Optimization started at 2023-03-11 02:17:26.633724
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Loading column definition...
Checking column definition...
Loading data...
Dropping columns / rows...
Checking for NA values...
Setting data types...
Dropping columns / rows...
Encoding data...
	Updated column definition:
		id: REAL_VALUED (ID)
		time: DATE (TIME)
		gl: REAL_VALUED (TARGET)
		fast_insulin: REAL_VALUED (OBSERVED_INPUT)
		slow_insulin: REAL_VALUED (OBSERVED_INPUT)
		calories: REAL_VALUED (OBSERVED_INPUT)
		balance: REAL_VALUED (OBSERVED_INPUT)
		quality: REAL_VALUED (OBSERVED_INPUT)
		HR: REAL_VALUED (OBSERVED_INPUT)
		BR: REAL_VALUED (OBSERVED_INPUT)
		Posture: REAL_VALUED (OBSERVED_INPUT)
		Activity: REAL_VALUED (OBSERVED_INPUT)
		HRV: REAL_VALUED (OBSERVED_INPUT)
		CoreTemp: REAL_VALUED (OBSERVED_INPUT)
		time_year: REAL_VALUED (KNOWN_INPUT)
		time_month: REAL_VALUED (KNOWN_INPUT)
		time_day: REAL_VALUED (KNOWN_INPUT)
		time_hour: REAL_VALUED (KNOWN_INPUT)
		time_minute: REAL_VALUED (KNOWN_INPUT)
Interpolating data...
	Dropped segments: 1
	Extracted segments: 8
	Interpolated values: 0
	Percent of values interpolated: 0.00%
Splitting data...
	Train: 4654 (47.17%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1140 (11.55%)
Scaling data...
	No scaling applied
Data formatting complete.
--------------------------------
--------------------------------
Loading column definition...
Checking column definition...
Loading data...
Dropping columns / rows...
Checking for NA values...
Setting data types...
Dropping columns / rows...
Encoding data...
	Updated column definition:
		id: REAL_VALUED (ID)
		time: DATE (TIME)
		gl: REAL_VALUED (TARGET)
		fast_insulin: REAL_VALUED (OBSERVED_INPUT)
		slow_insulin: REAL_VALUED (OBSERVED_INPUT)
		calories: REAL_VALUED (OBSERVED_INPUT)
		balance: REAL_VALUED (OBSERVED_INPUT)
		quality: REAL_VALUED (OBSERVED_INPUT)
		HR: REAL_VALUED (OBSERVED_INPUT)
		BR: REAL_VALUED (OBSERVED_INPUT)
		Posture: REAL_VALUED (OBSERVED_INPUT)
		Activity: REAL_VALUED (OBSERVED_INPUT)
		HRV: REAL_VALUED (OBSERVED_INPUT)
		CoreTemp: REAL_VALUED (OBSERVED_INPUT)
		time_year: REAL_VALUED (KNOWN_INPUT)
		time_month: REAL_VALUED (KNOWN_INPUT)
		time_day: REAL_VALUED (KNOWN_INPUT)
		time_hour: REAL_VALUED (KNOWN_INPUT)
		time_minute: REAL_VALUED (KNOWN_INPUT)
Interpolating data...
	Dropped segments: 1
	Extracted segments: 8
	Interpolated values: 0
	Percent of values interpolated: 0.00%
Splitting data...
	Train: 4654 (47.17%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1140 (11.55%)
Scaling data...
	No scaling applied
Data formatting complete.
--------------------------------
Current value: 0.15621991455554962, Current params: {'in_len': 192, 'max_samples_per_ts': 200, 'd_model': 96, 'n_heads': 4, 'num_encoder_layers': 4, 'num_decoder_layers': 4, 'dim_feedforward': 448, 'dropout': 0.16176295712601074, 'lr': 0.0005618097155944741, 'batch_size': 32, 'lr_epochs': 8, 'max_grad_norm': 0.3552528872164169}
Best value: 0.15621991455554962, Best params: {'in_len': 192, 'max_samples_per_ts': 200, 'd_model': 96, 'n_heads': 4, 'num_encoder_layers': 4, 'num_decoder_layers': 4, 'dim_feedforward': 448, 'dropout': 0.16176295712601074, 'lr': 0.0005618097155944741, 'batch_size': 32, 'lr_epochs': 8, 'max_grad_norm': 0.3552528872164169}
Current value: 0.12291247397661209, Current params: {'in_len': 96, 'max_samples_per_ts': 100, 'd_model': 64, 'n_heads': 4, 'num_encoder_layers': 2, 'num_decoder_layers': 3, 'dim_feedforward': 96, 'dropout': 0.10619859074051935, 'lr': 0.00045758846272260734, 'batch_size': 32, 'lr_epochs': 14, 'max_grad_norm': 0.3695571716433973}
Best value: 0.12291247397661209, Best params: {'in_len': 96, 'max_samples_per_ts': 100, 'd_model': 64, 'n_heads': 4, 'num_encoder_layers': 2, 'num_decoder_layers': 3, 'dim_feedforward': 96, 'dropout': 0.10619859074051935, 'lr': 0.00045758846272260734, 'batch_size': 32, 'lr_epochs': 14, 'max_grad_norm': 0.3695571716433973}
Current value: 0.15446673333644867, Current params: {'in_len': 120, 'max_samples_per_ts': 150, 'd_model': 32, 'n_heads': 4, 'num_encoder_layers': 3, 'num_decoder_layers': 4, 'dim_feedforward': 256, 'dropout': 0.19992045323340124, 'lr': 0.0005706575235644469, 'batch_size': 64, 'lr_epochs': 6, 'max_grad_norm': 0.40349583860516225}
Best value: 0.12291247397661209, Best params: {'in_len': 96, 'max_samples_per_ts': 100, 'd_model': 64, 'n_heads': 4, 'num_encoder_layers': 2, 'num_decoder_layers': 3, 'dim_feedforward': 96, 'dropout': 0.10619859074051935, 'lr': 0.00045758846272260734, 'batch_size': 32, 'lr_epochs': 14, 'max_grad_norm': 0.3695571716433973}
Current value: 0.11762730032205582, Current params: {'in_len': 180, 'max_samples_per_ts': 150, 'd_model': 128, 'n_heads': 2, 'num_encoder_layers': 1, 'num_decoder_layers': 1, 'dim_feedforward': 288, 'dropout': 0.08881060779085462, 'lr': 0.0006320939077316028, 'batch_size': 32, 'lr_epochs': 8, 'max_grad_norm': 0.445623305406162}
Best value: 0.11762730032205582, Best params: {'in_len': 180, 'max_samples_per_ts': 150, 'd_model': 128, 'n_heads': 2, 'num_encoder_layers': 1, 'num_decoder_layers': 1, 'dim_feedforward': 288, 'dropout': 0.08881060779085462, 'lr': 0.0006320939077316028, 'batch_size': 32, 'lr_epochs': 8, 'max_grad_norm': 0.445623305406162}
Current value: 0.09917581081390381, Current params: {'in_len': 180, 'max_samples_per_ts': 100, 'd_model': 32, 'n_heads': 4, 'num_encoder_layers': 1, 'num_decoder_layers': 3, 'dim_feedforward': 256, 'dropout': 0.019235624826036003, 'lr': 0.0007786223588561493, 'batch_size': 64, 'lr_epochs': 18, 'max_grad_norm': 0.6163841648171366}
Best value: 0.09917581081390381, Best params: {'in_len': 180, 'max_samples_per_ts': 100, 'd_model': 32, 'n_heads': 4, 'num_encoder_layers': 1, 'num_decoder_layers': 3, 'dim_feedforward': 256, 'dropout': 0.019235624826036003, 'lr': 0.0007786223588561493, 'batch_size': 64, 'lr_epochs': 18, 'max_grad_norm': 0.6163841648171366}
Current value: 0.03620939701795578, Current params: {'in_len': 120, 'max_samples_per_ts': 100, 'd_model': 96, 'n_heads': 4, 'num_encoder_layers': 3, 'num_decoder_layers': 3, 'dim_feedforward': 480, 'dropout': 0.039444406919825516, 'lr': 0.0006393313347141523, 'batch_size': 64, 'lr_epochs': 4, 'max_grad_norm': 0.7493498351403791}
Best value: 0.09917581081390381, Best params: {'in_len': 180, 'max_samples_per_ts': 100, 'd_model': 32, 'n_heads': 4, 'num_encoder_layers': 1, 'num_decoder_layers': 3, 'dim_feedforward': 256, 'dropout': 0.019235624826036003, 'lr': 0.0007786223588561493, 'batch_size': 64, 'lr_epochs': 18, 'max_grad_norm': 0.6163841648171366}
Current value: 0.0360909104347229, Current params: {'in_len': 108, 'max_samples_per_ts': 200, 'd_model': 64, 'n_heads': 4, 'num_encoder_layers': 3, 'num_decoder_layers': 3, 'dim_feedforward': 192, 'dropout': 0.1516361718706083, 'lr': 0.0003494174111062657, 'batch_size': 32, 'lr_epochs': 14, 'max_grad_norm': 0.8248325708519817}
Best value: 0.09917581081390381, Best params: {'in_len': 180, 'max_samples_per_ts': 100, 'd_model': 32, 'n_heads': 4, 'num_encoder_layers': 1, 'num_decoder_layers': 3, 'dim_feedforward': 256, 'dropout': 0.019235624826036003, 'lr': 0.0007786223588561493, 'batch_size': 64, 'lr_epochs': 18, 'max_grad_norm': 0.6163841648171366}
Current value: 0.13542138040065765, Current params: {'in_len': 120, 'max_samples_per_ts': 150, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 3, 'num_decoder_layers': 3, 'dim_feedforward': 416, 'dropout': 0.10706648855899013, 'lr': 0.0007218173941177725, 'batch_size': 64, 'lr_epochs': 20, 'max_grad_norm': 0.253273568144764}
Best value: 0.09917581081390381, Best params: {'in_len': 180, 'max_samples_per_ts': 100, 'd_model': 32, 'n_heads': 4, 'num_encoder_layers': 1, 'num_decoder_layers': 3, 'dim_feedforward': 256, 'dropout': 0.019235624826036003, 'lr': 0.0007786223588561493, 'batch_size': 64, 'lr_epochs': 18, 'max_grad_norm': 0.6163841648171366}
Current value: 0.04222766309976578, Current params: {'in_len': 108, 'max_samples_per_ts': 100, 'd_model': 128, 'n_heads': 4, 'num_encoder_layers': 4, 'num_decoder_layers': 4, 'dim_feedforward': 96, 'dropout': 0.14203031424084298, 'lr': 0.0004117934810656366, 'batch_size': 32, 'lr_epochs': 16, 'max_grad_norm': 0.5410626457665397}
Best value: 0.09917581081390381, Best params: {'in_len': 180, 'max_samples_per_ts': 100, 'd_model': 32, 'n_heads': 4, 'num_encoder_layers': 1, 'num_decoder_layers': 3, 'dim_feedforward': 256, 'dropout': 0.019235624826036003, 'lr': 0.0007786223588561493, 'batch_size': 64, 'lr_epochs': 18, 'max_grad_norm': 0.6163841648171366}
Current value: 0.04247082397341728, Current params: {'in_len': 120, 'max_samples_per_ts': 100, 'd_model': 96, 'n_heads': 4, 'num_encoder_layers': 1, 'num_decoder_layers': 2, 'dim_feedforward': 480, 'dropout': 0.05361579370828003, 'lr': 0.0007860918452332372, 'batch_size': 64, 'lr_epochs': 4, 'max_grad_norm': 0.7399532867513915}
Best value: 0.09917581081390381, Best params: {'in_len': 180, 'max_samples_per_ts': 100, 'd_model': 32, 'n_heads': 4, 'num_encoder_layers': 1, 'num_decoder_layers': 3, 'dim_feedforward': 256, 'dropout': 0.019235624826036003, 'lr': 0.0007786223588561493, 'batch_size': 64, 'lr_epochs': 18, 'max_grad_norm': 0.6163841648171366}
Current value: 0.08321572095155716, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.006954197405441606, 'lr': 0.0009888347584365398, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.12431538502039596}
Best value: 0.08321572095155716, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.006954197405441606, 'lr': 0.0009888347584365398, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.12431538502039596}
Current value: 0.07126187533140182, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.07941766828298569, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.00010220245396863527, 'lr': 0.0009990454368017882, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.10014520128162098}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.07620467245578766, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.0017786179625755294, 'lr': 0.000999332280870897, 'batch_size': 48, 'lr_epochs': 12, 'max_grad_norm': 0.1075595363776597}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.08651617914438248, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 2, 'dim_feedforward': 320, 'dropout': 0.05677277738919505, 'lr': 0.00012229958240375551, 'batch_size': 48, 'lr_epochs': 12, 'max_grad_norm': 0.22404732535732816}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.08295967429876328, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 2, 'dim_feedforward': 192, 'dropout': 0.030965815809432536, 'lr': 0.000883320801020018, 'batch_size': 48, 'lr_epochs': 10, 'max_grad_norm': 0.23838745166442293}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.0777851864695549, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 1, 'num_decoder_layers': 1, 'dim_feedforward': 512, 'dropout': 0.07445104460078959, 'lr': 0.000888734054686481, 'batch_size': 48, 'lr_epochs': 2, 'max_grad_norm': 0.9924894897090295}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.08179383724927902, Current params: {'in_len': 168, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 2, 'dim_feedforward': 384, 'dropout': 0.028401995319999603, 'lr': 0.000900668893210839, 'batch_size': 48, 'lr_epochs': 16, 'max_grad_norm': 0.18195292425249346}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.03723764419555664, Current params: {'in_len': 132, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 3, 'num_decoder_layers': 1, 'dim_feedforward': 192, 'dropout': 0.05869562578467423, 'lr': 0.00014938722552895452, 'batch_size': 48, 'lr_epochs': 12, 'max_grad_norm': 0.3110714616930665}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.04076450318098068, Current params: {'in_len': 168, 'max_samples_per_ts': 100, 'd_model': 96, 'n_heads': 2, 'num_encoder_layers': 1, 'num_decoder_layers': 1, 'dim_feedforward': 32, 'dropout': 0.0031096703234556877, 'lr': 0.000816652198447964, 'batch_size': 48, 'lr_epochs': 16, 'max_grad_norm': 0.4753978535323456}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.027165455743670464, Current params: {'in_len': 168, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 2, 'dim_feedforward': 320, 'dropout': 0.12376495963246974, 'lr': 0.00024381252545675123, 'batch_size': 48, 'lr_epochs': 10, 'max_grad_norm': 0.1617112057858674}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.08316344767808914, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 1, 'num_decoder_layers': 1, 'dim_feedforward': 512, 'dropout': 0.07804323948293732, 'lr': 0.0009514505535065749, 'batch_size': 48, 'lr_epochs': 2, 'max_grad_norm': 0.9481553230346735}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.012350221164524555, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 1, 'num_decoder_layers': 1, 'dim_feedforward': 416, 'dropout': 0.07824128133217473, 'lr': 0.0008862263991505526, 'batch_size': 48, 'lr_epochs': 2, 'max_grad_norm': 0.5923894938506666}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.08978420495986938, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 512, 'dropout': 0.044896597071623585, 'lr': 0.0009075378005974392, 'batch_size': 48, 'lr_epochs': 8, 'max_grad_norm': 0.6839705618085112}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.03908338025212288, Current params: {'in_len': 132, 'max_samples_per_ts': 100, 'd_model': 96, 'n_heads': 2, 'num_encoder_layers': 1, 'num_decoder_layers': 2, 'dim_feedforward': 352, 'dropout': 0.02306863509244298, 'lr': 0.0008334530260416829, 'batch_size': 48, 'lr_epochs': 6, 'max_grad_norm': 0.9549337816467314}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.01386152021586895, Current params: {'in_len': 180, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 448, 'dropout': 0.014477932404733447, 'lr': 0.0007159491405344161, 'batch_size': 32, 'lr_epochs': 18, 'max_grad_norm': 0.8749267964467301}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.036430392414331436, Current params: {'in_len': 168, 'max_samples_per_ts': 100, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 1, 'num_decoder_layers': 1, 'dim_feedforward': 224, 'dropout': 0.18137114781981056, 'lr': 0.0009506975622370544, 'batch_size': 64, 'lr_epochs': 14, 'max_grad_norm': 0.29501489014321425}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.08817168325185776, Current params: {'in_len': 132, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 2, 'dim_feedforward': 320, 'dropout': 0.07211781105033674, 'lr': 0.0006979938685337162, 'batch_size': 48, 'lr_epochs': 4, 'max_grad_norm': 0.5277527875114019}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.04040784016251564, Current params: {'in_len': 156, 'max_samples_per_ts': 150, 'd_model': 96, 'n_heads': 2, 'num_encoder_layers': 3, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.03627291403129318, 'lr': 0.0008521240356108369, 'batch_size': 48, 'lr_epochs': 18, 'max_grad_norm': 0.16822302561473068}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.03447655215859413, Current params: {'in_len': 192, 'max_samples_per_ts': 200, 'd_model': 128, 'n_heads': 2, 'num_encoder_layers': 4, 'num_decoder_layers': 2, 'dim_feedforward': 448, 'dropout': 0.12903862063601912, 'lr': 0.000997867454286544, 'batch_size': 32, 'lr_epochs': 6, 'max_grad_norm': 0.29923105631370894}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.0239623561501503, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 1, 'num_decoder_layers': 1, 'dim_feedforward': 448, 'dropout': 0.09282504305670601, 'lr': 0.0009415764392733724, 'batch_size': 48, 'lr_epochs': 10, 'max_grad_norm': 0.8772931671776152}
Best value: 0.07126187533140182, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.004477179738500273, 'lr': 0.0009878205948958725, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1013531645985869}
Current value: 0.07053025811910629, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.07967790216207504, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.014155850121493695, 'lr': 0.0009356451626229198, 'batch_size': 48, 'lr_epochs': 18, 'max_grad_norm': 0.1405046905225267}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.08047908544540405, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 288, 'dropout': 0.01277102728992411, 'lr': 0.0004994828423963994, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.36508485955459735}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.04121486097574234, Current params: {'in_len': 168, 'max_samples_per_ts': 100, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 416, 'dropout': 0.0006865671323733444, 'lr': 0.0009999814423625906, 'batch_size': 32, 'lr_epochs': 16, 'max_grad_norm': 0.20564954245600164}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.012179480865597725, Current params: {'in_len': 180, 'max_samples_per_ts': 50, 'd_model': 96, 'n_heads': 2, 'num_encoder_layers': 3, 'num_decoder_layers': 1, 'dim_feedforward': 288, 'dropout': 0.04707828818858884, 'lr': 0.0008511883622872499, 'batch_size': 48, 'lr_epochs': 18, 'max_grad_norm': 0.10180085848412383}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.040353674441576004, Current params: {'in_len': 192, 'max_samples_per_ts': 100, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 2, 'dim_feedforward': 480, 'dropout': 0.02570633694637617, 'lr': 0.0007873305541470869, 'batch_size': 64, 'lr_epochs': 12, 'max_grad_norm': 0.2721119222647633}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.08453821390867233, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 96, 'n_heads': 4, 'num_encoder_layers': 1, 'num_decoder_layers': 1, 'dim_feedforward': 256, 'dropout': 0.06611929649355686, 'lr': 0.0006104139468698455, 'batch_size': 48, 'lr_epochs': 8, 'max_grad_norm': 0.4198189267859883}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.03752657398581505, Current params: {'in_len': 180, 'max_samples_per_ts': 100, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 3, 'num_decoder_layers': 4, 'dim_feedforward': 384, 'dropout': 0.10750614940716587, 'lr': 0.0007484100689289499, 'batch_size': 64, 'lr_epochs': 14, 'max_grad_norm': 0.32571300511949663}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.030719351023435593, Current params: {'in_len': 168, 'max_samples_per_ts': 150, 'd_model': 64, 'n_heads': 4, 'num_encoder_layers': 1, 'num_decoder_layers': 1, 'dim_feedforward': 128, 'dropout': 0.1995471212237871, 'lr': 0.0009342858496221845, 'batch_size': 48, 'lr_epochs': 18, 'max_grad_norm': 0.489059687397601}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.01205049455165863, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 3, 'num_decoder_layers': 3, 'dim_feedforward': 320, 'dropout': 0.03887762984587643, 'lr': 0.0008701023920011286, 'batch_size': 32, 'lr_epochs': 20, 'max_grad_norm': 0.6516823790633725}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.07352794706821442, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0016222499911825474, 'lr': 0.0009731895314541878, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.14386663581968623}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.07450918853282928, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 416, 'dropout': 0.019932048042805305, 'lr': 0.0009544754562806506, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.19717063232998694}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.07859444618225098, Current params: {'in_len': 132, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 416, 'dropout': 0.010738919549732858, 'lr': 0.0009576436458561299, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.18682670894650752}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.0352303646504879, Current params: {'in_len': 156, 'max_samples_per_ts': 100, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.021130255875480092, 'lr': 0.0009653406255636999, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.14296992215870474}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.015699749812483788, Current params: {'in_len': 96, 'max_samples_per_ts': 50, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 2, 'dim_feedforward': 384, 'dropout': 0.0054584694261455196, 'lr': 0.0009211389066510139, 'batch_size': 48, 'lr_epochs': 18, 'max_grad_norm': 0.2150850839874573}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.011677617207169533, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 416, 'dropout': 0.02093186616822279, 'lr': 0.000826051824940634, 'batch_size': 64, 'lr_epochs': 20, 'max_grad_norm': 0.10381290100714055}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.012924645096063614, Current params: {'in_len': 168, 'max_samples_per_ts': 50, 'd_model': 96, 'n_heads': 4, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 352, 'dropout': 0.032222106171181285, 'lr': 0.00041601830883496995, 'batch_size': 48, 'lr_epochs': 16, 'max_grad_norm': 0.257051903023813}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.05268150195479393, Current params: {'in_len': 156, 'max_samples_per_ts': 200, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 4, 'dim_feedforward': 480, 'dropout': 0.007772148726860089, 'lr': 0.0009828442478603786, 'batch_size': 32, 'lr_epochs': 18, 'max_grad_norm': 0.13239393012875372}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
Current value: 0.0349893644452095, Current params: {'in_len': 144, 'max_samples_per_ts': 100, 'd_model': 32, 'n_heads': 2, 'num_encoder_layers': 3, 'num_decoder_layers': 2, 'dim_feedforward': 320, 'dropout': 0.019816823445820406, 'lr': 0.0009122954928546191, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.18733286245116212}
Best value: 0.07053025811910629, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'd_model': 64, 'n_heads': 2, 'num_encoder_layers': 2, 'num_decoder_layers': 1, 'dim_feedforward': 384, 'dropout': 0.0026811942171770446, 'lr': 0.000998963295875978, 'batch_size': 48, 'lr_epochs': 20, 'max_grad_norm': 0.1004169110387992}
--------------------------------
Loading column definition...
Checking column definition...
Loading data...
Dropping columns / rows...
Checking for NA values...
Setting data types...
Dropping columns / rows...
Encoding data...
	Updated column definition:
		id: REAL_VALUED (ID)
		time: DATE (TIME)
		gl: REAL_VALUED (TARGET)
		fast_insulin: REAL_VALUED (OBSERVED_INPUT)
		slow_insulin: REAL_VALUED (OBSERVED_INPUT)
		calories: REAL_VALUED (OBSERVED_INPUT)
		balance: REAL_VALUED (OBSERVED_INPUT)
		quality: REAL_VALUED (OBSERVED_INPUT)
		HR: REAL_VALUED (OBSERVED_INPUT)
		BR: REAL_VALUED (OBSERVED_INPUT)
		Posture: REAL_VALUED (OBSERVED_INPUT)
		Activity: REAL_VALUED (OBSERVED_INPUT)
		HRV: REAL_VALUED (OBSERVED_INPUT)
		CoreTemp: REAL_VALUED (OBSERVED_INPUT)
		time_year: REAL_VALUED (KNOWN_INPUT)
		time_month: REAL_VALUED (KNOWN_INPUT)
		time_day: REAL_VALUED (KNOWN_INPUT)
		time_hour: REAL_VALUED (KNOWN_INPUT)
		time_minute: REAL_VALUED (KNOWN_INPUT)
Interpolating data...
	Dropped segments: 1
	Extracted segments: 8
	Interpolated values: 0
	Percent of values interpolated: 0.00%
Splitting data...
	Train: 4654 (47.17%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1140 (11.55%)
Scaling data...
	No scaling applied
Data formatting complete.
--------------------------------
	Train: 4654 (47.17%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1140 (11.55%)
	No scaling applied
		Model Seed: 10 Seed: 1 ID mean of (MSE, MAE): [1568.2565     27.994318]
		Model Seed: 10 Seed: 1 OOD mean of (MSE, MAE) stats: [2860.8647     39.995556]
		Model Seed: 10 Seed: 1 ID median of (MSE, MAE): [597.8711    21.706085]
		Model Seed: 10 Seed: 1 OOD median of (MSE, MAE) stats: [1835.9122    36.93257]
		Model Seed: 10 Seed: 1 ID likelihoods: -10.597798604006073
		Model Seed: 10 Seed: 1 OOD likelihoods: -10.898378440331133
		Model Seed: 10 Seed: 1 ID calibration errors: [0.42269783 0.34099526 0.17741313 0.10005728 0.09200535 0.04543676
 0.07905236 0.08195526 0.05733586 0.1518315  0.05189484 0.18860515]
		Model Seed: 10 Seed: 1 OOD calibration errors: [0.55009158 0.46684896 0.2947879  0.21447452 0.23363671 0.16462457
 0.31911207 0.3922876  0.37817966 0.5951653  0.47965072 0.73371076]
	Train: 4514 (45.75%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1280 (12.97%)
	No scaling applied
		Model Seed: 10 Seed: 2 ID mean of (MSE, MAE): [2166.0374    32.81605]
		Model Seed: 10 Seed: 2 OOD mean of (MSE, MAE) stats: [980.06177   23.241457]
		Model Seed: 10 Seed: 2 ID median of (MSE, MAE): [906.6264    27.087156]
		Model Seed: 10 Seed: 2 OOD median of (MSE, MAE) stats: [490.94418   19.494476]
		Model Seed: 10 Seed: 2 ID likelihoods: -10.759266038675506
		Model Seed: 10 Seed: 2 OOD likelihoods: -10.362745865037791
		Model Seed: 10 Seed: 2 ID calibration errors: [0.48303879 0.33334494 0.27593832 0.22250646 0.17558388 0.16510343
 0.13282563 0.11886548 0.16809259 0.13681476 0.14033849 0.14285416]
		Model Seed: 10 Seed: 2 OOD calibration errors: [0.38421534 0.22881994 0.16237091 0.07748858 0.02556251 0.00514983
 0.00888321 0.02209488 0.01614575 0.04180961 0.05973289 0.06369844]
	Model Seed: 10 ID mean of (MSE, MAE): [1867.147      30.405186]
	Model Seed: 10 OOD mean of (MSE, MAE): [1920.4633     31.618507]
	Model Seed: 10 ID median of (MSE, MAE): [752.2488    24.396622]
	Model Seed: 10 OOD median of (MSE, MAE): [1163.4282     28.213524]
	Model Seed: 10 ID likelihoods: -10.67853232134079
	Model Seed: 10 OOD likelihoods: -10.630562152684462
	Model Seed: 10 ID calibration errors: [0.45286831 0.3371701  0.22667573 0.16128187 0.13379461 0.1052701
 0.105939   0.10041037 0.11271422 0.14432313 0.09611666 0.16572966]
	Model Seed: 10 OOD calibration errors: [0.46715346 0.34783445 0.2285794  0.14598155 0.12959961 0.0848872
 0.16399764 0.20719124 0.1971627  0.31848746 0.26969181 0.3987046 ]
	Train: 4654 (47.17%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1140 (11.55%)
	No scaling applied
		Model Seed: 11 Seed: 1 ID mean of (MSE, MAE): [1568.2565     27.994318]
		Model Seed: 11 Seed: 1 OOD mean of (MSE, MAE) stats: [2860.8647     39.995556]
		Model Seed: 11 Seed: 1 ID median of (MSE, MAE): [597.8711    21.706085]
		Model Seed: 11 Seed: 1 OOD median of (MSE, MAE) stats: [1835.9122    36.93257]
		Model Seed: 11 Seed: 1 ID likelihoods: -10.597798604006073
		Model Seed: 11 Seed: 1 OOD likelihoods: -10.898378440331133
		Model Seed: 11 Seed: 1 ID calibration errors: [0.42269783 0.34099526 0.17741313 0.10005728 0.09200535 0.04543676
 0.07905236 0.08195526 0.05733586 0.1518315  0.05189484 0.18860515]
		Model Seed: 11 Seed: 1 OOD calibration errors: [0.55009158 0.46684896 0.2947879  0.21447452 0.23363671 0.16462457
 0.31911207 0.3922876  0.37817966 0.5951653  0.47965072 0.73371076]
	Train: 4514 (45.75%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1280 (12.97%)
	No scaling applied
		Model Seed: 11 Seed: 2 ID mean of (MSE, MAE): [2166.0374    32.81605]
		Model Seed: 11 Seed: 2 OOD mean of (MSE, MAE) stats: [980.06177   23.241457]
		Model Seed: 11 Seed: 2 ID median of (MSE, MAE): [906.6264    27.087156]
		Model Seed: 11 Seed: 2 OOD median of (MSE, MAE) stats: [490.94418   19.494476]
		Model Seed: 11 Seed: 2 ID likelihoods: -10.759266038675506
		Model Seed: 11 Seed: 2 OOD likelihoods: -10.362745865037791
		Model Seed: 11 Seed: 2 ID calibration errors: [0.48303879 0.33334494 0.27593832 0.22250646 0.17558388 0.16510343
 0.13282563 0.11886548 0.16809259 0.13681476 0.14033849 0.14285416]
		Model Seed: 11 Seed: 2 OOD calibration errors: [0.38421534 0.22881994 0.16237091 0.07748858 0.02556251 0.00514983
 0.00888321 0.02209488 0.01614575 0.04180961 0.05973289 0.06369844]
	Model Seed: 11 ID mean of (MSE, MAE): [1867.147      30.405186]
	Model Seed: 11 OOD mean of (MSE, MAE): [1920.4633     31.618507]
	Model Seed: 11 ID median of (MSE, MAE): [752.2488    24.396622]
	Model Seed: 11 OOD median of (MSE, MAE): [1163.4282     28.213524]
	Model Seed: 11 ID likelihoods: -10.67853232134079
	Model Seed: 11 OOD likelihoods: -10.630562152684462
	Model Seed: 11 ID calibration errors: [0.45286831 0.3371701  0.22667573 0.16128187 0.13379461 0.1052701
 0.105939   0.10041037 0.11271422 0.14432313 0.09611666 0.16572966]
	Model Seed: 11 OOD calibration errors: [0.46715346 0.34783445 0.2285794  0.14598155 0.12959961 0.0848872
 0.16399764 0.20719124 0.1971627  0.31848746 0.26969181 0.3987046 ]
	Train: 4654 (47.17%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1140 (11.55%)
	No scaling applied
		Model Seed: 12 Seed: 1 ID mean of (MSE, MAE): [1568.2565     27.994318]
		Model Seed: 12 Seed: 1 OOD mean of (MSE, MAE) stats: [2860.8647     39.995556]
		Model Seed: 12 Seed: 1 ID median of (MSE, MAE): [597.8711    21.706085]
		Model Seed: 12 Seed: 1 OOD median of (MSE, MAE) stats: [1835.9122    36.93257]
		Model Seed: 12 Seed: 1 ID likelihoods: -10.597798604006073
		Model Seed: 12 Seed: 1 OOD likelihoods: -10.898378440331133
		Model Seed: 12 Seed: 1 ID calibration errors: [0.42269783 0.34099526 0.17741313 0.10005728 0.09200535 0.04543676
 0.07905236 0.08195526 0.05733586 0.1518315  0.05189484 0.18860515]
		Model Seed: 12 Seed: 1 OOD calibration errors: [0.55009158 0.46684896 0.2947879  0.21447452 0.23363671 0.16462457
 0.31911207 0.3922876  0.37817966 0.5951653  0.47965072 0.73371076]
	Train: 4514 (45.75%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1280 (12.97%)
	No scaling applied
		Model Seed: 12 Seed: 2 ID mean of (MSE, MAE): [2166.0374    32.81605]
		Model Seed: 12 Seed: 2 OOD mean of (MSE, MAE) stats: [980.06177   23.241457]
		Model Seed: 12 Seed: 2 ID median of (MSE, MAE): [906.6264    27.087156]
		Model Seed: 12 Seed: 2 OOD median of (MSE, MAE) stats: [490.94418   19.494476]
		Model Seed: 12 Seed: 2 ID likelihoods: -10.759266038675506
		Model Seed: 12 Seed: 2 OOD likelihoods: -10.362745865037791
		Model Seed: 12 Seed: 2 ID calibration errors: [0.48303879 0.33334494 0.27593832 0.22250646 0.17558388 0.16510343
 0.13282563 0.11886548 0.16809259 0.13681476 0.14033849 0.14285416]
		Model Seed: 12 Seed: 2 OOD calibration errors: [0.38421534 0.22881994 0.16237091 0.07748858 0.02556251 0.00514983
 0.00888321 0.02209488 0.01614575 0.04180961 0.05973289 0.06369844]
	Model Seed: 12 ID mean of (MSE, MAE): [1867.147      30.405186]
	Model Seed: 12 OOD mean of (MSE, MAE): [1920.4633     31.618507]
	Model Seed: 12 ID median of (MSE, MAE): [752.2488    24.396622]
	Model Seed: 12 OOD median of (MSE, MAE): [1163.4282     28.213524]
	Model Seed: 12 ID likelihoods: -10.67853232134079
	Model Seed: 12 OOD likelihoods: -10.630562152684462
	Model Seed: 12 ID calibration errors: [0.45286831 0.3371701  0.22667573 0.16128187 0.13379461 0.1052701
 0.105939   0.10041037 0.11271422 0.14432313 0.09611666 0.16572966]
	Model Seed: 12 OOD calibration errors: [0.46715346 0.34783445 0.2285794  0.14598155 0.12959961 0.0848872
 0.16399764 0.20719124 0.1971627  0.31848746 0.26969181 0.3987046 ]
	Train: 4654 (47.17%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1140 (11.55%)
	No scaling applied
		Model Seed: 13 Seed: 1 ID mean of (MSE, MAE): [1568.2565     27.994318]
		Model Seed: 13 Seed: 1 OOD mean of (MSE, MAE) stats: [2860.8647     39.995556]
		Model Seed: 13 Seed: 1 ID median of (MSE, MAE): [597.8711    21.706085]
		Model Seed: 13 Seed: 1 OOD median of (MSE, MAE) stats: [1835.9122    36.93257]
		Model Seed: 13 Seed: 1 ID likelihoods: -10.597798604006073
		Model Seed: 13 Seed: 1 OOD likelihoods: -10.898378440331133
		Model Seed: 13 Seed: 1 ID calibration errors: [0.42269783 0.34099526 0.17741313 0.10005728 0.09200535 0.04543676
 0.07905236 0.08195526 0.05733586 0.1518315  0.05189484 0.18860515]
		Model Seed: 13 Seed: 1 OOD calibration errors: [0.55009158 0.46684896 0.2947879  0.21447452 0.23363671 0.16462457
 0.31911207 0.3922876  0.37817966 0.5951653  0.47965072 0.73371076]
	Train: 4514 (45.75%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1280 (12.97%)
	No scaling applied
		Model Seed: 13 Seed: 2 ID mean of (MSE, MAE): [2166.0374    32.81605]
		Model Seed: 13 Seed: 2 OOD mean of (MSE, MAE) stats: [980.06177   23.241457]
		Model Seed: 13 Seed: 2 ID median of (MSE, MAE): [906.6264    27.087156]
		Model Seed: 13 Seed: 2 OOD median of (MSE, MAE) stats: [490.94418   19.494476]
		Model Seed: 13 Seed: 2 ID likelihoods: -10.759266038675506
		Model Seed: 13 Seed: 2 OOD likelihoods: -10.362745865037791
		Model Seed: 13 Seed: 2 ID calibration errors: [0.48303879 0.33334494 0.27593832 0.22250646 0.17558388 0.16510343
 0.13282563 0.11886548 0.16809259 0.13681476 0.14033849 0.14285416]
		Model Seed: 13 Seed: 2 OOD calibration errors: [0.38421534 0.22881994 0.16237091 0.07748858 0.02556251 0.00514983
 0.00888321 0.02209488 0.01614575 0.04180961 0.05973289 0.06369844]
	Model Seed: 13 ID mean of (MSE, MAE): [1867.147      30.405186]
	Model Seed: 13 OOD mean of (MSE, MAE): [1920.4633     31.618507]
	Model Seed: 13 ID median of (MSE, MAE): [752.2488    24.396622]
	Model Seed: 13 OOD median of (MSE, MAE): [1163.4282     28.213524]
	Model Seed: 13 ID likelihoods: -10.67853232134079
	Model Seed: 13 OOD likelihoods: -10.630562152684462
	Model Seed: 13 ID calibration errors: [0.45286831 0.3371701  0.22667573 0.16128187 0.13379461 0.1052701
 0.105939   0.10041037 0.11271422 0.14432313 0.09611666 0.16572966]
	Model Seed: 13 OOD calibration errors: [0.46715346 0.34783445 0.2285794  0.14598155 0.12959961 0.0848872
 0.16399764 0.20719124 0.1971627  0.31848746 0.26969181 0.3987046 ]
	Train: 4654 (47.17%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1140 (11.55%)
	No scaling applied
		Model Seed: 14 Seed: 1 ID mean of (MSE, MAE): [1568.2565     27.994318]
		Model Seed: 14 Seed: 1 OOD mean of (MSE, MAE) stats: [2860.8647     39.995556]
		Model Seed: 14 Seed: 1 ID median of (MSE, MAE): [597.8711    21.706085]
		Model Seed: 14 Seed: 1 OOD median of (MSE, MAE) stats: [1835.9122    36.93257]
		Model Seed: 14 Seed: 1 ID likelihoods: -10.597798604006073
		Model Seed: 14 Seed: 1 OOD likelihoods: -10.898378440331133
		Model Seed: 14 Seed: 1 ID calibration errors: [0.42269783 0.34099526 0.17741313 0.10005728 0.09200535 0.04543676
 0.07905236 0.08195526 0.05733586 0.1518315  0.05189484 0.18860515]
		Model Seed: 14 Seed: 1 OOD calibration errors: [0.55009158 0.46684896 0.2947879  0.21447452 0.23363671 0.16462457
 0.31911207 0.3922876  0.37817966 0.5951653  0.47965072 0.73371076]
	Train: 4514 (45.75%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1280 (12.97%)
	No scaling applied
		Model Seed: 14 Seed: 2 ID mean of (MSE, MAE): [2166.0374    32.81605]
		Model Seed: 14 Seed: 2 OOD mean of (MSE, MAE) stats: [980.06177   23.241457]
		Model Seed: 14 Seed: 2 ID median of (MSE, MAE): [906.6264    27.087156]
		Model Seed: 14 Seed: 2 OOD median of (MSE, MAE) stats: [490.94418   19.494476]
		Model Seed: 14 Seed: 2 ID likelihoods: -10.759266038675506
		Model Seed: 14 Seed: 2 OOD likelihoods: -10.362745865037791
		Model Seed: 14 Seed: 2 ID calibration errors: [0.48303879 0.33334494 0.27593832 0.22250646 0.17558388 0.16510343
 0.13282563 0.11886548 0.16809259 0.13681476 0.14033849 0.14285416]
		Model Seed: 14 Seed: 2 OOD calibration errors: [0.38421534 0.22881994 0.16237091 0.07748858 0.02556251 0.00514983
 0.00888321 0.02209488 0.01614575 0.04180961 0.05973289 0.06369844]
	Model Seed: 14 ID mean of (MSE, MAE): [1867.147      30.405186]
	Model Seed: 14 OOD mean of (MSE, MAE): [1920.4633     31.618507]
	Model Seed: 14 ID median of (MSE, MAE): [752.2488    24.396622]
	Model Seed: 14 OOD median of (MSE, MAE): [1163.4282     28.213524]
	Model Seed: 14 ID likelihoods: -10.67853232134079
	Model Seed: 14 OOD likelihoods: -10.630562152684462
	Model Seed: 14 ID calibration errors: [0.45286831 0.3371701  0.22667573 0.16128187 0.13379461 0.1052701
 0.105939   0.10041037 0.11271422 0.14432313 0.09611666 0.16572966]
	Model Seed: 14 OOD calibration errors: [0.46715346 0.34783445 0.2285794  0.14598155 0.12959961 0.0848872
 0.16399764 0.20719124 0.1971627  0.31848746 0.26969181 0.3987046 ]
	Train: 4654 (47.17%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1140 (11.55%)
	No scaling applied
		Model Seed: 15 Seed: 1 ID mean of (MSE, MAE): [1568.2565     27.994318]
		Model Seed: 15 Seed: 1 OOD mean of (MSE, MAE) stats: [2860.8647     39.995556]
		Model Seed: 15 Seed: 1 ID median of (MSE, MAE): [597.8711    21.706085]
		Model Seed: 15 Seed: 1 OOD median of (MSE, MAE) stats: [1835.9122    36.93257]
		Model Seed: 15 Seed: 1 ID likelihoods: -10.597798604006073
		Model Seed: 15 Seed: 1 OOD likelihoods: -10.898378440331133
		Model Seed: 15 Seed: 1 ID calibration errors: [0.42269783 0.34099526 0.17741313 0.10005728 0.09200535 0.04543676
 0.07905236 0.08195526 0.05733586 0.1518315  0.05189484 0.18860515]
		Model Seed: 15 Seed: 1 OOD calibration errors: [0.55009158 0.46684896 0.2947879  0.21447452 0.23363671 0.16462457
 0.31911207 0.3922876  0.37817966 0.5951653  0.47965072 0.73371076]
	Train: 4514 (45.75%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1280 (12.97%)
	No scaling applied
		Model Seed: 15 Seed: 2 ID mean of (MSE, MAE): [2166.0374    32.81605]
		Model Seed: 15 Seed: 2 OOD mean of (MSE, MAE) stats: [980.06177   23.241457]
		Model Seed: 15 Seed: 2 ID median of (MSE, MAE): [906.6264    27.087156]
		Model Seed: 15 Seed: 2 OOD median of (MSE, MAE) stats: [490.94418   19.494476]
		Model Seed: 15 Seed: 2 ID likelihoods: -10.759266038675506
		Model Seed: 15 Seed: 2 OOD likelihoods: -10.362745865037791
		Model Seed: 15 Seed: 2 ID calibration errors: [0.48303879 0.33334494 0.27593832 0.22250646 0.17558388 0.16510343
 0.13282563 0.11886548 0.16809259 0.13681476 0.14033849 0.14285416]
		Model Seed: 15 Seed: 2 OOD calibration errors: [0.38421534 0.22881994 0.16237091 0.07748858 0.02556251 0.00514983
 0.00888321 0.02209488 0.01614575 0.04180961 0.05973289 0.06369844]
	Model Seed: 15 ID mean of (MSE, MAE): [1867.147      30.405186]
	Model Seed: 15 OOD mean of (MSE, MAE): [1920.4633     31.618507]
	Model Seed: 15 ID median of (MSE, MAE): [752.2488    24.396622]
	Model Seed: 15 OOD median of (MSE, MAE): [1163.4282     28.213524]
	Model Seed: 15 ID likelihoods: -10.67853232134079
	Model Seed: 15 OOD likelihoods: -10.630562152684462
	Model Seed: 15 ID calibration errors: [0.45286831 0.3371701  0.22667573 0.16128187 0.13379461 0.1052701
 0.105939   0.10041037 0.11271422 0.14432313 0.09611666 0.16572966]
	Model Seed: 15 OOD calibration errors: [0.46715346 0.34783445 0.2285794  0.14598155 0.12959961 0.0848872
 0.16399764 0.20719124 0.1971627  0.31848746 0.26969181 0.3987046 ]
	Train: 4654 (47.17%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1140 (11.55%)
	No scaling applied
		Model Seed: 16 Seed: 1 ID mean of (MSE, MAE): [1568.2565     27.994318]
		Model Seed: 16 Seed: 1 OOD mean of (MSE, MAE) stats: [2860.8647     39.995556]
		Model Seed: 16 Seed: 1 ID median of (MSE, MAE): [597.8711    21.706085]
		Model Seed: 16 Seed: 1 OOD median of (MSE, MAE) stats: [1835.9122    36.93257]
		Model Seed: 16 Seed: 1 ID likelihoods: -10.597798604006073
		Model Seed: 16 Seed: 1 OOD likelihoods: -10.898378440331133
		Model Seed: 16 Seed: 1 ID calibration errors: [0.42269783 0.34099526 0.17741313 0.10005728 0.09200535 0.04543676
 0.07905236 0.08195526 0.05733586 0.1518315  0.05189484 0.18860515]
		Model Seed: 16 Seed: 1 OOD calibration errors: [0.55009158 0.46684896 0.2947879  0.21447452 0.23363671 0.16462457
 0.31911207 0.3922876  0.37817966 0.5951653  0.47965072 0.73371076]
	Train: 4514 (45.75%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1280 (12.97%)
	No scaling applied
		Model Seed: 16 Seed: 2 ID mean of (MSE, MAE): [2166.0374    32.81605]
		Model Seed: 16 Seed: 2 OOD mean of (MSE, MAE) stats: [980.06177   23.241457]
		Model Seed: 16 Seed: 2 ID median of (MSE, MAE): [906.6264    27.087156]
		Model Seed: 16 Seed: 2 OOD median of (MSE, MAE) stats: [490.94418   19.494476]
		Model Seed: 16 Seed: 2 ID likelihoods: -10.759266038675506
		Model Seed: 16 Seed: 2 OOD likelihoods: -10.362745865037791
		Model Seed: 16 Seed: 2 ID calibration errors: [0.48303879 0.33334494 0.27593832 0.22250646 0.17558388 0.16510343
 0.13282563 0.11886548 0.16809259 0.13681476 0.14033849 0.14285416]
		Model Seed: 16 Seed: 2 OOD calibration errors: [0.38421534 0.22881994 0.16237091 0.07748858 0.02556251 0.00514983
 0.00888321 0.02209488 0.01614575 0.04180961 0.05973289 0.06369844]
	Model Seed: 16 ID mean of (MSE, MAE): [1867.147      30.405186]
	Model Seed: 16 OOD mean of (MSE, MAE): [1920.4633     31.618507]
	Model Seed: 16 ID median of (MSE, MAE): [752.2488    24.396622]
	Model Seed: 16 OOD median of (MSE, MAE): [1163.4282     28.213524]
	Model Seed: 16 ID likelihoods: -10.67853232134079
	Model Seed: 16 OOD likelihoods: -10.630562152684462
	Model Seed: 16 ID calibration errors: [0.45286831 0.3371701  0.22667573 0.16128187 0.13379461 0.1052701
 0.105939   0.10041037 0.11271422 0.14432313 0.09611666 0.16572966]
	Model Seed: 16 OOD calibration errors: [0.46715346 0.34783445 0.2285794  0.14598155 0.12959961 0.0848872
 0.16399764 0.20719124 0.1971627  0.31848746 0.26969181 0.3987046 ]
	Train: 4654 (47.17%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1140 (11.55%)
	No scaling applied
		Model Seed: 17 Seed: 1 ID mean of (MSE, MAE): [1568.2565     27.994318]
		Model Seed: 17 Seed: 1 OOD mean of (MSE, MAE) stats: [2860.8647     39.995556]
		Model Seed: 17 Seed: 1 ID median of (MSE, MAE): [597.8711    21.706085]
		Model Seed: 17 Seed: 1 OOD median of (MSE, MAE) stats: [1835.9122    36.93257]
		Model Seed: 17 Seed: 1 ID likelihoods: -10.597798604006073
		Model Seed: 17 Seed: 1 OOD likelihoods: -10.898378440331133
		Model Seed: 17 Seed: 1 ID calibration errors: [0.42269783 0.34099526 0.17741313 0.10005728 0.09200535 0.04543676
 0.07905236 0.08195526 0.05733586 0.1518315  0.05189484 0.18860515]
		Model Seed: 17 Seed: 1 OOD calibration errors: [0.55009158 0.46684896 0.2947879  0.21447452 0.23363671 0.16462457
 0.31911207 0.3922876  0.37817966 0.5951653  0.47965072 0.73371076]
	Train: 4514 (45.75%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1280 (12.97%)
	No scaling applied
		Model Seed: 17 Seed: 2 ID mean of (MSE, MAE): [2166.0374    32.81605]
		Model Seed: 17 Seed: 2 OOD mean of (MSE, MAE) stats: [980.06177   23.241457]
		Model Seed: 17 Seed: 2 ID median of (MSE, MAE): [906.6264    27.087156]
		Model Seed: 17 Seed: 2 OOD median of (MSE, MAE) stats: [490.94418   19.494476]
		Model Seed: 17 Seed: 2 ID likelihoods: -10.759266038675506
		Model Seed: 17 Seed: 2 OOD likelihoods: -10.362745865037791
		Model Seed: 17 Seed: 2 ID calibration errors: [0.48303879 0.33334494 0.27593832 0.22250646 0.17558388 0.16510343
 0.13282563 0.11886548 0.16809259 0.13681476 0.14033849 0.14285416]
		Model Seed: 17 Seed: 2 OOD calibration errors: [0.38421534 0.22881994 0.16237091 0.07748858 0.02556251 0.00514983
 0.00888321 0.02209488 0.01614575 0.04180961 0.05973289 0.06369844]
	Model Seed: 17 ID mean of (MSE, MAE): [1867.147      30.405186]
	Model Seed: 17 OOD mean of (MSE, MAE): [1920.4633     31.618507]
	Model Seed: 17 ID median of (MSE, MAE): [752.2488    24.396622]
	Model Seed: 17 OOD median of (MSE, MAE): [1163.4282     28.213524]
	Model Seed: 17 ID likelihoods: -10.67853232134079
	Model Seed: 17 OOD likelihoods: -10.630562152684462
	Model Seed: 17 ID calibration errors: [0.45286831 0.3371701  0.22667573 0.16128187 0.13379461 0.1052701
 0.105939   0.10041037 0.11271422 0.14432313 0.09611666 0.16572966]
	Model Seed: 17 OOD calibration errors: [0.46715346 0.34783445 0.2285794  0.14598155 0.12959961 0.0848872
 0.16399764 0.20719124 0.1971627  0.31848746 0.26969181 0.3987046 ]
	Train: 4654 (47.17%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1140 (11.55%)
	No scaling applied
		Model Seed: 18 Seed: 1 ID mean of (MSE, MAE): [1568.2565     27.994318]
		Model Seed: 18 Seed: 1 OOD mean of (MSE, MAE) stats: [2860.8647     39.995556]
		Model Seed: 18 Seed: 1 ID median of (MSE, MAE): [597.8711    21.706085]
		Model Seed: 18 Seed: 1 OOD median of (MSE, MAE) stats: [1835.9122    36.93257]
		Model Seed: 18 Seed: 1 ID likelihoods: -10.597798604006073
		Model Seed: 18 Seed: 1 OOD likelihoods: -10.898378440331133
		Model Seed: 18 Seed: 1 ID calibration errors: [0.42269783 0.34099526 0.17741313 0.10005728 0.09200535 0.04543676
 0.07905236 0.08195526 0.05733586 0.1518315  0.05189484 0.18860515]
		Model Seed: 18 Seed: 1 OOD calibration errors: [0.55009158 0.46684896 0.2947879  0.21447452 0.23363671 0.16462457
 0.31911207 0.3922876  0.37817966 0.5951653  0.47965072 0.73371076]
	Train: 4514 (45.75%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1280 (12.97%)
	No scaling applied
		Model Seed: 18 Seed: 2 ID mean of (MSE, MAE): [2166.0374    32.81605]
		Model Seed: 18 Seed: 2 OOD mean of (MSE, MAE) stats: [980.06177   23.241457]
		Model Seed: 18 Seed: 2 ID median of (MSE, MAE): [906.6264    27.087156]
		Model Seed: 18 Seed: 2 OOD median of (MSE, MAE) stats: [490.94418   19.494476]
		Model Seed: 18 Seed: 2 ID likelihoods: -10.759266038675506
		Model Seed: 18 Seed: 2 OOD likelihoods: -10.362745865037791
		Model Seed: 18 Seed: 2 ID calibration errors: [0.48303879 0.33334494 0.27593832 0.22250646 0.17558388 0.16510343
 0.13282563 0.11886548 0.16809259 0.13681476 0.14033849 0.14285416]
		Model Seed: 18 Seed: 2 OOD calibration errors: [0.38421534 0.22881994 0.16237091 0.07748858 0.02556251 0.00514983
 0.00888321 0.02209488 0.01614575 0.04180961 0.05973289 0.06369844]
	Model Seed: 18 ID mean of (MSE, MAE): [1867.147      30.405186]
	Model Seed: 18 OOD mean of (MSE, MAE): [1920.4633     31.618507]
	Model Seed: 18 ID median of (MSE, MAE): [752.2488    24.396622]
	Model Seed: 18 OOD median of (MSE, MAE): [1163.4282     28.213524]
	Model Seed: 18 ID likelihoods: -10.67853232134079
	Model Seed: 18 OOD likelihoods: -10.630562152684462
	Model Seed: 18 ID calibration errors: [0.45286831 0.3371701  0.22667573 0.16128187 0.13379461 0.1052701
 0.105939   0.10041037 0.11271422 0.14432313 0.09611666 0.16572966]
	Model Seed: 18 OOD calibration errors: [0.46715346 0.34783445 0.2285794  0.14598155 0.12959961 0.0848872
 0.16399764 0.20719124 0.1971627  0.31848746 0.26969181 0.3987046 ]
	Train: 4654 (47.17%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1140 (11.55%)
	No scaling applied
		Model Seed: 19 Seed: 1 ID mean of (MSE, MAE): [1568.2565     27.994318]
		Model Seed: 19 Seed: 1 OOD mean of (MSE, MAE) stats: [2860.8647     39.995556]
		Model Seed: 19 Seed: 1 ID median of (MSE, MAE): [597.8711    21.706085]
		Model Seed: 19 Seed: 1 OOD median of (MSE, MAE) stats: [1835.9122    36.93257]
		Model Seed: 19 Seed: 1 ID likelihoods: -10.597798604006073
		Model Seed: 19 Seed: 1 OOD likelihoods: -10.898378440331133
		Model Seed: 19 Seed: 1 ID calibration errors: [0.42269783 0.34099526 0.17741313 0.10005728 0.09200535 0.04543676
 0.07905236 0.08195526 0.05733586 0.1518315  0.05189484 0.18860515]
		Model Seed: 19 Seed: 1 OOD calibration errors: [0.55009158 0.46684896 0.2947879  0.21447452 0.23363671 0.16462457
 0.31911207 0.3922876  0.37817966 0.5951653  0.47965072 0.73371076]
	Train: 4514 (45.75%)
	Val: 2016 (20.43%)
	Test: 2057 (20.85%)
	Test OOD: 1280 (12.97%)
	No scaling applied
		Model Seed: 19 Seed: 2 ID mean of (MSE, MAE): [2166.0374    32.81605]
		Model Seed: 19 Seed: 2 OOD mean of (MSE, MAE) stats: [980.06177   23.241457]
		Model Seed: 19 Seed: 2 ID median of (MSE, MAE): [906.6264    27.087156]
		Model Seed: 19 Seed: 2 OOD median of (MSE, MAE) stats: [490.94418   19.494476]
		Model Seed: 19 Seed: 2 ID likelihoods: -10.759266038675506
		Model Seed: 19 Seed: 2 OOD likelihoods: -10.362745865037791
		Model Seed: 19 Seed: 2 ID calibration errors: [0.48303879 0.33334494 0.27593832 0.22250646 0.17558388 0.16510343
 0.13282563 0.11886548 0.16809259 0.13681476 0.14033849 0.14285416]
		Model Seed: 19 Seed: 2 OOD calibration errors: [0.38421534 0.22881994 0.16237091 0.07748858 0.02556251 0.00514983
 0.00888321 0.02209488 0.01614575 0.04180961 0.05973289 0.06369844]
	Model Seed: 19 ID mean of (MSE, MAE): [1867.147      30.405186]
	Model Seed: 19 OOD mean of (MSE, MAE): [1920.4633     31.618507]
	Model Seed: 19 ID median of (MSE, MAE): [752.2488    24.396622]
	Model Seed: 19 OOD median of (MSE, MAE): [1163.4282     28.213524]
	Model Seed: 19 ID likelihoods: -10.67853232134079
	Model Seed: 19 OOD likelihoods: -10.630562152684462
	Model Seed: 19 ID calibration errors: [0.45286831 0.3371701  0.22667573 0.16128187 0.13379461 0.1052701
 0.105939   0.10041037 0.11271422 0.14432313 0.09611666 0.16572966]
	Model Seed: 19 OOD calibration errors: [0.46715346 0.34783445 0.2285794  0.14598155 0.12959961 0.0848872
 0.16399764 0.20719124 0.1971627  0.31848746 0.26969181 0.3987046 ]
ID mean of (MSE, MAE): [1867.146728515625, 30.40518569946289] +- [0.000244140625, 0.0] +- [298.89045    2.410866] 
OOD mean of (MSE, MAE): [1920.463134765625, 31.61850357055664] +- [0.0001220703125, 3.814697265625e-06] +- [940.401465    8.3770495] 
ID median of (MSE, MAE): [752.2488403320312, 24.396621704101562] +- [6.103515625e-05, 0.0] +- [154.37765     2.6905355] 
OOD median of (MSE, MAE): [1163.4281005859375, 28.21352767944336] +- [0.0001220703125, 3.814697265625e-06] +- [672.48401    8.719047] 
ID likelihoods: -10.678532321340787 +- 1.7763568394002505e-15 +- 0.08073371733471646 
OOD likelihoods: -10.630562152684462 +- 0.0 +- 0.26781628764667076 
ID calibration errors: [0.4528683093371667, 0.33717009950360494, 0.22667572606185837, 0.1612818669841198, 0.13379461377776772, 0.10527009725747394, 0.10593899508097296, 0.10041036814087725, 0.11271422474787171, 0.1443231284113114, 0.0961166640461804, 0.16572965566811157] +- [0.0, 5.551115123125783e-17, 0.0, 0.0, 0.0, 0.0, 0.0, 2.7755575615628914e-17, 0.0, 2.7755575615628914e-17, 1.3877787807814457e-17, 0.0] +- [0.03017048 0.00382516 0.04926259 0.06122459 0.04178926 0.05983334
 0.02688663 0.01845511 0.05537837 0.00750837 0.04422183 0.02287549] 
OOD calibration errors: [0.4671534582228155, 0.3478344512055441, 0.2285794030809017, 0.14598155083213005, 0.12959961266059575, 0.08488719770390883, 0.16399764146475418, 0.207191240477075, 0.19716270244713546, 0.31848745586416405, 0.26969180587746117, 0.3987045966941438] +- [0.0, 5.551115123125783e-17, 5.551115123125783e-17, 0.0, 2.7755575615628914e-17, 1.3877787807814457e-17, 2.7755575615628914e-17, 0.0, 2.7755575615628914e-17, 5.551115123125783e-17, 5.551115123125783e-17, 0.0] +- [0.08293812 0.11901451 0.0662085  0.06849297 0.1040371  0.07973737
 0.15511443 0.18509636 0.18101695 0.27667784 0.20995892 0.33500616] 
