Experiment overview:

Experiment parameters:

Number of evaluations

Result names and types

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trial_index,arm_name,trial_status,generation_method,generation_node,ACCURACY,RUNTIME,recent_samples_size,batch_size,divergence,detect_batch,statistic,significance,subsets,feature_id
0,0_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,160.000000000000000000000000000000,1388,4733,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.334707790672779059715225002947,14,18
1,1_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,170.000000000000000000000000000000,4522,2109,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.803561006955802459295057360578,98,18
2,2_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,164.000000000000000000000000000000,2893,3465,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.734514283167198245294571279373,35,18
3,3_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,194.000000000000000000000000000000,1024,797,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.142810671063140048886452859733,56,18
4,4_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,171.000000000000000000000000000000,197,2912,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.461441646676510552893546446285,44,18
5,5_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,250.000000000000000000000000000000,3329,249,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.930232957210391786517789114441,72,18
6,6_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,167.000000000000000000000000000000,4239,4183,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.623374173445627066669771920715,11,18
7,7_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,248.000000000000000000000000000000,2373,1554,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.031732470808550711227624674393,76,18
8,8_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,167.000000000000000000000000000000,2103,3190,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.212940394125878812436880593850,48,18
9,9_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,166.000000000000000000000000000000,3982,1151,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.679727283425629158486458436528,63,18
10,10_0,COMPLETED,Sobol,GenerationStep_0,0.110000000000000000555111512313,153.000000000000000000000000000000,3608,4385,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.873820837678387807301305656438,6,18
11,11_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,159.000000000000000000000000000000,484,2381,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.280292732911184450106389931534,84,18
12,12_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,175.000000000000000000000000000000,757,3829,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.086274819966405624782623817737,22,18
13,13_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,243.000000000000000000000000000000,2633,1830,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.553001792561262806025013105682,94,18
14,14_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,144.000000000000000000000000000000,4799,2642,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.984892661837860949880507632770,26,18
15,15_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,218.000000000000000000000000000000,1677,598,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.391424473775550729737204846970,60,18
16,16_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,168.000000000000000000000000000000,1758,2686,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.511572800386697057462015436613,96,18
17,17_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,161.000000000000000000000000000000,4875,330,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.105044181350618603953961383013,17,18
18,18_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,160.000000000000000000000000000000,2714,3952,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.419254619514569659699532166997,53,18
19,19_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,154.000000000000000000000000000000,833,1639,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.948534646889194865693184510747,37,18
20,20_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,174.000000000000000000000000000000,409,4654,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.630441492110490830924618421705,75,18
21,21_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,183.000000000000000000000000000000,3527,2337,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.223972789779305458957026075950,40,18
22,22_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,225.000000000000000000000000000000,3906,3380,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.284792177790775868917449997753,80,18
23,23_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,150.000000000000000000000000000000,2022,1030,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.814012288460507993370640633657,8,18
24,24_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,165.000000000000000000000000000000,2292,4227,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.878937011752277563836344143056,67,18
25,25_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,218.000000000000000000000000000000,4163,1285,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.474484151858836411541631150612,45,18
26,26_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,165.000000000000000000000000000000,3248,3034,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.034343632331117986133950381600,87,18
27,27_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,185.000000000000000000000000000000,122,58,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.565453952198848086574400895188,2,18
28,28_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,216.000000000000000000000000000000,1100,3734,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.760000019595026943264315377746,90,18
29,29_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,162.000000000000000000000000000000,2974,753,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.355609069168567659691149174250,24,18
30,30_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,163.000000000000000000000000000000,4629,3900,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,7,18
31,31_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,173.000000000000000000000000000000,3903,4509,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,1,18
32,32_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,152.000000000000000000000000000000,5000,2652,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,12,18
33,33_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,202.000000000000000000000000000000,4038,4975,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,1,18
34,34_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,166.000000000000000000000000000000,5000,4324,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,34,18
35,35_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,265.000000000000000000000000000000,4846,5000,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.240655509600742556486352441425,1,18
36,36_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,195.000000000000000000000000000000,5000,2770,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,1,18
37,37_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,269.000000000000000000000000000000,1839,5000,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,1,18
38,38_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,3767,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,26,18
39,39_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,181.000000000000000000000000000000,3932,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,1,18
40,40_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,203.000000000000000000000000000000,4629,3035,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,1,18
41,41_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,158.000000000000000000000000000000,4629,2740,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,45,18
42,42_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,272.000000000000000000000000000000,5000,5000,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.201478277693281526694590866100,1,18
43,43_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,177.000000000000000000000000000000,1,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,9,18
44,44_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,204.000000000000000000000000000000,5000,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.240407605245393035664491776515,1,18
45,45_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,194.000000000000000000000000000000,5000,1866,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,1,18
46,46_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,202.000000000000000000000000000000,975,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,1,18
47,47_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,158.000000000000000000000000000000,1392,3921,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,1,18
48,48_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,199.000000000000000000000000000000,5000,3501,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,1,18
49,49_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,201.000000000000000000000000000000,4139,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.945203731515823708519974388764,1,18
50,50_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,203.000000000000000000000000000000,4143,4283,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,1,18
51,51_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,173.000000000000000000000000000000,4834,5000,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,26,18
52,52_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,274.000000000000000000000000000000,1,5000,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,1,18
53,53_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,271.000000000000000000000000000000,1,5000,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,1,18
54,54_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,5000,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,18,18
55,55_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,189.000000000000000000000000000000,5000,3705,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,1,18
56,56_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,169.000000000000000000000000000000,5000,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,57,18
57,57_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,275.000000000000000000000000000000,5000,5000,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,1,18
58,58_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,271.000000000000000000000000000000,997,3719,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,1,18
59,59_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,199.000000000000000000000000000000,1870,3777,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,1,18
60,60_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,3273,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,50,18
61,61_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,164.000000000000000000000000000000,1459,4377,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.798652737604423124118113719305,9,18
62,62_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,178.000000000000000000000000000000,3670,4988,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,31,18
63,63_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,218.000000000000000000000000000000,2625,1129,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.106400262070895035293460750836,9,18
64,64_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,155.000000000000000000000000000000,3974,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,24,18
65,65_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,165.000000000000000000000000000000,2297,4331,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,9,18
66,66_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,160.000000000000000000000000000000,4314,4348,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,9,18
67,67_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,156.000000000000000000000000000000,4785,889,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,8,18
68,68_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,157.000000000000000000000000000000,71,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,47,18
69,69_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,4917,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,63,18
70,70_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,160.000000000000000000000000000000,677,4996,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,39,18
71,71_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,4036,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,63,18
72,72_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,172.000000000000000000000000000000,860,823,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,31,18
73,73_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,154.000000000000000000000000000000,1,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,28,18
74,74_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,163.000000000000000000000000000000,2878,426,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,8,18
75,75_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,187.000000000000000000000000000000,2765,1851,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,8,18
76,76_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,148.000000000000000000000000000000,3591,114,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,31,18
77,77_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,169.000000000000000000000000000000,4331,4299,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,9,18
78,78_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,487.000000000000000000000000000000,4984,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,31,18
79,79_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,180.000000000000000000000000000000,1021,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,76,18
80,80_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,162.000000000000000000000000000000,4881,370,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,31,18
81,81_0,FAILED,BoTorch,GenerationStep_1,,,1469,1,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,7,18
82,82_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,175.000000000000000000000000000000,697,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,43,18
83,83_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,178.000000000000000000000000000000,2139,3,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,14,18
84,84_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,80,4990,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,34,18
85,85_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,178.000000000000000000000000000000,305,1418,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,31,18
86,86_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,167.000000000000000000000000000000,728,4257,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,19,18
87,87_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,159.000000000000000000000000000000,858,402,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,27,18
88,88_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,176.000000000000000000000000000000,202,4401,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,8,18
89,89_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,148.000000000000000000000000000000,4453,1146,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,25,18
90,90_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,371.000000000000000000000000000000,4536,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,25,18
91,91_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,154.000000000000000000000000000000,2906,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,54,18
92,92_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,156.000000000000000000000000000000,1944,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,37,18
93,93_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,3040,101,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.994683436155499811626157224964,35,18
94,94_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,694,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,51,18
95,95_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,164.000000000000000000000000000000,2053,42,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.681772899939666499058432691527,25,18
96,96_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,248.000000000000000000000000000000,1732,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,28,18
97,97_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,172.000000000000000000000000000000,2629,4974,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,23,18
98,98_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,168.000000000000000000000000000000,4796,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.908461312498064388343266273296,57,18
99,99_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,1749,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,27,18
100,100_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,168.000000000000000000000000000000,1358,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,30,18
101,101_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,154.000000000000000000000000000000,4030,4995,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,20,18
102,102_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,841.000000000000000000000000000000,3918,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.956268729564748953819730559189,40,18
103,103_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,154.000000000000000000000000000000,3117,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,40,18
104,104_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,178.000000000000000000000000000000,4728,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,49,18
105,105_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,83,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.869183693337429064129651123949,60,18
106,106_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,164.000000000000000000000000000000,4992,104,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,5,18
107,107_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,158.000000000000000000000000000000,4116,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.849726765810585593641235391260,59,18
108,108_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,252.000000000000000000000000000000,4069,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.705129399302528825366209730419,37,18
109,109_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,172.000000000000000000000000000000,4383,76,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.790958366655155620250639003643,41,18
110,110_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,183.000000000000000000000000000000,4172,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,59,18
111,111_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,153.000000000000000000000000000000,3170,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.733996835680496451104204425064,67,18
112,112_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,166.000000000000000000000000000000,3734,4766,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.799706900349194893529158889578,55,18
113,113_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,176.000000000000000000000000000000,2062,1885,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,25,18
114,114_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,4658,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,33,18
115,115_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,2640,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,52,18
116,116_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,173.000000000000000000000000000000,1434,4614,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,54,18
117,117_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,184.000000000000000000000000000000,4922,4889,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.948675493612214482119782132941,62,18
118,118_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,2468,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,54,18
119,119_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,246.000000000000000000000000000000,3540,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,48,18
120,120_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,162.000000000000000000000000000000,2836,4951,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.951106729282999996399894371280,55,18
121,121_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,166.000000000000000000000000000000,3508,4956,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.942944071520434023980783422303,46,18
122,122_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,179.000000000000000000000000000000,2071,4995,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.942046932505637091281869288650,50,18
123,123_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,160.000000000000000000000000000000,3464,4996,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.711682803071962744034806291893,64,18
124,124_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,170.000000000000000000000000000000,309,4955,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.794419622717998263894401134166,69,18
125,125_0,FAILED,BoTorch,GenerationStep_1,,,4376,1,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.954929505617819862628437022067,42,18
126,126_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,256.000000000000000000000000000000,4818,8,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.827156692981249497265139325464,45,18
127,127_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,323.000000000000000000000000000000,557,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.856390992722781296819789531583,44,18
128,128_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,177.000000000000000000000000000000,2208,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,48,18
129,129_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,3013,4986,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.637656764032622613846967851714,53,18
130,130_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,230.000000000000000000000000000000,1479,5,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.892977192383421880528260317078,39,18
131,131_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,176.000000000000000000000000000000,465,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.870333161025382606723610479094,50,18
132,132_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,170.000000000000000000000000000000,2093,4937,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.864534143863924908757212506316,48,18
133,133_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,171.000000000000000000000000000000,586,4906,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.835700477730318302960199616791,49,18
134,134_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,171.000000000000000000000000000000,699,4911,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.963483888645868979949682398001,52,18
135,135_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,1271,4988,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.578591194850345491573762046755,49,18
136,136_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,203,4987,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.529983107266492892151177329652,49,18
137,137_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,169.000000000000000000000000000000,4974,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.853489945480502498753594409209,48,18
138,138_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,1517,4997,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.362959075184791646329074410460,62,18
139,139_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,152.000000000000000000000000000000,2064,11,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.862187930042830341648141256883,43,18
140,140_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,190.000000000000000000000000000000,922,4925,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.824625148626242920890661025624,37,18
141,141_0,FAILED,BoTorch,GenerationStep_1,,,4574,1,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.924716112617456120759129589715,49,18
142,142_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,180.000000000000000000000000000000,450,4976,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.796527718918443539131146735599,50,18
143,143_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,3648,4992,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.740979359726319453116616386978,51,18
144,144_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,159.000000000000000000000000000000,2688,4990,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.654960106710993028578116081917,64,18
145,145_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,187.000000000000000000000000000000,4783,36,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.854056509799596796916887342377,43,18
146,146_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,1828.000000000000000000000000000000,1480,1,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.669363217563233026119462465431,51,18
147,147_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,165.000000000000000000000000000000,1246,4952,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.889209946828804276286462027201,48,18
148,148_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,256.000000000000000000000000000000,3746,52,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.803350766342207811554487761896,57,18
149,149_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,186.000000000000000000000000000000,2985,4975,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.788271233864577203220846968179,42,18
150,150_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,159.000000000000000000000000000000,4748,4421,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.555846070670165226523806722980,34,18
151,151_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,5000,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,75,18
152,152_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,156.000000000000000000000000000000,2869,73,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.593906346120359085460904680076,62,18
153,153_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,158.000000000000000000000000000000,4517,20,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.310842982999767036833560496234,60,18
154,154_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,577,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.567784872579856325636171732185,63,18
155,155_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,171.000000000000000000000000000000,23,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.418744563396118374409837770145,63,18
156,156_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,157.000000000000000000000000000000,2729,39,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.250537550429379085592529463611,1,18
157,157_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,191.000000000000000000000000000000,4445,4945,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.553463057263575142208367196872,78,18
158,158_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,168.000000000000000000000000000000,3111,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.258406454023100251582434339070,73,18
159,159_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,263.000000000000000000000000000000,1999,3351,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.536391505330800932860313423589,1,18
160,160_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,166.000000000000000000000000000000,2595,4990,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.448589104756003775875683459162,91,18
161,161_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,189.000000000000000000000000000000,3603,4997,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.599925456268910806123528800526,58,18
162,162_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,254.000000000000000000000000000000,2196,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.185606328426649314078389352289,52,18
163,163_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,169.000000000000000000000000000000,4979,4989,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.516603183404800492262154421041,76,18
164,164_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,166.000000000000000000000000000000,924,4044,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.118212500806625778193570397434,1,18
165,165_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,187.000000000000000000000000000000,4269,4978,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.134777974983599224678698647040,85,18
166,166_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,247,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.726712533938276727951688371832,60,18
167,167_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,177.000000000000000000000000000000,3733,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.573754268580216164608032158867,56,18
168,168_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,226.000000000000000000000000000000,1673,3144,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.651676807804699942394677236734,100,18
169,169_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,206.000000000000000000000000000000,558,4015,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.528887586316514779483100028301,99,18
170,170_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,190.000000000000000000000000000000,2146,4983,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.630139352640926020399092521984,73,18
171,171_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,154.000000000000000000000000000000,4099,23,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.110047472546976318752420809233,77,18
172,172_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,174.000000000000000000000000000000,436,3,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.580596344141102060909531701327,57,18
173,173_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,211.000000000000000000000000000000,4566,5000,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.795814672059951822014056688204,100,18
174,174_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,170.000000000000000000000000000000,4440,4683,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.504251301586549516287050209939,54,18
175,175_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,168.000000000000000000000000000000,3296,4484,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.560761716915243790815281954565,54,18
176,176_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,152.000000000000000000000000000000,251,67,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.476438335392710410332028914127,99,18
177,177_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,170.000000000000000000000000000000,4293,4087,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.557709929535631587782518181484,100,18
178,178_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,178.000000000000000000000000000000,3580,4988,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.567495455311943564069565582031,53,18
179,179_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,4684,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.127751278860934097680868148927,49,18
180,180_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,3525,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.330828932980087808690683459645,71,18
181,181_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,167.000000000000000000000000000000,4427,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.352271224135521754394773097374,86,18
182,182_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,431.000000000000000000000000000000,3267,3,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.624766376640799481201327125746,33,18
183,183_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,167.000000000000000000000000000000,4390,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.413446527036719302383716012628,76,18
184,184_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,157.000000000000000000000000000000,2475,4999,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.294453282055232856695425880389,55,18
185,185_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,179.000000000000000000000000000000,771,4977,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.113899603429575238533466574609,67,18
186,186_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,179.000000000000000000000000000000,99,2455,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,1,18
187,187_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,1909,4982,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.197519866872421429038197970840,93,18
188,188_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,163.000000000000000000000000000000,3928,7,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.254667059090713188140853162622,18,18
189,189_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,176.000000000000000000000000000000,604,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.291173682702841329916765289454,100,18
190,190_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,186.000000000000000000000000000000,3523,4992,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.436019209818019504165675925833,65,18
191,191_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,164.000000000000000000000000000000,4526,2163,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.278550700287180541536713462847,50,18
192,192_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,162.000000000000000000000000000000,803,243,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.879148895509584860086249591404,23,18
193,193_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,175.000000000000000000000000000000,3096,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.152363621721112757478877597350,70,18
194,194_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,178.000000000000000000000000000000,3749,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.069324964441046188490247459413,89,18
195,195_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,172.000000000000000000000000000000,3624,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.389886798398336675841591159042,83,18
196,196_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,218.000000000000000000000000000000,1990,2529,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.308829363163968917405100000906,100,18
197,197_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,179.000000000000000000000000000000,138,4996,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.463592181330260366678430727916,98,18
198,198_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,154.000000000000000000000000000000,2345,340,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.086694748607183527711050885500,1,18
199,199_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,153.000000000000000000000000000000,1419,911,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.093925449241626060570276024464,81,18
200,200_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,190.000000000000000000000000000000,447,4927,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.079945358040472327965098031655,86,18
201,201_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,177.000000000000000000000000000000,3780,2470,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.333844029335272685088398247899,1,18
202,202_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,258.000000000000000000000000000000,755,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.411993754901355202502344354798,19,18
203,203_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,169.000000000000000000000000000000,2870,1230,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.109473937073183216761584901633,1,18
204,204_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,170.000000000000000000000000000000,1363,2470,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.261258881892254268830555474779,1,18
205,205_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,174.000000000000000000000000000000,1935,2488,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.258559291354009235242017439305,1,18
206,206_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,167.000000000000000000000000000000,2522,527,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.110385612690713730277281001690,69,18
207,207_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,165.000000000000000000000000000000,2615,1216,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.359405619860090730366408706686,17,18
208,208_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,209.000000000000000000000000000000,3540,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.072887982390899136442286021520,100,18
209,209_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,156.000000000000000000000000000000,1985,485,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.045081483432110248943391894727,1,18
210,210_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,2253,4997,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.601302421209272575097770641150,2,18
211,211_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,1660,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.197952502857284329262910205216,100,18
212,212_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,154.000000000000000000000000000000,1822,19,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.745341560945151027084421002655,22,18
213,213_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,38,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.313687856049134905500608283546,100,18
214,214_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,179.000000000000000000000000000000,182,4975,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.575939888372505981450899525953,61,18
215,215_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,172.000000000000000000000000000000,4571,4966,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.668367691322164003686623345857,91,18
216,216_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,168.000000000000000000000000000000,978,1710,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.564636987444755655829453644401,100,18
217,217_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,2396,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.626528710158745005820435380883,97,18
218,218_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,166.000000000000000000000000000000,3259,2001,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.550425528984511247543309764296,1,18
219,219_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,274.000000000000000000000000000000,4288,2536,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.734640667982121242651771808596,1,18
220,220_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,176.000000000000000000000000000000,1034,4981,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.271532934562568051273245828270,98,18
221,221_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,164.000000000000000000000000000000,71,1841,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.584709136678398744280116261507,100,18
222,222_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,168.000000000000000000000000000000,2541,2009,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,1,18
223,223_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,174.000000000000000000000000000000,4946,2022,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.876945914736689036139694053418,6,18
224,224_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,147.000000000000000000000000000000,1714,2214,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.847934741077982834234205711255,1,18
225,225_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,154.000000000000000000000000000000,4330,45,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.625539980330139244735221382143,38,18
226,226_0,FAILED,BoTorch,GenerationStep_1,,,4356,1,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.472377965615630002194080816480,18,18
227,227_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,163.000000000000000000000000000000,984,1048,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.306425580236084349472491794586,100,18
228,228_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,168.000000000000000000000000000000,3709,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.371549097647711767944400662600,100,18
229,229_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,166.000000000000000000000000000000,3200,2312,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.890021463444505611661838884174,15,18
230,230_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,152.000000000000000000000000000000,3393,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.658362852805459919380837163772,92,18
231,231_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,168.000000000000000000000000000000,612,2874,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.737772507044829550792996997188,63,18
232,232_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,163.000000000000000000000000000000,3422,1359,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.548958688694927809947898822429,100,18
233,233_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,183.000000000000000000000000000000,1375,2308,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.937136766135336807259648139734,8,18
234,234_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,172.000000000000000000000000000000,3162,2830,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.578989248122768196402887497243,1,18
235,235_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,158.000000000000000000000000000000,1268,1004,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.159689955220053192430640365274,1,18
236,236_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,164.000000000000000000000000000000,4393,1526,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.531815602825272737419481927645,1,18
237,237_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,186.000000000000000000000000000000,370,2318,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.749803554418284168292530011968,99,18
238,238_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,261.000000000000000000000000000000,2207,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.284746219331729055745938694599,1,18
239,239_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,179.000000000000000000000000000000,3821,2009,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.847242085634734243271282139176,100,18
240,240_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,246.000000000000000000000000000000,1,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.016712083968115763743522705909,1,18
241,241_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,438.000000000000000000000000000000,4614,473,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.480480669917425218251594287722,100,18
242,242_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,158.000000000000000000000000000000,3924,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.198945181052117503472942416920,100,18
243,243_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,227.000000000000000000000000000000,3184,982,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.848453749661952527638675292110,59,18
244,244_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,247.000000000000000000000000000000,1718,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.496093875975889719853739734390,66,18
245,245_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,154.000000000000000000000000000000,4980,4991,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.039905280450509131306979782039,84,18
246,246_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,170.000000000000000000000000000000,1072,4997,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.909081605933382941309162106336,66,18
247,247_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,255.000000000000000000000000000000,1,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,1,18
248,248_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,158.000000000000000000000000000000,217,2711,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.633063244573447092555795734370,99,18
249,249_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,2621,4985,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.478575435260937487313981364423,58,18
250,250_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,166.000000000000000000000000000000,412,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.670829564866629257657848484087,100,18
251,251_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,153.000000000000000000000000000000,586,10,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.456008578042043644096281695965,47,18
252,252_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,259.000000000000000000000000000000,1363,1,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.232819848016414948643770799208,1,18
253,253_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,182.000000000000000000000000000000,1560,948,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.409707589431484886510759224620,37,18
254,254_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,175.000000000000000000000000000000,3796,3915,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.184379353751378566306229345173,49,18
255,255_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,166.000000000000000000000000000000,1748,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.424181162429504654554079934314,100,18
256,256_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,175.000000000000000000000000000000,3881,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.915933536738566700385888452729,100,18
257,257_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,202.000000000000000000000000000000,816,1597,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.068369048983478508674238582898,10,18
258,258_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,184.000000000000000000000000000000,3866,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.693079507602069933014377056679,97,18
259,259_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,249.000000000000000000000000000000,2776,781,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.157974112551707818186130793947,100,18
260,260_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,2228,4987,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.805509744899368329562605595129,97,18
261,261_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,294.000000000000000000000000000000,5000,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,64,18
262,262_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,188.000000000000000000000000000000,123,324,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.477835351943631025939396295144,45,18
263,263_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,258.000000000000000000000000000000,281,1968,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.112109880439055814571602809337,100,18
264,264_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,354.000000000000000000000000000000,1409,770,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.076863577623117490533743989545,100,18
265,265_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,243.000000000000000000000000000000,2690,696,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.830473812920998311071230091329,1,18
266,266_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,173.000000000000000000000000000000,3596,1392,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,1,18
267,267_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,218.000000000000000000000000000000,344,1977,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,100,18
268,268_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,266.000000000000000000000000000000,4907,2047,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.317924538130451017625688336921,1,18
269,269_0,FAILED,BoTorch,GenerationStep_1,,,2099,1,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.446426440987481520927815381583,100,18
270,270_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,317.000000000000000000000000000000,3094,1311,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.986511291299952741695733493543,100,18
271,271_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,155.000000000000000000000000000000,4332,2078,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.131522626636114747800121449472,93,18
272,272_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,153.000000000000000000000000000000,3275,3328,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.013592107545771531240674789842,19,18
273,273_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,341.000000000000000000000000000000,306,18,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.077847882920296623820988202169,64,18
274,274_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,4152,4996,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.143597912966797636169502538905,91,18
275,275_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,256.000000000000000000000000000000,1,1,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,1,18
276,276_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,334.000000000000000000000000000000,4833,1,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.098975589100230160211069119214,1,18
277,277_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,183.000000000000000000000000000000,3894,1953,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.094228693898011375851275772675,84,18
278,278_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,160.000000000000000000000000000000,4059,3616,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.010751941666767853705843904777,5,18
279,279_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,170.000000000000000000000000000000,107,8,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.097582564590392717462918881210,100,18
280,280_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,175.000000000000000000000000000000,4881,4938,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.126452789315836089389932794802,94,18
281,281_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,251.000000000000000000000000000000,1,1,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,100,18
282,282_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,186.000000000000000000000000000000,767,2707,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.721606438960322726927643088857,61,18
283,283_0,FAILED,BoTorch,GenerationStep_1,,,1,1,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,100,18
284,284_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,151.000000000000000000000000000000,3726,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.357373127679982138804604119287,25,18
285,285_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,234.000000000000000000000000000000,3939,2816,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.140920743116528413008126108252,100,18
286,286_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,160.000000000000000000000000000000,1,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,100,18
287,287_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,168.000000000000000000000000000000,1,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,12,18
288,288_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,186.000000000000000000000000000000,3701,2026,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.081152975153974016286895221128,13,18
289,289_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,342.000000000000000000000000000000,4935,1,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,1,18
290,290_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,178.000000000000000000000000000000,608,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.957041233438205574124424401816,100,18
291,291_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,173.000000000000000000000000000000,84,2534,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.711735780773521842235140866251,100,18
292,292_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,175.000000000000000000000000000000,3730,3248,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.054234525020434437569960550718,1,18
293,293_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,145.000000000000000000000000000000,4137,2243,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,100,18
294,294_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,199.000000000000000000000000000000,4649,2719,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.023212308502367894752493882038,1,18
295,295_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,150.000000000000000000000000000000,5000,2893,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,1,18
296,296_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,154.000000000000000000000000000000,767,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.514044649320343549625533796643,100,18
297,297_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,158.000000000000000000000000000000,1778,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.244524520373307768794646221977,100,18
298,298_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,171.000000000000000000000000000000,4603,4993,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.479237314603225827980281792406,69,18
299,299_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,175.000000000000000000000000000000,1156,2696,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.063602735232553039845804221386,68,18
300,300_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,360.000000000000000000000000000000,1299,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.442003750469169065517860417458,25,18
301,301_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,189.000000000000000000000000000000,340,7,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.326808690375793131632775612161,13,18
302,302_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,220.000000000000000000000000000000,1623,1880,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.856048081950081085622628052079,83,18
303,303_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,227.000000000000000000000000000000,1711,1960,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.331916221187973869977128060782,93,18
304,304_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,151.000000000000000000000000000000,2666,38,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.344369598155097766944976456216,12,18
305,305_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,252.000000000000000000000000000000,954,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.122903296255934324499037302303,1,18
306,306_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,155.000000000000000000000000000000,3738,1985,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.871760005406904237013065994688,76,18
307,307_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,2026,4995,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.790886319168898066500617005659,63,18
308,308_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,209.000000000000000000000000000000,1007,2020,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.340666918872791046890569077732,89,18
309,309_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,183.000000000000000000000000000000,297,4978,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.456183787010513208670658968913,78,18
310,310_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,177.000000000000000000000000000000,1971,264,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.847676462875100678040496404719,5,18
311,311_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,160.000000000000000000000000000000,4125,4996,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.160706099241871486205113228607,71,18
312,312_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,249.000000000000000000000000000000,1,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,1,18
313,313_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,216.000000000000000000000000000000,69,1942,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.542172941286717557929364375013,100,18
314,314_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,172.000000000000000000000000000000,1637,4991,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.662669513155208389498795895634,83,18
315,315_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,202.000000000000000000000000000000,2916,2374,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.335163902715107819840767433561,1,18
316,316_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,265.000000000000000000000000000000,1654,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.493225709736339446020991772457,1,18
317,317_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,165.000000000000000000000000000000,87,8,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.304731012489604713433521965271,17,18
318,318_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,4171,4997,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.544769593239822769170643823600,66,18
319,319_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,216.000000000000000000000000000000,1695,1917,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.377907514552969503451862465226,100,18
320,320_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,3106.000000000000000000000000000000,1394,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.175514444478157838336329632511,60,18
321,321_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,184.000000000000000000000000000000,3494,2622,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.461790165466877899902442550228,100,18
322,322_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,170.000000000000000000000000000000,79,3537,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.406043996445283827956274080861,100,18
323,323_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,153.000000000000000000000000000000,4132,3373,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.435132596793479931029935414699,92,18
324,324_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,214.000000000000000000000000000000,1,3533,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,100,18
325,325_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,175.000000000000000000000000000000,519,4995,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.332713237298378206041604698839,93,18
326,326_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,180.000000000000000000000000000000,863,638,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.296259178792066479868339001769,40,18
327,327_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,222.000000000000000000000000000000,626,501,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.857050145559736353817470444483,1,18
328,328_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,184.000000000000000000000000000000,3822,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,100,18
329,329_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,174.000000000000000000000000000000,3610,4990,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.763404332557443443540989846952,65,18
330,330_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,156.000000000000000000000000000000,4950,4998,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.291629054541738375050385911891,34,18
331,331_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,159.000000000000000000000000000000,3095,36,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.243926685320758979136357424977,25,18
332,332_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,167.000000000000000000000000000000,1929,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,83,18
333,333_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,961.000000000000000000000000000000,1996,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.712076573726334594027775892755,42,18
334,334_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,186.000000000000000000000000000000,2369,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.963493595632778698778508896794,78,18
335,335_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,248.000000000000000000000000000000,2570,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.449417102352789143981937058925,39,18
336,336_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,1589,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.730365350654139544595011557249,25,18
337,337_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,2229,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,100,18
338,338_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,178.000000000000000000000000000000,736,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,100,18
339,339_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,155.000000000000000000000000000000,1580,2696,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.282376084756491230187691598985,30,18
340,340_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,150.000000000000000000000000000000,4718,1511,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.279887248483093931117338115655,30,18
341,341_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,380,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,79,18
342,342_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,167.000000000000000000000000000000,3355,1896,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.246007286178869855053363835395,28,18
343,343_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,173.000000000000000000000000000000,2321,1730,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.290893493552753845676761557115,31,18
344,344_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,2250,4989,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.417645127979385244465504456457,30,18
345,345_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,162.000000000000000000000000000000,1027,1653,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.272564363275486942761460795737,28,18
346,346_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,3206,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.205446690348524835245669351025,28,18
347,347_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,178.000000000000000000000000000000,2545,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,83,18
348,348_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,776.000000000000000000000000000000,2611,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.727684756130504761806321312179,38,18
349,349_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,177.000000000000000000000000000000,28,2461,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,26,18
350,350_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,156.000000000000000000000000000000,1684,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.069943302710977414693438447557,39,18
351,351_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,177.000000000000000000000000000000,4309,4997,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.422973622075365318639939005152,91,18
352,352_0,FAILED,BoTorch,GenerationStep_1,,,124,1,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.934929830994409849509452214988,30,18
353,353_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,181.000000000000000000000000000000,4353,4427,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.083236664580750782671536569524,21,18
354,354_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,182.000000000000000000000000000000,2125,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,61,18
355,355_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,174.000000000000000000000000000000,4897,1319,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.716019019120593180893763474160,41,18
356,356_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,253.000000000000000000000000000000,880,1,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.058762623956483725495836267783,33,18
357,357_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,2064,4988,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.266051986807579965432779545154,7,18
358,358_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,165.000000000000000000000000000000,1212,3241,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.318827051749125900403925015780,34,18
359,359_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,1738,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,90,18
360,360_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,175.000000000000000000000000000000,4264,4,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.669929667903024328445837909385,4,18
361,361_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,751,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.459669003541051390815397326151,12,18
362,362_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,204.000000000000000000000000000000,4255,3,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.302338525772746169462124044003,27,18
363,363_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,174.000000000000000000000000000000,1390,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.341987543985469599761017889250,47,18
364,364_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,157.000000000000000000000000000000,91,1243,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,36,18
365,365_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,169.000000000000000000000000000000,2820,1055,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,35,18
366,366_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,153.000000000000000000000000000000,1884,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.605297260158806360585970196553,19,18
367,367_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,157.000000000000000000000000000000,3921,316,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.656690998562199146348916656279,9,18
368,368_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,170.000000000000000000000000000000,4384,688,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.668578334512732963546000064525,11,18
369,369_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,160.000000000000000000000000000000,1019,4996,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.678950633562116823149779065716,23,18
370,370_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,155.000000000000000000000000000000,1,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.887547186846744051713642420509,100,18
371,371_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,260.000000000000000000000000000000,3702,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.644525654534366720938010075770,9,18
372,372_0,FAILED,BoTorch,GenerationStep_1,,,1,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,100,18
373,373_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,186.000000000000000000000000000000,2363,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.184058542823018506107146663453,100,18
374,374_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,142.000000000000000000000000000000,929,1465,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,37,18
375,375_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,157.000000000000000000000000000000,3278,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.552070867431792677848534367513,29,18
376,376_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,2624,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.499971092237103786892760126648,82,18
377,377_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,168.000000000000000000000000000000,1709,4979,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.247132371618027896209568439190,42,18
378,378_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,168.000000000000000000000000000000,644,1200,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,30,18
379,379_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,163.000000000000000000000000000000,4338,895,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,39,18
380,380_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,270.000000000000000000000000000000,732,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.249311734207934510010318263085,8,18
381,381_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,166.000000000000000000000000000000,1477,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.249895274645276471714439026073,1,18
382,382_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,156.000000000000000000000000000000,4253,662,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.699538456673195474522231052106,10,18
383,383_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,2361,1509,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.080909685842985870629640032803,55,18
384,384_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,221.000000000000000000000000000000,2973,979,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.828244286455516509626306742575,75,18
385,385_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,172.000000000000000000000000000000,3669,1347,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.120005420946155003103505976014,35,18
386,386_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,155.000000000000000000000000000000,2349,305,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.718218799859649981165432564012,7,18
387,387_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,156.000000000000000000000000000000,3589,3371,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.912244987543807339669399425475,94,18
388,388_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,192.000000000000000000000000000000,1570,1557,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.760909663139571867951360673032,78,18
389,389_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,165.000000000000000000000000000000,2335,63,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.722759293954206460952605084458,2,18
390,390_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,156.000000000000000000000000000000,662,47,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.976185263055129648357421956462,25,18
391,391_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,514,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.458580291508977277992187282507,42,18
392,392_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,154.000000000000000000000000000000,559,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.318254722021802494147380002687,38,18
393,393_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,169.000000000000000000000000000000,324,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.594765373972881317143901469535,84,18
394,394_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,289.000000000000000000000000000000,4300,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.052344238355634328807575172959,15,18
395,395_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,625.000000000000000000000000000000,1110,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,35,18
396,396_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,249.000000000000000000000000000000,1452,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,88,18
397,397_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,159.000000000000000000000000000000,46,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.580021620917104074877101993479,16,18
398,398_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,148.000000000000000000000000000000,1817,1572,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.020130093486339394048822981631,56,18
399,399_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,181.000000000000000000000000000000,4555,1206,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.871262617184335508291326277686,56,18
400,400_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,159.000000000000000000000000000000,3471,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.643356938141023682042884956900,11,18
401,401_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,159.000000000000000000000000000000,39,1978,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,36,18
402,402_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,157.000000000000000000000000000000,4869,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.578229796250764094267537984706,22,18
403,403_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,258,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.641448530580428055714037327562,83,18
404,404_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,216.000000000000000000000000000000,4894,1199,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,1,18
405,405_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,179.000000000000000000000000000000,2696,3014,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.622092728258392457796333019360,4,18
406,406_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,173.000000000000000000000000000000,134,1556,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,70,18
407,407_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,248.000000000000000000000000000000,4883,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,86,18
408,408_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,157.000000000000000000000000000000,1406,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.031159887486647951798657274480,75,18
409,409_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,159.000000000000000000000000000000,4974,2317,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.793863740060205769211165716115,57,18
410,410_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,163.000000000000000000000000000000,1532,1564,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,13,18
411,411_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,149.000000000000000000000000000000,606,1544,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,45,18
412,412_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,171.000000000000000000000000000000,4703,1502,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.509628022197689545258469934197,30,18
413,413_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,156.000000000000000000000000000000,2892,1490,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,62,18
414,414_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,150.000000000000000000000000000000,4225,1510,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,49,18
415,415_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,4486,3194,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.600914160764143945314685879566,5,18
416,416_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,169.000000000000000000000000000000,4954,1865,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.748480690556642680455468052969,53,18
417,417_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,154.000000000000000000000000000000,3390,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.594662708815007379214989668981,5,18
418,418_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,268.000000000000000000000000000000,1762,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,26,18
419,419_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,184.000000000000000000000000000000,4821,2200,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.817351810227535779951324457215,63,18
420,420_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,171.000000000000000000000000000000,3826,3196,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.759904862645299217227545796050,30,18
421,421_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,168.000000000000000000000000000000,2529,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.335636762472702798731916118413,51,18
422,422_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,183.000000000000000000000000000000,1339,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.063588971104119831889711633721,83,18
423,423_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,178.000000000000000000000000000000,4274,3225,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.817469143932815556219395602966,20,18
424,424_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,180.000000000000000000000000000000,1238,3183,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.533169733646343901867226122704,35,18
425,425_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,171.000000000000000000000000000000,4925,1152,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.580150838732663820529467102460,47,18
426,426_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,180.000000000000000000000000000000,4983,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.693878631221701347442376572872,95,18
427,427_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,166.000000000000000000000000000000,3611,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.523609276749398389050327295990,38,18
428,428_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,169.000000000000000000000000000000,4736,3213,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.553205869151155216201232178719,41,18
429,429_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,252.000000000000000000000000000000,4559,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.561654704943130300698328483122,50,18
430,430_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,157.000000000000000000000000000000,4267,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.379152413182160141769116989963,44,18
431,431_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,167.000000000000000000000000000000,785,5000,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.634569301942294994312021572114,44,18
432,432_0,FAILED,BoTorch,GenerationStep_1,,,4566,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,95,18
433,433_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,167.000000000000000000000000000000,2075,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.308287074595370469420885228828,23,18
434,434_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,206.000000000000000000000000000000,5000,3228,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,98,18
435,435_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,183.000000000000000000000000000000,5000,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.471874307087767841384362554891,100,18
436,436_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,270.000000000000000000000000000000,5000,1977,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,92,18
437,437_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,169.000000000000000000000000000000,3733,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.764849018797922775014797025506,87,18
438,438_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,256.000000000000000000000000000000,3399,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,68,18
439,439_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,168.000000000000000000000000000000,2482,3309,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.681383307997232900987683024141,15,18
440,440_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,157.000000000000000000000000000000,4181,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.703293760313980720333404406119,32,18
441,441_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,260.000000000000000000000000000000,2428,1,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,1,18
442,442_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,638,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.049174368323468914399043683261,58,18
443,443_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,2927,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.485439098480861419560028480191,15,18
444,444_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,159.000000000000000000000000000000,3460,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.382536405184915218047336793461,81,18
445,445_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,1386,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.847770047713114816012591745675,74,18
446,446_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,158.000000000000000000000000000000,1486,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.334011335982646262454665020414,12,18
447,447_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,174.000000000000000000000000000000,4585,3154,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.496765339891618751266832987312,47,18
448,448_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,3893,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.263399993189391723458214755738,17,18
449,449_0,FAILED,BoTorch,GenerationStep_1,,,1473,1,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,24,18
450,450_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,248.000000000000000000000000000000,1222,1687,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.687267467617988625505631716806,93,18
451,451_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,231.000000000000000000000000000000,3317,3833,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.103299027387052780313503319576,49,18
452,452_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,206.000000000000000000000000000000,4843,84,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.310903581099584713776096123183,74,18
453,453_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,237.000000000000000000000000000000,1888,2933,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.894875029992312165383339106484,18,18
454,454_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,344.000000000000000000000000000000,1751,817,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.594679551897570490837097167969,62,18
455,455_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,231.000000000000000000000000000000,4042,3666,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.197836134903132926377011813202,5,18
456,456_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,233.000000000000000000000000000000,2828,2419,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.403491469865664864613563622697,79,18
457,457_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,206.000000000000000000000000000000,147,4565,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.800337918758392330431661321199,37,18
458,458_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,240.000000000000000000000000000000,383,473,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.972235790509730524178166888305,52,18
459,459_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,227.000000000000000000000000000000,2594,2702,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.324914494300261147419206508857,8,18
460,460_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,277.000000000000000000000000000000,4276,1449,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.025694986309856178430255368994,83,18
461,461_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,220.000000000000000000000000000000,1518,4225,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.673012774737551833226234521135,27,18
462,462_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,325.000000000000000000000000000000,2280,2025,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.817272898025810712319128015224,95,18
463,463_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,162.000000000000000000000000000000,4452,4801,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.481826601816341293371692700021,40,18
464,464_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,323.000000000000000000000000000000,3707,1050,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.180657875075936308340018854324,65,18
465,465_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,254.000000000000000000000000000000,831,3279,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.516100663503631928996639999241,20,18
466,466_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,216.000000000000000000000000000000,700,1235,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.465390311261638989215327910642,45,18
467,467_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,291.000000000000000000000000000000,3528,3385,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.863219548776745781992758566048,90,18
468,468_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,242.000000000000000000000000000000,4585,2134,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.532780190004035869577592166024,15,18
469,469_0,COMPLETED,Sobol,SOBOL,0.110000000000000000555111512313,235.000000000000000000000000000000,2460,4988,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.134955055125057704623614540651,70,18
470,470_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,283.000000000000000000000000000000,4547,185,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,56,18
471,471_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2469,1,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,66,18
472,472_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,165.000000000000000000000000000000,1966,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,6,18
473,473_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,280.000000000000000000000000000000,1520,1193,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,65,18
474,474_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,289.000000000000000000000000000000,175,1193,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,65,18
475,475_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,243.000000000000000000000000000000,3160,1156,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.400634249445028789438083549612,37,18
476,476_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,178.000000000000000000000000000000,3074,445,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.346880819084105285199370882765,24,18
477,477_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,263.000000000000000000000000000000,758,1174,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.375650976004533565522081062227,41,18
478,478_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,247.000000000000000000000000000000,1433,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.043768027874757642337044671876,44,18
479,479_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,227.000000000000000000000000000000,4237,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.389620432740783617653335113573,16,18
480,480_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,242.000000000000000000000000000000,1153,1194,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,40,18
481,481_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,786.000000000000000000000000000000,2654,1,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,32,18
482,482_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,235.000000000000000000000000000000,659,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.787467901606951126147748709627,1,18
483,483_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,244.000000000000000000000000000000,3731,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.103542114743755880867936980394,51,18
484,484_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,207.000000000000000000000000000000,2408,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.367861483072062567245552600070,36,18
485,485_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,421.000000000000000000000000000000,1,1196,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,100,18
486,486_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,279.000000000000000000000000000000,1549,1190,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.957290358836716870349903274473,48,18
487,487_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,270.000000000000000000000000000000,607,1195,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,24,18
488,488_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,475.000000000000000000000000000000,3642,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,22,18
489,489_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,264.000000000000000000000000000000,4612,3143,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.560588706003079750850304208143,16,18
490,490_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,554.000000000000000000000000000000,3868,4,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,56,18
491,491_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4936,1,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,66,18
492,492_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,397.000000000000000000000000000000,3770,931,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,100,18
493,493_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,418.000000000000000000000000000000,3040,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,94,18
494,494_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,200.000000000000000000000000000000,3087,165,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,26,18
495,495_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,317.000000000000000000000000000000,1,401,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,62,18
496,496_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,230.000000000000000000000000000000,1167,136,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,1,18
497,497_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,218.000000000000000000000000000000,2172,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.257872172572936153578382345586,35,18
498,498_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,228.000000000000000000000000000000,2837,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.707627000812216411240740399080,15,18
499,499_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,228.000000000000000000000000000000,538,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.385273710933928092980949031698,16,18
500,500_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,1793.000000000000000000000000000000,3806,2,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,67,18
501,501_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,1083.000000000000000000000000000000,324,7,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.549205736883173534224056311359,67,18
502,502_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,381.000000000000000000000000000000,847,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,60,18
503,503_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4302,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,99,18
504,504_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,220.000000000000000000000000000000,4765,173,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,38,18
505,505_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,289.000000000000000000000000000000,4696,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,80,18
506,506_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,232.000000000000000000000000000000,2962,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.121612544672135600154483370261,58,18
507,507_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3782,1,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,66,18
508,508_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3344,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,86,18
509,509_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,237.000000000000000000000000000000,4354,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.751006317623352348533671829500,73,18
510,510_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,372.000000000000000000000000000000,4659,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,15,18
511,511_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,217.000000000000000000000000000000,2869,127,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,25,18
512,512_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,398.000000000000000000000000000000,681,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,8,18
513,513_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,224.000000000000000000000000000000,1747,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.380264456975674625116567995065,4,18
514,514_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,238.000000000000000000000000000000,579,74,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,33,18
515,515_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,393.000000000000000000000000000000,4142,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,64,18
516,516_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,192.000000000000000000000000000000,1908,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.389904134471331598188470479727,23,18
517,517_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,231.000000000000000000000000000000,4145,125,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,62,18
518,518_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,478.000000000000000000000000000000,3183,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,18,18
519,519_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,410.000000000000000000000000000000,85,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,57,18
520,520_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,459.000000000000000000000000000000,805,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,100,18
521,521_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,235.000000000000000000000000000000,3347,194,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,33,18
522,522_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,241.000000000000000000000000000000,1,570,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,48,18
523,523_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,300.000000000000000000000000000000,3020,5000,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.411522114759718449228387271432,100,18
524,524_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,270.000000000000000000000000000000,4318,1186,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,79,18
525,525_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,235.000000000000000000000000000000,3453,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.385530111436187261642061230305,40,18
526,526_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,294.000000000000000000000000000000,4524,1181,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,15,18
527,527_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,269.000000000000000000000000000000,3974,601,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,65,18
528,528_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,544.000000000000000000000000000000,925,1,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,25,18
529,529_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,285.000000000000000000000000000000,2900,3146,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.380536043260624012862791687439,67,18
530,530_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,245.000000000000000000000000000000,4533,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.401631533921990857560757604006,7,18
531,531_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,221.000000000000000000000000000000,503,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.394269084724394813790127045650,22,18
532,532_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,220.000000000000000000000000000000,2471,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.636331762807469725196085619245,12,18
533,533_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,354.000000000000000000000000000000,4585,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,11,18
534,534_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,709.000000000000000000000000000000,1605,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,31,18
535,535_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,247.000000000000000000000000000000,3644,200,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,53,18
536,536_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,289.000000000000000000000000000000,1872,4126,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,60,18
537,537_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,237.000000000000000000000000000000,857,199,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,20,18
538,538_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,222.000000000000000000000000000000,4529,186,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,1,18
539,539_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1224,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,77,18
540,540_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,224.000000000000000000000000000000,693,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.750415002641694361962265702459,87,18
541,541_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,389.000000000000000000000000000000,3170,1,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,20,18
542,542_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,275.000000000000000000000000000000,1737,4129,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,71,18
543,543_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,223.000000000000000000000000000000,3364,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.865799775462901100020474132180,44,18
544,544_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,227.000000000000000000000000000000,488,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.262060369431374173831983398486,38,18
545,545_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,270.000000000000000000000000000000,3662,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,93,18
546,546_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,399.000000000000000000000000000000,605,1,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,17,18
547,547_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,264.000000000000000000000000000000,2852,1,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,40,18
548,548_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,193.000000000000000000000000000000,1491,4155,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,7,18
549,549_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,268.000000000000000000000000000000,3526,4123,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,19,18
550,550_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,200.000000000000000000000000000000,382,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.604209496798831957597997188714,41,18
551,551_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,278.000000000000000000000000000000,4456,4098,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,50,18
552,552_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,408.000000000000000000000000000000,1074,1,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,44,18
553,553_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,246.000000000000000000000000000000,2218,4092,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,11,18
554,554_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,223.000000000000000000000000000000,1963,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.284228709044490268631477647432,80,18
555,555_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2009,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,95,18
556,556_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,225.000000000000000000000000000000,1662,4995,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.203350442053126329033929664547,41,18
557,557_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,268.000000000000000000000000000000,1971,1199,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,7,18
558,558_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,325.000000000000000000000000000000,21,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,75,18
559,559_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,250.000000000000000000000000000000,915,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.340762592922592089106359480866,87,18
560,560_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,239.000000000000000000000000000000,3255,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.342660423821788462372950334611,20,18
561,561_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,170.000000000000000000000000000000,1282,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.029658741822726323450476471066,95,18
562,562_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,468.000000000000000000000000000000,906,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,14,18
563,563_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,226.000000000000000000000000000000,4261,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.367669310936033943981726679340,78,18
564,564_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,222.000000000000000000000000000000,4899,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.803679035002526798159294685320,8,18
565,565_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,225.000000000000000000000000000000,2565,119,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,44,18
566,566_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,254.000000000000000000000000000000,4250,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.394849518405396449072242148759,62,18
567,567_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,242.000000000000000000000000000000,973,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.977660211633602838077194974176,66,18
568,568_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,218.000000000000000000000000000000,3780,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.279815832084910731669680217237,46,18
569,569_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,251.000000000000000000000000000000,1871,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.550776368590207510145262403967,71,18
570,570_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,243.000000000000000000000000000000,1061,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,77,18
571,571_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,235.000000000000000000000000000000,1808,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.667057939179555692277290290804,51,18
572,572_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,243.000000000000000000000000000000,1241,4971,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.045778809609228095223176779882,32,18
573,573_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,256.000000000000000000000000000000,1920,1219,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,6,18
574,574_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,241.000000000000000000000000000000,1675,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.466533439866213761604285537032,32,18
575,575_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,297.000000000000000000000000000000,261,4999,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.165533499491835961459074155755,3,18
576,576_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,242.000000000000000000000000000000,1077,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.219202541123252325760972780699,10,18
577,577_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,214.000000000000000000000000000000,1077,4979,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.194818765619795331023311746321,27,18
578,578_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2578,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,92,18
579,579_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,230.000000000000000000000000000000,174,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.878134542350330837834349040349,34,18
580,580_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,206,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,88,18
581,581_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,448.000000000000000000000000000000,3754,1186,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,100,18
582,582_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,251.000000000000000000000000000000,3547,4165,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,83,18
583,583_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,241.000000000000000000000000000000,760,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.619174658171962710717650679726,27,18
584,584_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,245.000000000000000000000000000000,212,1206,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,32,18
585,585_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,235.000000000000000000000000000000,2577,4450,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,33,18
586,586_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,285.000000000000000000000000000000,4972,4147,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,100,18
587,587_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,267.000000000000000000000000000000,3106,3617,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,62,18
588,588_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,285.000000000000000000000000000000,2457,4168,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,92,18
589,589_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,282.000000000000000000000000000000,2962,4179,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,84,18
590,590_0,FAILED,BoTorch,BOTORCH_MODULAR,,,795,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,77,18
591,591_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,256.000000000000000000000000000000,3156,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.428042674290308067241994649521,22,18
592,592_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,233.000000000000000000000000000000,3450,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.070431669239394756321104296148,10,18
593,593_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,218.000000000000000000000000000000,431,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.993210956990805748922923612554,11,18
594,594_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,229.000000000000000000000000000000,2858,1493,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,34,18
595,595_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,224.000000000000000000000000000000,2444,108,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,77,18
596,596_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,314.000000000000000000000000000000,4020,1,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,57,18
597,597_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,238.000000000000000000000000000000,4423,1259,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,29,18
598,598_0,FAILED,BoTorch,BOTORCH_MODULAR,,,173,1,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,21,18
599,599_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,217.000000000000000000000000000000,41,174,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,94,18
600,600_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,212.000000000000000000000000000000,4582,1610,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,100,18
601,601_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,242.000000000000000000000000000000,3346,1617,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,32,18
602,602_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,222.000000000000000000000000000000,3394,1556,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,20,18
603,603_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,303.000000000000000000000000000000,1261,1186,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,43,18
604,604_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,187.000000000000000000000000000000,1,769,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,10,18
605,605_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,342.000000000000000000000000000000,1,4092,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,3,18
606,606_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1,1,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,22,18
607,607_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,276.000000000000000000000000000000,802,4128,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,59,18
608,608_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,231.000000000000000000000000000000,2816,4124,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,35,18
609,609_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,224.000000000000000000000000000000,4601,3151,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.558128050208732462422744902142,100,18
610,610_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,209.000000000000000000000000000000,3850,3096,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,38,18
611,611_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,242.000000000000000000000000000000,5000,2377,str\(\'H\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,61,18
612,612_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,253.000000000000000000000000000000,1,4170,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,55,18
613,613_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,244.000000000000000000000000000000,4647,3220,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.984945034131976648872353052866,52,18
614,614_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,239.000000000000000000000000000000,5000,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.603543687443552356874931774655,28,18
615,615_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,100,18
616,616_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,216.000000000000000000000000000000,1,1177,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,88,18
617,617_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,256.000000000000000000000000000000,2639,3137,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.851401632317881573719375865039,26,18
618,618_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,293.000000000000000000000000000000,2667,2500,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,55,18
619,619_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,254.000000000000000000000000000000,890,2333,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,51,18
620,372_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,100,18
621,621_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,271.000000000000000000000000000000,3,4366,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.378568094384947650965500542952,24,18
622,622_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,218.000000000000000000000000000000,1360,3437,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,100,18
623,623_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,550.000000000000000000000000000000,828,248,str\(\'H\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,100,18
624,624_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,203.000000000000000000000000000000,5000,4414,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,19,18
625,625_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,225.000000000000000000000000000000,1,258,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,31,18
626,626_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,204.000000000000000000000000000000,5000,4422,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,63,18
627,627_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,219.000000000000000000000000000000,472,5000,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.378895524091516999742879079349,73,18
628,628_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,252.000000000000000000000000000000,4177,4409,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,100,18
629,629_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,222.000000000000000000000000000000,374,5000,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'stdev\'\),0.812203548429660915353167638386,70,18
630,630_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4591,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,77,18
631,631_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,223.000000000000000000000000000000,1451,5000,str\(\'H\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.713236795675060930577160434041,82,18
632,632_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,175.000000000000000000000000000000,1856,4439,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,51,18
633,633_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,220.000000000000000000000000000000,2417,1499,str\(\'H\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,86,18
634,634_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,189.000000000000000000000000000000,5000,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.391831418180480506308072108368,57,18
635,635_0,FAILED,BoTorch,BOTORCH_MODULAR,,,986,1,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,22,18
636,636_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,277.000000000000000000000000000000,3334,4396,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,68,18
637,637_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,243.000000000000000000000000000000,1,3035,str\(\'KL\'\),1.000000000000000000000000000000,str\(\'stdev\'\),0.001000000000000000020816681712,46,18
638,638_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,237.000000000000000000000000000000,3601,4981,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.787696829030552492234562578233,29,18
639,639_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,210.000000000000000000000000000000,4469,5000,str\(\'KL\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,93,18
640,640_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,201.000000000000000000000000000000,4223,5000,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.186758697613400692638663258549,85,18
641,641_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,1160,1,str\(\'H\'\),2.000000000000000000000000000000,str\(\'stdev\'\),0.998999999999999999111821580300,77,18
642,642_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,263.000000000000000000000000000000,4330,4686,str\(\'KL\'\),3.000000000000000000000000000000,str\(\'tstat\'\),0.001000000000000000020816681712,43,18
643,643_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,1055,1188,str\(\'H\'\),2.000000000000000000000000000000,str\(\'tstat\'\),0.998999999999999999111821580300,60,18

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start_time,end_time,run_time,program_string,recent_samples_size,batch_size,divergence,detect_batch,statistic,significance,subsets,feature_id,ACCURACY,RUNTIME,exit_code,signal,hostname,OO_Info_runtime,OO_Info_peak_memory,OO_Info_mean_memory,OO_Info_lpd,OO_Info_portion_req_label,OO_Info_SLURM_JOB_ID
1744999019,1744999261,242,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3317 feature_id 18 batch_size 3833 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.10329902738705278 subsets 49,3317,3833,str\(\'H\'\),1,str\(\'tstat\'\),0.10329902738705278,49,18,0.1,231,0,None,i7175,231,688.12109375,688.0919471153846,-1,0.1432897196261682,4548076
1744999000,1744999262,262,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1222 feature_id 18 batch_size 1687 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.6872674676179886 subsets 93,1222,1687,str\(\'KL\'\),3,str\(\'stdev\'\),0.6872674676179886,93,18,0.1,248,0,None,i7175,248,687.8671875,687.8382523148148,-1,0.16817445482866045,4548054
1744999059,1744999280,221,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4843 feature_id 18 batch_size 84 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.3109035810995847 subsets 74,4843,84,str\(\'KL\'\),3,str\(\'tstat\'\),0.3109035810995847,74,18,0.1,206,0,None,i7171,206,689.45703125,689.4242527173913,-1,0.0753644859813084,4548109
1744999100,1744999351,251,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1888 feature_id 18 batch_size 2933 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.8948750299923122 subsets 18,1888,2933,str\(\'H\'\),1,str\(\'stdev\'\),0.8948750299923122,18,18,0.1,237,0,None,i7170,237,688.18359375,687.8514122596154,-1,0.1461931464174455,4548148
1744999181,1744999428,247,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4042 feature_id 18 batch_size 3666 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.19783613490313293 subsets 5,4042,3666,str\(\'KL\'\),3,str\(\'tstat\'\),0.19783613490313293,5,18,0.1,231,0,None,i7169,231,688.2265625,688.1891526442307,-1,0.1827289719626168,4548224
1744999239,1744999484,245,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2828 feature_id 18 batch_size 2419 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.40349146986566486 subsets 79,2828,2419,str\(\'H\'\),2,str\(\'tstat\'\),0.40349146986566486,79,18,0.1,233,0,None,i7171,233,689.33203125,689.2823016826923,-1,0.2110031152647975,4548268
1744999300,1744999509,209,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4452 feature_id 18 batch_size 4801 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.4818266018163413 subsets 40,4452,4801,str\(\'H\'\),3,str\(\'stdev\'\),0.4818266018163413,40,18,0.1,162,0,None,i7165,162,687.9921875,684.2442434210526,-1,0.17947663551401868,4548645
1744999152,1744999510,358,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1751 feature_id 18 batch_size 817 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.5946795518975705 subsets 62,1751,817,str\(\'H\'\),1,str\(\'stdev\'\),0.5946795518975705,62,18,0.1,344,0,None,i7173,344,687.9921875,687.9652660472973,-1,0.28505919003115265,4548190
1744999283,1744999511,228,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 147 feature_id 18 batch_size 4565 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.8003379187583923 subsets 37,147,4565,str\(\'KL\'\),2,str\(\'stdev\'\),0.8003379187583923,37,18,0.1,206,0,None,i7168,206,687.26953125,686.9983016304348,-1,0.17065420560747663,4548325
1744999289,1744999543,254,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2594 feature_id 18 batch_size 2702 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.32491449430026115 subsets 8,2594,2702,str\(\'KL\'\),1,str\(\'stdev\'\),0.32491449430026115,8,18,0.1,227,0,None,i7167,227,687.3984375,687.3778125,-1,0.2693582554517134,4548415
1744999285,1744999554,269,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 383 feature_id 18 batch_size 473 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.9722357905097305 subsets 52,383,473,str\(\'H\'\),3,str\(\'tstat\'\),0.9722357905097305,52,18,0.1,240,0,None,i7167,240,687.1953125,687.1623263888889,-1,0.21674766355140188,4548370
1744999304,1744999586,282,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1518 feature_id 18 batch_size 4225 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.6730127747375518 subsets 27,1518,4225,str\(\'KL\'\),2,str\(\'tstat\'\),0.6730127747375518,27,18,0.1,220,0,None,i7166,220,688.16015625,687.1111653645834,-1,0.21059190031152647,4548532
1744999304,1744999637,333,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4276 feature_id 18 batch_size 1449 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.02569498630985618 subsets 83,4276,1449,str\(\'H\'\),2,str\(\'stdev\'\),0.02569498630985618,83,18,0.1,277,0,None,i7166,277,688.11328125,686.724609375,-1,0.25278504672897195,4548475
1744999400,1744999670,270,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 831 feature_id 18 batch_size 3279 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.5161006635036319 subsets 20,831,3279,str\(\'H\'\),3,str\(\'tstat\'\),0.5161006635036319,20,18,0.1,254,0,None,i7170,254,688.046875,687.6929408482143,-1,0.24515887850467288,4548754
1744999450,1744999677,227,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 700 feature_id 18 batch_size 1235 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.465390311261639 subsets 45,700,1235,str\(\'KL\'\),2,str\(\'stdev\'\),0.465390311261639,45,18,0.1,216,0,None,i7171,216,688.953125,688.9117838541666,-1,0.17515264797507787,4548797
1744999341,1744999677,336,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3707 feature_id 18 batch_size 1050 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.1806578750759363 subsets 65,3707,1050,str\(\'KL\'\),1,str\(\'stdev\'\),0.1806578750759363,65,18,0.1,323,0,None,i7172,323,688.07421875,688.0583705357143,-1,0.35452959501557635,4548700
1744999300,1744999695,395,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2280 feature_id 18 batch_size 2025 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.8172728980258107 subsets 95,2280,2025,str\(\'KL\'\),1,str\(\'tstat\'\),0.8172728980258107,95,18,0.1,325,0,None,i7165,325,688.26171875,682.8911830357143,-1,0.4542056074766355,4548586
1744999571,1744999827,256,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4585 feature_id 18 batch_size 2134 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.5327801900040359 subsets 15,4585,2134,str\(\'KL\'\),1,str\(\'tstat\'\),0.5327801900040359,15,18,0.1,242,0,None,i7170,242,688.5,688.4675925925926,-1,0.2393271028037383,4548902
1744999520,1744999829,309,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3528 feature_id 18 batch_size 3385 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.8632195487767458 subsets 90,3528,3385,str\(\'H\'\),2,str\(\'tstat\'\),0.8632195487767458,90,18,0.1,291,0,None,i7172,291,687.75390625,687.7117919921875,-1,0.2530841121495327,4548861
1744999586,1744999842,256,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2460 feature_id 18 batch_size 4988 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.1349550551250577 subsets 70,2460,4988,str\(\'H\'\),3,str\(\'stdev\'\),0.1349550551250577,70,18,0.11,235,0,None,i7168,235,688.3125,688.041015625,-1,0.18646728971962617,4548949
1745000173,1745000471,298,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4547 feature_id 18 batch_size 185 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 56,4547,185,str\(\'H\'\),2,str\(\'stdev\'\),0.999,56,18,0.1,283,0,None,i7180,283,687.5,687.46875,-1,0.12679127725856698,4549586
1745000481,1745000660,179,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1966 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 6,1966,5000,str\(\'H\'\),3,str\(\'tstat\'\),0.001,6,18,0.11,165,0,None,i7168,165,689.04296875,688.7826891447369,-1,0.18691588785046728,4549913
1745000800,1745001095,295,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1520 feature_id 18 batch_size 1193 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 65,1520,1193,str\(\'KL\'\),2,str\(\'tstat\'\),0.999,65,18,0.11,280,0,None,i7172,280,687.5703125,687.5309895833333,-1,0.2426791277258567,4550286
1745001183,1745001486,303,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 175 feature_id 18 batch_size 1193 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 65,175,1193,str\(\'H\'\),2,str\(\'tstat\'\),0.999,65,18,0.11,289,0,None,i7170,289,687.93359375,687.8951612903226,-1,0.2426791277258567,4550798
1745001421,1745001675,254,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3160 feature_id 18 batch_size 1156 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.4006342494450288 subsets 37,3160,1156,str\(\'H\'\),2,str\(\'tstat\'\),0.4006342494450288,37,18,0.1,243,0,None,i7168,243,688.23046875,688.1880787037037,-1,0.24878504672897198,4551205
1745001687,1745001904,217,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3074 feature_id 18 batch_size 445 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.3468808190841053 subsets 24,3074,445,str\(\'H\'\),2,str\(\'stdev\'\),0.3468808190841053,24,18,0.1,178,0,None,i7165,178,688.81640625,688.1599609375,-1,0.17190031152647975,4551540
1745001972,1745002249,277,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 758 feature_id 18 batch_size 1174 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.37565097600453357 subsets 41,758,1174,str\(\'KL\'\),2,str\(\'tstat\'\),0.37565097600453357,41,18,0.1,263,0,None,i7172,263,688.734375,688.6546336206897,-1,0.17990031152647976,4551825
1745002183,1745002452,269,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1433 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.04376802787475764 subsets 44,1433,5000,str\(\'KL\'\),2,str\(\'tstat\'\),0.04376802787475764,44,18,0.11,247,0,None,i7172,247,689.24609375,688.992042824074,-1,0.18691588785046728,4552067
1745002502,1745002743,241,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4237 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.3896204327407836 subsets 16,4237,5000,str\(\'H\'\),2,str\(\'tstat\'\),0.3896204327407836,16,18,0.11,227,0,None,i7173,227,688.24609375,687.9528125,-1,0.18691588785046728,4552277
1745002752,1745003012,260,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1153 feature_id 18 batch_size 1194 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 40,1153,1194,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,40,18,0.1,242,0,None,i7173,242,687.73828125,687.703125,-1,0.24290342679127727,4552445
1745003234,1745003484,250,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 659 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.7874679016069511 subsets 1,659,5000,str\(\'H\'\),3,str\(\'stdev\'\),0.7874679016069511,1,18,0.11,235,0,None,i7172,235,688.3203125,687.9914362980769,-1,0.18691588785046728,4552859
1745003502,1745003759,257,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3731 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.10354211474375588 subsets 51,3731,5000,str\(\'KL\'\),3,str\(\'tstat\'\),0.10354211474375588,51,18,0.11,244,0,None,i7172,244,688.1875,687.9419849537037,-1,0.18691588785046728,4553085
1745002993,1745003792,799,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2654 feature_id 18 batch_size 1 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 32,2654,1,str\(\'KL\'\),2,str\(\'stdev\'\),0.999,32,18,0.11,786,0,None,i7173,786,693.91796875,690.5719039351852,-1,0.04737694704049844,4552601
1745003682,1745003902,220,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2408 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.36786148307206257 subsets 36,2408,5000,str\(\'H\'\),2,str\(\'tstat\'\),0.36786148307206257,36,18,0.11,207,0,None,i7170,207,689.41015625,689.1083559782609,-1,0.18691588785046728,4553227
1745004042,1745004475,433,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 1196 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 100,1,1196,str\(\'H\'\),2,str\(\'tstat\'\),0.999,100,18,0.1,421,0,None,i7180,421,687.46484375,687.4309027777778,-1,0.24335202492211838,4553524
1745004343,1745004637,294,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1549 feature_id 18 batch_size 1190 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.9572903588367169 subsets 48,1549,1190,str\(\'H\'\),2,str\(\'tstat\'\),0.9572903588367169,48,18,0.1,279,0,None,i7169,279,688.16015625,688.1283854166667,-1,0.33097819314641747,4553821
1745004602,1745004886,284,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 607 feature_id 18 batch_size 1195 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 24,607,1195,str\(\'KL\'\),2,str\(\'tstat\'\),0.999,24,18,0.11,270,0,None,i7180,270,688.71875,688.6788793103449,-1,0.2431277258566978,4554103
1745004862,1745005353,491,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3642 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 22,3642,1,str\(\'H\'\),2,str\(\'tstat\'\),0.001,22,18,0.11,475,0,None,i7167,475,693.296875,690.094765625,-1,0.04737694704049844,4554441
1745005244,1745005525,281,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4612 feature_id 18 batch_size 3143 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.5605887060030798 subsets 16,4612,3143,str\(\'H\'\),3,str\(\'stdev\'\),0.5605887060030798,16,18,0.1,264,0,None,i7169,264,688.125,688.0988685344828,-1,0.23499065420560747,4554779
1745005847,1745006416,569,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3868 feature_id 18 batch_size 4 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 56,3868,4,str\(\'H\'\),2,str\(\'tstat\'\),0.001,56,18,0.1,554,0,None,i7170,554,688.4765625,688.4594558189655,-1,0.017345794392523366,4555307
1745006233,1745006646,413,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3770 feature_id 18 batch_size 931 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 100,3770,931,str\(\'H\'\),1,str\(\'stdev\'\),0.999,100,18,0.1,397,0,None,i7173,397,687.73828125,687.7191220238095,-1,0.16241744548286605,4555640
1745006654,1745006868,214,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3087 feature_id 18 batch_size 165 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 26,3087,165,str\(\'H\'\),2,str\(\'tstat\'\),0.001,26,18,0.1,200,0,None,i7169,200,688.79296875,688.7510653409091,-1,0.08406230529595016,4555987
1745006445,1745006876,431,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3040 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 94,3040,1,str\(\'H\'\),3,str\(\'stdev\'\),0.999,94,18,0.11,418,0,None,i7180,418,695,691.4446910511364,-1,0.04737694704049844,4555808
1745007044,1745007378,334,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 401 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.001 subsets 62,1,401,str\(\'H\'\),2,str\(\'stdev\'\),0.001,62,18,0.1,317,0,None,i7170,317,687.4296875,687.3969439338235,-1,0.21986292834890966,4556340
1745007265,1745007515,250,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1167 feature_id 18 batch_size 136 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 1,1167,136,str\(\'H\'\),3,str\(\'stdev\'\),0.999,1,18,0.1,230,0,None,i7173,230,688.4296875,688.408203125,-1,0.10337694704049845,4556542
1745007444,1745007679,235,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2172 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.25787217257293615 subsets 35,2172,5000,str\(\'H\'\),3,str\(\'tstat\'\),0.25787217257293615,35,18,0.11,218,0,None,i7168,218,689.7265625,689.4036458333334,-1,0.18691588785046728,4556719
1745007707,1745007950,243,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2837 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.7076270008122164 subsets 15,2837,5000,str\(\'H\'\),3,str\(\'stdev\'\),0.7076270008122164,15,18,0.11,228,0,None,i7170,228,688.12109375,687.79578125,-1,0.18691588785046728,4556968
1745008047,1745008288,241,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 538 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.3852737109339281 subsets 16,538,5000,str\(\'H\'\),3,str\(\'tstat\'\),0.3852737109339281,16,18,0.11,228,0,None,i7168,228,688.3671875,688.06078125,-1,0.18691588785046728,4557313
1745008724,1745009125,401,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 847 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 60,847,1,str\(\'H\'\),3,str\(\'stdev\'\),0.999,60,18,0.11,381,0,None,i7185,381,597.49609375,576.1899771341464,-1,0.04737694704049844,4557948
1745009104,1745009340,236,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4765 feature_id 18 batch_size 173 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 38,4765,173,str\(\'H\'\),3,str\(\'stdev\'\),0.999,38,18,0.1,220,0,None,i7173,220,688.40234375,688.37646484375,-1,0.09054205607476636,4558274
1745009263,1745009567,304,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4696 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 80,4696,1,str\(\'H\'\),3,str\(\'stdev\'\),0.999,80,18,0.11,289,0,None,i7168,289,695.265625,692.1188256048387,-1,0.04737694704049844,4558405
1745008464,1745009570,1106,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 324 feature_id 18 batch_size 7 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.5492057368831735 subsets 67,324,7,str\(\'KL\'\),2,str\(\'stdev\'\),0.5492057368831735,67,18,0.1,1083,0,None,i7185,1083,615.86328125,570.0670396959459,-1,0.039426791277258566,4557722
1745009525,1745009774,249,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2962 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.1216125446721356 subsets 58,2962,5000,str\(\'H\'\),2,str\(\'tstat\'\),0.1216125446721356,58,18,0.11,232,0,None,i7173,232,688,687.7076322115385,-1,0.18691588785046728,4558628
1745008304,1745010109,1805,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3806 feature_id 18 batch_size 2 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 67,3806,2,str\(\'KL\'\),2,str\(\'stdev\'\),0.999,67,18,0.1,1793,0,None,i7173,1793,689.109375,688.5421960851648,-1,0.020311526479750777,4557574
1745010285,1745010541,256,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4354 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.7510063176233523 subsets 73,4354,5000,str\(\'KL\'\),3,str\(\'stdev\'\),0.7510063176233523,73,18,0.11,237,0,None,i7185,237,618.08984375,573.8538161057693,-1,0.18691588785046728,4559241
1745012725,1745012953,228,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2869 feature_id 18 batch_size 127 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 25,2869,127,str\(\'H\'\),2,str\(\'stdev\'\),0.999,25,18,0.11,217,0,None,i7173,217,687.51953125,687.4796549479166,-1,0.08071028037383178,4561429
1745012568,1745012955,387,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4659 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 15,4659,1,str\(\'H\'\),3,str\(\'tstat\'\),0.001,15,18,0.11,372,0,None,i7170,372,695.28125,692.16796875,-1,0.04737694704049844,4561315
1745013047,1745013282,235,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1747 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.3802644569756746 subsets 4,1747,5000,str\(\'H\'\),3,str\(\'tstat\'\),0.3802644569756746,4,18,0.11,224,0,None,i7169,224,687.84375,687.54671875,-1,0.18691588785046728,4561703
1745012887,1745013309,422,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 681 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 8,681,1,str\(\'H\'\),3,str\(\'tstat\'\),0.001,8,18,0.11,398,0,None,i7185,398,628.14453125,584.5790550595239,-1,0.04737694704049844,4561561
1745013347,1745013601,254,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 579 feature_id 18 batch_size 74 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 33,579,74,str\(\'H\'\),2,str\(\'stdev\'\),0.999,33,18,0.1,238,0,None,i7170,238,688.59375,688.5608473557693,-1,0.08758878504672897,4561946
1745013667,1745014081,414,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4142 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 64,4142,1,str\(\'H\'\),3,str\(\'tstat\'\),0.001,64,18,0.11,393,0,None,i7185,393,592.96875,571.7344680059524,-1,0.04737694704049844,4562198
1745014006,1745014212,206,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1908 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.3899041344713316 subsets 23,1908,5000,str\(\'H\'\),2,str\(\'tstat\'\),0.3899041344713316,23,18,0.11,192,0,None,i7168,192,688.015625,687.701171875,-1,0.18691588785046728,4562525
1745014277,1745014520,243,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4145 feature_id 18 batch_size 125 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 62,4145,125,str\(\'H\'\),3,str\(\'stdev\'\),0.999,62,18,0.11,231,0,None,i7173,231,687.640625,687.6197415865385,-1,0.08566978193146417,4562730
1745014607,1745015099,492,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3183 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 18,3183,1,str\(\'H\'\),3,str\(\'stdev\'\),0.999,18,18,0.11,478,0,None,i7180,478,693.46875,690.41734375,-1,0.04737694704049844,4562992
1745015027,1745015450,423,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 85 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 57,85,1,str\(\'H\'\),3,str\(\'tstat\'\),0.999,57,18,0.11,410,0,None,i7173,410,694.80078125,691.7053052325581,-1,0.04737694704049844,4563357
1745015327,1745015800,473,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 805 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 100,805,1,str\(\'H\'\),3,str\(\'stdev\'\),0.999,100,18,0.11,459,0,None,i7170,459,694.0078125,690.8863118489584,-1,0.04737694704049844,4563626
1745015606,1745015857,251,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3347 feature_id 18 batch_size 194 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 33,3347,194,str\(\'H\'\),2,str\(\'stdev\'\),0.999,33,18,0.1,235,0,None,i7168,235,687.515625,687.4798677884615,-1,0.10878504672897196,4563867
1745016187,1745016455,268,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 570 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 48,1,570,str\(\'KL\'\),2,str\(\'stdev\'\),0.999,48,18,0.1,241,0,None,i7185,241,578.97265625,570.0065104166666,-1,0.13845482866043612,4564303
1745016906,1745017219,313,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3020 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.41152211475971845 subsets 100,3020,5000,str\(\'H\'\),1,str\(\'tstat\'\),0.41152211475971845,100,18,0.1,300,0,None,i7170,300,688.11328125,687.9298095703125,-1,0.18691588785046728,4564851
1745017547,1745017829,282,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4318 feature_id 18 batch_size 1186 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 79,4318,1186,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,79,18,0.1,270,0,None,i7173,270,688.6328125,686.3807920258621,-1,0.27066666666666667,4565364
1745018348,1745018603,255,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3453 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.38553011143618726 subsets 40,3453,5000,str\(\'KL\'\),2,str\(\'tstat\'\),0.38553011143618726,40,18,0.11,235,0,None,i7185,235,677.46484375,616.8834134615385,-1,0.18691588785046728,4566048
1745018958,1745019264,306,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4524 feature_id 18 batch_size 1181 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 15,4524,1181,str\(\'H\'\),1,str\(\'tstat\'\),0.999,15,18,0.1,294,0,None,i7170,294,689.53515625,689.50537109375,-1,0.4901682242990654,4566562
1745019828,1745020123,295,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3974 feature_id 18 batch_size 601 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 65,3974,601,str\(\'H\'\),2,str\(\'tstat\'\),0.001,65,18,0.1,269,0,None,i7185,269,582.0625,570.7047413793103,-1,0.11233644859813084,4567205
1745020729,1745021290,561,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 925 feature_id 18 batch_size 1 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 25,925,1,str\(\'KL\'\),2,str\(\'stdev\'\),0.999,25,18,0.11,544,0,None,i7170,544,693.37109375,690.2671326754386,-1,0.04737694704049844,4567892
1745021534,1745021836,302,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2900 feature_id 18 batch_size 3146 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.380536043260624 subsets 67,2900,3146,str\(\'H\'\),3,str\(\'stdev\'\),0.380536043260624,67,18,0.1,285,0,None,i7173,285,688.8203125,685.2240423387096,-1,0.31361993769470403,4568533
1745022191,1745022454,263,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4533 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.40163153392199086 subsets 7,4533,5000,str\(\'H\'\),3,str\(\'tstat\'\),0.40163153392199086,7,18,0.11,245,0,None,i7180,245,688.2578125,688.0135995370371,-1,0.18691588785046728,4569042
1745022449,1745022684,235,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 503 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.3942690847243948 subsets 22,503,5000,str\(\'H\'\),2,str\(\'tstat\'\),0.3942690847243948,22,18,0.11,221,0,None,i7170,221,688.21875,687.6859375,-1,0.18691588785046728,4569220
1745022770,1745023005,235,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2471 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.6363317628074697 subsets 12,2471,5000,str\(\'H\'\),2,str\(\'stdev\'\),0.6363317628074697,12,18,0.11,220,0,None,i7170,220,687.90234375,687.6474609375,-1,0.18691588785046728,4569451
1745023089,1745023456,367,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4585 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 11,4585,1,str\(\'H\'\),3,str\(\'stdev\'\),0.999,11,18,0.11,354,0,None,i7173,354,694.16796875,691.0259046052631,-1,0.04737694704049844,4569678
1745023629,1745023890,261,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3644 feature_id 18 batch_size 200 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 53,3644,200,str\(\'H\'\),2,str\(\'tstat\'\),0.001,53,18,0.11,247,0,None,i7170,247,688.875,688.8336226851852,-1,0.09470404984423676,4570120
1745023430,1745024150,720,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1605 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 31,1605,1,str\(\'H\'\),2,str\(\'stdev\'\),0.999,31,18,0.11,709,0,None,i7185,709,692.6875,689.5201198630137,-1,0.04737694704049844,4569950
1745024491,1745024799,308,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1872 feature_id 18 batch_size 4126 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 60,1872,4126,str\(\'H\'\),1,str\(\'stdev\'\),0.001,60,18,0.11,289,0,None,i7168,289,688.94921875,688.9281754032259,-1,0.3084859813084112,4570869
1745025070,1745025321,251,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 857 feature_id 18 batch_size 199 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 20,857,199,str\(\'H\'\),2,str\(\'tstat\'\),0.001,20,18,0.1,237,0,None,i7180,237,688,687.9562800480769,-1,0.08927102803738318,4571294
1745025380,1745025617,237,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4529 feature_id 18 batch_size 186 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 1,4529,186,str\(\'H\'\),3,str\(\'tstat\'\),0.001,1,18,0.1,222,0,None,i7185,222,687.875,687.843125,-1,0.07416822429906542,4571501
1745025972,1745026208,236,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 693 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.7504150026416944 subsets 87,693,5000,str\(\'H\'\),3,str\(\'stdev\'\),0.7504150026416944,87,18,0.11,224,0,None,i7173,224,686.42578125,686.100625,-1,0.18691588785046728,4571907
1745026212,1745026616,404,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3170 feature_id 18 batch_size 1 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 20,3170,1,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,20,18,0.11,389,0,None,i7173,389,694.12109375,690.9980945121952,-1,0.04737694704049844,4572054
1745026431,1745026719,288,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1737 feature_id 18 batch_size 4129 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 71,1737,4129,str\(\'H\'\),1,str\(\'stdev\'\),0.001,71,18,0.1,275,0,None,i7170,275,688.58984375,687.8268229166666,-1,0.25725856697819316,4572220
1745026730,1745026966,236,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3364 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.8657997754629011 subsets 44,3364,5000,str\(\'H\'\),2,str\(\'stdev\'\),0.8657997754629011,44,18,0.11,223,0,None,i7170,223,688.765625,688.4259375,-1,0.18691588785046728,4572444
1745027031,1745027272,241,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 488 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.2620603694313742 subsets 38,488,5000,str\(\'KL\'\),3,str\(\'tstat\'\),0.2620603694313742,38,18,0.11,227,0,None,i7185,227,688.93359375,688.68671875,-1,0.18691588785046728,4572673
1745027301,1745027586,285,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3662 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 93,3662,5000,str\(\'H\'\),2,str\(\'stdev\'\),0.999,93,18,0.11,270,0,None,i7180,270,689.28515625,688.9723868534483,-1,0.18691588785046728,4572889
1745027592,1745028008,416,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 605 feature_id 18 batch_size 1 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 17,605,1,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,17,18,0.11,399,0,None,i7173,399,694.38671875,691.2805989583334,-1,0.04737694704049844,4573107
1745027911,1745028184,273,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2852 feature_id 18 batch_size 1 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 40,2852,1,str\(\'KL\'\),3,str\(\'stdev\'\),0.999,40,18,0.11,264,0,None,i7168,264,693.51953125,690.534213362069,-1,0.04737694704049844,4573367
1745028140,1745028344,204,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1491 feature_id 18 batch_size 4155 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 7,1491,4155,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,7,18,0.1,193,0,None,i7168,193,687.89453125,687.8668323863636,-1,0.2588785046728972,4573542
1745028491,1745028771,280,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3526 feature_id 18 batch_size 4123 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 19,3526,4123,str\(\'H\'\),1,str\(\'stdev\'\),0.001,19,18,0.11,268,0,None,i7170,268,688.5078125,688.4876077586207,-1,0.3596386292834891,4573797
1745029101,1745029319,218,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 382 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.604209496798832 subsets 41,382,5000,str\(\'H\'\),3,str\(\'stdev\'\),0.604209496798832,41,18,0.11,200,0,None,i7169,200,689.01171875,688.7590553977273,-1,0.18691588785046728,4574246
1745029332,1745029627,295,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4456 feature_id 18 batch_size 4098 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 50,4456,4098,str\(\'H\'\),1,str\(\'stdev\'\),0.001,50,18,0.1,278,0,None,i7173,278,687.46875,687.4059895833333,-1,0.2553271028037383,4574409
1745029572,1745029996,424,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1074 feature_id 18 batch_size 1 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 44,1074,1,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,44,18,0.11,408,0,None,i7180,408,694.5,691.3271257267442,-1,0.04737694704049844,4574577
1745029871,1745030130,259,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2218 feature_id 18 batch_size 4092 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 11,2218,4092,str\(\'H\'\),1,str\(\'stdev\'\),0.001,11,18,0.1,246,0,None,i7169,246,688.0390625,687.7897858796297,-1,0.2549532710280374,4574806
1745030171,1745030404,233,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1963 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.28422870904449027 subsets 80,1963,5000,str\(\'H\'\),2,str\(\'tstat\'\),0.28422870904449027,80,18,0.11,223,0,None,i7169,223,689.40234375,689.12640625,-1,0.18691588785046728,4575032
1745030782,1745031023,241,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1662 feature_id 18 batch_size 4995 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.20335044205312633 subsets 41,1662,4995,str\(\'H\'\),2,str\(\'tstat\'\),0.20335044205312633,41,18,0.11,225,0,None,i7170,225,688.28515625,688.0040625,-1,0.12448598130841121,4575440
1745031022,1745031307,285,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1971 feature_id 18 batch_size 1199 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 7,1971,1199,str\(\'KL\'\),2,str\(\'tstat\'\),0.999,7,18,0.1,268,0,None,i7180,268,687.46484375,687.4241648706897,-1,0.24402492211838006,4575605
1745031311,1745031649,338,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 21 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 75,21,1,str\(\'H\'\),3,str\(\'stdev\'\),0.999,75,18,0.11,325,0,None,i7169,325,693.84375,690.7388392857143,-1,0.04737694704049844,4575822
1745031612,1745031873,261,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 915 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.3407625929225921 subsets 87,915,5000,str\(\'KL\'\),2,str\(\'tstat\'\),0.3407625929225921,87,18,0.11,250,0,None,i7170,250,689.3046875,689.0477430555555,-1,0.18691588785046728,4576039
1745031972,1745032223,251,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3255 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.34266042382178846 subsets 20,3255,5000,str\(\'KL\'\),3,str\(\'tstat\'\),0.34266042382178846,20,18,0.11,239,0,None,i7185,239,688.5546875,688.2865084134615,-1,0.18691588785046728,4576292
1745032291,1745032474,183,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1282 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.029658741822726323 subsets 95,1282,5000,str\(\'H\'\),2,str\(\'tstat\'\),0.029658741822726323,95,18,0.11,170,0,None,i7168,170,687.8515625,687.5627055921053,-1,0.18691588785046728,4576534
1745032632,1745033114,482,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 906 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 14,906,1,str\(\'H\'\),2,str\(\'stdev\'\),0.999,14,18,0.11,468,0,None,i7180,468,693.90625,689.9214764030612,-1,0.04737694704049844,4576774
1745032972,1745033209,237,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4261 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.36766931093603394 subsets 78,4261,5000,str\(\'H\'\),3,str\(\'tstat\'\),0.36766931093603394,78,18,0.11,226,0,None,i7173,226,687.90234375,687.6003125,-1,0.18691588785046728,4576995
1745033233,1745033469,236,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4899 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.8036790350025268 subsets 8,4899,5000,str\(\'H\'\),2,str\(\'stdev\'\),0.8036790350025268,8,18,0.11,222,0,None,i7173,222,689.0078125,687.6290625,-1,0.18691588785046728,4577162
1745033593,1745033836,243,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2565 feature_id 18 batch_size 119 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 44,2565,119,str\(\'KL\'\),3,str\(\'stdev\'\),0.999,44,18,0.1,225,0,None,i7185,225,687.40625,687.38234375,-1,0.07562616822429906,4577390
1745033933,1745034203,270,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4250 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.39484951840539645 subsets 62,4250,5000,str\(\'KL\'\),3,str\(\'tstat\'\),0.39484951840539645,62,18,0.11,254,0,None,i7173,254,688.015625,687.7822265625,-1,0.18691588785046728,4577619
1745034273,1745034528,255,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 973 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.9776602116336028 subsets 66,973,5000,str\(\'KL\'\),3,str\(\'stdev\'\),0.9776602116336028,66,18,0.11,242,0,None,i7173,242,687.6640625,687.433738425926,-1,0.18691588785046728,4577851
1745034633,1745034866,233,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3780 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.27981583208491073 subsets 46,3780,5000,str\(\'H\'\),3,str\(\'tstat\'\),0.27981583208491073,46,18,0.11,218,0,None,i7170,218,689.140625,688.78955078125,-1,0.18691588785046728,4578098
1745035014,1745035282,268,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1871 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.5507763685902075 subsets 71,1871,5000,str\(\'KL\'\),2,str\(\'tstat\'\),0.5507763685902075,71,18,0.11,251,0,None,i7173,251,689.87109375,689.6177455357143,-1,0.18691588785046728,4578369
1745035644,1745035900,256,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1061 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 77,1061,5000,str\(\'KL\'\),2,str\(\'tstat\'\),0.001,77,18,0.11,243,0,None,i7185,243,687.6875,687.4207175925926,-1,0.18691588785046728,4578799
1745035884,1745036131,247,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1808 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.6670579391795557 subsets 51,1808,5000,str\(\'KL\'\),2,str\(\'stdev\'\),0.6670579391795557,51,18,0.11,235,0,None,i7170,235,689.85546875,689.5821814903846,-1,0.18691588785046728,4578970
1745036134,1745036388,254,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1241 feature_id 18 batch_size 4971 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.045778809609228095 subsets 32,1241,4971,str\(\'H\'\),2,str\(\'tstat\'\),0.045778809609228095,32,18,0.1,243,0,None,i7173,243,687.50390625,687.4411168981482,-1,0.3097196261682243,4579138
1745036534,1745036802,268,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1920 feature_id 18 batch_size 1219 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 6,1920,1219,str\(\'H\'\),3,str\(\'tstat\'\),0.999,6,18,0.1,256,0,None,i7185,256,687.25,687.2126116071429,-1,0.2333208722741433,4579372
1745036904,1745037159,255,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1675 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.46653343986621376 subsets 32,1675,5000,str\(\'KL\'\),3,str\(\'tstat\'\),0.46653343986621376,32,18,0.11,241,0,None,i7173,241,688.48046875,688.24609375,-1,0.18691588785046728,4579617
1745037214,1745037528,314,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 261 feature_id 18 batch_size 4999 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.16553349949183596 subsets 3,261,4999,str\(\'H\'\),2,str\(\'tstat\'\),0.16553349949183596,3,18,0.1,297,0,None,i7173,297,689.44140625,689.3912353515625,-1,0.41114018691588783,4579799
1745037624,1745037879,255,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1077 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.21920254112325233 subsets 10,1077,5000,str\(\'KL\'\),2,str\(\'tstat\'\),0.21920254112325233,10,18,0.11,242,0,None,i7185,242,688.6328125,688.3919270833334,-1,0.18691588785046728,4580036
1745037954,1745038181,227,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1077 feature_id 18 batch_size 4979 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.19481876561979533 subsets 27,1077,4979,str\(\'KL\'\),3,str\(\'tstat\'\),0.19481876561979533,27,18,0.11,214,0,None,i7173,214,689.27734375,688.9747721354166,-1,0.18613084112149533,4580258
1745038704,1745038946,242,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 174 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.8781345423503308 subsets 34,174,5000,str\(\'KL\'\),2,str\(\'stdev\'\),0.8781345423503308,34,18,0.11,230,0,None,i7185,230,688.94140625,688.66671875,-1,0.18691588785046728,4580763
1745039695,1745040155,460,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3754 feature_id 18 batch_size 1186 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 100,3754,1186,str\(\'KL\'\),2,str\(\'tstat\'\),0.999,100,18,0.1,448,0,None,i7173,448,688.41796875,688.3864694148937,-1,0.2854454828660436,4581415
1745040415,1745040684,269,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3547 feature_id 18 batch_size 4165 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 83,3547,4165,str\(\'H\'\),3,str\(\'stdev\'\),0.001,83,18,0.1,251,0,None,i7173,251,689.234375,688.8042689732143,-1,0.25950155763239874,4581828
1745040795,1745041051,256,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 760 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.6191746581719627 subsets 27,760,5000,str\(\'KL\'\),2,str\(\'stdev\'\),0.6191746581719627,27,18,0.11,241,0,None,i7180,241,687.73046875,687.4519675925926,-1,0.18691588785046728,4582047
1745041166,1745041427,261,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 212 feature_id 18 batch_size 1206 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 32,212,1206,str\(\'KL\'\),2,str\(\'tstat\'\),0.999,32,18,0.1,245,0,None,i7185,245,687.72265625,687.6776620370371,-1,0.21553894080996885,4582271
1745042036,1745042284,248,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2577 feature_id 18 batch_size 4450 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 33,2577,4450,str\(\'H\'\),1,str\(\'stdev\'\),0.999,33,18,0.1,235,0,None,i7173,235,688.4375,688.2282151442307,-1,0.05545171339563863,4582815
1745042776,1745043077,301,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4972 feature_id 18 batch_size 4147 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 100,4972,4147,str\(\'H\'\),1,str\(\'stdev\'\),0.001,100,18,0.11,285,0,None,i7185,285,687.7578125,687.4469506048387,-1,0.15502803738317758,4583236
1745043756,1745044037,281,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3106 feature_id 18 batch_size 3617 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 62,3106,3617,str\(\'H\'\),1,str\(\'stdev\'\),0.001,62,18,0.1,267,0,None,i7185,267,688.82421875,688.8073814655172,-1,0.18028660436137073,4583828
1745044166,1745044467,301,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2457 feature_id 18 batch_size 4168 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 92,2457,4168,str\(\'H\'\),1,str\(\'stdev\'\),0.001,92,18,0.1,285,0,None,i7185,285,688.765625,688.7449596774194,-1,0.20775077881619938,4584163
1745044576,1745044870,294,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2962 feature_id 18 batch_size 4179 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 84,2962,4179,str\(\'H\'\),1,str\(\'stdev\'\),0.001,84,18,0.1,282,0,None,i7185,282,688.1953125,688.171370967742,-1,0.20829906542056076,4584421
1745045546,1745045817,271,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3156 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.42804267429030807 subsets 22,3156,5000,str\(\'H\'\),3,str\(\'tstat\'\),0.42804267429030807,22,18,0.11,256,0,None,i7180,256,689.22265625,688.8976004464286,-1,0.18691588785046728,4585104
1745045907,1745046156,249,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3450 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.07043166923939476 subsets 10,3450,5000,str\(\'KL\'\),3,str\(\'tstat\'\),0.07043166923939476,10,18,0.11,233,0,None,i7173,233,590.796875,572.7952223557693,-1,0.18691588785046728,4585292
1745046273,1745046516,243,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 431 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.9932109569908057 subsets 11,431,5000,str\(\'H\'\),3,str\(\'stdev\'\),0.9932109569908057,11,18,0.11,218,0,None,i7173,218,668.484375,602.2052408854166,-1,0.18691588785046728,4585502
1745047097,1745047345,248,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2858 feature_id 18 batch_size 1493 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 34,2858,1493,str\(\'KL\'\),1,str\(\'tstat\'\),0.001,34,18,0.1,229,0,None,i7185,229,683.68359375,607.489375,-1,0.13023052959501558,4585961
1745047887,1745048130,243,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2444 feature_id 18 batch_size 108 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 77,2444,108,str\(\'H\'\),3,str\(\'stdev\'\),0.999,77,18,0.1,224,0,None,i7180,224,689.17578125,689.15046875,-1,0.08209345794392524,4586409
1745048246,1745048578,332,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4020 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 57,4020,1,str\(\'H\'\),3,str\(\'tstat\'\),0.001,57,18,0.11,314,0,None,i7170,314,671.90625,580.0649126838235,-1,0.04737694704049844,4586611
1745049147,1745049402,255,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4423 feature_id 18 batch_size 1259 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 29,4423,1259,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,29,18,0.1,238,0,None,i7173,238,683.0546875,598.6663161057693,-1,0.1790404984423676,4587128
1745049838,1745049864,26,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 173 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 21,173,1,str\(\'H\'\),1,str\(\'stdev\'\),0.999,21,18,None,None,1,None,i7173,,,,,,4587522
1745050577,1745050818,241,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 41 feature_id 18 batch_size 174 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 94,41,174,str\(\'H\'\),3,str\(\'stdev\'\),0.999,94,18,0.1,217,0,None,i7173,217,629.59375,583.7410481770834,-1,0.08022429906542056,4587930
1745051249,1745051484,235,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4582 feature_id 18 batch_size 1610 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 100,4582,1610,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,100,18,0.1,212,0,None,i7185,212,642.19921875,579.4453125,-1,0.08024922118380062,4588294
1745052018,1745052280,262,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3346 feature_id 18 batch_size 1617 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 32,3346,1617,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,32,18,0.1,242,0,None,i7173,242,673.92578125,624.0026041666666,-1,0.12089719626168224,4588717
1745052738,1745052975,237,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3394 feature_id 18 batch_size 1556 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 20,3394,1556,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,20,18,0.1,222,0,None,i7180,222,689.34375,689.015,-1,0.07755763239875389,4589079
1745053358,1745053677,319,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1261 feature_id 18 batch_size 1186 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 43,1261,1186,str\(\'KL\'\),1,str\(\'tstat\'\),0.999,43,18,0.1,303,0,None,i7185,303,686.91796875,660.7359138257576,-1,0.5071277258566979,4589427
1745054306,1745054522,216,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 769 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 10,1,769,str\(\'H\'\),3,str\(\'tstat\'\),0.999,10,18,0.1,187,0,None,i7170,187,673.54296875,601.7204241071429,-1,0.19548909657320873,4589942
1745054899,1745055261,362,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 4092 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 3,1,4092,str\(\'H\'\),1,str\(\'stdev\'\),0.001,3,18,0.1,342,0,None,i7173,342,679.2421875,582.8331925675676,-1,0.7909408099688473,4590257
1745055618,1745055638,20,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 1 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 22,1,1,str\(\'H\'\),1,str\(\'tstat\'\),0.001,22,18,None,None,1,None,i7183,,,,,,4590647
1745056439,1745056733,294,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 802 feature_id 18 batch_size 4128 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.001 subsets 59,802,4128,str\(\'H\'\),2,str\(\'stdev\'\),0.001,59,18,0.1,276,0,None,i7185,276,683.97265625,660.5315104166667,-1,0.3086355140186916,4591114
1745057540,1745057788,248,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2816 feature_id 18 batch_size 4124 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 35,2816,4124,str\(\'H\'\),3,str\(\'tstat\'\),0.001,35,18,0.1,231,0,None,i7185,231,686.0078125,601.6726262019231,-1,0.15416822429906543,4591671
1745058420,1745058654,234,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4601 feature_id 18 batch_size 3151 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.5581280502087325 subsets 100,4601,3151,str\(\'H\'\),3,str\(\'stdev\'\),0.5581280502087325,100,18,0.1,224,0,None,i7183,224,687.59765625,687.56859375,-1,0.15705919003115265,4592147
1745059687,1745059915,228,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3850 feature_id 18 batch_size 3096 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 38,3850,3096,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,38,18,0.1,209,0,None,i7183,209,688.7109375,688.280910326087,-1,0.11573831775700935,4592839
1745060558,1745060819,261,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 5000 feature_id 18 batch_size 2377 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 61,5000,2377,str\(\'H\'\),3,str\(\'stdev\'\),0.001,61,18,0.1,242,0,None,i7185,242,616.953125,577.1176215277778,-1,0.26657943925233646,4593287
1745061481,1745061755,274,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 4170 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 55,1,4170,str\(\'H\'\),1,str\(\'tstat\'\),0.001,55,18,0.1,253,0,None,i7185,253,671.5859375,595.2811104910714,-1,0.15588785046728973,4593792
1745062640,1745062895,255,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4647 feature_id 18 batch_size 3220 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.9849450341319766 subsets 52,4647,3220,str\(\'H\'\),2,str\(\'stdev\'\),0.9849450341319766,52,18,0.1,244,0,None,i7183,244,689.359375,689.3177083333334,-1,0.20062305295950156,4594422
1745063541,1745063796,255,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 5000 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.6035436874435524 subsets 28,5000,5000,str\(\'KL\'\),3,str\(\'stdev\'\),0.6035436874435524,28,18,0.11,239,0,None,i7185,239,686.15234375,600.8871694711538,-1,0.18691588785046728,4594889
1745065441,1745065674,233,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 1177 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 88,1,1177,str\(\'H\'\),3,str\(\'tstat\'\),0.999,88,18,0.1,216,0,None,i7183,216,688.98046875,688.9444986979166,-1,0.23909034267912774,4595897
1745066422,1745066702,280,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2639 feature_id 18 batch_size 3137 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.8514016323178816 subsets 26,2639,3137,str\(\'KL\'\),3,str\(\'stdev\'\),0.8514016323178816,26,18,0.1,256,0,None,i7185,256,667.625,587.0972377232143,-1,0.3127227414330218,4596404
1745067286,1745067611,325,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2667 feature_id 18 batch_size 2500 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 55,2667,2500,str\(\'H\'\),1,str\(\'stdev\'\),0.001,55,18,0.1,293,0,None,i7185,293,671.66796875,597.4332275390625,-1,0.40498442367601245,4596811
1745068142,1745068410,268,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 890 feature_id 18 batch_size 2333 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.001 subsets 51,890,2333,str\(\'KL\'\),2,str\(\'stdev\'\),0.001,51,18,0.1,254,0,None,i7185,254,685.6015625,608.8974609375,-1,0.23257320872274143,4597273
1745070083,1745070370,287,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3 feature_id 18 batch_size 4366 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.37856809438494765 subsets 24,3,4366,str\(\'KL\'\),3,str\(\'tstat\'\),0.37856809438494765,24,18,0.1,271,0,None,i7180,271,687.171875,686.5899739583333,-1,0.2720249221183801,4598268
1745071164,1745071397,233,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1360 feature_id 18 batch_size 3437 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 100,1360,3437,str\(\'H\'\),3,str\(\'tstat\'\),0.001,100,18,0.1,218,0,None,i7180,218,687.9765625,687.4518229166666,-1,0.17131464174454827,4598790
1745071922,1745072489,567,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 828 feature_id 18 batch_size 248 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 100,828,248,str\(\'H\'\),1,str\(\'tstat\'\),0.001,100,18,0.1,550,0,None,i7183,550,678.44140625,581.9267406798245,-1,0.12361370716510904,4599168
1745072863,1745073084,221,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 5000 feature_id 18 batch_size 4414 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 19,5000,4414,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,19,18,0.11,203,0,None,i7183,203,681.05078125,625.3423913043479,-1,0.05500311526479751,4599668
1745073963,1745074206,243,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 258 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 31,1,258,str\(\'H\'\),2,str\(\'tstat\'\),0.001,31,18,0.1,225,0,None,i7185,225,645.67578125,585.8840625,-1,0.08358878504672897,4600207
1745074703,1745074925,222,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 5000 feature_id 18 batch_size 4422 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 63,5000,4422,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,63,18,0.11,204,0,None,i7185,204,651.23828125,579.3486752717391,-1,0.05510280373831776,4600548
1745075111,1745075346,235,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 472 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.378895524091517 subsets 73,472,5000,str\(\'H\'\),3,str\(\'tstat\'\),0.378895524091517,73,18,0.11,219,0,None,i7185,219,639.12109375,580.9117838541666,-1,0.18691588785046728,4600742
1745075523,1745075794,271,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4177 feature_id 18 batch_size 4409 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 100,4177,4409,str\(\'KL\'\),1,str\(\'tstat\'\),0.999,100,18,0.1,252,0,None,i7183,252,681.60546875,597.9535435267857,-1,0.16482242990654206,4600948
1745075904,1745076143,239,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 374 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.8122035484296609 subsets 70,374,5000,str\(\'KL\'\),3,str\(\'stdev\'\),0.8122035484296609,70,18,0.11,222,0,None,i7183,222,646.46875,575.87828125,-1,0.18691588785046728,4601126
1745076863,1745077102,239,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1451 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.7132367956750609 subsets 82,1451,5000,str\(\'H\'\),1,str\(\'stdev\'\),0.7132367956750609,82,18,0.1,223,0,None,i7183,223,639.92578125,585.744375,-1,0.12461059190031153,4601582
1745077243,1745077430,187,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1856 feature_id 18 batch_size 4439 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 51,1856,4439,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,51,18,0.1,175,0,None,i7183,175,685.3671875,685.1263671875,-1,0.05531464174454829,4601768
1745078304,1745078538,234,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2417 feature_id 18 batch_size 1499 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 86,2417,1499,str\(\'H\'\),3,str\(\'tstat\'\),0.001,86,18,0.1,220,0,None,i7183,220,689.71484375,689.416015625,-1,0.09339563862928349,4602275
1745079444,1745079643,199,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 5000 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.3918314181804805 subsets 57,5000,5000,str\(\'KL\'\),2,str\(\'tstat\'\),0.3918314181804805,57,18,0.11,189,0,None,i7183,189,689.37109375,689.0701264880952,-1,0.18691588785046728,4602806
1745080389,1745080409,20,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 986 feature_id 18 batch_size 1 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 22,986,1,str\(\'KL\'\),1,str\(\'tstat\'\),0.001,22,18,None,None,1,None,i7185,,,,,,4603227
1745081372,1745081666,294,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3334 feature_id 18 batch_size 4396 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 68,3334,4396,str\(\'KL\'\),2,str\(\'stdev\'\),0.999,68,18,0.1,277,0,None,i7183,277,688.53515625,688.4872395833333,-1,0.2738940809968847,4603677
1745082341,1745082599,258,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 3035 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 46,1,3035,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,46,18,0.1,243,0,None,i7183,243,688.18359375,688.1589988425926,-1,0.2269158878504673,4604107
1745082763,1745083018,255,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3601 feature_id 18 batch_size 4981 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.7876968290305525 subsets 29,3601,4981,str\(\'H\'\),2,str\(\'stdev\'\),0.7876968290305525,29,18,0.11,237,0,None,i7185,237,688.53125,688.1966646634615,-1,0.1862056074766355,4604288
1745083229,1745083454,225,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4469 feature_id 18 batch_size 5000 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 93,4469,5000,str\(\'KL\'\),2,str\(\'tstat\'\),0.001,93,18,0.11,210,0,None,i7183,210,689.48828125,689.1574388586956,-1,0.18691588785046728,4604478
1745083661,1745083879,218,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4223 feature_id 18 batch_size 5000 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.1867586976134007 subsets 85,4223,5000,str\(\'H\'\),2,str\(\'tstat\'\),0.1867586976134007,85,18,0.11,201,0,None,i7183,201,688.23046875,687.9074388586956,-1,0.18691588785046728,4604665
1745084674,1745084956,282,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4330 feature_id 18 batch_size 4686 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 43,4330,4686,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,43,18,0.1,263,0,None,i7185,263,689.05859375,688.729525862069,-1,0.17517757009345794,4604921
1745085073,1745085373,300,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1055 feature_id 18 batch_size 1188 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 60,1055,1188,str\(\'H\'\),2,str\(\'tstat\'\),0.999,60,18,0.1,283,0,None,i7185,283,687.3515625,687.3060735887096,-1,0.24155763239875389,4604969

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⚠ Job 4549775 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4549775/4549775_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4549775/4549775_0_log.out
----------------------------------------
submitit INFO (2025-04-18 20:18:38,438) - Starting with JobEnvironment(job_id=4549775, hostname=i7185, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-18 20:18:38,440) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4549775/4549775_submitted.pkl
slurmstepd: error: *** JOB 4549775 ON i7185 CANCELLED AT 2025-04-18T20:48:45 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4549775/4549775_0_log.err not found


⚠ Job 4555472 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4555472/4555472_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4555472/4555472_0_log.out
----------------------------------------
submitit INFO (2025-04-18 21:53:39,467) - Starting with JobEnvironment(job_id=4555472, hostname=i7173, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-18 21:53:39,473) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4555472/4555472_submitted.pkl
slurmstepd: error: *** JOB 4555472 ON i7173 CANCELLED AT 2025-04-18T22:23:49 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4555472/4555472_0_log.err not found


⚠ Job 4558105 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4558105/4558105_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4558105/4558105_0_log.out
----------------------------------------
submitit INFO (2025-04-18 22:41:40,269) - Starting with JobEnvironment(job_id=4558105, hostname=i7173, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-18 22:41:40,274) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4558105/4558105_submitted.pkl
slurmstepd: error: *** JOB 4558105 ON i7173 CANCELLED AT 2025-04-18T23:11:51 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4558105/4558105_0_log.err not found


⚠ Job 4571713 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4571713/4571713_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4571713/4571713_0_log.out
----------------------------------------
submitit INFO (2025-04-19 03:21:17,201) - Starting with JobEnvironment(job_id=4571713, hostname=i7185, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-19 03:21:17,216) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4571713/4571713_submitted.pkl
slurmstepd: error: *** JOB 4571713 ON i7185 CANCELLED AT 2025-04-19T03:51:32 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4571713/4571713_0_log.err not found


⚠ Job 4575236 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4575236/4575236_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4575236/4575236_0_log.out
----------------------------------------
submitit INFO (2025-04-19 04:41:18,134) - Starting with JobEnvironment(job_id=4575236, hostname=i7170, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-19 04:41:18,142) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4575236/4575236_submitted.pkl
slurmstepd: error: *** JOB 4575236 ON i7170 CANCELLED AT 2025-04-19T05:11:34 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4575236/4575236_0_log.err not found


⚠ Job 4580498 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4580498/4580498_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4580498/4580498_0_log.out
----------------------------------------
submitit INFO (2025-04-19 06:51:50,566) - Starting with JobEnvironment(job_id=4580498, hostname=i7170, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-19 06:51:50,568) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4580498/4580498_submitted.pkl
slurmstepd: error: *** JOB 4580498 ON i7170 CANCELLED AT 2025-04-19T07:22:10 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4580498/4580498_0_log.err not found


⚠ Job 4584919 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4584919/4584919_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4584919/4584919_0_log.out
----------------------------------------
submitit INFO (2025-04-19 08:47:12,448) - Starting with JobEnvironment(job_id=4584919, hostname=i7170, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-19 08:47:12,451) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4584919/4584919_submitted.pkl
slurmstepd: error: *** JOB 4584919 ON i7170 CANCELLED AT 2025-04-19T09:17:14 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4584919/4584919_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4587522/4587522_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4590647/4590647_0_log.err not found


⚠ Job 4595347 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4595347/4595347_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4595347/4595347_0_log.out
----------------------------------------
submitit INFO (2025-04-19 14:07:00,807) - Starting with JobEnvironment(job_id=4595347, hostname=i7183, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-19 14:07:00,820) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4595347/4595347_submitted.pkl
slurmstepd: error: *** JOB 4595347 ON i7183 CANCELLED AT 2025-04-19T14:36:56 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4595347/4595347_0_log.err not found


⚠ Job 4597769 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4597769/4597769_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4597769/4597769_0_log.out
----------------------------------------
submitit INFO (2025-04-19 15:24:58,277) - Starting with JobEnvironment(job_id=4597769, hostname=i7183, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-19 15:24:58,295) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4597769/4597769_submitted.pkl
slurmstepd: error: *** JOB 4597769 ON i7183 CANCELLED AT 2025-04-19T15:54:59 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4597769/4597769_0_log.err not found


⚠ Job 4601424 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4601424/4601424_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4601424/4601424_0_log.out
----------------------------------------
submitit INFO (2025-04-19 17:29:00,375) - Starting with JobEnvironment(job_id=4601424, hostname=i7183, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-19 17:29:00,377) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4601424/4601424_submitted.pkl
slurmstepd: error: *** JOB 4601424 ON i7183 CANCELLED AT 2025-04-19T17:59:02 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4601424/4601424_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4603227/4603227_0_log.err not found

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Download »cpu_ram_usage.csv« as file

timestamp,ram_usage_mb,cpu_usage_percent
1744998965,642.328125,21.9
1744998965,642.328125,21.7
1744998965,642.38671875,20.7
1744998965,642.38671875,18.2
1744998965,642.38671875,16.1
1744998965,642.38671875,21.3
1744998965,642.38671875,16.7
1744999587,704.921875,32.4
1744999587,704.921875,30.8
1744999587,704.921875,24.9
1744999587,704.921875,20.0
1745005245,930.7734375,9.6
1745005245,930.7734375,7.4
1745005245,930.7734375,7.0
1745005245,930.7734375,8.9
1745010290,932.02734375,7.3
1745010290,932.02734375,7.1
1745010290,932.02734375,7.0
1745010290,932.02734375,2.4
1745021527,943.7265625,7.2
1745021527,943.7265625,6.6
1745021527,943.7265625,6.4
1745021527,943.7265625,7.0
1745028492,978.50390625,7.4
1745028493,978.50390625,7.3
1745028493,978.50390625,7.1
1745028493,978.50390625,10.9
1745035024,984.00390625,7.4
1745035024,984.00390625,7.3
1745035024,984.00390625,7.4
1745035024,984.00390625,10.3
1745044574,991.125,7.4
1745044574,991.125,7.0
1745044574,991.125,6.9
1745044574,991.125,10.0
1745058414,1040.63671875,7.4
1745058414,1040.63671875,6.1
1745058414,1040.63671875,6.7
1745058414,1040.63671875,2.7
1745075901,1064.47265625,7.5
1745075901,1064.47265625,6.5
1745075901,1064.47265625,6.9
1745075901,1064.47265625,6.1

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Correlation Heatmap Explanation

This is a heatmap that visualizes the correlation between numerical columns in a dataset. The values represented in the heatmap show the strength and direction of relationships between different variables.

How It Works

The heatmap uses a matrix to represent correlations between each pair of numerical columns. The calculation behind this is based on the concept of "correlation," which measures how strongly two variables are related. A correlation can be positive, negative, or zero:

• Positive correlation: Both variables increase or decrease together (e.g., if the temperature rises, ice cream sales increase).
• Negative correlation: As one variable increases, the other decreases (e.g., as the price of a product rises, the demand for it decreases).
• Zero correlation: There is no relationship between the two variables (e.g., height and shoe size might show zero correlation in some contexts).

Color Scale: Yellow to Purple (Viridis)

The heatmap uses a color scale called "Viridis," which ranges from yellow to purple. Here's what the colors represent:

• Yellow (brightest): A strong positive correlation (close to +1). This indicates that as one variable increases, the other increases in a very predictable manner.
• Green: A moderate positive correlation. Variables are still positively related, but the relationship is not as strong.
• Blue: A weak or near-zero correlation. There is a small or no discernible relationship between the variables.
• Purple (darkest): A strong negative correlation (close to -1). This indicates that as one variable increases, the other decreases in a very predictable manner.

What the Heatmap Shows

In the heatmap, each cell represents the correlation between two numerical columns. The color of the cell is determined by the correlation coefficient: from yellow for strong positive correlations, through green and blue for weaker correlations, to purple for strong negative correlations.

•  Job-Infos

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			}
			
			.share_folder_buttons {
				margin-top: 10px;
				margin-bottom: 10px;
			}
			
			.nav_tab_button {
				margin: 10px;
			}
			
			.header_table {
				margin: 10px;
			}
			
			.no_border {
				border: unset !important;
			}
			
			.gui_table {
				padding: 5px !important;
			}
			
			.gui_parameter_row {
			}
			
			.gui_parameter_row_cell {
				border: unset !important;
			}
			
			.gui_param_table {
				width: 95%;
				margin: unset !important;
			}
			
			table td, table tr,
			.parameterRow table {
				padding: 2px !important;
			}
			
			.parameterRow table {
				margin: 0px;
				border: unset;
			}
			
			.parameterRow > td {
				border: 0px !important;
			}
			
			.parameter_config_table td, .parameter_config_table tr, #config_table th, #config_table td, #hidden_config_table th, #hidden_config_table td {
				border: 0px !important;
			}
			
			.green_text {
				color: green;
			}
			
			.remove_parameter {
				white-space: pre;
			}
			
			select {
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				background-color: #fff;
				color: #222;
				padding: 5px 30px 5px 5px;
				border: 1px solid #555;
				border-radius: 5px;
				cursor: pointer;
				outline: none;
				transition: all 0.3s ease;
				background:
					url("data:image/svg+xml;charset=UTF-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 10 6'%3E%3Cpath fill='%23888' d='M0 0l5 6 5-6z'/%3E%3C/svg%3E")
					no-repeat right 10px center,
				linear-gradient(180deg, #fff, #ecebe5 86%, #d8d0c4);
				background-size: 12px, auto;
			}
			
			select:hover {
				border-color: #888;
			}
			
			select:focus {
				border-color: #4caf50;
				box-shadow: 0 0 5px rgba(76, 175, 80, 0.5);
			}
			
			select::-ms-expand {
				display: none;
			}
			
			input, textarea {
				border-radius: 5px;
			}
			
			#search {
				width: 200px;
				max-width: 70%;
				background-image: url(images/search.svg);
				background-repeat: no-repeat;
				background-size: auto 40px;
				height: 40px;
				line-height: 40px;
				padding-left: 40px;
				box-sizing: border-box;
			}
			
			input[type="checkbox"] {
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				width: 25px;
				height: 25px;
				border: 2px solid #3498db;
				border-radius: 5px;
				background-color: #fff;
				position: relative;
				cursor: pointer;
				transition: all 0.3s ease;
				width: 25px !important;
			}
			
			input[type="checkbox"]:checked {
				background-color: #3498db;
				border-color: #2980b9;
			}
			
			input[type="checkbox"]:checked::before {
				content: '✔';
				position: absolute;
				left: 4px;
				top: 2px;
				color: #fff;
			}
			
			input[type="checkbox"]:hover {
				border-color: #2980b9;
				background-color: #3caffc;
			}
			
			.toc {
				margin-bottom: 20px;
			}
			
			.toc li {
				margin-bottom: 5px;
			}
			
			.toc a {
				text-decoration: none;
				color: #007bff;
			}
			
			.toc a:hover {
				text-decoration: underline;
			}
			
			.table-container {
				width: 100%;
				overflow-x: auto;
			}
			
			.section-header {
				background-color: #1d6f9a !important;
				color: white;
			}
			
			.warning {
				color: red;
			}
			
			.li_list a {
				text-decoration: none;
			}
			
			.gridjs-td {
				white-space: nowrap;
			}
			
			th, td {
				border: 1px solid gray !important;
			}
			
			.no_border {
				border: 0px !important;
			}
			
			.no_break {
			
			}
			
			img {
				user-select: none;
				pointer-events: none;
			}
			
			#config_table, #hidden_config_table {
				user-select: none;
			}
			
			.copy_clipboard_button {
				margin-bottom: 10px;
			}
			
			.badge_table {
				background-color: unset !important;
			}
			
			.make_markable {
				user-select: text;
			}
			
			.header-container {
				display: flex;
				flex-wrap: wrap;
				align-items: center;
				justify-content: space-between;
				gap: 1rem;
				padding: 10px;
				background: var(--header-bg, #fff);
				border-bottom: 1px solid #ccc;
			}
			
			.header-logo-group {
				display: flex;
				gap: 1rem;
				align-items: center;
				flex: 1 1 auto;
				min-width: 200px;
			}
			
			.logo-img {
				max-height: 45px;
				height: auto;
				width: auto;
				object-fit: contain;
				pointer-events: unset;
			}
			
			.header-badges {
				flex-direction: column;
				gap: 5px;
				align-items: flex-start;
				flex: 0 1 auto;
				margin-top: auto;
				margin-bottom: auto;
			}
			
			.badge-img {
				height: auto;
				max-width: 130px;
				margin-top: 3px;
			}
			
			.header-tabs {
				margin-top: 10px;
				display: flex;
				flex-wrap: wrap;
				gap: 10px;
				flex: 2 1 100%;
				justify-content: center;
			}
			
			.nav-tab {
				display: inline-block;
				text-decoration: none;
				padding: 8px 16px;
				border-radius: 20px;
				background: linear-gradient(to right, #4a90e2, #357ABD);
				color: white;
				font-weight: bold;
				white-space: nowrap;
				transition: background 0.2s ease-in-out, transform 0.2s;
				box-shadow: 0 2px 4px rgba(0,0,0,0.2);
			}
			
			.nav-tab:hover {
				background: linear-gradient(to right, #5aa0f2, #4a90e2);
				transform: translateY(-2px);
			}
			
			.current-tag {
				padding-left: 10px;
				font-size: 0.9rem;
				color: #666;
			}
			
			.header-theme-toggle {
				flex: 1 1 auto;
				align-items: center;
				margin-top: 20px;
				min-width: 120px;
			}
			
			.switch {
				position: relative;
				display: inline-block;
				width: 60px;
				height: 30px;
			}
			
			.switch input {
				display: none;
			}
			
			.slider {
				position: absolute;
				top: 0; left: 0; right: 0; bottom: 0;
				background-color: #ccc;
				border-radius: 34px;
				cursor: pointer;
			}
			
			.slider::before {
				content: "";
				position: absolute;
				height: 24px;
				width: 24px;
				left: 3px;
				bottom: 3px;
				background-color: white;
				transition: .4s;
				border-radius: 50%;
			}
			
			input:checked + .slider {
				background-color: #2196F3;
			}
			
			input:checked + .slider::before {
				transform: translateX(30px);
			}
			
			@media (max-width: 768px) {
				.header-logo-group,
				.header-badges,
				.header-theme-toggle {
					justify-content: center;
					flex: 1 1 100%;
					text-align: center;
					width: inherit;
				}
			
				.logo-img {
					max-height: 50px;
					pointer-events: unset;
				}
			
				.badge-img {
					max-width: 100px;
				}
			
				.hide_on_mobile {
					display: none;
				}
			
				.nav-tab {
					font-size: 0.9rem;
					padding: 6px 12px;
				}
			
				.header_button {
					white-space: pre;
					font-size: 2em;
				}
			}
			
			.header_button {
				white-space: pre;
				margin-top: 20px;
				margin: 5px;
			}
			
			.line_break_anywhere {
				line-break: anywhere;
			}
			
			.responsive-container {
				display: flex;
				flex-wrap: wrap;
				justify-content: space-between;
				gap: 20px;
			}
			
			.responsive-container .half {
				flex: 1 1 48%;
				box-sizing: border-box;
				min-width: 500px;
			}
			
			.config-section table {
				width: 100%;
				border-collapse: collapse;
			}
			
			@media (max-width: 768px) {
				.responsive-container .half {
					flex: 1 1 100%;
				}
			}
			
			@keyframes spin {
				0% {
					transform: rotate(0deg);
				}
			
				100% {
					transform: rotate(360deg);
				}
			}
			
			.rotate {
				animation: spin 2s linear infinite;
				display: inline-block;
			}
			
			input::placeholder {
				font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
			}
			
			.gridjs-th-content {
				overflow: visible !important;
			}
			
			.error_text {
				color: red;
			}
			
			h1, h2, h3, h4, h5, h6 {
				margin-top: 1em;
				font-weight: bold;
				color: #333;
				border-left: 5px solid #ccc;
				padding-left: 0.5em;
			}
			
			.no_cursive {
				font-style: normal;	
			}
			
			.caveat {
				background-color: #fff8b3;
				border: 1px solid #f2d600;
				padding: 1em 1em 1em 70px;
				position: relative;
				font-family: sans-serif;
				color: #665500;
				margin: 1em 0;
				border-radius: 4px;
			}
			
			.caveat h1, .caveat h2, .caveat h3, .caveat h4 {
				margin-top: 0;
				margin-bottom: 0.5em;
				font-weight: bold;
			}
			
			.caveat::before {
				content: "⚠️";
				font-size: 50px;
				line-height: 1;
				position: absolute;
				left: 10px;
				top: 50%;
				transform: translateY(-50%);
				pointer-events: none;
				user-select: none;
			}
			
			.caveat.warning::before { content: "⚠️"; }
			.caveat.stop::before { content: "🛑"; }
			.caveat.exclamation::before { content: "❗"; }
			.caveat.alarm::before { content: "🚨"; }
			.caveat.tip::before { content: "💡"; }
			
			.tutorial_icon {
				display: inline-block;
				font-size: 1.3em;
				line-height: 1;
				vertical-align: middle;
				transform: translateY(-10%);
				padding: 0.2em 0;
			}
			
			.highlight {
				background-color: yellow;
				font-weight: bold;
			}
			
			#searchResults li {
				opacity: 0;
				transform: translateY(8px);
				animation: fadeInUp 0.3s ease-out forwards;
				animation-delay: 0.05s;
				list-style: none;
				margin-bottom: 5px;
			}
			
			@keyframes fadeInUp {
				to {
					opacity: 1;
					transform: translateY(0);
				}
			}
			
			.search_headline {
				font-weight: bold;
				margin-top: 1em;
				margin-bottom: 0.3em;
				color: #444;
			}
			
			.search_share_path {
				color: black;
				display: block ruby;
				margin-top: 20px;
			}
			
			@media print {
				#scads_bar {
					display: none !important;
				}
			}
			

			/*! XP.css v0.2.6 - https: //botoxparty.github.io/XP.css/ */
			body{
				color: #222
			}
			
			.surface{
				background: #ece9d8
			}
			
			u{
				text-decoration: none;
				border-bottom: .5px solid #222
			}
			
			a{
				color: #00f
			}
			
			a: focus{
				outline: 1px dotted #00f
			}
			
			code,code *{
				font-family: monospace
			}
			
			pre{
				display: block;
				padding: 12px 8px;
				background-color: #000;
				color: silver;
				font-size: 1rem;
				margin: 0;
				overflow: scroll;
			}
			
			summary: focus{
				outline: 1px dotted #000
			}
			
			: :-webkit-scrollbar{
				width: 16px
			}
			
			: :-webkit-scrollbar: horizontal{
				height: 17px
			}
			
			: :-webkit-scrollbar-track{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='2' height='2' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 0H0v1h1v1h1V1H1V0z' fill='silver'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 0H1v1H0v1h1V1h1V0z' fill='%23fff'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-thumb{
				background-color: #dfdfdf;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
			}
			
			: :-webkit-scrollbar-button: horizontal: end: increment,: :-webkit-scrollbar-button: horizontal: start: decrement,: :-webkit-scrollbar-button: vertical: end: increment,: :-webkit-scrollbar-button: vertical: start: decrement{
				display: block
			}
			
			: :-webkit-scrollbar-button: vertical: start{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 6H7v1H6v1H5v1H4v1h7V9h-1V8H9V7H8V6z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: vertical: end{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 6H4v1h1v1h1v1h1v1h1V9h1V8h1V7h1V6z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: horizontal: start{
				width: 16px;
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 4H8v1H7v1H6v1H5v1h1v1h1v1h1v1h1V4z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: horizontal: end{
				width: 16px;
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7 4H6v7h1v-1h1V9h1V8h1V7H9V6H8V5H7V4z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			button{
				border: none;
				background: #ece9d8;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf;
				border-radius: 0;
				min-width: 75px;
				min-height: 23px;
				padding: 0 12px
			}
			
			button: not(: disabled).active,button: not(: disabled): active{
				box-shadow: inset -1px -1px #fff,inset 1px 1px #0a0a0a,inset -2px -2px #dfdfdf,inset 2px 2px grey
			}
			
			button.focused,button: focus{
				outline: 1px dotted #000;
				outline-offset: -4px
			}
			
			label{
				display: inline-flex;
				align-items: center
			}
			
			textarea{
				padding: 3px 4px;
				border: none;
				background-color: #fff;
				box-sizing: border-box;
				-webkit-appearance: none;
				-moz-appearance: none;
				appearance: none;
				border-radius: 0
			}
			
			textarea: focus{
				outline: none
			}
			
			select: focus option{
				color: #000;
				background-color: #fff
			}
			
			.vertical-bar{
				width: 4px;
				height: 20px;
				background: silver;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
			}
			
			&: disabled,&: disabled+label{
				color: grey;
				text-shadow: 1px 1px 0 #fff
			}
			
			input[type=radio]+label{
				line-height: 13px;
				position: relative;
				margin-left: 19px
			}
			
			input[type=radio]+label: before{
				content: "";
				position: absolute;
				top: 0;
				left: -19px;
				display: inline-block;
				width: 13px;
				height: 13px;
				margin-right: 6px;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='%23fff'/%3E%3C/svg%3E")
			}
			
			input[type=radio]: active+label: before{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
			}
			
			input[type=radio]: checked+label: after{
				content: "";
				display: block;
				width: 5px;
				height: 5px;
				top: 5px;
				left: -14px;
				position: absolute;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=radio][disabled]+label: before{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
			}
			
			input[type=radio][disabled]: checked+label: after{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='gray'/%3E%3C/svg%3E")
			}
			
			input[type=email],input[type=password]{
				padding: 3px 4px;
				border: 1px solid #7f9db9;
				background-color: #fff;
				box-sizing: border-box;
				-webkit-appearance: none;
				-moz-appearance: none;
				appearance: none;
				border-radius: 0;
				height: 21px;
				line-height: 2
			}
			
			input[type=email]: focus,input[type=password]: focus{
				outline: none
			}
			
			input[type=range]{
				-webkit-appearance: none;
				width: 100%;
				background: transparent
			}
			
			input[type=range]: focus{
				outline: none
			}
			
			input[type=range]: :-webkit-slider-thumb{
				-webkit-appearance: none;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=range]: :-moz-range-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=range]: :-webkit-slider-runnable-track{
				background: #000;
				border-right: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
			}
			
			input[type=range]: :-moz-range-track{
				background: #000;
				border-right: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
			}
			
			input[type=range].has-box-indicator: :-webkit-slider-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=range].has-box-indicator: :-moz-range-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			.is-vertical{
				display: inline-block;
				width: 4px;
				height: 150px;
				transform: translateY(50%)
			}
			
			.is-vertical>input[type=range]{
				width: 150px;
				height: 4px;
				margin: 0 16px 0 10px;
				transform-origin: left;
				transform: rotate(270deg) translateX(calc(-50% + 8px))
			}
			
			.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
				border-left: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
			}
			
			.is-vertical>input[type=range]: :-moz-range-track{
				border-left: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
			}
			
			.is-vertical>input[type=range]: :-webkit-slider-thumb{
				transform: translateY(-8px) scaleX(-1)
			}
			
			.is-vertical>input[type=range]: :-moz-range-thumb{
				transform: translateY(2px) scaleX(-1)
			}
			
			.is-vertical>input[type=range].has-box-indicator: :-webkit-slider-thumb{
				transform: translateY(-10px) scaleX(-1)
			}
			
			.is-vertical>input[type=range].has-box-indicator: :-moz-range-thumb{
				transform: translateY(0) scaleX(-1)
			}
			
			.window{
				font-size: 11px;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #dfdfdf,inset -2px -2px grey,inset 2px 2px #fff;
				background: #ece9d8;
				padding: 3px
			}
			
			.window fieldset{
				margin-bottom: 9px
			}
			
			.title-bar{
				background: #000;
				padding: 3px 2px 3px 3px;
				display: flex;
				justify-content: space-between;
				align-items: center
			}
			
			.title-bar-text{
				font-weight: 700;
				color: #fff;
				letter-spacing: 0;
				margin-right: 24px
			}
			
			.title-bar-controls button{
				padding: 0;
				display: block;
				min-width: 16px;
				min-height: 14px
			}
			
			.title-bar-controls button: focus{
				outline: none
			}
			
			.window-body{
				margin: 8px
			}
			
			.window-body pre{
				margin: -8px
			}
			
			.status-bar{
				margin: 0 1px;
				display: flex;
				gap: 1px
			}
			
			.status-bar-field{
				box-shadow: inset -1px -1px #dfdfdf,inset 1px 1px grey;
				flex-grow: 1;
				padding: 2px 3px;
				margin: 0
			}
			
			ul.tree-view{
				display: block;
				background: #fff;
				padding: 6px;
				margin: 0
			}
			
			ul.tree-view li{
				list-style-type: none;
				margin-top: 3px
			}
			
			ul.tree-view a{
				text-decoration: none;
				color: #000
			}
			
			ul.tree-view a: focus{
				background-color: #2267cb;
				color: #fff
			}
			
			ul.tree-view ul{
				margin-top: 3px;
				margin-left: 16px;
				padding-left: 16px;
				border-left: 1px dotted grey
			}
			
			ul.tree-view ul>li{
				position: relative
			}
			
			ul.tree-view ul>li: before{
				content: "";
				display: block;
				position: absolute;
				left: -16px;
				top: 6px;
				width: 12px;
				border-bottom: 1px dotted grey
			}
			
			ul.tree-view ul>li: last-child: after{
				content: "";
				display: block;
				position: absolute;
				left: -20px;
				top: 7px;
				bottom: 0;
				width: 8px;
				background: #fff
			}
			
			ul.tree-view ul details>summary: before{
				margin-left: -22px;
				position: relative;
				z-index: 1
			}
			
			ul.tree-view details{
				margin-top: 0
			}
			
			ul.tree-view details>summary: before{
				text-align: center;
				display: block;
				float: left;
				content: "+";
				border: 1px solid grey;
				width: 8px;
				height: 9px;
				line-height: 9px;
				margin-right: 5px;
				padding-left: 1px;
				background-color: #fff
			}
			
			ul.tree-view details[open] summary{
				margin-bottom: 0
			}
			
			ul.tree-view details[open]>summary: before{
				content: "-"
			}
			
			fieldset{
				border-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='5' height='5' fill='gray' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h5v5H0V2h2v1h1V2H0' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h4v4H0V1h1v2h2V1H0'/%3E%3C/svg%3E") 2;
				padding: 10px;
				padding-block-start: 8px;
				margin: 0
			}
			
			legend{
				background: #ece9d8
			}
			
			menu[role=tablist]{
				position: relative;
				margin: 0 0 -2px;
				text-indent: 0;
				list-style-type: none;
				display: flex;
				padding-left: 3px
			}
			
			menu[role=tablist] button{
				z-index: 1;
				display: block;
				color: #222;
				text-decoration: none;
				min-width: unset
			}
			
			menu[role=tablist] button[aria-selected=true]{
				padding-bottom: 2px;margin-top: -2px;background-color: #ece9d8;position: relative;z-index: 8;margin-left: -3px;margin-bottom: 1px
			}
			
			menu[role=tablist] button: focus{
				outline: 1px dotted #222;outline-offset: -4px
			}
			
			menu[role=tablist].justified button{
				flex-grow: 1;text-align: center
			}
			
			[role=tabpanel]{
				padding: 14px;clear: both;background: linear-gradient(180deg,#fcfcfe,#f4f3ee);border: 1px solid #919b9c;position: relative;z-index: 2;margin-bottom: 9px
			}
			
			: :-webkit-scrollbar{
				width: 17px
			}
			
			: :-webkit-scrollbar-corner{
				background: #dfdfdf
			}
			
			: :-webkit-scrollbar-track: vertical{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 1' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1'/%3E%3Cpath stroke='%23f3f1ec' d='M1 0h1'/%3E%3Cpath stroke='%23f4f1ec' d='M2 0h1'/%3E%3Cpath stroke='%23f4f3ee' d='M3 0h1'/%3E%3Cpath stroke='%23f5f4ef' d='M4 0h1'/%3E%3Cpath stroke='%23f6f5f0' d='M5 0h1'/%3E%3Cpath stroke='%23f7f7f3' d='M6 0h1'/%3E%3Cpath stroke='%23f9f8f4' d='M7 0h1'/%3E%3Cpath stroke='%23f9f9f7' d='M8 0h1'/%3E%3Cpath stroke='%23fbfbf8' d='M9 0h1'/%3E%3Cpath stroke='%23fbfbf9' d='M10 0h2'/%3E%3Cpath stroke='%23fdfdfa' d='M12 0h1'/%3E%3Cpath stroke='%23fefefb' d='M13 0h3'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-track: horizontal{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 1 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1M0 16h1'/%3E%3Cpath stroke='%23f3f1ec' d='M0 1h1'/%3E%3Cpath stroke='%23f4f1ec' d='M0 2h1'/%3E%3Cpath stroke='%23f4f3ee' d='M0 3h1'/%3E%3Cpath stroke='%23f5f4ef' d='M0 4h1'/%3E%3Cpath stroke='%23f6f5f0' d='M0 5h1'/%3E%3Cpath stroke='%23f7f7f3' d='M0 6h1'/%3E%3Cpath stroke='%23f9f8f4' d='M0 7h1'/%3E%3Cpath stroke='%23f9f9f7' d='M0 8h1'/%3E%3Cpath stroke='%23fbfbf8' d='M0 9h1'/%3E%3Cpath stroke='%23fbfbf9' d='M0 10h1m-1 1h1'/%3E%3Cpath stroke='%23fdfdfa' d='M0 12h1'/%3E%3Cpath stroke='%23fefefb' d='M0 13h1m-1 1h1m-1 1h1'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-thumb{
				background-position: 50%;
				background-repeat: no-repeat;
				background-color: #c8d6fb;
				background-size: 7px;
				border: 1px solid #fff;
				border-radius: 2px;
				box-shadow: inset -3px 0 #bad1fc,inset 1px 1px #b7caf5
			}
			
			: :-webkit-scrollbar-thumb: vertical{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 7 8' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h6M0 2h6M0 4h6M0 6h6'/%3E%3Cpath stroke='%23bad1fc' d='M6 0h1M6 2h1M6 4h1'/%3E%3Cpath stroke='%23c8d6fb' d='M0 1h1M0 3h1M0 5h1M0 7h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h6M1 3h6M1 5h6M1 7h6'/%3E%3Cpath stroke='%23bad3fc' d='M6 6h1'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-thumb: horizontal{
				background-size: 8px;background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 8 7' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h1m1 0h1m1 0h1m1 0h1M0 1h1m1 0h1m1 0h1m1 0h1M0 2h1m1 0h1m1 0h1m1 0h1M0 3h1m1 0h1m1 0h1m1 0h1M0 4h1m1 0h1m1 0h1m1 0h1M0 5h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23c8d6fb' d='M1 0h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h1m1 0h1m1 0h1m1 0h1M1 2h1m1 0h1m1 0h1m1 0h1M1 3h1m1 0h1m1 0h1m1 0h1M1 4h1m1 0h1m1 0h1m1 0h1M1 5h1m1 0h1m1 0h1m1 0h1M1 6h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23bad1fc' d='M0 6h1m1 0h1'/%3E%3Cpath stroke='%23bad3fc' d='M4 6h1m1 0h1'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: vertical: start{
				height: 17px;
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 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			}
			
			: :-webkit-scrollbar-button: vertical: end{
				height: 17px;
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			}
			
			: :-webkit-scrollbar-button: horizontal: start{
				width: 17px;
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			}
			
			: :-webkit-scrollbar-button: horizontal: end{
				width: 17px;
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			}
			
			.window{
				box-shadow: inset -1px -1px #00138c,inset 1px 1px #0831d9,inset -2px -2px #001ea0,inset 2px 2px #166aee,inset -3px -3px #003bda,inset 3px 3px #0855dd;
				border-top-left-radius: 8px;
				border-top-right-radius: 8px;
				padding: 0 0 3px;
				-webkit-font-smoothing: antialiased
			}
			
			.title-bar{
				background: linear-gradient(180deg,#0997ff,#0053ee 8%,#0050ee 40%,#06f 88%,#06f 93%,#005bff 95%,#003dd7 96%,#003dd7);
				padding: 3px 5px 3px 3px;
				border-top: 1px solid #0831d9;
				border-left: 1px solid #0831d9;
				border-right: 1px solid #001ea0;
				border-top-left-radius: 8px;
				border-top-right-radius: 7px;
				font-size: 13px;
				text-shadow: 1px 1px #0f1089;
				height: 21px
			}
			
			.title-bar-text{
				padding-left: 3px
			}
			
			.title-bar-controls{
				display: flex
			}
			
			.title-bar-controls button{
				min-width: 21px;
				min-height: 21px;
				margin-left: 2px;
				background-repeat: no-repeat;
				background-position: 50%;
				box-shadow: none;
				background-color: #0050ee;
				transition: background .1s;
				border: none
			}
			
			.title-bar-controls button: active,.title-bar-controls button: focus,.title-bar-controls button: hover{
				box-shadow: none!important
			}
			
			.title-bar-controls button[aria-label=Minimize]{
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stroke='%239e3217' d='M2 10h1'/%3E%3Cpath stroke='%23b4381a' d='M3 10h1'/%3E%3Cpath stroke='%23df9a87' d='M10 10h1m-2 1h1m-2 2h1'/%3E%3Cpath stroke='%23c6441f' d='M13 10h1m3 0h1m-8 3h1m-1 3h1'/%3E%3Cpath stroke='%23c74520' d='M14 10h2m-6 4h1m-1 1h1m7 2h1m-7 1h1m4 0h1'/%3E%3Cpath stroke='%23c7451f' d='M16 10h1m1 2h1'/%3E%3Cpath stroke='%237b2711' d='M1 11h1'/%3E%3Cpath stroke='%23a13217' d='M2 11h1'/%3E%3Cpath stroke='%23b7391a' d='M3 11h1'/%3E%3Cpath stroke='%23e09b88' d='M11 11h1'/%3E%3Cpath stroke='%23e29d89' d='M12 11h1'/%3E%3Cpath stroke='%23c94621' d='M13 11h1m-3 2h1'/%3E%3Cpath stroke='%23ca4721' d='M14 11h1m2 1h1m-7 2h1m-1 1h1m0 2h1m2 1h1'/%3E%3Cpath stroke='%23ca4821' d='M15 11h1m1 6h1'/%3E%3Cpath stroke='%23c94620' d='M16 11h1m1 3h1m-8 2h1m6 0h1'/%3E%3Cpath stroke='%23c84620' d='M17 11h1m0 2h1'/%3E%3Cpath stroke='%23a53418' d='M2 12h1'/%3E%3Cpath stroke='%23b83a1b' d='M3 12h1'/%3E%3Cpath stroke='%23e19d89' d='M11 12h1'/%3E%3Cpath stroke='%23e39e89' d='M12 12h1'/%3E%3Cpath stroke='%23e0947c' d='M13 12h1'/%3E%3Cpath stroke='%23cc4a22' d='M14 12h1m-3 2h1m4 0h1m-6 1h1'/%3E%3Cpath stroke='%23cd4a22' d='M15 12h1m0 1h1m0 2h1m-5 1h1m1 1h1'/%3E%3Cpath stroke='%23cb4922' d='M16 12h1m0 1h1m-5 4h1'/%3E%3Cpath stroke='%23c3411e' d='M19 12h1m-1 1h1m-1 4h1m-8 2h2m3 0h1'/%3E%3Cpath stroke='%23a93618' d='M2 13h1'/%3E%3Cpath stroke='%23dd9987' d='M7 13h1m-2 2h1'/%3E%3Cpath stroke='%23e39f8a' d='M12 13h1'/%3E%3Cpath stroke='%23e59f8b' d='M13 13h1'/%3E%3Cpath stroke='%23e5a08b' d='M14 13h1m-2 1h1'/%3E%3Cpath stroke='%23ce4c23' d='M15 13h1m0 3h1'/%3E%3Cpath stroke='%23882b13' d='M1 14h1'/%3E%3Cpath stroke='%23e6a08b' d='M14 14h1'/%3E%3Cpath stroke='%23e6a18b' d='M15 14h1m-2 1h1'/%3E%3Cpath stroke='%23ce4b23' d='M16 14h1m-4 1h1'/%3E%3Cpath stroke='%238b2c14' d='M1 15h1m-1 1h1'/%3E%3Cpath stroke='%23ac3619' d='M2 15h1'/%3E%3Cpath stroke='%23d76b48' d='M15 15h1'/%3E%3Cpath stroke='%23cf4c23' d='M16 15h1m-2 1h1'/%3E%3Cpath stroke='%23c94721' d='M18 15h1m-3 3h1'/%3E%3Cpath stroke='%23bb3c1b' d='M3 16h1'/%3E%3Cpath stroke='%23bf3e1d' d='M6 16h1'/%3E%3Cpath stroke='%23cb4821' d='M12 16h1'/%3E%3Cpath stroke='%23cd4b23' d='M14 16h1'/%3E%3Cpath stroke='%23cc4922' d='M17 16h1m-4 1h1m1 0h1'/%3E%3Cpath stroke='%238d2d14' d='M1 17h1'/%3E%3Cpath stroke='%23bc3c1b' d='M3 17h1m-1 1h1'/%3E%3Cpath stroke='%23c84520' d='M11 17h1m1 1h1'/%3E%3Cpath stroke='%23ae3719' d='M2 18h1'/%3E%3Cpath stroke='%23c94720' d='M14 18h1'/%3E%3Cpath stroke='%23c95839' d='M19 18h1'/%3E%3Cpath stroke='%23a7bdf0' d='M0 19h1m0 1h1'/%3E%3Cpath stroke='%23ead7d3' d='M1 19h1'/%3E%3Cpath stroke='%23b34e35' d='M2 19h1'/%3E%3Cpath stroke='%23c03e1c' d='M8 19h1'/%3E%3Cpath stroke='%23c9583a' d='M18 19h1'/%3E%3Cpath stroke='%23f3dbd4' d='M19 19h1'/%3E%3Cpath stroke='%23a7bcef' d='M20 19h1m-2 1h1'/%3E%3C/svg%3E")
			}
			
			.status-bar{
				margin: 0 3px;
				box-shadow: inset 0 1px 2px grey;
				padding: 2px 1px;
				gap: 0
			}
			
			.status-bar-field{
				-webkit-font-smoothing: antialiased;
				box-shadow: none;
				padding: 1px 2px;
				border-right: 1px solid rgba(208,206,191,.75);
				border-left: 1px solid hsla(0,0%,100%,.75)
			}
			
			.status-bar-field: first-of-type{
				border-left: none
			}
			
			.status-bar-field: last-of-type{
				border-right: none
			}
			
			button{
				-webkit-font-smoothing: antialiased;
				box-sizing: border-box;
				border: 1px solid #003c74;
				background: linear-gradient(180deg,#fff,#ecebe5 86%,#d8d0c4);
				box-shadow: none;
				border-radius: 3px
			}
			
			button: not(: disabled).active,button: not(: disabled): active{
				box-shadow: none;
				background: linear-gradient(180deg,#cdcac3,#e3e3db 8%,#e5e5de 94%,#f2f2f1)
			}
			
			button: not(: disabled): hover{
				box-shadow: inset -1px 1px #fff0cf,inset 1px 2px #fdd889,inset -2px 2px #fbc761,inset 2px -2px #e5a01a
			}
			
			button.focused,button: focus{
				box-shadow: inset -1px 1px #cee7ff,inset 1px 2px #98b8ea,inset -2px 2px #bcd4f6,inset 1px -1px #89ade4,inset 2px -2px #89ade4
			}
			
			button: :-moz-focus-inner{
				border: 0
			}
			
			input,label,option,select,textarea{
				-webkit-font-smoothing: antialiased
			}
			
			input[type=radio]{
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				margin: 0;
				background: 0;
				position: fixed;
				opacity: 0;
				border: none
			}
			
			input[type=radio]+label{
				line-height: 16px
			}
			
			input[type=radio]+label: before{
				background: linear-gradient(135deg,#dcdcd7,#fff);
				border-radius: 50%;
				border: 1px solid #1d5281
			}
			
			input[type=radio]: not([disabled]): not(: active)+label: hover: before{
				box-shadow: inset -2px -2px #f8b636,inset 2px 2px #fedf9c
			}
			
			input[type=radio]: active+label: before{
				background: linear-gradient(135deg,#b0b0a7,#e3e1d2)
			}
			
			input[type=radio]: checked+label: after{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23a9dca6' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%234dbf4a' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23a0d29e' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%2355d551' d='M1 1h1'/%3E%3Cpath stroke='%2343c33f' d='M2 1h1'/%3E%3Cpath stroke='%2329a826' d='M3 1h1'/%3E%3Cpath stroke='%239acc98' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%2342c33f' d='M1 2h1'/%3E%3Cpath stroke='%2338b935' d='M2 2h1'/%3E%3Cpath stroke='%2321a121' d='M3 2h1'/%3E%3Cpath stroke='%23269623' d='M4 2h1'/%3E%3Cpath stroke='%232aa827' d='M1 3h1'/%3E%3Cpath stroke='%2322a220' d='M2 3h1'/%3E%3Cpath stroke='%23139210' d='M3 3h1'/%3E%3Cpath stroke='%2398c897' d='M4 3h1'/%3E%3Cpath stroke='%23249624' d='M2 4h1'/%3E%3Cpath stroke='%2398c997' d='M3 4h1'/%3E%3C/svg%3E")
			}
			
			input[type=radio]: focus+label{
				outline: 1px dotted #000
			}
			
			input[type=radio][disabled]+label: before{
				border: 1px solid #cac8bb;
				background: #fff
			}
			
			input[type=radio][disabled]: checked+label: after{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e8e6da' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23d2ceb5' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23e5e3d4' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%23d7d3bd' d='M1 1h1'/%3E%3Cpath stroke='%23d0ccb2' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23c7c2a2' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%23e2dfd0' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%23cdc8ac' d='M2 2h1'/%3E%3Cpath stroke='%23c5bf9f' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%23c3bd9c' d='M4 2h1'/%3E%3Cpath stroke='%23bfb995' d='M3 3h1'/%3E%3Cpath stroke='%23e2dfcf' d='M4 3h1M3 4h1'/%3E%3Cpath stroke='%23c4be9d' d='M2 4h1'/%3E%3C/svg%3E")
			}
			
			input[type=email],input[type=password],textarea: :selection{
				background: #2267cb;
				color: #fff
			}
			
			input[type=range]: :-webkit-slider-thumb{
				height: 21px;
				width: 11px;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
				transform: translateY(-8px)
			}
			
			input[type=range]: :-moz-range-thumb{
				height: 21px;
				width: 11px;
				border: 0;
				border-radius: 0;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
				transform: translateY(2px)
			}
			
			input[type=range]: :-webkit-slider-runnable-track{
				width: 100%;
				height: 2px;
				box-sizing: border-box;
				background: #ecebe4;
				border-right: 1px solid #f3f2ea;
				border-bottom: 1px solid #f3f2ea;
				border-radius: 2px;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
			}
			
			input[type=range]: :-moz-range-track{
				width: 100%;
				height: 2px;
				box-sizing: border-box;
				background: #ecebe4;
				border-right: 1px solid #f3f2ea;
				border-bottom: 1px solid #f3f2ea;
				border-radius: 2px;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
			}
			
			input[type=range].has-box-indicator: :-webkit-slider-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(-10px)
			}
			
			input[type=range].has-box-indicator: :-moz-range-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(0)
			}
			
			.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
				border-left: 1px solid #f3f2ea;
				border-right: 0;
				border-bottom: 1px solid #f3f2ea;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
			}
			
			.is-vertical>input[type=range]: :-moz-range-track{
				border-left: 1px solid #f3f2ea;
				border-right: 0;
				border-bottom: 1px solid #f3f2ea;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
			}
			
			fieldset{
				box-shadow: none;
				background: #fff;
				border: 1px solid #d0d0bf;
				border-radius: 4px;
				padding-top: 10px
			}
			
			legend{
				background: transparent;
				color: #0046d5
			}
			
			.field-row{
				display: flex;
				align-items: center
			}
			
			.field-row>*+*{
				margin-left: 6px
			}
			
			[class^=field-row]+[class^=field-row]{
				margin-top: 6px
			}
			
			.field-row-stacked{
				display: flex;
				flex-direction: column
			}
			
			.field-row-stacked *+*{
				margin-top: 6px
			}
			
			menu[role=tablist] button{
				background: linear-gradient(180deg,#fff,#fafaf9 26%,#f0f0ea 95%,#ecebe5);
				margin-left: -1px;
				margin-right: 2px;
				border-radius: 0;
				border-color: #91a7b4;
				border-top-right-radius: 3px;
				border-top-left-radius: 3px;
				padding: 0 12px 3px
			}
			
			menu[role=tablist] button: hover{
				box-shadow: unset;
				border-top: 1px solid #e68b2c;
				box-shadow: inset 0 2px #ffc73c
			}
			
			menu[role=tablist] button[aria-selected=true]{
				border-color: #919b9c;
				margin-right: -1px;
				border-bottom: 1px solid transparent;
				border-top: 1px solid #e68b2c;
				box-shadow: inset 0 2px #ffc73c
			}
			
			menu[role=tablist] button[aria-selected=true]: first-of-type: before{
				content: "";
				display: block;
				position: absolute;
				z-index: -1;
				top: 100%;
				left: -1px;
				height: 2px;
				width: 0;
				border-left: 1px solid #919b9c
			}
			
			[role=tabpanel]{
				box-shadow: inset 1px 1px #fcfcfe,inset -1px -1px #fcfcfe,1px 2px 2px 0 rgba(208,206,191,.75)
			}
			
			ul.tree-view{
				-webkit-font-smoothing: auto;
				border: 1px solid #7f9db9;
				padding: 2px 5px
			}
			
			@keyframes sliding{
				0%{
					transform: translateX(-30px)
				}
			
				to{
					transform: translateX(100%)
				}
			}
			
			progress{
				box-sizing: border-box;
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				height: 14px;
				border: 1px solid #686868;
				border-radius: 4px;
				padding: 1px 2px 1px 0;
				overflow: hidden;
				background-color: #fff;
				-webkit-box-shadow: inset 0 0 1px 0 #686868;
				-moz-box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress,progress: not([value]){
				box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress: not([value]){
				-moz-box-shadow: inset 0 0 1px 0 #686868;
				-webkit-box-shadow: inset 0 0 1px 0 #686868;
				height: 14px
			}
			
			progress[value]: :-webkit-progress-bar{
				background-color: transparent
			}
			
			progress[value]: :-webkit-progress-value{
				border-radius: 2px;
				background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
			}
			
			progress[value]: :-moz-progress-bar{
				border-radius: 2px;
				background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
			}
			
			progress: not([value]): :-webkit-progress-bar{
				width: 100%;
				background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff);
				animation: sliding 2s linear 0s infinite
			}
			
			progress: not([value]): :-webkit-progress-bar: not([value]){
				animation: sliding 2s linear 0s infinite;
				background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
			}
			
			progress: not([value]){
				position: relative
			}
			
			progress: not([value]): before{
				box-sizing: border-box;
				content: "";
				position: absolute;
				top: 0;
				left: 0;
				width: 100%;
				height: 100%;
				background-color: #fff;
				-webkit-box-shadow: inset 0 0 1px 0 #686868;
				-moz-box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress: not([value]): before,progress: not([value]): before: not([value]){
				box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress: not([value]): before: not([value]){
				-moz-box-shadow: inset 0 0 1px 0 #686868;
				-webkit-box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress: not([value]): after{
				box-sizing: border-box;
				content: "";
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2460 feature_id 18 batch_size 4988 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.1349550551250577 subsets 70",
			        2460,
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			        "str\\(\\'H\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4547 feature_id 18 batch_size 185 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 56",
			        4547,
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			        "str\\(\\'H\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1966 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 6",
			        1966,
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			        "str\\(\\'H\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1520 feature_id 18 batch_size 1193 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.999 subsets 65",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 175 feature_id 18 batch_size 1193 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.999 subsets 65",
			        175,
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			        "str\\(\\'H\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3160 feature_id 18 batch_size 1156 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.4006342494450288 subsets 37",
			        3160,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3074 feature_id 18 batch_size 445 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.3468808190841053 subsets 24",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 758 feature_id 18 batch_size 1174 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.37565097600453357 subsets 41",
			        758,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1433 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.04376802787475764 subsets 44",
			        1433,
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			        "str\\(\\'KL\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4237 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.3896204327407836 subsets 16",
			        4237,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1153 feature_id 18 batch_size 1194 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.999 subsets 40",
			        1153,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 659 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.7874679016069511 subsets 1",
			        659,
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			        "str\\(\\'H\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3731 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.10354211474375588 subsets 51",
			        3731,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2654 feature_id 18 batch_size 1 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 32",
			        2654,
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			        "str\\(\\'KL\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2408 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.36786148307206257 subsets 36",
			        2408,
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			        "str\\(\\'H\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 1196 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.999 subsets 100",
			        1,
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			        "str\\(\\'H\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1549 feature_id 18 batch_size 1190 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.9572903588367169 subsets 48",
			        1549,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 607 feature_id 18 batch_size 1195 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.999 subsets 24",
			        607,
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			        "str\\(\\'KL\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3642 feature_id 18 batch_size 1 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 22",
			        3642,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4612 feature_id 18 batch_size 3143 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.5605887060030798 subsets 16",
			        4612,
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			        "str\\(\\'H\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3868 feature_id 18 batch_size 4 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 56",
			        3868,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3770 feature_id 18 batch_size 931 divergence str\\(\\'H\\'\\) detect_batch 1 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 100",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3087 feature_id 18 batch_size 165 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 26",
			        3087,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3040 feature_id 18 batch_size 1 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 94",
			        3040,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 401 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.001 subsets 62",
			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1167 feature_id 18 batch_size 136 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 1",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2172 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.25787217257293615 subsets 35",
			        2172,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2837 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.7076270008122164 subsets 15",
			        2837,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 538 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.3852737109339281 subsets 16",
			        538,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 847 feature_id 18 batch_size 1 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 60",
			        847,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4765 feature_id 18 batch_size 173 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 38",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4696 feature_id 18 batch_size 1 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 80",
			        4696,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 324 feature_id 18 batch_size 7 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.5492057368831735 subsets 67",
			        324,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2962 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.1216125446721356 subsets 58",
			        2962,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3806 feature_id 18 batch_size 2 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 67",
			        3806,
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			        "str\\(\\'KL\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4354 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.7510063176233523 subsets 73",
			        4354,
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			        "str\\(\\'KL\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2869 feature_id 18 batch_size 127 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 25",
			        2869,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4659 feature_id 18 batch_size 1 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 15",
			        4659,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1747 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.3802644569756746 subsets 4",
			        1747,
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			        "str\\(\\'H\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 681 feature_id 18 batch_size 1 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 8",
			        681,
			        1,
			        "str\\(\\'H\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 579 feature_id 18 batch_size 74 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 33",
			        579,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4142 feature_id 18 batch_size 1 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 64",
			        4142,
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			        "str\\(\\'H\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1908 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.3899041344713316 subsets 23",
			        1908,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4145 feature_id 18 batch_size 125 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 62",
			        4145,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3183 feature_id 18 batch_size 1 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 18",
			        3183,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 85 feature_id 18 batch_size 1 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.999 subsets 57",
			        85,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 805 feature_id 18 batch_size 1 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 100",
			        805,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3347 feature_id 18 batch_size 194 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 33",
			        3347,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 570 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 48",
			        1,
			        570,
			        "str\\(\\'KL\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3020 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 1 statistic str\\(\\'tstat\\'\\) significance 0.41152211475971845 subsets 100",
			        3020,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4318 feature_id 18 batch_size 1186 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.999 subsets 79",
			        4318,
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			        "str\\(\\'KL\\'\\)",
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			    [
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			        255,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3453 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.38553011143618726 subsets 40",
			        3453,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4524 feature_id 18 batch_size 1181 divergence str\\(\\'H\\'\\) detect_batch 1 statistic str\\(\\'tstat\\'\\) significance 0.999 subsets 15",
			        4524,
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			        "str\\(\\'H\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3974 feature_id 18 batch_size 601 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 65",
			        3974,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 925 feature_id 18 batch_size 1 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 25",
			        925,
			        1,
			        "str\\(\\'KL\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2900 feature_id 18 batch_size 3146 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.380536043260624 subsets 67",
			        2900,
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			        "str\\(\\'H\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4533 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.40163153392199086 subsets 7",
			        4533,
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			        "str\\(\\'H\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 503 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.3942690847243948 subsets 22",
			        503,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2471 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.6363317628074697 subsets 12",
			        2471,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4585 feature_id 18 batch_size 1 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 11",
			        4585,
			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3644 feature_id 18 batch_size 200 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 53",
			        3644,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1605 feature_id 18 batch_size 1 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 31",
			        1605,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1872 feature_id 18 batch_size 4126 divergence str\\(\\'H\\'\\) detect_batch 1 statistic str\\(\\'stdev\\'\\) significance 0.001 subsets 60",
			        1872,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 857 feature_id 18 batch_size 199 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 20",
			        857,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4529 feature_id 18 batch_size 186 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 1",
			        4529,
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			        "str\\(\\'H\\'\\)",
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			    [
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			        236,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 693 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.7504150026416944 subsets 87",
			        693,
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			        "str\\(\\'H\\'\\)",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3170 feature_id 18 batch_size 1 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 20",
			        3170,
			        1,
			        "str\\(\\'KL\\'\\)",
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			        0.001,
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			    [
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			        288,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1737 feature_id 18 batch_size 4129 divergence str\\(\\'H\\'\\) detect_batch 1 statistic str\\(\\'stdev\\'\\) significance 0.001 subsets 71",
			        1737,
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			        "str\\(\\'H\\'\\)",
			        1,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3364 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.8657997754629011 subsets 44",
			        3364,
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			    [
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			        241,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 488 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.2620603694313742 subsets 38",
			        488,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3662 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 93",
			        3662,
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			        "str\\(\\'H\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 605 feature_id 18 batch_size 1 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 17",
			        605,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2852 feature_id 18 batch_size 1 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 40",
			        2852,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1491 feature_id 18 batch_size 4155 divergence str\\(\\'KL\\'\\) detect_batch 1 statistic str\\(\\'stdev\\'\\) significance 0.001 subsets 7",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3526 feature_id 18 batch_size 4123 divergence str\\(\\'H\\'\\) detect_batch 1 statistic str\\(\\'stdev\\'\\) significance 0.001 subsets 19",
			        3526,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 382 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.604209496798832 subsets 41",
			        382,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4456 feature_id 18 batch_size 4098 divergence str\\(\\'H\\'\\) detect_batch 1 statistic str\\(\\'stdev\\'\\) significance 0.001 subsets 50",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1074 feature_id 18 batch_size 1 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 44",
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			        691.3271257267442,
			        -1,
			        0.04737694704049844,
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			    [
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			        259,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2218 feature_id 18 batch_size 4092 divergence str\\(\\'H\\'\\) detect_batch 1 statistic str\\(\\'stdev\\'\\) significance 0.001 subsets 11",
			        2218,
			        4092,
			        "str\\(\\'H\\'\\)",
			        1,
			        "str\\(\\'stdev\\'\\)",
			        0.001,
			        11,
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			        "None",
			        "i7169",
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			    [
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			        233,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1963 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.28422870904449027 subsets 80",
			        1963,
			        5000,
			        "str\\(\\'H\\'\\)",
			        2,
			        "str\\(\\'tstat\\'\\)",
			        0.28422870904449027,
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			    [
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			        241,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1662 feature_id 18 batch_size 4995 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.20335044205312633 subsets 41",
			        1662,
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			        "str\\(\\'H\\'\\)",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1971 feature_id 18 batch_size 1199 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.999 subsets 7",
			        1971,
			        1199,
			        "str\\(\\'KL\\'\\)",
			        2,
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			        0.999,
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			    [
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			        338,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 21 feature_id 18 batch_size 1 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 75",
			        21,
			        1,
			        "str\\(\\'H\\'\\)",
			        3,
			        "str\\(\\'stdev\\'\\)",
			        0.999,
			        75,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 915 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.3407625929225921 subsets 87",
			        915,
			        5000,
			        "str\\(\\'KL\\'\\)",
			        2,
			        "str\\(\\'tstat\\'\\)",
			        0.3407625929225921,
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			    [
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			        251,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3255 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.34266042382178846 subsets 20",
			        3255,
			        5000,
			        "str\\(\\'KL\\'\\)",
			        3,
			        "str\\(\\'tstat\\'\\)",
			        0.34266042382178846,
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			        183,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1282 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.029658741822726323 subsets 95",
			        1282,
			        5000,
			        "str\\(\\'H\\'\\)",
			        2,
			        "str\\(\\'tstat\\'\\)",
			        0.029658741822726323,
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			        482,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 906 feature_id 18 batch_size 1 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 14",
			        906,
			        1,
			        "str\\(\\'H\\'\\)",
			        2,
			        "str\\(\\'stdev\\'\\)",
			        0.999,
			        14,
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			    [
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			        237,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4261 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.36766931093603394 subsets 78",
			        4261,
			        5000,
			        "str\\(\\'H\\'\\)",
			        3,
			        "str\\(\\'tstat\\'\\)",
			        0.36766931093603394,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4899 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.8036790350025268 subsets 8",
			        4899,
			        5000,
			        "str\\(\\'H\\'\\)",
			        2,
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			        0.8036790350025268,
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			    [
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			        243,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2565 feature_id 18 batch_size 119 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 44",
			        2565,
			        119,
			        "str\\(\\'KL\\'\\)",
			        3,
			        "str\\(\\'stdev\\'\\)",
			        0.999,
			        44,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4250 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.39484951840539645 subsets 62",
			        4250,
			        5000,
			        "str\\(\\'KL\\'\\)",
			        3,
			        "str\\(\\'tstat\\'\\)",
			        0.39484951840539645,
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			    [
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			        255,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 973 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.9776602116336028 subsets 66",
			        973,
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			        "str\\(\\'KL\\'\\)",
			        3,
			        "str\\(\\'stdev\\'\\)",
			        0.9776602116336028,
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			        "i7173",
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			    [
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			        233,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3780 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.27981583208491073 subsets 46",
			        3780,
			        5000,
			        "str\\(\\'H\\'\\)",
			        3,
			        "str\\(\\'tstat\\'\\)",
			        0.27981583208491073,
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			    [
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			        268,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1871 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.5507763685902075 subsets 71",
			        1871,
			        5000,
			        "str\\(\\'KL\\'\\)",
			        2,
			        "str\\(\\'tstat\\'\\)",
			        0.5507763685902075,
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			    [
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			        256,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1061 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 77",
			        1061,
			        5000,
			        "str\\(\\'KL\\'\\)",
			        2,
			        "str\\(\\'tstat\\'\\)",
			        0.001,
			        77,
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			    [
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			        247,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1808 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.6670579391795557 subsets 51",
			        1808,
			        5000,
			        "str\\(\\'KL\\'\\)",
			        2,
			        "str\\(\\'stdev\\'\\)",
			        0.6670579391795557,
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			    [
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			        254,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1241 feature_id 18 batch_size 4971 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.045778809609228095 subsets 32",
			        1241,
			        4971,
			        "str\\(\\'H\\'\\)",
			        2,
			        "str\\(\\'tstat\\'\\)",
			        0.045778809609228095,
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			    [
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			        268,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1920 feature_id 18 batch_size 1219 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.999 subsets 6",
			        1920,
			        1219,
			        "str\\(\\'H\\'\\)",
			        3,
			        "str\\(\\'tstat\\'\\)",
			        0.999,
			        6,
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			    [
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			        255,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1675 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.46653343986621376 subsets 32",
			        1675,
			        5000,
			        "str\\(\\'KL\\'\\)",
			        3,
			        "str\\(\\'tstat\\'\\)",
			        0.46653343986621376,
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			    [
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			        314,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 261 feature_id 18 batch_size 4999 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.16553349949183596 subsets 3",
			        261,
			        4999,
			        "str\\(\\'H\\'\\)",
			        2,
			        "str\\(\\'tstat\\'\\)",
			        0.16553349949183596,
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			    [
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			        255,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1077 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.21920254112325233 subsets 10",
			        1077,
			        5000,
			        "str\\(\\'KL\\'\\)",
			        2,
			        "str\\(\\'tstat\\'\\)",
			        0.21920254112325233,
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			    [
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			        227,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1077 feature_id 18 batch_size 4979 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.19481876561979533 subsets 27",
			        1077,
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			        "str\\(\\'KL\\'\\)",
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			        0.19481876561979533,
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			        214,
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			    [
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			        242,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 174 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.8781345423503308 subsets 34",
			        174,
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			        "str\\(\\'KL\\'\\)",
			        2,
			        "str\\(\\'stdev\\'\\)",
			        0.8781345423503308,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3754 feature_id 18 batch_size 1186 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.999 subsets 100",
			        3754,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3547 feature_id 18 batch_size 4165 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.001 subsets 83",
			        3547,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 760 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.6191746581719627 subsets 27",
			        760,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 212 feature_id 18 batch_size 1206 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.999 subsets 32",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2577 feature_id 18 batch_size 4450 divergence str\\(\\'H\\'\\) detect_batch 1 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 33",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4972 feature_id 18 batch_size 4147 divergence str\\(\\'H\\'\\) detect_batch 1 statistic str\\(\\'stdev\\'\\) significance 0.001 subsets 100",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2457 feature_id 18 batch_size 4168 divergence str\\(\\'H\\'\\) detect_batch 1 statistic str\\(\\'stdev\\'\\) significance 0.001 subsets 92",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2962 feature_id 18 batch_size 4179 divergence str\\(\\'H\\'\\) detect_batch 1 statistic str\\(\\'stdev\\'\\) significance 0.001 subsets 84",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3156 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.42804267429030807 subsets 22",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3450 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.07043166923939476 subsets 10",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 431 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.9932109569908057 subsets 11",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3394 feature_id 18 batch_size 1556 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 20",
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			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 4092 divergence str\\(\\'H\\'\\) detect_batch 1 statistic str\\(\\'stdev\\'\\) significance 0.001 subsets 3",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 802 feature_id 18 batch_size 4128 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.001 subsets 59",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2816 feature_id 18 batch_size 4124 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 35",
			        2816,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4601 feature_id 18 batch_size 3151 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.5581280502087325 subsets 100",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3850 feature_id 18 batch_size 3096 divergence str\\(\\'KL\\'\\) detect_batch 1 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 38",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 5000 feature_id 18 batch_size 2377 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.001 subsets 61",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 4170 divergence str\\(\\'H\\'\\) detect_batch 1 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 55",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4647 feature_id 18 batch_size 3220 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.9849450341319766 subsets 52",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 5000 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.6035436874435524 subsets 28",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 1177 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.999 subsets 88",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2639 feature_id 18 batch_size 3137 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.8514016323178816 subsets 26",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2667 feature_id 18 batch_size 2500 divergence str\\(\\'H\\'\\) detect_batch 1 statistic str\\(\\'stdev\\'\\) significance 0.001 subsets 55",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 890 feature_id 18 batch_size 2333 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.001 subsets 51",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 828 feature_id 18 batch_size 248 divergence str\\(\\'H\\'\\) detect_batch 1 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 100",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 5000 feature_id 18 batch_size 4414 divergence str\\(\\'KL\\'\\) detect_batch 1 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 19",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1 feature_id 18 batch_size 258 divergence str\\(\\'H\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.001 subsets 31",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 5000 feature_id 18 batch_size 4422 divergence str\\(\\'KL\\'\\) detect_batch 1 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 63",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 472 feature_id 18 batch_size 5000 divergence str\\(\\'H\\'\\) detect_batch 3 statistic str\\(\\'tstat\\'\\) significance 0.378895524091517 subsets 73",
			        472,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4177 feature_id 18 batch_size 4409 divergence str\\(\\'KL\\'\\) detect_batch 1 statistic str\\(\\'tstat\\'\\) significance 0.999 subsets 100",
			        4177,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 374 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 3 statistic str\\(\\'stdev\\'\\) significance 0.8122035484296609 subsets 70",
			        374,
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			        1451,
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			        1856,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 5000 feature_id 18 batch_size 5000 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'tstat\\'\\) significance 0.3918314181804805 subsets 57",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3334 feature_id 18 batch_size 4396 divergence str\\(\\'KL\\'\\) detect_batch 2 statistic str\\(\\'stdev\\'\\) significance 0.999 subsets 68",
			        3334,
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			        1745075511.4981668,
			        20,
			        1,
			        5
			    ],
			    [
			        1745075512.0025492,
			        20,
			        1,
			        5
			    ],
			    [
			        1745075513.4001145,
			        20,
			        2,
			        10
			    ],
			    [
			        1745075513.8643332,
			        20,
			        2,
			        10
			    ],
			    [
			        1745075523.6470246,
			        20,
			        1,
			        5
			    ],
			    [
			        1745075523.8567607,
			        20,
			        1,
			        5
			    ],
			    [
			        1745075883.2492864,
			        20,
			        1,
			        5
			    ],
			    [
			        1745075883.6248791,
			        20,
			        1,
			        5
			    ],
			    [
			        1745075885.018819,
			        20,
			        2,
			        10
			    ],
			    [
			        1745075885.4792094,
			        20,
			        2,
			        10
			    ],
			    [
			        1745075894.6103566,
			        20,
			        1,
			        5
			    ],
			    [
			        1745075894.9000762,
			        20,
			        1,
			        5
			    ]
			];
			var tab_main_worker_cpu_ram_csv_json = [
			    [
			        1744998965,
			        642.328125,
			        21.9
			    ],
			    [
			        1744998965,
			        642.328125,
			        21.7
			    ],
			    [
			        1744998965,
			        642.38671875,
			        20.7
			    ],
			    [
			        1744998965,
			        642.38671875,
			        18.2
			    ],
			    [
			        1744998965,
			        642.38671875,
			        16.1
			    ],
			    [
			        1744998965,
			        642.38671875,
			        21.3
			    ],
			    [
			        1744998965,
			        642.38671875,
			        16.7
			    ],
			    [
			        1744999587,
			        704.921875,
			        32.4
			    ],
			    [
			        1744999587,
			        704.921875,
			        30.8
			    ],
			    [
			        1744999587,
			        704.921875,
			        24.9
			    ],
			    [
			        1744999587,
			        704.921875,
			        20
			    ],
			    [
			        1745005245,
			        930.7734375,
			        9.6
			    ],
			    [
			        1745005245,
			        930.7734375,
			        7.4
			    ],
			    [
			        1745005245,
			        930.7734375,
			        7
			    ],
			    [
			        1745005245,
			        930.7734375,
			        8.9
			    ],
			    [
			        1745010290,
			        932.02734375,
			        7.3
			    ],
			    [
			        1745010290,
			        932.02734375,
			        7.1
			    ],
			    [
			        1745010290,
			        932.02734375,
			        7
			    ],
			    [
			        1745010290,
			        932.02734375,
			        2.4
			    ],
			    [
			        1745021527,
			        943.7265625,
			        7.2
			    ],
			    [
			        1745021527,
			        943.7265625,
			        6.6
			    ],
			    [
			        1745021527,
			        943.7265625,
			        6.4
			    ],
			    [
			        1745021527,
			        943.7265625,
			        7
			    ],
			    [
			        1745028492,
			        978.50390625,
			        7.4
			    ],
			    [
			        1745028493,
			        978.50390625,
			        7.3
			    ],
			    [
			        1745028493,
			        978.50390625,
			        7.1
			    ],
			    [
			        1745028493,
			        978.50390625,
			        10.9
			    ],
			    [
			        1745035024,
			        984.00390625,
			        7.4
			    ],
			    [
			        1745035024,
			        984.00390625,
			        7.3
			    ],
			    [
			        1745035024,
			        984.00390625,
			        7.4
			    ],
			    [
			        1745035024,
			        984.00390625,
			        10.3
			    ],
			    [
			        1745044574,
			        991.125,
			        7.4
			    ],
			    [
			        1745044574,
			        991.125,
			        7
			    ],
			    [
			        1745044574,
			        991.125,
			        6.9
			    ],
			    [
			        1745044574,
			        991.125,
			        10
			    ],
			    [
			        1745058414,
			        1040.63671875,
			        7.4
			    ],
			    [
			        1745058414,
			        1040.63671875,
			        6.1
			    ],
			    [
			        1745058414,
			        1040.63671875,
			        6.7
			    ],
			    [
			        1745058414,
			        1040.63671875,
			        2.7
			    ],
			    [
			        1745075901,
			        1064.47265625,
			        7.5
			    ],
			    [
			        1745075901,
			        1064.47265625,
			        6.5
			    ],
			    [
			        1745075901,
			        1064.47265625,
			        6.9
			    ],
			    [
			        1745075901,
			        1064.47265625,
			        6.1
			    ]
			];
			var tab_main_worker_cpu_ram_headers_json = [
			    "timestamp",
			    "ram_usage_mb",
			    "cpu_usage_percent"
			];
			
			"use strict";
			
			function add_default_layout_data (layout, no_height = 0) {
				layout["width"] = get_graph_width();
				if (!no_height) {
					layout["height"] = get_graph_height();
				}
				layout["paper_bgcolor"] = 'rgba(0,0,0,0)';
				layout["plot_bgcolor"] = 'rgba(0,0,0,0)';
			
				return layout;
			}
			
			function get_marker_size() {
				return 12;
			}
			
			function get_text_color() {
				return theme == "dark" ? "white" : "black";
			}
			
			function get_font_size() {
				return 14;
			}
			
			function get_graph_height() {
				return 800;
			}
			
			function get_font_data() {
				return {
					size: get_font_size(),
					color: get_text_color()
				}
			}
			
			function get_axis_title_data(name, axis_type = "") {
				if(axis_type) {
					return {
						text: name,
						type: axis_type,
						font: get_font_data()
					};
				}
			
				return {
					text: name,
					font: get_font_data()
				};
			}
			
			function get_graph_width() {
				var width = document.body.clientWidth || window.innerWidth || document.documentElement.clientWidth;
				return Math.max(800, Math.floor(width * 0.9));
			}
			
			function createTable(data, headers, table_name) {
				if (!$("#" + table_name).length) {
					console.error("#" + table_name + " not found");
					return;
				}
			
				new gridjs.Grid({
					columns: headers,
					data: data,
					search: true,
					sort: true,
					ellipsis: false
				}).render(document.getElementById(table_name));
			
				if (typeof apply_theme_based_on_system_preferences === 'function') {
					apply_theme_based_on_system_preferences();
				}
			
				colorize_table_entries();
			
				add_colorize_to_gridjs_table();
			}
			
			function download_as_file(id, filename) {
				var text = $("#" + id).text();
				var blob = new Blob([text], {
					type: "text/plain"
				});
				var link = document.createElement("a");
				link.href = URL.createObjectURL(blob);
				link.download = filename;
				document.body.appendChild(link);
				link.click();
				document.body.removeChild(link);
			}
			
			function copy_to_clipboard_from_id (id) {
				var text = $("#" + id).text();
			
				copy_to_clipboard(text);
			}
			
			function copy_to_clipboard(text) {
				if (!navigator.clipboard) {
					let textarea = document.createElement("textarea");
					textarea.value = text;
					document.body.appendChild(textarea);
					textarea.select();
					try {
						document.execCommand("copy");
					} catch (err) {
						console.error("Copy failed:", err);
					}
					document.body.removeChild(textarea);
					return;
				}
			
				navigator.clipboard.writeText(text).then(() => {
					console.log("Text copied to clipboard");
				}).catch(err => {
					console.error("Failed to copy text:", err);
				});
			}
			
			function filterNonEmptyRows(data) {
				var new_data = [];
				for (var row_idx = 0; row_idx <  data.length; row_idx++) {
					var line = data[row_idx];
			
					var line_has_empty_data = false;
			
					for (var col_idx = 0; col_idx < line.length; col_idx++) {
						var col_header_name = tab_results_headers_json[col_idx];
						var single_data_point = line[col_idx];
			
						if(single_data_point === "" && !special_col_names.includes(col_header_name)) {
							line_has_empty_data = true;
							continue;
						}
					}
			
					if(!line_has_empty_data) {
						new_data.push(line);
					}
				}
			
				return new_data;
			}
			
			function make_text_in_parallel_plot_nicer() {
				$(".parcoords g > g > text").each(function() {
					if (theme == "dark") {
						$(this)
							.css("text-shadow", "unset")
							.css("font-size", "0.9em")
							.css("fill", "white")
							.css("stroke", "black")
							.css("stroke-width", "2px")
							.css("paint-order", "stroke fill");
					} else {
						$(this)
							.css("text-shadow", "unset")
							.css("font-size", "0.9em")
							.css("fill", "black")
							.css("stroke", "unset")
							.css("stroke-width", "unset")
							.css("paint-order", "stroke fill");
					}
				});
			}
			
			function createParallelPlot(dataArray, headers, resultNames, ignoreColumns = []) {
				if ($("#parallel-plot").data("loaded") == "true") {
					return;
				}
			
				dataArray = filterNonEmptyRows(dataArray);
				const ignoreSet = new Set(ignoreColumns);
				const numericalCols = [];
				const categoricalCols = [];
				const categoryMappings = {};
			
				headers.forEach((header, colIndex) => {
					if (ignoreSet.has(header)) return;
			
					const values = dataArray.map(row => row[colIndex]);
					if (values.every(val => !isNaN(parseFloat(val)))) {
						numericalCols.push({ name: header, index: colIndex });
					} else {
						categoricalCols.push({ name: header, index: colIndex });
						const uniqueValues = [...new Set(values)];
						categoryMappings[header] = Object.fromEntries(uniqueValues.map((val, i) => [val, i]));
					}
				});
			
				const dimensions = [];
			
				numericalCols.forEach(col => {
					dimensions.push({
						label: col.name,
						values: dataArray.map(row => parseFloat(row[col.index])),
						range: [
							Math.min(...dataArray.map(row => parseFloat(row[col.index]))),
							Math.max(...dataArray.map(row => parseFloat(row[col.index])))
						]
					});
				});
			
				categoricalCols.forEach(col => {
					dimensions.push({
						label: col.name,
						values: dataArray.map(row => categoryMappings[col.name][row[col.index]]),
						tickvals: Object.values(categoryMappings[col.name]),
						ticktext: Object.keys(categoryMappings[col.name])
					});
				});
			
				let colorScale = null;
				let colorValues = null;
			
				if (resultNames.length > 1) {
					let selectBox = '<select id="result-select" style="margin-bottom: 10px;">';
					selectBox += '<option value="none">No color</option>';
					var k = 0;
					resultNames.forEach(resultName => {
						var minMax = result_min_max[k];
						if(minMax === undefined) {
							minMax = "min [automatically chosen]"
						}
						selectBox += `<option value="${resultName}">${resultName} (${minMax})</option>`;
						k = k + 1;
					});
					selectBox += '</select>';
					$("#parallel-plot").before(selectBox);
			
					$("#result-select").change(function() {
						const selectedResult = $(this).val();
						if (selectedResult === "none") {
							colorValues = null;
							colorScale = null;
						} else {
							const resultCol = numericalCols.find(col => col.name.toLowerCase() === selectedResult.toLowerCase());
							colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
			
							let minResult = Math.min(...colorValues);
							let maxResult = Math.max(...colorValues);
			
							var _result_min_max_idx = result_names.indexOf(selectedResult);
			
							let invertColor = false;
							if (result_min_max.length > _result_min_max_idx) {
								invertColor = result_min_max[_result_min_max_idx] === "max";
							}
			
							colorScale = invertColor
								? [[0, 'red'], [1, 'green']]
								: [[0, 'green'], [1, 'red']];
						}
						updatePlot();
					});
				} else {
					let invertColor = false;
					if (Object.keys(result_min_max).length == 1) {
						invertColor = result_min_max[0] === "max";
					}
			
					colorScale = invertColor
						? [[0, 'red'], [1, 'green']]
						: [[0, 'green'], [1, 'red']];
			
					const resultCol = numericalCols.find(col => col.name.toLowerCase() === resultNames[0].toLowerCase());
					colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
				}
			
				function updatePlot() {
					const trace = {
						type: 'parcoords',
						dimensions: dimensions,
						line: colorValues ? { color: colorValues, colorscale: colorScale } : {},
						unselected: {
							line: {
								color: get_text_color(),
								opacity: 0
							}
						},
					};
			
					dimensions.forEach(dim => {
						if (!dim.line) {
							dim.line = {};
						}
						if (!dim.line.color) {
							dim.line.color = 'rgba(169,169,169, 0.01)';
						}
					});
			
					Plotly.newPlot('parallel-plot', [trace], add_default_layout_data({}));
			
					make_text_in_parallel_plot_nicer();
				}
			
				updatePlot();
			
				$("#parallel-plot").data("loaded", "true");
			
				make_text_in_parallel_plot_nicer();
			}
			
			function plotWorkerUsage() {
				if($("#workerUsagePlot").data("loaded") == "true") {
					return;
				}
				var data = tab_worker_usage_csv_json;
				if (!Array.isArray(data) || data.length === 0) {
					console.error("Invalid or empty data provided.");
					return;
				}
			
				let timestamps = [];
				let desiredWorkers = [];
				let realWorkers = [];
			
				for (let i = 0; i < data.length; i++) {
					let entry = data[i];
			
					if (!Array.isArray(entry) || entry.length < 3) {
						console.warn("Skipping invalid entry:", entry);
						continue;
					}
			
					let unixTime = parseFloat(entry[0]);
					let desired = parseInt(entry[1], 10);
					let real = parseInt(entry[2], 10);
			
					if (isNaN(unixTime) || isNaN(desired) || isNaN(real)) {
						console.warn("Skipping invalid numerical values:", entry);
						continue;
					}
			
					timestamps.push(new Date(unixTime * 1000).toISOString());
					desiredWorkers.push(desired);
					realWorkers.push(real);
				}
			
				let trace1 = {
					x: timestamps,
					y: desiredWorkers,
					mode: 'lines+markers',
					name: 'Desired Workers',
					line: {
						color: 'blue'
					}
				};
			
				let trace2 = {
					x: timestamps,
					y: realWorkers,
					mode: 'lines+markers',
					name: 'Real Workers',
					line: {
						color: 'red'
					}
				};
			
				let layout = {
					title: "Worker Usage Over Time",
					xaxis: {
						title: get_axis_title_data("Time", "date")
					},
					yaxis: {
						title: get_axis_title_data("Number of Workers")
					},
					legend: {
						x: 0,
						y: 1
					}
				};
			
				Plotly.newPlot('workerUsagePlot', [trace1, trace2], add_default_layout_data(layout));
				$("#workerUsagePlot").data("loaded", "true");
			}
			
			function plotCPUAndRAMUsage() {
				if($("#mainWorkerCPURAM").data("loaded") == "true") {
					return;
				}
				var timestamps = tab_main_worker_cpu_ram_csv_json.map(row => new Date(row[0] * 1000));
				var ramUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[1]);
				var cpuUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[2]);
			
				var trace1 = {
					x: timestamps,
					y: cpuUsage,
					mode: 'lines+markers',
					marker: {
						size: get_marker_size(),
					},
					name: 'CPU Usage (%)',
					type: 'scatter',
					yaxis: 'y1'
				};
			
				var trace2 = {
					x: timestamps,
					y: ramUsage,
					mode: 'lines+markers',
					marker: {
						size: get_marker_size(),
					},
					name: 'RAM Usage (MB)',
					type: 'scatter',
					yaxis: 'y2'
				};
			
				var layout = {
					title: 'CPU and RAM Usage Over Time',
					xaxis: {
						title: get_axis_title_data("Timestamp", "date"),
						tickmode: 'array',
						tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
						ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
						tickangle: -45
					},
					yaxis: {
						title: get_axis_title_data("CPU Usage (%)"),
						rangemode: 'tozero'
					},
					yaxis2: {
						title: get_axis_title_data("RAM Usage (MB)"),
						overlaying: 'y',
						side: 'right',
						rangemode: 'tozero'
					},
					legend: {
						x: 0.1,
						y: 0.9
					}
				};
			
				var data = [trace1, trace2];
				Plotly.newPlot('mainWorkerCPURAM', data, add_default_layout_data(layout));
				$("#mainWorkerCPURAM").data("loaded", "true");
			}
			
			function plotScatter2d() {
				if ($("#plotScatter2d").data("loaded") == "true") {
					return;
				}
			
				var plotDiv = document.getElementById("plotScatter2d");
			
				var minInput = document.getElementById("minValue");
				var maxInput = document.getElementById("maxValue");
			
				if (!minInput || !maxInput) {
					minInput = document.createElement("input");
					minInput.id = "minValue";
					minInput.type = "number";
					minInput.placeholder = "Min Value";
					minInput.step = "any";
			
					maxInput = document.createElement("input");
					maxInput.id = "maxValue";
					maxInput.type = "number";
					maxInput.placeholder = "Max Value";
					maxInput.step = "any";
			
					var inputContainer = document.createElement("div");
					inputContainer.style.marginBottom = "10px";
					inputContainer.appendChild(minInput);
					inputContainer.appendChild(maxInput);
					plotDiv.appendChild(inputContainer);
				}
			
				var resultSelect = document.getElementById("resultSelect");
				if (result_names.length > 1 && !resultSelect) {
					resultSelect = document.createElement("select");
					resultSelect.id = "resultSelect";
					resultSelect.style.marginBottom = "10px";
			
					var sortedResults = [...result_names].sort();
					sortedResults.forEach(result => {
						var option = document.createElement("option");
						option.value = result;
						option.textContent = result;
						resultSelect.appendChild(option);
					});
			
					var selectContainer = document.createElement("div");
					selectContainer.style.marginBottom = "10px";
					selectContainer.appendChild(resultSelect);
					plotDiv.appendChild(selectContainer);
				}
			
				minInput.addEventListener("input", updatePlots);
				maxInput.addEventListener("input", updatePlots);
				if (resultSelect) {
					resultSelect.addEventListener("change", updatePlots);
				}
			
				updatePlots();
			
				async function updatePlots() {
					var minValue = parseFloat(minInput.value);
					var maxValue = parseFloat(maxInput.value);
					if (isNaN(minValue)) minValue = -Infinity;
					if (isNaN(maxValue)) maxValue = Infinity;
			
					while (plotDiv.children.length > 2) {
						plotDiv.removeChild(plotDiv.lastChild);
					}
			
					var selectedResult = resultSelect ? resultSelect.value : result_names[0];
			
					var resultIndex = tab_results_headers_json.findIndex(header =>
						header.toLowerCase() === selectedResult.toLowerCase()
					);
					var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
			
					var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
					var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
			
					if (minValue !== -Infinity) minResult = Math.max(minResult, minValue);
					if (maxValue !== Infinity) maxResult = Math.min(maxResult, maxValue);
			
					var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
			
					var numericColumns = tab_results_headers_json.filter(col =>
						!special_col_names.includes(col) && !result_names.includes(col) &&
						tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
					);
			
					if (numericColumns.length < 2) {
						console.error("Not enough columns for Scatter-Plots");
						return;
					}
			
					for (let i = 0; i < numericColumns.length; i++) {
						for (let j = i + 1; j < numericColumns.length; j++) {
							let xCol = numericColumns[i];
							let yCol = numericColumns[j];
			
							let xIndex = tab_results_headers_json.indexOf(xCol);
							let yIndex = tab_results_headers_json.indexOf(yCol);
			
							let data = tab_results_csv_json.map(row => ({
								x: parseFloat(row[xIndex]),
								y: parseFloat(row[yIndex]),
								result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
							}));
			
							data = data.filter(d => d.result >= minResult && d.result <= maxResult);
			
							let layoutTitle = `${xCol} (x) vs ${yCol} (y), result: ${selectedResult}`;
							let layout = {
								title: layoutTitle,
								xaxis: {
									title: get_axis_title_data(xCol)
								},
								yaxis: {
									title: get_axis_title_data(yCol)
								},
								showlegend: false
							};
							let subDiv = document.createElement("div");
			
							let spinnerContainer = document.createElement("div");
							spinnerContainer.style.display = "flex";
							spinnerContainer.style.alignItems = "center";
							spinnerContainer.style.justifyContent = "center";
							spinnerContainer.style.width = layout.width + "px";
							spinnerContainer.style.height = layout.height + "px";
							spinnerContainer.style.position = "relative";
			
							let spinner = document.createElement("div");
							spinner.className = "spinner";
							spinner.style.width = "40px";
							spinner.style.height = "40px";
			
							let loadingText = document.createElement("span");
							loadingText.innerText = `Loading ${layoutTitle}`;
							loadingText.style.marginLeft = "10px";
			
							spinnerContainer.appendChild(spinner);
							spinnerContainer.appendChild(loadingText);
			
							plotDiv.appendChild(spinnerContainer);
			
							await new Promise(resolve => setTimeout(resolve, 50));
			
							let colors = data.map(d => {
								if (d.result === null) {
									return 'rgb(0, 0, 0)';
								} else {
									let norm = (d.result - minResult) / (maxResult - minResult);
									if (invertColor) {
										norm = 1 - norm;
									}
									return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
								}
							});
			
							let trace = {
								x: data.map(d => d.x),
								y: data.map(d => d.y),
								mode: 'markers',
								marker: {
									size: get_marker_size(),
									color: data.map(d => d.result !== null ? d.result : null),
									colorscale: invertColor ? [
										[0, 'red'],
										[1, 'green']
									] : [
										[0, 'green'],
										[1, 'red']
									],
									colorbar: {
										title: 'Result',
										tickvals: [minResult, maxResult],
										ticktext: [`${minResult}`, `${maxResult}`]
									},
									symbol: data.map(d => d.result === null ? 'x' : 'circle'),
								},
								text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
								type: 'scatter',
								showlegend: false
							};
			
							try {
								plotDiv.replaceChild(subDiv, spinnerContainer);
							} catch (err) {
								//
							}
							Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
						}
					}
				}
			
				$("#plotScatter2d").data("loaded", "true");
			}
			
			function plotScatter3d() {
				if ($("#plotScatter3d").data("loaded") == "true") {
					return;
				}
			
				var plotDiv = document.getElementById("plotScatter3d");
				if (!plotDiv) {
					console.error("Div element with id 'plotScatter3d' not found");
					return;
				}
				plotDiv.innerHTML = "";
			
				var minInput3d = document.getElementById("minValue3d");
				var maxInput3d = document.getElementById("maxValue3d");
			
				if (!minInput3d || !maxInput3d) {
					minInput3d = document.createElement("input");
					minInput3d.id = "minValue3d";
					minInput3d.type = "number";
					minInput3d.placeholder = "Min Value";
					minInput3d.step = "any";
			
					maxInput3d = document.createElement("input");
					maxInput3d.id = "maxValue3d";
					maxInput3d.type = "number";
					maxInput3d.placeholder = "Max Value";
					maxInput3d.step = "any";
			
					var inputContainer3d = document.createElement("div");
					inputContainer3d.style.marginBottom = "10px";
					inputContainer3d.appendChild(minInput3d);
					inputContainer3d.appendChild(maxInput3d);
					plotDiv.appendChild(inputContainer3d);
				}
			
				var select3d = document.getElementById("select3dScatter");
				if (result_names.length > 1 && !select3d) {
					if (!select3d) {
						select3d = document.createElement("select");
						select3d.id = "select3dScatter";
						select3d.style.marginBottom = "10px";
						select3d.innerHTML = result_names.map(name => `<option value="${name}">${name}</option>`).join("");
			
						select3d.addEventListener("change", updatePlots3d);
			
						plotDiv.appendChild(select3d);
					}
				}
			
				minInput3d.addEventListener("input", updatePlots3d);
				maxInput3d.addEventListener("input", updatePlots3d);
			
				updatePlots3d();
			
				async function updatePlots3d() {
					var selectedResult = select3d ? select3d.value : result_names[0];
					var minValue3d = parseFloat(minInput3d.value);
					var maxValue3d = parseFloat(maxInput3d.value);
			
					if (isNaN(minValue3d)) minValue3d = -Infinity;
					if (isNaN(maxValue3d)) maxValue3d = Infinity;
			
					while (plotDiv.children.length > 2) {
						plotDiv.removeChild(plotDiv.lastChild);
					}
			
					var resultIndex = tab_results_headers_json.findIndex(header =>
						header.toLowerCase() === selectedResult.toLowerCase()
					);
					var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
			
					var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
					var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
			
					if (minValue3d !== -Infinity) minResult = Math.max(minResult, minValue3d);
					if (maxValue3d !== Infinity) maxResult = Math.min(maxResult, maxValue3d);
			
					var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
			
					var numericColumns = tab_results_headers_json.filter(col =>
						!special_col_names.includes(col) && !result_names.includes(col) &&
						tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
					);
			
					if (numericColumns.length < 3) {
						console.error("Not enough columns for 3D scatter plots");
						return;
					}
			
					for (let i = 0; i < numericColumns.length; i++) {
						for (let j = i + 1; j < numericColumns.length; j++) {
							for (let k = j + 1; k < numericColumns.length; k++) {
								let xCol = numericColumns[i];
								let yCol = numericColumns[j];
								let zCol = numericColumns[k];
			
								let xIndex = tab_results_headers_json.indexOf(xCol);
								let yIndex = tab_results_headers_json.indexOf(yCol);
								let zIndex = tab_results_headers_json.indexOf(zCol);
			
								let data = tab_results_csv_json.map(row => ({
									x: parseFloat(row[xIndex]),
									y: parseFloat(row[yIndex]),
									z: parseFloat(row[zIndex]),
									result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
								}));
			
								data = data.filter(d => d.result >= minResult && d.result <= maxResult);
			
								let layoutTitle = `${xCol} (x) vs ${yCol} (y) vs ${zCol} (z), result: ${selectedResult}`;
								let layout = {
									title: layoutTitle,
									scene: {
										xaxis: {
											title: get_axis_title_data(xCol)
										},
										yaxis: {
											title: get_axis_title_data(yCol)
										},
										zaxis: {
											title: get_axis_title_data(zCol)
										}
									},
									showlegend: false
								};
			
								let spinnerContainer = document.createElement("div");
								spinnerContainer.style.display = "flex";
								spinnerContainer.style.alignItems = "center";
								spinnerContainer.style.justifyContent = "center";
								spinnerContainer.style.width = layout.width + "px";
								spinnerContainer.style.height = layout.height + "px";
								spinnerContainer.style.position = "relative";
			
								let spinner = document.createElement("div");
								spinner.className = "spinner";
								spinner.style.width = "40px";
								spinner.style.height = "40px";
			
								let loadingText = document.createElement("span");
								loadingText.innerText = `Loading ${layoutTitle}`;
								loadingText.style.marginLeft = "10px";
			
								spinnerContainer.appendChild(spinner);
								spinnerContainer.appendChild(loadingText);
			
								plotDiv.appendChild(spinnerContainer);
			
								await new Promise(resolve => setTimeout(resolve, 50));
			
								let colors = data.map(d => {
									if (d.result === null) {
										return 'rgb(0, 0, 0)';
									} else {
										let norm = (d.result - minResult) / (maxResult - minResult);
										if (invertColor) {
											norm = 1 - norm;
										}
										return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
									}
								});
			
								let trace = {
									x: data.map(d => d.x),
									y: data.map(d => d.y),
									z: data.map(d => d.z),
									mode: 'markers',
									marker: {
										size: get_marker_size(),
										color: data.map(d => d.result !== null ? d.result : null),
										colorscale: invertColor ? [
											[0, 'red'],
											[1, 'green']
										] : [
											[0, 'green'],
											[1, 'red']
										],
										colorbar: {
											title: 'Result',
											tickvals: [minResult, maxResult],
											ticktext: [`${minResult}`, `${maxResult}`]
										},
									},
									text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
									type: 'scatter3d',
									showlegend: false
								};
			
								let subDiv = document.createElement("div");
								try {
									plotDiv.replaceChild(subDiv, spinnerContainer);
								} catch (err) {
									//
								}
								Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
							}
						}
					}
				}
			
				$("#plotScatter3d").data("loaded", "true");
			}
			
			async function plot_worker_cpu_ram() {
				if($("#worker_cpu_ram_pre").data("loaded") == "true") {
					return;
				}
			
				const logData = $("#worker_cpu_ram_pre").text();
				const regex = /^Unix-Timestamp: (\d+), Hostname: ([\w-]+), CPU: ([\d.]+)%, RAM: ([\d.]+) MB \/ ([\d.]+) MB$/;
			
				const hostData = {};
			
				logData.split("\n").forEach(line => {
					line = line.trim();
					const match = line.match(regex);
					if (match) {
						const timestamp = new Date(parseInt(match[1]) * 1000);
						const hostname = match[2];
						const cpu = parseFloat(match[3]);
						const ram = parseFloat(match[4]);
			
						if (!hostData[hostname]) {
							hostData[hostname] = { timestamps: [], cpuUsage: [], ramUsage: [] };
						}
			
						hostData[hostname].timestamps.push(timestamp);
						hostData[hostname].cpuUsage.push(cpu);
						hostData[hostname].ramUsage.push(ram);
					}
				});
			
				if (!Object.keys(hostData).length) {
					console.log("No valid data found");
					return;
				}
			
				const container = document.getElementById("cpuRamWorkerChartContainer");
				container.innerHTML = "";
			
				var i = 1;
			
				Object.entries(hostData).forEach(([hostname, { timestamps, cpuUsage, ramUsage }], index) => {
					const chartId = `workerChart_${index}`;
					const chartDiv = document.createElement("div");
					chartDiv.id = chartId;
					chartDiv.style.marginBottom = "40px";
					container.appendChild(chartDiv);
			
					const cpuTrace = {
						x: timestamps,
						y: cpuUsage,
						mode: "lines+markers",
						name: "CPU Usage (%)",
						yaxis: "y1",
						line: {
							color: "red"
						}
					};
			
					const ramTrace = {
						x: timestamps,
						y: ramUsage,
						mode: "lines+markers",
						name: "RAM Usage (MB)",
						yaxis: "y2",
						line: {
							color: "blue"
						}
					};
			
					const layout = {
						title: `Worker CPU and RAM Usage - ${hostname}`,
						xaxis: {
							title: get_axis_title_data("Timestamp", "date")
						},
						yaxis: {
							title: get_axis_title_data("CPU Usage (%)"),
							side: "left",
							color: "red"
						},
						yaxis2: {
							title: get_axis_title_data("RAM Usage (MB)"),
							side: "right",
							overlaying: "y",
							color: "blue"
						},
						showlegend: true
					};
			
					Plotly.newPlot(chartId, [cpuTrace, ramTrace], add_default_layout_data(layout));
					i++;
				});
			
				$("#plot_worker_cpu_ram_button").remove();
				$("#worker_cpu_ram_pre").data("loaded", "true");
			}
			
			function load_log_file(log_nr, filename) {
				var pre_id = `single_run_${log_nr}_pre`;
			
				if (!$("#" + pre_id).data("loaded")) {
					const params = new URLSearchParams(window.location.search);
			
					const user_id = params.get('user_id');
					const experiment_name = params.get('experiment_name');
					const run_nr = params.get('run_nr');
			
					var url = `get_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}&filename=${filename}`;
			
					fetch(url)
						.then(response => response.json())
						.then(data => {
							if (data.data) {
								$("#" + pre_id).html(data.data);
								$("#" + pre_id).data("loaded", true);
							} else {
								log(`No 'data' key found in response.`);
							}
			
							$("#spinner_log_" + log_nr).remove();
						})
						.catch(error => {
							log(`Error loading log: ${error}`);
							$("#spinner_log_" + log_nr).remove();
						});
				}
			}
			
			function load_debug_log () {
				var pre_id = `here_debuglogs_go`;
			
				if (!$("#" + pre_id).data("loaded")) {
					const params = new URLSearchParams(window.location.search);
			
					const user_id = params.get('user_id');
					const experiment_name = params.get('experiment_name');
					const run_nr = params.get('run_nr');
			
					var url = `get_debug_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}`;
			
					fetch(url)
						.then(response => response.json())
						.then(data => {
							$("#debug_log_spinner").remove();
			
							if (data.data) {
								try {
									$("#" + pre_id).html(data.data);
								} catch (err) {
									$("#" + pre_id).text(`Error loading data: ${err}`);
								}
			
								$("#" + pre_id).data("loaded", true);
			
								if (typeof apply_theme_based_on_system_preferences === 'function') {
									apply_theme_based_on_system_preferences();
								}
							} else {
								log(`No 'data' key found in response.`);
							}
						})
						.catch(error => {
							log(`Error loading log: ${error}`);
							$("#debug_log_spinner").remove();
						});
				}
			}
			
			function plotBoxplot() {
				if ($("#plotBoxplot").data("loaded") == "true") {
					return;
				}
				var numericColumns = tab_results_headers_json.filter(col =>
					!special_col_names.includes(col) && !result_names.includes(col) &&
					tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
				);
			
				if (numericColumns.length < 1) {
					console.error("Not enough numeric columns for Boxplot");
					return;
				}
			
				var resultIndex = tab_results_headers_json.findIndex(function(header) {
					return result_names.includes(header.toLowerCase());
				});
				var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
				var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
				var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
			
				var plotDiv = document.getElementById("plotBoxplot");
				plotDiv.innerHTML = "";
			
				let traces = numericColumns.map(col => {
					let index = tab_results_headers_json.indexOf(col);
					let data = tab_results_csv_json.map(row => parseFloat(row[index]));
			
					return {
						y: data,
						type: 'box',
						name: col,
						boxmean: 'sd',
						marker: {
							color: 'rgb(0, 255, 0)'
						},
					};
				});
			
				let layout = {
					title: 'Boxplot of Numerical Columns',
					xaxis: {
						title: get_axis_title_data("Columns")
					},
					yaxis: {
						title: get_axis_title_data("Value")
					},
					showlegend: false
				};
			
				Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
				$("#plotBoxplot").data("loaded", "true");
			}
			
			function plotHeatmap() {
				if ($("#plotHeatmap").data("loaded") === "true") {
					return;
				}
			
				var numericColumns = tab_results_headers_json.filter(col => {
					if (special_col_names.includes(col) || result_names.includes(col)) {
						return false;
					}
					let index = tab_results_headers_json.indexOf(col);
					return tab_results_csv_json.every(row => {
						let value = parseFloat(row[index]);
						return !isNaN(value) && isFinite(value);
					});
				});
			
				if (numericColumns.length < 2) {
					console.error("Not enough valid numeric columns for Heatmap");
					return;
				}
			
				var columnData = numericColumns.map(col => {
					let index = tab_results_headers_json.indexOf(col);
					return tab_results_csv_json.map(row => parseFloat(row[index]));
				});
			
				var dataMatrix = numericColumns.map((_, i) =>
					numericColumns.map((_, j) => {
						let values = columnData[i].map((val, index) => (val + columnData[j][index]) / 2);
						return values.reduce((a, b) => a + b, 0) / values.length;
					})
				);
			
				var trace = {
					z: dataMatrix,
					x: numericColumns,
					y: numericColumns,
					colorscale: 'Viridis',
					type: 'heatmap'
				};
			
				var layout = {
					xaxis: {
						title: get_axis_title_data("Columns")
					},
					yaxis: {
						title: get_axis_title_data("Columns")
					},
					showlegend: false
				};
			
				var plotDiv = document.getElementById("plotHeatmap");
				plotDiv.innerHTML = "";
			
				Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
				$("#plotHeatmap").data("loaded", "true");
			}
			
			function plotHistogram() {
				if ($("#plotHistogram").data("loaded") == "true") {
					return;
				}
			
				var numericColumns = tab_results_headers_json.filter(col =>
					!special_col_names.includes(col) && !result_names.includes(col) &&
					tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
				);
			
				if (numericColumns.length < 1) {
					console.error("Not enough columns for Histogram");
					return;
				}
			
				var plotDiv = document.getElementById("plotHistogram");
				plotDiv.innerHTML = "";
			
				const colorPalette = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0', '#ffb3e6'];
			
				let traces = numericColumns.map((col, index) => {
					let data = tab_results_csv_json.map(row => parseFloat(row[tab_results_headers_json.indexOf(col)]));
			
					return {
						x: data,
						type: 'histogram',
						name: col,
						opacity: 0.7,
						marker: {
							color: colorPalette[index % colorPalette.length]
						},
						autobinx: true
					};
				});
			
				let layout = {
					title: 'Histogram of Numerical Columns',
					xaxis: {
						title: get_axis_title_data("Value")
					},
					yaxis: {
						title: get_axis_title_data("Frequency")
					},
					showlegend: true,
					barmode: 'overlay'
				};
			
				Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
				$("#plotHistogram").data("loaded", "true");
			}
			
			function plotViolin() {
				if ($("#plotViolin").data("loaded") == "true") {
					return;
				}
				var numericColumns = tab_results_headers_json.filter(col =>
					!special_col_names.includes(col) && !result_names.includes(col) &&
					tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
				);
			
				if (numericColumns.length < 1) {
					console.error("Not enough columns for Violin Plot");
					return;
				}
			
				var plotDiv = document.getElementById("plotViolin");
				plotDiv.innerHTML = "";
			
				let traces = numericColumns.map(col => {
					let index = tab_results_headers_json.indexOf(col);
					let data = tab_results_csv_json.map(row => parseFloat(row[index]));
			
					return {
						y: data,
						type: 'violin',
						name: col,
						box: {
							visible: true
						},
						line: {
							color: 'rgb(0, 255, 0)'
						},
						marker: {
							color: 'rgb(0, 255, 0)'
						},
						meanline: {
							visible: true
						},
					};
				});
			
				let layout = {
					title: 'Violin Plot of Numerical Columns',
					yaxis: {
						title: get_axis_title_data("Value")
					},
					xaxis: {
						title: get_axis_title_data("Columns")
					},
					showlegend: false
				};
			
				Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
				$("#plotViolin").data("loaded", "true");
			}
			
			function plotExitCodesPieChart() {
				if ($("#plotExitCodesPieChart").data("loaded") == "true") {
					return;
				}
			
				var exitCodes = tab_job_infos_csv_json.map(row => row[tab_job_infos_headers_json.indexOf("exit_code")]);
			
				var exitCodeCounts = exitCodes.reduce(function(counts, exitCode) {
					counts[exitCode] = (counts[exitCode] || 0) + 1;
					return counts;
				}, {});
			
				var labels = Object.keys(exitCodeCounts);
				var values = Object.values(exitCodeCounts);
			
				var plotDiv = document.getElementById("plotExitCodesPieChart");
				plotDiv.innerHTML = "";
			
				var trace = {
					labels: labels,
					values: values,
					type: 'pie',
					hoverinfo: 'label+percent',
					textinfo: 'label+value',
					marker: {
						colors: ['#ff9999','#66b3ff','#99ff99','#ffcc99','#c2c2f0']
					}
				};
			
				var layout = {
					title: 'Exit Code Distribution',
					showlegend: true
				};
			
				Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
				$("#plotExitCodesPieChart").data("loaded", "true");
			}
			
			function plotResultEvolution() {
				if ($("#plotResultEvolution").data("loaded") == "true") {
					return;
				}
			
				result_names.forEach(resultName => {
					var relevantColumns = tab_results_headers_json.filter(col =>
						!special_col_names.includes(col) && !col.startsWith("OO_Info") && col.toLowerCase() !== resultName.toLowerCase()
					);
			
					var xColumnIndex = tab_results_headers_json.indexOf("trial_index");
					var resultIndex = tab_results_headers_json.indexOf(resultName);
			
					let data = tab_results_csv_json.map(row => ({
						x: row[xColumnIndex],
						y: parseFloat(row[resultIndex])
					}));
			
					data.sort((a, b) => a.x - b.x);
			
					let xData = data.map(item => item.x);
					let yData = data.map(item => item.y);
			
					let trace = {
						x: xData,
						y: yData,
						mode: 'lines+markers',
						name: resultName,
						line: {
							shape: 'linear'
						},
						marker: {
							size: get_marker_size()
						}
					};
			
					let layout = {
						title: `Evolution of ${resultName} over time`,
						xaxis: {
							title: get_axis_title_data("Trial-Index")
						},
						yaxis: {
							title: get_axis_title_data(resultName)
						},
						showlegend: true
					};
			
					let subDiv = document.createElement("div");
					document.getElementById("plotResultEvolution").appendChild(subDiv);
			
					Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
				});
			
				$("#plotResultEvolution").data("loaded", "true");
			}
			
			function plotResultPairs() {
				if ($("#plotResultPairs").data("loaded") == "true") {
					return;
				}
			
				var plotDiv = document.getElementById("plotResultPairs");
				plotDiv.innerHTML = "";
			
				for (let i = 0; i < result_names.length; i++) {
					for (let j = i + 1; j < result_names.length; j++) {
						let xName = result_names[i];
						let yName = result_names[j];
			
						let xIndex = tab_results_headers_json.indexOf(xName);
						let yIndex = tab_results_headers_json.indexOf(yName);
			
						let data = tab_results_csv_json
							.filter(row => row[xIndex] !== "" && row[yIndex] !== "")
							.map(row => ({
								x: parseFloat(row[xIndex]),
								y: parseFloat(row[yIndex]),
								status: row[tab_results_headers_json.indexOf("trial_status")]
							}));
			
						let colors = data.map(d => d.status === "COMPLETED" ? 'green' : (d.status === "FAILED" ? 'red' : 'gray'));
			
						let trace = {
							x: data.map(d => d.x),
							y: data.map(d => d.y),
							mode: 'markers',
							marker: {
								size: get_marker_size(),
								color: colors
							},
							text: data.map(d => `Status: ${d.status}`),
							type: 'scatter',
							showlegend: false
						};
			
						let layout = {
							xaxis: {
								title: get_axis_title_data(xName)
							},
							yaxis: {
								title: get_axis_title_data(yName)
							},
							showlegend: false
						};
			
						let subDiv = document.createElement("div");
						plotDiv.appendChild(subDiv);
			
						Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
					}
				}
			
				$("#plotResultPairs").data("loaded", "true");
			}
			
			function add_up_down_arrows_for_scrolling () {
				const upArrow = document.createElement('div');
				const downArrow = document.createElement('div');
			
				const style = document.createElement('style');
				style.innerHTML = `
					.scroll-arrow {
						position: fixed;
						right: 10px;
						z-index: 100;
						cursor: pointer;
						font-size: 25px;
						display: none;
						background-color: green;
						color: white;
						padding: 5px;
						outline: 2px solid white;
						box-shadow: 0 0 10px rgba(0, 0, 0, 0.5);
						transition: background-color 0.3s, transform 0.3s;
					}
					.scroll-arrow:hover {
						background-color: darkgreen;
						transform: scale(1.1);
					}
					#up-arrow {
						top: 10px;
					}
					#down-arrow {
						bottom: 10px;
					}
				`;
				document.head.appendChild(style);
			
				upArrow.id = "up-arrow";
				upArrow.classList.add("scroll-arrow");
				upArrow.classList.add("invert_in_dark_mode");
				upArrow.innerHTML = "&#8593;";
			
				downArrow.id = "down-arrow";
				downArrow.classList.add("scroll-arrow");
				downArrow.classList.add("invert_in_dark_mode");
				downArrow.innerHTML = "&#8595;";
			
				document.body.appendChild(upArrow);
				document.body.appendChild(downArrow);
			
				function checkScrollPosition() {
					const scrollPosition = window.scrollY;
					const pageHeight = document.documentElement.scrollHeight;
					const windowHeight = window.innerHeight;
			
					if (scrollPosition > 0) {
						upArrow.style.display = "block";
					} else {
						upArrow.style.display = "none";
					}
			
					if (scrollPosition + windowHeight < pageHeight) {
						downArrow.style.display = "block";
					} else {
						downArrow.style.display = "none";
					}
				}
			
				window.addEventListener("scroll", checkScrollPosition);
			
				upArrow.addEventListener("click", function () {
					window.scrollTo({ top: 0, behavior: 'smooth' });
				});
			
				downArrow.addEventListener("click", function () {
					window.scrollTo({ top: document.documentElement.scrollHeight, behavior: 'smooth' });
				});
			
				checkScrollPosition();
			
				if (typeof apply_theme_based_on_system_preferences === 'function') {
					apply_theme_based_on_system_preferences();
				}
			}
			
			function plotGPUUsage() {
				if ($("#tab_gpu_usage").data("loaded") === "true") {
					return;
				}
			
				Object.keys(gpu_usage).forEach(node => {
					const nodeData = gpu_usage[node];
			
					var timestamps = [];
					var gpuUtilizations = [];
					var temperatures = [];
			
					nodeData.forEach(entry => {
						try {
							var timestamp = new Date(entry[0]* 1000);
							var utilization = parseFloat(entry[1]);
							var temperature = parseFloat(entry[2]);
			
							if (!isNaN(timestamp) && !isNaN(utilization) && !isNaN(temperature)) {
								timestamps.push(timestamp);
								gpuUtilizations.push(utilization);
								temperatures.push(temperature);
							} else {
								console.warn("Invalid data point:", entry);
							}
						} catch (error) {
							console.error("Error processing GPU data entry:", error, entry);
						}
					});
			
					var trace1 = {
						x: timestamps,
						y: gpuUtilizations,
						mode: 'lines+markers',
						marker: {
							size: get_marker_size(),
						},
						name: 'GPU Utilization (%)',
						type: 'scatter',
						yaxis: 'y1'
					};
			
					var trace2 = {
						x: timestamps,
						y: temperatures,
						mode: 'lines+markers',
						marker: {
							size: get_marker_size(),
						},
						name: 'GPU Temperature (°C)',
						type: 'scatter',
						yaxis: 'y2'
					};
			
					var layout = {
						title: 'GPU Usage Over Time - ' + node,
						xaxis: {
							title: get_axis_title_data("Timestamp", "date"),
							tickmode: 'array',
							tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
							ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
							tickangle: -45
						},
						yaxis: {
							title: get_axis_title_data("GPU Utilization (%)"),
							overlaying: 'y',
							rangemode: 'tozero'
						},
						yaxis2: {
							title: get_axis_title_data("GPU Temperature (°C)"),
							overlaying: 'y',
							side: 'right',
							position: 0.85,
							rangemode: 'tozero'
						},
						legend: {
							x: 0.1,
							y: 0.9
						}
					};
			
					var divId = 'gpu_usage_plot_' + node;
			
					if (!document.getElementById(divId)) {
						var div = document.createElement('div');
						div.id = divId;
						div.className = 'gpu-usage-plot';
						document.getElementById('tab_gpu_usage').appendChild(div);
					}
			
					var plotData = [trace1, trace2];
			
					Plotly.newPlot(divId, plotData, add_default_layout_data(layout));
				});
			
				$("#tab_gpu_usage").data("loaded", "true");
			}
			
			function plotResultsDistributionByGenerationMethod() {
				if ("true" === $("#plotResultsDistributionByGenerationMethod").data("loaded")) {
					return;
				}
			
				var res_col = result_names[0];
				var gen_method_col = "generation_node";
			
				var data = {};
			
				tab_results_csv_json.forEach(row => {
					var gen_method = row[tab_results_headers_json.indexOf(gen_method_col)];
					var result = row[tab_results_headers_json.indexOf(res_col)];
			
					if (!data[gen_method]) {
						data[gen_method] = [];
					}
					data[gen_method].push(result);
				});
			
				var traces = Object.keys(data).map(method => {
					return {
						y: data[method],
						type: 'box',
						name: method,
						boxpoints: 'outliers',
						jitter: 0.5,
						pointpos: 0
					};
				});
			
				var layout = {
					title: 'Distribution of Results by Generation Method',
					yaxis: {
						title: get_axis_title_data(res_col)
					},
					xaxis: {
						title: get_axis_title_data("Generation Method")
					},
					boxmode: 'group'
				};
			
				Plotly.newPlot("plotResultsDistributionByGenerationMethod", traces, add_default_layout_data(layout));
				$("#plotResultsDistributionByGenerationMethod").data("loaded", "true");
			}
			
			function plotJobStatusDistribution() {
				if ($("#plotJobStatusDistribution").data("loaded") === "true") {
					return;
				}
			
				var status_col = "trial_status";
				var status_counts = {};
			
				tab_results_csv_json.forEach(row => {
					var status = row[tab_results_headers_json.indexOf(status_col)];
					if (status) {
						status_counts[status] = (status_counts[status] || 0) + 1;
					}
				});
			
				var statuses = Object.keys(status_counts);
				var counts = Object.values(status_counts);
			
				var colors = statuses.map((status, i) =>
					status === "FAILED" ? "#FF0000" : `hsl(${30 + ((i * 137) % 330)}, 70%, 50%)`
				);
			
				var trace = {
					x: statuses,
					y: counts,
					type: 'bar',
					marker: { color: colors }
				};
			
				var layout = {
					title: 'Distribution of Job Status',
					xaxis: { title: 'Trial Status' },
					yaxis: { title: 'Nr. of jobs' }
				};
			
				Plotly.newPlot("plotJobStatusDistribution", [trace], add_default_layout_data(layout));
				$("#plotJobStatusDistribution").data("loaded", "true");
			}
			
			function _colorize_table_entries_by_generation_method () {
				document.querySelectorAll('[data-column-id="generation_node"]').forEach(el => {
					let text = el.textContent.toLowerCase();
					let color = text.includes("manual") ? "green" :
						text.includes("sobol") ? "orange" :
						text.includes("saasbo") ? "pink" :
						text.includes("uniform") ? "lightblue" :
						text.includes("legacy_gpei") ? "sienna" :
						text.includes("bo_mixed") ? "aqua" :
						text.includes("randomforest") ? "darkseagreen" :
						text.includes("external_generator") ? "purple" :
						text.includes("botorch") ? "yellow" : "";
					if (color !== "") {
						el.style.backgroundColor = color;
					}
					el.classList.add("invert_in_dark_mode");
				});
			}
			
			function _colorize_table_entries_by_trial_status () {
				document.querySelectorAll('[data-column-id="trial_status"]').forEach(el => {
					let color = el.textContent.includes("COMPLETED") ? "lightgreen" :
						el.textContent.includes("RUNNING") ? "orange" :
						el.textContent.includes("FAILED") ? "red" :
						el.textContent.includes("ABANDONED") ? "yellow" : "";
					if (color) el.style.backgroundColor = color;
					el.classList.add("invert_in_dark_mode");
				});
			}
			
			function _colorize_table_entries_by_run_time() {
				let cells = [...document.querySelectorAll('[data-column-id="run_time"]')];
				if (cells.length === 0) return;
			
				let values = cells.map(el => parseFloat(el.textContent)).filter(v => !isNaN(v));
				if (values.length === 0) return;
			
				let min = Math.min(...values);
				let max = Math.max(...values);
				let range = max - min || 1;
			
				cells.forEach(el => {
					let value = parseFloat(el.textContent);
					if (isNaN(value)) return;
			
					let ratio = (value - min) / range;
					let red = Math.round(255 * ratio);
					let green = Math.round(255 * (1 - ratio));
			
					el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
					el.classList.add("invert_in_dark_mode");
				});
			}
			
			function _colorize_table_entries_by_results() {
				result_names.forEach((name, index) => {
					let minMax = result_min_max[index];
					let selector_query = `[data-column-id="${name}"]`;
					let cells = [...document.querySelectorAll(selector_query)];
					if (cells.length === 0) return;
			
					let values = cells.map(el => parseFloat(el.textContent)).filter(v => v > 0 && !isNaN(v));
					if (values.length === 0) return;
			
					let logValues = values.map(v => Math.log(v));
					let logMin = Math.min(...logValues);
					let logMax = Math.max(...logValues);
					let logRange = logMax - logMin || 1;
			
					cells.forEach(el => {
						let value = parseFloat(el.textContent);
						if (isNaN(value) || value <= 0) return;
			
						let logValue = Math.log(value);
						let ratio = (logValue - logMin) / logRange;
						if (minMax === "max") ratio = 1 - ratio;
			
						let red = Math.round(255 * ratio);
						let green = Math.round(255 * (1 - ratio));
			
						el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
						el.classList.add("invert_in_dark_mode");
					});
				});
			}
			
			function _colorize_table_entries_by_generation_node_or_hostname() {
				["hostname", "generation_node"].forEach(element => {
					let selector_query = '[data-column-id="' + element + '"]:not(.gridjs-th)';
					let cells = [...document.querySelectorAll(selector_query)];
					if (cells.length === 0) return;
			
					let uniqueValues = [...new Set(cells.map(el => el.textContent.trim()))];
					let colorMap = {};
			
					uniqueValues.forEach((value, index) => {
						let hue = Math.round((360 / uniqueValues.length) * index);
						colorMap[value] = `hsl(${hue}, 70%, 60%)`;
					});
			
					cells.forEach(el => {
						let value = el.textContent.trim();
						if (colorMap[value]) {
							el.style.backgroundColor = colorMap[value];
							el.classList.add("invert_in_dark_mode");
						}
					});
				});
			}
			
			function colorize_table_entries () {
				setTimeout(() => {
					if (typeof result_names !== "undefined" && Array.isArray(result_names) && result_names.length > 0) {
						_colorize_table_entries_by_trial_status();
						_colorize_table_entries_by_results();
						_colorize_table_entries_by_run_time();
						_colorize_table_entries_by_generation_method();
						_colorize_table_entries_by_generation_node_or_hostname();
						if (typeof apply_theme_based_on_system_preferences === 'function') {
							apply_theme_based_on_system_preferences();
						}
					}
				}, 300);
			}
			
			function add_colorize_to_gridjs_table () {
				let searchInput = document.querySelector(".gridjs-search-input");
				if (searchInput) {
					searchInput.addEventListener("input", colorize_table_entries);
				}
			}
			
			function updatePreWidths() {
				var width = window.innerWidth * 0.95;
				var pres = document.getElementsByTagName('pre');
				for (var i = 0; i < pres.length; i++) {
					pres[i].style.width = width + 'px';
				}
			}
			
			function demo_mode(nr_sec = 3) {
				let i = 0;
				let tabs = $('menu[role="tablist"] > button');
			
				setInterval(() => {
					tabs.attr('aria-selected', 'false').removeClass('active');
			
					let tab = tabs.eq(i % tabs.length);
					tab.attr('aria-selected', 'true').addClass('active');
			
					tab.trigger('click');
			
					i++;
				}, nr_sec * 1000);
			}
			
			function resizePlotlyCharts() {
				const plotlyElements = document.querySelectorAll('.js-plotly-plot');
			
				if (plotlyElements.length) {
					const windowWidth = window.innerWidth;
					const windowHeight = window.innerHeight;
			
					const newWidth = windowWidth * 0.9;
					const newHeight = windowHeight * 0.9;
			
					plotlyElements.forEach(function(element, index) {
						const layout = {
							width: newWidth,
							height: newHeight,
							plot_bgcolor: 'rgba(0, 0, 0, 0)',
							paper_bgcolor: 'rgba(0, 0, 0, 0)',
						};
			
						Plotly.relayout(element, layout)
					});
				}
			
				make_text_in_parallel_plot_nicer();
				apply_theme_based_on_system_preferences();
			}
			
			window.addEventListener('load', updatePreWidths);
			window.addEventListener('resize', updatePreWidths);
			
			$(document).ready(function() {
				colorize_table_entries();
			
				add_up_down_arrows_for_scrolling();
			
				add_colorize_to_gridjs_table();
			});
			
			window.addEventListener('resize', function() {
				resizePlotlyCharts();
			});
			
			"use strict";
			
			function get_row_by_index(idx) {
				if (!Object.keys(window).includes("tab_results_csv_json")) {
					error("tab_results_csv_json is not defined");
					return;
				}
			
				if (!Object.keys(window).includes("tab_results_headers_json")) {
					error("tab_results_headers_json is not defined");
					return;
				}
			
			
				var trial_index_col_idx = tab_results_headers_json.indexOf("trial_index");
			
				if(trial_index_col_idx == -1) {
					error(`"trial_index" could not be found in tab_results_headers_json. Cannot continue`);
			
					return null;
				}
			
				for (var i = 0; i < tab_results_csv_json.length; i++) {
					var row = tab_results_csv_json[i];
					var trial_index = row[trial_index_col_idx];
			
					if (trial_index == idx) {
						return row;
					}
				}
			
				return null;
			}
			
			function load_pareto_graph_from_idxs () {
				if (!Object.keys(window).includes("pareto_idxs")) {
					error("pareto_idxs is not defined");
					return;
				}
			
				if (!Object.keys(window).includes("tab_results_csv_json")) {
					error("tab_results_csv_json is not defined");
					return;
				}
			
				if (!Object.keys(window).includes("tab_results_headers_json")) {
					error("tab_results_headers_json is not defined");
					return;
				}
			
				if(pareto_idxs === null) {
					var err_msg = "pareto_idxs is null. Cannot plot or create tables from empty data. This can be caused by a defective <tt>pareto_idxs.json</tt> file. Please try reloading, or re-calculating the pareto-front and re-submitting if this problem persists.";
					$("#pareto_from_idxs_table").html(`<div class="caveat alarm">${err_msg}</div>`);
					return;	
				}
			
				var table = get_pareto_table_data_from_idx();
			
				var html_tables = createParetoTablesFromData(table);
			
				$("#pareto_from_idxs_table").html(html_tables);
			
				renderParetoFrontPlots(table);
			
				apply_theme_based_on_system_preferences();
			}
			
			function renderParetoFrontPlots(data) {
				try {
					let container = document.getElementById("pareto_front_idxs_plot_container");
					if (!container) {
						console.error("DIV with id 'pareto_front_idxs_plot_container' not found.");
						return;
					}
			
					container.innerHTML = "";
			
					if(data === undefined || data === null) {
						var err_msg = "There was an error getting the data for Pareto-Fronts. See the developer's console to see further details.";
						$("#pareto_from_idxs_table").html(`<div class="caveat alarm">${err_msg}</div>`);
						return;
					}
			
					Object.keys(data).forEach((key, idx) => {
						if (!key.startsWith("Pareto front for ")) return;
			
						let label = key.replace("Pareto front for ", "");
						let [xKey, yKey] = label.split("/");
			
						if (!xKey || !yKey) {
							console.warn("Could not extract two objectives from key:", key);
							return;
						}
			
						let entries = data[key];
						let x = [];
						let y = [];
						let hoverTexts = [];
			
						entries.forEach((entry) => {
							let results = entry.results || {};
							let values = entry.values || {};
			
							let xVal = (results[xKey] || [])[0];
							let yVal = (results[yKey] || [])[0];
			
							if (xVal === undefined || yVal === undefined) {
								console.warn("Missing values for", xKey, yKey, "in", entry);
								return;
							}
			
							x.push(xVal);
							y.push(yVal);
			
							let hoverInfo = [];
			
							if ("trial_index" in values) {
								hoverInfo.push(`<b>Trial Index:</b> ${values.trial_index[0]}`);
							}
			
							Object.keys(values)
								.filter(k => k !== "trial_index")
								.sort()
								.forEach(k => {
									hoverInfo.push(`<b>${k}:</b> ${values[k][0]}`);
								});
			
							Object.keys(results)
								.sort()
								.forEach(k => {
									hoverInfo.push(`<b>${k}:</b> ${results[k][0]}`);
								});
			
							hoverTexts.push(hoverInfo.join("<br>"));
						});
			
						let wrapper = document.createElement("div");
						wrapper.style.marginBottom = "30px";
			
						let titleEl = document.createElement("h3");
						titleEl.textContent = `Pareto Front: ${xKey} (${getMinMaxByResultName(xKey)}) vs ${yKey} (${getMinMaxByResultName(yKey)})`;
						wrapper.appendChild(titleEl);
			
						let divId = `pareto_plot_${idx}`;
						let plotDiv = document.createElement("div");
						plotDiv.id = divId;
						plotDiv.style.width = "100%";
						plotDiv.style.height = "400px";
			
						wrapper.appendChild(plotDiv);
						container.appendChild(wrapper);
			
						let trace = {
							x: x,
							y: y,
							text: hoverTexts,
							hoverinfo: "text",
							mode: "markers",
							type: "scatter",
							marker: {
								size: 8,
								color: 'rgb(31, 119, 180)',
								line: {
									width: 1,
									color: 'black'
								}
							},
							name: label
						};
			
						let layout = {
							xaxis: { title: { text: xKey } },
							yaxis: { title: { text: yKey } },
							margin: { t: 10, l: 60, r: 20, b: 50 },
							hovermode: "closest",
							showlegend: false
						};
			
						Plotly.newPlot(divId, [trace], add_default_layout_data(layout, 1));
					});
				} catch (e) {
					console.error("Error while rendering Pareto front plots:", e);
				}
			}
			
			function createParetoTablesFromData(data) {
				try {
					var container = document.createElement("div");
			
					var parsedData;
					try {
						parsedData = typeof data === "string" ? JSON.parse(data) : data;
					} catch (e) {
						console.error("JSON parsing failed:", e);
						return container;
					}
			
					for (var sectionTitle in parsedData) {
						if (!parsedData.hasOwnProperty(sectionTitle)) {
							continue;
						}
			
						var sectionData = parsedData[sectionTitle];
						var heading = document.createElement("h2");
						heading.textContent = sectionTitle;
						container.appendChild(heading);
			
						var table = document.createElement("table");
						table.style.borderCollapse = "collapse";
						table.style.marginBottom = "2em";
						table.style.width = "100%";
			
						var thead = document.createElement("thead");
						var headerRow = document.createElement("tr");
			
						var allValueKeys = new Set();
						var allResultKeys = new Set();
			
						sectionData.forEach(entry => {
							var values = entry.values || {};
							var results = entry.results || {};
			
							Object.keys(values).forEach(key => {
								allValueKeys.add(key);
							});
			
							Object.keys(results).forEach(key => {
								allResultKeys.add(key);
							});
						});
			
						var sortedValueKeys = Array.from(allValueKeys).sort();
						var sortedResultKeys = Array.from(allResultKeys).sort();
			
						if (sortedValueKeys.includes("trial_index")) {
							sortedValueKeys = sortedValueKeys.filter(k => k !== "trial_index");
							sortedValueKeys.unshift("trial_index");
						}
			
						var allColumns = [...sortedValueKeys, ...sortedResultKeys];
			
						allColumns.forEach(col => {
							var th = document.createElement("th");
							th.textContent = col;
							th.style.border = "1px solid black";
							th.style.padding = "4px";
							headerRow.appendChild(th);
						});
			
						thead.appendChild(headerRow);
						table.appendChild(thead);
			
						var tbody = document.createElement("tbody");
			
						sectionData.forEach(entry => {
							var tr = document.createElement("tr");
			
							allColumns.forEach(col => {
								var td = document.createElement("td");
								td.style.border = "1px solid black";
								td.style.padding = "4px";
			
								var value = null;
			
								if (col in entry.values) {
									value = entry.values[col];
								} else if (col in entry.results) {
									value = entry.results[col];
								}
			
								if (Array.isArray(value)) {
									td.textContent = value.join(", ");
								} else {
									td.textContent = value !== null && value !== undefined ? value : "";
								}
			
								tr.appendChild(td);
							});
			
							tbody.appendChild(tr);
						});
			
						table.appendChild(tbody);
						container.appendChild(table);
					}
			
					return container;
				} catch (err) {
					console.error("Unexpected error:", err);
					var errorDiv = document.createElement("div");
					errorDiv.textContent = "Error generating tables.";
					return errorDiv;
				}
			}
			
			function get_pareto_table_data_from_idx () {
				if (!Object.keys(window).includes("pareto_idxs")) {
					error("pareto_idxs is not defined");
					return;
				}
			
				if (!Object.keys(window).includes("tab_results_csv_json")) {
					error("tab_results_csv_json is not defined");
					return;
				}
			
				if (!Object.keys(window).includes("tab_results_headers_json")) {
					error("tab_results_headers_json is not defined");
					return;
				}
			
				var x_keys = Object.keys(pareto_idxs);
			
				var tables = {};
			
				for (var i = 0; i < x_keys.length; i++) {
					var x_key = x_keys[i];
					var y_keys = Object.keys(pareto_idxs[x_key]);
			
					for (var j = 0; j < y_keys.length; j++) {
						var y_key = y_keys[j];
			
						var indices = pareto_idxs[x_key][y_key];
			
						for (var k = 0; k < indices.length; k++) {
							var idx = indices[k];
							var row = get_row_by_index(idx);
			
							if(row === null) {
								error(`Error getting the row for index ${idx}`);
								return;
							}
			
							var row_dict = {
								"results": {},
								"values": {},
							};
			
							for (var l = 0; l < tab_results_headers_json.length; l++) {
								var header = tab_results_headers_json[l];
			
								if (!special_col_names.includes(header) || header == "trial_index") {
									var val = row[l];
			
									if (result_names.includes(header)) {
										if (!Object.keys(row_dict["results"]).includes(header)) {
											row_dict["results"][header] = [];
										}
			
										row_dict["results"][header].push(val);
									} else {
										if (!Object.keys(row_dict["values"]).includes(header)) {
											row_dict["values"][header] = [];
										}
										row_dict["values"][header].push(val);
									}
								}
								
							}
			
							var table_key = `Pareto front for ${x_key}/${y_key}`;
			
							if(!Object.keys(tables).includes(table_key)) {
								tables[table_key] = [];
							}
			
							tables[table_key].push(row_dict);
						}
			
					}
				}
			
				return tables;
			}
			
			function getMinMaxByResultName(resultName) {
				try {
					if (typeof resultName !== "string") {
						error("Parameter resultName must be a string");
						return;
					}
			
					if (!Array.isArray(result_names)) {
						error("Global variable result_names is not an array or undefined");
						return;
					}
					if (!Array.isArray(result_min_max)) {
						error("Global variable result_min_max is not an array or undefined");
						return;
					}
			
					if (result_names.length !== result_min_max.length) {
						error("Global arrays result_names and result_min_max must have the same length");
						return;
					}
			
					var index = result_names.indexOf(resultName);
					if (index === -1) {
						error("Result name '" + resultName + "' not found in result_names");
						return;
					}
			
					var minMaxValue = result_min_max[index];
			
					if (minMaxValue !== "min" && minMaxValue !== "max") {
						error("Value for result name '" + resultName + "' is invalid: expected 'min' or 'max'");
						return;
					}
			
					return minMaxValue;
			
				} catch (e) {
					error("Unexpected error: " + e.message);
				}
			}
			

			$(document).ready(function() {
				colorize_table_entries();;
				plotWorkerUsage();;
				plotCPUAndRAMUsage();;
				createParallelPlot(tab_results_csv_json, tab_results_headers_json, result_names, special_col_names);;
				plotScatter2d();;
				plotScatter3d();
				plotJobStatusDistribution();;
				plotBoxplot();;
				plotViolin();;
				plotHistogram();;
				plotHeatmap();;
				plotResultPairs();;
				plotResultEvolution();;
				plotExitCodesPieChart();

				colorize_table_entries();
			});
		</script>

					<h1>&nbsp;Overview</h1>
			<h2>Experiment overview:         </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> Setting</th><th>Value </th></tr></thead><tbody><tr><td> Max. nr. evaluations</td><td>940   </td></tr><tr><td> Number random steps</td><td>20    </td></tr><tr><td> Nr. of workers (parameter)</td><td>20    </td></tr><tr><td> Main process memory (GB)</td><td>8     </td></tr><tr><td> Worker memory (GB)</td><td>1     </td></tr><tr><td> Nr. imported jobs</td><td>440   </td></tr></tbody></table><h2>Experiment parameters:                                                    </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> Name</th><th>Type</th><th>Lower bound</th><th>Upper bound</th><th>Values</th><th>Type</th><th>Log Scale? </th></tr></thead><tbody><tr><td> recent_samples_size</td><td>int</td><td>1</td><td>5000</td><td></td><td>int</td><td>No         </td></tr><tr><td> feature_id</td><td>FixedParameter</td><td></td><td></td><td>18</td><td></td><td></td></tr><tr><td> batch_size</td><td>int</td><td>1</td><td>5000</td><td></td><td>int</td><td>No         </td></tr><tr><td> divergence</td><td>choice</td><td></td><td></td><td>str\(\&#039;H\&#039;\), str\(\&#039;KL\&#039;\)</td><td></td><td></td></tr><tr><td> detect_batch</td><td>choice</td><td></td><td></td><td>1, 2, 3</td><td></td><td></td></tr><tr><td> statistic</td><td>choice</td><td></td><td></td><td>str\(\&#039;stdev\&#039;\), str\(\&#039;tstat\&#039;\)</td><td></td><td></td></tr><tr><td> significance</td><td>float</td><td>0.001</td><td>0.999</td><td></td><td>float</td><td>No         </td></tr><tr><td> subsets</td><td>int</td><td>1</td><td>100</td><td></td><td>int</td><td>No         </td></tr></tbody></table><h2>Number of evaluations</h2>
			<table>
			<tbody>
			<tr>
			<th>Failed</th>
			<th>Succeeded</th>
			<th>Running</th>
			<th>Total</th>
			</tr>
			<tr>
			<td>23</td>
			<td>616</td>
			<td>5</td>
			<td>644</td>
			</tr>
			</tbody>
			</table>
			<h2>Result names and types</h2>
			<table>
			<tr><th>name</th><th>min/max</th></tr>
			<tr>
			<td>ACCURACY</td>
			<td>max</td>
			</tr>
			<tr>
			<td>RUNTIME</td>
			<td>min</td>
			</tr>
			</table>
			<br>
			<h2>Git-Version</h2>
			<tt>Commit: 2223ae6553abdd3e288f4b391080b763a7a48477
			</tt>
			<h1>&nbsp;Results</h1>
			<div id='tab_results_csv_table'></div>
			<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("tab_results_csv_table_pre")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download &raquo;results.csv&laquo; as file</button>
<pre id='tab_results_csv_table_pre'>trial_index,arm_name,trial_status,generation_method,generation_node,ACCURACY,RUNTIME,recent_samples_size,batch_size,divergence,detect_batch,statistic,significance,subsets,feature_id
0,0_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,160.000000000000000000000000000000,1388,4733,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.334707790672779059715225002947,14,18
1,1_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,170.000000000000000000000000000000,4522,2109,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.803561006955802459295057360578,98,18
2,2_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,164.000000000000000000000000000000,2893,3465,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.734514283167198245294571279373,35,18
3,3_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,194.000000000000000000000000000000,1024,797,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.142810671063140048886452859733,56,18
4,4_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,171.000000000000000000000000000000,197,2912,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.461441646676510552893546446285,44,18
5,5_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,250.000000000000000000000000000000,3329,249,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.930232957210391786517789114441,72,18
6,6_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,167.000000000000000000000000000000,4239,4183,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.623374173445627066669771920715,11,18
7,7_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,248.000000000000000000000000000000,2373,1554,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.031732470808550711227624674393,76,18
8,8_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,167.000000000000000000000000000000,2103,3190,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.212940394125878812436880593850,48,18
9,9_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,166.000000000000000000000000000000,3982,1151,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.679727283425629158486458436528,63,18
10,10_0,COMPLETED,Sobol,GenerationStep_0,0.110000000000000000555111512313,153.000000000000000000000000000000,3608,4385,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.873820837678387807301305656438,6,18
11,11_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,159.000000000000000000000000000000,484,2381,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.280292732911184450106389931534,84,18
12,12_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,175.000000000000000000000000000000,757,3829,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.086274819966405624782623817737,22,18
13,13_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,243.000000000000000000000000000000,2633,1830,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.553001792561262806025013105682,94,18
14,14_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,144.000000000000000000000000000000,4799,2642,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.984892661837860949880507632770,26,18
15,15_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,218.000000000000000000000000000000,1677,598,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.391424473775550729737204846970,60,18
16,16_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,168.000000000000000000000000000000,1758,2686,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.511572800386697057462015436613,96,18
17,17_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,161.000000000000000000000000000000,4875,330,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.105044181350618603953961383013,17,18
18,18_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,160.000000000000000000000000000000,2714,3952,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.419254619514569659699532166997,53,18
19,19_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,154.000000000000000000000000000000,833,1639,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.948534646889194865693184510747,37,18
20,20_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,174.000000000000000000000000000000,409,4654,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.630441492110490830924618421705,75,18
21,21_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,183.000000000000000000000000000000,3527,2337,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.223972789779305458957026075950,40,18
22,22_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,225.000000000000000000000000000000,3906,3380,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.284792177790775868917449997753,80,18
23,23_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,150.000000000000000000000000000000,2022,1030,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.814012288460507993370640633657,8,18
24,24_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,165.000000000000000000000000000000,2292,4227,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.878937011752277563836344143056,67,18
25,25_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,218.000000000000000000000000000000,4163,1285,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.474484151858836411541631150612,45,18
26,26_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,165.000000000000000000000000000000,3248,3034,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.034343632331117986133950381600,87,18
27,27_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,185.000000000000000000000000000000,122,58,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.565453952198848086574400895188,2,18
28,28_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,216.000000000000000000000000000000,1100,3734,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.760000019595026943264315377746,90,18
29,29_0,COMPLETED,Sobol,GenerationStep_0,0.100000000000000005551115123126,162.000000000000000000000000000000,2974,753,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.355609069168567659691149174250,24,18
30,30_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,163.000000000000000000000000000000,4629,3900,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,7,18
31,31_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,173.000000000000000000000000000000,3903,4509,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,18
32,32_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,152.000000000000000000000000000000,5000,2652,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,12,18
33,33_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,202.000000000000000000000000000000,4038,4975,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,18
34,34_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,166.000000000000000000000000000000,5000,4324,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,34,18
35,35_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,265.000000000000000000000000000000,4846,5000,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.240655509600742556486352441425,1,18
36,36_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,195.000000000000000000000000000000,5000,2770,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,18
37,37_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,269.000000000000000000000000000000,1839,5000,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,18
38,38_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,3767,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,26,18
39,39_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,181.000000000000000000000000000000,3932,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,18
40,40_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,203.000000000000000000000000000000,4629,3035,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,18
41,41_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,158.000000000000000000000000000000,4629,2740,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,45,18
42,42_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,272.000000000000000000000000000000,5000,5000,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.201478277693281526694590866100,1,18
43,43_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,177.000000000000000000000000000000,1,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,9,18
44,44_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,204.000000000000000000000000000000,5000,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.240407605245393035664491776515,1,18
45,45_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,194.000000000000000000000000000000,5000,1866,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,18
46,46_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,202.000000000000000000000000000000,975,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,18
47,47_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,158.000000000000000000000000000000,1392,3921,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,18
48,48_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,199.000000000000000000000000000000,5000,3501,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,18
49,49_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,201.000000000000000000000000000000,4139,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.945203731515823708519974388764,1,18
50,50_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,203.000000000000000000000000000000,4143,4283,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,18
51,51_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,173.000000000000000000000000000000,4834,5000,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,26,18
52,52_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,274.000000000000000000000000000000,1,5000,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,18
53,53_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,271.000000000000000000000000000000,1,5000,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,18
54,54_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,5000,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,18,18
55,55_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,189.000000000000000000000000000000,5000,3705,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,18
56,56_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,169.000000000000000000000000000000,5000,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,57,18
57,57_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,275.000000000000000000000000000000,5000,5000,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,18
58,58_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,271.000000000000000000000000000000,997,3719,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,18
59,59_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,199.000000000000000000000000000000,1870,3777,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,18
60,60_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,3273,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,50,18
61,61_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,164.000000000000000000000000000000,1459,4377,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.798652737604423124118113719305,9,18
62,62_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,178.000000000000000000000000000000,3670,4988,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,31,18
63,63_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,218.000000000000000000000000000000,2625,1129,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.106400262070895035293460750836,9,18
64,64_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,155.000000000000000000000000000000,3974,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,24,18
65,65_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,165.000000000000000000000000000000,2297,4331,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,9,18
66,66_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,160.000000000000000000000000000000,4314,4348,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,9,18
67,67_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,156.000000000000000000000000000000,4785,889,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,8,18
68,68_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,157.000000000000000000000000000000,71,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,47,18
69,69_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,4917,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,63,18
70,70_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,160.000000000000000000000000000000,677,4996,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,39,18
71,71_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,4036,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,63,18
72,72_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,172.000000000000000000000000000000,860,823,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,31,18
73,73_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,154.000000000000000000000000000000,1,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,28,18
74,74_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,163.000000000000000000000000000000,2878,426,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,8,18
75,75_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,187.000000000000000000000000000000,2765,1851,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,8,18
76,76_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,148.000000000000000000000000000000,3591,114,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,31,18
77,77_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,169.000000000000000000000000000000,4331,4299,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,9,18
78,78_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,487.000000000000000000000000000000,4984,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,31,18
79,79_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,180.000000000000000000000000000000,1021,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,76,18
80,80_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,162.000000000000000000000000000000,4881,370,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,31,18
81,81_0,FAILED,BoTorch,GenerationStep_1,,,1469,1,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,7,18
82,82_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,175.000000000000000000000000000000,697,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,43,18
83,83_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,178.000000000000000000000000000000,2139,3,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,14,18
84,84_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,80,4990,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,34,18
85,85_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,178.000000000000000000000000000000,305,1418,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,31,18
86,86_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,167.000000000000000000000000000000,728,4257,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,19,18
87,87_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,159.000000000000000000000000000000,858,402,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,27,18
88,88_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,176.000000000000000000000000000000,202,4401,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,8,18
89,89_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,148.000000000000000000000000000000,4453,1146,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,25,18
90,90_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,371.000000000000000000000000000000,4536,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,25,18
91,91_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,154.000000000000000000000000000000,2906,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,54,18
92,92_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,156.000000000000000000000000000000,1944,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,37,18
93,93_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,3040,101,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.994683436155499811626157224964,35,18
94,94_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,694,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,51,18
95,95_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,164.000000000000000000000000000000,2053,42,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.681772899939666499058432691527,25,18
96,96_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,248.000000000000000000000000000000,1732,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,28,18
97,97_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,172.000000000000000000000000000000,2629,4974,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,23,18
98,98_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,168.000000000000000000000000000000,4796,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.908461312498064388343266273296,57,18
99,99_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,1749,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,27,18
100,100_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,168.000000000000000000000000000000,1358,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,30,18
101,101_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,154.000000000000000000000000000000,4030,4995,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,20,18
102,102_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,841.000000000000000000000000000000,3918,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.956268729564748953819730559189,40,18
103,103_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,154.000000000000000000000000000000,3117,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,40,18
104,104_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,178.000000000000000000000000000000,4728,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,49,18
105,105_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,83,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.869183693337429064129651123949,60,18
106,106_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,164.000000000000000000000000000000,4992,104,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,5,18
107,107_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,158.000000000000000000000000000000,4116,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.849726765810585593641235391260,59,18
108,108_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,252.000000000000000000000000000000,4069,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.705129399302528825366209730419,37,18
109,109_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,172.000000000000000000000000000000,4383,76,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.790958366655155620250639003643,41,18
110,110_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,183.000000000000000000000000000000,4172,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,59,18
111,111_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,153.000000000000000000000000000000,3170,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.733996835680496451104204425064,67,18
112,112_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,166.000000000000000000000000000000,3734,4766,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.799706900349194893529158889578,55,18
113,113_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,176.000000000000000000000000000000,2062,1885,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,25,18
114,114_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,4658,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,33,18
115,115_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,2640,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,52,18
116,116_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,173.000000000000000000000000000000,1434,4614,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,54,18
117,117_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,184.000000000000000000000000000000,4922,4889,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.948675493612214482119782132941,62,18
118,118_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,2468,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,54,18
119,119_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,246.000000000000000000000000000000,3540,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,48,18
120,120_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,162.000000000000000000000000000000,2836,4951,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.951106729282999996399894371280,55,18
121,121_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,166.000000000000000000000000000000,3508,4956,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.942944071520434023980783422303,46,18
122,122_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,179.000000000000000000000000000000,2071,4995,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.942046932505637091281869288650,50,18
123,123_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,160.000000000000000000000000000000,3464,4996,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.711682803071962744034806291893,64,18
124,124_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,170.000000000000000000000000000000,309,4955,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.794419622717998263894401134166,69,18
125,125_0,FAILED,BoTorch,GenerationStep_1,,,4376,1,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.954929505617819862628437022067,42,18
126,126_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,256.000000000000000000000000000000,4818,8,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.827156692981249497265139325464,45,18
127,127_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,323.000000000000000000000000000000,557,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.856390992722781296819789531583,44,18
128,128_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,177.000000000000000000000000000000,2208,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,48,18
129,129_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,3013,4986,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.637656764032622613846967851714,53,18
130,130_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,230.000000000000000000000000000000,1479,5,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.892977192383421880528260317078,39,18
131,131_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,176.000000000000000000000000000000,465,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.870333161025382606723610479094,50,18
132,132_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,170.000000000000000000000000000000,2093,4937,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.864534143863924908757212506316,48,18
133,133_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,171.000000000000000000000000000000,586,4906,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.835700477730318302960199616791,49,18
134,134_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,171.000000000000000000000000000000,699,4911,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.963483888645868979949682398001,52,18
135,135_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,1271,4988,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.578591194850345491573762046755,49,18
136,136_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,203,4987,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.529983107266492892151177329652,49,18
137,137_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,169.000000000000000000000000000000,4974,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.853489945480502498753594409209,48,18
138,138_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,1517,4997,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.362959075184791646329074410460,62,18
139,139_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,152.000000000000000000000000000000,2064,11,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.862187930042830341648141256883,43,18
140,140_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,190.000000000000000000000000000000,922,4925,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.824625148626242920890661025624,37,18
141,141_0,FAILED,BoTorch,GenerationStep_1,,,4574,1,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.924716112617456120759129589715,49,18
142,142_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,180.000000000000000000000000000000,450,4976,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.796527718918443539131146735599,50,18
143,143_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,3648,4992,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.740979359726319453116616386978,51,18
144,144_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,159.000000000000000000000000000000,2688,4990,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.654960106710993028578116081917,64,18
145,145_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,187.000000000000000000000000000000,4783,36,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.854056509799596796916887342377,43,18
146,146_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,1828.000000000000000000000000000000,1480,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.669363217563233026119462465431,51,18
147,147_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,165.000000000000000000000000000000,1246,4952,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.889209946828804276286462027201,48,18
148,148_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,256.000000000000000000000000000000,3746,52,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.803350766342207811554487761896,57,18
149,149_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,186.000000000000000000000000000000,2985,4975,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.788271233864577203220846968179,42,18
150,150_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,159.000000000000000000000000000000,4748,4421,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.555846070670165226523806722980,34,18
151,151_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,5000,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,75,18
152,152_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,156.000000000000000000000000000000,2869,73,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.593906346120359085460904680076,62,18
153,153_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,158.000000000000000000000000000000,4517,20,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.310842982999767036833560496234,60,18
154,154_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,577,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.567784872579856325636171732185,63,18
155,155_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,171.000000000000000000000000000000,23,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.418744563396118374409837770145,63,18
156,156_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,157.000000000000000000000000000000,2729,39,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.250537550429379085592529463611,1,18
157,157_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,191.000000000000000000000000000000,4445,4945,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.553463057263575142208367196872,78,18
158,158_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,168.000000000000000000000000000000,3111,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.258406454023100251582434339070,73,18
159,159_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,263.000000000000000000000000000000,1999,3351,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.536391505330800932860313423589,1,18
160,160_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,166.000000000000000000000000000000,2595,4990,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.448589104756003775875683459162,91,18
161,161_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,189.000000000000000000000000000000,3603,4997,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.599925456268910806123528800526,58,18
162,162_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,254.000000000000000000000000000000,2196,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.185606328426649314078389352289,52,18
163,163_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,169.000000000000000000000000000000,4979,4989,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.516603183404800492262154421041,76,18
164,164_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,166.000000000000000000000000000000,924,4044,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.118212500806625778193570397434,1,18
165,165_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,187.000000000000000000000000000000,4269,4978,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.134777974983599224678698647040,85,18
166,166_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,247,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.726712533938276727951688371832,60,18
167,167_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,177.000000000000000000000000000000,3733,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.573754268580216164608032158867,56,18
168,168_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,226.000000000000000000000000000000,1673,3144,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.651676807804699942394677236734,100,18
169,169_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,206.000000000000000000000000000000,558,4015,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.528887586316514779483100028301,99,18
170,170_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,190.000000000000000000000000000000,2146,4983,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.630139352640926020399092521984,73,18
171,171_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,154.000000000000000000000000000000,4099,23,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.110047472546976318752420809233,77,18
172,172_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,174.000000000000000000000000000000,436,3,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.580596344141102060909531701327,57,18
173,173_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,211.000000000000000000000000000000,4566,5000,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.795814672059951822014056688204,100,18
174,174_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,170.000000000000000000000000000000,4440,4683,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.504251301586549516287050209939,54,18
175,175_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,168.000000000000000000000000000000,3296,4484,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.560761716915243790815281954565,54,18
176,176_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,152.000000000000000000000000000000,251,67,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.476438335392710410332028914127,99,18
177,177_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,170.000000000000000000000000000000,4293,4087,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.557709929535631587782518181484,100,18
178,178_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,178.000000000000000000000000000000,3580,4988,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.567495455311943564069565582031,53,18
179,179_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,4684,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.127751278860934097680868148927,49,18
180,180_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,3525,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.330828932980087808690683459645,71,18
181,181_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,167.000000000000000000000000000000,4427,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.352271224135521754394773097374,86,18
182,182_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,431.000000000000000000000000000000,3267,3,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.624766376640799481201327125746,33,18
183,183_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,167.000000000000000000000000000000,4390,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.413446527036719302383716012628,76,18
184,184_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,157.000000000000000000000000000000,2475,4999,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.294453282055232856695425880389,55,18
185,185_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,179.000000000000000000000000000000,771,4977,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.113899603429575238533466574609,67,18
186,186_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,179.000000000000000000000000000000,99,2455,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,18
187,187_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,1909,4982,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.197519866872421429038197970840,93,18
188,188_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,163.000000000000000000000000000000,3928,7,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.254667059090713188140853162622,18,18
189,189_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,176.000000000000000000000000000000,604,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.291173682702841329916765289454,100,18
190,190_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,186.000000000000000000000000000000,3523,4992,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.436019209818019504165675925833,65,18
191,191_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,164.000000000000000000000000000000,4526,2163,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.278550700287180541536713462847,50,18
192,192_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,162.000000000000000000000000000000,803,243,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.879148895509584860086249591404,23,18
193,193_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,175.000000000000000000000000000000,3096,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.152363621721112757478877597350,70,18
194,194_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,178.000000000000000000000000000000,3749,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.069324964441046188490247459413,89,18
195,195_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,172.000000000000000000000000000000,3624,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.389886798398336675841591159042,83,18
196,196_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,218.000000000000000000000000000000,1990,2529,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.308829363163968917405100000906,100,18
197,197_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,179.000000000000000000000000000000,138,4996,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.463592181330260366678430727916,98,18
198,198_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,154.000000000000000000000000000000,2345,340,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.086694748607183527711050885500,1,18
199,199_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,153.000000000000000000000000000000,1419,911,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.093925449241626060570276024464,81,18
200,200_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,190.000000000000000000000000000000,447,4927,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.079945358040472327965098031655,86,18
201,201_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,177.000000000000000000000000000000,3780,2470,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.333844029335272685088398247899,1,18
202,202_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,258.000000000000000000000000000000,755,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.411993754901355202502344354798,19,18
203,203_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,169.000000000000000000000000000000,2870,1230,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.109473937073183216761584901633,1,18
204,204_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,170.000000000000000000000000000000,1363,2470,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.261258881892254268830555474779,1,18
205,205_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,174.000000000000000000000000000000,1935,2488,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.258559291354009235242017439305,1,18
206,206_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,167.000000000000000000000000000000,2522,527,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.110385612690713730277281001690,69,18
207,207_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,165.000000000000000000000000000000,2615,1216,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.359405619860090730366408706686,17,18
208,208_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,209.000000000000000000000000000000,3540,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.072887982390899136442286021520,100,18
209,209_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,156.000000000000000000000000000000,1985,485,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.045081483432110248943391894727,1,18
210,210_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,2253,4997,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.601302421209272575097770641150,2,18
211,211_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,1660,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.197952502857284329262910205216,100,18
212,212_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,154.000000000000000000000000000000,1822,19,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.745341560945151027084421002655,22,18
213,213_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,38,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.313687856049134905500608283546,100,18
214,214_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,179.000000000000000000000000000000,182,4975,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.575939888372505981450899525953,61,18
215,215_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,172.000000000000000000000000000000,4571,4966,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.668367691322164003686623345857,91,18
216,216_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,168.000000000000000000000000000000,978,1710,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.564636987444755655829453644401,100,18
217,217_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,2396,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.626528710158745005820435380883,97,18
218,218_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,166.000000000000000000000000000000,3259,2001,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.550425528984511247543309764296,1,18
219,219_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,274.000000000000000000000000000000,4288,2536,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.734640667982121242651771808596,1,18
220,220_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,176.000000000000000000000000000000,1034,4981,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.271532934562568051273245828270,98,18
221,221_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,164.000000000000000000000000000000,71,1841,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.584709136678398744280116261507,100,18
222,222_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,168.000000000000000000000000000000,2541,2009,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,18
223,223_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,174.000000000000000000000000000000,4946,2022,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.876945914736689036139694053418,6,18
224,224_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,147.000000000000000000000000000000,1714,2214,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.847934741077982834234205711255,1,18
225,225_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,154.000000000000000000000000000000,4330,45,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.625539980330139244735221382143,38,18
226,226_0,FAILED,BoTorch,GenerationStep_1,,,4356,1,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.472377965615630002194080816480,18,18
227,227_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,163.000000000000000000000000000000,984,1048,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.306425580236084349472491794586,100,18
228,228_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,168.000000000000000000000000000000,3709,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.371549097647711767944400662600,100,18
229,229_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,166.000000000000000000000000000000,3200,2312,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.890021463444505611661838884174,15,18
230,230_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,152.000000000000000000000000000000,3393,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.658362852805459919380837163772,92,18
231,231_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,168.000000000000000000000000000000,612,2874,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.737772507044829550792996997188,63,18
232,232_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,163.000000000000000000000000000000,3422,1359,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.548958688694927809947898822429,100,18
233,233_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,183.000000000000000000000000000000,1375,2308,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.937136766135336807259648139734,8,18
234,234_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,172.000000000000000000000000000000,3162,2830,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.578989248122768196402887497243,1,18
235,235_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,158.000000000000000000000000000000,1268,1004,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.159689955220053192430640365274,1,18
236,236_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,164.000000000000000000000000000000,4393,1526,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.531815602825272737419481927645,1,18
237,237_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,186.000000000000000000000000000000,370,2318,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.749803554418284168292530011968,99,18
238,238_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,261.000000000000000000000000000000,2207,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.284746219331729055745938694599,1,18
239,239_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,179.000000000000000000000000000000,3821,2009,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.847242085634734243271282139176,100,18
240,240_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,246.000000000000000000000000000000,1,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.016712083968115763743522705909,1,18
241,241_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,438.000000000000000000000000000000,4614,473,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.480480669917425218251594287722,100,18
242,242_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,158.000000000000000000000000000000,3924,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.198945181052117503472942416920,100,18
243,243_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,227.000000000000000000000000000000,3184,982,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.848453749661952527638675292110,59,18
244,244_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,247.000000000000000000000000000000,1718,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.496093875975889719853739734390,66,18
245,245_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,154.000000000000000000000000000000,4980,4991,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.039905280450509131306979782039,84,18
246,246_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,170.000000000000000000000000000000,1072,4997,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.909081605933382941309162106336,66,18
247,247_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,255.000000000000000000000000000000,1,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,18
248,248_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,158.000000000000000000000000000000,217,2711,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.633063244573447092555795734370,99,18
249,249_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,2621,4985,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.478575435260937487313981364423,58,18
250,250_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,166.000000000000000000000000000000,412,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.670829564866629257657848484087,100,18
251,251_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,153.000000000000000000000000000000,586,10,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.456008578042043644096281695965,47,18
252,252_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,259.000000000000000000000000000000,1363,1,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.232819848016414948643770799208,1,18
253,253_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,182.000000000000000000000000000000,1560,948,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.409707589431484886510759224620,37,18
254,254_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,175.000000000000000000000000000000,3796,3915,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.184379353751378566306229345173,49,18
255,255_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,166.000000000000000000000000000000,1748,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.424181162429504654554079934314,100,18
256,256_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,175.000000000000000000000000000000,3881,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.915933536738566700385888452729,100,18
257,257_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,202.000000000000000000000000000000,816,1597,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.068369048983478508674238582898,10,18
258,258_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,184.000000000000000000000000000000,3866,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.693079507602069933014377056679,97,18
259,259_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,249.000000000000000000000000000000,2776,781,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.157974112551707818186130793947,100,18
260,260_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,2228,4987,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.805509744899368329562605595129,97,18
261,261_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,294.000000000000000000000000000000,5000,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,64,18
262,262_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,188.000000000000000000000000000000,123,324,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.477835351943631025939396295144,45,18
263,263_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,258.000000000000000000000000000000,281,1968,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.112109880439055814571602809337,100,18
264,264_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,354.000000000000000000000000000000,1409,770,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.076863577623117490533743989545,100,18
265,265_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,243.000000000000000000000000000000,2690,696,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.830473812920998311071230091329,1,18
266,266_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,173.000000000000000000000000000000,3596,1392,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,18
267,267_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,218.000000000000000000000000000000,344,1977,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,18
268,268_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,266.000000000000000000000000000000,4907,2047,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.317924538130451017625688336921,1,18
269,269_0,FAILED,BoTorch,GenerationStep_1,,,2099,1,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.446426440987481520927815381583,100,18
270,270_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,317.000000000000000000000000000000,3094,1311,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.986511291299952741695733493543,100,18
271,271_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,155.000000000000000000000000000000,4332,2078,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.131522626636114747800121449472,93,18
272,272_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,153.000000000000000000000000000000,3275,3328,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.013592107545771531240674789842,19,18
273,273_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,341.000000000000000000000000000000,306,18,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.077847882920296623820988202169,64,18
274,274_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,4152,4996,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.143597912966797636169502538905,91,18
275,275_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,256.000000000000000000000000000000,1,1,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,18
276,276_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,334.000000000000000000000000000000,4833,1,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.098975589100230160211069119214,1,18
277,277_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,183.000000000000000000000000000000,3894,1953,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.094228693898011375851275772675,84,18
278,278_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,160.000000000000000000000000000000,4059,3616,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.010751941666767853705843904777,5,18
279,279_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,170.000000000000000000000000000000,107,8,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.097582564590392717462918881210,100,18
280,280_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,175.000000000000000000000000000000,4881,4938,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.126452789315836089389932794802,94,18
281,281_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,251.000000000000000000000000000000,1,1,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,100,18
282,282_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,186.000000000000000000000000000000,767,2707,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.721606438960322726927643088857,61,18
283,283_0,FAILED,BoTorch,GenerationStep_1,,,1,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,18
284,284_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,151.000000000000000000000000000000,3726,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.357373127679982138804604119287,25,18
285,285_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,234.000000000000000000000000000000,3939,2816,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.140920743116528413008126108252,100,18
286,286_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,160.000000000000000000000000000000,1,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,18
287,287_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,168.000000000000000000000000000000,1,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,12,18
288,288_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,186.000000000000000000000000000000,3701,2026,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.081152975153974016286895221128,13,18
289,289_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,342.000000000000000000000000000000,4935,1,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,18
290,290_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,178.000000000000000000000000000000,608,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.957041233438205574124424401816,100,18
291,291_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,173.000000000000000000000000000000,84,2534,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.711735780773521842235140866251,100,18
292,292_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,175.000000000000000000000000000000,3730,3248,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.054234525020434437569960550718,1,18
293,293_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,145.000000000000000000000000000000,4137,2243,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,18
294,294_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,199.000000000000000000000000000000,4649,2719,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.023212308502367894752493882038,1,18
295,295_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,150.000000000000000000000000000000,5000,2893,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,18
296,296_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,154.000000000000000000000000000000,767,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.514044649320343549625533796643,100,18
297,297_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,158.000000000000000000000000000000,1778,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.244524520373307768794646221977,100,18
298,298_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,171.000000000000000000000000000000,4603,4993,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.479237314603225827980281792406,69,18
299,299_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,175.000000000000000000000000000000,1156,2696,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.063602735232553039845804221386,68,18
300,300_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,360.000000000000000000000000000000,1299,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.442003750469169065517860417458,25,18
301,301_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,189.000000000000000000000000000000,340,7,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.326808690375793131632775612161,13,18
302,302_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,220.000000000000000000000000000000,1623,1880,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.856048081950081085622628052079,83,18
303,303_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,227.000000000000000000000000000000,1711,1960,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.331916221187973869977128060782,93,18
304,304_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,151.000000000000000000000000000000,2666,38,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.344369598155097766944976456216,12,18
305,305_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,252.000000000000000000000000000000,954,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.122903296255934324499037302303,1,18
306,306_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,155.000000000000000000000000000000,3738,1985,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.871760005406904237013065994688,76,18
307,307_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,2026,4995,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.790886319168898066500617005659,63,18
308,308_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,209.000000000000000000000000000000,1007,2020,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.340666918872791046890569077732,89,18
309,309_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,183.000000000000000000000000000000,297,4978,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.456183787010513208670658968913,78,18
310,310_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,177.000000000000000000000000000000,1971,264,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.847676462875100678040496404719,5,18
311,311_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,160.000000000000000000000000000000,4125,4996,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.160706099241871486205113228607,71,18
312,312_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,249.000000000000000000000000000000,1,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,18
313,313_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,216.000000000000000000000000000000,69,1942,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.542172941286717557929364375013,100,18
314,314_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,172.000000000000000000000000000000,1637,4991,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.662669513155208389498795895634,83,18
315,315_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,202.000000000000000000000000000000,2916,2374,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.335163902715107819840767433561,1,18
316,316_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,265.000000000000000000000000000000,1654,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.493225709736339446020991772457,1,18
317,317_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,165.000000000000000000000000000000,87,8,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.304731012489604713433521965271,17,18
318,318_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,4171,4997,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.544769593239822769170643823600,66,18
319,319_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,216.000000000000000000000000000000,1695,1917,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.377907514552969503451862465226,100,18
320,320_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,3106.000000000000000000000000000000,1394,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.175514444478157838336329632511,60,18
321,321_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,184.000000000000000000000000000000,3494,2622,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.461790165466877899902442550228,100,18
322,322_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,170.000000000000000000000000000000,79,3537,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.406043996445283827956274080861,100,18
323,323_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,153.000000000000000000000000000000,4132,3373,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.435132596793479931029935414699,92,18
324,324_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,214.000000000000000000000000000000,1,3533,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,18
325,325_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,175.000000000000000000000000000000,519,4995,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.332713237298378206041604698839,93,18
326,326_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,180.000000000000000000000000000000,863,638,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.296259178792066479868339001769,40,18
327,327_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,222.000000000000000000000000000000,626,501,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.857050145559736353817470444483,1,18
328,328_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,184.000000000000000000000000000000,3822,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,18
329,329_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,174.000000000000000000000000000000,3610,4990,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.763404332557443443540989846952,65,18
330,330_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,156.000000000000000000000000000000,4950,4998,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.291629054541738375050385911891,34,18
331,331_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,159.000000000000000000000000000000,3095,36,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.243926685320758979136357424977,25,18
332,332_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,167.000000000000000000000000000000,1929,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,83,18
333,333_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,961.000000000000000000000000000000,1996,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.712076573726334594027775892755,42,18
334,334_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,186.000000000000000000000000000000,2369,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.963493595632778698778508896794,78,18
335,335_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,248.000000000000000000000000000000,2570,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.449417102352789143981937058925,39,18
336,336_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,1589,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.730365350654139544595011557249,25,18
337,337_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,2229,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,18
338,338_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,178.000000000000000000000000000000,736,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,18
339,339_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,155.000000000000000000000000000000,1580,2696,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.282376084756491230187691598985,30,18
340,340_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,150.000000000000000000000000000000,4718,1511,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.279887248483093931117338115655,30,18
341,341_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,380,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,79,18
342,342_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,167.000000000000000000000000000000,3355,1896,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.246007286178869855053363835395,28,18
343,343_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,173.000000000000000000000000000000,2321,1730,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.290893493552753845676761557115,31,18
344,344_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,2250,4989,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.417645127979385244465504456457,30,18
345,345_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,162.000000000000000000000000000000,1027,1653,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.272564363275486942761460795737,28,18
346,346_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,3206,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.205446690348524835245669351025,28,18
347,347_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,178.000000000000000000000000000000,2545,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,83,18
348,348_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,776.000000000000000000000000000000,2611,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.727684756130504761806321312179,38,18
349,349_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,177.000000000000000000000000000000,28,2461,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,26,18
350,350_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,156.000000000000000000000000000000,1684,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.069943302710977414693438447557,39,18
351,351_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,177.000000000000000000000000000000,4309,4997,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.422973622075365318639939005152,91,18
352,352_0,FAILED,BoTorch,GenerationStep_1,,,124,1,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.934929830994409849509452214988,30,18
353,353_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,181.000000000000000000000000000000,4353,4427,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.083236664580750782671536569524,21,18
354,354_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,182.000000000000000000000000000000,2125,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,61,18
355,355_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,174.000000000000000000000000000000,4897,1319,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.716019019120593180893763474160,41,18
356,356_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,253.000000000000000000000000000000,880,1,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.058762623956483725495836267783,33,18
357,357_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,2064,4988,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.266051986807579965432779545154,7,18
358,358_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,165.000000000000000000000000000000,1212,3241,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.318827051749125900403925015780,34,18
359,359_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,1738,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,90,18
360,360_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,175.000000000000000000000000000000,4264,4,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.669929667903024328445837909385,4,18
361,361_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,751,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.459669003541051390815397326151,12,18
362,362_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,204.000000000000000000000000000000,4255,3,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.302338525772746169462124044003,27,18
363,363_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,174.000000000000000000000000000000,1390,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.341987543985469599761017889250,47,18
364,364_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,157.000000000000000000000000000000,91,1243,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,36,18
365,365_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,169.000000000000000000000000000000,2820,1055,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,35,18
366,366_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,153.000000000000000000000000000000,1884,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.605297260158806360585970196553,19,18
367,367_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,157.000000000000000000000000000000,3921,316,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.656690998562199146348916656279,9,18
368,368_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,170.000000000000000000000000000000,4384,688,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.668578334512732963546000064525,11,18
369,369_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,160.000000000000000000000000000000,1019,4996,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.678950633562116823149779065716,23,18
370,370_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,155.000000000000000000000000000000,1,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.887547186846744051713642420509,100,18
371,371_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,260.000000000000000000000000000000,3702,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.644525654534366720938010075770,9,18
372,372_0,FAILED,BoTorch,GenerationStep_1,,,1,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,100,18
373,373_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,186.000000000000000000000000000000,2363,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.184058542823018506107146663453,100,18
374,374_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,142.000000000000000000000000000000,929,1465,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,37,18
375,375_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,157.000000000000000000000000000000,3278,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.552070867431792677848534367513,29,18
376,376_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,2624,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.499971092237103786892760126648,82,18
377,377_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,168.000000000000000000000000000000,1709,4979,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.247132371618027896209568439190,42,18
378,378_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,168.000000000000000000000000000000,644,1200,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,30,18
379,379_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,163.000000000000000000000000000000,4338,895,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,39,18
380,380_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,270.000000000000000000000000000000,732,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.249311734207934510010318263085,8,18
381,381_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,166.000000000000000000000000000000,1477,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.249895274645276471714439026073,1,18
382,382_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,156.000000000000000000000000000000,4253,662,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.699538456673195474522231052106,10,18
383,383_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,2361,1509,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.080909685842985870629640032803,55,18
384,384_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,221.000000000000000000000000000000,2973,979,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.828244286455516509626306742575,75,18
385,385_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,172.000000000000000000000000000000,3669,1347,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.120005420946155003103505976014,35,18
386,386_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,155.000000000000000000000000000000,2349,305,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.718218799859649981165432564012,7,18
387,387_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,156.000000000000000000000000000000,3589,3371,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.912244987543807339669399425475,94,18
388,388_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,192.000000000000000000000000000000,1570,1557,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.760909663139571867951360673032,78,18
389,389_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,165.000000000000000000000000000000,2335,63,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.722759293954206460952605084458,2,18
390,390_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,156.000000000000000000000000000000,662,47,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.976185263055129648357421956462,25,18
391,391_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,514,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.458580291508977277992187282507,42,18
392,392_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,154.000000000000000000000000000000,559,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.318254722021802494147380002687,38,18
393,393_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,169.000000000000000000000000000000,324,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.594765373972881317143901469535,84,18
394,394_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,289.000000000000000000000000000000,4300,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.052344238355634328807575172959,15,18
395,395_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,625.000000000000000000000000000000,1110,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,35,18
396,396_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,249.000000000000000000000000000000,1452,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,88,18
397,397_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,159.000000000000000000000000000000,46,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.580021620917104074877101993479,16,18
398,398_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,148.000000000000000000000000000000,1817,1572,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.020130093486339394048822981631,56,18
399,399_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,181.000000000000000000000000000000,4555,1206,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.871262617184335508291326277686,56,18
400,400_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,159.000000000000000000000000000000,3471,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.643356938141023682042884956900,11,18
401,401_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,159.000000000000000000000000000000,39,1978,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,36,18
402,402_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,157.000000000000000000000000000000,4869,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.578229796250764094267537984706,22,18
403,403_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,163.000000000000000000000000000000,258,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.641448530580428055714037327562,83,18
404,404_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,216.000000000000000000000000000000,4894,1199,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,18
405,405_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,179.000000000000000000000000000000,2696,3014,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.622092728258392457796333019360,4,18
406,406_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,173.000000000000000000000000000000,134,1556,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,70,18
407,407_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,248.000000000000000000000000000000,4883,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,86,18
408,408_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,157.000000000000000000000000000000,1406,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.031159887486647951798657274480,75,18
409,409_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,159.000000000000000000000000000000,4974,2317,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.793863740060205769211165716115,57,18
410,410_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,163.000000000000000000000000000000,1532,1564,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,13,18
411,411_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,149.000000000000000000000000000000,606,1544,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,45,18
412,412_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,171.000000000000000000000000000000,4703,1502,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.509628022197689545258469934197,30,18
413,413_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,156.000000000000000000000000000000,2892,1490,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,62,18
414,414_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,150.000000000000000000000000000000,4225,1510,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,49,18
415,415_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,4486,3194,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.600914160764143945314685879566,5,18
416,416_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,169.000000000000000000000000000000,4954,1865,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.748480690556642680455468052969,53,18
417,417_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,154.000000000000000000000000000000,3390,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.594662708815007379214989668981,5,18
418,418_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,268.000000000000000000000000000000,1762,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,26,18
419,419_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,184.000000000000000000000000000000,4821,2200,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.817351810227535779951324457215,63,18
420,420_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,171.000000000000000000000000000000,3826,3196,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.759904862645299217227545796050,30,18
421,421_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,168.000000000000000000000000000000,2529,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.335636762472702798731916118413,51,18
422,422_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,183.000000000000000000000000000000,1339,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.063588971104119831889711633721,83,18
423,423_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,178.000000000000000000000000000000,4274,3225,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.817469143932815556219395602966,20,18
424,424_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,180.000000000000000000000000000000,1238,3183,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.533169733646343901867226122704,35,18
425,425_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,171.000000000000000000000000000000,4925,1152,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.580150838732663820529467102460,47,18
426,426_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,180.000000000000000000000000000000,4983,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.693878631221701347442376572872,95,18
427,427_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,166.000000000000000000000000000000,3611,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.523609276749398389050327295990,38,18
428,428_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,169.000000000000000000000000000000,4736,3213,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.553205869151155216201232178719,41,18
429,429_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,252.000000000000000000000000000000,4559,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.561654704943130300698328483122,50,18
430,430_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,157.000000000000000000000000000000,4267,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.379152413182160141769116989963,44,18
431,431_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,167.000000000000000000000000000000,785,5000,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.634569301942294994312021572114,44,18
432,432_0,FAILED,BoTorch,GenerationStep_1,,,4566,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,95,18
433,433_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,167.000000000000000000000000000000,2075,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.308287074595370469420885228828,23,18
434,434_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,206.000000000000000000000000000000,5000,3228,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,98,18
435,435_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,183.000000000000000000000000000000,5000,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.471874307087767841384362554891,100,18
436,436_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,270.000000000000000000000000000000,5000,1977,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,92,18
437,437_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,169.000000000000000000000000000000,3733,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.764849018797922775014797025506,87,18
438,438_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,256.000000000000000000000000000000,3399,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,68,18
439,439_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,168.000000000000000000000000000000,2482,3309,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.681383307997232900987683024141,15,18
440,440_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,157.000000000000000000000000000000,4181,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.703293760313980720333404406119,32,18
441,441_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,260.000000000000000000000000000000,2428,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,18
442,442_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,638,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.049174368323468914399043683261,58,18
443,443_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,164.000000000000000000000000000000,2927,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.485439098480861419560028480191,15,18
444,444_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,159.000000000000000000000000000000,3460,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.382536405184915218047336793461,81,18
445,445_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,1386,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.847770047713114816012591745675,74,18
446,446_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,158.000000000000000000000000000000,1486,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.334011335982646262454665020414,12,18
447,447_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,174.000000000000000000000000000000,4585,3154,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.496765339891618751266832987312,47,18
448,448_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,3893,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.263399993189391723458214755738,17,18
449,449_0,FAILED,BoTorch,GenerationStep_1,,,1473,1,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,24,18
450,450_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,248.000000000000000000000000000000,1222,1687,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.687267467617988625505631716806,93,18
451,451_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,231.000000000000000000000000000000,3317,3833,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.103299027387052780313503319576,49,18
452,452_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,206.000000000000000000000000000000,4843,84,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.310903581099584713776096123183,74,18
453,453_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,237.000000000000000000000000000000,1888,2933,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.894875029992312165383339106484,18,18
454,454_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,344.000000000000000000000000000000,1751,817,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.594679551897570490837097167969,62,18
455,455_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,231.000000000000000000000000000000,4042,3666,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.197836134903132926377011813202,5,18
456,456_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,233.000000000000000000000000000000,2828,2419,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.403491469865664864613563622697,79,18
457,457_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,206.000000000000000000000000000000,147,4565,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.800337918758392330431661321199,37,18
458,458_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,240.000000000000000000000000000000,383,473,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.972235790509730524178166888305,52,18
459,459_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,227.000000000000000000000000000000,2594,2702,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.324914494300261147419206508857,8,18
460,460_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,277.000000000000000000000000000000,4276,1449,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.025694986309856178430255368994,83,18
461,461_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,220.000000000000000000000000000000,1518,4225,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.673012774737551833226234521135,27,18
462,462_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,325.000000000000000000000000000000,2280,2025,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.817272898025810712319128015224,95,18
463,463_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,162.000000000000000000000000000000,4452,4801,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.481826601816341293371692700021,40,18
464,464_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,323.000000000000000000000000000000,3707,1050,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.180657875075936308340018854324,65,18
465,465_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,254.000000000000000000000000000000,831,3279,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.516100663503631928996639999241,20,18
466,466_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,216.000000000000000000000000000000,700,1235,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.465390311261638989215327910642,45,18
467,467_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,291.000000000000000000000000000000,3528,3385,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.863219548776745781992758566048,90,18
468,468_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,242.000000000000000000000000000000,4585,2134,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.532780190004035869577592166024,15,18
469,469_0,COMPLETED,Sobol,SOBOL,0.110000000000000000555111512313,235.000000000000000000000000000000,2460,4988,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.134955055125057704623614540651,70,18
470,470_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,283.000000000000000000000000000000,4547,185,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,56,18
471,471_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2469,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,66,18
472,472_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,165.000000000000000000000000000000,1966,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,6,18
473,473_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,280.000000000000000000000000000000,1520,1193,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,65,18
474,474_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,289.000000000000000000000000000000,175,1193,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,65,18
475,475_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,243.000000000000000000000000000000,3160,1156,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.400634249445028789438083549612,37,18
476,476_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,178.000000000000000000000000000000,3074,445,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.346880819084105285199370882765,24,18
477,477_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,263.000000000000000000000000000000,758,1174,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.375650976004533565522081062227,41,18
478,478_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,247.000000000000000000000000000000,1433,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.043768027874757642337044671876,44,18
479,479_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,227.000000000000000000000000000000,4237,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.389620432740783617653335113573,16,18
480,480_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,242.000000000000000000000000000000,1153,1194,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,40,18
481,481_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,786.000000000000000000000000000000,2654,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,32,18
482,482_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,235.000000000000000000000000000000,659,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.787467901606951126147748709627,1,18
483,483_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,244.000000000000000000000000000000,3731,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.103542114743755880867936980394,51,18
484,484_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,207.000000000000000000000000000000,2408,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.367861483072062567245552600070,36,18
485,485_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,421.000000000000000000000000000000,1,1196,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,18
486,486_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,279.000000000000000000000000000000,1549,1190,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.957290358836716870349903274473,48,18
487,487_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,270.000000000000000000000000000000,607,1195,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,24,18
488,488_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,475.000000000000000000000000000000,3642,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,22,18
489,489_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,264.000000000000000000000000000000,4612,3143,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.560588706003079750850304208143,16,18
490,490_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,554.000000000000000000000000000000,3868,4,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,56,18
491,491_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4936,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,66,18
492,492_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,397.000000000000000000000000000000,3770,931,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,100,18
493,493_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,418.000000000000000000000000000000,3040,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,94,18
494,494_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,200.000000000000000000000000000000,3087,165,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,26,18
495,495_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,317.000000000000000000000000000000,1,401,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,62,18
496,496_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,230.000000000000000000000000000000,1167,136,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,18
497,497_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,218.000000000000000000000000000000,2172,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.257872172572936153578382345586,35,18
498,498_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,228.000000000000000000000000000000,2837,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.707627000812216411240740399080,15,18
499,499_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,228.000000000000000000000000000000,538,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.385273710933928092980949031698,16,18
500,500_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,1793.000000000000000000000000000000,3806,2,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,67,18
501,501_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,1083.000000000000000000000000000000,324,7,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.549205736883173534224056311359,67,18
502,502_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,381.000000000000000000000000000000,847,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,60,18
503,503_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4302,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,99,18
504,504_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,220.000000000000000000000000000000,4765,173,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,38,18
505,505_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,289.000000000000000000000000000000,4696,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,80,18
506,506_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,232.000000000000000000000000000000,2962,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.121612544672135600154483370261,58,18
507,507_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3782,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,66,18
508,508_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3344,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,86,18
509,509_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,237.000000000000000000000000000000,4354,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.751006317623352348533671829500,73,18
510,510_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,372.000000000000000000000000000000,4659,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,15,18
511,511_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,217.000000000000000000000000000000,2869,127,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,25,18
512,512_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,398.000000000000000000000000000000,681,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,8,18
513,513_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,224.000000000000000000000000000000,1747,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.380264456975674625116567995065,4,18
514,514_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,238.000000000000000000000000000000,579,74,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,33,18
515,515_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,393.000000000000000000000000000000,4142,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,64,18
516,516_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,192.000000000000000000000000000000,1908,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.389904134471331598188470479727,23,18
517,517_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,231.000000000000000000000000000000,4145,125,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,62,18
518,518_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,478.000000000000000000000000000000,3183,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,18,18
519,519_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,410.000000000000000000000000000000,85,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,57,18
520,520_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,459.000000000000000000000000000000,805,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,100,18
521,521_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,235.000000000000000000000000000000,3347,194,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,33,18
522,522_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,241.000000000000000000000000000000,1,570,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,48,18
523,523_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,300.000000000000000000000000000000,3020,5000,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.411522114759718449228387271432,100,18
524,524_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,270.000000000000000000000000000000,4318,1186,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,79,18
525,525_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,235.000000000000000000000000000000,3453,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.385530111436187261642061230305,40,18
526,526_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,294.000000000000000000000000000000,4524,1181,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,15,18
527,527_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,269.000000000000000000000000000000,3974,601,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,65,18
528,528_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,544.000000000000000000000000000000,925,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,25,18
529,529_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,285.000000000000000000000000000000,2900,3146,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.380536043260624012862791687439,67,18
530,530_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,245.000000000000000000000000000000,4533,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.401631533921990857560757604006,7,18
531,531_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,221.000000000000000000000000000000,503,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.394269084724394813790127045650,22,18
532,532_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,220.000000000000000000000000000000,2471,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.636331762807469725196085619245,12,18
533,533_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,354.000000000000000000000000000000,4585,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,11,18
534,534_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,709.000000000000000000000000000000,1605,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,31,18
535,535_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,247.000000000000000000000000000000,3644,200,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,53,18
536,536_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,289.000000000000000000000000000000,1872,4126,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,60,18
537,537_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,237.000000000000000000000000000000,857,199,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,20,18
538,538_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,222.000000000000000000000000000000,4529,186,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,18
539,539_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1224,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,77,18
540,540_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,224.000000000000000000000000000000,693,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.750415002641694361962265702459,87,18
541,541_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,389.000000000000000000000000000000,3170,1,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,20,18
542,542_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,275.000000000000000000000000000000,1737,4129,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,71,18
543,543_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,223.000000000000000000000000000000,3364,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.865799775462901100020474132180,44,18
544,544_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,227.000000000000000000000000000000,488,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.262060369431374173831983398486,38,18
545,545_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,270.000000000000000000000000000000,3662,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,93,18
546,546_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,399.000000000000000000000000000000,605,1,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,17,18
547,547_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,264.000000000000000000000000000000,2852,1,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,40,18
548,548_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,193.000000000000000000000000000000,1491,4155,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,7,18
549,549_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,268.000000000000000000000000000000,3526,4123,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,19,18
550,550_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,200.000000000000000000000000000000,382,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.604209496798831957597997188714,41,18
551,551_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,278.000000000000000000000000000000,4456,4098,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,50,18
552,552_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,408.000000000000000000000000000000,1074,1,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,44,18
553,553_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,246.000000000000000000000000000000,2218,4092,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,11,18
554,554_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,223.000000000000000000000000000000,1963,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.284228709044490268631477647432,80,18
555,555_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2009,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,95,18
556,556_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,225.000000000000000000000000000000,1662,4995,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.203350442053126329033929664547,41,18
557,557_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,268.000000000000000000000000000000,1971,1199,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,7,18
558,558_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,325.000000000000000000000000000000,21,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,75,18
559,559_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,250.000000000000000000000000000000,915,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.340762592922592089106359480866,87,18
560,560_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,239.000000000000000000000000000000,3255,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.342660423821788462372950334611,20,18
561,561_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,170.000000000000000000000000000000,1282,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.029658741822726323450476471066,95,18
562,562_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,468.000000000000000000000000000000,906,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,14,18
563,563_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,226.000000000000000000000000000000,4261,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.367669310936033943981726679340,78,18
564,564_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,222.000000000000000000000000000000,4899,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.803679035002526798159294685320,8,18
565,565_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,225.000000000000000000000000000000,2565,119,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,44,18
566,566_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,254.000000000000000000000000000000,4250,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.394849518405396449072242148759,62,18
567,567_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,242.000000000000000000000000000000,973,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.977660211633602838077194974176,66,18
568,568_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,218.000000000000000000000000000000,3780,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.279815832084910731669680217237,46,18
569,569_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,251.000000000000000000000000000000,1871,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.550776368590207510145262403967,71,18
570,570_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,243.000000000000000000000000000000,1061,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,77,18
571,571_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,235.000000000000000000000000000000,1808,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.667057939179555692277290290804,51,18
572,572_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,243.000000000000000000000000000000,1241,4971,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.045778809609228095223176779882,32,18
573,573_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,256.000000000000000000000000000000,1920,1219,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,6,18
574,574_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,241.000000000000000000000000000000,1675,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.466533439866213761604285537032,32,18
575,575_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,297.000000000000000000000000000000,261,4999,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.165533499491835961459074155755,3,18
576,576_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,242.000000000000000000000000000000,1077,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.219202541123252325760972780699,10,18
577,577_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,214.000000000000000000000000000000,1077,4979,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.194818765619795331023311746321,27,18
578,578_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2578,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,92,18
579,579_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,230.000000000000000000000000000000,174,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.878134542350330837834349040349,34,18
580,580_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,206,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,88,18
581,581_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,448.000000000000000000000000000000,3754,1186,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,18
582,582_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,251.000000000000000000000000000000,3547,4165,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,83,18
583,583_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,241.000000000000000000000000000000,760,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.619174658171962710717650679726,27,18
584,584_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,245.000000000000000000000000000000,212,1206,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,32,18
585,585_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,235.000000000000000000000000000000,2577,4450,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,33,18
586,586_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,285.000000000000000000000000000000,4972,4147,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,18
587,587_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,267.000000000000000000000000000000,3106,3617,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,62,18
588,588_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,285.000000000000000000000000000000,2457,4168,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,92,18
589,589_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,282.000000000000000000000000000000,2962,4179,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,84,18
590,590_0,FAILED,BoTorch,BOTORCH_MODULAR,,,795,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,77,18
591,591_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,256.000000000000000000000000000000,3156,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.428042674290308067241994649521,22,18
592,592_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,233.000000000000000000000000000000,3450,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.070431669239394756321104296148,10,18
593,593_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,218.000000000000000000000000000000,431,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.993210956990805748922923612554,11,18
594,594_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,229.000000000000000000000000000000,2858,1493,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,34,18
595,595_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,224.000000000000000000000000000000,2444,108,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,77,18
596,596_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,314.000000000000000000000000000000,4020,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,57,18
597,597_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,238.000000000000000000000000000000,4423,1259,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,29,18
598,598_0,FAILED,BoTorch,BOTORCH_MODULAR,,,173,1,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,21,18
599,599_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,217.000000000000000000000000000000,41,174,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,94,18
600,600_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,212.000000000000000000000000000000,4582,1610,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,18
601,601_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,242.000000000000000000000000000000,3346,1617,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,32,18
602,602_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,222.000000000000000000000000000000,3394,1556,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,20,18
603,603_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,303.000000000000000000000000000000,1261,1186,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,43,18
604,604_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,187.000000000000000000000000000000,1,769,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,10,18
605,605_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,342.000000000000000000000000000000,1,4092,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,3,18
606,606_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1,1,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,22,18
607,607_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,276.000000000000000000000000000000,802,4128,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,59,18
608,608_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,231.000000000000000000000000000000,2816,4124,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,35,18
609,609_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,224.000000000000000000000000000000,4601,3151,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.558128050208732462422744902142,100,18
610,610_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,209.000000000000000000000000000000,3850,3096,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,38,18
611,611_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,242.000000000000000000000000000000,5000,2377,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,61,18
612,612_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,253.000000000000000000000000000000,1,4170,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,55,18
613,613_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,244.000000000000000000000000000000,4647,3220,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.984945034131976648872353052866,52,18
614,614_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,239.000000000000000000000000000000,5000,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.603543687443552356874931774655,28,18
615,615_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,18
616,616_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,216.000000000000000000000000000000,1,1177,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,88,18
617,617_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,256.000000000000000000000000000000,2639,3137,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.851401632317881573719375865039,26,18
618,618_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,293.000000000000000000000000000000,2667,2500,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,55,18
619,619_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,254.000000000000000000000000000000,890,2333,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,51,18
620,372_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,100,18
621,621_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,271.000000000000000000000000000000,3,4366,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.378568094384947650965500542952,24,18
622,622_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,218.000000000000000000000000000000,1360,3437,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,18
623,623_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,550.000000000000000000000000000000,828,248,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,18
624,624_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,203.000000000000000000000000000000,5000,4414,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,19,18
625,625_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,225.000000000000000000000000000000,1,258,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,31,18
626,626_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,204.000000000000000000000000000000,5000,4422,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,63,18
627,627_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,219.000000000000000000000000000000,472,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.378895524091516999742879079349,73,18
628,628_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,252.000000000000000000000000000000,4177,4409,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,18
629,629_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,222.000000000000000000000000000000,374,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.812203548429660915353167638386,70,18
630,630_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4591,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,77,18
631,631_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,223.000000000000000000000000000000,1451,5000,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.713236795675060930577160434041,82,18
632,632_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,175.000000000000000000000000000000,1856,4439,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,51,18
633,633_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,220.000000000000000000000000000000,2417,1499,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,86,18
634,634_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,189.000000000000000000000000000000,5000,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.391831418180480506308072108368,57,18
635,635_0,FAILED,BoTorch,BOTORCH_MODULAR,,,986,1,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,22,18
636,636_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,277.000000000000000000000000000000,3334,4396,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,68,18
637,637_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,243.000000000000000000000000000000,1,3035,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,46,18
638,638_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,237.000000000000000000000000000000,3601,4981,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.787696829030552492234562578233,29,18
639,639_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,210.000000000000000000000000000000,4469,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,93,18
640,640_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.110000000000000000555111512313,201.000000000000000000000000000000,4223,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.186758697613400692638663258549,85,18
641,641_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,1160,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,77,18
642,642_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,263.000000000000000000000000000000,4330,4686,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,43,18
643,643_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,1055,1188,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,60,18
</pre>
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			<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download &raquo;results.csv&laquo; as file</button>
			<script>
				createTable(tab_results_csv_json, tab_results_headers_json, 'tab_results_csv_table');</script>
			
			<h1>&nbsp;Errors</h1>
			<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("simple_pre_tab_tab_errors")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("simple_pre_tab_tab_errors", "oo_errors.txt")'> Download &raquo;oo_errors.txt&laquo; as file</button>
<pre id='simple_pre_tab_tab_errors'><span style="background-color: black; color: white">
⚠ Job 4549775 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4549775/4549775_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4549775/4549775_0_log.out
----------------------------------------
submitit INFO (2025-04-18 20:18:38,438) - Starting with JobEnvironment(job_id=4549775, hostname=i7185, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-18 20:18:38,440) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4549775/4549775_submitted.pkl
slurmstepd: error: *** JOB 4549775 ON i7185 CANCELLED AT 2025-04-18T20:48:45 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4549775/4549775_0_log.err not found


⚠ Job 4555472 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4555472/4555472_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4555472/4555472_0_log.out
----------------------------------------
submitit INFO (2025-04-18 21:53:39,467) - Starting with JobEnvironment(job_id=4555472, hostname=i7173, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-18 21:53:39,473) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4555472/4555472_submitted.pkl
slurmstepd: error: *** JOB 4555472 ON i7173 CANCELLED AT 2025-04-18T22:23:49 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4555472/4555472_0_log.err not found


⚠ Job 4558105 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4558105/4558105_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4558105/4558105_0_log.out
----------------------------------------
submitit INFO (2025-04-18 22:41:40,269) - Starting with JobEnvironment(job_id=4558105, hostname=i7173, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-18 22:41:40,274) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4558105/4558105_submitted.pkl
slurmstepd: error: *** JOB 4558105 ON i7173 CANCELLED AT 2025-04-18T23:11:51 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4558105/4558105_0_log.err not found


⚠ Job 4571713 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4571713/4571713_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4571713/4571713_0_log.out
----------------------------------------
submitit INFO (2025-04-19 03:21:17,201) - Starting with JobEnvironment(job_id=4571713, hostname=i7185, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-19 03:21:17,216) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4571713/4571713_submitted.pkl
slurmstepd: error: *** JOB 4571713 ON i7185 CANCELLED AT 2025-04-19T03:51:32 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4571713/4571713_0_log.err not found


⚠ Job 4575236 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4575236/4575236_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4575236/4575236_0_log.out
----------------------------------------
submitit INFO (2025-04-19 04:41:18,134) - Starting with JobEnvironment(job_id=4575236, hostname=i7170, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-19 04:41:18,142) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4575236/4575236_submitted.pkl
slurmstepd: error: *** JOB 4575236 ON i7170 CANCELLED AT 2025-04-19T05:11:34 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4575236/4575236_0_log.err not found


⚠ Job 4580498 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4580498/4580498_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4580498/4580498_0_log.out
----------------------------------------
submitit INFO (2025-04-19 06:51:50,566) - Starting with JobEnvironment(job_id=4580498, hostname=i7170, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-19 06:51:50,568) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4580498/4580498_submitted.pkl
slurmstepd: error: *** JOB 4580498 ON i7170 CANCELLED AT 2025-04-19T07:22:10 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4580498/4580498_0_log.err not found


⚠ Job 4584919 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4584919/4584919_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4584919/4584919_0_log.out
----------------------------------------
submitit INFO (2025-04-19 08:47:12,448) - Starting with JobEnvironment(job_id=4584919, hostname=i7170, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-19 08:47:12,451) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4584919/4584919_submitted.pkl
slurmstepd: error: *** JOB 4584919 ON i7170 CANCELLED AT 2025-04-19T09:17:14 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4584919/4584919_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4587522/4587522_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4590647/4590647_0_log.err not found


⚠ Job 4595347 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4595347/4595347_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4595347/4595347_0_log.out
----------------------------------------
submitit INFO (2025-04-19 14:07:00,807) - Starting with JobEnvironment(job_id=4595347, hostname=i7183, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-19 14:07:00,820) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4595347/4595347_submitted.pkl
slurmstepd: error: *** JOB 4595347 ON i7183 CANCELLED AT 2025-04-19T14:36:56 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4595347/4595347_0_log.err not found


⚠ Job 4597769 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4597769/4597769_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4597769/4597769_0_log.out
----------------------------------------
submitit INFO (2025-04-19 15:24:58,277) - Starting with JobEnvironment(job_id=4597769, hostname=i7183, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-19 15:24:58,295) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4597769/4597769_submitted.pkl
slurmstepd: error: *** JOB 4597769 ON i7183 CANCELLED AT 2025-04-19T15:54:59 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4597769/4597769_0_log.err not found


⚠ Job 4601424 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4601424/4601424_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4601424/4601424_0_log.out
----------------------------------------
submitit INFO (2025-04-19 17:29:00,375) - Starting with JobEnvironment(job_id=4601424, hostname=i7183, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-19 17:29:00,377) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4601424/4601424_submitted.pkl
slurmstepd: error: *** JOB 4601424 ON i7183 CANCELLED AT 2025-04-19T17:59:02 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4601424/4601424_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4603227/4603227_0_log.err not found

</span></pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("simple_pre_tab_tab_errors")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("simple_pre_tab_tab_errors", "oo_errors.txt")'> Download &raquo;oo_errors.txt&laquo; as file</button>
			
			<h1>&nbsp;Args Overview</h1>
			<h2>Arguments Overview:                                                                                      </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> Key</th><th>Value                                                                                                                                          </th></tr></thead><tbody><tr><td> config_yaml</td><td>None                                                                                                                                           </td></tr><tr><td> config_toml</td><td>None                                                                                                                                           </td></tr><tr><td> config_json</td><td>None                                                                                                                                           </td></tr><tr><td> num_random_steps</td><td>20                                                                                                                                             </td></tr><tr><td> max_eval</td><td>None                                                                                                                                           </td></tr><tr><td> run_program</td><td>None                                                                                                                                           </td></tr><tr><td> experiment_name</td><td>None                                                                                                                                           </td></tr><tr><td> mem_gb</td><td>1                                                                                                                                              </td></tr><tr><td> parameter</td><td>None                                                                                                                                           </td></tr><tr><td> continue_previous_job</td><td>/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_18_HoeffdingTreeClassifier_ACCURACY-RUNTIME/0 </td></tr><tr><td> experiment_constraints</td><td>None                                                                                                                                           </td></tr><tr><td> run_dir</td><td>runs                                                                                                                                           </td></tr><tr><td> seed</td><td>None                                                                                                                                           </td></tr><tr><td> decimalrounding</td><td>4                                                                                                                                              </td></tr><tr><td> enforce_sequential_optimization</td><td>False                                                                                                                                          </td></tr><tr><td> verbose_tqdm</td><td>False                                                                                                                                          </td></tr><tr><td> model</td><td>None                                                                                                                                           </td></tr><tr><td> gridsearch</td><td>False                                                                                                                                          </td></tr><tr><td> occ</td><td>False                                                                                                                                          </td></tr><tr><td> show_sixel_scatter</td><td>False                                                                                                                                          </td></tr><tr><td> show_sixel_general</td><td>False                                                                                                                                          </td></tr><tr><td> show_sixel_trial_index_result</td><td>False                                                                                                                                          </td></tr><tr><td> follow</td><td>False                                                                                                                                          </td></tr><tr><td> send_anonymized_usage_stats</td><td>False                                                                                                                                          </td></tr><tr><td> ui_url</td><td>None                                                                                                                                           </td></tr><tr><td> root_venv_dir</td><td>/home/s4122485                                                                                                                                 </td></tr><tr><td> exclude</td><td>None                                                                                                                                           </td></tr><tr><td> main_process_gb</td><td>8                                                                                                                                              </td></tr><tr><td> pareto_front_confidence</td><td>1                                                                                                                                              </td></tr><tr><td> max_nr_of_zero_results</td><td>10                                                                                                                                             </td></tr><tr><td> abbreviate_job_names</td><td>False                                                                                                                                          </td></tr><tr><td> orchestrator_file</td><td>None                                                                                                                                           </td></tr><tr><td> checkout_to_latest_tested_version</td><td>False                                                                                                                                          </td></tr><tr><td> live_share</td><td>False                                                                                                                                          </td></tr><tr><td> disable_tqdm</td><td>False                                                                                                                                          </td></tr><tr><td> workdir</td><td>False                                                                                                                                          </td></tr><tr><td> occ_type</td><td>euclid                                                                                                                                         </td></tr><tr><td> result_names</td><td>[&#039;RESULT=min&#039;]                                                                                                                                 </td></tr><tr><td> minkowski_p</td><td>2                                                                                                                                              </td></tr><tr><td> signed_weighted_euclidean_weights</td><td></td></tr><tr><td> generation_strategy</td><td>None                                                                                                                                           </td></tr><tr><td> generate_all_jobs_at_once</td><td>False                                                                                                                                          </td></tr><tr><td> revert_to_random_when_seemingly_exhausted</td><td>True                                                                                                                                           </td></tr><tr><td> load_data_from_existing_jobs</td><td>[]                                                                                                                                             </td></tr><tr><td> n_estimators_randomforest</td><td>100                                                                                                                                            </td></tr><tr><td> external_generator</td><td>None                                                                                                                                           </td></tr><tr><td> username</td><td>None                                                                                                                                           </td></tr><tr><td> max_failed_jobs</td><td>None                                                                                                                                           </td></tr><tr><td> num_parallel_jobs</td><td>20                                                                                                                                             </td></tr><tr><td> worker_timeout</td><td>30                                                                                                                                             </td></tr><tr><td> slurm_use_srun</td><td>False                                                                                                                                          </td></tr><tr><td> time</td><td></td></tr><tr><td> partition</td><td></td></tr><tr><td> reservation</td><td>None                                                                                                                                           </td></tr><tr><td> force_local_execution</td><td>False                                                                                                                                          </td></tr><tr><td> slurm_signal_delay_s</td><td>0                                                                                                                                              </td></tr><tr><td> nodes_per_job</td><td>1                                                                                                                                              </td></tr><tr><td> cpus_per_task</td><td>1                                                                                                                                              </td></tr><tr><td> account</td><td>None                                                                                                                                           </td></tr><tr><td> gpus</td><td>0                                                                                                                                              </td></tr><tr><td> run_mode</td><td>local                                                                                                                                          </td></tr><tr><td> verbose</td><td>False                                                                                                                                          </td></tr><tr><td> verbose_break_run_search_table</td><td>False                                                                                                                                          </td></tr><tr><td> debug</td><td>False                                                                                                                                          </td></tr><tr><td> no_sleep</td><td>False                                                                                                                                          </td></tr><tr><td> tests</td><td>False                                                                                                                                          </td></tr><tr><td> show_worker_percentage_table_at_end</td><td>False                                                                                                                                          </td></tr><tr><td> auto_exclude_defective_hosts</td><td>False                                                                                                                                          </td></tr><tr><td> run_tests_that_fail_on_taurus</td><td>False                                                                                                                                          </td></tr><tr><td> raise_in_eval</td><td>False                                                                                                                                          </td></tr><tr><td> show_ram_every_n_seconds</td><td>False                                                                                                                                          </td></tr></tbody></table>
			<h1>&nbsp;Worker-Usage</h1>
			<div class='invert_in_dark_mode' id='workerUsagePlot'></div><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_worker_usage")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("pre_tab_worker_usage", "worker_usage.csv")'> Download &raquo;worker_usage.csv&laquo; as file</button>
<pre id="pre_tab_worker_usage">1744998965.9128642,20,0,0
1744998974.7615535,20,0,0
1744998975.7262874,20,0,0
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1744998977.4750612,20,1,5
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1744998983.0807135,20,1,5
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1744998994.2740874,20,3,15
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1744999006.9752054,20,4,20
1744999019.3122056,20,4,20
1744999020.1971917,20,4,20
1744999020.371278,20,4,20
1744999022.0040336,20,5,25
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1744999057.3259547,20,7,35
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1744999520.5905614,20,13,65
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1744999559.7603977,20,10,50
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1744999670.8928616,20,7,35
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1745044775.400047,20,1,5
1745044785.8086596,20,1,5
1745044796.6413596,20,1,5
1745044807.0374973,20,1,5
1745044817.466646,20,1,5
1745044827.9288442,20,1,5
1745044838.821874,20,1,5
1745044849.2784996,20,1,5
1745044859.7042282,20,1,5
1745044870.0838082,20,1,5
1745044875.490193,20,1,5
1745044881.833787,20,0,0
1745044891.0649152,20,0,0
1745045223.2518837,20,0,0
1745045223.4091532,20,0,0
1745045225.0243254,20,1,5
1745045230.814057,20,1,5
1745045532.4893224,20,1,5
1745045532.938085,20,1,5
1745045534.2836375,20,2,10
1745045543.6701066,20,2,10
1745045896.9718304,20,2,10
1745045897.2095613,20,2,10
1745045898.6955268,20,3,15
1745045902.1171212,20,3,15
1745045910.8974774,20,2,10
1745045912.0472383,20,2,10
1745046257.7587223,20,2,10
1745046258.311056,20,2,10
1745046260.0503626,20,3,15
1745046263.4217389,20,3,15
1745046272.2851305,20,2,10
1745046272.4461107,20,2,10
1745047084.0084102,20,2,10
1745047084.2708893,20,2,10
1745047085.9839463,20,3,15
1745047101.48651,20,3,15
1745047105.9074166,20,2,10
1745047114.8066516,20,1,5
1745047114.8653162,20,1,5
1745047879.7891507,20,1,5
1745047880.0701406,20,1,5
1745047881.449929,20,2,10
1745047881.9061403,20,2,10
1745047890.5507371,20,1,5
1745047890.8884113,20,1,5
1745048240.4117796,20,1,5
1745048240.938812,20,1,5
1745048242.3278584,20,2,10
1745048242.765073,20,2,10
1745048252.0902665,20,1,5
1745048252.1577952,20,1,5
1745049134.162555,20,1,5
1745049134.4618313,20,1,5
1745049136.074132,20,2,10
1745049136.609786,20,2,10
1745049145.7033968,20,1,5
1745049146.1151824,20,1,5
1745049829.2423546,20,1,5
1745049829.5775588,20,1,5
1745049831.0349655,20,2,10
1745049831.451171,20,2,10
1745049840.6011403,20,1,5
1745049840.6780837,20,1,5
1745050560.3847163,20,1,5
1745050560.946478,20,1,5
1745050562.9834669,20,2,10
1745050563.234716,20,2,10
1745050573.1225011,20,1,5
1745050573.198703,20,1,5
1745051238.7020729,20,1,5
1745051238.9958317,20,1,5
1745051240.3776863,20,2,10
1745051240.8397248,20,2,10
1745051249.4682026,20,1,5
1745051249.8611093,20,1,5
1745052009.911402,20,1,5
1745052011.252493,20,1,5
1745052012.9219127,20,2,10
1745052013.3752203,20,2,10
1745052022.7188041,20,1,5
1745052022.8869557,20,1,5
1745052715.7510507,20,1,5
1745052716.0498738,20,1,5
1745052717.4237597,20,2,10
1745052717.8528376,20,2,10
1745052727.2497947,20,1,5
1745052727.3235092,20,1,5
1745053340.7327,20,1,5
1745053341.023235,20,1,5
1745053342.4071915,20,2,10
1745053342.8710163,20,2,10
1745053352.0918863,20,1,5
1745053352.2627528,20,1,5
1745054291.3392866,20,1,5
1745054292.9684496,20,1,5
1745054294.504282,20,2,10
1745054294.9434648,20,2,10
1745054303.71629,20,1,5
1745054303.8943813,20,1,5
1745054883.6998096,20,1,5
1745054884.0017905,20,1,5
1745054885.3743432,20,2,10
1745054885.8346663,20,2,10
1745054894.6336215,20,1,5
1745054894.868539,20,1,5
1745055599.511073,20,1,5
1745055599.944979,20,1,5
1745055601.3143795,20,2,10
1745055601.7557156,20,2,10
1745055611.2391717,20,1,5
1745055611.3022146,20,1,5
1745056418.4436226,20,1,5
1745056418.9473808,20,1,5
1745056420.35962,20,2,10
1745056420.6086857,20,2,10
1745056430.606742,20,1,5
1745056430.677493,20,1,5
1745057518.217379,20,1,5
1745057518.4885662,20,1,5
1745057520.0464256,20,2,10
1745057520.4742355,20,2,10
1745057530.3970945,20,1,5
1745057530.8317397,20,1,5
1745058396.172613,20,1,5
1745058396.461225,20,1,5
1745058398.0485,20,2,10
1745058398.531351,20,2,10
1745058407.81676,20,1,5
1745058407.9317617,20,1,5
1745058414.8430393,20,1,5
1745058425.643291,20,1,5
1745058436.3365505,20,1,5
1745058447.178729,20,1,5
1745058457.9186938,20,1,5
1745058468.5603004,20,1,5
1745058479.2694445,20,1,5
1745058489.92784,20,1,5
1745058500.9167302,20,1,5
1745058511.633426,20,1,5
1745058522.319413,20,1,5
1745058533.0271974,20,1,5
1745058543.7143424,20,1,5
1745058554.418455,20,1,5
1745058565.1320078,20,1,5
1745058575.9164562,20,1,5
1745058586.6694767,20,1,5
1745058597.3973885,20,1,5
1745058608.419123,20,1,5
1745058619.1618783,20,1,5
1745058630.4944565,20,1,5
1745058640.68441,20,1,5
1745058652.0248456,20,1,5
1745058657.4874072,20,1,5
1745058663.785289,20,0,0
1745058673.5319772,20,0,0
1745059667.201118,20,0,0
1745059667.391974,20,0,0
1745059669.0485976,20,1,5
1745059674.927647,20,1,5
1745060538.1808264,20,1,5
1745060538.4738266,20,1,5
1745060540.0144866,20,2,10
1745060540.4449608,20,2,10
1745060549.8365042,20,1,5
1745060551.0712497,20,1,5
1745061459.0369995,20,1,5
1745061459.3167405,20,1,5
1745061461.0506668,20,2,10
1745061461.502376,20,2,10
1745061470.4352903,20,1,5
1745061470.8481596,20,1,5
1745062618.0858598,20,1,5
1745062618.3724005,20,1,5
1745062620.059079,20,2,10
1745062620.5848265,20,2,10
1745062630.4128692,20,1,5
1745062630.4789252,20,1,5
1745063530.6101727,20,1,5
1745063530.987409,20,1,5
1745063532.3769896,20,2,10
1745063532.8212464,20,2,10
1745063542.4441452,20,1,5
1745063542.5195513,20,1,5
1745064406.5754762,20,1,5
1745064406.9496443,20,1,5
1745064408.32076,20,2,10
1745064408.76344,20,2,10
1745064418.4318626,20,1,5
1745064418.4949176,20,1,5
1745065434.190101,20,1,5
1745065434.457042,20,1,5
1745065436.0185401,20,2,10
1745065445.01032,20,2,10
1745066401.3229923,20,2,10
1745066401.8698847,20,2,10
1745066403.2973182,20,3,15
1745066419.0221574,20,3,15
1745066423.9782681,20,2,10
1745066432.8747911,20,1,5
1745066432.9414976,20,1,5
1745067205.8684583,20,1,5
1745067207.9662414,20,1,5
1745067209.420249,20,2,10
1745067209.837845,20,2,10
1745067218.836721,20,1,5
1745067219.0048141,20,1,5
1745068117.2288163,20,1,5
1745068117.4963214,20,1,5
1745068119.0068371,20,2,10
1745068119.470184,20,2,10
1745068128.50586,20,1,5
1745068128.854548,20,1,5
1745069082.0228086,20,1,5
1745069082.2942646,20,1,5
1745069084.006896,20,2,10
1745069084.4456377,20,2,10
1745069094.304368,20,1,5
1745069094.3642662,20,1,5
1745070072.5984974,20,1,5
1745070073.056452,20,1,5
1745070074.5988696,20,2,10
1745070083.812179,20,2,10
1745071146.9549737,20,2,10
1745071147.212516,20,2,10
1745071148.870825,20,3,15
1745071164.2983158,20,3,15
1745071168.533014,20,2,10
1745071178.1305518,20,1,5
1745071178.1960554,20,1,5
1745071908.6437738,20,1,5
1745071908.9595053,20,1,5
1745071910.3310406,20,2,10
1745071910.7783139,20,2,10
1745071919.8400114,20,1,5
1745071920.0090823,20,1,5
1745072854.4401293,20,1,5
1745072854.9352121,20,1,5
1745072856.3085613,20,2,10
1745072856.7684002,20,2,10
1745072866.581524,20,1,5
1745072866.8625247,20,1,5
1745073938.9807835,20,1,5
1745073939.2913268,20,1,5
1745073941.0536795,20,2,10
1745073941.4891796,20,2,10
1745073951.515738,20,1,5
1745073951.5782044,20,1,5
1745074678.4998167,20,1,5
1745074678.9858484,20,1,5
1745074680.376284,20,2,10
1745074680.9153843,20,2,10
1745074690.1112058,20,1,5
1745074690.2785912,20,1,5
1745075082.5189452,20,1,5
1745075082.9794056,20,1,5
1745075084.4857044,20,2,10
1745075084.9848974,20,2,10
1745075094.1874106,20,1,5
1745075094.2643518,20,1,5
1745075511.4981668,20,1,5
1745075512.0025492,20,1,5
1745075513.4001145,20,2,10
1745075513.8643332,20,2,10
1745075523.6470246,20,1,5
1745075523.8567607,20,1,5
1745075883.2492864,20,1,5
1745075883.6248791,20,1,5
1745075885.018819,20,2,10
1745075885.4792094,20,2,10
1745075894.6103566,20,1,5
1745075894.9000762,20,1,5
</pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_worker_usage")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("pre_tab_worker_usage", "worker_usage.csv")'> Download &raquo;worker_usage.csv&laquo; as file</button>
			
			<h1>&nbsp;CPU/RAM-Usage (main)</h1>
			<div class='invert_in_dark_mode' id='mainWorkerCPURAM'></div><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("pre_tab_main_worker_cpu_ram", "cpu_ram_usage.csv")'> Download &raquo;cpu_ram_usage.csv&laquo; as file</button>
<pre id="pre_tab_main_worker_cpu_ram">timestamp,ram_usage_mb,cpu_usage_percent
1744998965,642.328125,21.9
1744998965,642.328125,21.7
1744998965,642.38671875,20.7
1744998965,642.38671875,18.2
1744998965,642.38671875,16.1
1744998965,642.38671875,21.3
1744998965,642.38671875,16.7
1744999587,704.921875,32.4
1744999587,704.921875,30.8
1744999587,704.921875,24.9
1744999587,704.921875,20.0
1745005245,930.7734375,9.6
1745005245,930.7734375,7.4
1745005245,930.7734375,7.0
1745005245,930.7734375,8.9
1745010290,932.02734375,7.3
1745010290,932.02734375,7.1
1745010290,932.02734375,7.0
1745010290,932.02734375,2.4
1745021527,943.7265625,7.2
1745021527,943.7265625,6.6
1745021527,943.7265625,6.4
1745021527,943.7265625,7.0
1745028492,978.50390625,7.4
1745028493,978.50390625,7.3
1745028493,978.50390625,7.1
1745028493,978.50390625,10.9
1745035024,984.00390625,7.4
1745035024,984.00390625,7.3
1745035024,984.00390625,7.4
1745035024,984.00390625,10.3
1745044574,991.125,7.4
1745044574,991.125,7.0
1745044574,991.125,6.9
1745044574,991.125,10.0
1745058414,1040.63671875,7.4
1745058414,1040.63671875,6.1
1745058414,1040.63671875,6.7
1745058414,1040.63671875,2.7
1745075901,1064.47265625,7.5
1745075901,1064.47265625,6.5
1745075901,1064.47265625,6.9
1745075901,1064.47265625,6.1
</pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("pre_tab_main_worker_cpu_ram", "cpu_ram_usage.csv")'> Download &raquo;cpu_ram_usage.csv&laquo; as file</button>
			
			<h1>&nbsp;Parallel Plot</h1>
			<div class="invert_in_dark_mode" id="parallel-plot"></div>
			<h1>&nbsp;Scatter-2D</h1>
			<div class='invert_in_dark_mode' id='plotScatter2d'></div>
			<h1>&nbsp;Scatter-3D</h1>
			<div class='invert_in_dark_mode' id='plotScatter3d'></div>
			<h1>&nbsp;Job Status Distribution</h1>
			<div class="invert_in_dark_mode" id="plotJobStatusDistribution"></div>
			<h1>&nbsp;Boxplots</h1>
			<div class="invert_in_dark_mode" id="plotBoxplot"></div>
			<h1>&nbsp;Violin</h1>
			<div class="invert_in_dark_mode" id="plotViolin"></div>
			<h1>&nbsp;Histogram</h1>
			<div class="invert_in_dark_mode" id="plotHistogram"></div>
			<h1>&nbsp;Heatmap</h1>
			<div class="invert_in_dark_mode" id="plotHeatmap"></div><br>
			    <h1>Correlation Heatmap Explanation</h1>
			
			    <p>
			        This is a heatmap that visualizes the correlation between numerical columns in a dataset. The values represented in the heatmap show the strength and direction of relationships between different variables.
			    </p>
			
			    <h2>How It Works</h2>
			    <p>
			        The heatmap uses a matrix to represent correlations between each pair of numerical columns. The calculation behind this is based on the concept of "correlation," which measures how strongly two variables are related. A correlation can be positive, negative, or zero:
			    </p>
			    <ul>
			        <li><strong>Positive correlation</strong>: Both variables increase or decrease together (e.g., if the temperature rises, ice cream sales increase).</li>
			        <li><strong>Negative correlation</strong>: As one variable increases, the other decreases (e.g., as the price of a product rises, the demand for it decreases).</li>
			        <li><strong>Zero correlation</strong>: There is no relationship between the two variables (e.g., height and shoe size might show zero correlation in some contexts).</li>
			    </ul>
			
			    <h2>Color Scale: Yellow to Purple (Viridis)</h2>
			    <p>
			        The heatmap uses a color scale called "Viridis," which ranges from yellow to purple. Here's what the colors represent:
			    </p>
			    <ul>
			        <li><strong>Yellow (brightest)</strong>: A strong positive correlation (close to +1). This indicates that as one variable increases, the other increases in a very predictable manner.</li>
			        <li><strong>Green</strong>: A moderate positive correlation. Variables are still positively related, but the relationship is not as strong.</li>
			        <li><strong>Blue</strong>: A weak or near-zero correlation. There is a small or no discernible relationship between the variables.</li>
			        <li><strong>Purple (darkest)</strong>: A strong negative correlation (close to -1). This indicates that as one variable increases, the other decreases in a very predictable manner.</li>
			    </ul>
			
			    <h2>What the Heatmap Shows</h2>
			    <p>
			        In the heatmap, each cell represents the correlation between two numerical columns. The color of the cell is determined by the correlation coefficient: from yellow for strong positive correlations, through green and blue for weaker correlations, to purple for strong negative correlations.
			    </p>
			
			<h1>&nbsp;Result-Pairs</h1>
			<div class="invert_in_dark_mode" id="plotResultPairs"></div>
			<h1>&nbsp;Evolution</h1>
			<div class="invert_in_dark_mode" id="plotResultEvolution"></div>
			<h1>&nbsp;Exit-Codes</h1>
			<div class="invert_in_dark_mode" id="plotExitCodesPieChart"></div>
	</body>
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