Optimization started at 2023-03-10 20:47:13.800728
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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.
--------------------------------
Current value: 0.15886686742305756, Current params: {'in_len': 132, 'max_samples_per_ts': 200, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.12676107862364014, 'lr': 0.0019312547654878039, 'batch_size': 48, 'max_grad_norm': 0.2773913345076531}
Best value: 0.15886686742305756, Best params: {'in_len': 132, 'max_samples_per_ts': 200, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.12676107862364014, 'lr': 0.0019312547654878039, 'batch_size': 48, 'max_grad_norm': 0.2773913345076531}
Current value: 0.15684294700622559, Current params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 48, 'num_attention_heads': 1, 'dropout': 0.13279876240128743, 'lr': 0.0058514111987203, 'batch_size': 32, 'max_grad_norm': 0.5844674635826104}
Best value: 0.15684294700622559, Best params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 48, 'num_attention_heads': 1, 'dropout': 0.13279876240128743, 'lr': 0.0058514111987203, 'batch_size': 32, 'max_grad_norm': 0.5844674635826104}
Current value: 0.12982258200645447, Current params: {'in_len': 108, 'max_samples_per_ts': 100, 'hidden_size': 208, 'num_attention_heads': 1, 'dropout': 0.14232858882396734, 'lr': 0.00690418921523732, 'batch_size': 48, 'max_grad_norm': 0.17948288068110593}
Best value: 0.12982258200645447, Best params: {'in_len': 108, 'max_samples_per_ts': 100, 'hidden_size': 208, 'num_attention_heads': 1, 'dropout': 0.14232858882396734, 'lr': 0.00690418921523732, 'batch_size': 48, 'max_grad_norm': 0.17948288068110593}
Current value: 0.08786167949438095, Current params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.22416068086344332, 'lr': 0.0010338003374630667, 'batch_size': 48, 'max_grad_norm': 0.02961300627311521}
Best value: 0.08786167949438095, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.22416068086344332, 'lr': 0.0010338003374630667, 'batch_size': 48, 'max_grad_norm': 0.02961300627311521}
Current value: 0.10750354081392288, Current params: {'in_len': 96, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 4, 'dropout': 0.24051741342818447, 'lr': 0.009184936685446083, 'batch_size': 64, 'max_grad_norm': 0.560470741991441}
Best value: 0.08786167949438095, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.22416068086344332, 'lr': 0.0010338003374630667, 'batch_size': 48, 'max_grad_norm': 0.02961300627311521}
Current value: 2.5716521739959717, Current params: {'in_len': 180, 'max_samples_per_ts': 200, 'hidden_size': 16, 'num_attention_heads': 2, 'dropout': 0.21615110146082173, 'lr': 0.00010166908397330965, 'batch_size': 32, 'max_grad_norm': 0.8650516393520816}
Best value: 0.08786167949438095, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.22416068086344332, 'lr': 0.0010338003374630667, 'batch_size': 48, 'max_grad_norm': 0.02961300627311521}
Current value: 1.3387428522109985, Current params: {'in_len': 120, 'max_samples_per_ts': 100, 'hidden_size': 208, 'num_attention_heads': 1, 'dropout': 0.12964261604863128, 'lr': 0.006472370247160903, 'batch_size': 48, 'max_grad_norm': 0.9661845225800542}
Best value: 0.08786167949438095, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.22416068086344332, 'lr': 0.0010338003374630667, 'batch_size': 48, 'max_grad_norm': 0.02961300627311521}
Current value: 1.0494663715362549, Current params: {'in_len': 180, 'max_samples_per_ts': 150, 'hidden_size': 48, 'num_attention_heads': 2, 'dropout': 0.20834405747811888, 'lr': 0.0016579640301128263, 'batch_size': 32, 'max_grad_norm': 0.02902178397527138}
Best value: 0.08786167949438095, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.22416068086344332, 'lr': 0.0010338003374630667, 'batch_size': 48, 'max_grad_norm': 0.02961300627311521}
Current value: 0.10508692264556885, Current params: {'in_len': 108, 'max_samples_per_ts': 200, 'hidden_size': 16, 'num_attention_heads': 3, 'dropout': 0.26343548479938417, 'lr': 0.009844425064122852, 'batch_size': 64, 'max_grad_norm': 0.7475709011675336}
Best value: 0.08786167949438095, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.22416068086344332, 'lr': 0.0010338003374630667, 'batch_size': 48, 'max_grad_norm': 0.02961300627311521}
Current value: 0.4825736880302429, Current params: {'in_len': 180, 'max_samples_per_ts': 50, 'hidden_size': 240, 'num_attention_heads': 3, 'dropout': 0.2978701435393552, 'lr': 0.00776128998346198, 'batch_size': 48, 'max_grad_norm': 0.6084914461479276}
Best value: 0.08786167949438095, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.22416068086344332, 'lr': 0.0010338003374630667, 'batch_size': 48, 'max_grad_norm': 0.02961300627311521}
Current value: 0.0855717733502388, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 4, 'dropout': 0.171771363465495, 'lr': 0.0038365815473094074, 'batch_size': 64, 'max_grad_norm': 0.33664643670146954}
Best value: 0.0855717733502388, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 4, 'dropout': 0.171771363465495, 'lr': 0.0038365815473094074, 'batch_size': 64, 'max_grad_norm': 0.33664643670146954}
Current value: 0.8711184859275818, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 4, 'dropout': 0.17638505881959593, 'lr': 0.004179811489545207, 'batch_size': 64, 'max_grad_norm': 0.3620983234330707}
Best value: 0.0855717733502388, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 4, 'dropout': 0.171771363465495, 'lr': 0.0038365815473094074, 'batch_size': 64, 'max_grad_norm': 0.33664643670146954}
Current value: 1.0876935720443726, Current params: {'in_len': 156, 'max_samples_per_ts': 100, 'hidden_size': 160, 'num_attention_heads': 3, 'dropout': 0.1855271301279035, 'lr': 0.003875169702936771, 'batch_size': 64, 'max_grad_norm': 0.04032716492431543}
Best value: 0.0855717733502388, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 4, 'dropout': 0.171771363465495, 'lr': 0.0038365815473094074, 'batch_size': 64, 'max_grad_norm': 0.33664643670146954}
Current value: 0.9320995807647705, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 128, 'num_attention_heads': 4, 'dropout': 0.17423542230733874, 'lr': 0.002624241844871482, 'batch_size': 48, 'max_grad_norm': 0.4099914614768978}
Best value: 0.0855717733502388, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 4, 'dropout': 0.171771363465495, 'lr': 0.0038365815473094074, 'batch_size': 64, 'max_grad_norm': 0.33664643670146954}
Current value: 0.09576284885406494, Current params: {'in_len': 156, 'max_samples_per_ts': 150, 'hidden_size': 176, 'num_attention_heads': 2, 'dropout': 0.22885186286202036, 'lr': 0.0007333253120417751, 'batch_size': 64, 'max_grad_norm': 0.1919968993365223}
Best value: 0.0855717733502388, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 4, 'dropout': 0.171771363465495, 'lr': 0.0038365815473094074, 'batch_size': 64, 'max_grad_norm': 0.33664643670146954}
Current value: 1.1054641008377075, Current params: {'in_len': 132, 'max_samples_per_ts': 100, 'hidden_size': 112, 'num_attention_heads': 3, 'dropout': 0.15853949395195463, 'lr': 0.003306214719127956, 'batch_size': 48, 'max_grad_norm': 0.16353417829366984}
Best value: 0.0855717733502388, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 4, 'dropout': 0.171771363465495, 'lr': 0.0038365815473094074, 'batch_size': 64, 'max_grad_norm': 0.33664643670146954}
Current value: 0.41222527623176575, Current params: {'in_len': 192, 'max_samples_per_ts': 50, 'hidden_size': 256, 'num_attention_heads': 4, 'dropout': 0.10228200184856442, 'lr': 0.004992502176560144, 'batch_size': 64, 'max_grad_norm': 0.419084957231196}
Best value: 0.0855717733502388, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 4, 'dropout': 0.171771363465495, 'lr': 0.0038365815473094074, 'batch_size': 64, 'max_grad_norm': 0.33664643670146954}
Current value: 0.08598052710294724, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.2593374558912927, 'lr': 0.001579966957445297, 'batch_size': 32, 'max_grad_norm': 0.29435575807621883}
Best value: 0.0855717733502388, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 4, 'dropout': 0.171771363465495, 'lr': 0.0038365815473094074, 'batch_size': 64, 'max_grad_norm': 0.33664643670146954}
Current value: 1.043116807937622, Current params: {'in_len': 168, 'max_samples_per_ts': 150, 'hidden_size': 144, 'num_attention_heads': 2, 'dropout': 0.28181165402086905, 'lr': 0.0028054512968528713, 'batch_size': 32, 'max_grad_norm': 0.29625938224864845}
Best value: 0.0855717733502388, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 4, 'dropout': 0.171771363465495, 'lr': 0.0038365815473094074, 'batch_size': 64, 'max_grad_norm': 0.33664643670146954}
Current value: 0.9189298748970032, Current params: {'in_len': 144, 'max_samples_per_ts': 100, 'hidden_size': 96, 'num_attention_heads': 3, 'dropout': 0.25391379793060004, 'lr': 0.004793588863495541, 'batch_size': 32, 'max_grad_norm': 0.422819531744681}
Best value: 0.0855717733502388, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 4, 'dropout': 0.171771363465495, 'lr': 0.0038365815473094074, 'batch_size': 64, 'max_grad_norm': 0.33664643670146954}
Current value: 0.8694673180580139, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 176, 'num_attention_heads': 1, 'dropout': 0.19958808099840136, 'lr': 0.0020386034290309435, 'batch_size': 32, 'max_grad_norm': 0.705493278022435}
Best value: 0.0855717733502388, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 4, 'dropout': 0.171771363465495, 'lr': 0.0038365815473094074, 'batch_size': 64, 'max_grad_norm': 0.33664643670146954}
Current value: 0.07809664309024811, Current params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.2609868669096288, 'lr': 0.0009610885012392324, 'batch_size': 48, 'max_grad_norm': 0.09882285619941597}
Best value: 0.07809664309024811, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.2609868669096288, 'lr': 0.0009610885012392324, 'batch_size': 48, 'max_grad_norm': 0.09882285619941597}
Current value: 0.09215456247329712, Current params: {'in_len': 132, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.2719569873584485, 'lr': 0.0011381690335394148, 'batch_size': 64, 'max_grad_norm': 0.2668490999712754}
Best value: 0.07809664309024811, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.2609868669096288, 'lr': 0.0009610885012392324, 'batch_size': 48, 'max_grad_norm': 0.09882285619941597}
Current value: 1.101582407951355, Current params: {'in_len': 168, 'max_samples_per_ts': 100, 'hidden_size': 144, 'num_attention_heads': 1, 'dropout': 0.2456672661618426, 'lr': 0.00026566302620601237, 'batch_size': 48, 'max_grad_norm': 0.12795677469775824}
Best value: 0.07809664309024811, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.2609868669096288, 'lr': 0.0009610885012392324, 'batch_size': 48, 'max_grad_norm': 0.09882285619941597}
Current value: 0.07298249751329422, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 0.09536338597536087, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 256, 'num_attention_heads': 2, 'dropout': 0.29722651249886334, 'lr': 0.0038065747063316803, 'batch_size': 48, 'max_grad_norm': 0.45698150190042996}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 1.1807467937469482, Current params: {'in_len': 120, 'max_samples_per_ts': 100, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.28111445761828663, 'lr': 0.0027922357696854427, 'batch_size': 48, 'max_grad_norm': 0.6838106938132074}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 0.8417448401451111, Current params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 3, 'dropout': 0.19049449269253507, 'lr': 0.004496775423938381, 'batch_size': 64, 'max_grad_norm': 0.5398150444209677}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 1.0132801532745361, Current params: {'in_len': 108, 'max_samples_per_ts': 100, 'hidden_size': 176, 'num_attention_heads': 4, 'dropout': 0.1613038535331377, 'lr': 0.005527783219837643, 'batch_size': 32, 'max_grad_norm': 0.4949833526720624}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 0.09821286797523499, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 240, 'num_attention_heads': 2, 'dropout': 0.2863973375502712, 'lr': 0.0021955674646090464, 'batch_size': 48, 'max_grad_norm': 0.34082993264386674}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 0.09802642464637756, Current params: {'in_len': 132, 'max_samples_per_ts': 50, 'hidden_size': 208, 'num_attention_heads': 3, 'dropout': 0.10903248733584607, 'lr': 0.003343469428221673, 'batch_size': 64, 'max_grad_norm': 0.09691656514831326}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 0.08770710974931717, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.26553521082539755, 'lr': 0.001586982132817211, 'batch_size': 32, 'max_grad_norm': 0.2370278613723391}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 0.929293155670166, Current params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.24003309915391913, 'lr': 0.0033158344643385213, 'batch_size': 32, 'max_grad_norm': 0.30989817034107103}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 0.8206408023834229, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 208, 'num_attention_heads': 1, 'dropout': 0.26868921290899933, 'lr': 0.002271257269584529, 'batch_size': 32, 'max_grad_norm': 0.25431006553059304}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 0.08273610472679138, Current params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 240, 'num_attention_heads': 1, 'dropout': 0.14883665653592573, 'lr': 0.0011812255140926909, 'batch_size': 32, 'max_grad_norm': 0.3783129504935836}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 0.9905962944030762, Current params: {'in_len': 108, 'max_samples_per_ts': 100, 'hidden_size': 240, 'num_attention_heads': 2, 'dropout': 0.1480886912526041, 'lr': 0.0007452486915105506, 'batch_size': 32, 'max_grad_norm': 0.6191638204264054}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 0.844304621219635, Current params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 1, 'dropout': 0.15600013667467344, 'lr': 0.00589711986008888, 'batch_size': 32, 'max_grad_norm': 0.36870166954004985}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 0.09661521762609482, Current params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 256, 'num_attention_heads': 2, 'dropout': 0.12210219088467136, 'lr': 0.0011737980323961706, 'batch_size': 48, 'max_grad_norm': 0.48366084647139473}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 1.2399725914001465, Current params: {'in_len': 108, 'max_samples_per_ts': 100, 'hidden_size': 224, 'num_attention_heads': 1, 'dropout': 0.1708573770240278, 'lr': 0.007132111068839467, 'batch_size': 48, 'max_grad_norm': 0.21811013740107096}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 0.8622709512710571, Current params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 208, 'num_attention_heads': 2, 'dropout': 0.13685951536537785, 'lr': 0.000457934888925197, 'batch_size': 32, 'max_grad_norm': 0.5526887453423364}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 1.4031270742416382, Current params: {'in_len': 96, 'max_samples_per_ts': 150, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.22249776967178664, 'lr': 0.005444172845680214, 'batch_size': 32, 'max_grad_norm': 0.8132603811299979}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 0.08466890454292297, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.25108270167772917, 'lr': 0.0016688996042422619, 'batch_size': 32, 'max_grad_norm': 0.3217414280314366}
Best value: 0.07298249751329422, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 2, 'dropout': 0.2912549497565282, 'lr': 0.003296494254281515, 'batch_size': 32, 'max_grad_norm': 0.5085748603709166}
Current value: 0.0706271082162857, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.24678432437358133, 'lr': 0.001651871789627224, 'batch_size': 32, 'max_grad_norm': 0.3799279146945382}
Best value: 0.0706271082162857, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.24678432437358133, 'lr': 0.001651871789627224, 'batch_size': 32, 'max_grad_norm': 0.3799279146945382}
Current value: 0.0693221315741539, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 240, 'num_attention_heads': 1, 'dropout': 0.2354005483884536, 'lr': 0.0014372065280028868, 'batch_size': 32, 'max_grad_norm': 0.08770929102027172}
Best value: 0.0693221315741539, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 240, 'num_attention_heads': 1, 'dropout': 0.2354005483884536, 'lr': 0.0014372065280028868, 'batch_size': 32, 'max_grad_norm': 0.08770929102027172}
Current value: 0.832660436630249, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 240, 'num_attention_heads': 1, 'dropout': 0.23540933216862522, 'lr': 0.0012111364509748083, 'batch_size': 32, 'max_grad_norm': 0.09339247331400885}
Best value: 0.0693221315741539, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 240, 'num_attention_heads': 1, 'dropout': 0.2354005483884536, 'lr': 0.0014372065280028868, 'batch_size': 32, 'max_grad_norm': 0.08770929102027172}
Current value: 0.9402987360954285, Current params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 240, 'num_attention_heads': 1, 'dropout': 0.20905238784428914, 'lr': 0.0024792993862634448, 'batch_size': 32, 'max_grad_norm': 0.0652625366025785}
Best value: 0.0693221315741539, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 240, 'num_attention_heads': 1, 'dropout': 0.2354005483884536, 'lr': 0.0014372065280028868, 'batch_size': 32, 'max_grad_norm': 0.08770929102027172}
Current value: 0.08484712243080139, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 1, 'dropout': 0.27426663822097075, 'lr': 0.000632457078118916, 'batch_size': 32, 'max_grad_norm': 0.013952330337586138}
Best value: 0.0693221315741539, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 240, 'num_attention_heads': 1, 'dropout': 0.2354005483884536, 'lr': 0.0014372065280028868, 'batch_size': 32, 'max_grad_norm': 0.08770929102027172}
Current value: 0.8556360006332397, Current params: {'in_len': 132, 'max_samples_per_ts': 200, 'hidden_size': 256, 'num_attention_heads': 1, 'dropout': 0.2903205493893886, 'lr': 0.0017955749239548515, 'batch_size': 32, 'max_grad_norm': 0.1519958538660086}
Best value: 0.0693221315741539, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 240, 'num_attention_heads': 1, 'dropout': 0.2354005483884536, 'lr': 0.0014372065280028868, 'batch_size': 32, 'max_grad_norm': 0.08770929102027172}
Current value: 1.181713342666626, Current params: {'in_len': 96, 'max_samples_per_ts': 100, 'hidden_size': 208, 'num_attention_heads': 1, 'dropout': 0.23303787613768231, 'lr': 0.0009290863792835277, 'batch_size': 32, 'max_grad_norm': 0.9891178716015838}
Best value: 0.0693221315741539, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 240, 'num_attention_heads': 1, 'dropout': 0.2354005483884536, 'lr': 0.0014372065280028868, 'batch_size': 32, 'max_grad_norm': 0.08770929102027172}
Current value: 0.8176308870315552, Current params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 176, 'num_attention_heads': 2, 'dropout': 0.22041674232114375, 'lr': 0.0013364542291261355, 'batch_size': 32, 'max_grad_norm': 0.39186692853420224}
Best value: 0.0693221315741539, Best params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 240, 'num_attention_heads': 1, 'dropout': 0.2354005483884536, 'lr': 0.0014372065280028868, 'batch_size': 32, 'max_grad_norm': 0.08770929102027172}
--------------------------------
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): [809.93200538  18.84889986]
		Model Seed: 10 Seed: 1 OOD mean of (MSE, MAE) stats: [785.39425305  20.32960977]
		Model Seed: 10 Seed: 1 ID median of (MSE, MAE): [288.5542277   14.43192164]
		Model Seed: 10 Seed: 1 OOD median of (MSE, MAE) stats: [411.26225289  17.28735733]
		Model Seed: 10 Seed: 1 ID likelihoods: 0
		Model Seed: 10 Seed: 1 OOD likelihoods: 0
		Model Seed: 10 Seed: 1 ID calibration errors: [0.14401532 0.14088503 0.09061628 0.07249082 0.0799829  0.10434033
 0.13462556 0.16005713 0.17183249 0.20176896 0.22042826 0.24318749]
		Model Seed: 10 Seed: 1 OOD calibration errors: [0.34568315 0.36427991 0.38125498 0.46288149 0.48492767 0.52248853
 0.55636541 0.56858428 0.595501   0.6428542  0.62118461 0.65451261]
	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): [1250.45705557   21.72910963]
		Model Seed: 10 Seed: 2 OOD mean of (MSE, MAE) stats: [554.28021581  16.50887688]
		Model Seed: 10 Seed: 2 ID median of (MSE, MAE): [309.73018871  14.67611599]
		Model Seed: 10 Seed: 2 OOD median of (MSE, MAE) stats: [239.75810617  13.09682274]
		Model Seed: 10 Seed: 2 ID likelihoods: 0
		Model Seed: 10 Seed: 2 OOD likelihoods: 0
		Model Seed: 10 Seed: 2 ID calibration errors: [0.27857028 0.09696414 0.02085205 0.02120885 0.05219519 0.07572932
 0.1045222  0.1238862  0.13595139 0.15437905 0.15744645 0.17599878]
		Model Seed: 10 Seed: 2 OOD calibration errors: [0.43473323 0.25938251 0.16430669 0.11494738 0.0888409  0.08610208
 0.09357155 0.10945322 0.11014963 0.11623749 0.13088087 0.13868983]
	Model Seed: 10 ID mean of (MSE, MAE): [1030.19453047   20.28900474]
	Model Seed: 10 OOD mean of (MSE, MAE): [669.83723443  18.41924332]
	Model Seed: 10 ID median of (MSE, MAE): [299.1422082   14.55401882]
	Model Seed: 10 OOD median of (MSE, MAE): [325.51017953  15.19209003]
	Model Seed: 10 ID likelihoods: 0.0
	Model Seed: 10 OOD likelihoods: 0.0
	Model Seed: 10 ID calibration errors: [0.2112928  0.11892458 0.05573416 0.04684984 0.06608904 0.09003483
 0.11957388 0.14197167 0.15389194 0.17807401 0.18893736 0.20959314]
	Model Seed: 10 OOD calibration errors: [0.39020819 0.31183121 0.27278083 0.28891444 0.28688429 0.30429531
 0.32496848 0.33901875 0.35282531 0.37954585 0.37603274 0.39660122]
	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): [1516.14718249   25.61612342]
		Model Seed: 11 Seed: 1 OOD mean of (MSE, MAE) stats: [922.59625361  19.80105101]
		Model Seed: 11 Seed: 1 ID median of (MSE, MAE): [467.01130978  18.42146873]
		Model Seed: 11 Seed: 1 OOD median of (MSE, MAE) stats: [347.24740415  15.28496933]
		Model Seed: 11 Seed: 1 ID likelihoods: 0
		Model Seed: 11 Seed: 1 OOD likelihoods: 0
		Model Seed: 11 Seed: 1 ID calibration errors: [0.65858923 0.74739161 0.59677342 0.47403827 0.38451373 0.34300878
 0.33988703 0.32192042 0.31199682 0.34702861 0.36155653 0.37889124]
		Model Seed: 11 Seed: 1 OOD calibration errors: [0.67415486 0.33002133 0.17037706 0.12706263 0.15006159 0.19306504
 0.2387891  0.28135527 0.33200212 0.38256195 0.40455293 0.47617552]
	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): [1271.37459187   22.64129635]
		Model Seed: 11 Seed: 2 OOD mean of (MSE, MAE) stats: [587.30837126  17.33178158]
		Model Seed: 11 Seed: 2 ID median of (MSE, MAE): [385.50266946  16.5913372 ]
		Model Seed: 11 Seed: 2 OOD median of (MSE, MAE) stats: [225.46427819  13.01939392]
		Model Seed: 11 Seed: 2 ID likelihoods: 0
		Model Seed: 11 Seed: 2 OOD likelihoods: 0
		Model Seed: 11 Seed: 2 ID calibration errors: [1.41844738 0.99398879 0.43183778 0.21778923 0.16372783 0.1709094
 0.18997503 0.20268239 0.22262197 0.22986246 0.23678899 0.24256315]
		Model Seed: 11 Seed: 2 OOD calibration errors: [1.9002928  1.55080117 0.97036879 0.55619807 0.33976436 0.25495849
 0.21930528 0.23696865 0.22749888 0.21576627 0.21966098 0.22534224]
	Model Seed: 11 ID mean of (MSE, MAE): [1393.76088718   24.12870988]
	Model Seed: 11 OOD mean of (MSE, MAE): [754.95231243  18.5664163 ]
	Model Seed: 11 ID median of (MSE, MAE): [426.25698962  17.50640297]
	Model Seed: 11 OOD median of (MSE, MAE): [286.35584117  14.15218163]
	Model Seed: 11 ID likelihoods: 0.0
	Model Seed: 11 OOD likelihoods: 0.0
	Model Seed: 11 ID calibration errors: [1.03851831 0.8706902  0.5143056  0.34591375 0.27412078 0.25695909
 0.26493103 0.26230141 0.26730939 0.28844554 0.29917276 0.31072719]
	Model Seed: 11 OOD calibration errors: [1.28722383 0.94041125 0.57037293 0.34163035 0.24491297 0.22401176
 0.22904719 0.25916196 0.2797505  0.29916411 0.31210696 0.35075888]
	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): [837.84919789  19.25494466]
		Model Seed: 12 Seed: 1 OOD mean of (MSE, MAE) stats: [553.62551012  16.27195746]
		Model Seed: 12 Seed: 1 ID median of (MSE, MAE): [314.98758711  15.09233856]
		Model Seed: 12 Seed: 1 OOD median of (MSE, MAE) stats: [246.38064649  12.97593689]
		Model Seed: 12 Seed: 1 ID likelihoods: 0
		Model Seed: 12 Seed: 1 OOD likelihoods: 0
		Model Seed: 12 Seed: 1 ID calibration errors: [0.28291077 0.18372893 0.10397164 0.09125115 0.11531653 0.14429227
 0.16816439 0.17989865 0.18773874 0.20701298 0.20725254 0.21635878]
		Model Seed: 12 Seed: 1 OOD calibration errors: [0.19214566 0.25588558 0.18822928 0.1515208  0.12469366 0.11622459
 0.12823405 0.12457678 0.13346723 0.14160646 0.1344811  0.15327037]
	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): [1136.53922098   21.86200129]
		Model Seed: 12 Seed: 2 OOD mean of (MSE, MAE) stats: [465.0341975   15.06124485]
		Model Seed: 12 Seed: 2 ID median of (MSE, MAE): [360.74745525  15.87142245]
		Model Seed: 12 Seed: 2 OOD median of (MSE, MAE) stats: [234.83943642  12.71414185]
		Model Seed: 12 Seed: 2 ID likelihoods: 0
		Model Seed: 12 Seed: 2 OOD likelihoods: 0
		Model Seed: 12 Seed: 2 ID calibration errors: [0.3441754  0.29912157 0.28426346 0.34123082 0.3780381  0.3879523
 0.39522474 0.38794227 0.38236621 0.37788211 0.38738292 0.38437061]
		Model Seed: 12 Seed: 2 OOD calibration errors: [0.12609372 0.0801795  0.02920165 0.04414953 0.08803194 0.12828762
 0.16957861 0.18574236 0.20498201 0.21097352 0.22199411 0.2335872 ]
	Model Seed: 12 ID mean of (MSE, MAE): [987.19420944  20.55847298]
	Model Seed: 12 OOD mean of (MSE, MAE): [509.32985381  15.66660115]
	Model Seed: 12 ID median of (MSE, MAE): [337.86752118  15.48188051]
	Model Seed: 12 OOD median of (MSE, MAE): [240.61004145  12.84503937]
	Model Seed: 12 ID likelihoods: 0.0
	Model Seed: 12 OOD likelihoods: 0.0
	Model Seed: 12 ID calibration errors: [0.31354308 0.24142525 0.19411755 0.21624098 0.24667732 0.26612228
 0.28169457 0.28392046 0.28505247 0.29244754 0.29731773 0.3003647 ]
	Model Seed: 12 OOD calibration errors: [0.15911969 0.16803254 0.10871546 0.09783516 0.1063628  0.12225611
 0.14890633 0.15515957 0.16922462 0.17628999 0.17823761 0.19342878]
	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): [761.01834554  18.40178664]
		Model Seed: 13 Seed: 1 OOD mean of (MSE, MAE) stats: [567.59151516  17.0872162 ]
		Model Seed: 13 Seed: 1 ID median of (MSE, MAE): [286.02294744  14.33576393]
		Model Seed: 13 Seed: 1 OOD median of (MSE, MAE) stats: [292.37407936  14.18325186]
		Model Seed: 13 Seed: 1 ID likelihoods: 0
		Model Seed: 13 Seed: 1 OOD likelihoods: 0
		Model Seed: 13 Seed: 1 ID calibration errors: [0.018474   0.00740792 0.01498653 0.03424145 0.05643292 0.08243436
 0.11299506 0.1333958  0.13647781 0.15519146 0.16432581 0.17994718]
		Model Seed: 13 Seed: 1 OOD calibration errors: [0.2061752  0.27854626 0.24440379 0.261548   0.270364   0.28297459
 0.29569381 0.30451767 0.32743588 0.34806116 0.32499639 0.3181268 ]
	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): [1069.09613409   21.48453274]
		Model Seed: 13 Seed: 2 OOD mean of (MSE, MAE) stats: [521.65121639  16.60844508]
		Model Seed: 13 Seed: 2 ID median of (MSE, MAE): [357.62741981  15.88501867]
		Model Seed: 13 Seed: 2 OOD median of (MSE, MAE) stats: [292.00559609  14.26072311]
		Model Seed: 13 Seed: 2 ID likelihoods: 0
		Model Seed: 13 Seed: 2 OOD likelihoods: 0
		Model Seed: 13 Seed: 2 ID calibration errors: [0.00370973 0.02341382 0.06214331 0.10325307 0.15009131 0.16821082
 0.21951557 0.22130183 0.23671224 0.25523733 0.25702855 0.28154705]
		Model Seed: 13 Seed: 2 OOD calibration errors: [0.10479999 0.10404677 0.09807722 0.11540322 0.158079   0.18306125
 0.20155677 0.22525907 0.2209064  0.21333892 0.23783125 0.24610605]
	Model Seed: 13 ID mean of (MSE, MAE): [915.05723981  19.94315969]
	Model Seed: 13 OOD mean of (MSE, MAE): [544.62136578  16.84783064]
	Model Seed: 13 ID median of (MSE, MAE): [321.82518362  15.1103913 ]
	Model Seed: 13 OOD median of (MSE, MAE): [292.18983773  14.22198749]
	Model Seed: 13 ID likelihoods: 0.0
	Model Seed: 13 OOD likelihoods: 0.0
	Model Seed: 13 ID calibration errors: [0.01109186 0.01541087 0.03856492 0.06874726 0.10326211 0.12532259
 0.16625531 0.17734882 0.18659502 0.2052144  0.21067718 0.23074711]
	Model Seed: 13 OOD calibration errors: [0.15548759 0.19129651 0.1712405  0.18847561 0.2142215  0.23301792
 0.24862529 0.26488837 0.27417114 0.28070004 0.28141382 0.28211642]
	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): [790.46088287  18.47528945]
		Model Seed: 14 Seed: 1 OOD mean of (MSE, MAE) stats: [714.47804205  18.01455841]
		Model Seed: 14 Seed: 1 ID median of (MSE, MAE): [323.16457539  15.06708749]
		Model Seed: 14 Seed: 1 OOD median of (MSE, MAE) stats: [314.51532093  14.37392394]
		Model Seed: 14 Seed: 1 ID likelihoods: 0
		Model Seed: 14 Seed: 1 OOD likelihoods: 0
		Model Seed: 14 Seed: 1 ID calibration errors: [0.11959338 0.19057276 0.27864431 0.27291647 0.25235193 0.23414365
 0.20982063 0.18988676 0.18012905 0.17201807 0.15454966 0.15572717]
		Model Seed: 14 Seed: 1 OOD calibration errors: [0.17470265 0.40420679 0.47381736 0.50553468 0.4993554  0.46730393
 0.47527991 0.48093271 0.4908166  0.515317   0.49381911 0.5125188 ]
	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): [1000.08060577   20.72229958]
		Model Seed: 14 Seed: 2 OOD mean of (MSE, MAE) stats: [681.68579552  18.9939781 ]
		Model Seed: 14 Seed: 2 ID median of (MSE, MAE): [365.23088328  16.70856349]
		Model Seed: 14 Seed: 2 OOD median of (MSE, MAE) stats: [345.8701876   15.60425631]
		Model Seed: 14 Seed: 2 ID likelihoods: 0
		Model Seed: 14 Seed: 2 OOD likelihoods: 0
		Model Seed: 14 Seed: 2 ID calibration errors: [0.76372226 0.54015284 0.35553935 0.22667906 0.16816253 0.13630436
 0.14202552 0.12337687 0.12583873 0.1452062  0.14342799 0.15752395]
		Model Seed: 14 Seed: 2 OOD calibration errors: [0.84396683 0.68486015 0.61808304 0.50984013 0.37460088 0.29549701
 0.25331283 0.25656127 0.24854961 0.23325119 0.23386109 0.23106925]
	Model Seed: 14 ID mean of (MSE, MAE): [895.27074432  19.59879451]
	Model Seed: 14 OOD mean of (MSE, MAE): [698.08191879  18.50426826]
	Model Seed: 14 ID median of (MSE, MAE): [344.19772934  15.88782549]
	Model Seed: 14 OOD median of (MSE, MAE): [330.19275426  14.98909012]
	Model Seed: 14 ID likelihoods: 0.0
	Model Seed: 14 OOD likelihoods: 0.0
	Model Seed: 14 ID calibration errors: [0.44165782 0.3653628  0.31709183 0.24979776 0.21025723 0.185224
 0.17592308 0.15663181 0.15298389 0.15861214 0.14898882 0.15662556]
	Model Seed: 14 OOD calibration errors: [0.50933474 0.54453347 0.5459502  0.5076874  0.43697814 0.38140047
 0.36429637 0.36874699 0.3696831  0.3742841  0.3638401  0.37179402]
	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): [825.45217925  20.5402783 ]
		Model Seed: 15 Seed: 1 OOD mean of (MSE, MAE) stats: [612.95697993  17.84299373]
		Model Seed: 15 Seed: 1 ID median of (MSE, MAE): [391.40717517  17.53604635]
		Model Seed: 15 Seed: 1 OOD median of (MSE, MAE) stats: [282.51530874  14.49262206]
		Model Seed: 15 Seed: 1 ID likelihoods: 0
		Model Seed: 15 Seed: 1 OOD likelihoods: 0
		Model Seed: 15 Seed: 1 ID calibration errors: [1.37379926 1.11903155 0.81644829 0.61530396 0.49452147 0.40741646
 0.35469203 0.31578241 0.27512726 0.2626852  0.23380281 0.23354691]
		Model Seed: 15 Seed: 1 OOD calibration errors: [1.42795912 1.21535639 0.82954053 0.60630524 0.4977239  0.41191914
 0.40428478 0.40603375 0.39694371 0.39782203 0.37814147 0.38425022]
	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): [1351.02052519   22.12694906]
		Model Seed: 15 Seed: 2 OOD mean of (MSE, MAE) stats: [690.15472818  18.4127288 ]
		Model Seed: 15 Seed: 2 ID median of (MSE, MAE): [288.62399945  13.92387009]
		Model Seed: 15 Seed: 2 OOD median of (MSE, MAE) stats: [315.25420505  14.63504855]
		Model Seed: 15 Seed: 2 ID likelihoods: 0
		Model Seed: 15 Seed: 2 OOD likelihoods: 0
		Model Seed: 15 Seed: 2 ID calibration errors: [0.18002505 0.10528187 0.14110912 0.18937767 0.24439497 0.3102581
 0.36025361 0.41820274 0.42872445 0.44514287 0.44794644 0.46133759]
		Model Seed: 15 Seed: 2 OOD calibration errors: [0.32325172 0.18917593 0.20393217 0.27068018 0.32488496 0.36948438
 0.41517985 0.46858429 0.4893122  0.49049626 0.50907631 0.51915689]
	Model Seed: 15 ID mean of (MSE, MAE): [1088.23635222   21.33361368]
	Model Seed: 15 OOD mean of (MSE, MAE): [651.55585405  18.12786126]
	Model Seed: 15 ID median of (MSE, MAE): [340.01558731  15.72995822]
	Model Seed: 15 OOD median of (MSE, MAE): [298.88475689  14.5638353 ]
	Model Seed: 15 ID likelihoods: 0.0
	Model Seed: 15 OOD likelihoods: 0.0
	Model Seed: 15 ID calibration errors: [0.77691216 0.61215671 0.47877871 0.40234082 0.36945822 0.35883728
 0.35747282 0.36699257 0.35192585 0.35391404 0.34087463 0.34744225]
	Model Seed: 15 OOD calibration errors: [0.87560542 0.70226616 0.51673635 0.43849271 0.41130443 0.39070176
 0.40973231 0.43730902 0.44312795 0.44415914 0.44360889 0.45170356]
	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): [1207.54502251   21.95965276]
		Model Seed: 16 Seed: 1 OOD mean of (MSE, MAE) stats: [611.41081709  16.98426202]
		Model Seed: 16 Seed: 1 ID median of (MSE, MAE): [354.0074726   15.42623583]
		Model Seed: 16 Seed: 1 OOD median of (MSE, MAE) stats: [287.48020153  13.66539224]
		Model Seed: 16 Seed: 1 ID likelihoods: 0
		Model Seed: 16 Seed: 1 OOD likelihoods: 0
		Model Seed: 16 Seed: 1 ID calibration errors: [0.10047241 0.02322081 0.00819953 0.04441106 0.09531914 0.11710626
 0.15451685 0.17304095 0.19079858 0.23108677 0.23083396 0.24932303]
		Model Seed: 16 Seed: 1 OOD calibration errors: [0.03191735 0.0218156  0.04792197 0.1182928  0.17224116 0.2099195
 0.25832763 0.27394525 0.30391221 0.32999521 0.32542131 0.35614951]
	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): [1095.46772396   20.4440259 ]
		Model Seed: 16 Seed: 2 OOD mean of (MSE, MAE) stats: [639.79226154  18.63829856]
		Model Seed: 16 Seed: 2 ID median of (MSE, MAE): [265.30333477  13.64912669]
		Model Seed: 16 Seed: 2 OOD median of (MSE, MAE) stats: [295.53237205  14.75951385]
		Model Seed: 16 Seed: 2 ID likelihoods: 0
		Model Seed: 16 Seed: 2 OOD likelihoods: 0
		Model Seed: 16 Seed: 2 ID calibration errors: [0.73113339 0.43344786 0.19249224 0.10327709 0.08250493 0.07427511
 0.09727873 0.10401411 0.11994263 0.14438401 0.15537469 0.19141959]
		Model Seed: 16 Seed: 2 OOD calibration errors: [1.09442899 0.88822448 0.653985   0.50786301 0.40385006 0.34365907
 0.30808812 0.31159411 0.29457276 0.31383528 0.30641202 0.30401602]
	Model Seed: 16 ID mean of (MSE, MAE): [1151.50637324   21.20183933]
	Model Seed: 16 OOD mean of (MSE, MAE): [625.60153932  17.81128029]
	Model Seed: 16 ID median of (MSE, MAE): [309.65540369  14.53768126]
	Model Seed: 16 OOD median of (MSE, MAE): [291.50628679  14.21245305]
	Model Seed: 16 ID likelihoods: 0.0
	Model Seed: 16 OOD likelihoods: 0.0
	Model Seed: 16 ID calibration errors: [0.4158029  0.22833434 0.10034588 0.07384408 0.08891204 0.09569068
 0.12589779 0.13852753 0.15537061 0.18773539 0.19310433 0.22037131]
	Model Seed: 16 OOD calibration errors: [0.56317317 0.45502004 0.35095349 0.3130779  0.28804561 0.27678928
 0.28320788 0.29276968 0.29924249 0.32191524 0.31591666 0.33008276]
	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): [1185.99005566   23.15714833]
		Model Seed: 17 Seed: 1 OOD mean of (MSE, MAE) stats: [692.06377206  18.89427656]
		Model Seed: 17 Seed: 1 ID median of (MSE, MAE): [434.9301839   17.40300751]
		Model Seed: 17 Seed: 1 OOD median of (MSE, MAE) stats: [375.54702894  16.04584249]
		Model Seed: 17 Seed: 1 ID likelihoods: 0
		Model Seed: 17 Seed: 1 OOD likelihoods: 0
		Model Seed: 17 Seed: 1 ID calibration errors: [0.07367193 0.03394692 0.06218714 0.10477798 0.14711503 0.17237327
 0.21711882 0.23393479 0.25690474 0.29586203 0.30005543 0.32239002]
		Model Seed: 17 Seed: 1 OOD calibration errors: [0.14302639 0.04881463 0.05071193 0.07112425 0.10231548 0.14058768
 0.17768532 0.21087109 0.23070055 0.24737644 0.23666864 0.22843178]
	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): [1069.50977949   20.67027406]
		Model Seed: 17 Seed: 2 OOD mean of (MSE, MAE) stats: [639.77241312  17.85629064]
		Model Seed: 17 Seed: 2 ID median of (MSE, MAE): [313.66230705  14.77047094]
		Model Seed: 17 Seed: 2 OOD median of (MSE, MAE) stats: [221.26596845  12.81722864]
		Model Seed: 17 Seed: 2 ID likelihoods: 0
		Model Seed: 17 Seed: 2 OOD likelihoods: 0
		Model Seed: 17 Seed: 2 ID calibration errors: [0.79457575 0.25920479 0.1400023  0.09625325 0.1150912  0.11580469
 0.1422371  0.15200199 0.17402397 0.20504101 0.19991188 0.21688545]
		Model Seed: 17 Seed: 2 OOD calibration errors: [1.73514779 1.11805343 0.81795367 0.59695974 0.42752554 0.33798675
 0.26900332 0.28084303 0.26729222 0.26233085 0.2701592  0.270514  ]
	Model Seed: 17 ID mean of (MSE, MAE): [1127.74991758   21.9137112 ]
	Model Seed: 17 OOD mean of (MSE, MAE): [665.91809259  18.3752836 ]
	Model Seed: 17 ID median of (MSE, MAE): [374.29624547  16.08673922]
	Model Seed: 17 OOD median of (MSE, MAE): [298.40649869  14.43153556]
	Model Seed: 17 ID likelihoods: 0.0
	Model Seed: 17 OOD likelihoods: 0.0
	Model Seed: 17 ID calibration errors: [0.43412384 0.14657586 0.10109472 0.10051562 0.13110312 0.14408898
 0.17967796 0.19296839 0.21546435 0.25045152 0.24998366 0.26963773]
	Model Seed: 17 OOD calibration errors: [0.93908709 0.58343403 0.4343328  0.334042   0.26492051 0.23928722
 0.22334432 0.24585706 0.24899638 0.25485364 0.25341392 0.24947289]
	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): [858.80555673  19.15227646]
		Model Seed: 18 Seed: 1 OOD mean of (MSE, MAE) stats: [676.67490018  18.2510559 ]
		Model Seed: 18 Seed: 1 ID median of (MSE, MAE): [301.13103683  14.63098462]
		Model Seed: 18 Seed: 1 OOD median of (MSE, MAE) stats: [306.14688238  14.89242172]
		Model Seed: 18 Seed: 1 ID likelihoods: 0
		Model Seed: 18 Seed: 1 OOD likelihoods: 0
		Model Seed: 18 Seed: 1 ID calibration errors: [0.02487633 0.12284401 0.1637729  0.18185192 0.209756   0.23585006
 0.2502257  0.25261402 0.26352188 0.29502102 0.29068833 0.31384176]
		Model Seed: 18 Seed: 1 OOD calibration errors: [0.41464053 0.46103606 0.43146788 0.4322851  0.39269945 0.41214839
 0.44920321 0.43707176 0.47035496 0.49799363 0.47577496 0.50577218]
	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): [1369.85483899   22.7879524 ]
		Model Seed: 18 Seed: 2 OOD mean of (MSE, MAE) stats: [600.44752759  17.58066956]
		Model Seed: 18 Seed: 2 ID median of (MSE, MAE): [352.77569583  15.70977402]
		Model Seed: 18 Seed: 2 OOD median of (MSE, MAE) stats: [315.59551971  15.19375102]
		Model Seed: 18 Seed: 2 ID likelihoods: 0
		Model Seed: 18 Seed: 2 OOD likelihoods: 0
		Model Seed: 18 Seed: 2 ID calibration errors: [0.47498661 0.29777271 0.24675804 0.26138698 0.29875449 0.3385513
 0.37462393 0.40114003 0.42396364 0.45987161 0.46136445 0.49294134]
		Model Seed: 18 Seed: 2 OOD calibration errors: [0.25485411 0.11804176 0.22214076 0.29747792 0.37470995 0.42220075
 0.46584516 0.48958534 0.50084741 0.52365318 0.55860634 0.53882587]
	Model Seed: 18 ID mean of (MSE, MAE): [1114.33019786   20.97011443]
	Model Seed: 18 OOD mean of (MSE, MAE): [638.56121388  17.91586273]
	Model Seed: 18 ID median of (MSE, MAE): [326.95336633  15.17037932]
	Model Seed: 18 OOD median of (MSE, MAE): [310.87120104  15.04308637]
	Model Seed: 18 ID likelihoods: 0.0
	Model Seed: 18 OOD likelihoods: 0.0
	Model Seed: 18 ID calibration errors: [0.24993147 0.21030836 0.20526547 0.22161945 0.25425525 0.28720068
 0.31242481 0.32687703 0.34374276 0.37744631 0.37602639 0.40339155]
	Model Seed: 18 OOD calibration errors: [0.33474732 0.28953891 0.32680432 0.36488151 0.3837047  0.41717457
 0.45752418 0.46332855 0.48560119 0.5108234  0.51719065 0.52229903]
	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): [854.72694625  18.13435938]
		Model Seed: 19 Seed: 1 OOD mean of (MSE, MAE) stats: [580.95263115  17.57383244]
		Model Seed: 19 Seed: 1 ID median of (MSE, MAE): [228.1778367   12.88178635]
		Model Seed: 19 Seed: 1 OOD median of (MSE, MAE) stats: [274.92895211  14.69033432]
		Model Seed: 19 Seed: 1 ID likelihoods: 0
		Model Seed: 19 Seed: 1 OOD likelihoods: 0
		Model Seed: 19 Seed: 1 ID calibration errors: [0.34490548 0.2973332  0.2152714  0.1869605  0.16117757 0.15292038
 0.16092463 0.15329749 0.14446176 0.17719449 0.16718905 0.18851933]
		Model Seed: 19 Seed: 1 OOD calibration errors: [0.92896464 0.92381338 0.76360982 0.65600267 0.52104896 0.43043221
 0.35678954 0.33697795 0.26608202 0.23093826 0.23259569 0.18439166]
	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): [1243.3293583    22.93706385]
		Model Seed: 19 Seed: 2 OOD mean of (MSE, MAE) stats: [537.34015341  16.80842926]
		Model Seed: 19 Seed: 2 ID median of (MSE, MAE): [404.4091484   17.11142476]
		Model Seed: 19 Seed: 2 OOD median of (MSE, MAE) stats: [287.60789327  14.51282469]
		Model Seed: 19 Seed: 2 ID likelihoods: 0
		Model Seed: 19 Seed: 2 OOD likelihoods: 0
		Model Seed: 19 Seed: 2 ID calibration errors: [0.23611921 0.22666371 0.20248332 0.21895436 0.24569245 0.24695476
 0.25481008 0.28309028 0.29774337 0.30582067 0.31223688 0.3242487 ]
		Model Seed: 19 Seed: 2 OOD calibration errors: [0.25232707 0.12708675 0.07482683 0.06166398 0.07702543 0.09987407
 0.13358227 0.15823428 0.18754966 0.20273856 0.21386929 0.23383625]
	Model Seed: 19 ID mean of (MSE, MAE): [1049.02815228   20.53571161]
	Model Seed: 19 OOD mean of (MSE, MAE): [559.14639228  17.19113085]
	Model Seed: 19 ID median of (MSE, MAE): [316.29349255  14.99660556]
	Model Seed: 19 OOD median of (MSE, MAE): [281.26842269  14.60157951]
	Model Seed: 19 ID likelihoods: 0.0
	Model Seed: 19 OOD likelihoods: 0.0
	Model Seed: 19 ID calibration errors: [0.29051234 0.26199846 0.20887736 0.20295743 0.20343501 0.19993757
 0.20786736 0.21819389 0.22110257 0.24150758 0.23971296 0.25638402]
	Model Seed: 19 OOD calibration errors: [0.59064586 0.52545006 0.41921832 0.35883332 0.2990372  0.26515314
 0.24518591 0.24760611 0.22681584 0.21683841 0.22323249 0.20911396]
ID mean of (MSE, MAE): [1075.2328604390173, 21.047313206145184] +- [134.25424429191096, 1.2145184627761128] +- [110.44012298   0.69323728] 
OOD mean of (MSE, MAE): [631.7605777366329, 17.742577841017418] +- [71.07930156106596, 0.8783286053851015] +- [40.0138897   0.36250351] 
ID median of (MSE, MAE): [339.65037273097585, 15.506188265482583] +- [35.0572927244597, 0.831140120934197] +- [0.71093747 0.01647584] 
OOD median of (MSE, MAE): [295.5795820252479, 14.425287842750553] +- [23.84592371544366, 0.6321589003052863] +- [18.26022573  0.36391738] 
ID likelihoods: 0.0 +- 0.0
OOD likelihoods: 0.0 +- 0.0
ID calibration errors: [0.41833865798874736, 0.3071187434267175, 0.22141761908977178, 0.19288269890389068, 0.19475701062434342, 0.2009417985553171, 0.21917186081076317, 0.22657335674902393, 0.2333438856634195, 0.25338484661463967, 0.25447958173650415, 0.27052845631626293] +- [0.27899780163532417, 0.24130241854072892, 0.1588575403398916, 0.1146475090538869, 0.09140688206505902, 0.0850498255730924, 0.07668392693947518, 0.07600491076078777, 0.07182073799480011, 0.07048831008737684, 0.06886090414131164, 0.06860073526043192] +- [0.10420785 0.02048247 0.01366952 0.01494166 0.00489171 0.00155322
 0.00887479 0.01519051 0.02144497 0.01889789 0.02141134 0.02235516] 
OOD calibration errors: [0.5804632904825169, 0.47118141795086166, 0.37171052006916394, 0.32338704188634904, 0.2936372151058321, 0.28540875426304885, 0.2934838259891267, 0.30738460521012934, 0.31494385261176416, 0.32585739196719443, 0.3264993834905561, 0.335737151590657] +- [0.3428411263946652, 0.2287762082328931, 0.14786019772142558, 0.11000449894683308, 0.09337432083871698, 0.08588650417654883, 0.08991857688515094, 0.08991187159454724, 0.09299661770024985, 0.09772392884773447, 0.09708996550578936, 0.10005873698846118] +- [0.12652633 0.04080383 0.01357706 0.01586873 0.02790591 0.03329761
 0.04058145 0.03510204 0.03977777 0.04759524 0.03626424 0.04162279] 
