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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)
		time_second: REAL_VALUED (KNOWN_INPUT)
Interpolating data...
	Dropped segments: 17
	Extracted segments: 15
	Interpolated values: 561
	Percent of values interpolated: 4.37%
Splitting data...
	Train: 7686 (66.68%)
	Val: 2160 (18.74%)
	Test: 2980 (25.85%)
Scaling data...
	No scaling applied
Data formatting complete.
--------------------------------
Current value: 0.08835827559232712, Current params: {'in_len': 96, 'max_samples_per_ts': 100, 'hidden_size': 240, 'num_attention_heads': 3, 'dropout': 0.22072118157443252, 'lr': 0.006654905076364479, 'batch_size': 32, 'max_grad_norm': 0.3943459619417496}
Best value: 0.08835827559232712, Best params: {'in_len': 96, 'max_samples_per_ts': 100, 'hidden_size': 240, 'num_attention_heads': 3, 'dropout': 0.22072118157443252, 'lr': 0.006654905076364479, 'batch_size': 32, 'max_grad_norm': 0.3943459619417496}
Current value: 0.0683748722076416, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 2, 'dropout': 0.12741373527767025, 'lr': 0.003335349656791939, 'batch_size': 32, 'max_grad_norm': 0.03463336165471304}
Best value: 0.0683748722076416, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 2, 'dropout': 0.12741373527767025, 'lr': 0.003335349656791939, 'batch_size': 32, 'max_grad_norm': 0.03463336165471304}
Current value: 0.07283150404691696, Current params: {'in_len': 108, 'max_samples_per_ts': 50, 'hidden_size': 32, 'num_attention_heads': 1, 'dropout': 0.15323492562505053, 'lr': 0.005137910225523188, 'batch_size': 64, 'max_grad_norm': 0.14875133849091807}
Best value: 0.0683748722076416, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 2, 'dropout': 0.12741373527767025, 'lr': 0.003335349656791939, 'batch_size': 32, 'max_grad_norm': 0.03463336165471304}
Current value: 0.07099808752536774, Current params: {'in_len': 180, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 4, 'dropout': 0.1261949015606756, 'lr': 0.0039929151717157645, 'batch_size': 32, 'max_grad_norm': 0.5158935361818553}
Best value: 0.0683748722076416, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 2, 'dropout': 0.12741373527767025, 'lr': 0.003335349656791939, 'batch_size': 32, 'max_grad_norm': 0.03463336165471304}
Current value: 0.09785456210374832, Current params: {'in_len': 120, 'max_samples_per_ts': 100, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.18994009891645497, 'lr': 0.0024672349836299076, 'batch_size': 32, 'max_grad_norm': 0.21617405724747263}
Best value: 0.0683748722076416, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 2, 'dropout': 0.12741373527767025, 'lr': 0.003335349656791939, 'batch_size': 32, 'max_grad_norm': 0.03463336165471304}
Current value: 0.2710210382938385, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 1, 'dropout': 0.14232553289872957, 'lr': 0.00028495124732555595, 'batch_size': 32, 'max_grad_norm': 0.3319807450653626}
Best value: 0.0683748722076416, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 2, 'dropout': 0.12741373527767025, 'lr': 0.003335349656791939, 'batch_size': 32, 'max_grad_norm': 0.03463336165471304}
Current value: 0.6452043056488037, Current params: {'in_len': 96, 'max_samples_per_ts': 150, 'hidden_size': 176, 'num_attention_heads': 2, 'dropout': 0.28343293514814316, 'lr': 0.004562519084051764, 'batch_size': 48, 'max_grad_norm': 0.26496623268750186}
Best value: 0.0683748722076416, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 2, 'dropout': 0.12741373527767025, 'lr': 0.003335349656791939, 'batch_size': 32, 'max_grad_norm': 0.03463336165471304}
Current value: 0.6563935875892639, Current params: {'in_len': 144, 'max_samples_per_ts': 200, 'hidden_size': 128, 'num_attention_heads': 1, 'dropout': 0.10197524763371753, 'lr': 0.00409712118609409, 'batch_size': 64, 'max_grad_norm': 0.2726155464534215}
Best value: 0.0683748722076416, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 2, 'dropout': 0.12741373527767025, 'lr': 0.003335349656791939, 'batch_size': 32, 'max_grad_norm': 0.03463336165471304}
Current value: 0.6552991271018982, Current params: {'in_len': 108, 'max_samples_per_ts': 200, 'hidden_size': 176, 'num_attention_heads': 3, 'dropout': 0.1017003364837795, 'lr': 0.006720139983932666, 'batch_size': 48, 'max_grad_norm': 0.3239490008957773}
Best value: 0.0683748722076416, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 2, 'dropout': 0.12741373527767025, 'lr': 0.003335349656791939, 'batch_size': 32, 'max_grad_norm': 0.03463336165471304}
Current value: 0.7276796102523804, Current params: {'in_len': 192, 'max_samples_per_ts': 50, 'hidden_size': 224, 'num_attention_heads': 1, 'dropout': 0.18402288941166942, 'lr': 0.006039169576973323, 'batch_size': 32, 'max_grad_norm': 0.6163745776465231}
Best value: 0.0683748722076416, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 2, 'dropout': 0.12741373527767025, 'lr': 0.003335349656791939, 'batch_size': 32, 'max_grad_norm': 0.03463336165471304}
Current value: 0.5945107936859131, Current params: {'in_len': 156, 'max_samples_per_ts': 150, 'hidden_size': 32, 'num_attention_heads': 2, 'dropout': 0.2285317962437095, 'lr': 0.009921726689984443, 'batch_size': 48, 'max_grad_norm': 0.014855902079314587}
Best value: 0.0683748722076416, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 2, 'dropout': 0.12741373527767025, 'lr': 0.003335349656791939, 'batch_size': 32, 'max_grad_norm': 0.03463336165471304}
Current value: 0.6699790358543396, Current params: {'in_len': 192, 'max_samples_per_ts': 100, 'hidden_size': 112, 'num_attention_heads': 4, 'dropout': 0.13875168133795787, 'lr': 0.0026018690550039638, 'batch_size': 32, 'max_grad_norm': 0.8112583914114181}
Best value: 0.0683748722076416, Best params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 2, 'dropout': 0.12741373527767025, 'lr': 0.003335349656791939, 'batch_size': 32, 'max_grad_norm': 0.03463336165471304}
Current value: 0.055125489830970764, Current params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.0637689083814621, Current params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 3, 'dropout': 0.1605455575011528, 'lr': 0.0018426987867095686, 'batch_size': 48, 'max_grad_norm': 0.6399581811979378}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.6645073294639587, Current params: {'in_len': 168, 'max_samples_per_ts': 100, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.16655682357175577, 'lr': 0.0006594786437881326, 'batch_size': 48, 'max_grad_norm': 0.6522791260129666}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.07021858543157578, Current params: {'in_len': 132, 'max_samples_per_ts': 50, 'hidden_size': 176, 'num_attention_heads': 3, 'dropout': 0.1653969209298347, 'lr': 0.0019850371839583926, 'batch_size': 64, 'max_grad_norm': 0.948670997806895}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.6610875725746155, Current params: {'in_len': 168, 'max_samples_per_ts': 150, 'hidden_size': 64, 'num_attention_heads': 4, 'dropout': 0.17056669761353582, 'lr': 0.0015004465833237807, 'batch_size': 48, 'max_grad_norm': 0.4970646335031389}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.6913893222808838, Current params: {'in_len': 180, 'max_samples_per_ts': 100, 'hidden_size': 144, 'num_attention_heads': 3, 'dropout': 0.12358870844934335, 'lr': 0.0012975793088317227, 'batch_size': 64, 'max_grad_norm': 0.6689017533498123}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.45951417088508606, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 48, 'num_attention_heads': 4, 'dropout': 0.15184788330198593, 'lr': 0.00296628712157815, 'batch_size': 48, 'max_grad_norm': 0.4498775252960292}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 1.0459412336349487, Current params: {'in_len': 168, 'max_samples_per_ts': 100, 'hidden_size': 16, 'num_attention_heads': 3, 'dropout': 0.20548453632117697, 'lr': 0.0012843348530524917, 'batch_size': 48, 'max_grad_norm': 0.7780405059029833}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.31956860423088074, Current params: {'in_len': 180, 'max_samples_per_ts': 50, 'hidden_size': 192, 'num_attention_heads': 4, 'dropout': 0.11791824620554413, 'lr': 0.00026307165496303837, 'batch_size': 64, 'max_grad_norm': 0.5603058646499245}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.34505587816238403, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 144, 'num_attention_heads': 2, 'dropout': 0.13776078420803609, 'lr': 0.003221631400113546, 'batch_size': 32, 'max_grad_norm': 0.42787016726097843}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.06265783309936523, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 2, 'dropout': 0.12048816588530861, 'lr': 0.003399113551961678, 'batch_size': 32, 'max_grad_norm': 0.5618733742676398}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.05886273831129074, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 2, 'dropout': 0.11481353579191711, 'lr': 0.002254196617459985, 'batch_size': 32, 'max_grad_norm': 0.5745935990294945}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.7266401052474976, Current params: {'in_len': 132, 'max_samples_per_ts': 100, 'hidden_size': 112, 'num_attention_heads': 2, 'dropout': 0.10962423218401449, 'lr': 0.0036369963726349194, 'batch_size': 32, 'max_grad_norm': 0.5509254600047145}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.2879154682159424, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 2, 'dropout': 0.11631752132270681, 'lr': 0.002343735908336991, 'batch_size': 32, 'max_grad_norm': 0.447614213522841}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.6423008441925049, Current params: {'in_len': 132, 'max_samples_per_ts': 100, 'hidden_size': 64, 'num_attention_heads': 2, 'dropout': 0.10111913055799734, 'lr': 0.002952554587104771, 'batch_size': 32, 'max_grad_norm': 0.7138485993569873}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.06396239250898361, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 2, 'dropout': 0.12937988550075097, 'lr': 0.0023695492905955657, 'batch_size': 32, 'max_grad_norm': 0.5594092226227828}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.6280338168144226, Current params: {'in_len': 120, 'max_samples_per_ts': 150, 'hidden_size': 80, 'num_attention_heads': 3, 'dropout': 0.11487532960621671, 'lr': 0.003756240418500722, 'batch_size': 32, 'max_grad_norm': 0.5922354834175907}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.07075617462396622, Current params: {'in_len': 132, 'max_samples_per_ts': 50, 'hidden_size': 128, 'num_attention_heads': 3, 'dropout': 0.1454131961221704, 'lr': 0.0011245409716807092, 'batch_size': 32, 'max_grad_norm': 0.3862165316904974}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.5017206072807312, Current params: {'in_len': 144, 'max_samples_per_ts': 100, 'hidden_size': 96, 'num_attention_heads': 2, 'dropout': 0.1331208342159266, 'lr': 0.002015813928299111, 'batch_size': 32, 'max_grad_norm': 0.5137352736739345}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.28540024161338806, Current params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 3, 'dropout': 0.11499603252120424, 'lr': 0.0015456666227249038, 'batch_size': 48, 'max_grad_norm': 0.6268010155303305}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.29263219237327576, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 3, 'dropout': 0.12966082715605365, 'lr': 0.003152514426581849, 'batch_size': 32, 'max_grad_norm': 0.7095141365021438}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.31592175364494324, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 128, 'num_attention_heads': 2, 'dropout': 0.15258336301082476, 'lr': 0.001973467680852255, 'batch_size': 32, 'max_grad_norm': 0.4857496775662282}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.3041163384914398, Current params: {'in_len': 180, 'max_samples_per_ts': 50, 'hidden_size': 256, 'num_attention_heads': 2, 'dropout': 0.1268817946325937, 'lr': 0.002882293938016673, 'batch_size': 48, 'max_grad_norm': 0.5844709780460918}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.2593821585178375, Current params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 64, 'num_attention_heads': 4, 'dropout': 0.1543671040144754, 'lr': 0.004431970216259738, 'batch_size': 32, 'max_grad_norm': 0.3901753200022312}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.33443400263786316, Current params: {'in_len': 120, 'max_samples_per_ts': 50, 'hidden_size': 208, 'num_attention_heads': 1, 'dropout': 0.1418590303148869, 'lr': 0.00341993031133932, 'batch_size': 32, 'max_grad_norm': 0.5051137084106796}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.44245967268943787, Current params: {'in_len': 156, 'max_samples_per_ts': 100, 'hidden_size': 96, 'num_attention_heads': 3, 'dropout': 0.12518265229155906, 'lr': 0.0007114411176183766, 'batch_size': 32, 'max_grad_norm': 0.5424480111705577}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.269827663898468, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 160, 'num_attention_heads': 2, 'dropout': 0.11018911706237552, 'lr': 0.002602688427391695, 'batch_size': 32, 'max_grad_norm': 0.5984958529855291}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.3111780881881714, Current params: {'in_len': 192, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 1, 'dropout': 0.1341516075301347, 'lr': 0.0038583167005596854, 'batch_size': 48, 'max_grad_norm': 0.6621057794298172}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.7076587080955505, Current params: {'in_len': 180, 'max_samples_per_ts': 200, 'hidden_size': 144, 'num_attention_heads': 4, 'dropout': 0.10655490371002524, 'lr': 0.0047634779130848424, 'batch_size': 48, 'max_grad_norm': 0.35121752135020917}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.2945404350757599, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 112, 'num_attention_heads': 2, 'dropout': 0.12309456535992525, 'lr': 0.001978744396451116, 'batch_size': 32, 'max_grad_norm': 0.5520433914514636}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.32123124599456787, Current params: {'in_len': 144, 'max_samples_per_ts': 50, 'hidden_size': 96, 'num_attention_heads': 2, 'dropout': 0.13179158715564976, 'lr': 0.002457717769254008, 'batch_size': 32, 'max_grad_norm': 0.4405529633690146}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.28051894903182983, Current params: {'in_len': 132, 'max_samples_per_ts': 50, 'hidden_size': 128, 'num_attention_heads': 2, 'dropout': 0.1199204333719879, 'lr': 0.0034236587349874854, 'batch_size': 32, 'max_grad_norm': 0.606357050559146}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.07008849084377289, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 1, 'dropout': 0.11024681674832873, 'lr': 0.0042901810456258335, 'batch_size': 32, 'max_grad_norm': 0.4818947632834149}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.6085343956947327, Current params: {'in_len': 120, 'max_samples_per_ts': 100, 'hidden_size': 112, 'num_attention_heads': 2, 'dropout': 0.13879672525673561, 'lr': 0.002338894225958209, 'batch_size': 32, 'max_grad_norm': 0.5435993505150085}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.3032844364643097, Current params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 48, 'num_attention_heads': 2, 'dropout': 0.14643379811486798, 'lr': 0.0017442103312600705, 'batch_size': 64, 'max_grad_norm': 0.6466108887432459}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.27010104060173035, Current params: {'in_len': 156, 'max_samples_per_ts': 50, 'hidden_size': 128, 'num_attention_heads': 1, 'dropout': 0.15788776205651653, 'lr': 0.003825864337938914, 'batch_size': 32, 'max_grad_norm': 0.13061716098742637}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.4498315155506134, Current params: {'in_len': 96, 'max_samples_per_ts': 100, 'hidden_size': 192, 'num_attention_heads': 3, 'dropout': 0.10331429538620389, 'lr': 0.000824547983532954, 'batch_size': 32, 'max_grad_norm': 0.5865477085356816}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
Current value: 0.067383773624897, Current params: {'in_len': 132, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 2, 'dropout': 0.1287587390892144, 'lr': 0.002826470595256566, 'batch_size': 48, 'max_grad_norm': 0.5207925018497276}
Best value: 0.055125489830970764, Best params: {'in_len': 168, 'max_samples_per_ts': 50, 'hidden_size': 80, 'num_attention_heads': 4, 'dropout': 0.12792080253276716, 'lr': 0.003164601797779577, 'batch_size': 32, 'max_grad_norm': 0.5265925565310886}
--------------------------------
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)
		time_second: REAL_VALUED (KNOWN_INPUT)
Interpolating data...
	Dropped segments: 17
	Extracted segments: 15
	Interpolated values: 561
	Percent of values interpolated: 4.37%
Splitting data...
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
Scaling data...
	No scaling applied
Data formatting complete.
--------------------------------
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 10 Seed: 1 ID mean of (MSE, MAE): [400.88553132  13.40345615]
		Model Seed: 10 Seed: 1 OOD mean of (MSE, MAE) stats: [373.30886771  12.09080303]
		Model Seed: 10 Seed: 1 ID median of (MSE, MAE): [154.46256293  10.15841309]
		Model Seed: 10 Seed: 1 OOD median of (MSE, MAE) stats: [103.8220313   8.6082716]
		Model Seed: 10 Seed: 1 ID likelihoods: 0
		Model Seed: 10 Seed: 1 OOD likelihoods: 0
		Model Seed: 10 Seed: 1 ID calibration errors: [0.02740155 0.01329677 0.01823784 0.02801337 0.02395928 0.03486371
 0.0381549  0.0392209  0.03420616 0.03642495 0.03358841 0.03363758]
		Model Seed: 10 Seed: 1 OOD calibration errors: [0.01066297 0.01137651 0.01522419 0.02351147 0.03153663 0.04221566
 0.04500901 0.04551814 0.04819606 0.05251405 0.04262413 0.04563701]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 10 Seed: 2 ID mean of (MSE, MAE): [425.55809473  13.60084178]
		Model Seed: 10 Seed: 2 OOD mean of (MSE, MAE) stats: [386.18041591  12.63655524]
		Model Seed: 10 Seed: 2 ID median of (MSE, MAE): [146.25965248   9.77794758]
		Model Seed: 10 Seed: 2 OOD median of (MSE, MAE) stats: [133.6332234    9.73108164]
		Model Seed: 10 Seed: 2 ID likelihoods: 0
		Model Seed: 10 Seed: 2 OOD likelihoods: 0
		Model Seed: 10 Seed: 2 ID calibration errors: [0.03487167 0.01906723 0.01251654 0.00872514 0.00922349 0.01232317
 0.01770296 0.01935363 0.02411942 0.02374936 0.0246649  0.03071005]
		Model Seed: 10 Seed: 2 OOD calibration errors: [0.07002717 0.03649739 0.02799264 0.0275828  0.02747252 0.02729044
 0.03852498 0.03981258 0.04433463 0.03506757 0.04088237 0.03172592]
	Model Seed: 10 ID mean of (MSE, MAE): [413.22181303  13.50214896]
	Model Seed: 10 OOD mean of (MSE, MAE): [379.74464181  12.36367913]
	Model Seed: 10 ID median of (MSE, MAE): [150.3611077    9.96818034]
	Model Seed: 10 OOD median of (MSE, MAE): [118.72762735   9.16967662]
	Model Seed: 10 ID likelihoods: 0.0
	Model Seed: 10 OOD likelihoods: 0.0
	Model Seed: 10 ID calibration errors: [0.03113661 0.016182   0.01537719 0.01836925 0.01659139 0.02359344
 0.02792893 0.02928726 0.02916279 0.03008715 0.02912666 0.03217382]
	Model Seed: 10 OOD calibration errors: [0.04034507 0.02393695 0.02160842 0.02554713 0.02950458 0.03475305
 0.041767   0.04266536 0.04626535 0.04379081 0.04175325 0.03868147]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 11 Seed: 1 ID mean of (MSE, MAE): [343.31417611  12.44003313]
		Model Seed: 11 Seed: 1 OOD mean of (MSE, MAE) stats: [436.91608418  12.80425016]
		Model Seed: 11 Seed: 1 ID median of (MSE, MAE): [129.24763155   9.28410053]
		Model Seed: 11 Seed: 1 OOD median of (MSE, MAE) stats: [129.86819615   9.15906302]
		Model Seed: 11 Seed: 1 ID likelihoods: 0
		Model Seed: 11 Seed: 1 OOD likelihoods: 0
		Model Seed: 11 Seed: 1 ID calibration errors: [0.31796751 0.31376344 0.17119439 0.15940688 0.12251589 0.13741697
 0.11725502 0.11718713 0.09576065 0.09660237 0.09401744 0.0931072 ]
		Model Seed: 11 Seed: 1 OOD calibration errors: [0.27502263 0.13493204 0.05298539 0.07803104 0.09106677 0.10093023
 0.11664044 0.12512595 0.13803835 0.16652802 0.17837784 0.1938225 ]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 11 Seed: 2 ID mean of (MSE, MAE): [505.57503219  15.44329049]
		Model Seed: 11 Seed: 2 OOD mean of (MSE, MAE) stats: [439.14321394  14.24188928]
		Model Seed: 11 Seed: 2 ID median of (MSE, MAE): [147.19305159  10.48152892]
		Model Seed: 11 Seed: 2 OOD median of (MSE, MAE) stats: [161.0790689   11.17268626]
		Model Seed: 11 Seed: 2 ID likelihoods: 0
		Model Seed: 11 Seed: 2 OOD likelihoods: 0
		Model Seed: 11 Seed: 2 ID calibration errors: [0.9574002  0.74416758 0.54321052 0.38362853 0.29164195 0.24392847
 0.21244522 0.2017941  0.20041782 0.21130461 0.20938815 0.21910937]
		Model Seed: 11 Seed: 2 OOD calibration errors: [0.96882701 0.85556608 0.71455789 0.51706987 0.44589054 0.38986385
 0.29993052 0.28633447 0.25252367 0.2418144  0.23061869 0.254195  ]
	Model Seed: 11 ID mean of (MSE, MAE): [424.44460415  13.94166181]
	Model Seed: 11 OOD mean of (MSE, MAE): [438.02964906  13.52306972]
	Model Seed: 11 ID median of (MSE, MAE): [138.22034157   9.88281473]
	Model Seed: 11 OOD median of (MSE, MAE): [145.47363253  10.16587464]
	Model Seed: 11 ID likelihoods: 0.0
	Model Seed: 11 OOD likelihoods: 0.0
	Model Seed: 11 ID calibration errors: [0.63768386 0.52896551 0.35720246 0.2715177  0.20707892 0.19067272
 0.16485012 0.15949061 0.14808924 0.15395349 0.15170279 0.15610828]
	Model Seed: 11 OOD calibration errors: [0.62192482 0.49524906 0.38377164 0.29755045 0.26847865 0.24539704
 0.20828548 0.20573021 0.19528101 0.20417121 0.20449826 0.22400875]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 12 Seed: 1 ID mean of (MSE, MAE): [405.89511778  13.85569034]
		Model Seed: 12 Seed: 1 OOD mean of (MSE, MAE) stats: [343.33479047  12.16983055]
		Model Seed: 12 Seed: 1 ID median of (MSE, MAE): [149.90322107  10.40394306]
		Model Seed: 12 Seed: 1 OOD median of (MSE, MAE) stats: [136.89765721   9.63696416]
		Model Seed: 12 Seed: 1 ID likelihoods: 0
		Model Seed: 12 Seed: 1 OOD likelihoods: 0
		Model Seed: 12 Seed: 1 ID calibration errors: [0.0493167  0.03016004 0.02716556 0.02978983 0.03063888 0.02828135
 0.02862066 0.02762801 0.03100441 0.03326908 0.03324427 0.03933296]
		Model Seed: 12 Seed: 1 OOD calibration errors: [0.0359065  0.02256295 0.02209882 0.02302811 0.04010837 0.05340417
 0.06978566 0.09431497 0.11178172 0.15640003 0.17103874 0.20198679]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 12 Seed: 2 ID mean of (MSE, MAE): [461.92582731  14.39110288]
		Model Seed: 12 Seed: 2 OOD mean of (MSE, MAE) stats: [450.2731225  13.9740478]
		Model Seed: 12 Seed: 2 ID median of (MSE, MAE): [162.4692083   10.44815063]
		Model Seed: 12 Seed: 2 OOD median of (MSE, MAE) stats: [183.4926105  11.1270024]
		Model Seed: 12 Seed: 2 ID likelihoods: 0
		Model Seed: 12 Seed: 2 OOD likelihoods: 0
		Model Seed: 12 Seed: 2 ID calibration errors: [0.02940369 0.04721685 0.06948645 0.08344311 0.08084631 0.09316869
 0.09727241 0.09702706 0.1046414  0.10324077 0.1135468  0.1217995 ]
		Model Seed: 12 Seed: 2 OOD calibration errors: [0.03732052 0.0882669  0.09998858 0.11246365 0.14058847 0.16009449
 0.18291364 0.18708837 0.19960948 0.19764373 0.20911977 0.2010235 ]
	Model Seed: 12 ID mean of (MSE, MAE): [433.91047254  14.12339661]
	Model Seed: 12 OOD mean of (MSE, MAE): [396.80395648  13.07193918]
	Model Seed: 12 ID median of (MSE, MAE): [156.18621468  10.42604685]
	Model Seed: 12 OOD median of (MSE, MAE): [160.19513386  10.38198328]
	Model Seed: 12 ID likelihoods: 0.0
	Model Seed: 12 OOD likelihoods: 0.0
	Model Seed: 12 ID calibration errors: [0.0393602  0.03868844 0.048326   0.05661647 0.0557426  0.06072502
 0.06294653 0.06232754 0.06782291 0.06825493 0.07339553 0.08056623]
	Model Seed: 12 OOD calibration errors: [0.03661351 0.05541492 0.0610437  0.06774588 0.09034842 0.10674933
 0.12634965 0.14070167 0.1556956  0.17702188 0.19007926 0.20150515]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 13 Seed: 1 ID mean of (MSE, MAE): [460.34006094  14.10260127]
		Model Seed: 13 Seed: 1 OOD mean of (MSE, MAE) stats: [402.91469192  12.95073052]
		Model Seed: 13 Seed: 1 ID median of (MSE, MAE): [139.75450477  10.16892878]
		Model Seed: 13 Seed: 1 OOD median of (MSE, MAE) stats: [139.56860682   9.71543058]
		Model Seed: 13 Seed: 1 ID likelihoods: 0
		Model Seed: 13 Seed: 1 OOD likelihoods: 0
		Model Seed: 13 Seed: 1 ID calibration errors: [0.14704126 0.10241388 0.1069159  0.12572341 0.12090253 0.11506668
 0.11476826 0.10381934 0.10763432 0.11111373 0.11568536 0.12782202]
		Model Seed: 13 Seed: 1 OOD calibration errors: [0.13335896 0.10135313 0.135626   0.12458968 0.10163621 0.11231249
 0.11091324 0.12052385 0.12235734 0.13895183 0.13132896 0.13527558]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 13 Seed: 2 ID mean of (MSE, MAE): [471.40596486  13.96532184]
		Model Seed: 13 Seed: 2 OOD mean of (MSE, MAE) stats: [412.3259831   12.95219226]
		Model Seed: 13 Seed: 2 ID median of (MSE, MAE): [136.32560176   9.72469139]
		Model Seed: 13 Seed: 2 OOD median of (MSE, MAE) stats: [123.31821251   9.35279401]
		Model Seed: 13 Seed: 2 ID likelihoods: 0
		Model Seed: 13 Seed: 2 OOD likelihoods: 0
		Model Seed: 13 Seed: 2 ID calibration errors: [0.45271058 0.25816373 0.21376172 0.16784655 0.14063729 0.13935169
 0.14061076 0.12168307 0.13078544 0.12693675 0.15358648 0.16691587]
		Model Seed: 13 Seed: 2 OOD calibration errors: [0.31979657 0.08234937 0.0549319  0.0393299  0.03934708 0.03954359
 0.03941407 0.04244549 0.04103456 0.04617809 0.05179329 0.04682566]
	Model Seed: 13 ID mean of (MSE, MAE): [465.8730129   14.03396155]
	Model Seed: 13 OOD mean of (MSE, MAE): [407.62033751  12.95146139]
	Model Seed: 13 ID median of (MSE, MAE): [138.04005327   9.94681009]
	Model Seed: 13 OOD median of (MSE, MAE): [131.44340967   9.53411229]
	Model Seed: 13 ID likelihoods: 0.0
	Model Seed: 13 OOD likelihoods: 0.0
	Model Seed: 13 ID calibration errors: [0.29987592 0.1802888  0.16033881 0.14678498 0.13076991 0.12720918
 0.12768951 0.1127512  0.11920988 0.11902524 0.13463592 0.14736894]
	Model Seed: 13 OOD calibration errors: [0.22657776 0.09185125 0.09527895 0.08195979 0.07049164 0.07592804
 0.07516366 0.08148467 0.08169595 0.09256496 0.09156113 0.09105062]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 14 Seed: 1 ID mean of (MSE, MAE): [475.95816598  14.71863348]
		Model Seed: 14 Seed: 1 OOD mean of (MSE, MAE) stats: [396.03133951  12.43446884]
		Model Seed: 14 Seed: 1 ID median of (MSE, MAE): [137.7640934    9.88502645]
		Model Seed: 14 Seed: 1 OOD median of (MSE, MAE) stats: [110.94536474   8.64511935]
		Model Seed: 14 Seed: 1 ID likelihoods: 0
		Model Seed: 14 Seed: 1 OOD likelihoods: 0
		Model Seed: 14 Seed: 1 ID calibration errors: [0.07486952 0.03521317 0.04802173 0.05521092 0.0657453  0.06875492
 0.07058835 0.07001882 0.07961206 0.08174187 0.08050169 0.08613964]
		Model Seed: 14 Seed: 1 OOD calibration errors: [0.13409826 0.03008345 0.01813881 0.0138775  0.01570893 0.01913405
 0.02516529 0.02506497 0.02265674 0.02581905 0.02367706 0.02886215]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 14 Seed: 2 ID mean of (MSE, MAE): [598.99300299  16.29379471]
		Model Seed: 14 Seed: 2 OOD mean of (MSE, MAE) stats: [615.50525554  14.95867007]
		Model Seed: 14 Seed: 2 ID median of (MSE, MAE): [166.6875574   11.01508745]
		Model Seed: 14 Seed: 2 OOD median of (MSE, MAE) stats: [134.84351456   9.92293866]
		Model Seed: 14 Seed: 2 ID likelihoods: 0
		Model Seed: 14 Seed: 2 OOD likelihoods: 0
		Model Seed: 14 Seed: 2 ID calibration errors: [0.00622823 0.03093106 0.0572858  0.08408723 0.10318037 0.11824845
 0.13149137 0.12625907 0.1314291  0.14936883 0.15107615 0.16819491]
		Model Seed: 14 Seed: 2 OOD calibration errors: [0.0845927  0.07390371 0.06119915 0.06703698 0.0886125  0.086958
 0.09769852 0.11335102 0.11195692 0.11956227 0.13104176 0.1284006 ]
	Model Seed: 14 ID mean of (MSE, MAE): [537.47558448  15.50621409]
	Model Seed: 14 OOD mean of (MSE, MAE): [505.76829752  13.69656946]
	Model Seed: 14 ID median of (MSE, MAE): [152.2258254   10.45005695]
	Model Seed: 14 OOD median of (MSE, MAE): [122.89443965   9.28402901]
	Model Seed: 14 ID likelihoods: 0.0
	Model Seed: 14 OOD likelihoods: 0.0
	Model Seed: 14 ID calibration errors: [0.04054887 0.03307211 0.05265377 0.06964908 0.08446283 0.09350168
 0.10103986 0.09813895 0.10552058 0.11555535 0.11578892 0.12716728]
	Model Seed: 14 OOD calibration errors: [0.10934548 0.05199358 0.03966898 0.04045724 0.05216071 0.05304602
 0.0614319  0.069208   0.06730683 0.07269066 0.07735941 0.07863138]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 15 Seed: 1 ID mean of (MSE, MAE): [414.5792558  13.5622223]
		Model Seed: 15 Seed: 1 OOD mean of (MSE, MAE) stats: [394.89399137  12.87015496]
		Model Seed: 15 Seed: 1 ID median of (MSE, MAE): [130.85455483   9.419391  ]
		Model Seed: 15 Seed: 1 OOD median of (MSE, MAE) stats: [130.45142743  10.01666705]
		Model Seed: 15 Seed: 1 ID likelihoods: 0
		Model Seed: 15 Seed: 1 OOD likelihoods: 0
		Model Seed: 15 Seed: 1 ID calibration errors: [0.48066791 0.34815783 0.24355842 0.18855492 0.15916262 0.12747225
 0.10763807 0.08668591 0.09194318 0.07529327 0.07923139 0.07583532]
		Model Seed: 15 Seed: 1 OOD calibration errors: [0.60478083 0.41480678 0.20443282 0.14482298 0.12302897 0.1308401
 0.12276409 0.10016894 0.10482188 0.09633569 0.08355658 0.09985013]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 15 Seed: 2 ID mean of (MSE, MAE): [3147.15361954   40.93361792]
		Model Seed: 15 Seed: 2 OOD mean of (MSE, MAE) stats: [1321.68528018   27.94652389]
		Model Seed: 15 Seed: 2 ID median of (MSE, MAE): [999.85077841  29.72765001]
		Model Seed: 15 Seed: 2 OOD median of (MSE, MAE) stats: [567.00471673  21.53390121]
		Model Seed: 15 Seed: 2 ID likelihoods: 0
		Model Seed: 15 Seed: 2 OOD likelihoods: 0
		Model Seed: 15 Seed: 2 ID calibration errors: [0.1195431  0.13223867 0.14758285 0.15369199 0.16290268 0.16732354
 0.1717391  0.18594392 0.19389913 0.1963411  0.2036937  0.21293342]
		Model Seed: 15 Seed: 2 OOD calibration errors: [0.2477333  0.21547532 0.16460933 0.20416486 0.22599254 0.23797325
 0.23700961 0.23430765 0.27445235 0.34924541 0.41372634 0.3996854 ]
	Model Seed: 15 ID mean of (MSE, MAE): [1780.86643767   27.24792011]
	Model Seed: 15 OOD mean of (MSE, MAE): [858.28963577  20.40833943]
	Model Seed: 15 ID median of (MSE, MAE): [565.35266662  19.5735205 ]
	Model Seed: 15 OOD median of (MSE, MAE): [348.72807208  15.77528413]
	Model Seed: 15 ID likelihoods: 0.0
	Model Seed: 15 OOD likelihoods: 0.0
	Model Seed: 15 ID calibration errors: [0.30010551 0.24019825 0.19557063 0.17112345 0.16103265 0.14739789
 0.13968858 0.13631491 0.14292116 0.13581718 0.14146254 0.14438437]
	Model Seed: 15 OOD calibration errors: [0.42625707 0.31514105 0.18452108 0.17449392 0.17451075 0.18440668
 0.17988685 0.16723829 0.18963712 0.22279055 0.24864146 0.24976777]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 16 Seed: 1 ID mean of (MSE, MAE): [423.28145786  13.66546321]
		Model Seed: 16 Seed: 1 OOD mean of (MSE, MAE) stats: [344.32281267  12.0664279 ]
		Model Seed: 16 Seed: 1 ID median of (MSE, MAE): [136.25151582   9.89973338]
		Model Seed: 16 Seed: 1 OOD median of (MSE, MAE) stats: [118.34128128   9.13234266]
		Model Seed: 16 Seed: 1 ID likelihoods: 0
		Model Seed: 16 Seed: 1 OOD likelihoods: 0
		Model Seed: 16 Seed: 1 ID calibration errors: [0.05710366 0.07708424 0.06530188 0.06043683 0.05843    0.05514849
 0.04791279 0.04424578 0.04375617 0.04831047 0.0423688  0.05441025]
		Model Seed: 16 Seed: 1 OOD calibration errors: [0.21452542 0.15456723 0.09621064 0.08042175 0.06237716 0.05017658
 0.05088703 0.059736   0.0541208  0.06227135 0.06203946 0.06151693]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 16 Seed: 2 ID mean of (MSE, MAE): [459.54245996  14.70503465]
		Model Seed: 16 Seed: 2 OOD mean of (MSE, MAE) stats: [429.616802    13.94069808]
		Model Seed: 16 Seed: 2 ID median of (MSE, MAE): [160.11963859  10.93302409]
		Model Seed: 16 Seed: 2 OOD median of (MSE, MAE) stats: [152.52386075  11.10079924]
		Model Seed: 16 Seed: 2 ID likelihoods: 0
		Model Seed: 16 Seed: 2 OOD likelihoods: 0
		Model Seed: 16 Seed: 2 ID calibration errors: [0.66532748 0.3111092  0.31983668 0.31439477 0.29100235 0.2355325
 0.21912981 0.19894914 0.19955581 0.18461836 0.1730684  0.17741622]
		Model Seed: 16 Seed: 2 OOD calibration errors: [0.96689561 0.35482111 0.42197446 0.42905728 0.43345085 0.36638304
 0.30816043 0.29808329 0.2290748  0.22884841 0.19107656 0.19978503]
	Model Seed: 16 ID mean of (MSE, MAE): [441.41195891  14.18524893]
	Model Seed: 16 OOD mean of (MSE, MAE): [386.96980734  13.00356299]
	Model Seed: 16 ID median of (MSE, MAE): [148.18557721  10.41637874]
	Model Seed: 16 OOD median of (MSE, MAE): [135.43257102  10.11657095]
	Model Seed: 16 ID likelihoods: 0.0
	Model Seed: 16 OOD likelihoods: 0.0
	Model Seed: 16 ID calibration errors: [0.36121557 0.19409672 0.19256928 0.1874158  0.17471618 0.1453405
 0.1335213  0.12159746 0.12165599 0.11646442 0.1077186  0.11591323]
	Model Seed: 16 OOD calibration errors: [0.59071052 0.25469417 0.25909255 0.25473952 0.24791401 0.20827981
 0.17952373 0.17890965 0.1415978  0.14555988 0.12655801 0.13065098]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 17 Seed: 1 ID mean of (MSE, MAE): [486.04395482  14.5815223 ]
		Model Seed: 17 Seed: 1 OOD mean of (MSE, MAE) stats: [377.18140888  11.95901248]
		Model Seed: 17 Seed: 1 ID median of (MSE, MAE): [155.24800707  10.22014395]
		Model Seed: 17 Seed: 1 OOD median of (MSE, MAE) stats: [115.24189504   8.91326555]
		Model Seed: 17 Seed: 1 ID likelihoods: 0
		Model Seed: 17 Seed: 1 OOD likelihoods: 0
		Model Seed: 17 Seed: 1 ID calibration errors: [0.26601281 0.15782536 0.11870388 0.10864647 0.08297098 0.0727038
 0.07463057 0.07710922 0.07927837 0.07507617 0.07692411 0.08303419]
		Model Seed: 17 Seed: 1 OOD calibration errors: [0.08066498 0.02307174 0.0083949  0.01640391 0.01872832 0.03062212
 0.03650942 0.04840768 0.05669565 0.07135875 0.07777853 0.09685718]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 17 Seed: 2 ID mean of (MSE, MAE): [662.84819989  16.86691812]
		Model Seed: 17 Seed: 2 OOD mean of (MSE, MAE) stats: [572.89604607  15.54711986]
		Model Seed: 17 Seed: 2 ID median of (MSE, MAE): [178.65948442  10.99352105]
		Model Seed: 17 Seed: 2 OOD median of (MSE, MAE) stats: [180.82298555  10.88525836]
		Model Seed: 17 Seed: 2 ID likelihoods: 0
		Model Seed: 17 Seed: 2 OOD likelihoods: 0
		Model Seed: 17 Seed: 2 ID calibration errors: [0.709442   0.43003237 0.31095391 0.17803053 0.10940797 0.09752588
 0.08209367 0.06663618 0.06965704 0.05816982 0.06565961 0.06744965]
		Model Seed: 17 Seed: 2 OOD calibration errors: [0.35253483 0.30253698 0.19094327 0.10577899 0.08101161 0.06189792
 0.05592062 0.04987873 0.03882077 0.0394127  0.04308242 0.0369148 ]
	Model Seed: 17 ID mean of (MSE, MAE): [574.44607736  15.72422021]
	Model Seed: 17 OOD mean of (MSE, MAE): [475.03872747  13.75306617]
	Model Seed: 17 ID median of (MSE, MAE): [166.95374574  10.6068325 ]
	Model Seed: 17 OOD median of (MSE, MAE): [148.0324403    9.89926195]
	Model Seed: 17 ID likelihoods: 0.0
	Model Seed: 17 OOD likelihoods: 0.0
	Model Seed: 17 ID calibration errors: [0.4877274  0.29392886 0.21482889 0.1433385  0.09618947 0.08511484
 0.07836212 0.0718727  0.0744677  0.06662299 0.07129186 0.07524192]
	Model Seed: 17 OOD calibration errors: [0.2165999  0.16280436 0.09966908 0.06109145 0.04986997 0.04626002
 0.04621502 0.0491432  0.04775821 0.05538572 0.06043048 0.06688599]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 18 Seed: 1 ID mean of (MSE, MAE): [390.29096813  13.14575201]
		Model Seed: 18 Seed: 1 OOD mean of (MSE, MAE) stats: [352.06148106  11.84521853]
		Model Seed: 18 Seed: 1 ID median of (MSE, MAE): [140.39340499   9.87884967]
		Model Seed: 18 Seed: 1 OOD median of (MSE, MAE) stats: [106.08375582   8.55428537]
		Model Seed: 18 Seed: 1 ID likelihoods: 0
		Model Seed: 18 Seed: 1 OOD likelihoods: 0
		Model Seed: 18 Seed: 1 ID calibration errors: [0.16341258 0.07371658 0.03773724 0.04377942 0.03398141 0.03838729
 0.03896587 0.04436923 0.03734673 0.04533098 0.03697137 0.03374246]
		Model Seed: 18 Seed: 1 OOD calibration errors: [0.07597562 0.06268876 0.03515242 0.02824171 0.02508765 0.02399621
 0.02484717 0.02062434 0.01908973 0.02627905 0.02133925 0.02217028]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 18 Seed: 2 ID mean of (MSE, MAE): [412.17092635  13.50827177]
		Model Seed: 18 Seed: 2 OOD mean of (MSE, MAE) stats: [359.55686803  12.13228828]
		Model Seed: 18 Seed: 2 ID median of (MSE, MAE): [119.08134832   9.36357307]
		Model Seed: 18 Seed: 2 OOD median of (MSE, MAE) stats: [122.33854228   9.35754967]
		Model Seed: 18 Seed: 2 ID likelihoods: 0
		Model Seed: 18 Seed: 2 OOD likelihoods: 0
		Model Seed: 18 Seed: 2 ID calibration errors: [2.21546077 1.25183343 0.81639738 0.51971229 0.37189486 0.25983859
 0.20439948 0.15477094 0.13572557 0.12237729 0.10897517 0.11385911]
		Model Seed: 18 Seed: 2 OOD calibration errors: [2.262484   0.84735781 0.53325177 0.31939531 0.22767968 0.14881323
 0.09706468 0.08971665 0.07814406 0.06166465 0.0648249  0.07397998]
	Model Seed: 18 ID mean of (MSE, MAE): [401.23094724  13.32701189]
	Model Seed: 18 OOD mean of (MSE, MAE): [355.80917455  11.9887534 ]
	Model Seed: 18 ID median of (MSE, MAE): [129.73737665   9.62121137]
	Model Seed: 18 OOD median of (MSE, MAE): [114.21114905   8.95591752]
	Model Seed: 18 ID likelihoods: 0.0
	Model Seed: 18 OOD likelihoods: 0.0
	Model Seed: 18 ID calibration errors: [1.18943667 0.66277501 0.42706731 0.28174585 0.20293814 0.14911294
 0.12168267 0.09957009 0.08653615 0.08385413 0.07297327 0.07380079]
	Model Seed: 18 OOD calibration errors: [1.16922981 0.45502328 0.2842021  0.17381851 0.12638366 0.08640472
 0.06095593 0.05517049 0.0486169  0.04397185 0.04308208 0.04807513]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 19 Seed: 1 ID mean of (MSE, MAE): [444.45234504  14.27605664]
		Model Seed: 19 Seed: 1 OOD mean of (MSE, MAE) stats: [368.58154839  12.25533723]
		Model Seed: 19 Seed: 1 ID median of (MSE, MAE): [136.27216167   9.71428331]
		Model Seed: 19 Seed: 1 OOD median of (MSE, MAE) stats: [127.60913036   9.41671499]
		Model Seed: 19 Seed: 1 ID likelihoods: 0
		Model Seed: 19 Seed: 1 OOD likelihoods: 0
		Model Seed: 19 Seed: 1 ID calibration errors: [0.84564687 0.56890772 0.41437947 0.34578656 0.28753543 0.23508706
 0.21351684 0.19276196 0.20331709 0.17507273 0.1694571  0.17456205]
		Model Seed: 19 Seed: 1 OOD calibration errors: [0.70377785 0.50237173 0.30618059 0.21214879 0.15685351 0.11629965
 0.07402395 0.04437208 0.03570827 0.02631512 0.0230312  0.01802063]
	Train: 9056 (64.79%)
	Val: 1774 (12.69%)
	Test: 1848 (13.22%)
	Test OOD: 1300 (9.30%)
	No scaling applied
		Model Seed: 19 Seed: 2 ID mean of (MSE, MAE): [470.60607163  14.19339732]
		Model Seed: 19 Seed: 2 OOD mean of (MSE, MAE) stats: [381.04657058  12.15525989]
		Model Seed: 19 Seed: 2 ID median of (MSE, MAE): [145.99166888   9.99851131]
		Model Seed: 19 Seed: 2 OOD median of (MSE, MAE) stats: [111.27682827   8.60678101]
		Model Seed: 19 Seed: 2 ID likelihoods: 0
		Model Seed: 19 Seed: 2 OOD likelihoods: 0
		Model Seed: 19 Seed: 2 ID calibration errors: [0.07473576 0.04231558 0.06341055 0.06366214 0.06068109 0.0636829
 0.07313598 0.07750487 0.09265224 0.10172605 0.12789459 0.15601846]
		Model Seed: 19 Seed: 2 OOD calibration errors: [0.2150349  0.06212363 0.03998195 0.04007917 0.04782056 0.04093734
 0.03389334 0.03741534 0.03446053 0.02487431 0.02690877 0.0259307 ]
	Model Seed: 19 ID mean of (MSE, MAE): [457.52920833  14.23472698]
	Model Seed: 19 OOD mean of (MSE, MAE): [374.81405948  12.20529856]
	Model Seed: 19 ID median of (MSE, MAE): [141.13191528   9.85639731]
	Model Seed: 19 OOD median of (MSE, MAE): [119.44297932   9.011748  ]
	Model Seed: 19 ID likelihoods: 0.0
	Model Seed: 19 OOD likelihoods: 0.0
	Model Seed: 19 ID calibration errors: [0.46019132 0.30561165 0.23889501 0.20472435 0.17410826 0.14938498
 0.14332641 0.13513341 0.14798466 0.13839939 0.14867585 0.16529026]
	Model Seed: 19 OOD calibration errors: [0.45940638 0.28224768 0.17308127 0.12611398 0.10233704 0.07861849
 0.05395864 0.04089371 0.0350844  0.02559472 0.02496998 0.02197566]
ID mean of (MSE, MAE): [593.0410116608191, 15.582651115348401] +- [399.3447597567472, 3.9561486824505057] +- [168.53690828   1.80750803] 
OOD mean of (MSE, MAE): [457.88882870055966, 13.696573943006461] +- [140.71877391122854, 2.311360601466144] +- [78.93412708  1.35195052] 
ID median of (MSE, MAE): [188.63948241226143, 11.074824937184653] +- [125.97334430155323, 2.849687159263695] +- [47.6243166   1.17154361] 
OOD median of (MSE, MAE): [154.45814548093878, 10.229445838928225] +- [66.26175304156081, 1.9107712687482843] +- [32.57521086  1.04963341] 
ID likelihoods: 0.0 +- 0.0
OOD likelihoods: 0.0 +- 0.0
ID calibration errors: [0.38472819301027616, 0.24938073580641543, 0.19028293523455164, 0.1551285444942502, 0.13036303362509213, 0.11720532001897892, 0.11010360349111618, 0.10264841301552027, 0.10433710465856741, 0.10280342806128188, 0.10467719505799802, 0.11180151144351913] +- [0.33249436727449455, 0.20191225052756603, 0.1255029961735573, 0.08322230474720109, 0.061449953679861516, 0.04774852061108876, 0.04004256960738472, 0.03723635939529502, 0.037448832331213835, 0.03706082365223835, 0.039162279433836884, 0.04183384429014039] +- [0.14178416 0.07732683 0.0651613  0.04059368 0.0317788  0.02588707
 0.02489847 0.02234378 0.02395119 0.02497987 0.0284782  0.03163914] 
OOD calibration errors: [0.3897010318309633, 0.21883563020360075, 0.16019377534702123, 0.1303517878829, 0.12119994365897191, 0.11198432070412548, 0.10335378558459832, 0.10311452516365524, 0.10089391568427727, 0.10835422360173766, 0.11089333166142479, 0.11512328894187085] +- [0.33016447674279753, 0.16079902940751714, 0.11284681431026665, 0.08798117327019557, 0.07921979826059876, 0.07012125458209881, 0.060865424659932596, 0.060128278331719076, 0.059843484679671814, 0.06918496101486492, 0.07420142794575374, 0.07806496737340306] +- [0.16282363 0.0730542  0.07074932 0.05584409 0.05458669 0.04399119
 0.03569926 0.03472883 0.02954726 0.02607693 0.02941416 0.02472337] 
