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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)
		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: 160
	Extracted segments: 152
	Interpolated values: 8003
	Percent of values interpolated: 8.57%
Splitting data...
	Train: 57159 (68.64%)
	Val: 16704 (20.06%)
	Test: 19521 (23.44%)
Scaling data...
	No scaling applied
Data formatting complete.
--------------------------------
Current value: 0.06408796459436417, Current params: {'in_len': 132, 'max_samples_per_ts': 100, 'hidden_size': 48, 'num_attention_heads': 2, 'dropout': 0.2857086798287012, 'lr': 0.00824164301411041, 'batch_size': 48, 'max_grad_norm': 0.6913541369100363}
Best value: 0.06408796459436417, Best params: {'in_len': 132, 'max_samples_per_ts': 100, 'hidden_size': 48, 'num_attention_heads': 2, 'dropout': 0.2857086798287012, 'lr': 0.00824164301411041, 'batch_size': 48, 'max_grad_norm': 0.6913541369100363}
Current value: 0.06674056500196457, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 16, 'num_attention_heads': 4, 'dropout': 0.2193319065508743, 'lr': 0.00890340224330123, 'batch_size': 64, 'max_grad_norm': 0.9094075813885965}
Best value: 0.06408796459436417, Best params: {'in_len': 132, 'max_samples_per_ts': 100, 'hidden_size': 48, 'num_attention_heads': 2, 'dropout': 0.2857086798287012, 'lr': 0.00824164301411041, 'batch_size': 48, 'max_grad_norm': 0.6913541369100363}
Current value: 0.06387852877378464, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 4, 'dropout': 0.16328283154419143, 'lr': 0.007457238168782765, 'batch_size': 48, 'max_grad_norm': 0.6788221831516876}
Best value: 0.06387852877378464, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 4, 'dropout': 0.16328283154419143, 'lr': 0.007457238168782765, 'batch_size': 48, 'max_grad_norm': 0.6788221831516876}
Current value: 0.07188338786363602, Current params: {'in_len': 144, 'max_samples_per_ts': 100, 'hidden_size': 224, 'num_attention_heads': 4, 'dropout': 0.18162000935602088, 'lr': 0.0005166752137461828, 'batch_size': 64, 'max_grad_norm': 0.9090685760224372}
Best value: 0.06387852877378464, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 4, 'dropout': 0.16328283154419143, 'lr': 0.007457238168782765, 'batch_size': 48, 'max_grad_norm': 0.6788221831516876}
Current value: 0.07230956852436066, Current params: {'in_len': 108, 'max_samples_per_ts': 100, 'hidden_size': 96, 'num_attention_heads': 3, 'dropout': 0.24701653010540128, 'lr': 0.008770479882281426, 'batch_size': 32, 'max_grad_norm': 0.07918869316254969}
Best value: 0.06387852877378464, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 4, 'dropout': 0.16328283154419143, 'lr': 0.007457238168782765, 'batch_size': 48, 'max_grad_norm': 0.6788221831516876}
Current value: 0.3602733314037323, Current params: {'in_len': 120, 'max_samples_per_ts': 200, 'hidden_size': 176, 'num_attention_heads': 4, 'dropout': 0.15203077729251085, 'lr': 0.008156633860044206, 'batch_size': 32, 'max_grad_norm': 0.35474985599040154}
Best value: 0.06387852877378464, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 4, 'dropout': 0.16328283154419143, 'lr': 0.007457238168782765, 'batch_size': 48, 'max_grad_norm': 0.6788221831516876}
Current value: 0.06673804670572281, Current params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 256, 'num_attention_heads': 2, 'dropout': 0.2485232377814099, 'lr': 0.00566275546990402, 'batch_size': 48, 'max_grad_norm': 0.3619662762128023}
Best value: 0.06387852877378464, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 4, 'dropout': 0.16328283154419143, 'lr': 0.007457238168782765, 'batch_size': 48, 'max_grad_norm': 0.6788221831516876}
Current value: 0.2214611917734146, Current params: {'in_len': 120, 'max_samples_per_ts': 150, 'hidden_size': 176, 'num_attention_heads': 4, 'dropout': 0.2958269372662624, 'lr': 0.0008794414419415577, 'batch_size': 32, 'max_grad_norm': 0.6369659753130538}
Best value: 0.06387852877378464, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 4, 'dropout': 0.16328283154419143, 'lr': 0.007457238168782765, 'batch_size': 48, 'max_grad_norm': 0.6788221831516876}
Current value: 0.059402547776699066, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.28143581943667584, 'lr': 0.0016575091913418386, 'batch_size': 48, 'max_grad_norm': 0.6273502645339284}
Best value: 0.059402547776699066, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.28143581943667584, 'lr': 0.0016575091913418386, 'batch_size': 48, 'max_grad_norm': 0.6273502645339284}
Current value: 0.06431552022695541, Current params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 16, 'num_attention_heads': 1, 'dropout': 0.1995253356677017, 'lr': 0.0056530359659329515, 'batch_size': 48, 'max_grad_norm': 0.6772138553099127}
Best value: 0.059402547776699066, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.28143581943667584, 'lr': 0.0016575091913418386, 'batch_size': 48, 'max_grad_norm': 0.6273502645339284}
Current value: 0.23268082737922668, Current params: {'in_len': 132, 'max_samples_per_ts': 150, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.10510512625566208, 'lr': 0.002712809434495395, 'batch_size': 64, 'max_grad_norm': 0.07077427352805449}
Best value: 0.059402547776699066, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.28143581943667584, 'lr': 0.0016575091913418386, 'batch_size': 48, 'max_grad_norm': 0.6273502645339284}
Current value: 0.06392079591751099, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 3, 'dropout': 0.14736379297490781, 'lr': 0.0035671999198697325, 'batch_size': 48, 'max_grad_norm': 0.4943737221798048}
Best value: 0.059402547776699066, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.28143581943667584, 'lr': 0.0016575091913418386, 'batch_size': 48, 'max_grad_norm': 0.6273502645339284}
Current value: 0.05988479405641556, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 2, 'dropout': 0.1570320821212351, 'lr': 0.0041358894015594535, 'batch_size': 48, 'max_grad_norm': 0.7974736416394427}
Best value: 0.059402547776699066, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.28143581943667584, 'lr': 0.0016575091913418386, 'batch_size': 48, 'max_grad_norm': 0.6273502645339284}
Current value: 0.21857228875160217, Current params: {'in_len': 132, 'max_samples_per_ts': 100, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.1023532891177918, 'lr': 0.0025867634670148883, 'batch_size': 48, 'max_grad_norm': 0.8159326142097275}
Best value: 0.059402547776699066, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.28143581943667584, 'lr': 0.0016575091913418386, 'batch_size': 48, 'max_grad_norm': 0.6273502645339284}
Current value: 0.22141921520233154, Current params: {'in_len': 144, 'max_samples_per_ts': 200, 'hidden_size': 128, 'num_attention_heads': 2, 'dropout': 0.2596441955843235, 'lr': 0.004310232325621764, 'batch_size': 32, 'max_grad_norm': 0.5015279999264644}
Best value: 0.059402547776699066, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.28143581943667584, 'lr': 0.0016575091913418386, 'batch_size': 48, 'max_grad_norm': 0.6273502645339284}
Current value: 0.0644090548157692, Current params: {'in_len': 132, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.13120643747306746, 'lr': 0.0016872447340363721, 'batch_size': 64, 'max_grad_norm': 0.8115361913056531}
Best value: 0.059402547776699066, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.28143581943667584, 'lr': 0.0016575091913418386, 'batch_size': 48, 'max_grad_norm': 0.6273502645339284}
Current value: 0.2242697924375534, Current params: {'in_len': 144, 'max_samples_per_ts': 150, 'hidden_size': 128, 'num_attention_heads': 2, 'dropout': 0.2170962033073382, 'lr': 0.00451571453805049, 'batch_size': 48, 'max_grad_norm': 0.9906878455043249}
Best value: 0.059402547776699066, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.28143581943667584, 'lr': 0.0016575091913418386, 'batch_size': 48, 'max_grad_norm': 0.6273502645339284}
Current value: 0.24451668560504913, Current params: {'in_len': 132, 'max_samples_per_ts': 100, 'hidden_size': 48, 'num_attention_heads': 3, 'dropout': 0.18468351139376793, 'lr': 0.0019491278417010228, 'batch_size': 32, 'max_grad_norm': 0.2687432427902292}
Best value: 0.059402547776699066, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.28143581943667584, 'lr': 0.0016575091913418386, 'batch_size': 48, 'max_grad_norm': 0.6273502645339284}
Current value: 0.21644987165927887, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 1, 'dropout': 0.2710084500633989, 'lr': 0.0069054040421142795, 'batch_size': 64, 'max_grad_norm': 0.5583762040254486}
Best value: 0.059402547776699066, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.28143581943667584, 'lr': 0.0016575091913418386, 'batch_size': 48, 'max_grad_norm': 0.6273502645339284}
Current value: 0.23130500316619873, Current params: {'in_len': 120, 'max_samples_per_ts': 100, 'hidden_size': 48, 'num_attention_heads': 2, 'dropout': 0.2248669237548152, 'lr': 0.003442361509524188, 'batch_size': 48, 'max_grad_norm': 0.7813457910803117}
Best value: 0.059402547776699066, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.28143581943667584, 'lr': 0.0016575091913418386, 'batch_size': 48, 'max_grad_norm': 0.6273502645339284}
Current value: 0.06113678216934204, Current params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.13049387990666342, 'lr': 0.0013629979521577558, 'batch_size': 48, 'max_grad_norm': 0.5497970731359247}
Best value: 0.059402547776699066, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.28143581943667584, 'lr': 0.0016575091913418386, 'batch_size': 48, 'max_grad_norm': 0.6273502645339284}
Current value: 0.22467051446437836, Current params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 1, 'dropout': 0.12413706838674843, 'lr': 0.0012563638015083045, 'batch_size': 48, 'max_grad_norm': 0.5973307330305396}
Best value: 0.059402547776699066, Best params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.28143581943667584, 'lr': 0.0016575091913418386, 'batch_size': 48, 'max_grad_norm': 0.6273502645339284}
Current value: 0.05902719497680664, Current params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.20155994594097137, Current params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 2, 'dropout': 0.16054037103231636, 'lr': 0.00036854915485606994, 'batch_size': 48, 'max_grad_norm': 0.4378143158450921}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.26563721895217896, Current params: {'in_len': 96, 'max_samples_per_ts': 100, 'hidden_size': 208, 'num_attention_heads': 1, 'dropout': 0.1701588296608713, 'lr': 0.002284708496013049, 'batch_size': 48, 'max_grad_norm': 0.1989474668620782}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.20100060105323792, Current params: {'in_len': 132, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 2, 'dropout': 0.11492145988399739, 'lr': 0.0001947202281836894, 'batch_size': 32, 'max_grad_norm': 0.41152208729428297}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.3130767047405243, Current params: {'in_len': 144, 'max_samples_per_ts': 100, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.1374492624907371, 'lr': 0.003292385168292926, 'batch_size': 64, 'max_grad_norm': 0.7463077774169304}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.20115506649017334, Current params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 3, 'dropout': 0.19975712034734133, 'lr': 0.006248183205661442, 'batch_size': 48, 'max_grad_norm': 0.2847771340087779}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.37116995453834534, Current params: {'in_len': 132, 'max_samples_per_ts': 150, 'hidden_size': 160, 'num_attention_heads': 2, 'dropout': 0.17988428877048077, 'lr': 0.003916505295236191, 'batch_size': 48, 'max_grad_norm': 0.8708422086182052}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.37139326333999634, Current params: {'in_len': 132, 'max_samples_per_ts': 100, 'hidden_size': 64, 'num_attention_heads': 2, 'dropout': 0.28225425949769944, 'lr': 0.005241446969102369, 'batch_size': 48, 'max_grad_norm': 0.7346462526706292}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.24270814657211304, Current params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 240, 'num_attention_heads': 1, 'dropout': 0.14001691748197087, 'lr': 0.0010601737563862138, 'batch_size': 48, 'max_grad_norm': 0.6241067040585597}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.19822677969932556, Current params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.11224403887037529, 'lr': 0.001581265718724247, 'batch_size': 48, 'max_grad_norm': 0.5630096557476613}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.06012151017785072, Current params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 32, 'num_attention_heads': 1, 'dropout': 0.12319590539211929, 'lr': 0.0027337395772644045, 'batch_size': 48, 'max_grad_norm': 0.47293654145739983}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.19821935892105103, Current params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 16, 'num_attention_heads': 1, 'dropout': 0.12246028867126299, 'lr': 0.0027551459378183413, 'batch_size': 48, 'max_grad_norm': 0.4606139194931113}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.06065146625041962, Current params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 32, 'num_attention_heads': 1, 'dropout': 0.1515600378090321, 'lr': 0.00207314624040631, 'batch_size': 64, 'max_grad_norm': 0.7056239475050748}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.3810405433177948, Current params: {'in_len': 96, 'max_samples_per_ts': 100, 'hidden_size': 32, 'num_attention_heads': 2, 'dropout': 0.2342621368770466, 'lr': 0.009736990864957613, 'batch_size': 48, 'max_grad_norm': 0.35415067218882146}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.059564147144556046, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.1664386220360726, 'lr': 0.003066709116222042, 'batch_size': 48, 'max_grad_norm': 0.1962634054298737}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.06150364130735397, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 2, 'dropout': 0.17525464631110094, 'lr': 0.004369327217566061, 'batch_size': 32, 'max_grad_norm': 0.20423637855874832}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.26203420758247375, Current params: {'in_len': 144, 'max_samples_per_ts': 100, 'hidden_size': 192, 'num_attention_heads': 1, 'dropout': 0.18819820813371482, 'lr': 0.0007149131105421216, 'batch_size': 64, 'max_grad_norm': 0.1312177082870965}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.06060860678553581, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 3, 'dropout': 0.20846252769895304, 'lr': 0.004938137202685003, 'batch_size': 32, 'max_grad_norm': 0.952026225749674}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.28984084725379944, Current params: {'in_len': 144, 'max_samples_per_ts': 200, 'hidden_size': 96, 'num_attention_heads': 1, 'dropout': 0.160335127988894, 'lr': 0.0031715761973511144, 'batch_size': 48, 'max_grad_norm': 0.0207309850285744}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.061475273221731186, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 32, 'num_attention_heads': 1, 'dropout': 0.14385789781150377, 'lr': 0.003814455147643883, 'batch_size': 48, 'max_grad_norm': 0.39464915853944293}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.1965402364730835, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 48, 'num_attention_heads': 1, 'dropout': 0.16910301847360576, 'lr': 0.002895010578616628, 'batch_size': 48, 'max_grad_norm': 0.3243877000988664}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.06280604004859924, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 1, 'dropout': 0.299045850317508, 'lr': 0.001971389568447744, 'batch_size': 48, 'max_grad_norm': 0.6386566652859994}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.19613094627857208, Current params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.11608945804621028, 'lr': 0.0024004345075675, 'batch_size': 48, 'max_grad_norm': 0.4900064596341788}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.21278616786003113, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 16, 'num_attention_heads': 1, 'dropout': 0.15357169240925517, 'lr': 0.0007319310862755876, 'batch_size': 48, 'max_grad_norm': 0.20732900754811778}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.19630104303359985, Current params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 48, 'num_attention_heads': 2, 'dropout': 0.13264137238038165, 'lr': 0.002975294322170404, 'batch_size': 48, 'max_grad_norm': 0.8600070785699117}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.2906997799873352, Current params: {'in_len': 132, 'max_samples_per_ts': 100, 'hidden_size': 32, 'num_attention_heads': 4, 'dropout': 0.1903699630849944, 'lr': 0.0001533246960731395, 'batch_size': 48, 'max_grad_norm': 0.6755498013922985}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.06392975151538849, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 128, 'num_attention_heads': 1, 'dropout': 0.1033559203026502, 'lr': 0.0038259494571942394, 'batch_size': 48, 'max_grad_norm': 0.13842087487010668}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
Current value: 0.2078913003206253, Current params: {'in_len': 132, 'max_samples_per_ts': 50, 'hidden_size': 176, 'num_attention_heads': 1, 'dropout': 0.1567318358030029, 'lr': 0.004819654999010018, 'batch_size': 48, 'max_grad_norm': 0.29455005271219925}
Best value: 0.05902719497680664, Best params: {'in_len': 96, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 1, 'dropout': 0.12663651999137013, 'lr': 0.0003909069464830342, 'batch_size': 48, 'max_grad_norm': 0.42691316697261855}
--------------------------------
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)
		Age: REAL_VALUED (STATIC_INPUT)
		BMI: REAL_VALUED (STATIC_INPUT)
		A1C: REAL_VALUED (STATIC_INPUT)
		FBG: REAL_VALUED (STATIC_INPUT)
		ogtt.2hr: REAL_VALUED (STATIC_INPUT)
		insulin: REAL_VALUED (STATIC_INPUT)
		hs.CRP: REAL_VALUED (STATIC_INPUT)
		Tchol: REAL_VALUED (STATIC_INPUT)
		Trg: REAL_VALUED (STATIC_INPUT)
		HDL: REAL_VALUED (STATIC_INPUT)
		LDL: REAL_VALUED (STATIC_INPUT)
		mean_glucose: REAL_VALUED (STATIC_INPUT)
		sd_glucose: REAL_VALUED (STATIC_INPUT)
		range_glucose: REAL_VALUED (STATIC_INPUT)
		min_glucose: REAL_VALUED (STATIC_INPUT)
		max_glucose: REAL_VALUED (STATIC_INPUT)
		quartile.25_glucose: REAL_VALUED (STATIC_INPUT)
		median_glucose: REAL_VALUED (STATIC_INPUT)
		quartile.75_glucose: REAL_VALUED (STATIC_INPUT)
		mean_slope: REAL_VALUED (STATIC_INPUT)
		max_slope: REAL_VALUED (STATIC_INPUT)
		number_Random140: REAL_VALUED (STATIC_INPUT)
		number_Random200: REAL_VALUED (STATIC_INPUT)
		percent_below.80: REAL_VALUED (STATIC_INPUT)
		se_glucose_mean: REAL_VALUED (STATIC_INPUT)
		numGE: REAL_VALUED (STATIC_INPUT)
		mage: REAL_VALUED (STATIC_INPUT)
		j_index: REAL_VALUED (STATIC_INPUT)
		IQR: REAL_VALUED (STATIC_INPUT)
		modd: REAL_VALUED (STATIC_INPUT)
		distance_traveled: REAL_VALUED (STATIC_INPUT)
		coef_variation: REAL_VALUED (STATIC_INPUT)
		number_Random140_normByDays: REAL_VALUED (STATIC_INPUT)
		number_Random200_normByDays: REAL_VALUED (STATIC_INPUT)
		numGE_normByDays: REAL_VALUED (STATIC_INPUT)
		distance_traveled_normByDays: REAL_VALUED (STATIC_INPUT)
		diagnosis: REAL_VALUED (STATIC_INPUT)
		freq_low: REAL_VALUED (STATIC_INPUT)
		freq_moderate: REAL_VALUED (STATIC_INPUT)
		freq_severe: REAL_VALUED (STATIC_INPUT)
		glucotype: REAL_VALUED (STATIC_INPUT)
		Height: REAL_VALUED (STATIC_INPUT)
		Weight: REAL_VALUED (STATIC_INPUT)
		Insulin_rate_dd: REAL_VALUED (STATIC_INPUT)
		perc_cgm_prediabetic_range: REAL_VALUED (STATIC_INPUT)
		perc_cgm_diabetic_range: REAL_VALUED (STATIC_INPUT)
		SSPG: 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: 160
	Extracted segments: 152
	Interpolated values: 8003
	Percent of values interpolated: 8.57%
Splitting data...
	Train: 62461 (61.57%)
	Val: 12357 (12.18%)
	Test: 16517 (16.28%)
	Test OOD: 10113 (9.97%)
Scaling data...
	No scaling applied
Data formatting complete.
--------------------------------
	Train: 62090 (61.20%)
	Val: 12502 (12.32%)
	Test: 16648 (16.41%)
	Test OOD: 10208 (10.06%)
	No scaling applied
		Model Seed: 10 Seed: 1 ID mean of (MSE, MAE): [193.62347718   8.78643212]
		Model Seed: 10 Seed: 1 OOD mean of (MSE, MAE) stats: [159.14070788   8.16083711]
		Model Seed: 10 Seed: 1 ID median of (MSE, MAE): [56.58068209  6.23571587]
		Model Seed: 10 Seed: 1 OOD median of (MSE, MAE) stats: [51.1067401   5.90874958]
		Model Seed: 10 Seed: 1 ID likelihoods: 0
		Model Seed: 10 Seed: 1 OOD likelihoods: 0
		Model Seed: 10 Seed: 1 ID calibration errors: [0.04042846 0.02548976 0.02196035 0.01814555 0.01819798 0.01792785
 0.01899351 0.01974955 0.02001435 0.01976838 0.01915802 0.01846531]
		Model Seed: 10 Seed: 1 OOD calibration errors: [0.07342347 0.04304852 0.03143214 0.02071021 0.01484957 0.00898187
 0.00768968 0.0071127  0.00557151 0.00605763 0.00708894 0.00820229]
	Train: 64804 (63.70%)
	Val: 12349 (12.14%)
	Test: 16419 (16.14%)
	Test OOD: 8164 (8.02%)
	No scaling applied
		Model Seed: 10 Seed: 2 ID mean of (MSE, MAE): [218.04519931   9.57522532]
		Model Seed: 10 Seed: 2 OOD mean of (MSE, MAE) stats: [220.49381059  10.17941358]
		Model Seed: 10 Seed: 2 ID median of (MSE, MAE): [68.92803524  6.97519493]
		Model Seed: 10 Seed: 2 OOD median of (MSE, MAE) stats: [85.86249539  7.77304999]
		Model Seed: 10 Seed: 2 ID likelihoods: 0
		Model Seed: 10 Seed: 2 OOD likelihoods: 0
		Model Seed: 10 Seed: 2 ID calibration errors: [0.24792694 0.11134346 0.05932613 0.04491538 0.04864552 0.05415047
 0.06155551 0.06569297 0.07039334 0.07411779 0.07701017 0.0826505 ]
		Model Seed: 10 Seed: 2 OOD calibration errors: [0.29866822 0.15670949 0.09045238 0.07666238 0.07763402 0.08778741
 0.09446594 0.10231488 0.10124344 0.10807627 0.10887926 0.1082556 ]
	Model Seed: 10 ID mean of (MSE, MAE): [205.83433824   9.18082872]
	Model Seed: 10 OOD mean of (MSE, MAE): [189.81725923   9.17012535]
	Model Seed: 10 ID median of (MSE, MAE): [62.75435867  6.6054554 ]
	Model Seed: 10 OOD median of (MSE, MAE): [68.48461775  6.84089979]
	Model Seed: 10 ID likelihoods: 0.0
	Model Seed: 10 OOD likelihoods: 0.0
	Model Seed: 10 ID calibration errors: [0.1441777  0.06841661 0.04064324 0.03153046 0.03342175 0.03603916
 0.04027451 0.04272126 0.04520384 0.04694309 0.0480841  0.05055791]
	Model Seed: 10 OOD calibration errors: [0.18604584 0.099879   0.06094226 0.0486863  0.0462418  0.04838464
 0.05107781 0.05471379 0.05340747 0.05706695 0.0579841  0.05822894]
	Train: 62090 (61.20%)
	Val: 12502 (12.32%)
	Test: 16648 (16.41%)
	Test OOD: 10208 (10.06%)
	No scaling applied
		Model Seed: 11 Seed: 1 ID mean of (MSE, MAE): [186.63930934   8.66477172]
		Model Seed: 11 Seed: 1 OOD mean of (MSE, MAE) stats: [154.87963541   8.04517939]
		Model Seed: 11 Seed: 1 ID median of (MSE, MAE): [57.50205893  6.22215621]
		Model Seed: 11 Seed: 1 OOD median of (MSE, MAE) stats: [52.55084075  5.9278911 ]
		Model Seed: 11 Seed: 1 ID likelihoods: 0
		Model Seed: 11 Seed: 1 OOD likelihoods: 0
		Model Seed: 11 Seed: 1 ID calibration errors: [0.03327738 0.01894417 0.01038257 0.00698397 0.00586462 0.00467547
 0.00437991 0.00493682 0.00501135 0.0045918  0.00395139 0.0041771 ]
		Model Seed: 11 Seed: 1 OOD calibration errors: [0.01867572 0.01101017 0.01026759 0.00996098 0.00901853 0.01189081
 0.01353739 0.01445487 0.01574898 0.0161625  0.01753099 0.0197837 ]
	Train: 64804 (63.70%)
	Val: 12349 (12.14%)
	Test: 16419 (16.14%)
	Test OOD: 8164 (8.02%)
	No scaling applied
		Model Seed: 11 Seed: 2 ID mean of (MSE, MAE): [202.54021949   9.31537595]
		Model Seed: 11 Seed: 2 OOD mean of (MSE, MAE) stats: [192.63801944   9.40823231]
		Model Seed: 11 Seed: 2 ID median of (MSE, MAE): [66.83143012  6.89331659]
		Model Seed: 11 Seed: 2 OOD median of (MSE, MAE) stats: [72.4361211   7.15058263]
		Model Seed: 11 Seed: 2 ID likelihoods: 0
		Model Seed: 11 Seed: 2 OOD likelihoods: 0
		Model Seed: 11 Seed: 2 ID calibration errors: [0.58462234 0.23901017 0.12018498 0.06590676 0.04532662 0.03841679
 0.03962556 0.03978893 0.04360567 0.04706821 0.04721709 0.05255216]
		Model Seed: 11 Seed: 2 OOD calibration errors: [0.55649941 0.22572146 0.11236679 0.06145318 0.04271419 0.03748005
 0.03609835 0.03900521 0.0415449  0.04655323 0.04968411 0.0531616 ]
	Model Seed: 11 ID mean of (MSE, MAE): [194.58976441   8.99007383]
	Model Seed: 11 OOD mean of (MSE, MAE): [173.75882743   8.72670585]
	Model Seed: 11 ID median of (MSE, MAE): [62.16674452  6.5577364 ]
	Model Seed: 11 OOD median of (MSE, MAE): [62.49348093  6.53923686]
	Model Seed: 11 ID likelihoods: 0.0
	Model Seed: 11 OOD likelihoods: 0.0
	Model Seed: 11 ID calibration errors: [0.30894986 0.12897717 0.06528377 0.03644536 0.02559562 0.02154613
 0.02200274 0.02236287 0.02430851 0.02583001 0.02558424 0.02836463]
	Model Seed: 11 OOD calibration errors: [0.28758757 0.11836581 0.06131719 0.03570708 0.02586636 0.02468543
 0.02481787 0.02673004 0.02864694 0.03135787 0.03360755 0.03647265]
	Train: 62090 (61.20%)
	Val: 12502 (12.32%)
	Test: 16648 (16.41%)
	Test OOD: 10208 (10.06%)
	No scaling applied
		Model Seed: 12 Seed: 1 ID mean of (MSE, MAE): [225.67233341   9.58957872]
		Model Seed: 12 Seed: 1 OOD mean of (MSE, MAE) stats: [202.09954649   9.17672205]
		Model Seed: 12 Seed: 1 ID median of (MSE, MAE): [67.79327062  6.80600659]
		Model Seed: 12 Seed: 1 OOD median of (MSE, MAE) stats: [61.67962542  6.56478119]
		Model Seed: 12 Seed: 1 ID likelihoods: 0
		Model Seed: 12 Seed: 1 OOD likelihoods: 0
		Model Seed: 12 Seed: 1 ID calibration errors: [0.01440289 0.0191944  0.03001254 0.03723753 0.04175271 0.04969288
 0.0561677  0.06124943 0.06431347 0.06957027 0.07162557 0.07515631]
		Model Seed: 12 Seed: 1 OOD calibration errors: [0.01854852 0.02007673 0.02984364 0.03581867 0.0415532  0.04668767
 0.05119562 0.0560208  0.05732033 0.05990975 0.05983682 0.0624886 ]
	Train: 64804 (63.70%)
	Val: 12349 (12.14%)
	Test: 16419 (16.14%)
	Test OOD: 8164 (8.02%)
	No scaling applied
		Model Seed: 12 Seed: 2 ID mean of (MSE, MAE): [228.65525189   9.78397459]
		Model Seed: 12 Seed: 2 OOD mean of (MSE, MAE) stats: [219.83623048  10.11476497]
		Model Seed: 12 Seed: 2 ID median of (MSE, MAE): [72.11562517  7.11896833]
		Model Seed: 12 Seed: 2 OOD median of (MSE, MAE) stats: [85.48520812  7.71055126]
		Model Seed: 12 Seed: 2 ID likelihoods: 0
		Model Seed: 12 Seed: 2 OOD likelihoods: 0
		Model Seed: 12 Seed: 2 ID calibration errors: [0.3010144  0.10900063 0.04345226 0.02521396 0.02899311 0.03919449
 0.0506181  0.06021935 0.07257427 0.07698256 0.08055344 0.08785928]
		Model Seed: 12 Seed: 2 OOD calibration errors: [0.2933662  0.11938425 0.05093196 0.03996395 0.04131364 0.05280378
 0.06722046 0.07561934 0.08084024 0.09095994 0.09453696 0.0995354 ]
	Model Seed: 12 ID mean of (MSE, MAE): [227.16379265   9.68677665]
	Model Seed: 12 OOD mean of (MSE, MAE): [210.96788849   9.64574351]
	Model Seed: 12 ID median of (MSE, MAE): [69.95444789  6.96248746]
	Model Seed: 12 OOD median of (MSE, MAE): [73.58241677  7.13766623]
	Model Seed: 12 ID likelihoods: 0.0
	Model Seed: 12 OOD likelihoods: 0.0
	Model Seed: 12 ID calibration errors: [0.15770865 0.06409751 0.0367324  0.03122575 0.03537291 0.04444368
 0.0533929  0.06073439 0.06844387 0.07327642 0.07608951 0.08150779]
	Model Seed: 12 OOD calibration errors: [0.15595736 0.06973049 0.0403878  0.03789131 0.04143342 0.04974572
 0.05920804 0.06582007 0.06908028 0.07543484 0.07718689 0.081012  ]
	Train: 62090 (61.20%)
	Val: 12502 (12.32%)
	Test: 16648 (16.41%)
	Test OOD: 10208 (10.06%)
	No scaling applied
		Model Seed: 13 Seed: 1 ID mean of (MSE, MAE): [229.33342413   9.73774838]
		Model Seed: 13 Seed: 1 OOD mean of (MSE, MAE) stats: [186.25502699   9.15376679]
		Model Seed: 13 Seed: 1 ID median of (MSE, MAE): [69.27207923  6.96914355]
		Model Seed: 13 Seed: 1 OOD median of (MSE, MAE) stats: [69.6572536   6.92991702]
		Model Seed: 13 Seed: 1 ID likelihoods: 0
		Model Seed: 13 Seed: 1 OOD likelihoods: 0
		Model Seed: 13 Seed: 1 ID calibration errors: [0.18152493 0.10975697 0.09096053 0.08782596 0.0882439  0.09891672
 0.10533391 0.11450899 0.12025854 0.12378633 0.12836194 0.13469151]
		Model Seed: 13 Seed: 1 OOD calibration errors: [0.1027469  0.05599417 0.05182636 0.0678209  0.0856632  0.10972483
 0.12580158 0.13745215 0.147675   0.15745473 0.16908303 0.17657826]
	Train: 64804 (63.70%)
	Val: 12349 (12.14%)
	Test: 16419 (16.14%)
	Test OOD: 8164 (8.02%)
	No scaling applied
		Model Seed: 13 Seed: 2 ID mean of (MSE, MAE): [221.404021     9.55828214]
		Model Seed: 13 Seed: 2 OOD mean of (MSE, MAE) stats: [220.60603142   9.88303825]
		Model Seed: 13 Seed: 2 ID median of (MSE, MAE): [65.80106489  6.85161146]
		Model Seed: 13 Seed: 2 OOD median of (MSE, MAE) stats: [73.22844102  7.15869522]
		Model Seed: 13 Seed: 2 ID likelihoods: 0
		Model Seed: 13 Seed: 2 OOD likelihoods: 0
		Model Seed: 13 Seed: 2 ID calibration errors: [0.15867317 0.0553215  0.02642002 0.0284824  0.03831929 0.05098313
 0.05896039 0.06736486 0.07383686 0.08070652 0.0827328  0.09004975]
		Model Seed: 13 Seed: 2 OOD calibration errors: [0.17986138 0.05769419 0.02394526 0.03244878 0.04175445 0.0567813
 0.06962886 0.07404813 0.08631295 0.09756054 0.09977871 0.10771202]
	Model Seed: 13 ID mean of (MSE, MAE): [225.36872257   9.64801526]
	Model Seed: 13 OOD mean of (MSE, MAE): [203.4305292    9.51840252]
	Model Seed: 13 ID median of (MSE, MAE): [67.53657206  6.9103775 ]
	Model Seed: 13 OOD median of (MSE, MAE): [71.44284731  7.04430612]
	Model Seed: 13 ID likelihoods: 0.0
	Model Seed: 13 OOD likelihoods: 0.0
	Model Seed: 13 ID calibration errors: [0.17009905 0.08253924 0.05869028 0.05815418 0.06328159 0.07494992
 0.08214715 0.09093693 0.0970477  0.10224642 0.10554737 0.11237063]
	Model Seed: 13 OOD calibration errors: [0.14130414 0.05684418 0.03788581 0.05013484 0.06370883 0.08325306
 0.09771522 0.10575014 0.11699397 0.12750764 0.13443087 0.14214514]
	Train: 62090 (61.20%)
	Val: 12502 (12.32%)
	Test: 16648 (16.41%)
	Test OOD: 10208 (10.06%)
	No scaling applied
		Model Seed: 14 Seed: 1 ID mean of (MSE, MAE): [193.79432346   9.10606613]
		Model Seed: 14 Seed: 1 OOD mean of (MSE, MAE) stats: [179.33693438   8.8580998 ]
		Model Seed: 14 Seed: 1 ID median of (MSE, MAE): [64.32782078  6.78141673]
		Model Seed: 14 Seed: 1 OOD median of (MSE, MAE) stats: [63.66954883  6.68924713]
		Model Seed: 14 Seed: 1 ID likelihoods: 0
		Model Seed: 14 Seed: 1 OOD likelihoods: 0
		Model Seed: 14 Seed: 1 ID calibration errors: [0.36290866 0.15420657 0.07840344 0.06099608 0.05532116 0.05867967
 0.05903921 0.0621597  0.06348286 0.06802331 0.07142435 0.07535205]
		Model Seed: 14 Seed: 1 OOD calibration errors: [0.31675707 0.17197516 0.11658669 0.10457202 0.09269501 0.09820977
 0.09636994 0.09885815 0.10081815 0.09957368 0.10014509 0.10170029]
	Train: 64804 (63.70%)
	Val: 12349 (12.14%)
	Test: 16419 (16.14%)
	Test OOD: 8164 (8.02%)
	No scaling applied
		Model Seed: 14 Seed: 2 ID mean of (MSE, MAE): [203.87345699   9.0944497 ]
		Model Seed: 14 Seed: 2 OOD mean of (MSE, MAE) stats: [186.44196186   8.94012066]
		Model Seed: 14 Seed: 2 ID median of (MSE, MAE): [63.30175741  6.6273632 ]
		Model Seed: 14 Seed: 2 OOD median of (MSE, MAE) stats: [61.26493973  6.50472482]
		Model Seed: 14 Seed: 2 ID likelihoods: 0
		Model Seed: 14 Seed: 2 OOD likelihoods: 0
		Model Seed: 14 Seed: 2 ID calibration errors: [0.05645042 0.04831316 0.04919224 0.05700383 0.06372599 0.07248623
 0.07913165 0.08327669 0.08789266 0.08527556 0.08408872 0.08589331]
		Model Seed: 14 Seed: 2 OOD calibration errors: [0.05814504 0.05364482 0.05514148 0.0609933  0.06883539 0.08161831
 0.09060369 0.09985272 0.10750465 0.1136707  0.11591807 0.12304537]
	Model Seed: 14 ID mean of (MSE, MAE): [198.83389023   9.10025792]
	Model Seed: 14 OOD mean of (MSE, MAE): [182.88944812   8.89911023]
	Model Seed: 14 ID median of (MSE, MAE): [63.81478909  6.70438997]
	Model Seed: 14 OOD median of (MSE, MAE): [62.46724428  6.59698598]
	Model Seed: 14 ID likelihoods: 0.0
	Model Seed: 14 OOD likelihoods: 0.0
	Model Seed: 14 ID calibration errors: [0.20967954 0.10125986 0.06379784 0.05899996 0.05952357 0.06558295
 0.06908543 0.07271819 0.07568776 0.07664943 0.07775653 0.08062268]
	Model Seed: 14 OOD calibration errors: [0.18745106 0.11280999 0.08586409 0.08278266 0.0807652  0.08991404
 0.09348681 0.09935544 0.1041614  0.10662219 0.10803158 0.11237283]
	Train: 62090 (61.20%)
	Val: 12502 (12.32%)
	Test: 16648 (16.41%)
	Test OOD: 10208 (10.06%)
	No scaling applied
		Model Seed: 15 Seed: 1 ID mean of (MSE, MAE): [192.78877797   8.71477552]
		Model Seed: 15 Seed: 1 OOD mean of (MSE, MAE) stats: [153.03383102   8.05259125]
		Model Seed: 15 Seed: 1 ID median of (MSE, MAE): [53.05433812  6.07110556]
		Model Seed: 15 Seed: 1 OOD median of (MSE, MAE) stats: [50.06745982  5.86509228]
		Model Seed: 15 Seed: 1 ID likelihoods: 0
		Model Seed: 15 Seed: 1 OOD likelihoods: 0
		Model Seed: 15 Seed: 1 ID calibration errors: [0.12591772 0.05398372 0.05335649 0.05008777 0.04755952 0.0443944
 0.04262861 0.04148096 0.0425468  0.04172818 0.03791897 0.03699593]
		Model Seed: 15 Seed: 1 OOD calibration errors: [0.21478846 0.09987642 0.06636127 0.04483198 0.03520678 0.02230199
 0.01684926 0.01229773 0.00878439 0.00686675 0.00641474 0.00520572]
	Train: 64804 (63.70%)
	Val: 12349 (12.14%)
	Test: 16419 (16.14%)
	Test OOD: 8164 (8.02%)
	No scaling applied
		Model Seed: 15 Seed: 2 ID mean of (MSE, MAE): [216.25088058   9.58123309]
		Model Seed: 15 Seed: 2 OOD mean of (MSE, MAE) stats: [208.78216202   9.82683665]
		Model Seed: 15 Seed: 2 ID median of (MSE, MAE): [70.66664483  7.05857277]
		Model Seed: 15 Seed: 2 OOD median of (MSE, MAE) stats: [83.06835424  7.67755667]
		Model Seed: 15 Seed: 2 ID likelihoods: 0
		Model Seed: 15 Seed: 2 OOD likelihoods: 0
		Model Seed: 15 Seed: 2 ID calibration errors: [0.50458088 0.20030124 0.08230534 0.04855362 0.04121709 0.04378201
 0.05018116 0.05570075 0.06366246 0.06933172 0.07243024 0.07766878]
		Model Seed: 15 Seed: 2 OOD calibration errors: [0.65628509 0.27632784 0.13417227 0.09149087 0.0744972  0.07239006
 0.0702697  0.07566704 0.07249259 0.07990937 0.07966679 0.07991337]
	Model Seed: 15 ID mean of (MSE, MAE): [204.51982928   9.1480043 ]
	Model Seed: 15 OOD mean of (MSE, MAE): [180.90799652   8.93971395]
	Model Seed: 15 ID median of (MSE, MAE): [61.86049148  6.56483916]
	Model Seed: 15 OOD median of (MSE, MAE): [66.56790703  6.77132448]
	Model Seed: 15 ID likelihoods: 0.0
	Model Seed: 15 OOD likelihoods: 0.0
	Model Seed: 15 ID calibration errors: [0.3152493  0.12714248 0.06783091 0.04932069 0.04438831 0.04408821
 0.04640488 0.04859085 0.05310463 0.05552995 0.0551746  0.05733236]
	Model Seed: 15 OOD calibration errors: [0.43553678 0.18810213 0.10026677 0.06816143 0.05485199 0.04734603
 0.04355948 0.04398239 0.04063849 0.04338806 0.04304077 0.04255954]
	Train: 62090 (61.20%)
	Val: 12502 (12.32%)
	Test: 16648 (16.41%)
	Test OOD: 10208 (10.06%)
	No scaling applied
		Model Seed: 16 Seed: 1 ID mean of (MSE, MAE): [194.85770146   9.09225851]
		Model Seed: 16 Seed: 1 OOD mean of (MSE, MAE) stats: [176.23449665   8.83970571]
		Model Seed: 16 Seed: 1 ID median of (MSE, MAE): [63.25899632  6.6304973 ]
		Model Seed: 16 Seed: 1 OOD median of (MSE, MAE) stats: [63.9207793   6.72036648]
		Model Seed: 16 Seed: 1 ID likelihoods: 0
		Model Seed: 16 Seed: 1 OOD likelihoods: 0
		Model Seed: 16 Seed: 1 ID calibration errors: [0.1637194  0.08010114 0.04220381 0.03062274 0.02484542 0.02391065
 0.02246808 0.02074614 0.0189537  0.01937405 0.01935865 0.02055988]
		Model Seed: 16 Seed: 1 OOD calibration errors: [0.21516766 0.1336039  0.09726193 0.0856418  0.07590212 0.08118552
 0.07974617 0.07855332 0.07800417 0.07437329 0.07074767 0.07293058]
	Train: 64804 (63.70%)
	Val: 12349 (12.14%)
	Test: 16419 (16.14%)
	Test OOD: 8164 (8.02%)
	No scaling applied
		Model Seed: 16 Seed: 2 ID mean of (MSE, MAE): [185.78704362   8.59368209]
		Model Seed: 16 Seed: 2 OOD mean of (MSE, MAE) stats: [175.79929704   8.68119523]
		Model Seed: 16 Seed: 2 ID median of (MSE, MAE): [54.75603087  6.1502835 ]
		Model Seed: 16 Seed: 2 OOD median of (MSE, MAE) stats: [60.45702715  6.41215611]
		Model Seed: 16 Seed: 2 ID likelihoods: 0
		Model Seed: 16 Seed: 2 OOD likelihoods: 0
		Model Seed: 16 Seed: 2 ID calibration errors: [0.20693739 0.08483731 0.04530101 0.0259216  0.01822303 0.01350803
 0.01332797 0.0150561  0.01774577 0.01978823 0.0196706  0.02407367]
		Model Seed: 16 Seed: 2 OOD calibration errors: [0.2485504  0.10085876 0.05398611 0.0349831  0.02342435 0.01883859
 0.01491506 0.01407099 0.01298168 0.01411132 0.01452144 0.01691141]
	Model Seed: 16 ID mean of (MSE, MAE): [190.32237254   8.8429703 ]
	Model Seed: 16 OOD mean of (MSE, MAE): [176.01689684   8.76045047]
	Model Seed: 16 ID median of (MSE, MAE): [59.0075136  6.3903904]
	Model Seed: 16 OOD median of (MSE, MAE): [62.18890322  6.56626129]
	Model Seed: 16 ID likelihoods: 0.0
	Model Seed: 16 OOD likelihoods: 0.0
	Model Seed: 16 ID calibration errors: [0.18532839 0.08246923 0.04375241 0.02827217 0.02153422 0.01870934
 0.01789803 0.01790112 0.01834973 0.01958114 0.01951462 0.02231678]
	Model Seed: 16 OOD calibration errors: [0.23185903 0.11723133 0.07562402 0.06031245 0.04966324 0.05001205
 0.04733061 0.04631215 0.04549292 0.0442423  0.04263456 0.044921  ]
	Train: 62090 (61.20%)
	Val: 12502 (12.32%)
	Test: 16648 (16.41%)
	Test OOD: 10208 (10.06%)
	No scaling applied
		Model Seed: 17 Seed: 1 ID mean of (MSE, MAE): [191.84389014   8.8877404 ]
		Model Seed: 17 Seed: 1 OOD mean of (MSE, MAE) stats: [170.57600668   8.55214289]
		Model Seed: 17 Seed: 1 ID median of (MSE, MAE): [57.43910499  6.37917089]
		Model Seed: 17 Seed: 1 OOD median of (MSE, MAE) stats: [58.48366172  6.49479675]
		Model Seed: 17 Seed: 1 ID likelihoods: 0
		Model Seed: 17 Seed: 1 OOD likelihoods: 0
		Model Seed: 17 Seed: 1 ID calibration errors: [0.11684543 0.06419296 0.04171845 0.03455624 0.02927498 0.02787532
 0.02661439 0.02665243 0.02624546 0.02737566 0.02910515 0.02949496]
		Model Seed: 17 Seed: 1 OOD calibration errors: [0.18650891 0.13074015 0.10649168 0.09196359 0.08207079 0.07829112
 0.07005405 0.06978409 0.06774796 0.06395983 0.06253421 0.06516688]
	Train: 64804 (63.70%)
	Val: 12349 (12.14%)
	Test: 16419 (16.14%)
	Test OOD: 8164 (8.02%)
	No scaling applied
		Model Seed: 17 Seed: 2 ID mean of (MSE, MAE): [214.14887841   9.46906969]
		Model Seed: 17 Seed: 2 OOD mean of (MSE, MAE) stats: [210.48769076   9.78641598]
		Model Seed: 17 Seed: 2 ID median of (MSE, MAE): [66.81562738  6.93195311]
		Model Seed: 17 Seed: 2 OOD median of (MSE, MAE) stats: [76.49927245  7.34622415]
		Model Seed: 17 Seed: 2 ID likelihoods: 0
		Model Seed: 17 Seed: 2 OOD likelihoods: 0
		Model Seed: 17 Seed: 2 ID calibration errors: [0.43367955 0.18321887 0.09156863 0.05551956 0.04699293 0.04806626
 0.05401135 0.05934967 0.06520044 0.06788221 0.06895182 0.07238845]
		Model Seed: 17 Seed: 2 OOD calibration errors: [0.44972324 0.20438715 0.11967722 0.08622836 0.07600792 0.06865004
 0.06700367 0.0677902  0.06674097 0.07304151 0.07765952 0.07716613]
	Model Seed: 17 ID mean of (MSE, MAE): [202.99638427   9.17840504]
	Model Seed: 17 OOD mean of (MSE, MAE): [190.53184872   9.16927943]
	Model Seed: 17 ID median of (MSE, MAE): [62.12736619  6.655562  ]
	Model Seed: 17 OOD median of (MSE, MAE): [67.49146709  6.92051045]
	Model Seed: 17 ID likelihoods: 0.0
	Model Seed: 17 OOD likelihoods: 0.0
	Model Seed: 17 ID calibration errors: [0.27526249 0.12370591 0.06664354 0.0450379  0.03813395 0.03797079
 0.04031287 0.04300105 0.04572295 0.04762893 0.04902848 0.05094171]
	Model Seed: 17 OOD calibration errors: [0.31811608 0.16756365 0.11308445 0.08909598 0.07903936 0.07347058
 0.06852886 0.06878715 0.06724446 0.06850067 0.07009686 0.07116651]
	Train: 62090 (61.20%)
	Val: 12502 (12.32%)
	Test: 16648 (16.41%)
	Test OOD: 10208 (10.06%)
	No scaling applied
		Model Seed: 18 Seed: 1 ID mean of (MSE, MAE): [183.70974532   8.67861089]
		Model Seed: 18 Seed: 1 OOD mean of (MSE, MAE) stats: [161.55778965   8.22666905]
		Model Seed: 18 Seed: 1 ID median of (MSE, MAE): [55.48304483  6.20907402]
		Model Seed: 18 Seed: 1 OOD median of (MSE, MAE) stats: [53.86217512  6.14600754]
		Model Seed: 18 Seed: 1 ID likelihoods: 0
		Model Seed: 18 Seed: 1 OOD likelihoods: 0
		Model Seed: 18 Seed: 1 ID calibration errors: [0.01764816 0.01502635 0.01243222 0.01151308 0.01011222 0.01259958
 0.01318142 0.01504267 0.01548173 0.01855994 0.01907923 0.02115994]
		Model Seed: 18 Seed: 1 OOD calibration errors: [0.07989975 0.0713278  0.06303531 0.0543867  0.04417468 0.0459304
 0.04387888 0.04414791 0.04021161 0.03976685 0.03706045 0.03769708]
	Train: 64804 (63.70%)
	Val: 12349 (12.14%)
	Test: 16419 (16.14%)
	Test OOD: 8164 (8.02%)
	No scaling applied
		Model Seed: 18 Seed: 2 ID mean of (MSE, MAE): [205.39203987   9.10329034]
		Model Seed: 18 Seed: 2 OOD mean of (MSE, MAE) stats: [213.2131892    9.58233902]
		Model Seed: 18 Seed: 2 ID median of (MSE, MAE): [58.03160368  6.32162317]
		Model Seed: 18 Seed: 2 OOD median of (MSE, MAE) stats: [68.94315298  6.80751197]
		Model Seed: 18 Seed: 2 ID likelihoods: 0
		Model Seed: 18 Seed: 2 OOD likelihoods: 0
		Model Seed: 18 Seed: 2 ID calibration errors: [0.02094466 0.01775251 0.02438487 0.03805987 0.04926454 0.05747327
 0.06538144 0.07122453 0.07314594 0.0740418  0.07381646 0.07622577]
		Model Seed: 18 Seed: 2 OOD calibration errors: [0.03079559 0.02000831 0.02805532 0.04228168 0.05120944 0.06392537
 0.07566779 0.08079812 0.08905715 0.09412208 0.09760834 0.10157334]
	Model Seed: 18 ID mean of (MSE, MAE): [194.5508926    8.89095061]
	Model Seed: 18 OOD mean of (MSE, MAE): [187.38548942   8.90450404]
	Model Seed: 18 ID median of (MSE, MAE): [56.75732426  6.26534859]
	Model Seed: 18 OOD median of (MSE, MAE): [61.40266405  6.47675975]
	Model Seed: 18 ID likelihoods: 0.0
	Model Seed: 18 OOD likelihoods: 0.0
	Model Seed: 18 ID calibration errors: [0.01929641 0.01638943 0.01840854 0.02478648 0.02968838 0.03503643
 0.03928143 0.0431336  0.04431384 0.04630087 0.04644784 0.04869285]
	Model Seed: 18 OOD calibration errors: [0.05534767 0.04566805 0.04554532 0.04833419 0.04769206 0.05492788
 0.05977334 0.06247301 0.06463438 0.06694446 0.06733439 0.06963521]
	Train: 62090 (61.20%)
	Val: 12502 (12.32%)
	Test: 16648 (16.41%)
	Test OOD: 10208 (10.06%)
	No scaling applied
		Model Seed: 19 Seed: 1 ID mean of (MSE, MAE): [188.70699299   8.96661851]
		Model Seed: 19 Seed: 1 OOD mean of (MSE, MAE) stats: [175.48686139   8.99022437]
		Model Seed: 19 Seed: 1 ID median of (MSE, MAE): [60.76790444  6.58864705]
		Model Seed: 19 Seed: 1 OOD median of (MSE, MAE) stats: [66.10668682  6.92013677]
		Model Seed: 19 Seed: 1 ID likelihoods: 0
		Model Seed: 19 Seed: 1 OOD likelihoods: 0
		Model Seed: 19 Seed: 1 ID calibration errors: [0.27747225 0.14521794 0.08787363 0.05945538 0.04602596 0.03730917
 0.03132513 0.02682182 0.022903   0.02259381 0.02287826 0.02310015]
		Model Seed: 19 Seed: 1 OOD calibration errors: [0.39811355 0.25650304 0.19309965 0.15673457 0.13247267 0.12768067
 0.11938249 0.11494241 0.11367612 0.1091518  0.10489376 0.10254926]
	Train: 64804 (63.70%)
	Val: 12349 (12.14%)
	Test: 16419 (16.14%)
	Test OOD: 8164 (8.02%)
	No scaling applied
		Model Seed: 19 Seed: 2 ID mean of (MSE, MAE): [202.75278831   9.04463892]
		Model Seed: 19 Seed: 2 OOD mean of (MSE, MAE) stats: [190.75790377   9.04087576]
		Model Seed: 19 Seed: 2 ID median of (MSE, MAE): [60.55491137  6.44341119]
		Model Seed: 19 Seed: 2 OOD median of (MSE, MAE) stats: [60.71126749  6.49839624]
		Model Seed: 19 Seed: 2 ID likelihoods: 0
		Model Seed: 19 Seed: 2 OOD likelihoods: 0
		Model Seed: 19 Seed: 2 ID calibration errors: [0.08150026 0.06196144 0.05854884 0.05923152 0.06412667 0.06886191
 0.07156244 0.07072832 0.07084919 0.07073775 0.07056456 0.0719634 ]
		Model Seed: 19 Seed: 2 OOD calibration errors: [0.0998904  0.07452754 0.06927491 0.07229236 0.07259334 0.07489512
 0.08075985 0.0775577  0.07746127 0.07906389 0.07644915 0.07978675]
	Model Seed: 19 ID mean of (MSE, MAE): [195.72989065   9.00562871]
	Model Seed: 19 OOD mean of (MSE, MAE): [183.12238258   9.01555006]
	Model Seed: 19 ID median of (MSE, MAE): [60.6614079   6.51602912]
	Model Seed: 19 OOD median of (MSE, MAE): [63.40897715  6.7092665 ]
	Model Seed: 19 ID likelihoods: 0.0
	Model Seed: 19 OOD likelihoods: 0.0
	Model Seed: 19 ID calibration errors: [0.17948626 0.10358969 0.07321124 0.05934345 0.05507631 0.05308554
 0.05144379 0.04877507 0.04687609 0.04666578 0.04672141 0.04753177]
	Model Seed: 19 OOD calibration errors: [0.24900198 0.16551529 0.13118728 0.11451346 0.102533   0.1012879
 0.10007117 0.09625006 0.09556869 0.09410785 0.09067145 0.09116801]
ID mean of (MSE, MAE): [203.99098774331262, 9.167191135511214] +- [12.074038685924442, 0.27298750971084584] +- [5.8939902  0.14473105] 
OOD mean of (MSE, MAE): [187.88285665550947, 9.074958540827721] +- [11.055267444952946, 0.29023857338047393] +- [16.022773   0.4693647] 
ID median of (MSE, MAE): [62.664101565620115, 6.613261600335439] +- [3.6287052808817983, 0.2014914812736706] +- [2.11617153 0.12396822] 
OOD median of (MSE, MAE): [65.95305255698507, 6.76032174428304] +- [4.036896120986605, 0.21249491373302015] +- [6.84257541 0.34362316] 
ID likelihoods: 0.0 +- 0.0
OOD likelihoods: 0.0 +- 0.0
ID calibration errors: [0.1965237647486731, 0.08985871309716298, 0.05349941757321185, 0.04231164003127567, 0.04060166296138527, 0.0431452151234978, 0.0462243734801671, 0.0490875341669613, 0.051905891418023466, 0.054065203163644215, 0.05499487063842462, 0.05802391032508962] +- [0.08359328782112004, 0.0331870606573474, 0.016767160742088606, 0.01286044420114261, 0.013744456533148076, 0.016801880758573254, 0.018437346200924014, 0.020604846816253033, 0.022208145584709627, 0.02322984497966381, 0.02422262622564618, 0.025443989885571706] +- [0.06310924 0.02124732 0.00656901 0.00256921 0.00388182 0.00554704
 0.00821119 0.00975268 0.01198477 0.01252803 0.01270872 0.0141086 ] 
OOD calibration errors: [0.22482074954252135, 0.11417099246545388, 0.0752104977191507, 0.06356196879473265, 0.059179524709940534, 0.062302733712089955, 0.0645569218204363, 0.06701742349255646, 0.06858690200325326, 0.0715172827934184, 0.07250190227034646, 0.07496818275842188] +- [0.10050746608363031, 0.045978121330406733, 0.03031782570278586, 0.023911348989379824, 0.021432960566284732, 0.022453218252217542, 0.024007205464023115, 0.024818356610884013, 0.027376150153756856, 0.028774527457439327, 0.029942741574223292, 0.03157367230851491] +- [0.06235775 0.01475539 0.00141013 0.00368217 0.00218113 0.00078573
 0.00210642 0.00365501 0.00503108 0.0081896  0.00896833 0.00973792] 
