
  10          	124.74107   	223.11403   	0.63381     	0           
  100         	98.30713    	186.66130   	0.83408     	0           
---> try to load best state
              	98.30713    	186.66130   	0.83408     	0                eval state : test_on_restore;
              	98.30713    	186.66130   	0.83408     	0                eval state : test_on_last_epoch;
---> final result
              	98.30713    	186.66130   	0.83408     	0                eval state : final;
              	98.30713    	186.66130   	0.83408     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 1 
  interp_data: False 

----------------------------------------
---> 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           	178.59821   	302.61151   	0.42124     	0           
  10          	177.11867   	300.35889   	0.43536     	0           
  100         	163.57587   	279.92032   	0.54745     	0           
  300         	141.27914   	245.65298   	0.68483     	1           
  500         	126.71380   	221.32901   	0.75538     	1           
  1000        	101.88340   	178.40077   	0.84983     	3           
---> try to load best state
              	101.88340   	178.40077   	0.84983     	3                eval state : test_on_restore;
              	101.88340   	178.40077   	0.84983     	3                eval state : test_on_last_epoch;
---> final result
              	101.88340   	178.40077   	0.84983     	3                eval state : final;
----------> finish x-yl training

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

  Demo lar cigp 
  seed: 1 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	192.00822   	285.68372   	-0.34337    	0           
  10          	185.76704   	281.28070   	-0.28065    	0           
  100         	258.97269   	411.62863   	-1.34584    	0           
---> try to load best state
              	258.97269   	411.62863   	-1.34584    	0                eval state : test_on_restore;
              	258.97269   	411.62863   	-1.34584    	0                eval state : test_on_last_epoch;
---> final result
              	258.97269   	411.62863   	-1.34584    	0                eval state : final;
              	258.97269   	411.62863   	-1.34584    	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 1 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	184.73618   	282.99493   	-0.26246    	0           
  10          	178.93327   	279.76843   	-0.24308    	0           
  100         	225.37148   	394.31781   	-1.10077    	0           
---> try to load best state
              	225.37148   	394.31781   	-1.10077    	0                eval state : test_on_restore;
              	225.37148   	394.31781   	-1.10077    	0                eval state : test_on_last_epoch;
---> final result
              	225.37148   	394.31781   	-1.10077    	0                eval state : final;
              	225.37148   	394.31781   	-1.10077    	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 1 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	132.89825   	212.59302   	0.54709     	0           
  10          	129.31190   	210.75256   	0.59190     	0           
  100         	126.08904   	214.62431   	0.73646     	0           
---> try to load best state
              	126.08904   	214.62431   	0.73646     	0                eval state : test_on_restore;
              	126.08904   	214.62431   	0.73646     	0                eval state : test_on_last_epoch;
---> final result
              	126.08904   	214.62431   	0.73646     	0                eval state : final;
              	126.08904   	214.62431   	0.73646     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 1 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	112.54119   	190.20099   	0.67172     	0           
  10          	110.71251   	189.84877   	0.70256     	0           
  100         	89.61092    	157.99248   	0.86655     	0           
---> try to load best state
              	89.61092    	157.99248   	0.86655     	0                eval state : test_on_restore;
              	89.61092    	157.99248   	0.86655     	0                eval state : test_on_last_epoch;
---> final result
              	89.61092    	157.99248   	0.86655     	0                eval state : final;
              	89.61092    	157.99248   	0.86655     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 2 
  interp_data: False 

----------------------------------------
---> 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           	173.74155   	334.46317   	0.34726     	0           
  10          	172.35533   	332.10190   	0.36470     	0           
  100         	160.53122   	310.73581   	0.49882     	0           
  300         	142.60095   	275.76715   	0.65226     	0           
  500         	130.60809   	252.00232   	0.72724     	2           
  1000        	109.52187   	209.76738   	0.82739     	4           
---> try to load best state
              	109.52187   	209.76738   	0.82739     	4                eval state : test_on_restore;
              	109.52187   	209.76738   	0.82739     	4                eval state : test_on_last_epoch;
---> final result
              	109.52187   	209.76738   	0.82739     	4                eval state : final;
----------> finish x-yl training

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

  Demo lar cigp 
  seed: 2 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	221.67212   	327.64087   	-1.33151    	0           
  10          	215.90520   	323.85773   	-1.32784    	0           
  100         	301.35941   	461.00311   	-7.33172    	0           
---> try to load best state
              	301.35941   	461.00311   	-7.33172    	0                eval state : test_on_restore;
              	301.35941   	461.00311   	-7.33172    	0                eval state : test_on_last_epoch;
---> final result
              	301.35941   	461.00311   	-7.33172    	0                eval state : final;
              	301.35941   	461.00311   	-7.33172    	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 2 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	191.22476   	300.64746   	-0.21439    	0           
  10          	185.55756   	299.85800   	-0.16272    	0           
  100         	204.65590   	379.95056   	-0.05411    	0           
---> try to load best state
              	204.65590   	379.95056   	-0.05411    	0                eval state : test_on_restore;
              	204.65590   	379.95056   	-0.05411    	0                eval state : test_on_last_epoch;
---> final result
              	204.65590   	379.95056   	-0.05411    	0                eval state : final;
              	204.65590   	379.95056   	-0.05411    	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 2 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	158.36862   	260.91489   	0.34233     	0           
  10          	151.18004   	258.05945   	0.41373     	0           
  100         	133.00394   	260.64355   	0.68460     	0           
---> try to load best state
              	133.00394   	260.64355   	0.68460     	0                eval state : test_on_restore;
              	133.00394   	260.64355   	0.68460     	0                eval state : test_on_last_epoch;
---> final result
              	133.00394   	260.64355   	0.68460     	0                eval state : final;
              	133.00394   	260.64355   	0.68460     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 2 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	127.37759   	226.06615   	0.59223     	0           
  10          	122.22816   	224.22551   	0.63487     	0           
  100         	90.08623    	180.14153   	0.85274     	0           
---> try to load best state
              	90.08623    	180.14153   	0.85274     	0                eval state : test_on_restore;
              	90.08623    	180.14153   	0.85274     	0                eval state : test_on_last_epoch;
---> final result
              	90.08623    	180.14153   	0.85274     	0                eval state : final;
              	90.08623    	180.14153   	0.85274     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 3 
  interp_data: False 

----------------------------------------
---> 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           	190.19420   	346.10031   	0.05073     	0           
  10          	188.35249   	342.78323   	0.08269     	0           
  100         	171.96010   	312.76385   	0.32758     	0           
  300         	147.71516   	265.29257   	0.59212     	1           
  500         	131.57915   	235.04662   	0.70670     	2           
  1000        	105.94860   	188.88913   	0.82916     	3           
---> try to load best state
              	105.94860   	188.88913   	0.82916     	3                eval state : test_on_restore;
              	105.94860   	188.88913   	0.82916     	3                eval state : test_on_last_epoch;
---> final result
              	105.94860   	188.88913   	0.82916     	3                eval state : final;
----------> finish x-yl training

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

  Demo lar cigp 
  seed: 3 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	217.20979   	319.39508   	-1.57202    	0           
  10          	214.72755   	320.50610   	-1.80004    	0           
  100         	311.19675   	498.44370   	-14.04673   	0           
---> try to load best state
              	311.19675   	498.44370   	-14.04673   	0                eval state : test_on_restore;
              	311.19675   	498.44370   	-14.04673   	0                eval state : test_on_last_epoch;
---> final result
              	311.19675   	498.44370   	-14.04673   	0                eval state : final;
              	311.19675   	498.44370   	-14.04673   	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 3 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	181.05925   	278.65448   	-0.27185    	0           
  10          	173.50085   	274.74100   	-0.15266    	0           
  100         	204.14566   	347.78922   	-0.19317    	0           
---> try to load best state
              	204.14566   	347.78922   	-0.19317    	0                eval state : test_on_restore;
              	204.14566   	347.78922   	-0.19317    	0                eval state : test_on_last_epoch;
---> final result
              	204.14566   	347.78922   	-0.19317    	0                eval state : final;
              	204.14566   	347.78922   	-0.19317    	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 3 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	138.07104   	226.81816   	0.45109     	0           
  10          	134.15182   	226.54709   	0.51392     	0           
  100         	135.44888   	235.56810   	0.66254     	0           
---> try to load best state
              	135.44888   	235.56810   	0.66254     	0                eval state : test_on_restore;
              	135.44888   	235.56810   	0.66254     	0                eval state : test_on_last_epoch;
---> final result
              	135.44888   	235.56810   	0.66254     	0                eval state : final;
              	135.44888   	235.56810   	0.66254     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 3 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	118.82770   	205.18399   	0.64906     	0           
  10          	116.77979   	204.38399   	0.68704     	0           
  100         	93.74556    	168.68935   	0.86087     	0           
---> try to load best state
              	93.74556    	168.68935   	0.86087     	0                eval state : test_on_restore;
              	93.74556    	168.68935   	0.86087     	0                eval state : test_on_last_epoch;
---> final result
              	93.74556    	168.68935   	0.86087     	0                eval state : final;
              	93.74556    	168.68935   	0.86087     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> 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           	135.78708   	241.20796   	0.78973     	0           
  10          	135.16093   	239.71599   	0.79409     	0           
  100         	129.75902   	226.75250   	0.82699     	0           
  300         	118.67701   	204.69368   	0.86559     	1           
  500         	108.11190   	187.74069   	0.88647     	1           
  1000        	89.72105    	157.15001   	0.91848     	3           
---> try to load best state
              	89.72105    	157.15001   	0.91848     	3                eval state : test_on_restore;
              	89.72105    	157.15001   	0.91848     	3                eval state : test_on_last_epoch;
---> final result
              	89.72105    	157.15001   	0.91848     	3                eval state : final;
----------> finish x-yl training

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

  Demo lar cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	207.74733   	302.27673   	-0.90320    	0           
  10          	198.81500   	294.48544   	-1.00953    	0           
  100         	293.98633   	476.78665   	-6.53326    	0           
---> try to load best state
              	293.98633   	476.78665   	-6.53326    	0                eval state : test_on_restore;
              	293.98633   	476.78665   	-6.53326    	0                eval state : test_on_last_epoch;
---> final result
              	293.98633   	476.78665   	-6.53326    	0                eval state : final;
              	293.98633   	476.78665   	-6.53326    	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	136.54436   	213.95355   	0.49817     	0           
  10          	126.07634   	203.96756   	0.56848     	0           
  100         	124.43133   	227.36819   	0.68629     	0           
---> try to load best state
              	124.43133   	227.36819   	0.68629     	0                eval state : test_on_restore;
              	124.43133   	227.36819   	0.68629     	0                eval state : test_on_last_epoch;
---> final result
              	124.43133   	227.36819   	0.68629     	0                eval state : final;
              	124.43133   	227.36819   	0.68629     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	119.77045   	194.57362   	0.66413     	0           
  10          	115.24751   	188.29855   	0.70269     	0           
  100         	112.91947   	189.22128   	0.83347     	0           
---> try to load best state
              	112.91947   	189.22128   	0.83347     	0                eval state : test_on_restore;
              	112.91947   	189.22128   	0.83347     	0                eval state : test_on_last_epoch;
---> final result
              	112.91947   	189.22128   	0.83347     	0                eval state : final;
              	112.91947   	189.22128   	0.83347     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> 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': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, '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
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	95.39234    	159.61665   	0.78874     	0           
  10          	94.84371    	160.42284   	0.81309     	0           
  100         	85.16317    	143.61328   	0.92851     	0           
---> try to load best state
              	85.16317    	143.61328   	0.92851     	0                eval state : test_on_restore;
              	85.16317    	143.61328   	0.92851     	0                eval state : test_on_last_epoch;
---> final result
              	85.16317    	143.61328   	0.92851     	0                eval state : final;
              	85.16317    	143.61328   	0.92851     	0                eval state : final;module_name : LarGP;cp_record_file : True;
