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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)
		Height: REAL_VALUED (STATIC_INPUT)
		Weight: REAL_VALUED (STATIC_INPUT)
		Gender: REAL_VALUED (STATIC_INPUT)
		Race: REAL_VALUED (STATIC_INPUT)
		EduLevel: REAL_VALUED (STATIC_INPUT)
		AnnualInc: REAL_VALUED (STATIC_INPUT)
		MaritalStatus: REAL_VALUED (STATIC_INPUT)
		DaysWkEx: REAL_VALUED (STATIC_INPUT)
		DaysWkDrinkAlc: REAL_VALUED (STATIC_INPUT)
		DaysMonBingeAlc: REAL_VALUED (STATIC_INPUT)
		T1DDiagAge: REAL_VALUED (STATIC_INPUT)
		NumHospDKA: REAL_VALUED (STATIC_INPUT)
		NumSHSinceT1DDiag: REAL_VALUED (STATIC_INPUT)
		InsDeliveryMethod: REAL_VALUED (STATIC_INPUT)
		UnitsInsTotal: REAL_VALUED (STATIC_INPUT)
		NumMeterCheckDay: REAL_VALUED (STATIC_INPUT)
		Aspirin: REAL_VALUED (STATIC_INPUT)
		Simvastatin: REAL_VALUED (STATIC_INPUT)
		Lisinopril: REAL_VALUED (STATIC_INPUT)
		Vitamin D: REAL_VALUED (STATIC_INPUT)
		Multivitamin preparation: REAL_VALUED (STATIC_INPUT)
		Omeprazole: REAL_VALUED (STATIC_INPUT)
		atorvastatin: REAL_VALUED (STATIC_INPUT)
		Synthroid: REAL_VALUED (STATIC_INPUT)
		vitamin D3: REAL_VALUED (STATIC_INPUT)
		Hypertension: REAL_VALUED (STATIC_INPUT)
		Hyperlipidemia: REAL_VALUED (STATIC_INPUT)
		Hypothyroidism: REAL_VALUED (STATIC_INPUT)
		Depression: REAL_VALUED (STATIC_INPUT)
		Coronary artery disease: REAL_VALUED (STATIC_INPUT)
		Diabetic peripheral neuropathy: REAL_VALUED (STATIC_INPUT)
		Dyslipidemia: REAL_VALUED (STATIC_INPUT)
		Chronic kidney disease: REAL_VALUED (STATIC_INPUT)
		Osteoporosis: REAL_VALUED (STATIC_INPUT)
		Proliferative diabetic retinopathy: REAL_VALUED (STATIC_INPUT)
		Hypercholesterolemia: REAL_VALUED (STATIC_INPUT)
		Erectile dysfunction: REAL_VALUED (STATIC_INPUT)
		Type I diabetes mellitus: REAL_VALUED (STATIC_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: 1416
	Extracted segments: 681
	Interpolated values: 140564
	Percent of values interpolated: 24.24%
Splitting data...
	Train: 357814 (68.58%)
	Val: 96960 (18.58%)
	Test: 125008 (23.96%)
Scaling data...
	No scaling applied
Data formatting complete.
--------------------------------
Current value: 0.09474323689937592, Current params: {'in_len': 180, 'max_samples_per_ts': 150, 'hidden_size': 80, 'num_attention_heads': 3, 'dropout': 0.22869834840044573, 'lr': 0.0003965761934934222, 'batch_size': 48, 'max_grad_norm': 0.49510422682222904}
Best value: 0.09474323689937592, Best params: {'in_len': 180, 'max_samples_per_ts': 150, 'hidden_size': 80, 'num_attention_heads': 3, 'dropout': 0.22869834840044573, 'lr': 0.0003965761934934222, 'batch_size': 48, 'max_grad_norm': 0.49510422682222904}
Current value: 0.08801671862602234, Current params: {'in_len': 180, 'max_samples_per_ts': 50, 'hidden_size': 48, 'num_attention_heads': 3, 'dropout': 0.1540959423204858, 'lr': 0.004506789334668041, 'batch_size': 32, 'max_grad_norm': 0.5248505158679704}
Best value: 0.08801671862602234, Best params: {'in_len': 180, 'max_samples_per_ts': 50, 'hidden_size': 48, 'num_attention_heads': 3, 'dropout': 0.1540959423204858, 'lr': 0.004506789334668041, 'batch_size': 32, 'max_grad_norm': 0.5248505158679704}
Current value: 0.10275181382894516, Current params: {'in_len': 180, 'max_samples_per_ts': 200, 'hidden_size': 144, 'num_attention_heads': 2, 'dropout': 0.10678587815280127, 'lr': 0.009524038981658654, 'batch_size': 32, 'max_grad_norm': 0.7377520660603355}
Best value: 0.08801671862602234, Best params: {'in_len': 180, 'max_samples_per_ts': 50, 'hidden_size': 48, 'num_attention_heads': 3, 'dropout': 0.1540959423204858, 'lr': 0.004506789334668041, 'batch_size': 32, 'max_grad_norm': 0.5248505158679704}
Current value: 0.09100145101547241, Current params: {'in_len': 144, 'max_samples_per_ts': 150, 'hidden_size': 208, 'num_attention_heads': 1, 'dropout': 0.2668922473608931, 'lr': 0.008390793030900966, 'batch_size': 64, 'max_grad_norm': 0.1236168715185916}
Best value: 0.08801671862602234, Best params: {'in_len': 180, 'max_samples_per_ts': 50, 'hidden_size': 48, 'num_attention_heads': 3, 'dropout': 0.1540959423204858, 'lr': 0.004506789334668041, 'batch_size': 32, 'max_grad_norm': 0.5248505158679704}
Current value: 0.08783876895904541, Current params: {'in_len': 144, 'max_samples_per_ts': 200, 'hidden_size': 16, 'num_attention_heads': 2, 'dropout': 0.21982871186596353, 'lr': 0.005424839041416716, 'batch_size': 48, 'max_grad_norm': 0.9730429066069279}
Best value: 0.08783876895904541, Best params: {'in_len': 144, 'max_samples_per_ts': 200, 'hidden_size': 16, 'num_attention_heads': 2, 'dropout': 0.21982871186596353, 'lr': 0.005424839041416716, 'batch_size': 48, 'max_grad_norm': 0.9730429066069279}
Current value: 0.08874011039733887, Current params: {'in_len': 192, 'max_samples_per_ts': 200, 'hidden_size': 48, 'num_attention_heads': 2, 'dropout': 0.16566179842901974, 'lr': 0.0013732530569643122, 'batch_size': 32, 'max_grad_norm': 0.3798847873061824}
Best value: 0.08783876895904541, Best params: {'in_len': 144, 'max_samples_per_ts': 200, 'hidden_size': 16, 'num_attention_heads': 2, 'dropout': 0.21982871186596353, 'lr': 0.005424839041416716, 'batch_size': 48, 'max_grad_norm': 0.9730429066069279}
Current value: 0.09055887162685394, Current params: {'in_len': 132, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 3, 'dropout': 0.23729539052561813, 'lr': 0.003836132732292066, 'batch_size': 32, 'max_grad_norm': 0.6789495438478501}
Best value: 0.08783876895904541, Best params: {'in_len': 144, 'max_samples_per_ts': 200, 'hidden_size': 16, 'num_attention_heads': 2, 'dropout': 0.21982871186596353, 'lr': 0.005424839041416716, 'batch_size': 48, 'max_grad_norm': 0.9730429066069279}
Current value: 0.30399829149246216, Current params: {'in_len': 108, 'max_samples_per_ts': 150, 'hidden_size': 176, 'num_attention_heads': 2, 'dropout': 0.17306707251383288, 'lr': 0.006206794309649241, 'batch_size': 48, 'max_grad_norm': 0.5592346779568934}
Best value: 0.08783876895904541, Best params: {'in_len': 144, 'max_samples_per_ts': 200, 'hidden_size': 16, 'num_attention_heads': 2, 'dropout': 0.21982871186596353, 'lr': 0.005424839041416716, 'batch_size': 48, 'max_grad_norm': 0.9730429066069279}
Current value: 0.0793653354048729, Current params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.25305944938410385, 'lr': 0.0012085771132017077, 'batch_size': 32, 'max_grad_norm': 0.7315689984828031}
Best value: 0.0793653354048729, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.25305944938410385, 'lr': 0.0012085771132017077, 'batch_size': 32, 'max_grad_norm': 0.7315689984828031}
Current value: 0.304718941450119, Current params: {'in_len': 120, 'max_samples_per_ts': 100, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2504329571526954, 'lr': 0.0009733518247976271, 'batch_size': 32, 'max_grad_norm': 0.3890448492324838}
Best value: 0.0793653354048729, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.25305944938410385, 'lr': 0.0012085771132017077, 'batch_size': 32, 'max_grad_norm': 0.7315689984828031}
Current value: 0.2994464635848999, Current params: {'in_len': 96, 'max_samples_per_ts': 100, 'hidden_size': 128, 'num_attention_heads': 4, 'dropout': 0.29577563659526673, 'lr': 0.002649475608462103, 'batch_size': 64, 'max_grad_norm': 0.9908938663703903}
Best value: 0.0793653354048729, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.25305944938410385, 'lr': 0.0012085771132017077, 'batch_size': 32, 'max_grad_norm': 0.7315689984828031}
Current value: 0.29833847284317017, Current params: {'in_len': 156, 'max_samples_per_ts': 200, 'hidden_size': 16, 'num_attention_heads': 1, 'dropout': 0.20552610006042146, 'lr': 0.006416610594376341, 'batch_size': 48, 'max_grad_norm': 0.9671675400096977}
Best value: 0.0793653354048729, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.25305944938410385, 'lr': 0.0012085771132017077, 'batch_size': 32, 'max_grad_norm': 0.7315689984828031}
Current value: 0.2811185121536255, Current params: {'in_len': 156, 'max_samples_per_ts': 200, 'hidden_size': 16, 'num_attention_heads': 1, 'dropout': 0.28434021772266593, 'lr': 0.006128818874820053, 'batch_size': 48, 'max_grad_norm': 0.8275992262175671}
Best value: 0.0793653354048729, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.25305944938410385, 'lr': 0.0012085771132017077, 'batch_size': 32, 'max_grad_norm': 0.7315689984828031}
Current value: 0.08572036772966385, Current params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.21279090200581557, 'lr': 0.003021022170069057, 'batch_size': 64, 'max_grad_norm': 0.8636699127004543}
Best value: 0.0793653354048729, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.25305944938410385, 'lr': 0.0012085771132017077, 'batch_size': 32, 'max_grad_norm': 0.7315689984828031}
Current value: 0.2757709324359894, Current params: {'in_len': 96, 'max_samples_per_ts': 150, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.1974546479498289, 'lr': 0.0026950609948999935, 'batch_size': 64, 'max_grad_norm': 0.8070823048399995}
Best value: 0.0793653354048729, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.25305944938410385, 'lr': 0.0012085771132017077, 'batch_size': 32, 'max_grad_norm': 0.7315689984828031}
Current value: 0.08298715949058533, Current params: {'in_len': 108, 'max_samples_per_ts': 200, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.2646324451651484, 'lr': 0.0022127063068408796, 'batch_size': 64, 'max_grad_norm': 0.6325670329227235}
Best value: 0.0793653354048729, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.25305944938410385, 'lr': 0.0012085771132017077, 'batch_size': 32, 'max_grad_norm': 0.7315689984828031}
Current value: 0.2923476994037628, Current params: {'in_len': 120, 'max_samples_per_ts': 100, 'hidden_size': 160, 'num_attention_heads': 4, 'dropout': 0.25792624379804685, 'lr': 0.0017211961006106968, 'batch_size': 64, 'max_grad_norm': 0.6228918674194983}
Best value: 0.0793653354048729, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.25305944938410385, 'lr': 0.0012085771132017077, 'batch_size': 32, 'max_grad_norm': 0.7315689984828031}
Current value: 0.32823485136032104, Current params: {'in_len': 108, 'max_samples_per_ts': 150, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.27359313427353293, 'lr': 0.00021239208711752874, 'batch_size': 48, 'max_grad_norm': 0.042979523991507174}
Best value: 0.0793653354048729, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.25305944938410385, 'lr': 0.0012085771132017077, 'batch_size': 32, 'max_grad_norm': 0.7315689984828031}
Current value: 0.2719453275203705, Current params: {'in_len': 120, 'max_samples_per_ts': 200, 'hidden_size': 240, 'num_attention_heads': 1, 'dropout': 0.24538105424504428, 'lr': 0.00212165290557411, 'batch_size': 32, 'max_grad_norm': 0.27189731807014605}
Best value: 0.0793653354048729, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.25305944938410385, 'lr': 0.0012085771132017077, 'batch_size': 32, 'max_grad_norm': 0.7315689984828031}
Current value: 0.2858860492706299, Current params: {'in_len': 108, 'max_samples_per_ts': 150, 'hidden_size': 256, 'num_attention_heads': 2, 'dropout': 0.2977502097768895, 'lr': 0.0037928271350544476, 'batch_size': 64, 'max_grad_norm': 0.6918263612187123}
Best value: 0.0793653354048729, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.25305944938410385, 'lr': 0.0012085771132017077, 'batch_size': 32, 'max_grad_norm': 0.7315689984828031}
Current value: 0.2916630208492279, Current params: {'in_len': 132, 'max_samples_per_ts': 200, 'hidden_size': 128, 'num_attention_heads': 1, 'dropout': 0.19165686640498183, 'lr': 0.0037034350756994378, 'batch_size': 48, 'max_grad_norm': 0.40656985117088895}
Best value: 0.0793653354048729, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.25305944938410385, 'lr': 0.0012085771132017077, 'batch_size': 32, 'max_grad_norm': 0.7315689984828031}
Current value: 0.08495630323886871, Current params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.22108496976661782, 'lr': 0.003172815649053539, 'batch_size': 64, 'max_grad_norm': 0.8467764227153112}
Best value: 0.0793653354048729, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.25305944938410385, 'lr': 0.0012085771132017077, 'batch_size': 32, 'max_grad_norm': 0.7315689984828031}
Current value: 0.07913843542337418, Current params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.23386673765413968, 'lr': 0.0018769954660235563, 'batch_size': 64, 'max_grad_norm': 0.8748220727487463}
Best value: 0.07913843542337418, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.23386673765413968, 'lr': 0.0018769954660235563, 'batch_size': 64, 'max_grad_norm': 0.8748220727487463}
Current value: 0.28106486797332764, Current params: {'in_len': 108, 'max_samples_per_ts': 200, 'hidden_size': 48, 'num_attention_heads': 1, 'dropout': 0.2616046053023917, 'lr': 0.001075324167117865, 'batch_size': 64, 'max_grad_norm': 0.7514815071140891}
Best value: 0.07913843542337418, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.23386673765413968, 'lr': 0.0018769954660235563, 'batch_size': 64, 'max_grad_norm': 0.8748220727487463}
Current value: 0.28206852078437805, Current params: {'in_len': 96, 'max_samples_per_ts': 150, 'hidden_size': 160, 'num_attention_heads': 2, 'dropout': 0.2401016730674648, 'lr': 0.0019732179619418437, 'batch_size': 64, 'max_grad_norm': 0.6107851312578784}
Best value: 0.07913843542337418, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.23386673765413968, 'lr': 0.0018769954660235563, 'batch_size': 64, 'max_grad_norm': 0.8748220727487463}
Current value: 0.2806072235107422, Current params: {'in_len': 108, 'max_samples_per_ts': 200, 'hidden_size': 112, 'num_attention_heads': 1, 'dropout': 0.2764765838530255, 'lr': 0.0009493354077337399, 'batch_size': 64, 'max_grad_norm': 0.8958298170086814}
Best value: 0.07913843542337418, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.23386673765413968, 'lr': 0.0018769954660235563, 'batch_size': 64, 'max_grad_norm': 0.8748220727487463}
Current value: 0.27332010865211487, Current params: {'in_len': 120, 'max_samples_per_ts': 150, 'hidden_size': 64, 'num_attention_heads': 2, 'dropout': 0.2357827125838408, 'lr': 0.0048509524370300115, 'batch_size': 48, 'max_grad_norm': 0.7569105609549378}
Best value: 0.07913843542337418, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.23386673765413968, 'lr': 0.0018769954660235563, 'batch_size': 64, 'max_grad_norm': 0.8748220727487463}
Current value: 0.2694808840751648, Current params: {'in_len': 132, 'max_samples_per_ts': 200, 'hidden_size': 144, 'num_attention_heads': 1, 'dropout': 0.2824849874857351, 'lr': 0.002363148908582315, 'batch_size': 48, 'max_grad_norm': 0.6475705582859987}
Best value: 0.07913843542337418, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.23386673765413968, 'lr': 0.0018769954660235563, 'batch_size': 64, 'max_grad_norm': 0.8748220727487463}
Current value: 0.31474536657333374, Current params: {'in_len': 96, 'max_samples_per_ts': 100, 'hidden_size': 112, 'num_attention_heads': 3, 'dropout': 0.25764302639411213, 'lr': 0.00026079797347159945, 'batch_size': 64, 'max_grad_norm': 0.887205972378821}
Best value: 0.07913843542337418, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.23386673765413968, 'lr': 0.0018769954660235563, 'batch_size': 64, 'max_grad_norm': 0.8748220727487463}
Current value: 0.2819129228591919, Current params: {'in_len': 108, 'max_samples_per_ts': 150, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.23166369990059213, 'lr': 0.0075700411875922, 'batch_size': 32, 'max_grad_norm': 0.44153996907885046}
Best value: 0.07913843542337418, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.23386673765413968, 'lr': 0.0018769954660235563, 'batch_size': 64, 'max_grad_norm': 0.8748220727487463}
Current value: 0.27899032831192017, Current params: {'in_len': 168, 'max_samples_per_ts': 200, 'hidden_size': 176, 'num_attention_heads': 2, 'dropout': 0.18646296935931006, 'lr': 0.001486794078178733, 'batch_size': 64, 'max_grad_norm': 0.4845039087260298}
Best value: 0.07913843542337418, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.23386673765413968, 'lr': 0.0018769954660235563, 'batch_size': 64, 'max_grad_norm': 0.8748220727487463}
Current value: 0.08727876096963882, Current params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.2223145740158499, 'lr': 0.0032377630278836154, 'batch_size': 64, 'max_grad_norm': 0.7873406161555072}
Best value: 0.07913843542337418, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.23386673765413968, 'lr': 0.0018769954660235563, 'batch_size': 64, 'max_grad_norm': 0.8748220727487463}
Current value: 0.27683570981025696, Current params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.22277751618499675, 'lr': 0.0032597778365518774, 'batch_size': 64, 'max_grad_norm': 0.9107429786387459}
Best value: 0.07913843542337418, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.23386673765413968, 'lr': 0.0018769954660235563, 'batch_size': 64, 'max_grad_norm': 0.8748220727487463}
Current value: 0.27157413959503174, Current params: {'in_len': 108, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.24946083809094294, 'lr': 0.00421422489003135, 'batch_size': 64, 'max_grad_norm': 0.5658392314759281}
Best value: 0.07913843542337418, Best params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.23386673765413968, 'lr': 0.0018769954660235563, 'batch_size': 64, 'max_grad_norm': 0.8748220727487463}
Current value: 0.07627468556165695, Current params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.14180635714600212, 'lr': 0.0007559568420232418, 'batch_size': 64, 'max_grad_norm': 0.7057947208255668}
Best value: 0.07627468556165695, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.14180635714600212, 'lr': 0.0007559568420232418, 'batch_size': 64, 'max_grad_norm': 0.7057947208255668}
Current value: 0.08005280047655106, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.11636903783605461, 'lr': 0.0005442639484917811, 'batch_size': 64, 'max_grad_norm': 0.6872733559888218}
Best value: 0.07627468556165695, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.14180635714600212, 'lr': 0.0007559568420232418, 'batch_size': 64, 'max_grad_norm': 0.7057947208255668}
Current value: 0.07754843682050705, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 48, 'num_attention_heads': 2, 'dropout': 0.12683107338345095, 'lr': 0.000766445990154049, 'batch_size': 48, 'max_grad_norm': 0.7064066205200504}
Best value: 0.07627468556165695, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.14180635714600212, 'lr': 0.0007559568420232418, 'batch_size': 64, 'max_grad_norm': 0.7057947208255668}
Current value: 0.08472408354282379, Current params: {'in_len': 132, 'max_samples_per_ts': 50, 'hidden_size': 32, 'num_attention_heads': 3, 'dropout': 0.1293833257269471, 'lr': 0.0007446530382314315, 'batch_size': 32, 'max_grad_norm': 0.7222607161749214}
Best value: 0.07627468556165695, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.14180635714600212, 'lr': 0.0007559568420232418, 'batch_size': 64, 'max_grad_norm': 0.7057947208255668}
Current value: 0.0785297229886055, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 2, 'dropout': 0.13639468744456168, 'lr': 0.0014867501939919894, 'batch_size': 48, 'max_grad_norm': 0.2953216134035859}
Best value: 0.07627468556165695, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.14180635714600212, 'lr': 0.0007559568420232418, 'batch_size': 64, 'max_grad_norm': 0.7057947208255668}
Current value: 0.08972711116075516, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 32, 'num_attention_heads': 2, 'dropout': 0.13906342885172598, 'lr': 0.009641729448866211, 'batch_size': 48, 'max_grad_norm': 0.2926708594772486}
Best value: 0.07627468556165695, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.14180635714600212, 'lr': 0.0007559568420232418, 'batch_size': 64, 'max_grad_norm': 0.7057947208255668}
Current value: 0.25924739241600037, Current params: {'in_len': 192, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 2, 'dropout': 0.15042069194001795, 'lr': 0.0015506241532358064, 'batch_size': 48, 'max_grad_norm': 0.22798341743393685}
Best value: 0.07627468556165695, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.14180635714600212, 'lr': 0.0007559568420232418, 'batch_size': 64, 'max_grad_norm': 0.7057947208255668}
Current value: 0.07997370511293411, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 48, 'num_attention_heads': 2, 'dropout': 0.10240245228059314, 'lr': 0.0013771612741770634, 'batch_size': 32, 'max_grad_norm': 0.16293834087865292}
Best value: 0.07627468556165695, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.14180635714600212, 'lr': 0.0007559568420232418, 'batch_size': 64, 'max_grad_norm': 0.7057947208255668}
Current value: 0.08315221965312958, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 3, 'dropout': 0.11922436280442539, 'lr': 0.00018527539578123053, 'batch_size': 48, 'max_grad_norm': 0.5331301623232102}
Best value: 0.07627468556165695, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.14180635714600212, 'lr': 0.0007559568420232418, 'batch_size': 64, 'max_grad_norm': 0.7057947208255668}
Current value: 0.3021567761898041, Current params: {'in_len': 96, 'max_samples_per_ts': 100, 'hidden_size': 32, 'num_attention_heads': 2, 'dropout': 0.16164136610342897, 'lr': 0.0009650674579928318, 'batch_size': 32, 'max_grad_norm': 0.7838287428273203}
Best value: 0.07627468556165695, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.14180635714600212, 'lr': 0.0007559568420232418, 'batch_size': 64, 'max_grad_norm': 0.7057947208255668}
Current value: 0.07596633583307266, Current params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 2, 'dropout': 0.1504541564537306, 'lr': 0.0018430630797167395, 'batch_size': 48, 'max_grad_norm': 0.9530046023189843}
Best value: 0.07596633583307266, Best params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 2, 'dropout': 0.1504541564537306, 'lr': 0.0018430630797167395, 'batch_size': 48, 'max_grad_norm': 0.9530046023189843}
Current value: 0.08067645877599716, Current params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 2, 'dropout': 0.14378733538782998, 'lr': 0.0017877027667314, 'batch_size': 48, 'max_grad_norm': 0.937464014866254}
Best value: 0.07596633583307266, Best params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 2, 'dropout': 0.1504541564537306, 'lr': 0.0018430630797167395, 'batch_size': 48, 'max_grad_norm': 0.9530046023189843}
Current value: 0.07903483510017395, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 128, 'num_attention_heads': 2, 'dropout': 0.12982977876933344, 'lr': 0.002713236959805231, 'batch_size': 48, 'max_grad_norm': 0.9994163390126644}
Best value: 0.07596633583307266, Best params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 2, 'dropout': 0.1504541564537306, 'lr': 0.0018430630797167395, 'batch_size': 48, 'max_grad_norm': 0.9530046023189843}
Current value: 0.07725610584020615, Current params: {'in_len': 132, 'max_samples_per_ts': 50, 'hidden_size': 128, 'num_attention_heads': 3, 'dropout': 0.12632150119281293, 'lr': 0.0026182747784861153, 'batch_size': 48, 'max_grad_norm': 0.9865961016128295}
Best value: 0.07596633583307266, Best params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 2, 'dropout': 0.1504541564537306, 'lr': 0.0018430630797167395, 'batch_size': 48, 'max_grad_norm': 0.9530046023189843}
Current value: 0.07792942970991135, Current params: {'in_len': 132, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 3, 'dropout': 0.11262849357417276, 'lr': 0.0006229290704763214, 'batch_size': 48, 'max_grad_norm': 0.3232236212418971}
Best value: 0.07596633583307266, Best params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 2, 'dropout': 0.1504541564537306, 'lr': 0.0018430630797167395, 'batch_size': 48, 'max_grad_norm': 0.9530046023189843}
Current value: 0.2565698027610779, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 3, 'dropout': 0.11336847396368255, 'lr': 0.0006738639956425143, 'batch_size': 48, 'max_grad_norm': 0.9472775943705964}
Best value: 0.07596633583307266, Best params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 2, 'dropout': 0.1504541564537306, 'lr': 0.0018430630797167395, 'batch_size': 48, 'max_grad_norm': 0.9530046023189843}
--------------------------------
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)
		Height: REAL_VALUED (STATIC_INPUT)
		Weight: REAL_VALUED (STATIC_INPUT)
		Gender: REAL_VALUED (STATIC_INPUT)
		Race: REAL_VALUED (STATIC_INPUT)
		EduLevel: REAL_VALUED (STATIC_INPUT)
		AnnualInc: REAL_VALUED (STATIC_INPUT)
		MaritalStatus: REAL_VALUED (STATIC_INPUT)
		DaysWkEx: REAL_VALUED (STATIC_INPUT)
		DaysWkDrinkAlc: REAL_VALUED (STATIC_INPUT)
		DaysMonBingeAlc: REAL_VALUED (STATIC_INPUT)
		T1DDiagAge: REAL_VALUED (STATIC_INPUT)
		NumHospDKA: REAL_VALUED (STATIC_INPUT)
		NumSHSinceT1DDiag: REAL_VALUED (STATIC_INPUT)
		InsDeliveryMethod: REAL_VALUED (STATIC_INPUT)
		UnitsInsTotal: REAL_VALUED (STATIC_INPUT)
		NumMeterCheckDay: REAL_VALUED (STATIC_INPUT)
		Aspirin: REAL_VALUED (STATIC_INPUT)
		Simvastatin: REAL_VALUED (STATIC_INPUT)
		Lisinopril: REAL_VALUED (STATIC_INPUT)
		Vitamin D: REAL_VALUED (STATIC_INPUT)
		Multivitamin preparation: REAL_VALUED (STATIC_INPUT)
		Omeprazole: REAL_VALUED (STATIC_INPUT)
		atorvastatin: REAL_VALUED (STATIC_INPUT)
		Synthroid: REAL_VALUED (STATIC_INPUT)
		vitamin D3: REAL_VALUED (STATIC_INPUT)
		Hypertension: REAL_VALUED (STATIC_INPUT)
		Hyperlipidemia: REAL_VALUED (STATIC_INPUT)
		Hypothyroidism: REAL_VALUED (STATIC_INPUT)
		Depression: REAL_VALUED (STATIC_INPUT)
		Coronary artery disease: REAL_VALUED (STATIC_INPUT)
		Diabetic peripheral neuropathy: REAL_VALUED (STATIC_INPUT)
		Dyslipidemia: REAL_VALUED (STATIC_INPUT)
		Chronic kidney disease: REAL_VALUED (STATIC_INPUT)
		Osteoporosis: REAL_VALUED (STATIC_INPUT)
		Proliferative diabetic retinopathy: REAL_VALUED (STATIC_INPUT)
		Hypercholesterolemia: REAL_VALUED (STATIC_INPUT)
		Erectile dysfunction: REAL_VALUED (STATIC_INPUT)
		Type I diabetes mellitus: REAL_VALUED (STATIC_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: 1416
	Extracted segments: 681
	Interpolated values: 140564
	Percent of values interpolated: 24.24%
Splitting data...
	Train: 431798 (69.72%)
	Val: 57067 (9.21%)
	Test: 72421 (11.69%)
	Test OOD: 58048 (9.37%)
Scaling data...
	No scaling applied
Data formatting complete.
--------------------------------
	Train: 425413 (68.73%)
	Val: 56593 (9.14%)
	Test: 72022 (11.64%)
	Test OOD: 64922 (10.49%)
	No scaling applied
		Model Seed: 10 Seed: 1 ID mean of (MSE, MAE): [751.18687403  17.44435723]
		Model Seed: 10 Seed: 1 OOD mean of (MSE, MAE) stats: [893.30095816  18.69457716]
		Model Seed: 10 Seed: 1 ID median of (MSE, MAE): [235.20743714  12.6427021 ]
		Model Seed: 10 Seed: 1 OOD median of (MSE, MAE) stats: [267.42869315  13.40165075]
		Model Seed: 10 Seed: 1 ID likelihoods: 0
		Model Seed: 10 Seed: 1 OOD likelihoods: 0
		Model Seed: 10 Seed: 1 ID calibration errors: [0.07754914 0.04082213 0.03017668 0.02709583 0.02375461 0.02427416
 0.02491749 0.02805371 0.030802   0.03475008 0.03955691 0.04277068]
		Model Seed: 10 Seed: 1 OOD calibration errors: [0.03692305 0.02873999 0.02754342 0.02831676 0.02914347 0.03117281
 0.03353218 0.03776452 0.04050508 0.043249   0.04790377 0.05077791]
	Train: 429990 (69.47%)
	Val: 56876 (9.19%)
	Test: 72378 (11.69%)
	Test OOD: 59706 (9.65%)
	No scaling applied
		Model Seed: 10 Seed: 2 ID mean of (MSE, MAE): [714.43285417  17.00837596]
		Model Seed: 10 Seed: 2 OOD mean of (MSE, MAE) stats: [716.78932365  17.01342593]
		Model Seed: 10 Seed: 2 ID median of (MSE, MAE): [220.22554834  12.18617884]
		Model Seed: 10 Seed: 2 OOD median of (MSE, MAE) stats: [224.97278508  12.2933108 ]
		Model Seed: 10 Seed: 2 ID likelihoods: 0
		Model Seed: 10 Seed: 2 OOD likelihoods: 0
		Model Seed: 10 Seed: 2 ID calibration errors: [0.25384963 0.09737338 0.04787493 0.02500094 0.01652696 0.01283619
 0.01171679 0.01269906 0.01479485 0.01655237 0.01908532 0.02160914]
		Model Seed: 10 Seed: 2 OOD calibration errors: [0.26052593 0.11636387 0.0646903  0.04198927 0.03295326 0.0295921
 0.02756126 0.02902192 0.02952763 0.03128943 0.03313639 0.03591593]
	Model Seed: 10 ID mean of (MSE, MAE): [732.8098641  17.2263666]
	Model Seed: 10 OOD mean of (MSE, MAE): [805.0451409   17.85400155]
	Model Seed: 10 ID median of (MSE, MAE): [227.71649274  12.41444047]
	Model Seed: 10 OOD median of (MSE, MAE): [246.20073912  12.84748077]
	Model Seed: 10 ID likelihoods: 0.0
	Model Seed: 10 OOD likelihoods: 0.0
	Model Seed: 10 ID calibration errors: [0.16569938 0.06909775 0.03902581 0.02604839 0.02014079 0.01855517
 0.01831714 0.02037639 0.02279843 0.02565123 0.02932112 0.03218991]
	Model Seed: 10 OOD calibration errors: [0.14872449 0.07255193 0.04611686 0.03515301 0.03104836 0.03038245
 0.03054672 0.03339322 0.03501636 0.03726922 0.04052008 0.04334692]
	Train: 425413 (68.73%)
	Val: 56593 (9.14%)
	Test: 72022 (11.64%)
	Test OOD: 64922 (10.49%)
	No scaling applied
		Model Seed: 11 Seed: 1 ID mean of (MSE, MAE): [813.3801725   17.86328702]
		Model Seed: 11 Seed: 1 OOD mean of (MSE, MAE) stats: [923.15443997  18.91484616]
		Model Seed: 11 Seed: 1 ID median of (MSE, MAE): [236.17259207  12.59715986]
		Model Seed: 11 Seed: 1 OOD median of (MSE, MAE) stats: [271.67827074  13.43767357]
		Model Seed: 11 Seed: 1 ID likelihoods: 0
		Model Seed: 11 Seed: 1 OOD likelihoods: 0
		Model Seed: 11 Seed: 1 ID calibration errors: [0.0297242  0.02705165 0.03094203 0.03758766 0.04413888 0.04942382
 0.05441819 0.06023297 0.06532399 0.06904753 0.07415966 0.07701181]
		Model Seed: 11 Seed: 1 OOD calibration errors: [0.02674134 0.02391957 0.02648005 0.03005201 0.0341488  0.03879688
 0.04333149 0.04764698 0.05209156 0.05503804 0.05906953 0.06212626]
	Train: 429990 (69.47%)
	Val: 56876 (9.19%)
	Test: 72378 (11.69%)
	Test OOD: 59706 (9.65%)
	No scaling applied
		Model Seed: 11 Seed: 2 ID mean of (MSE, MAE): [711.67546166  16.76210168]
		Model Seed: 11 Seed: 2 OOD mean of (MSE, MAE) stats: [718.90540367  16.84138132]
		Model Seed: 11 Seed: 2 ID median of (MSE, MAE): [206.7416132   11.86118698]
		Model Seed: 11 Seed: 2 OOD median of (MSE, MAE) stats: [216.89246478  12.14200401]
		Model Seed: 11 Seed: 2 ID likelihoods: 0
		Model Seed: 11 Seed: 2 OOD likelihoods: 0
		Model Seed: 11 Seed: 2 ID calibration errors: [0.34296442 0.1531407  0.08066248 0.04857226 0.03365747 0.02619498
 0.02310114 0.02312253 0.02470371 0.02569153 0.02866111 0.03079057]
		Model Seed: 11 Seed: 2 OOD calibration errors: [0.31576237 0.16043599 0.08911811 0.06140512 0.04727547 0.03960639
 0.03504358 0.03503704 0.03404441 0.03462823 0.03549453 0.03759326]
	Model Seed: 11 ID mean of (MSE, MAE): [762.52781708  17.31269435]
	Model Seed: 11 OOD mean of (MSE, MAE): [821.02992182  17.87811374]
	Model Seed: 11 ID median of (MSE, MAE): [221.45710263  12.22917342]
	Model Seed: 11 OOD median of (MSE, MAE): [244.28536776  12.78983879]
	Model Seed: 11 ID likelihoods: 0.0
	Model Seed: 11 OOD likelihoods: 0.0
	Model Seed: 11 ID calibration errors: [0.18634431 0.09009618 0.05580226 0.04307996 0.03889817 0.0378094
 0.03875966 0.04167775 0.04501385 0.04736953 0.05141038 0.05390119]
	Model Seed: 11 OOD calibration errors: [0.17125185 0.09217778 0.05779908 0.04572856 0.04071213 0.03920163
 0.03918754 0.04134201 0.04306799 0.04483314 0.04728203 0.04985976]
	Train: 425413 (68.73%)
	Val: 56593 (9.14%)
	Test: 72022 (11.64%)
	Test OOD: 64922 (10.49%)
	No scaling applied
		Model Seed: 12 Seed: 1 ID mean of (MSE, MAE): [770.12571957  17.44645395]
		Model Seed: 12 Seed: 1 OOD mean of (MSE, MAE) stats: [861.35861308  18.32127875]
		Model Seed: 12 Seed: 1 ID median of (MSE, MAE): [232.04957976  12.50449435]
		Model Seed: 12 Seed: 1 OOD median of (MSE, MAE) stats: [255.95492604  13.07652585]
		Model Seed: 12 Seed: 1 ID likelihoods: 0
		Model Seed: 12 Seed: 1 OOD likelihoods: 0
		Model Seed: 12 Seed: 1 ID calibration errors: [0.03845026 0.03681049 0.03790783 0.04317019 0.05080339 0.05716377
 0.06414149 0.0704485  0.07589249 0.08060549 0.08562839 0.08933406]
		Model Seed: 12 Seed: 1 OOD calibration errors: [0.04355196 0.04093994 0.04038902 0.04044489 0.04310835 0.04637501
 0.04991721 0.05422162 0.05683797 0.05892589 0.06195543 0.0643793 ]
	Train: 429990 (69.47%)
	Val: 56876 (9.19%)
	Test: 72378 (11.69%)
	Test OOD: 59706 (9.65%)
	No scaling applied
		Model Seed: 12 Seed: 2 ID mean of (MSE, MAE): [737.90955288  17.16739931]
		Model Seed: 12 Seed: 2 OOD mean of (MSE, MAE) stats: [761.23392348  17.34123295]
		Model Seed: 12 Seed: 2 ID median of (MSE, MAE): [218.78012761  12.21247609]
		Model Seed: 12 Seed: 2 OOD median of (MSE, MAE) stats: [226.48984587  12.38163567]
		Model Seed: 12 Seed: 2 ID likelihoods: 0
		Model Seed: 12 Seed: 2 OOD likelihoods: 0
		Model Seed: 12 Seed: 2 ID calibration errors: [0.20641934 0.10252839 0.06462027 0.04876178 0.0430178  0.04013783
 0.03886677 0.0406805  0.04441519 0.04577684 0.04778951 0.05090625]
		Model Seed: 12 Seed: 2 OOD calibration errors: [0.20115829 0.11059339 0.07797872 0.06587281 0.06049189 0.05875097
 0.05842079 0.06082976 0.06101374 0.06310937 0.06399461 0.06751612]
	Model Seed: 12 ID mean of (MSE, MAE): [754.01763622  17.30692663]
	Model Seed: 12 OOD mean of (MSE, MAE): [811.29626828  17.83125585]
	Model Seed: 12 ID median of (MSE, MAE): [225.41485369  12.35848522]
	Model Seed: 12 OOD median of (MSE, MAE): [241.22238596  12.72908076]
	Model Seed: 12 ID likelihoods: 0.0
	Model Seed: 12 OOD likelihoods: 0.0
	Model Seed: 12 ID calibration errors: [0.1224348  0.06966944 0.05126405 0.04596598 0.0469106  0.0486508
 0.05150413 0.0555645  0.06015384 0.06319116 0.06670895 0.07012016]
	Model Seed: 12 OOD calibration errors: [0.12235513 0.07576667 0.05918387 0.05315885 0.05180012 0.05256299
 0.054169   0.05752569 0.05892586 0.06101763 0.06297502 0.06594771]
	Train: 425413 (68.73%)
	Val: 56593 (9.14%)
	Test: 72022 (11.64%)
	Test OOD: 64922 (10.49%)
	No scaling applied
		Model Seed: 13 Seed: 1 ID mean of (MSE, MAE): [748.5037767   17.33968832]
		Model Seed: 13 Seed: 1 OOD mean of (MSE, MAE) stats: [834.48484469  18.1278312 ]
		Model Seed: 13 Seed: 1 ID median of (MSE, MAE): [229.54031419  12.46915785]
		Model Seed: 13 Seed: 1 OOD median of (MSE, MAE) stats: [249.01726981  12.89530786]
		Model Seed: 13 Seed: 1 ID likelihoods: 0
		Model Seed: 13 Seed: 1 OOD likelihoods: 0
		Model Seed: 13 Seed: 1 ID calibration errors: [0.01790157 0.01430631 0.01259099 0.0147838  0.0184127  0.02173961
 0.02534227 0.02914004 0.0321655  0.03526683 0.03850426 0.04190872]
		Model Seed: 13 Seed: 1 OOD calibration errors: [0.05567716 0.02188139 0.01512255 0.01438354 0.01635467 0.01932832
 0.02240444 0.02553266 0.02942448 0.03159101 0.03580811 0.03869962]
	Train: 429990 (69.47%)
	Val: 56876 (9.19%)
	Test: 72378 (11.69%)
	Test OOD: 59706 (9.65%)
	No scaling applied
		Model Seed: 13 Seed: 2 ID mean of (MSE, MAE): [696.01728132  16.34962595]
		Model Seed: 13 Seed: 2 OOD mean of (MSE, MAE) stats: [691.42286335  16.28655906]
		Model Seed: 13 Seed: 2 ID median of (MSE, MAE): [198.58565443  11.44758606]
		Model Seed: 13 Seed: 2 OOD median of (MSE, MAE) stats: [202.66418109  11.54786174]
		Model Seed: 13 Seed: 2 ID likelihoods: 0
		Model Seed: 13 Seed: 2 OOD likelihoods: 0
		Model Seed: 13 Seed: 2 ID calibration errors: [0.04182728 0.01544525 0.00676077 0.00983869 0.01500848 0.02119826
 0.02798131 0.033344   0.03981355 0.044771   0.04984654 0.05132315]
		Model Seed: 13 Seed: 2 OOD calibration errors: [0.02994626 0.01460455 0.00753336 0.00810958 0.01013737 0.01227099
 0.0153889  0.01829279 0.02068434 0.0222119  0.02448553 0.02605111]
	Model Seed: 13 ID mean of (MSE, MAE): [722.26052901  16.84465714]
	Model Seed: 13 OOD mean of (MSE, MAE): [762.95385402  17.20719513]
	Model Seed: 13 ID median of (MSE, MAE): [214.06298431  11.95837196]
	Model Seed: 13 OOD median of (MSE, MAE): [225.84072545  12.2215848 ]
	Model Seed: 13 ID likelihoods: 0.0
	Model Seed: 13 OOD likelihoods: 0.0
	Model Seed: 13 ID calibration errors: [0.02986443 0.01487578 0.00967588 0.01231124 0.01671059 0.02146894
 0.02666179 0.03124202 0.03598952 0.04001892 0.0441754  0.04661594]
	Model Seed: 13 OOD calibration errors: [0.04281171 0.01824297 0.01132795 0.01124656 0.01324602 0.01579966
 0.01889667 0.02191273 0.02505441 0.02690146 0.03014682 0.03237536]
	Train: 425413 (68.73%)
	Val: 56593 (9.14%)
	Test: 72022 (11.64%)
	Test OOD: 64922 (10.49%)
	No scaling applied
		Model Seed: 14 Seed: 1 ID mean of (MSE, MAE): [723.5285755   16.97188727]
		Model Seed: 14 Seed: 1 OOD mean of (MSE, MAE) stats: [850.25328783  18.11866613]
		Model Seed: 14 Seed: 1 ID median of (MSE, MAE): [222.06175671  12.15872653]
		Model Seed: 14 Seed: 1 OOD median of (MSE, MAE) stats: [245.80925598  12.79717636]
		Model Seed: 14 Seed: 1 ID likelihoods: 0
		Model Seed: 14 Seed: 1 OOD likelihoods: 0
		Model Seed: 14 Seed: 1 ID calibration errors: [0.01650927 0.01824378 0.01715367 0.01862037 0.02230327 0.0244044
 0.02830136 0.0319452  0.03587241 0.03949547 0.04437943 0.04757857]
		Model Seed: 14 Seed: 1 OOD calibration errors: [0.03497049 0.0172802  0.01403353 0.01459999 0.01625296 0.01983244
 0.02282869 0.02633102 0.03032625 0.03358412 0.03712543 0.04071877]
	Train: 429990 (69.47%)
	Val: 56876 (9.19%)
	Test: 72378 (11.69%)
	Test OOD: 59706 (9.65%)
	No scaling applied
		Model Seed: 14 Seed: 2 ID mean of (MSE, MAE): [748.55802317  17.51287456]
		Model Seed: 14 Seed: 2 OOD mean of (MSE, MAE) stats: [741.17001331  17.22648011]
		Model Seed: 14 Seed: 2 ID median of (MSE, MAE): [229.59060302  12.55437469]
		Model Seed: 14 Seed: 2 OOD median of (MSE, MAE) stats: [224.58674955  12.3769776 ]
		Model Seed: 14 Seed: 2 ID likelihoods: 0
		Model Seed: 14 Seed: 2 OOD likelihoods: 0
		Model Seed: 14 Seed: 2 ID calibration errors: [0.51332757 0.27634991 0.1742507  0.1259112  0.10737725 0.09943892
 0.0959208  0.09388553 0.09354789 0.09601021 0.09846615 0.09723026]
		Model Seed: 14 Seed: 2 OOD calibration errors: [0.42754448 0.21042695 0.1274338  0.0858064  0.06841367 0.05891145
 0.0550462  0.05466501 0.05474681 0.0538143  0.05566529 0.05583636]
	Model Seed: 14 ID mean of (MSE, MAE): [736.04329933  17.24238091]
	Model Seed: 14 OOD mean of (MSE, MAE): [795.71165057  17.67257312]
	Model Seed: 14 ID median of (MSE, MAE): [225.82617986  12.35655061]
	Model Seed: 14 OOD median of (MSE, MAE): [235.19800277  12.58707698]
	Model Seed: 14 ID likelihoods: 0.0
	Model Seed: 14 OOD likelihoods: 0.0
	Model Seed: 14 ID calibration errors: [0.26491842 0.14729684 0.09570218 0.07226578 0.06484026 0.06192166
 0.06211108 0.06291537 0.06471015 0.06775284 0.07142279 0.07240442]
	Model Seed: 14 OOD calibration errors: [0.23125748 0.11385357 0.07073367 0.05020319 0.04233332 0.03937194
 0.03893744 0.04049801 0.04253653 0.04369921 0.04639536 0.04827757]
	Train: 425413 (68.73%)
	Val: 56593 (9.14%)
	Test: 72022 (11.64%)
	Test OOD: 64922 (10.49%)
	No scaling applied
		Model Seed: 15 Seed: 1 ID mean of (MSE, MAE): [794.32208384  17.7426288 ]
		Model Seed: 15 Seed: 1 OOD mean of (MSE, MAE) stats: [867.77561187  18.36958833]
		Model Seed: 15 Seed: 1 ID median of (MSE, MAE): [242.92422541  12.75999133]
		Model Seed: 15 Seed: 1 OOD median of (MSE, MAE) stats: [260.0878377   13.15047836]
		Model Seed: 15 Seed: 1 ID likelihoods: 0
		Model Seed: 15 Seed: 1 OOD likelihoods: 0
		Model Seed: 15 Seed: 1 ID calibration errors: [0.05636738 0.02888762 0.02533392 0.02876708 0.03477263 0.04199902
 0.04826783 0.05431601 0.06012183 0.06638917 0.07262876 0.07729658]
		Model Seed: 15 Seed: 1 OOD calibration errors: [0.03348902 0.02873748 0.02823603 0.02937665 0.03328687 0.03735611
 0.04091241 0.04617254 0.05091166 0.0544513  0.05924776 0.06286482]
	Train: 429990 (69.47%)
	Val: 56876 (9.19%)
	Test: 72378 (11.69%)
	Test OOD: 59706 (9.65%)
	No scaling applied
		Model Seed: 15 Seed: 2 ID mean of (MSE, MAE): [767.70303196  17.55997101]
		Model Seed: 15 Seed: 2 OOD mean of (MSE, MAE) stats: [809.57802304  17.73711458]
		Model Seed: 15 Seed: 2 ID median of (MSE, MAE): [231.31646543  12.52010886]
		Model Seed: 15 Seed: 2 OOD median of (MSE, MAE) stats: [230.43834079  12.49224027]
		Model Seed: 15 Seed: 2 ID likelihoods: 0
		Model Seed: 15 Seed: 2 OOD likelihoods: 0
		Model Seed: 15 Seed: 2 ID calibration errors: [0.06288184 0.02966515 0.02279275 0.02371872 0.02537525 0.02955881
 0.03517222 0.04156452 0.04905335 0.05477875 0.06217155 0.06963566]
		Model Seed: 15 Seed: 2 OOD calibration errors: [0.01929    0.01354878 0.01415278 0.01817986 0.02202557 0.02692251
 0.03251932 0.03843885 0.04408265 0.0501169  0.05701043 0.06428237]
	Model Seed: 15 ID mean of (MSE, MAE): [781.0125579   17.65129991]
	Model Seed: 15 OOD mean of (MSE, MAE): [838.67681745  18.05335146]
	Model Seed: 15 ID median of (MSE, MAE): [237.12034542  12.64005009]
	Model Seed: 15 OOD median of (MSE, MAE): [245.26308925  12.82135932]
	Model Seed: 15 ID likelihoods: 0.0
	Model Seed: 15 OOD likelihoods: 0.0
	Model Seed: 15 ID calibration errors: [0.05962461 0.02927638 0.02406334 0.0262429  0.03007394 0.03577891
 0.04172003 0.04794027 0.05458759 0.06058396 0.06740015 0.07346612]
	Model Seed: 15 OOD calibration errors: [0.02638951 0.02114313 0.02119441 0.02377826 0.02765622 0.03213931
 0.03671586 0.0423057  0.04749716 0.0522841  0.05812909 0.06357359]
	Train: 425413 (68.73%)
	Val: 56593 (9.14%)
	Test: 72022 (11.64%)
	Test OOD: 64922 (10.49%)
	No scaling applied
		Model Seed: 16 Seed: 1 ID mean of (MSE, MAE): [760.24278579  17.29030461]
		Model Seed: 16 Seed: 1 OOD mean of (MSE, MAE) stats: [848.83763565  18.1648695 ]
		Model Seed: 16 Seed: 1 ID median of (MSE, MAE): [222.80821369  12.2440691 ]
		Model Seed: 16 Seed: 1 OOD median of (MSE, MAE) stats: [246.71058553  12.88731241]
		Model Seed: 16 Seed: 1 ID likelihoods: 0
		Model Seed: 16 Seed: 1 OOD likelihoods: 0
		Model Seed: 16 Seed: 1 ID calibration errors: [0.00845904 0.01187741 0.01447327 0.01780124 0.02007528 0.02382906
 0.027981   0.03319975 0.03671417 0.04169997 0.0485033  0.05314481]
		Model Seed: 16 Seed: 1 OOD calibration errors: [0.00392351 0.01042979 0.01550058 0.02014569 0.02439082 0.02818235
 0.03201595 0.0371603  0.04241998 0.04546979 0.05093005 0.05595799]
	Train: 429990 (69.47%)
	Val: 56876 (9.19%)
	Test: 72378 (11.69%)
	Test OOD: 59706 (9.65%)
	No scaling applied
		Model Seed: 16 Seed: 2 ID mean of (MSE, MAE): [741.33816105  17.33280173]
		Model Seed: 16 Seed: 2 OOD mean of (MSE, MAE) stats: [740.6054665   17.24831076]
		Model Seed: 16 Seed: 2 ID median of (MSE, MAE): [220.35729236  12.31737709]
		Model Seed: 16 Seed: 2 OOD median of (MSE, MAE) stats: [225.77264003  12.41437658]
		Model Seed: 16 Seed: 2 ID likelihoods: 0
		Model Seed: 16 Seed: 2 OOD likelihoods: 0
		Model Seed: 16 Seed: 2 ID calibration errors: [0.57680505 0.27461983 0.16520438 0.1137423  0.0906547  0.07889494
 0.07359329 0.0682854  0.06579023 0.06530253 0.06466607 0.06353331]
		Model Seed: 16 Seed: 2 OOD calibration errors: [0.46644111 0.17544909 0.10157465 0.06666101 0.05254396 0.04485743
 0.04126563 0.04053314 0.04065574 0.04064046 0.04260113 0.04477029]
	Model Seed: 16 ID mean of (MSE, MAE): [750.79047342  17.31155317]
	Model Seed: 16 OOD mean of (MSE, MAE): [794.72155108  17.70659013]
	Model Seed: 16 ID median of (MSE, MAE): [221.58275303  12.28072309]
	Model Seed: 16 OOD median of (MSE, MAE): [236.24161278  12.65084449]
	Model Seed: 16 ID likelihoods: 0.0
	Model Seed: 16 OOD likelihoods: 0.0
	Model Seed: 16 ID calibration errors: [0.29263205 0.14324862 0.08983883 0.06577177 0.05536499 0.051362
 0.05078714 0.05074258 0.0512522  0.05350125 0.05658468 0.05833906]
	Model Seed: 16 OOD calibration errors: [0.23518231 0.09293944 0.05853762 0.04340335 0.03846739 0.03651989
 0.03664079 0.03884672 0.04153786 0.04305512 0.04676559 0.05036414]
	Train: 425413 (68.73%)
	Val: 56593 (9.14%)
	Test: 72022 (11.64%)
	Test OOD: 64922 (10.49%)
	No scaling applied
		Model Seed: 17 Seed: 1 ID mean of (MSE, MAE): [751.21266486  17.40800022]
		Model Seed: 17 Seed: 1 OOD mean of (MSE, MAE) stats: [876.00210556  18.65672886]
		Model Seed: 17 Seed: 1 ID median of (MSE, MAE): [228.99270547  12.50215371]
		Model Seed: 17 Seed: 1 OOD median of (MSE, MAE) stats: [260.13842822  13.29063288]
		Model Seed: 17 Seed: 1 ID likelihoods: 0
		Model Seed: 17 Seed: 1 OOD likelihoods: 0
		Model Seed: 17 Seed: 1 ID calibration errors: [0.24363202 0.12637437 0.07840826 0.06066909 0.05737675 0.0559839
 0.05815824 0.06102051 0.06449022 0.06768729 0.07217323 0.07507291]
		Model Seed: 17 Seed: 1 OOD calibration errors: [0.21758434 0.09354042 0.05223269 0.04129472 0.03907715 0.04196179
 0.04616666 0.05135986 0.05691924 0.06216892 0.06774175 0.07285624]
	Train: 429990 (69.47%)
	Val: 56876 (9.19%)
	Test: 72378 (11.69%)
	Test OOD: 59706 (9.65%)
	No scaling applied
		Model Seed: 17 Seed: 2 ID mean of (MSE, MAE): [661.12511207  16.41082545]
		Model Seed: 17 Seed: 2 OOD mean of (MSE, MAE) stats: [677.81619531  16.57897211]
		Model Seed: 17 Seed: 2 ID median of (MSE, MAE): [205.98088797  11.82436434]
		Model Seed: 17 Seed: 2 OOD median of (MSE, MAE) stats: [213.08358675  11.99868647]
		Model Seed: 17 Seed: 2 ID likelihoods: 0
		Model Seed: 17 Seed: 2 OOD likelihoods: 0
		Model Seed: 17 Seed: 2 ID calibration errors: [0.31054513 0.15958974 0.10421629 0.08118067 0.0646925  0.05163169
 0.04390952 0.03716116 0.0330803  0.03108405 0.03035465 0.02804289]
		Model Seed: 17 Seed: 2 OOD calibration errors: [0.18169228 0.09013482 0.06206209 0.04541928 0.03486781 0.02681073
 0.02254765 0.02080607 0.02034489 0.01903002 0.02025325 0.02071971]
	Model Seed: 17 ID mean of (MSE, MAE): [706.16888847  16.90941284]
	Model Seed: 17 OOD mean of (MSE, MAE): [776.90915043  17.61785048]
	Model Seed: 17 ID median of (MSE, MAE): [217.48679672  12.16325903]
	Model Seed: 17 OOD median of (MSE, MAE): [236.61100749  12.64465968]
	Model Seed: 17 ID likelihoods: 0.0
	Model Seed: 17 OOD likelihoods: 0.0
	Model Seed: 17 ID calibration errors: [0.27708857 0.14298205 0.09131228 0.07092488 0.06103463 0.05380779
 0.05103388 0.04909084 0.04878526 0.04938567 0.05126394 0.0515579 ]
	Model Seed: 17 OOD calibration errors: [0.19963831 0.09183762 0.05714739 0.043357   0.03697248 0.03438626
 0.03435715 0.03608296 0.03863207 0.04059947 0.0439975  0.04678797]
	Train: 425413 (68.73%)
	Val: 56593 (9.14%)
	Test: 72022 (11.64%)
	Test OOD: 64922 (10.49%)
	No scaling applied
		Model Seed: 18 Seed: 1 ID mean of (MSE, MAE): [747.19789158  17.26466649]
		Model Seed: 18 Seed: 1 OOD mean of (MSE, MAE) stats: [849.21288378  18.27874879]
		Model Seed: 18 Seed: 1 ID median of (MSE, MAE): [231.5375783  12.4401199]
		Model Seed: 18 Seed: 1 OOD median of (MSE, MAE) stats: [258.85511499  13.14464442]
		Model Seed: 18 Seed: 1 ID likelihoods: 0
		Model Seed: 18 Seed: 1 OOD likelihoods: 0
		Model Seed: 18 Seed: 1 ID calibration errors: [0.03418659 0.03131795 0.02554568 0.02713788 0.03044392 0.03421325
 0.03758675 0.04225958 0.0462339  0.05006556 0.05418235 0.05802295]
		Model Seed: 18 Seed: 1 OOD calibration errors: [0.05923398 0.05603768 0.0494232  0.04783818 0.04900213 0.05038701
 0.05290738 0.05495633 0.05803357 0.06007294 0.06343449 0.06610806]
	Train: 429990 (69.47%)
	Val: 56876 (9.19%)
	Test: 72378 (11.69%)
	Test OOD: 59706 (9.65%)
	No scaling applied
		Model Seed: 18 Seed: 2 ID mean of (MSE, MAE): [716.56497718  16.97027879]
		Model Seed: 18 Seed: 2 OOD mean of (MSE, MAE) stats: [752.85692731  17.27187769]
		Model Seed: 18 Seed: 2 ID median of (MSE, MAE): [220.55779062  12.16209602]
		Model Seed: 18 Seed: 2 OOD median of (MSE, MAE) stats: [228.37727655  12.34504986]
		Model Seed: 18 Seed: 2 ID likelihoods: 0
		Model Seed: 18 Seed: 2 OOD likelihoods: 0
		Model Seed: 18 Seed: 2 ID calibration errors: [0.16042985 0.0585766  0.02300842 0.01137295 0.00769217 0.00715716
 0.00777588 0.00950471 0.01188344 0.01384496 0.01601676 0.01819348]
		Model Seed: 18 Seed: 2 OOD calibration errors: [0.15273815 0.06763114 0.03590003 0.02705226 0.02362198 0.02360541
 0.02319436 0.0250101  0.02620175 0.02758008 0.02924539 0.03144492]
	Model Seed: 18 ID mean of (MSE, MAE): [731.88143438  17.11747264]
	Model Seed: 18 OOD mean of (MSE, MAE): [801.03490555  17.77531324]
	Model Seed: 18 ID median of (MSE, MAE): [226.04768446  12.30110796]
	Model Seed: 18 OOD median of (MSE, MAE): [243.61619577  12.74484714]
	Model Seed: 18 ID likelihoods: 0.0
	Model Seed: 18 OOD likelihoods: 0.0
	Model Seed: 18 ID calibration errors: [0.09730822 0.04494727 0.02427705 0.01925541 0.01906804 0.0206852
 0.02268132 0.02588214 0.02905867 0.03195526 0.03509956 0.03810821]
	Model Seed: 18 OOD calibration errors: [0.10598607 0.06183441 0.04266162 0.03744522 0.03631206 0.03699621
 0.03805087 0.03998322 0.04211766 0.04382651 0.04633994 0.04877649]
	Train: 425413 (68.73%)
	Val: 56593 (9.14%)
	Test: 72022 (11.64%)
	Test OOD: 64922 (10.49%)
	No scaling applied
		Model Seed: 19 Seed: 1 ID mean of (MSE, MAE): [715.36496094  16.91927386]
		Model Seed: 19 Seed: 1 OOD mean of (MSE, MAE) stats: [843.75552851  18.13551937]
		Model Seed: 19 Seed: 1 ID median of (MSE, MAE): [220.1982048   12.18824704]
		Model Seed: 19 Seed: 1 OOD median of (MSE, MAE) stats: [249.05582114  12.88875802]
		Model Seed: 19 Seed: 1 ID likelihoods: 0
		Model Seed: 19 Seed: 1 OOD likelihoods: 0
		Model Seed: 19 Seed: 1 ID calibration errors: [0.09390928 0.0503011  0.03342389 0.02944202 0.03105103 0.03363612
 0.03667213 0.04088457 0.04465188 0.04788867 0.05239059 0.05611952]
		Model Seed: 19 Seed: 1 OOD calibration errors: [0.14642948 0.05736925 0.0331288  0.02555293 0.02679166 0.03045773
 0.03495131 0.04018882 0.04602876 0.04937412 0.0548285  0.06000618]
	Train: 429990 (69.47%)
	Val: 56876 (9.19%)
	Test: 72378 (11.69%)
	Test OOD: 59706 (9.65%)
	No scaling applied
		Model Seed: 19 Seed: 2 ID mean of (MSE, MAE): [735.83417767  17.21035745]
		Model Seed: 19 Seed: 2 OOD mean of (MSE, MAE) stats: [724.66214536  17.10474163]
		Model Seed: 19 Seed: 2 ID median of (MSE, MAE): [229.13143372  12.39713669]
		Model Seed: 19 Seed: 2 OOD median of (MSE, MAE) stats: [231.12592503  12.41382726]
		Model Seed: 19 Seed: 2 ID likelihoods: 0
		Model Seed: 19 Seed: 2 OOD likelihoods: 0
		Model Seed: 19 Seed: 2 ID calibration errors: [0.16098615 0.06387231 0.03537858 0.02262525 0.01801349 0.0163416
 0.01602228 0.01749831 0.02013468 0.02140651 0.02451101 0.02650188]
		Model Seed: 19 Seed: 2 OOD calibration errors: [0.16861131 0.07992959 0.0466098  0.03344729 0.02783347 0.02617175
 0.02526843 0.02668946 0.02701822 0.02851489 0.03017961 0.03341828]
	Model Seed: 19 ID mean of (MSE, MAE): [725.5995693   17.06481565]
	Model Seed: 19 OOD mean of (MSE, MAE): [784.20883693  17.6201305 ]
	Model Seed: 19 ID median of (MSE, MAE): [224.66481926  12.29269187]
	Model Seed: 19 OOD median of (MSE, MAE): [240.09087309  12.65129264]
	Model Seed: 19 ID likelihoods: 0.0
	Model Seed: 19 OOD likelihoods: 0.0
	Model Seed: 19 ID calibration errors: [0.12744771 0.05708671 0.03440123 0.02603364 0.02453226 0.02498886
 0.02634721 0.02919144 0.03239328 0.03464759 0.0384508  0.0413107 ]
	Model Seed: 19 OOD calibration errors: [0.15752039 0.06864942 0.0398693  0.02950011 0.02731256 0.02831474
 0.03010987 0.03343914 0.03652349 0.0389445  0.04250406 0.04671223]
ID mean of (MSE, MAE): [740.311206921082, 17.198757983856215] +- [20.73972607613783, 0.21875099308818896] +- [17.19534361  0.17029679] 
OOD mean of (MSE, MAE): [799.1588097037978, 17.721637519307944] +- [20.703683301348818, 0.21337679742509888] +- [65.65478121  0.65662791] 
ID median of (MSE, MAE): [224.13800121176274, 12.29948537349701] +- [5.9094211796246485, 0.16619175216725668] +- [6.01125954 0.15119681] 
OOD median of (MSE, MAE): [239.45699994208843, 12.66880653699239] +- [5.876218982959516, 0.169458030758546] +- [17.01662039  0.42820951] 
ID likelihoods: 0.0 +- 0.0
OOD likelihoods: 0.0 +- 0.0
ID calibration errors: [0.16233624994278353, 0.08085770286276539, 0.05153628914952787, 0.040789996619752106, 0.037757426746965354, 0.03750287344254394, 0.038992337621819424, 0.041462328290693976, 0.04447428011513484, 0.04740574082540648, 0.051183777244005094, 0.053801360148216015] +- [0.08740270828826942, 0.046242740406519774, 0.02952686545977973, 0.02115755470230342, 0.01734060410976889, 0.014956878288797616, 0.014099952655828064, 0.013370938310785225, 0.013220663121441354, 0.013443354739368137, 0.013750531912782454, 0.013948799305370659] +- [0.10066738 0.04225842 0.02094067 0.01028248 0.00444418 0.00083616
 0.00158634 0.00368776 0.00475256 0.00588387 0.00702691 0.0080247 ] 
OOD calibration errors: [0.14411172632896704, 0.07089969353080583, 0.04645717550796273, 0.03729741147629215, 0.03458606595541786, 0.034567509340039784, 0.0357611911688001, 0.038532939351994706, 0.04109093698973472, 0.04324303534792995, 0.046505549501223445, 0.04960217385447897] +- [0.06779101821476773, 0.0292985424607916, 0.017522457792984367, 0.012206811252756357, 0.009932021605524952, 0.008904372590290727, 0.008444614823144299, 0.008505938109959246, 0.00829068584679752, 0.008538083562816413, 0.00856035421242937, 0.009042252720311724] +- [0.07825929 0.03301212 0.01624819 0.00809688 0.00343038 0.00018246
 0.00213558 0.00360053 0.00525892 0.00614948 0.00729893 0.00784734] 
