OmniOpt2

Setting	Value
Model for non-random steps	BOTORCH_MODULAR
Max. nr. evaluations	500
Number random steps	20
Nr. of workers (parameter)	30
Main process memory (GB)	8
Worker memory (GB)	32

Name	Type	Lower bound	Upper bound	Values	Type	Log Scale?
recent_samples_size	range	1	5000		int	No
feature_id	fixed			23
batch_size	range	1	5000		int	No
divergence	choice			str\(\'H\'\), str\(\'KL\'\)
detect_batch	choice			1, 2, 3
statistic	choice			str\(\'stdev\'\), str\(\'tstat\'\)
significance	range	0.001	0.999		float	No
subsets	range	1	100		int	No

Failed	Succeeded	Running	Total
24	423	3	450

name	min/max
ACCURACY	max
RUNTIME	min

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
1743381735,1743381932,197,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2465 feature_id 23 batch_size 4178 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.39224478071928026 subsets 87,2465,4178,str\(\'H\'\),2,str\(\'stdev\'\),0.39224478071928026,87,23,0.11,187,0,None,i7178,187,688.60546875,688.5545014880952,-1,0.26031152647975075,3837014
1743381795,1743381973,178,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3778 feature_id 23 batch_size 1234 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.9288401540704071 subsets 21,3778,1234,str\(\'KL\'\),2,str\(\'tstat\'\),0.9288401540704071,21,23,0.12,168,0,None,i7182,168,688.6171875,688.5509868421053,-1,0.2211214953271028,3837031
1743382096,1743382292,196,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3481 feature_id 23 batch_size 2543 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.7054346216898412 subsets 59,3481,2543,str\(\'H\'\),1,str\(\'tstat\'\),0.7054346216898412,59,23,0.12,182,0,None,i7181,182,688.36328125,688.32421875,-1,0.3168847352024922,3837134
1743382396,1743382574,178,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 263 feature_id 23 batch_size 2050 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.24633616822957993 subsets 49,263,2050,str\(\'KL\'\),3,str\(\'stdev\'\),0.24633616822957993,49,23,0.12,168,0,None,i7176,168,688.41796875,688.381990131579,-1,0.22990654205607478,3837234
1743382606,1743382777,171,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 938 feature_id 23 batch_size 3412 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.28779023444466295 subsets 71,938,3412,str\(\'KL\'\),3,str\(\'stdev\'\),0.28779023444466295,71,23,0.12,160,0,None,i7176,160,689.046875,689.0095486111111,-1,0.17006853582554518,3837303
1743382816,1743383019,203,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2799 feature_id 23 batch_size 1337 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.7643025044798851 subsets 37,2799,1337,str\(\'H\'\),1,str\(\'tstat\'\),0.7643025044798851,37,23,0.13,188,0,None,i7175,188,688.25,688.2215401785714,-1,0.34986915887850467,3837372
1743382937,1743383108,171,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4924 feature_id 23 batch_size 4872 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.5993887814600021 subsets 99,4924,4872,str\(\'KL\'\),3,str\(\'tstat\'\),0.5993887814600021,99,23,0.13,162,0,None,i7176,162,687.671875,687.6130756578947,-1,0.18213084112149533,3837414
1743383328,1743383530,202,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1316 feature_id 23 batch_size 384 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.07559296454861761 subsets 9,1316,384,str\(\'H\'\),2,str\(\'stdev\'\),0.07559296454861761,9,23,0.13,190,0,None,i7167,190,687.4140625,687.3746448863636,-1,0.26317757009345794,3837548
1743383358,1743383554,196,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1567 feature_id 23 batch_size 3033 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.1375504678785801 subsets 65,1567,3033,str\(\'KL\'\),1,str\(\'tstat\'\),0.1375504678785801,65,23,0.12,184,0,None,i7175,184,687.9140625,687.879650297619,-1,0.3023551401869159,3837554
1743383627,1743383818,191,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4677 feature_id 23 batch_size 2217 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.6612091782968491 subsets 30,4677,2217,str\(\'H\'\),3,str\(\'stdev\'\),0.6612091782968491,30,23,0.12,175,0,None,i7167,175,688.26171875,687.7712890625,-1,0.24863551401869158,3837647
1743383775,1743383952,177,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3051 feature_id 23 batch_size 3995 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.9518646610863507 subsets 94,3051,3995,str\(\'KL\'\),1,str\(\'stdev\'\),0.9518646610863507,94,23,0.12,169,0,None,i7167,169,688.55078125,688.1206825657895,-1,0.14934579439252335,3837705
1743383844,1743384033,189,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 589 feature_id 23 batch_size 4376 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.05865658365562558 subsets 80,589,4376,str\(\'H\'\),2,str\(\'tstat\'\),0.05865658365562558,80,23,0.12,152,0,None,i7147,152,689.39453125,689.1067708333334,-1,0.10905919003115265,3837916
1743383845,1743384040,195,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4102 feature_id 23 batch_size 3589 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.8670089420378209 subsets 52,4102,3589,str\(\'H\'\),3,str\(\'stdev\'\),0.8670089420378209,52,23,0.11,161,0,None,i7135,161,689.4765625,689.2025082236842,-1,0.22361370716510903,3838028
1743383857,1743384047,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1985 feature_id 23 batch_size 3642 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.5487277844697237 subsets 24,1985,3642,str\(\'H\'\),3,str\(\'stdev\'\),0.5487277844697237,24,23,0.11,149,0,None,i7015,149,687.80859375,687.5349264705883,-1,0.18153271028037382,3838195
1743383846,1743384050,204,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 2135 feature_id 23 batch_size 1817 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.3303069289606065 subsets 44,2135,1817,str\(\'KL\'\),1,str\(\'tstat\'\),0.3303069289606065,44,23,0.11,162,0,None,i7020,162,688.57421875,688.5345394736842,-1,0.18113395638629284,3838125
1743383857,1743384058,201,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3316 feature_id 23 batch_size 4642 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.36187257880344986 subsets 46,3316,4642,str\(\'H\'\),3,str\(\'tstat\'\),0.36187257880344986,46,23,0.12,173,0,None,i7002,173,687.07421875,687.0283203125,-1,0.28922118380062306,3838328
1743383844,1743384059,215,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4275 feature_id 23 batch_size 1576 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.02492506910674274 subsets 82,4275,1576,str\(\'KL\'\),2,str\(\'tstat\'\),0.02492506910674274,82,23,0.12,176,0,None,i7007,176,688.38671875,688.3279296875,-1,0.1321993769470405,3838263
1743383842,1743384064,222,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 3152 feature_id 23 batch_size 211 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.5178921436090023 subsets 16,3152,211,str\(\'KL\'\),2,str\(\'stdev\'\),0.5178921436090023,16,23,0.13,184,0,None,i7143,184,687.51953125,687.4799107142857,-1,0.252411214953271,3837956
1743383840,1743384081,241,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 692 feature_id 23 batch_size 748 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.4754590410012752 subsets 2,692,748,str\(\'H\'\),2,str\(\'tstat\'\),0.4754590410012752,2,23,0.12,202,0,None,i7157,202,687.671875,687.6329823369565,-1,0.4877383177570093,3837820
1743384015,1743384187,172,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 851 feature_id 23 batch_size 3773 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.6313753892816604 subsets 32,851,3773,str\(\'KL\'\),1,str\(\'stdev\'\),0.6313753892816604,32,23,0.12,163,0,None,i7167,163,686.1875,686.1558388157895,-1,0.18806230529595017,3838462
1743383915,1743384237,322,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 449 feature_id 23 batch_size 145 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.8383912751246244 subsets 60,449,145,str\(\'KL\'\),1,str\(\'stdev\'\),0.8383912751246244,60,23,0.12,307,0,None,i7175,307,688.33984375,688.3246922348485,-1,0.310778816199377,3838373
1743384115,1743384286,171,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2907 feature_id 23 batch_size 858 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.17228526564128696 subsets 75,2907,858,str\(\'H\'\),3,str\(\'tstat\'\),0.17228526564128696,75,23,0.12,161,0,None,i7162,161,688.80859375,688.76953125,-1,0.17823052959501556,3838553
1743384139,1743384317,178,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1480 feature_id 23 batch_size 4782 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.7929494438041002 subsets 28,1480,4782,str\(\'KL\'\),3,str\(\'tstat\'\),0.7929494438041002,28,23,0.12,164,0,None,i7044,164,688.62890625,687.1800986842105,-1,0.2383551401869159,3838807
1743384140,1743384323,183,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 4524 feature_id 23 batch_size 2948 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.44294412072002887 subsets 10,4524,2948,str\(\'KL\'\),2,str\(\'tstat\'\),0.44294412072002887,10,23,0.12,163,0,None,i7146,163,688.56640625,688.5102796052631,-1,0.22041121495327104,3838656
1743384139,1743384335,196,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2650 feature_id 23 batch_size 3184 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.10935564760677517 subsets 6,2650,3184,str\(\'KL\'\),2,str\(\'stdev\'\),0.10935564760677517,6,23,0.12,177,0,None,i7012,177,688.97265625,687.4802734375,-1,0.3174080996884735,3838908
1743384139,1743384354,215,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1736 feature_id 23 batch_size 2426 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.9795306818615646 subsets 96,1736,2426,str\(\'H\'\),2,str\(\'stdev\'\),0.9795306818615646,96,23,0.13,200,0,None,i7138,200,688.60546875,688.2781929347826,-1,0.21161370716510902,3838727
1743384198,1743384395,197,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 109 feature_id 23 batch_size 2803 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.9012033982705325 subsets 16,109,2803,str\(\'H\'\),1,str\(\'tstat\'\),0.9012033982705325,16,23,0.12,183,0,None,i7162,183,688.859375,688.8214285714286,-1,0.3492834890965732,3839070
1743384140,1743384398,258,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 1112 feature_id 23 batch_size 1441 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.568582877740264 subsets 88,1112,1441,str\(\'H\'\),2,str\(\'tstat\'\),0.568582877740264,88,23,0.12,238,0,None,i7003,238,689.28515625,689.2415865384615,-1,0.262392523364486,3838990
1743384139,1743384406,267,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4785 feature_id 23 batch_size 587 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.2562562327124178 subsets 66,4785,587,str\(\'H\'\),1,str\(\'stdev\'\),0.2562562327124178,66,23,0.12,250,0,None,i7017,250,687.98828125,687.9707754629629,-1,0.36573208722741435,3838856
1743384275,1743384491,216,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 3649 feature_id 23 batch_size 1978 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.4247913518995047 subsets 78,3649,1978,str\(\'KL\'\),2,str\(\'stdev\'\),0.4247913518995047,78,23,0.13,204,0,None,i7179,204,688.453125,688.4172894021739,-1,0.2955140186915888,3839133
1743389845,1743389858,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2568 feature_id 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 1,2568,1,str\(\'KL\'\),1,str\(\'tstat\'\),0.001,1,23,None,None,1,None,i7147,3846102
1743389846,1743390004,158,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4691 feature_id 23 batch_size 1867 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 1,4691,1867,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,1,23,0.12,143,0,None,i7152,143,689.2265625,689.0264246323529,-1,0.06814953271028037,3846068
1743389845,1743390009,164,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 725 feature_id 23 batch_size 2123 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 100,725,2123,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,100,23,0.12,150,0,None,i7146,150,687.71484375,687.6656709558823,-1,0.10581931464174454,3846111
1743389846,1743390016,170,module load GCCcore/10.3.0 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 23 batch_size 5000 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 1,1,5000,str\(\'KL\'\),3,str\(\'stdev\'\),0.001,1,23,0.12,157,0,None,i7152,157,688.23046875,688.1931423611111,-1,0.24922118380062305,3846073
1743389849,1743390020,171,module load GCCcore/10.3.0 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 23 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 1,1,5000,str\(\'H\'\),3,str\(\'stdev\'\),0.001,1,23,0.12,158,0,None,i7152,158,687.42578125,687.3910590277778,-1,0.24922118380062305,3846067
1743389844,1743390022,178,module load GCCcore/10.3.0 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 23 batch_size 5000 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 100,5000,5000,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,100,23,0.12,160,0,None,i7143,160,689.046875,688.9958767361111,-1,0.24922118380062305,3846140
1743389845,1743390022,177,module load GCCcore/10.3.0 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 23 batch_size 5000 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 40,5000,5000,str\(\'KL\'\),2,str\(\'tstat\'\),0.999,40,23,0.12,160,0,None,i7144,160,688.703125,688.6402138157895,-1,0.24922118380062305,3846129
1743389846,1743390023,177,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4481 feature_id 23 batch_size 280 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 1,4481,280,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,1,23,0.12,161,0,None,i7152,161,688.58203125,688.546875,-1,0.18143302180685358,3846071
1743389845,1743390023,178,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3121 feature_id 23 batch_size 4314 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 100,3121,4314,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,100,23,0.13,160,0,None,i7151,160,687.26953125,651.9715711805555,-1,0.1612710280373832,3846081
1743389845,1743390029,184,module load GCCcore/10.3.0 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 23 batch_size 2472 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 1,1,2472,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,1,23,0.12,172,0,None,i7150,172,688.6953125,688.659375,-1,0.30803738317757007,3846089
1743389846,1743390029,183,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 4013 feature_id 23 batch_size 2862 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 100,4013,2862,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,100,23,0.12,167,0,None,i7152,167,688.03515625,687.9845805921053,-1,0.28530841121495326,3846072
1743389844,1743390034,190,module load GCCcore/10.3.0 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 23 batch_size 1259 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 100,5000,1259,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,100,23,0.14,171,0,None,i7149,171,687.39453125,687.3537109375,-1,0.2731713395638629,3846095
1743389845,1743390047,202,module load GCCcore/10.3.0 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 23 batch_size 5000 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 100,1,5000,str\(\'KL\'\),2,str\(\'tstat\'\),0.001,100,23,0.12,184,0,None,i7151,184,687.515625,651.6861979166666,-1,0.24922118380062305,3846077
1743389845,1743390047,202,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4888 feature_id 23 batch_size 4932 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.2440725286047339 subsets 100,4888,4932,str\(\'KL\'\),2,str\(\'tstat\'\),0.2440725286047339,100,23,0.12,188,0,None,i7153,188,689.234375,689.0167410714286,-1,0.2458317757009346,3846064
1743389846,1743390048,202,module load GCCcore/10.3.0 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 23 batch_size 5000 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 100,5000,5000,str\(\'H\'\),2,str\(\'tstat\'\),0.999,100,23,0.12,187,0,None,i7152,187,688.953125,688.8904389880952,-1,0.24922118380062305,3846070
1743389846,1743390048,202,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2032 feature_id 23 batch_size 4512 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 100,2032,4512,str\(\'KL\'\),2,str\(\'tstat\'\),0.999,100,23,0.12,184,0,None,i7152,184,687.60546875,687.5448288690476,-1,0.22489719626168225,3846069
1743389844,1743390066,222,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 23 batch_size 5000 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 1,5000,5000,str\(\'H\'\),2,str\(\'tstat\'\),0.999,1,23,0.12,203,0,None,i7149,203,688.84375,688.7997622282609,-1,0.47353271028037386,3846098
1743389845,1743390066,221,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 23 batch_size 4116 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 100,5000,4116,str\(\'KL\'\),2,str\(\'tstat\'\),0.001,100,23,0.13,207,0,None,i7151,207,688.52734375,652.3777173913044,-1,0.3077383177570093,3846083
1743389845,1743390067,222,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 1 feature_id 23 batch_size 5000 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 1,1,5000,str\(\'H\'\),2,str\(\'tstat\'\),0.001,1,23,0.12,203,0,None,i7153,203,687.3828125,687.1036005434783,-1,0.47353271028037386,3846066
1743389844,1743390078,234,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 5000 feature_id 23 batch_size 4359 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 1,5000,4359,str\(\'H\'\),2,str\(\'tstat\'\),0.001,1,23,0.12,218,0,None,i7143,218,687.828125,687.78125,-1,0.4639501557632399,3846133
1743389845,1743390079,234,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 1 feature_id 23 batch_size 4634 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 1,1,4634,str\(\'KL\'\),2,str\(\'tstat\'\),0.001,1,23,0.12,215,0,None,i7151,215,687.29296875,650.9777018229166,-1,0.494791277258567,3846074
1743389845,1743390099,254,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 2789 feature_id 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.32185047017589874 subsets 1,2789,1,str\(\'KL\'\),3,str\(\'stdev\'\),0.32185047017589874,1,23,0.12,236,0,None,i7151,236,688.19140625,653.9002403846154,-1,0.1649221183800623,3846076
1743389845,1743390104,259,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 1 feature_id 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 1,1,1,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,1,23,0.12,242,0,None,i7146,242,693.8359375,690.4706307870371,-1,0.1649221183800623,3846116
1743389845,1743390106,261,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 1 feature_id 23 batch_size 1 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 1,1,1,str\(\'H\'\),3,str\(\'tstat\'\),0.999,1,23,0.12,244,0,None,i7150,244,693.953125,690.5756655092592,-1,0.1649221183800623,3846086
1743389845,1743390111,266,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4641 feature_id 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 100,4641,1,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,100,23,0.12,247,0,None,i7151,247,688.3828125,653.9512442129629,-1,0.1649221183800623,3846075
1743389845,1743390112,267,module load GCCcore/10.3.0 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 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 1,5000,1,str\(\'KL\'\),2,str\(\'tstat\'\),0.001,1,23,0.12,252,0,None,i7145,252,694.8046875,691.4913504464286,-1,0.1649221183800623,3846123
1743389845,1743390124,279,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4945 feature_id 23 batch_size 3539 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 1,4945,3539,str\(\'H\'\),1,str\(\'stdev\'\),0.001,1,23,0.12,260,0,None,i7141,260,688.33203125,688.3187779017857,-1,0.9452834890965732,3846153
1743389850,1743390127,277,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 1 feature_id 23 batch_size 5000 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 1,1,5000,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,1,23,0.12,267,0,None,i7147,267,688.9765625,688.9608028017242,-1,0.97197507788162,3846107
1743389845,1743390129,284,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 1614 feature_id 23 batch_size 5000 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 1,1614,5000,str\(\'H\'\),1,str\(\'stdev\'\),0.001,1,23,0.12,266,0,None,i7151,266,687.9453125,650.8201778017242,-1,0.97197507788162,3846079
1743389873,1743390150,277,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 5000 feature_id 23 batch_size 5000 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 1,5000,5000,str\(\'H\'\),1,str\(\'stdev\'\),0.999,1,23,0.12,267,0,None,i7142,267,687.44921875,687.3810614224138,-1,0.97197507788162,3846147
1743394106,1743394271,165,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3443 feature_id 23 batch_size 2055 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.4837685172376809 subsets 25,3443,2055,str\(\'KL\'\),2,str\(\'stdev\'\),0.4837685172376809,25,23,0.12,156,0,None,i7006,156,689.16796875,688.8018663194445,-1,0.15364485981308412,3852726
1743394087,1743394271,184,module load GCCcore/10.3.0 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 23 batch_size 1260 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.994509569168529 subsets 3,5000,1260,str\(\'KL\'\),3,str\(\'tstat\'\),0.994509569168529,3,23,0.13,172,0,None,i7010,172,687.43359375,687.3908203125,-1,0.24200623052959502,3852707
1743394102,1743394284,182,module load GCCcore/10.3.0 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 23 batch_size 1342 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 62,5000,1342,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,62,23,0.12,168,0,None,i7012,168,688.0859375,688.0267269736842,-1,0.25937694704049846,3852716
1743394102,1743394285,183,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 3667 feature_id 23 batch_size 1582 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 73,3667,1582,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,73,23,0.13,173,0,None,i7029,173,687.96875,687.9322265625,-1,0.2905046728971963,3852711
1743394101,1743394285,184,module load GCCcore/10.3.0 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 23 batch_size 1143 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 24,5000,1143,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,24,23,0.13,173,0,None,i7017,173,687.8046875,687.769921875,-1,0.27419314641744547,3852714
1743394127,1743394285,158,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4665 feature_id 23 batch_size 4292 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.19929630005277102 subsets 4,4665,4292,str\(\'KL\'\),3,str\(\'tstat\'\),0.19929630005277102,4,23,0.11,149,0,None,i7059,149,688.671875,688.3681066176471,-1,0.10696573208722741,3852735
1743394107,1743394290,183,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 2429 feature_id 23 batch_size 3875 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 52,2429,3875,str\(\'KL\'\),2,str\(\'tstat\'\),0.001,52,23,0.12,171,0,None,i7017,171,688.8515625,688.7892578125,-1,0.24143302180685358,3852719
1743394106,1743394297,191,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2510 feature_id 23 batch_size 1229 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 41,2510,1229,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,41,23,0.13,177,0,None,i7005,177,687.42578125,687.38046875,-1,0.29669781931464173,3852727
1743394132,1743394297,165,module load GCCcore/10.3.0 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 23 batch_size 3810 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 52,1,3810,str\(\'KL\'\),2,str\(\'tstat\'\),0.001,52,23,0.11,155,0,None,i7009,155,689.71875,689.439453125,-1,0.14242990654205606,3852749
1743394107,1743394297,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 469 feature_id 23 batch_size 1403 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 79,469,1403,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,79,23,0.13,177,0,None,i7009,177,687.69140625,687.651171875,-1,0.27229906542056076,3852723
1743394132,1743394297,165,module load GCCcore/10.3.0 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 23 batch_size 2722 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 42,1,2722,str\(\'H\'\),1,str\(\'stdev\'\),0.999,42,23,0.11,157,0,None,i7016,157,687.72265625,687.4769965277778,-1,0.1356760124610592,3852744
1743394131,1743394304,173,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 891 feature_id 23 batch_size 2297 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.7855251115981852 subsets 29,891,2297,str\(\'H\'\),2,str\(\'stdev\'\),0.7855251115981852,29,23,0.13,162,0,None,i7168,162,688.87890625,688.8291529605264,-1,0.17173831775700935,3852741
1743394126,1743394304,178,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3859 feature_id 23 batch_size 1380 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.9941106216298343 subsets 15,3859,1380,str\(\'KL\'\),3,str\(\'tstat\'\),0.9941106216298343,15,23,0.12,169,0,None,i7019,169,688.47265625,688.4194078947369,-1,0.25023052959501557,3852738
1743394126,1743394311,185,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2130 feature_id 23 batch_size 1808 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.98634532357173 subsets 79,2130,1808,str\(\'KL\'\),3,str\(\'tstat\'\),0.98634532357173,79,23,0.12,175,0,None,i7155,175,687.58203125,687.5453125,-1,0.2905046728971963,3852732
1743394131,1743394314,183,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 4975 feature_id 23 batch_size 1255 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.8258023621790931 subsets 42,4975,1255,str\(\'KL\'\),3,str\(\'tstat\'\),0.8258023621790931,42,23,0.14,175,0,None,i7012,175,688.41015625,688.3701171875,-1,0.28786292834890964,3852746
1743394146,1743394316,170,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4921 feature_id 23 batch_size 1986 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.8019561957828987 subsets 11,4921,1986,str\(\'H\'\),2,str\(\'stdev\'\),0.8019561957828987,11,23,0.12,160,0,None,i7106,160,688.01953125,687.9635416666666,-1,0.19781931464174454,3852758
1743394146,1743394317,171,module load GCCcore/10.3.0 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 23 batch_size 2984 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 39,1,2984,str\(\'H\'\),2,str\(\'stdev\'\),0.999,39,23,0.12,153,0,None,i7030,153,689.2109375,688.8990885416666,-1,0.11155140186915888,3852760
1743394161,1743394320,159,module load GCCcore/10.3.0 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 23 batch_size 1771 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 81,5000,1771,str\(\'KL\'\),3,str\(\'stdev\'\),0.999,81,23,0.11,146,0,None,i7158,146,687.75390625,687.7111672794117,-1,0.08543302180685358,3852765
1743394146,1743394324,178,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2119 feature_id 23 batch_size 4927 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.2319401730491424 subsets 80,2119,4927,str\(\'KL\'\),3,str\(\'tstat\'\),0.2319401730491424,80,23,0.12,163,0,None,i7061,163,688.8671875,688.8075657894736,-1,0.24558255451713396,3852759
1743394147,1743394324,177,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4409 feature_id 23 batch_size 1966 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.9144731328819804 subsets 62,4409,1966,str\(\'KL\'\),3,str\(\'tstat\'\),0.9144731328819804,62,23,0.12,169,0,None,i7162,169,687.484375,687.438322368421,-1,0.24457320872274144,3852751
1743394146,1743394329,183,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 4913 feature_id 23 batch_size 1429 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 78,4913,1429,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,78,23,0.12,172,0,None,i7106,172,688.37109375,688.3298828125,-1,0.29561370716510904,3852757
1743394147,1743394331,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1550 feature_id 23 batch_size 1578 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.8952518484817298 subsets 63,1550,1578,str\(\'KL\'\),3,str\(\'tstat\'\),0.8952518484817298,63,23,0.13,173,0,None,i7141,173,686.0546875,686.015234375,-1,0.2700436137071651,3852755
1743394166,1743394331,165,module load GCCcore/10.3.0 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 23 batch_size 3669 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 47,5000,3669,str\(\'KL\'\),2,str\(\'tstat\'\),0.001,47,23,0.11,155,0,None,i7135,155,689.9140625,689.5985243055555,-1,0.0457196261682243,3852773
1743394166,1743394338,172,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4109 feature_id 23 batch_size 2970 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 45,4109,2970,str\(\'H\'\),2,str\(\'tstat\'\),0.001,45,23,0.12,159,0,None,i7183,159,688.15234375,688.0798611111111,-1,0.18504672897196262,3852771
1743394162,1743394340,178,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4855 feature_id 23 batch_size 2300 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.5888569706691094 subsets 30,4855,2300,str\(\'KL\'\),2,str\(\'stdev\'\),0.5888569706691094,30,23,0.13,166,0,None,i7129,166,688.35546875,688.2933799342105,-1,0.20062305295950156,3852769
1743394161,1743394345,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4920 feature_id 23 batch_size 3754 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.03730969265139453 subsets 10,4920,3754,str\(\'KL\'\),3,str\(\'tstat\'\),0.03730969265139453,10,23,0.12,171,0,None,i7169,171,688.84765625,688.8017578125,-1,0.2806728971962617,3852763
1743394162,1743394346,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4493 feature_id 23 batch_size 1156 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.8293951338364469 subsets 1,4493,1156,str\(\'KL\'\),3,str\(\'tstat\'\),0.8293951338364469,1,23,0.12,174,0,None,i7180,174,688.59375,688.5583984375,-1,0.277595015576324,3852761
1743394162,1743394352,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3909 feature_id 23 batch_size 1672 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 43,3909,1672,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,43,23,0.12,176,0,None,i7134,176,687.76171875,687.7265625,-1,0.28761370716510903,3852768
1743394147,1743394361,214,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 2347 feature_id 23 batch_size 1996 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.5895368358388237 subsets 1,2347,1996,str\(\'KL\'\),2,str\(\'stdev\'\),0.5895368358388237,1,23,0.13,204,0,None,i7146,204,688.7734375,688.730808423913,-1,0.47257320872274144,3852753
1743394166,1743394363,197,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 16 feature_id 23 batch_size 2923 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 44,16,2923,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,44,23,0.12,187,0,None,i7020,187,687.421875,687.3973214285714,-1,0.3642367601246106,3852775
1743397651,1743397826,175,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4756 feature_id 23 batch_size 1216 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 86,4756,1216,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,86,23,0.14,162,0,None,i7003,162,687.5078125,687.4621710526316,-1,0.06600623052959502,3857652
1743397651,1743397826,175,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 162 feature_id 23 batch_size 3830 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.6707193679388956 subsets 30,162,3830,str\(\'H\'\),2,str\(\'stdev\'\),0.6707193679388956,30,23,0.11,161,0,None,i7002,161,689.3125,689.262541118421,-1,0.14317757009345794,3857659
1743397651,1743397827,176,module load GCCcore/10.3.0 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 23 batch_size 1213 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 67,5000,1213,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,67,23,0.12,161,0,None,i7002,161,687.83984375,687.7983141447369,-1,0.1413582554517134,3857663
1743397673,1743397832,159,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4729 feature_id 23 batch_size 1217 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 99,4729,1217,str\(\'H\'\),3,str\(\'tstat\'\),0.001,99,23,0.13,147,0,None,i7140,147,687.53515625,687.4873621323529,-1,0.0660809968847352,3857683
1743397673,1743397832,159,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 443 feature_id 23 batch_size 1542 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 79,443,1542,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,79,23,0.12,148,0,None,i7181,148,687.79296875,687.3602941176471,-1,0.07116510903426791,3857675
1743397673,1743397837,164,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4483 feature_id 23 batch_size 4446 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.9059312582416825 subsets 10,4483,4446,str\(\'KL\'\),3,str\(\'stdev\'\),0.9059312582416825,10,23,0.12,153,0,None,i7141,153,689.62109375,689.3248697916666,-1,0.16620560747663551,3857679
1743397673,1743397839,166,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 540 feature_id 23 batch_size 4515 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.9623615192520757 subsets 30,540,4515,str\(\'KL\'\),2,str\(\'stdev\'\),0.9623615192520757,30,23,0.12,156,0,None,i7181,156,687.73046875,687.4657118055555,-1,0.11252336448598131,3857676
1743397651,1743397839,188,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3784 feature_id 23 batch_size 2363 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.232343844023395 subsets 19,3784,2363,str\(\'H\'\),2,str\(\'stdev\'\),0.232343844023395,19,23,0.12,170,0,None,i7002,170,688.04296875,688.00078125,-1,0.2061183800623053,3857657
1743397651,1743397839,188,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2819 feature_id 23 batch_size 2345 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.8637521073519481 subsets 100,2819,2345,str\(\'KL\'\),3,str\(\'stdev\'\),0.8637521073519481,100,23,0.12,173,0,None,i7002,173,688.73046875,688.6947265625,-1,0.23376947040498441,3857661
1743397669,1743397840,171,module load GCCcore/10.3.0 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 23 batch_size 1243 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 5,5000,1243,str\(\'H\'\),3,str\(\'tstat\'\),0.001,5,23,0.12,158,0,None,i7155,158,688.07421875,688.0279947916666,-1,0.12998130841121494,3857672
1743397669,1743397840,171,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4144 feature_id 23 batch_size 1235 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 16,4144,1235,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,16,23,0.13,157,0,None,i7156,157,688.44140625,688.1161024305555,-1,0.11359501557632398,3857670
1743397673,1743397844,171,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4314 feature_id 23 batch_size 2700 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 100,4314,2700,str\(\'KL\'\),3,str\(\'stdev\'\),0.999,100,23,0.13,157,0,None,i7154,157,688.015625,687.9685329861111,-1,0.13457943925233645,3857677
1743397651,1743397845,194,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2987 feature_id 23 batch_size 1251 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 10,2987,1251,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,10,23,0.12,180,0,None,i7003,180,688.96875,688.930078125,-1,0.19333333333333333,3857654
1743397651,1743397846,195,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2750 feature_id 23 batch_size 4025 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.705931805720645 subsets 34,2750,4025,str\(\'KL\'\),2,str\(\'tstat\'\),0.705931805720645,34,23,0.13,180,0,None,i7002,180,687.32421875,687.2630208333334,-1,0.30093457943925234,3857656
1743397669,1743397846,177,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4510 feature_id 23 batch_size 3482 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.637338122541147 subsets 27,4510,3482,str\(\'H\'\),2,str\(\'stdev\'\),0.637338122541147,27,23,0.12,166,0,None,i7180,166,687.54296875,687.4455180921053,-1,0.2603364485981308,3857665
1743397669,1743397847,178,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3379 feature_id 23 batch_size 1422 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 87,3379,1422,str\(\'KL\'\),3,str\(\'stdev\'\),0.001,87,23,0.12,170,0,None,i7159,170,687.5390625,687.5037109375,-1,0.2586043613707165,3857669
1743397670,1743397848,178,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4589 feature_id 23 batch_size 1209 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.4334081225859559 subsets 88,4589,1209,str\(\'KL\'\),3,str\(\'tstat\'\),0.4334081225859559,88,23,0.13,168,0,None,i7174,168,687.82421875,687.766447368421,-1,0.21613707165109033,3857668
1743397673,1743397851,178,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1321 feature_id 23 batch_size 3783 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 27,1321,3783,str\(\'KL\'\),2,str\(\'stdev\'\),0.999,27,23,0.12,165,0,None,i7140,165,687.59765625,687.5400904605264,-1,0.23570093457943925,3857684
1743397673,1743397851,178,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4332 feature_id 23 batch_size 1210 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.44768142874298683 subsets 72,4332,1210,str\(\'H\'\),3,str\(\'tstat\'\),0.44768142874298683,72,23,0.13,163,0,None,i7153,163,689.26953125,689.2162828947369,-1,0.18618068535825547,3857678
1743397689,1743397853,164,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4724 feature_id 23 batch_size 1232 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 21,4724,1232,str\(\'H\'\),3,str\(\'tstat\'\),0.001,21,23,0.13,153,0,None,i7186,153,687.48046875,687.4270833333334,-1,0.12861059190031152,3857685
1743397689,1743397854,165,module load GCCcore/10.3.0 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 23 batch_size 1195 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 93,5000,1195,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,93,23,0.12,156,0,None,i7149,156,687.6875,687.6404079861111,-1,0.124,3857691
1743397673,1743397856,183,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 489 feature_id 23 batch_size 1260 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.005969196969030885 subsets 99,489,1260,str\(\'KL\'\),3,str\(\'stdev\'\),0.005969196969030885,99,23,0.13,169,0,None,i7141,169,687.5859375,687.5458470394736,-1,0.24200623052959502,3857681
1743397669,1743397859,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4159 feature_id 23 batch_size 2078 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.6087090352419823 subsets 100,4159,2078,str\(\'KL\'\),3,str\(\'stdev\'\),0.6087090352419823,100,23,0.12,179,0,None,i7155,179,688.09765625,688.06953125,-1,0.2848348909657321,3857673
1743397669,1743397859,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4460 feature_id 23 batch_size 4045 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 45,4460,4045,str\(\'KL\'\),2,str\(\'tstat\'\),0.999,45,23,0.13,179,0,None,i7175,179,688.56640625,688.5009765625,-1,0.3024299065420561,3857667
1743397669,1743397865,196,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4802 feature_id 23 batch_size 4091 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.545999868939978 subsets 30,4802,4091,str\(\'H\'\),2,str\(\'tstat\'\),0.545999868939978,30,23,0.13,183,0,None,i7154,183,688.8671875,687.4300595238095,-1,0.3058691588785047,3857674
1743397674,1743397870,196,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4722 feature_id 23 batch_size 3867 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.2736024378655561 subsets 29,4722,3867,str\(\'H\'\),2,str\(\'stdev\'\),0.2736024378655561,29,23,0.12,181,0,None,i7141,181,688.4375,688.3878348214286,-1,0.33730841121495325,3857680
1743397669,1743397871,202,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 260 feature_id 23 batch_size 3363 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.9716855437254286 subsets 35,260,3363,str\(\'KL\'\),2,str\(\'tstat\'\),0.9716855437254286,35,23,0.12,189,0,None,i7155,189,687.94140625,687.8777901785714,-1,0.29334579439252334,3857671
1743397689,1743397873,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1758 feature_id 23 batch_size 4129 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.4482086729044833 subsets 31,1758,4129,str\(\'KL\'\),2,str\(\'tstat\'\),0.4482086729044833,31,23,0.13,171,0,None,i7176,171,688.76953125,688.703515625,-1,0.20580685358255452,3857687
1743397673,1743397876,203,module load GCCcore/10.3.0 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 23 batch_size 1057 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 91,3160,1057,str\(\'KL\'\),3,str\(\'stdev\'\),0.001,91,23,0.13,192,0,None,i7140,192,688.109375,688.0793678977273,-1,0.30294080996884737,3857682
1743397689,1743397879,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2202 feature_id 23 batch_size 3469 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.001 subsets 29,2202,3469,str\(\'H\'\),2,str\(\'stdev\'\),0.001,29,23,0.12,177,0,None,i7150,177,687.53515625,687.481640625,-1,0.3025919003115265,3857689
1743401731,1743401903,172,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3219 feature_id 23 batch_size 1622 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 100,3219,1622,str\(\'H\'\),3,str\(\'tstat\'\),0.001,100,23,0.12,158,0,None,i7163,158,687.82421875,680.146484375,-1,0.13678504672897196,3863111
1743401751,1743401934,183,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 4910 feature_id 23 batch_size 870 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 34,4910,870,str\(\'KL\'\),2,str\(\'stdev\'\),0.999,34,23,0.12,169,0,None,i7182,169,688.0390625,687.9839638157895,-1,0.19191277258566977,3863117
1743401756,1743401934,178,module load GCCcore/10.3.0 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 23 batch_size 1756 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 100,5000,1756,str\(\'H\'\),3,str\(\'tstat\'\),0.001,100,23,0.13,169,0,None,i7174,169,687.30078125,687.2456825657895,-1,0.21881619937694705,3863127
1743401751,1743401935,184,module load GCCcore/10.3.0 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 23 batch_size 1943 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.770536776039013 subsets 100,5000,1943,str\(\'H\'\),3,str\(\'tstat\'\),0.770536776039013,100,23,0.12,171,0,None,i7183,171,687.5546875,687.5126953125,-1,0.2656323987538941,3863114
1743401756,1743401941,185,module load GCCcore/10.3.0 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 23 batch_size 2758 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 1,5000,2758,str\(\'H\'\),3,str\(\'tstat\'\),0.999,1,23,0.12,170,0,None,i7161,170,687.34375,687.2979029605264,-1,0.27494080996884734,3863133
1743401752,1743401942,190,module load GCCcore/10.3.0 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 23 batch_size 367 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 39,1,367,str\(\'KL\'\),2,str\(\'stdev\'\),0.999,39,23,0.12,178,0,None,i7163,178,688.1015625,677.7572265625,-1,0.21494080996884735,3863124
1743401771,1743401951,180,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 395 feature_id 23 batch_size 1387 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 100,395,1387,str\(\'H\'\),3,str\(\'tstat\'\),0.001,100,23,0.14,165,0,None,i7176,165,688.00390625,687.9549753289474,-1,0.14792523364485982,3863141
1743401771,1743401955,184,module load GCCcore/10.3.0 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 23 batch_size 2404 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 100,5000,2404,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,100,23,0.12,169,0,None,i7179,169,688.24609375,688.0322779605264,-1,0.23965109034267912,3863139
1743401773,1743401956,183,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 5000 feature_id 23 batch_size 2010 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 29,5000,2010,str\(\'H\'\),2,str\(\'tstat\'\),0.999,29,23,0.12,169,0,None,i7170,169,688.2109375,688.1504934210526,-1,0.22542056074766356,3863145
1743401771,1743401962,191,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3088 feature_id 23 batch_size 867 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.001 subsets 40,3088,867,str\(\'KL\'\),2,str\(\'stdev\'\),0.001,40,23,0.13,180,0,None,i7172,180,687.51171875,687.4716796875,-1,0.30250467289719624,3863143
1743401791,1743401975,184,module load GCCcore/10.3.0 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 23 batch_size 974 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.001 subsets 34,5000,974,str\(\'KL\'\),2,str\(\'stdev\'\),0.001,34,23,0.12,171,0,None,i7171,171,688.890625,688.654296875,-1,0.2299688473520249,3863159
1743401817,1743401975,158,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 888 feature_id 23 batch_size 1200 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 100,888,1200,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,100,23,0.12,147,0,None,i7159,147,688.65625,687.9705882352941,-1,0.06480996884735203,3863185
1743401792,1743401975,183,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 5000 feature_id 23 batch_size 3691 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 100,5000,3691,str\(\'H\'\),3,str\(\'tstat\'\),0.999,100,23,0.12,172,0,None,i7170,172,687.3671875,687.328515625,-1,0.2759626168224299,3863161
1743401792,1743401976,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2817 feature_id 23 batch_size 918 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 40,2817,918,str\(\'H\'\),2,str\(\'stdev\'\),0.999,40,23,0.12,172,0,None,i7172,172,687.375,687.3306640625,-1,0.2381931464174455,3863158
1743401786,1743401977,191,module load GCCcore/10.3.0 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 23 batch_size 1017 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.001 subsets 35,1,1017,str\(\'KL\'\),2,str\(\'stdev\'\),0.001,35,23,0.13,182,0,None,i7183,182,688.8203125,688.778087797619,-1,0.2914766355140187,3863147
1743401786,1743401978,192,module load GCCcore/10.3.0 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 23 batch_size 4038 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 100,1,4038,str\(\'H\'\),3,str\(\'tstat\'\),0.999,100,23,0.13,183,0,None,i7175,183,688.703125,688.6610863095239,-1,0.30190654205607476,3863151
1743401791,1743401980,189,module load GCCcore/10.3.0 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 23 batch_size 1427 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 33,5000,1427,str\(\'KL\'\),2,str\(\'stdev\'\),0.999,33,23,0.12,177,0,None,i7182,177,687.4375,687.3970703125,-1,0.24181931464174455,3863156
1743401791,1743401982,191,module load GCCcore/10.3.0 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 23 batch_size 1623 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 100,5000,1623,str\(\'H\'\),3,str\(\'tstat\'\),0.999,100,23,0.12,178,0,None,i7162,178,688.44921875,688.39921875,-1,0.2986791277258567,3863165
1743401811,1743401995,184,module load GCCcore/10.3.0 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 23 batch_size 5000 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 61,1,5000,str\(\'H\'\),2,str\(\'tstat\'\),0.999,61,23,0.12,170,0,None,i7179,170,687.55859375,687.4714226973684,-1,0.24922118380062305,3863170
1743401812,1743402002,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4484 feature_id 23 batch_size 5000 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 73,4484,5000,str\(\'H\'\),1,str\(\'tstat\'\),0.999,73,23,0.12,180,0,None,i7185,180,689.125,689.0842633928571,-1,0.3115264797507788,3863167
1743401816,1743402013,197,module load GCCcore/10.3.0 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 23 batch_size 792 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.001 subsets 40,5000,792,str\(\'H\'\),2,str\(\'stdev\'\),0.001,40,23,0.12,186,0,None,i7167,186,688.0390625,687.9970238095239,-1,0.2960747663551402,3863179
1743401791,1743402045,254,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 1 feature_id 23 batch_size 1 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 68,1,1,str\(\'H\'\),3,str\(\'stdev\'\),0.999,68,23,0.12,245,0,None,i7163,245,688.9609375,676.7837094907408,-1,0.1649221183800623,3863163
1743401811,1743402059,248,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 1 feature_id 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 68,1,1,str\(\'KL\'\),3,str\(\'stdev\'\),0.001,68,23,0.12,238,0,None,i7169,238,694.046875,690.6380709134615,-1,0.1649221183800623,3863176
1743401786,1743403171,1385,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1456 feature_id 23 batch_size 1 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 34,1456,1,str\(\'H\'\),2,str\(\'stdev\'\),0.999,34,23,0.12,1374,0,None,i7173,1374,693.95703125,690.6592075892858,-1,0.1649221183800623,3863153
1743408149,1743408162,13,module load GCCcore/10.3.0 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 23 batch_size 1 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.4109767713552185 subsets 88,1,1,str\(\'H\'\),1,str\(\'stdev\'\),0.4109767713552185,88,23,None,None,1,None,i7180,3872626
1743408156,1743408169,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 327 feature_id 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 1,327,1,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,1,23,None,None,1,None,i7140,3872665
1743408156,1743408175,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4189 feature_id 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 100,4189,1,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,100,23,None,None,1,None,i7138,3872680
1743408156,1743408175,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2904 feature_id 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 100,2904,1,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,100,23,None,None,1,None,i7138,3872676
1743408157,1743408176,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4095 feature_id 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 100,4095,1,str\(\'KL\'\),1,str\(\'tstat\'\),0.999,100,23,None,None,1,None,i7169,3872638
1743408054,1743408239,185,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1303 feature_id 23 batch_size 2128 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.9324222050202282 subsets 100,1303,2128,str\(\'H\'\),3,str\(\'tstat\'\),0.9324222050202282,100,23,0.12,172,0,None,i7151,172,688.07421875,688.025390625,-1,0.2651713395638629,3872545
1743408059,1743408248,189,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3062 feature_id 23 batch_size 910 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.8651943370341769 subsets 100,3062,910,str\(\'KL\'\),3,str\(\'tstat\'\),0.8651943370341769,100,23,0.12,175,0,None,i7144,175,687.69140625,687.465625,-1,0.2925981308411215,3872558
1743408075,1743408259,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3363 feature_id 23 batch_size 79 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 100,3363,79,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,100,23,0.12,171,0,None,i7168,171,687.85546875,687.8162109375,-1,0.2549657320872274,3872566
1743408074,1743408264,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1274 feature_id 23 batch_size 2182 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 100,1274,2182,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,100,23,0.13,181,0,None,i7146,181,688.4609375,688.4200148809524,-1,0.3262803738317757,3872571
1743408094,1743408271,177,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1578 feature_id 23 batch_size 1855 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.42089140662715113 subsets 100,1578,1855,str\(\'H\'\),3,str\(\'tstat\'\),0.42089140662715113,100,23,0.12,165,0,None,i7142,165,687.69140625,687.6484375,-1,0.20624299065420562,3872593
1743408094,1743408284,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3191 feature_id 23 batch_size 627 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 100,3191,627,str\(\'H\'\),3,str\(\'tstat\'\),0.999,100,23,0.13,178,0,None,i7143,178,688.78125,688.746484375,-1,0.29542679127725857,3872589
1743408156,1743408321,165,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1893 feature_id 23 batch_size 861 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 100,1893,861,str\(\'H\'\),3,str\(\'tstat\'\),0.001,100,23,0.12,155,0,None,i7151,155,688.8203125,688.7738715277778,-1,0.10728971962616822,3872648
1743408054,1743408327,273,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 795 feature_id 23 batch_size 357 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 1,795,357,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,1,23,0.13,262,0,None,i7147,262,688.66796875,688.657192887931,-1,0.9715763239875389,3872550
1743408156,1743408340,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3116 feature_id 23 batch_size 914 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.6612898076464924 subsets 100,3116,914,str\(\'H\'\),3,str\(\'tstat\'\),0.6612898076464924,100,23,0.12,173,0,None,i7156,173,688.328125,688.283984375,-1,0.271214953271028,3872642
1743408157,1743408346,189,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3487 feature_id 23 batch_size 63 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.8860492125752661 subsets 100,3487,63,str\(\'H\'\),3,str\(\'tstat\'\),0.8860492125752661,100,23,0.12,173,0,None,i7142,173,688.4609375,688.4244140625,-1,0.24885981308411215,3872655
1743408157,1743408353,196,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 166 feature_id 23 batch_size 158 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.2899431291448234 subsets 10,166,158,str\(\'KL\'\),1,str\(\'stdev\'\),0.2899431291448234,10,23,0.12,183,0,None,i7137,183,687.92578125,687.9043898809524,-1,0.35242367601246105,3872684
1743408089,1743408367,278,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2070 feature_id 23 batch_size 938 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 1,2070,938,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,1,23,0.13,267,0,None,i7143,267,688.1015625,688.0839170258621,-1,0.9686105919003115,3872581
1743408114,1743408367,253,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3921 feature_id 23 batch_size 1 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 100,3921,1,str\(\'H\'\),3,str\(\'tstat\'\),0.001,100,23,0.12,240,0,None,i7147,240,693.2421875,689.8293269230769,-1,0.1649221183800623,3872604
1743408135,1743408408,273,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 4103 feature_id 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 1,4103,1,str\(\'KL\'\),3,str\(\'stdev\'\),0.001,1,23,0.12,261,0,None,i7175,261,694.08984375,687.8894127155172,-1,0.1649221183800623,3872618
1743408157,1743408412,255,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 743 feature_id 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.001 subsets 1,743,1,str\(\'KL\'\),2,str\(\'stdev\'\),0.001,1,23,0.12,239,0,None,i7135,239,694.11328125,690.6801382211538,-1,0.1649221183800623,3872695
1743408149,1743408428,279,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 789 feature_id 23 batch_size 973 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 1,789,973,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,1,23,0.13,269,0,None,i7157,269,688.01953125,688.0033674568965,-1,0.9693084112149533,3872635
1743408157,1743408555,398,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 97 feature_id 23 batch_size 927 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.5051970075249388 subsets 100,97,927,str\(\'KL\'\),1,str\(\'stdev\'\),0.5051970075249388,100,23,0.12,381,0,None,i7139,381,688.0234375,688.0100038109756,-1,0.38119626168224297,3872671
1743408114,1743408564,450,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 966 feature_id 23 batch_size 859 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 100,966,859,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,100,23,0.12,439,0,None,i7168,439,687.73046875,687.7160326086956,-1,0.4353644859813084,3872598
1743408150,1743408580,430,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 699 feature_id 23 batch_size 365 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 100,699,365,str\(\'H\'\),1,str\(\'tstat\'\),0.001,100,23,0.13,419,0,None,i7177,419,687.69921875,687.6827947443181,-1,0.1864797507788162,3872628
1743408157,1743408580,423,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 2129 feature_id 23 batch_size 950 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 100,2129,950,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,100,23,0.12,410,0,None,i7136,410,687.20703125,687.1979469476744,-1,0.4143302180685358,3872689
1743408054,1743408891,837,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 656 feature_id 23 batch_size 291 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 100,656,291,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,100,23,0.13,827,0,None,i7182,827,687.30859375,687.3025275735295,-1,0.38847352024922116,3872541
1743408134,1743409517,1383,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 26 feature_id 23 batch_size 120 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.42114807110555924 subsets 100,26,120,str\(\'H\'\),1,str\(\'tstat\'\),0.42114807110555924,100,23,0.13,1369,0,None,i7183,1369,687.61328125,687.6096841276978,-1,0.3050467289719626,3872615
1743408119,1743410205,2086,module load GCCcore/10.3.0 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 23 batch_size 81 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.5423888404187747 subsets 100,1,81,str\(\'KL\'\),1,str\(\'tstat\'\),0.5423888404187747,100,23,0.12,2072,0,None,i7144,2072,687.3359375,677.339490327381,-1,0.3300560747663551,3872610
1743415088,1743415270,182,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4602 feature_id 23 batch_size 3522 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 100,4602,3522,str\(\'H\'\),3,str\(\'tstat\'\),0.001,100,23,0.12,156,0,None,i7126,156,688.36328125,688.3114149305555,-1,0.13166355140186917,3881375
1743415088,1743415277,189,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4558 feature_id 23 batch_size 337 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 100,4558,337,str\(\'KL\'\),3,str\(\'stdev\'\),0.999,100,23,0.12,165,0,None,i7126,165,688.42578125,688.3980263157895,-1,0.23096573208722743,3881370
1743415082,1743415285,203,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4393 feature_id 23 batch_size 411 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 100,4393,411,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,100,23,0.12,172,0,None,i7127,172,687.39453125,687.357421875,-1,0.2560747663551402,3881361
1743415083,1743415285,202,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2582 feature_id 23 batch_size 960 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 33,2582,960,str\(\'H\'\),1,str\(\'tstat\'\),0.001,33,23,0.12,176,0,None,i7127,176,688.796875,688.755078125,-1,0.28710280373831776,3881360
1743415088,1743415289,201,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2174 feature_id 23 batch_size 3623 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 100,2174,3623,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,100,23,0.12,173,0,None,i7126,173,687.74609375,687.7,-1,0.31602492211838007,3881366
1743415093,1743415294,201,module load GCCcore/10.3.0 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 23 batch_size 259 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 100,5000,259,str\(\'H\'\),3,str\(\'stdev\'\),0.001,100,23,0.12,171,0,None,i7123,171,687.921875,687.8982421875,-1,0.26878504672897197,3881409
1743415107,1743415303,196,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4245 feature_id 23 batch_size 4214 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 100,4245,4214,str\(\'KL\'\),3,str\(\'stdev\'\),0.001,100,23,0.11,162,0,None,i7124,162,688.1171875,688.0838815789474,-1,0.2625545171339564,3881403
1743415090,1743415304,214,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 5000 feature_id 23 batch_size 3637 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 1,5000,3637,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,1,23,0.12,177,0,None,i7122,177,687.515625,687.4748046875,-1,0.3172461059190031,3881420
1743415107,1743415310,203,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4724 feature_id 23 batch_size 332 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.3293470994377879 subsets 100,4724,332,str\(\'KL\'\),3,str\(\'tstat\'\),0.3293470994377879,100,23,0.13,170,0,None,i7124,170,687.66015625,687.6229440789474,-1,0.24822429906542057,3881397
1743415107,1743415316,209,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 5000 feature_id 23 batch_size 548 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 1,5000,548,str\(\'KL\'\),3,str\(\'stdev\'\),0.999,1,23,0.11,174,0,None,i7124,174,688.58984375,688.5609375,-1,0.19803115264797508,3881391
1743415083,1743415317,234,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 1 feature_id 23 batch_size 2276 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.001 subsets 1,1,2276,str\(\'KL\'\),2,str\(\'stdev\'\),0.001,1,23,0.12,203,0,None,i7127,203,687.65625,687.6200747282609,-1,0.485595015576324,3881356
1743415093,1743415320,227,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 5000 feature_id 23 batch_size 3739 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.6618365018305045 subsets 100,5000,3739,str\(\'H\'\),1,str\(\'tstat\'\),0.6618365018305045,100,23,0.13,194,0,None,i7123,194,688.4609375,688.4287997159091,-1,0.2329595015576324,3881417
1743415093,1743415326,233,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 1775 feature_id 23 batch_size 3692 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 100,1775,3692,str\(\'H\'\),1,str\(\'tstat\'\),0.999,100,23,0.13,203,0,None,i7123,203,688.9140625,688.8797554347826,-1,0.23003115264797508,3881406
1743415087,1743415340,253,module load GCCcore/10.3.0 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 23 batch_size 1144 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 100,1,1144,str\(\'H\'\),1,str\(\'tstat\'\),0.001,100,23,0.11,226,0,None,i7126,226,688.421875,688.3946875,-1,0.1425545171339564,3881368
1743415107,1743415341,234,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 4771 feature_id 23 batch_size 402 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 1,4771,402,str\(\'H\'\),2,str\(\'tstat\'\),0.001,1,23,0.12,200,0,None,i7124,200,688.34765625,688.3100142045455,-1,0.4860436137071651,3881394
1743415093,1743415351,258,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 4134 feature_id 23 batch_size 2578 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.001 subsets 100,4134,2578,str\(\'KL\'\),2,str\(\'stdev\'\),0.001,100,23,0.13,230,0,None,i7123,230,687.4765625,687.44078125,-1,0.2891214953271028,3881412
1743415082,1743415355,273,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 4913 feature_id 23 batch_size 1010 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 100,4913,1010,str\(\'H\'\),1,str\(\'tstat\'\),0.001,100,23,0.12,243,0,None,i7127,243,687.859375,687.8311631944445,-1,0.15102803738317758,3881354
1743415142,1743415356,214,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 4896 feature_id 23 batch_size 2959 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.3607247161951347 subsets 100,4896,2959,str\(\'H\'\),3,str\(\'tstat\'\),0.3607247161951347,100,23,0.12,167,0,None,i7125,167,687.94140625,687.8972039473684,-1,0.2212336448598131,3881384
1743415142,1743415362,220,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 4333 feature_id 23 batch_size 2759 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 1,4333,2759,str\(\'KL\'\),3,str\(\'stdev\'\),0.001,1,23,0.12,176,0,None,i7125,176,688.76171875,688.7291015625,-1,0.30942056074766355,3881388
1743415093,1743415364,271,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 5000 feature_id 23 batch_size 914 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 100,5000,914,str\(\'H\'\),2,str\(\'tstat\'\),0.001,100,23,0.12,242,0,None,i7123,242,687.86328125,687.82421875,-1,0.14806230529595016,3881415
1743415087,1743415374,287,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 2931 feature_id 23 batch_size 578 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 1,2931,578,str\(\'H\'\),1,str\(\'stdev\'\),0.001,1,23,0.13,262,0,None,i7126,262,688.03125,688.0148168103449,-1,0.9690342679127726,3881372
1743415090,1743415376,286,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 841 feature_id 23 batch_size 1934 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 100,841,1934,str\(\'KL\'\),2,str\(\'tstat\'\),0.999,100,23,0.12,252,0,None,i7122,252,689.26171875,689.2198660714286,-1,0.3124859813084112,3881425
1743415083,1743415378,295,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 9 feature_id 23 batch_size 315 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.6623657316825299 subsets 1,9,315,str\(\'KL\'\),1,str\(\'stdev\'\),0.6623657316825299,1,23,0.13,265,0,None,i7127,265,687.703125,687.6908674568965,-1,0.9720996884735202,3881358
1743415083,1743415382,299,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4263 feature_id 23 batch_size 686 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 100,4263,686,str\(\'H\'\),2,str\(\'tstat\'\),0.001,100,23,0.11,273,0,None,i7127,273,687.83203125,687.796875,-1,0.16241744548286605,3881364
1743415142,1743415390,248,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 5000 feature_id 23 batch_size 3536 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 100,5000,3536,str\(\'KL\'\),1,str\(\'tstat\'\),0.999,100,23,0.12,200,0,None,i7125,200,687.734375,687.6937144886364,-1,0.22031152647975077,3881386
1743415142,1743415391,249,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 5000 feature_id 23 batch_size 542 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.001 subsets 1,5000,542,str\(\'KL\'\),2,str\(\'stdev\'\),0.001,1,23,0.12,203,0,None,i7125,203,686.9765625,686.9437839673913,-1,0.4862803738317757,3881380
1743415107,1743415400,293,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 530 feature_id 23 batch_size 845 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 1,530,845,str\(\'H\'\),1,str\(\'tstat\'\),0.001,1,23,0.13,261,0,None,i7124,261,687.59765625,687.5775862068965,-1,0.9648722741433022,3881400
1743415142,1743415453,311,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3730 feature_id 23 batch_size 375 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.831843570629203 subsets 1,3730,375,str\(\'KL\'\),1,str\(\'stdev\'\),0.831843570629203,1,23,0.13,267,0,None,i7125,267,687.48828125,687.4757543103449,-1,0.970417445482866,3881382
1743415142,1743415460,318,module load GCCcore/10.3.0 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 23 batch_size 2201 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 1,1,2201,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,1,23,0.12,268,0,None,i7125,268,688.01171875,687.9991918103449,-1,0.9624548286604362,3881378
1743415142,1743415478,336,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 4996 feature_id 23 batch_size 1990 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 100,4996,1990,str\(\'KL\'\),1,str\(\'tstat\'\),0.999,100,23,0.12,291,0,None,i7125,291,688.67578125,688.65576171875,-1,0.39664797507788163,3881376
1743417163,1743417182,19,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4119 feature_id 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.2813964759513176 subsets 1,4119,1,str\(\'KL\'\),1,str\(\'stdev\'\),0.2813964759513176,1,23,None,None,1,None,i7129,3884927
1743417161,1743417332,171,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4692 feature_id 23 batch_size 1300 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 7,4692,1300,str\(\'H\'\),1,str\(\'stdev\'\),0.999,7,23,0.13,156,0,None,i7133,156,687.953125,687.6976996527778,-1,0.12088473520249221,3884886
1743417161,1743417351,190,module load GCCcore/10.3.0 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 23 batch_size 2605 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.5269679726783049 subsets 100,5000,2605,str\(\'KL\'\),1,str\(\'stdev\'\),0.5269679726783049,100,23,0.12,177,0,None,i7133,177,689.75,689.35625,-1,0.09738317757009346,3884888
1743417162,1743417365,203,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4071 feature_id 23 batch_size 2745 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.5558828853294202 subsets 100,4071,2745,str\(\'KL\'\),3,str\(\'tstat\'\),0.5558828853294202,100,23,0.12,170,0,None,i7134,170,689.06640625,614.5559210526316,-1,0.20523364485981307,3884884
1743417163,1743417365,202,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1632 feature_id 23 batch_size 4782 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.36198876756227555 subsets 100,1632,4782,str\(\'H\'\),1,str\(\'tstat\'\),0.36198876756227555,100,23,0.11,178,0,None,i7130,178,688.78515625,597.8025390625,-1,0.11917757009345795,3884911
1743417163,1743417366,203,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4629 feature_id 23 batch_size 3768 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.46323887407373154 subsets 100,4629,3768,str\(\'KL\'\),3,str\(\'tstat\'\),0.46323887407373154,100,23,0.12,175,0,None,i7130,175,688.8203125,597.540625,-1,0.23476635514018693,3884915
1743417163,1743417366,203,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 956 feature_id 23 batch_size 1402 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 1,956,1402,str\(\'H\'\),3,str\(\'tstat\'\),0.999,1,23,0.13,174,0,None,i7132,174,672.10546875,630.188671875,-1,0.2720872274143302,3884893
1743417163,1743417377,214,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 5000 feature_id 23 batch_size 1089 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 100,5000,1089,str\(\'H\'\),1,str\(\'stdev\'\),0.999,100,23,0.14,197,0,None,i7131,197,687.82421875,687.443359375,-1,0.08142056074766355,3884907
1743417163,1743417384,221,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 2030 feature_id 23 batch_size 1005 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.30608117314615585 subsets 9,2030,1005,str\(\'KL\'\),2,str\(\'stdev\'\),0.30608117314615585,9,23,0.13,191,0,None,i7132,191,672.03515625,627.3986150568181,-1,0.2880373831775701,3884890
1743417162,1743417401,239,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 1104 feature_id 23 batch_size 1632 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 100,1104,1632,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,100,23,0.12,207,0,None,i7128,207,688.21875,609.7090692934783,-1,0.13778193146417445,3884933
1743417162,1743417401,239,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 4825 feature_id 23 batch_size 3211 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 100,4825,3211,str\(\'H\'\),2,str\(\'tstat\'\),0.999,100,23,0.12,214,0,None,i7128,214,688.24609375,607.87548828125,-1,0.20006230529595015,3884934
1743417162,1743417408,246,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3444 feature_id 23 batch_size 4646 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 100,3444,4646,str\(\'KL\'\),1,str\(\'tstat\'\),0.999,100,23,0.12,216,0,None,i7128,216,687.22265625,606.9158528645834,-1,0.28947040498442367,3884931
1743417163,1743417416,253,module load GCCcore/10.3.0 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 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 100,5000,1,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,100,23,0.12,238,0,None,i7129,238,693.921875,690.4130108173077,-1,0.1649221183800623,3884925
1743417163,1743417422,259,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 62 feature_id 23 batch_size 1597 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.39112881450575115 subsets 88,62,1597,str\(\'H\'\),1,str\(\'stdev\'\),0.39112881450575115,88,23,0.13,243,0,None,i7129,243,687.4609375,687.4366319444445,-1,0.29349532710280374,3884920
1743417162,1743417439,277,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 3554 feature_id 23 batch_size 1144 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.5174998145460016 subsets 100,3554,1144,str\(\'H\'\),2,str\(\'stdev\'\),0.5174998145460016,100,23,0.13,249,0,None,i7128,249,688.37109375,602.5831886574074,-1,0.17466666666666666,3884929
1743417163,1743417446,283,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4751 feature_id 23 batch_size 1815 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.683486297813867 subsets 1,4751,1815,str\(\'H\'\),1,str\(\'tstat\'\),0.683486297813867,1,23,0.13,266,0,None,i7131,266,687.32421875,687.3029364224138,-1,0.9702305295950155,3884900
1743417163,1743417446,283,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4229 feature_id 23 batch_size 1009 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.405191750255294 subsets 1,4229,1009,str\(\'H\'\),1,str\(\'stdev\'\),0.405191750255294,1,23,0.13,265,0,None,i7131,265,687.65234375,687.635775862069,-1,0.9683738317757009,3884904
1743417163,1743417446,283,module load GCCcore/10.3.0 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 23 batch_size 1856 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.40263077572055167 subsets 1,1,1856,str\(\'H\'\),1,str\(\'tstat\'\),0.40263077572055167,1,23,0.13,264,0,None,i7131,264,687.80859375,687.7858297413793,-1,0.9695825545171339,3884902
1743417163,1743417447,284,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 5000 feature_id 23 batch_size 4211 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 1,5000,4211,str\(\'H\'\),1,str\(\'tstat\'\),0.999,1,23,0.12,266,0,None,i7129,266,687.9453125,687.9278017241379,-1,0.9720872274143302,3884917
1743417163,1743417447,284,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 2639 feature_id 23 batch_size 4948 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.5731778382736398 subsets 1,2639,4948,str\(\'H\'\),1,str\(\'tstat\'\),0.5731778382736398,1,23,0.12,266,0,None,i7129,266,688.234375,688.2161907327586,-1,0.961607476635514,3884923
1743417163,1743417448,285,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 1 feature_id 23 batch_size 1264 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.9966263791507924 subsets 100,1,1264,str\(\'H\'\),2,str\(\'stdev\'\),0.9966263791507924,100,23,0.12,255,0,None,i7132,255,672.796875,613.4091796875,-1,0.17985046728971962,3884894
1743417163,1743417460,297,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3550 feature_id 23 batch_size 2669 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.7156517059168328 subsets 1,3550,2669,str\(\'KL\'\),1,str\(\'stdev\'\),0.7156517059168328,1,23,0.13,268,0,None,i7132,268,672.734375,611.5499730603449,-1,0.9728473520249221,3884896
1743417162,1743417464,302,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 1 feature_id 23 batch_size 1165 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 1,1,1165,str\(\'H\'\),1,str\(\'stdev\'\),0.999,1,23,0.13,273,0,None,i7128,273,687.58203125,597.03515625,-1,0.9622554517133957,3884935
1743417163,1743417465,302,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 1306 feature_id 23 batch_size 1836 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 1,1306,1836,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,1,23,0.13,277,0,None,i7130,277,688.19140625,584.0174479166667,-1,0.9588660436137072,3884909
1743417162,1743417487,325,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 65 feature_id 23 batch_size 1733 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 1,65,1733,str\(\'H\'\),1,str\(\'tstat\'\),0.999,1,23,0.13,292,0,None,i7134,292,688.78125,591.7991943359375,-1,0.9684610591900311,3884880
1743417163,1743417497,334,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 5000 feature_id 23 batch_size 1841 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 100,5000,1841,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,100,23,0.12,315,0,None,i7129,315,687.9765625,687.9592141544117,-1,0.4568473520249221,3884919
1743417163,1743417503,340,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4761 feature_id 23 batch_size 772 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.3976029259366454 subsets 99,4761,772,str\(\'KL\'\),2,str\(\'stdev\'\),0.3976029259366454,99,23,0.12,322,0,None,i7129,322,687.72265625,687.6970982142857,-1,0.25973831775700934,3884921
1743417163,1743417522,359,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 419 feature_id 23 batch_size 1817 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 100,419,1817,str\(\'H\'\),1,str\(\'tstat\'\),0.999,100,23,0.12,331,0,None,i7130,331,687.41796875,578.8936631944445,-1,0.4528348909657321,3884913
1743417163,1743417547,384,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 191 feature_id 23 batch_size 1393 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 100,191,1393,str\(\'H\'\),1,str\(\'stdev\'\),0.001,100,23,0.13,364,0,None,i7131,364,687.5390625,687.5171274038462,-1,0.4165981308411215,3884898
1743417162,1743417548,386,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 852 feature_id 23 batch_size 655 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.7805423345191682 subsets 100,852,655,str\(\'KL\'\),2,str\(\'stdev\'\),0.7805423345191682,100,23,0.13,353,0,None,i7134,353,688.2890625,585.8704769736842,-1,0.22853582554517135,3884882
1743419265,1743419278,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2636 feature_id 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 7,2636,1,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,7,23,None,None,1,None,i7126,3887555
1743419300,1743419313,13,module load GCCcore/10.3.0 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 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 16,1,1,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,16,23,None,None,1,None,i7182,3887583
1743419314,1743419327,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1782 feature_id 23 batch_size 1 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 1,1782,1,str\(\'H\'\),1,str\(\'stdev\'\),0.001,1,23,None,None,1,None,i7183,3887593
1743419341,1743419360,19,module load GCCcore/10.3.0 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 23 batch_size 1 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 99,5000,1,str\(\'H\'\),1,str\(\'stdev\'\),0.001,99,23,None,None,1,None,i7183,3887614
1743419380,1743419393,13,module load GCCcore/10.3.0 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 23 batch_size 1 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 52,1,1,str\(\'H\'\),1,str\(\'stdev\'\),0.001,52,23,None,None,1,None,i7173,3887657
1743419284,1743419512,228,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 1 feature_id 23 batch_size 821 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 50,1,821,str\(\'H\'\),1,str\(\'stdev\'\),0.999,50,23,0.13,219,0,None,i7179,219,688.5859375,688.56640625,-1,0.3331588785046729,3887576
1743419265,1743419543,278,module load GCCcore/10.3.0 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 23 batch_size 1539 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 1,5000,1539,str\(\'H\'\),1,str\(\'stdev\'\),0.999,1,23,0.13,259,0,None,i7127,259,689.015625,688.0447823660714,-1,0.9723239875389408,3887552
1743419269,1743419548,279,module load GCCcore/10.3.0 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 23 batch_size 1508 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 1,1,1508,str\(\'H\'\),1,str\(\'stdev\'\),0.001,1,23,0.13,265,0,None,i7127,265,688.7109375,687.7326239224138,-1,0.9710280373831776,3887542
1743419344,1743419559,215,module load GCCcore/10.3.0 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 23 batch_size 543 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.001 subsets 1,1,543,str\(\'H\'\),2,str\(\'stdev\'\),0.001,1,23,0.12,203,0,None,i7177,203,687.40625,687.3744904891304,-1,0.48717757009345797,3887624
1743419374,1743419563,189,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 644 feature_id 23 batch_size 2205 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 100,644,2205,str\(\'KL\'\),2,str\(\'stdev\'\),0.999,100,23,0.12,180,0,None,i7173,180,687.7109375,687.3126953125,-1,0.10990654205607477,3887652
1743419280,1743419565,285,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 5000 feature_id 23 batch_size 1471 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 1,5000,1471,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,1,23,0.13,273,0,None,i7182,273,688.48046875,688.4641927083334,-1,0.9649221183800623,3887566
1743419300,1743419566,266,module load GCCcore/10.3.0 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 23 batch_size 1660 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.001 subsets 100,1,1660,str\(\'H\'\),2,str\(\'stdev\'\),0.001,100,23,0.12,253,0,None,i7181,253,687.4921875,686.4468470982143,-1,0.244,3887587
1743419265,1743419567,302,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 1554 feature_id 23 batch_size 1511 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 1,1554,1511,str\(\'H\'\),1,str\(\'stdev\'\),0.999,1,23,0.13,273,0,None,i7128,273,684.90625,584.8421875,-1,0.9730093457943926,3887530
1743419264,1743419567,303,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 1112 feature_id 23 batch_size 1455 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 1,1112,1455,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,1,23,0.13,274,0,None,i7128,274,686.8359375,586.81015625,-1,0.9722866043613707,3887534
1743419281,1743419568,287,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 928 feature_id 23 batch_size 1149 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 1,928,1149,str\(\'KL\'\),1,str\(\'tstat\'\),0.999,1,23,0.13,273,0,None,i7185,273,688.6015625,688.584765625,-1,0.9630155763239875,3887560
1743419284,1743419576,292,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3904 feature_id 23 batch_size 1146 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 1,3904,1146,str\(\'H\'\),1,str\(\'tstat\'\),0.999,1,23,0.13,279,0,None,i7180,279,688.32421875,688.3049479166667,-1,0.9747165109034268,3887570
1743419365,1743419580,215,module load GCCcore/10.3.0 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 23 batch_size 2808 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.4452938986386773 subsets 1,5000,2808,str\(\'KL\'\),2,str\(\'stdev\'\),0.4452938986386773,1,23,0.12,205,0,None,i7181,205,688.6484375,688.6102241847826,-1,0.4649595015576324,3887635
1743419344,1743419593,249,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 5000 feature_id 23 batch_size 849 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 58,5000,849,str\(\'H\'\),1,str\(\'stdev\'\),0.001,58,23,0.13,238,0,None,i7175,238,688.5390625,688.5142728365385,-1,0.4405856697819315,3887630
1743419374,1743419596,222,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 1 feature_id 23 batch_size 1556 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.001 subsets 1,1,1556,str\(\'H\'\),2,str\(\'stdev\'\),0.001,1,23,0.12,211,0,None,i7174,211,687.22265625,687.1827799479166,-1,0.48473520249221186,3887648
1743419314,1743419599,285,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 5000 feature_id 23 batch_size 1651 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 1,5000,1651,str\(\'H\'\),1,str\(\'stdev\'\),0.999,1,23,0.13,274,0,None,i7180,274,687.79296875,687.778125,-1,0.9626043613707165,3887599
1743419320,1743419606,286,module load GCCcore/10.3.0 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 23 batch_size 1531 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 1,5000,1531,str\(\'H\'\),1,str\(\'stdev\'\),0.001,1,23,0.13,273,0,None,i7179,273,687.421875,687.4036458333334,-1,0.9671401869158879,3887605
1743419320,1743419606,286,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4649 feature_id 23 batch_size 507 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 1,4649,507,str\(\'H\'\),1,str\(\'tstat\'\),0.999,1,23,0.13,274,0,None,i7177,274,687.55078125,687.533203125,-1,0.9732959501557632,3887610
1743419340,1743419612,272,module load GCCcore/10.3.0 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 23 batch_size 1401 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 1,1,1401,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,1,23,0.13,262,0,None,i7181,262,687.3671875,687.3529094827586,-1,0.9701931464174455,3887619
1743419344,1743419625,281,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 546 feature_id 23 batch_size 1389 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 1,546,1389,str\(\'KL\'\),1,str\(\'tstat\'\),0.999,1,23,0.13,269,0,None,i7176,269,689.44921875,689.4299568965517,-1,0.9616697819314641,3887628
1743419265,1743419631,366,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3508 feature_id 23 batch_size 850 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 100,3508,850,str\(\'H\'\),1,str\(\'stdev\'\),0.999,100,23,0.12,350,0,None,i7127,350,689.0234375,688.0380067567568,-1,0.2822554517133956,3887547
1743419400,1743419635,235,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 4141 feature_id 23 batch_size 3742 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 100,4141,3742,str\(\'H\'\),2,str\(\'tstat\'\),0.999,100,23,0.12,221,0,None,i7183,221,688.43359375,688.37859375,-1,0.27977570093457943,3887665
1743419360,1743419645,285,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 1975 feature_id 23 batch_size 3236 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 1,1975,3236,str\(\'H\'\),1,str\(\'tstat\'\),0.999,1,23,0.12,269,0,None,i7174,269,687.99609375,687.9785829741379,-1,0.9428660436137072,3887640
1743419380,1743419665,285,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 4927 feature_id 23 batch_size 1039 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 1,4927,1039,str\(\'KL\'\),1,str\(\'tstat\'\),0.999,1,23,0.13,273,0,None,i7172,273,687.453125,687.4354166666667,-1,0.97201246105919,3887662
1743419404,1743419688,284,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 1 feature_id 23 batch_size 1357 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 1,1,1357,str\(\'H\'\),1,str\(\'stdev\'\),0.001,1,23,0.13,272,0,None,i7182,272,688.07421875,688.0625,-1,0.9727601246105919,3887675
1743419400,1743419729,329,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 489 feature_id 23 batch_size 582 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 75,489,582,str\(\'H\'\),1,str\(\'tstat\'\),0.999,75,23,0.12,316,0,None,i7173,316,688.4765625,688.4545036764706,-1,0.40613084112149533,3887671
1743422601,1743422614,13,module load GCCcore/10.3.0 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 23 batch_size 1 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 1,1,1,str\(\'H\'\),1,str\(\'tstat\'\),0.001,1,23,None,None,1,None,i7147,3892349
1743422562,1743422727,165,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2665 feature_id 23 batch_size 1451 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 42,2665,1451,str\(\'H\'\),2,str\(\'stdev\'\),0.999,42,23,0.11,146,0,None,i7098,146,688.0703125,687.6397058823529,-1,0.06549532710280374,3892314
1743422563,1743422733,170,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 480 feature_id 23 batch_size 1301 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 38,480,1301,str\(\'H\'\),1,str\(\'stdev\'\),0.999,38,23,0.13,156,0,None,i7098,156,689.62109375,689.3708767361111,-1,0.1209968847352025,3892311
1743422562,1743422745,183,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 5000 feature_id 23 batch_size 1438 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 56,5000,1438,str\(\'H\'\),1,str\(\'stdev\'\),0.999,56,23,0.12,170,0,None,i7098,170,687.5546875,687.5254934210526,-1,0.1901183800623053,3892306
1743422562,1743422746,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 636 feature_id 23 batch_size 1234 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 23,636,1234,str\(\'H\'\),1,str\(\'stdev\'\),0.999,23,23,0.13,166,0,None,i7098,166,688.09375,688.0625,-1,0.2211214953271028,3892309
1743422563,1743422747,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4970 feature_id 23 batch_size 1268 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 27,4970,1268,str\(\'H\'\),1,str\(\'stdev\'\),0.999,27,23,0.12,164,0,None,i7099,164,688.9296875,688.8986430921053,-1,0.19629906542056075,3892302
1743422562,1743422752,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 553 feature_id 23 batch_size 1224 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 14,553,1224,str\(\'H\'\),1,str\(\'stdev\'\),0.999,14,23,0.12,180,0,None,i7097,180,689.3046875,689.27265625,-1,0.3106417445482866,3892317
1743422582,1743422753,171,module load GCCcore/10.3.0 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 23 batch_size 1232 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 17,5000,1232,str\(\'H\'\),1,str\(\'stdev\'\),0.999,17,23,0.12,158,0,None,i7168,158,687.91015625,687.8728298611111,-1,0.1593146417445483,3892328
1743422562,1743422758,196,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2619 feature_id 23 batch_size 1028 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 22,2619,1028,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,22,23,0.12,186,0,None,i7097,186,688.171875,688.1469494047619,-1,0.34586915887850467,3892319
1743422562,1743422758,196,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4535 feature_id 23 batch_size 1350 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 47,4535,1350,str\(\'H\'\),1,str\(\'stdev\'\),0.999,47,23,0.12,178,0,None,i7099,178,688.08203125,688.053125,-1,0.2610716510903427,3892299
1743422562,1743422758,196,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 117 feature_id 23 batch_size 1005 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.3920041139824503 subsets 11,117,1005,str\(\'KL\'\),1,str\(\'stdev\'\),0.3920041139824503,11,23,0.12,185,0,None,i7096,185,688.16015625,688.1298363095239,-1,0.3506542056074766,3892322
1743422585,1743422768,183,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 5000 feature_id 23 batch_size 1306 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 38,5000,1306,str\(\'H\'\),1,str\(\'stdev\'\),0.999,38,23,0.13,168,0,None,i7142,168,688.75,688.4594983552631,-1,0.18665420560747664,3892339
1743422582,1743422773,191,module load GCCcore/10.3.0 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 23 batch_size 985 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.7532366014060013 subsets 61,1,985,str\(\'H\'\),3,str\(\'stdev\'\),0.7532366014060013,61,23,0.13,178,0,None,i7155,178,687.625,687.5837890625,-1,0.2451214953271028,3892332
1743422601,1743422778,177,module load GCCcore/10.3.0 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 23 batch_size 1514 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 30,5000,1514,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,30,23,0.12,168,0,None,i7153,168,689.1796875,689.1467927631579,-1,0.2014828660436137,3892347
1743422582,1743422785,203,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4794 feature_id 23 batch_size 1344 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.80025637286963 subsets 44,4794,1344,str\(\'H\'\),1,str\(\'stdev\'\),0.80025637286963,44,23,0.13,188,0,None,i7178,188,687.828125,685.2544642857143,-1,0.3100436137071651,3892326
1743422615,1743422785,170,module load GCCcore/10.3.0 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 23 batch_size 1483 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 24,5000,1483,str\(\'H\'\),1,str\(\'stdev\'\),0.999,24,23,0.12,156,0,None,i7142,156,688.3203125,688.2918836805555,-1,0.12292834890965731,3892363
1743422581,1743422790,209,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 714 feature_id 23 batch_size 1350 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.8391629153040667 subsets 53,714,1350,str\(\'H\'\),1,str\(\'stdev\'\),0.8391629153040667,53,23,0.13,196,0,None,i7183,196,688.1015625,688.0809659090909,-1,0.31962616822429907,3892325
1743422622,1743422793,171,module load GCCcore/10.3.0 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 23 batch_size 1500 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 29,1,1500,str\(\'H\'\),1,str\(\'stdev\'\),0.999,29,23,0.13,156,0,None,i7133,156,688.20703125,687.9431423611111,-1,0.10593146417445483,3892371
1743422582,1743422799,217,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 2162 feature_id 23 batch_size 1358 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.7454784048789843 subsets 40,2162,1358,str\(\'H\'\),2,str\(\'stdev\'\),0.7454784048789843,40,23,0.13,206,0,None,i7166,206,687.6953125,686.2486413043479,-1,0.212,3892330
1743422585,1743422799,214,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 5000 feature_id 23 batch_size 5000 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 100,5000,5000,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,100,23,0.12,203,0,None,i7142,203,688.39453125,688.366847826087,-1,0.3115264797507788,3892343
1743422585,1743422800,215,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4474 feature_id 23 batch_size 990 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 52,4474,990,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,52,23,0.12,202,0,None,i7147,202,689.03515625,689.0028872282609,-1,0.38242990654205605,3892336
1743422585,1743422805,220,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 4824 feature_id 23 batch_size 1596 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.28087837004429905 subsets 1,4824,1596,str\(\'KL\'\),2,str\(\'tstat\'\),0.28087837004429905,1,23,0.12,208,0,None,i7142,208,687.41015625,687.3739809782609,-1,0.47730841121495327,3892341
1743422615,1743422805,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4517 feature_id 23 batch_size 1478 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.8547933720012154 subsets 52,4517,1478,str\(\'H\'\),1,str\(\'stdev\'\),0.8547933720012154,52,23,0.12,180,0,None,i7145,180,688.1953125,688.1591796875,-1,0.23293457943925233,3892361
1743422585,1743422814,229,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1879 feature_id 23 batch_size 1249 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 1,1879,1249,str\(\'H\'\),2,str\(\'tstat\'\),0.001,1,23,0.13,217,0,None,i7149,217,689.21875,689.1793619791666,-1,0.4824797507788162,3892335
1743422615,1743422818,203,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4688 feature_id 23 batch_size 915 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 11,4688,915,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,11,23,0.13,191,0,None,i7167,191,688.125,687.6424005681819,-1,0.33065420560747666,3892357
1743422615,1743422825,210,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 67 feature_id 23 batch_size 808 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 13,67,808,str\(\'H\'\),1,str\(\'tstat\'\),0.999,13,23,0.13,195,0,None,i7183,195,689.08203125,689.0495383522727,-1,0.4181059190031153,3892355
1743422622,1743422825,203,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4617 feature_id 23 batch_size 1126 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.42732027715701953 subsets 19,4617,1126,str\(\'KL\'\),1,str\(\'stdev\'\),0.42732027715701953,19,23,0.12,189,0,None,i7138,189,687.4375,687.4149925595239,-1,0.3788411214953271,3892368
1743422602,1743422839,237,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 343 feature_id 23 batch_size 1251 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.9160409622655734 subsets 31,343,1251,str\(\'H\'\),1,str\(\'stdev\'\),0.9160409622655734,31,23,0.12,207,0,None,i7141,207,688.609375,636.3481657608696,-1,0.3180436137071651,3892352
1743422562,1743422840,278,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3460 feature_id 23 batch_size 1410 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 1,3460,1410,str\(\'KL\'\),1,str\(\'tstat\'\),0.001,1,23,0.13,267,0,None,i7097,267,688.71484375,688.6945043103449,-1,0.9590155763239875,3892320
1743422622,1743422906,284,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 4955 feature_id 23 batch_size 1139 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.43547661375957714 subsets 20,4955,1139,str\(\'H\'\),1,str\(\'stdev\'\),0.43547661375957714,20,23,0.12,271,0,None,i7107,271,687.45703125,679.594140625,-1,0.36902180685358255,3892374
1743427844,1743428021,177,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1472 feature_id 23 batch_size 1357 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 17,1472,1357,str\(\'H\'\),1,str\(\'stdev\'\),0.999,17,23,0.13,165,0,None,i7164,165,687.58203125,686.9161184210526,-1,0.22873520249221183,3898933
1743427844,1743428028,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 694 feature_id 23 batch_size 375 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 26,694,375,str\(\'KL\'\),1,str\(\'tstat\'\),0.001,26,23,0.12,172,0,None,i7163,172,687.63671875,687.60390625,-1,0.2570093457943925,3898937
1743427866,1743428043,177,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4378 feature_id 23 batch_size 1344 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 18,4378,1344,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,18,23,0.12,164,0,None,i7169,164,688.46484375,688.4311266447369,-1,0.19280996884735202,3898952
1743427884,1743428054,170,module load GCCcore/10.3.0 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 23 batch_size 1514 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 42,1,1514,str\(\'H\'\),3,str\(\'tstat\'\),0.001,42,23,0.12,159,0,None,i7182,159,688.77734375,687.3200954861111,-1,0.12601869158878504,3898962
1743427884,1743428068,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 146 feature_id 23 batch_size 830 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 20,146,830,str\(\'H\'\),3,str\(\'tstat\'\),0.999,20,23,0.13,173,0,None,i7167,173,688.29296875,688.2560546875,-1,0.24822429906542057,3898969
1743427866,1743428069,203,module load GCCcore/10.3.0 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 23 batch_size 405 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 27,1,405,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,27,23,0.13,189,0,None,i7166,189,688.59375,688.557849702381,-1,0.2728473520249221,3898957
1743427884,1743428093,209,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 1 feature_id 23 batch_size 525 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 67,1,525,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,67,23,0.13,199,0,None,i7172,199,688.76953125,688.7340198863636,-1,0.2616822429906542,3898966
1743427904,1743428094,190,module load GCCcore/10.3.0 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 23 batch_size 1697 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 15,1,1697,str\(\'H\'\),3,str\(\'tstat\'\),0.999,15,23,0.12,177,0,None,i7179,177,687.578125,687.4296875,-1,0.2922866043613707,3898982
1743427904,1743428095,191,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2569 feature_id 23 batch_size 371 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 48,2569,371,str\(\'KL\'\),1,str\(\'tstat\'\),0.001,48,23,0.12,179,0,None,i7168,179,688.41015625,688.372265625,-1,0.1941682242990654,3898986
1743427896,1743428098,202,module load GCCcore/10.3.0 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 23 batch_size 1338 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 11,1,1338,str\(\'H\'\),1,str\(\'tstat\'\),0.999,11,23,0.13,189,0,None,i7172,189,687.88671875,687.856212797619,-1,0.40014953271028036,3898978
1743427905,1743428102,197,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1083 feature_id 23 batch_size 331 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 16,1083,331,str\(\'KL\'\),1,str\(\'tstat\'\),0.001,16,23,0.12,184,0,None,i7166,184,689.40234375,689.3712797619048,-1,0.19798130841121495,3898989
1743427944,1743428109,165,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1380 feature_id 23 batch_size 1839 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 23,1380,1839,str\(\'H\'\),3,str\(\'tstat\'\),0.001,23,23,0.12,151,0,None,i7180,151,688.6953125,688.0705295138889,-1,0.06675389408099688,3899016
1743427926,1743428110,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3377 feature_id 23 batch_size 1686 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 100,3377,1686,str\(\'KL\'\),3,str\(\'stdev\'\),0.001,100,23,0.12,174,0,None,i7171,174,687.77734375,687.7421875,-1,0.26922118380062304,3899010
1743427924,1743428121,197,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 328 feature_id 23 batch_size 168 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 81,328,168,str\(\'H\'\),3,str\(\'tstat\'\),0.999,81,23,0.12,181,0,None,i7179,181,687.6015625,685.8928571428571,-1,0.2805233644859813,3899001
1743427864,1743428125,261,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 1608 feature_id 23 batch_size 355 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 59,1608,355,str\(\'H\'\),1,str\(\'stdev\'\),0.999,59,23,0.12,247,0,None,i7176,247,688.4453125,688.4230324074074,-1,0.3245607476635514,3898947
1743427924,1743428128,204,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 1 feature_id 23 batch_size 367 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 73,1,367,str\(\'KL\'\),3,str\(\'stdev\'\),0.001,73,23,0.13,187,0,None,i7185,187,688.16015625,688.1311383928571,-1,0.28606853582554514,3898992
1743427864,1743428130,266,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1961 feature_id 23 batch_size 364 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 56,1961,364,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,56,23,0.13,251,0,None,i7180,251,687.390625,686.9013671875,-1,0.3969221183800623,3898942
1743427944,1743428134,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 675 feature_id 23 batch_size 587 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 68,675,587,str\(\'H\'\),3,str\(\'tstat\'\),0.999,68,23,0.12,180,0,None,i7167,180,689.25390625,689.2171875,-1,0.2706417445482866,3899029
1743427925,1743428135,210,module load GCCcore/10.3.0 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 23 batch_size 1684 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 55,1,1684,str\(\'H\'\),2,str\(\'stdev\'\),0.999,55,23,0.12,194,0,None,i7183,194,687.921875,687.8840553977273,-1,0.24788785046728973,3898995
1743427944,1743428140,196,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 181 feature_id 23 batch_size 1312 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 13,181,1312,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,13,23,0.13,184,0,None,i7171,184,688.56640625,688.5438988095239,-1,0.35111526479750776,3899020
1743427945,1743428142,197,module load GCCcore/10.3.0 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 23 batch_size 925 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 1,1,925,str\(\'H\'\),3,str\(\'tstat\'\),0.999,1,23,0.12,182,0,None,i7185,182,689.1484375,689.110119047619,-1,0.28630529595015575,3899014
1743427924,1743428147,223,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1432 feature_id 23 batch_size 544 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 26,1432,544,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,26,23,0.12,208,0,None,i7181,208,688.34375,688.3269361413044,-1,0.4135077881619938,3898998
1743427926,1743428149,223,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 616 feature_id 23 batch_size 426 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 38,616,426,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,38,23,0.12,210,0,None,i7175,210,687.78515625,685.9568614130435,-1,0.3822056074766355,3899005
1743427945,1743428154,209,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 318 feature_id 23 batch_size 797 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 26,318,797,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,26,23,0.12,192,0,None,i7164,192,686.25390625,686.2279829545455,-1,0.40214330218068534,3899038
1743427926,1743428160,234,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 1 feature_id 23 batch_size 376 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 51,1,376,str\(\'H\'\),1,str\(\'stdev\'\),0.001,51,23,0.13,224,0,None,i7172,224,687.6796875,687.658125,-1,0.3795140186915888,3899008
1743427944,1743428160,216,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 308 feature_id 23 batch_size 1338 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 12,308,1338,str\(\'H\'\),1,str\(\'stdev\'\),0.001,12,23,0.13,197,0,None,i7164,197,687.52734375,687.5008877840909,-1,0.40014953271028036,3899035
1743427896,1743428168,272,module load GCCcore/10.3.0 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 23 batch_size 54 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 1,1,54,str\(\'KL\'\),1,str\(\'tstat\'\),0.001,1,23,0.12,259,0,None,i7178,259,687.40625,687.3804408482143,-1,0.8909158878504673,3898973
1743427944,1743428173,229,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2567 feature_id 23 batch_size 305 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 25,2567,305,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,25,23,0.13,218,0,None,i7168,218,689.40234375,689.373046875,-1,0.3779563862928349,3899026
1743427944,1743428198,254,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 4737 feature_id 23 batch_size 2892 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 100,4737,2892,str\(\'H\'\),1,str\(\'stdev\'\),0.001,100,23,0.12,243,0,None,i7166,243,688.5390625,688.5205439814815,-1,0.2882990654205608,3899032
1743427944,1743428222,278,module load GCCcore/10.3.0 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 23 batch_size 3860 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 1,5000,3860,str\(\'KL\'\),1,str\(\'tstat\'\),0.999,1,23,0.13,265,0,None,i7169,265,687.99609375,687.9808728448276,-1,0.9370841121495327,3899023
1743433849,1743434020,171,module load GCCcore/10.3.0 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 23 batch_size 505 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 29,472,505,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,29,23,0.13,159,0,None,i7164,159,688.859375,675.4754774305555,-1,0.1573208722741433,3905957
1743433848,1743434024,176,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3735 feature_id 23 batch_size 1770 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 26,3735,1770,str\(\'H\'\),1,str\(\'stdev\'\),0.999,26,23,0.12,163,0,None,i7159,163,691.37109375,690.0797697368421,-1,0.17644859813084113,3905961
1743433867,1743434052,185,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1028 feature_id 23 batch_size 1550 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 79,1028,1550,str\(\'H\'\),3,str\(\'tstat\'\),0.999,79,23,0.13,171,0,None,i7173,171,689.16015625,689.1154296875,-1,0.22618068535825545,3905971
1743433867,1743434058,191,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2615 feature_id 23 batch_size 724 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 86,2615,724,str\(\'H\'\),3,str\(\'stdev\'\),0.001,86,23,0.13,180,0,None,i7179,180,687.703125,687.6759672619048,-1,0.2886978193146417,3905966
1743433888,1743434060,172,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 348 feature_id 23 batch_size 867 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 61,348,867,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,61,23,0.12,161,0,None,i7173,161,687.65625,687.6079358552631,-1,0.17285981308411216,3905987
1743433888,1743434072,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1913 feature_id 23 batch_size 4636 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 100,1913,4636,str\(\'H\'\),3,str\(\'tstat\'\),0.999,100,23,0.12,174,0,None,i7156,174,687.43359375,687.117578125,-1,0.2888473520249221,3905993
1743433869,1743434073,204,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 1078 feature_id 23 batch_size 1777 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 22,1078,1777,str\(\'KL\'\),2,str\(\'tstat\'\),0.999,22,23,0.12,189,0,None,i7162,189,688.75390625,688.7087053571429,-1,0.3072398753894081,3905976
1743433869,1743434073,204,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 3973 feature_id 23 batch_size 3786 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 70,3973,3786,str\(\'H\'\),1,str\(\'tstat\'\),0.999,70,23,0.13,191,0,None,i7154,191,689.3515625,688.9389204545455,-1,0.2830654205607477,3905981
1743433908,1743434085,177,module load GCCcore/10.3.0 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 23 batch_size 4369 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 55,5000,4369,str\(\'KL\'\),1,str\(\'tstat\'\),0.999,55,23,0.13,167,0,None,i7159,167,687.8203125,687.7806332236842,-1,0.21776947040498443,3906008
1743433898,1743434089,191,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 77 feature_id 23 batch_size 496 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 47,77,496,str\(\'H\'\),3,str\(\'tstat\'\),0.999,47,23,0.12,181,0,None,i7156,181,686.078125,685.680431547619,-1,0.26576947040498444,3906000
1743433927,1743434092,165,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 692 feature_id 23 batch_size 928 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 46,692,928,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,46,23,0.12,156,0,None,i7147,156,688.81640625,688.7680121527778,-1,0.13876635514018693,3906024
1743433929,1743434094,165,module load GCCcore/10.3.0 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 23 batch_size 1867 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 18,1,1867,str\(\'KL\'\),3,str\(\'stdev\'\),0.999,18,23,0.12,155,0,None,i7146,155,688.0546875,687.8684895833334,-1,0.13794392523364485,3906031
1743433930,1743434108,178,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 272 feature_id 23 batch_size 1135 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 77,272,1135,str\(\'H\'\),1,str\(\'stdev\'\),0.999,77,23,0.11,165,0,None,i7145,165,687.80078125,687.5349506578947,-1,0.056573208722741436,3906036
1743433949,1743434115,166,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 721 feature_id 23 batch_size 1865 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 22,721,1865,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,22,23,0.12,154,0,None,i7128,154,687.453125,686.6833767361111,-1,0.1394392523364486,3906100
1743433950,1743434115,165,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4741 feature_id 23 batch_size 2097 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 24,4741,2097,str\(\'H\'\),1,str\(\'stdev\'\),0.999,24,23,0.13,155,0,None,i7161,155,689.37890625,689.3446180555555,-1,0.13065420560747665,3906043
1743433950,1743434121,171,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4617 feature_id 23 batch_size 2009 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 15,4617,2009,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,15,23,0.12,158,0,None,i7122,158,688.48828125,674.443359375,-1,0.10013707165109034,3906122
1743433949,1743434126,177,module load GCCcore/10.3.0 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 23 batch_size 2304 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 100,5000,2304,str\(\'H\'\),1,str\(\'stdev\'\),0.999,100,23,0.12,165,0,None,i7146,165,688.69921875,688.3081825657895,-1,0.05742056074766355,3906049
1743433949,1743434134,185,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4312 feature_id 23 batch_size 2227 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 74,4312,2227,str\(\'KL\'\),3,str\(\'stdev\'\),0.001,74,23,0.12,170,0,None,i7136,170,688.67578125,688.639802631579,-1,0.2497570093457944,3906086
1743433949,1743434134,185,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1207 feature_id 23 batch_size 423 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 18,1207,423,str\(\'KL\'\),2,str\(\'tstat\'\),0.999,18,23,0.13,174,0,None,i7127,174,687.66015625,687.3619140625,-1,0.2846355140186916,3906105
1743433950,1743434134,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 5 feature_id 23 batch_size 1694 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 27,5,1694,str\(\'H\'\),2,str\(\'stdev\'\),0.999,27,23,0.12,172,0,None,i7143,172,688.9453125,688.8966796875,-1,0.24950778816199376,3906055
1743433949,1743434140,191,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1444 feature_id 23 batch_size 724 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 84,1444,724,str\(\'KL\'\),3,str\(\'stdev\'\),0.001,84,23,0.13,175,0,None,i7138,175,687.94140625,687.908984375,-1,0.27967601246105916,3906076
1743433949,1743434140,191,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 223 feature_id 23 batch_size 750 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 50,223,750,str\(\'H\'\),3,str\(\'tstat\'\),0.999,50,23,0.13,179,0,None,i7137,179,689.16796875,689.137890625,-1,0.2928473520249221,3906080
1743433950,1743434141,191,module load GCCcore/10.3.0 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 23 batch_size 587 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 33,1,587,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,33,23,0.12,175,0,None,i7139,175,688.375,688.344140625,-1,0.27795638629283487,3906069
1743433950,1743434141,191,module load GCCcore/10.3.0 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 23 batch_size 4782 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 46,5000,4782,str\(\'KL\'\),1,str\(\'tstat\'\),0.999,46,23,0.12,174,0,None,i7141,174,687.8828125,686.943359375,-1,0.2383551401869159,3906061
1743433950,1743434141,191,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2530 feature_id 23 batch_size 1450 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 67,2530,1450,str\(\'H\'\),3,str\(\'stdev\'\),0.001,67,23,0.12,175,0,None,i7135,175,688.85546875,688.825390625,-1,0.2822554517133956,3906090
1743433949,1743434147,198,module load GCCcore/10.3.0 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 23 batch_size 4658 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 19,5000,4658,str\(\'H\'\),2,str\(\'tstat\'\),0.999,19,23,0.12,181,0,None,i7124,181,687.25390625,687.1623883928571,-1,0.34826168224299064,3906116
1743433950,1743434172,222,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 57 feature_id 23 batch_size 4598 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 76,57,4598,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,76,23,0.12,207,0,None,i7131,207,687.7265625,687.6847826086956,-1,0.22918380062305296,3906095
1743433908,1743434180,272,module load GCCcore/10.3.0 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 23 batch_size 2425 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 1,5000,2425,str\(\'H\'\),1,str\(\'tstat\'\),0.999,1,23,0.13,263,0,None,i7147,263,688.59375,688.5765086206897,-1,0.9722866043613707,3906017
1743433950,1743434185,235,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 638 feature_id 23 batch_size 1274 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 4,638,1274,str\(\'H\'\),1,str\(\'stdev\'\),0.999,4,23,0.12,223,0,None,i7121,223,688.859375,673.639375,-1,0.673607476635514,3906127
1743433950,1743434229,279,module load GCCcore/10.3.0 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 23 batch_size 2990 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 1,5000,2990,str\(\'KL\'\),1,str\(\'tstat\'\),0.999,1,23,0.13,265,0,None,i7125,265,689.1796875,689.1643318965517,-1,0.9438130841121495,3906110
1743438654,1743438667,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2754 feature_id 23 batch_size 1 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.25629605818009055 subsets 1,2754,1,str\(\'H\'\),1,str\(\'stdev\'\),0.25629605818009055,1,23,None,None,1,None,i7124,3910950
1743438521,1743438717,196,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 573 feature_id 23 batch_size 1327 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 5,573,1327,str\(\'H\'\),2,str\(\'tstat\'\),0.999,5,23,0.12,182,0,None,i7120,182,688.0390625,687.9920014880952,-1,0.32234267912772585,3910837
1743438531,1743438728,197,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4490 feature_id 23 batch_size 1754 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 31,4490,1754,str\(\'H\'\),1,str\(\'stdev\'\),0.001,31,23,0.12,183,0,None,i7120,183,687.671875,687.6462053571429,-1,0.34970716510903427,3910846
1743438521,1743438730,209,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 619 feature_id 23 batch_size 623 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.9758042003844143 subsets 60,619,623,str\(\'KL\'\),2,str\(\'stdev\'\),0.9758042003844143,60,23,0.12,195,0,None,i7121,195,687.49609375,687.4534801136364,-1,0.24066043613707166,3910834
1743438551,1743438736,185,module load GCCcore/10.3.0 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 23 batch_size 4008 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 100,5000,4008,str\(\'KL\'\),1,str\(\'tstat\'\),0.001,100,23,0.12,170,0,None,i7135,170,689.3203125,688.8856907894736,-1,0.1498317757009346,3910862
1743438531,1743438741,210,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 225 feature_id 23 batch_size 376 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.5468824853858335 subsets 37,225,376,str\(\'KL\'\),1,str\(\'stdev\'\),0.5468824853858335,37,23,0.13,194,0,None,i7120,194,688.46875,688.4444247159091,-1,0.3514018691588785,3910841
1743438581,1743438746,165,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4768 feature_id 23 batch_size 4295 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.3225888546783184 subsets 52,4768,4295,str\(\'KL\'\),1,str\(\'tstat\'\),0.3225888546783184,52,23,0.12,154,0,None,i7185,154,689.796875,689.5067274305555,-1,0.1070404984423676,3910881
1743438551,1743438747,196,module load GCCcore/10.3.0 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 23 batch_size 3803 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.2772765777580191 subsets 60,3974,3803,str\(\'KL\'\),1,str\(\'tstat\'\),0.2772765777580191,60,23,0.12,181,0,None,i7159,181,688.98828125,688.9512648809524,-1,0.28433644859813084,3910855
1743438552,1743438750,198,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 94 feature_id 23 batch_size 920 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.403174740095409 subsets 18,94,920,str\(\'H\'\),3,str\(\'tstat\'\),0.403174740095409,18,23,0.12,183,0,None,i7180,183,688.5625,688.5288318452381,-1,0.2731588785046729,3910851
1743438592,1743438763,171,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4014 feature_id 23 batch_size 3717 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.09737779528180568 subsets 49,4014,3717,str\(\'H\'\),1,str\(\'tstat\'\),0.09737779528180568,49,23,0.13,159,0,None,i7122,159,688.89453125,688.5266927083334,-1,0.09263551401869159,3910899
1743438581,1743438765,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1554 feature_id 23 batch_size 712 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.37602801753963633 subsets 43,1554,712,str\(\'H\'\),3,str\(\'tstat\'\),0.37602801753963633,43,23,0.12,173,0,None,i7144,173,687.87890625,687.8392578125,-1,0.24842367601246107,3910888
1743438572,1743438769,197,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4743 feature_id 23 batch_size 636 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.270865868883937 subsets 1,4743,636,str\(\'H\'\),3,str\(\'tstat\'\),0.270865868883937,1,23,0.13,183,0,None,i7131,183,687.61328125,687.5716145833334,-1,0.2683239875389408,3910876
1743438581,1743438771,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 940 feature_id 23 batch_size 654 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.35628384977146216 subsets 100,940,654,str\(\'H\'\),3,str\(\'stdev\'\),0.35628384977146216,100,23,0.13,179,0,None,i7171,179,687.89453125,687.8654296875,-1,0.2847725856697819,3910885
1743438572,1743438781,209,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 243 feature_id 23 batch_size 637 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.6336682740326635 subsets 52,243,637,str\(\'KL\'\),2,str\(\'stdev\'\),0.6336682740326635,52,23,0.12,194,0,None,i7169,194,687.37109375,687.3343394886364,-1,0.23813084112149532,3910872
1743438591,1743438781,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4794 feature_id 23 batch_size 3760 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 41,4794,3760,str\(\'KL\'\),1,str\(\'tstat\'\),0.999,41,23,0.12,174,0,None,i7152,174,687.61328125,687.57734375,-1,0.2811214953271028,3910894
1743438611,1743438795,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3132 feature_id 23 batch_size 2927 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.9065628002077891 subsets 100,3132,2927,str\(\'H\'\),1,str\(\'stdev\'\),0.9065628002077891,100,23,0.12,172,0,None,i7175,172,688.9765625,688.661328125,-1,0.07294704049844236,3910904
1743438612,1743438796,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4699 feature_id 23 batch_size 4215 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.16485588917190874 subsets 27,4699,4215,str\(\'KL\'\),1,str\(\'tstat\'\),0.16485588917190874,27,23,0.12,169,0,None,i7136,169,688.875,688.8340871710526,-1,0.2626168224299065,3910915
1743438617,1743438800,183,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 2744 feature_id 23 batch_size 3862 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 35,2744,3862,str\(\'H\'\),1,str\(\'tstat\'\),0.999,35,23,0.12,172,0,None,i7121,172,688.66015625,688.625,-1,0.24062305295950157,3910920
1743438641,1743438813,172,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4950 feature_id 23 batch_size 3098 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.5641485494883932 subsets 29,4950,3098,str\(\'KL\'\),1,str\(\'tstat\'\),0.5641485494883932,29,23,0.12,155,0,None,i7138,155,689.16015625,688.8192274305555,-1,0.11581308411214954,3910940
1743438552,1743438818,266,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4874 feature_id 23 batch_size 2904 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 100,4874,2904,str\(\'H\'\),1,str\(\'tstat\'\),0.999,100,23,0.12,253,0,None,i7122,253,687.640625,687.611328125,-1,0.3256822429906542,3910866
1743438632,1743438823,191,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3609 feature_id 23 batch_size 2285 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.6955947859896409 subsets 24,3609,2285,str\(\'KL\'\),1,str\(\'tstat\'\),0.6955947859896409,24,23,0.12,175,0,None,i7157,175,688.75390625,688.2205078125,-1,0.2562616822429907,3910926
1743438611,1743438833,222,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 23 batch_size 1444 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 1,5000,1444,str\(\'H\'\),2,str\(\'tstat\'\),0.999,1,23,0.12,208,0,None,i7157,208,688.96484375,688.4775815217391,-1,0.48583177570093455,3910909
1743438651,1743438835,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1654 feature_id 23 batch_size 3725 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 29,1654,3725,str\(\'KL\'\),1,str\(\'tstat\'\),0.001,29,23,0.1,169,0,None,i7142,169,689.546875,687.3046875,-1,0,3910947
1743438552,1743438838,286,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4939 feature_id 23 batch_size 2693 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 1,4939,2693,str\(\'H\'\),1,str\(\'tstat\'\),0.001,1,23,0.12,267,0,None,i7140,267,688.3203125,688.2983566810345,-1,0.9482616822429907,3910859
1743438631,1743438848,217,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 383 feature_id 23 batch_size 668 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 60,383,668,str\(\'KL\'\),2,str\(\'tstat\'\),0.999,60,23,0.12,204,0,None,i7139,204,688.43359375,688.3931725543479,-1,0.28301557632398755,3910932
1743438641,1743438855,214,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 783 feature_id 23 batch_size 1248 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.999 subsets 1,783,1248,str\(\'H\'\),2,str\(\'stdev\'\),0.999,1,23,0.13,204,0,None,i7159,204,687.7890625,687.7571331521739,-1,0.4820934579439252,3910937
1743438670,1743438861,191,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4047 feature_id 23 batch_size 1680 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.442991579959727 subsets 1,4047,1680,str\(\'H\'\),3,str\(\'tstat\'\),0.442991579959727,1,23,0.12,175,0,None,i7146,175,688.234375,688.1998046875,-1,0.2681744548286604,3910961
1743438672,1743438862,190,module load GCCcore/10.3.0 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 23 batch_size 588 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.4559433576366979 subsets 28,1222,588,str\(\'KL\'\),2,str\(\'tstat\'\),0.4559433576366979,28,23,0.12,179,0,None,i7122,179,688.4765625,688.4275390625,-1,0.2637757009345794,3910968
1743438671,1743438957,286,module load GCCcore/10.3.0 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 23 batch_size 2999 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 1,5000,2999,str\(\'KL\'\),1,str\(\'tstat\'\),0.001,1,23,0.13,272,0,None,i7176,272,687.546875,682.4365885416667,-1,0.9467289719626168,3910955
1743438692,1743439017,325,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 5000 feature_id 23 batch_size 4119 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 1,5000,4119,str\(\'KL\'\),1,str\(\'tstat\'\),0.001,1,23,0.12,312,0,None,i7183,312,688.97265625,688.9545036764706,-1,0.9503052959501558,3910976
1743446194,1743446366,172,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2095 feature_id 23 batch_size 2015 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 31,2095,2015,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,31,23,0.12,161,0,None,i7181,161,688.24609375,688.2138157894736,-1,0.12554517133956386,3918274
1743446214,1743446378,164,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4207 feature_id 23 batch_size 2569 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 27,4207,2569,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,27,23,0.12,150,0,None,i7158,150,689.01953125,688.6901041666666,-1,0.09603738317757009,3918297
1743446203,1743446386,183,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 737 feature_id 23 batch_size 1281 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 90,737,1281,str\(\'H\'\),3,str\(\'stdev\'\),0.001,90,23,0.12,173,0,None,i7161,173,687.8828125,687.8517578125,-1,0.26241744548286605,3918293
1743446194,1743446391,197,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1106 feature_id 23 batch_size 2116 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 76,1106,2116,str\(\'H\'\),3,str\(\'tstat\'\),0.999,76,23,0.12,186,0,None,i7170,186,688.81640625,688.7801339285714,-1,0.316411214953271,3918277
1743446203,1743446399,196,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 871 feature_id 23 batch_size 234 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.001 subsets 4,871,234,str\(\'KL\'\),2,str\(\'stdev\'\),0.001,4,23,0.12,187,0,None,i7179,187,687.30078125,687.262462797619,-1,0.3294953271028037,3918285
1743446203,1743446412,209,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 5000 feature_id 23 batch_size 4342 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 100,5000,4342,str\(\'H\'\),1,str\(\'tstat\'\),0.999,100,23,0.13,200,0,None,i7171,200,688.125,688.0857599431819,-1,0.21642367601246107,3918289
1743446193,1743446414,221,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 23 batch_size 3103 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 100,5000,3103,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,100,23,0.13,207,0,None,i7161,207,688.39453125,688.3609035326087,-1,0.232,3918281
1743446233,1743446417,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 23 feature_id 23 batch_size 1904 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 82,23,1904,str\(\'H\'\),3,str\(\'stdev\'\),0.999,82,23,0.12,172,0,None,i7162,172,688.5,688.4642578125,-1,0.16489719626168226,3918313
1743446255,1743446427,172,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4815 feature_id 23 batch_size 2970 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 27,4815,2970,str\(\'H\'\),1,str\(\'tstat\'\),0.999,27,23,0.12,159,0,None,i7127,159,688.11328125,688.0748697916666,-1,0.18504672897196262,3918384
1743446233,1743446430,197,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 720 feature_id 23 batch_size 2247 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 76,720,2247,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,76,23,0.12,184,0,None,i7167,184,687.37890625,687.3372395833334,-1,0.196,3918310
1743446233,1743446430,197,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2753 feature_id 23 batch_size 2494 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 30,2753,2494,str\(\'H\'\),1,str\(\'tstat\'\),0.999,30,23,0.12,183,0,None,i7178,183,688.73046875,688.6824776785714,-1,0.27970093457943923,3918307
1743446255,1743446433,178,module load GCCcore/10.3.0 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 23 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 29,5000,5000,str\(\'H\'\),3,str\(\'tstat\'\),0.999,29,23,0.12,160,0,None,i7128,160,687.515625,687.4671052631579,-1,0.24922118380062305,3918375
1743446255,1743446438,183,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 1059 feature_id 23 batch_size 388 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 15,1059,388,str\(\'KL\'\),3,str\(\'tstat\'\),0.001,15,23,0.12,168,0,None,i7131,168,688.3359375,688.2935855263158,-1,0.1885607476635514,3918364
1743446255,1743446439,184,module load GCCcore/10.3.0 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 23 batch_size 4478 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 18,5000,4478,str\(\'KL\'\),1,str\(\'tstat\'\),0.999,18,23,0.13,167,0,None,i7134,167,688.01953125,687.9833470394736,-1,0.223202492211838,3918356
1743446255,1743446439,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3872 feature_id 23 batch_size 2455 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 67,3872,2455,str\(\'KL\'\),3,str\(\'tstat\'\),0.999,67,23,0.11,162,0,None,i7126,162,687.98046875,687.9340049342105,-1,0.21414330218068536,3918395
1743446255,1743446439,184,module load GCCcore/10.3.0 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 23 batch_size 1951 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.999 subsets 1,986,1951,str\(\'H\'\),3,str\(\'stdev\'\),0.999,1,23,0.12,163,0,None,i7125,163,686.91015625,686.8762335526316,-1,0.2425171339563863,3918410
1743446255,1743446451,196,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4016 feature_id 23 batch_size 829 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 8,4016,829,str\(\'KL\'\),3,str\(\'stdev\'\),0.001,8,23,0.12,173,0,None,i7126,173,688.20703125,688.17578125,-1,0.2849968847352025,3918390
1743446255,1743446451,196,module load GCCcore/10.3.0 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 23 batch_size 1644 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.001 subsets 88,5000,1644,str\(\'KL\'\),3,str\(\'stdev\'\),0.001,88,23,0.12,171,0,None,i7125,171,688.28125,688.251171875,-1,0.28238006230529594,3918407
1743446255,1743446452,197,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4511 feature_id 23 batch_size 4324 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 51,4511,4324,str\(\'H\'\),2,str\(\'tstat\'\),0.999,51,23,0.12,185,0,None,i7137,185,689.171875,688.1899181547619,-1,0.21552647975077882,3918344
1743446174,1743446452,278,module load GCCcore/10.3.0 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 23 batch_size 3116 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 100,5000,3116,str\(\'H\'\),1,str\(\'tstat\'\),0.999,100,23,0.13,267,0,None,i7144,267,688.35546875,688.3267780172414,-1,0.4271152647975078,3918271
1743446255,1743446457,202,module load GCCcore/10.3.0 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 23 batch_size 2609 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 83,5000,2609,str\(\'H\'\),3,str\(\'tstat\'\),0.999,83,23,0.13,191,0,None,i7167,191,687.3125,687.2709517045455,-1,0.2925981308411215,3918328
1743446260,1743446458,198,module load GCCcore/10.3.0 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 23 batch_size 2104 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.001 subsets 50,5000,2104,str\(\'KL\'\),1,str\(\'stdev\'\),0.001,50,23,0.12,187,0,None,i7135,187,689.11328125,688.4892113095239,-1,0.28970716510903427,3918349
1743446256,1743446458,202,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4402 feature_id 23 batch_size 1973 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 16,4402,1973,str\(\'H\'\),1,str\(\'tstat\'\),0.999,16,23,0.12,191,0,None,i7129,191,688.11328125,686.0729758522727,-1,0.36878504672897194,3918371
1743446255,1743446464,209,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 5000 feature_id 23 batch_size 4452 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 28,5000,4452,str\(\'H\'\),1,str\(\'tstat\'\),0.999,28,23,0.12,186,0,None,i7125,186,688.8515625,688.821056547619,-1,0.3883364485981308,3918402
1743446262,1743446470,208,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4381 feature_id 23 batch_size 4855 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.999 subsets 59,4381,4855,str\(\'H\'\),3,str\(\'tstat\'\),0.999,59,23,0.12,159,0,None,i7124,159,688.98828125,688.9364149305555,-1,0.18149532710280375,3918424
1743446262,1743446495,233,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 5000 feature_id 23 batch_size 4704 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 72,5000,4704,str\(\'H\'\),2,str\(\'tstat\'\),0.999,72,23,0.13,186,0,None,i7124,186,689.09375,689.0355282738095,-1,0.35170093457943924,3918420
1743446233,1743446511,278,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 190 feature_id 23 batch_size 2102 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.999 subsets 1,190,2102,str\(\'H\'\),1,str\(\'tstat\'\),0.999,1,23,0.13,270,0,None,i7153,270,688.328125,688.3122306034483,-1,0.9704299065420561,3918316
1743446262,1743446514,252,module load GCCcore/10.3.0 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 23 batch_size 3117 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 91,5000,3117,str\(\'H\'\),1,str\(\'stdev\'\),0.999,91,23,0.12,206,0,None,i7124,206,687.328125,687.2982336956521,-1,0.31072897196261684,3918414
1743446255,1743446515,260,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 5000 feature_id 23 batch_size 3053 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 100,5000,3053,str\(\'KL\'\),2,str\(\'tstat\'\),0.999,100,23,0.12,248,0,None,i7141,248,689.40625,689.3606770833334,-1,0.342392523364486,3918335
1743446234,1743446518,284,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 1 feature_id 23 batch_size 167 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 1,1,167,str\(\'H\'\),1,str\(\'stdev\'\),0.999,1,23,0.13,269,0,None,i7143,269,687.4921875,685.0794719827586,-1,0.9573208722741433,3918321
1743452617,1743452630,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 98 feature_id 23 batch_size 1 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 41,98,1,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,41,23,None,None,1,None,i7171,3929910
1743452638,1743452651,13,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3059 feature_id 23 batch_size 1 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.17829409835588356 subsets 22,3059,1,str\(\'H\'\),1,str\(\'stdev\'\),0.17829409835588356,22,23,None,None,1,None,i7184,3929930
1743452578,1743452762,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 449 feature_id 23 batch_size 1325 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 25,449,1325,str\(\'KL\'\),1,str\(\'tstat\'\),0.001,25,23,0.12,173,0,None,i7178,173,687.8359375,687.79609375,-1,0.12368847352024923,3929885
1743452595,1743452780,185,module load GCCcore/10.3.0 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 23 batch_size 5000 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 100,5000,5000,str\(\'H\'\),3,str\(\'tstat\'\),0.001,100,23,0.12,171,0,None,i7180,171,688.45703125,688.4013671875,-1,0.24922118380062305,3929895
1743452597,1743452780,183,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 4469 feature_id 23 batch_size 1324 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'stdev\'\) significance 0.7288042327335007 subsets 100,4469,1324,str\(\'KL\'\),3,str\(\'stdev\'\),0.7288042327335007,100,23,0.12,174,0,None,i7173,174,688.2421875,688.208984375,-1,0.23906542056074767,3929905
1743452595,1743452785,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3815 feature_id 23 batch_size 609 divergence str\(\'KL\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.43530522759585716 subsets 60,3815,609,str\(\'KL\'\),3,str\(\'tstat\'\),0.43530522759585716,60,23,0.12,177,0,None,i7174,177,687.625,687.5892578125,-1,0.2504299065420561,3929901
1743452617,1743452800,183,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 4785 feature_id 23 batch_size 4219 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 85,4785,4219,str\(\'H\'\),1,str\(\'tstat\'\),0.001,85,23,0.11,170,0,None,i7171,170,689.4921875,689.2802220394736,-1,0.1577196261682243,3929914
1743452617,1743452807,190,module load GCCcore/10.3.0 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 23 batch_size 1267 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.375769138196921 subsets 100,1,1267,str\(\'H\'\),3,str\(\'tstat\'\),0.375769138196921,100,23,0.12,179,0,None,i7171,179,687.359375,687.3158203125,-1,0.1961246105919003,3929919
1743452565,1743452808,243,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 831 feature_id 23 batch_size 1302 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.6941005878506542 subsets 100,831,1302,str\(\'KL\'\),1,str\(\'stdev\'\),0.6941005878506542,100,23,0.13,230,0,None,i7181,230,688.7890625,688.5471754807693,-1,0.1535576323987539,3929884
1743452578,1743452814,236,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 855 feature_id 23 batch_size 630 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.685096351512105 subsets 1,855,630,str\(\'KL\'\),2,str\(\'stdev\'\),0.685096351512105,1,23,0.12,224,0,None,i7175,224,687.640625,687.6115625,-1,0.48672897196261683,3929890
1743452597,1743452818,221,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 769 feature_id 23 batch_size 740 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 1,769,740,str\(\'KL\'\),2,str\(\'tstat\'\),0.999,1,23,0.12,207,0,None,i7173,207,689.20703125,689.1642323369565,-1,0.4822554517133956,3929903
1743452617,1743452819,202,module load GCCcore/10.3.0 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 23 batch_size 4608 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.001 subsets 100,5000,4608,str\(\'H\'\),2,str\(\'tstat\'\),0.001,100,23,0.12,188,0,None,i7171,188,688.19921875,687.932849702381,-1,0.1148411214953271,3929917
1743452638,1743452822,184,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4664 feature_id 23 batch_size 4348 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.2559408705831887 subsets 100,4664,4348,str\(\'H\'\),3,str\(\'tstat\'\),0.2559408705831887,100,23,0.12,169,0,None,i7174,169,687.64453125,687.4183799342105,-1,0.16254205607476635,3929934
1743452637,1743452827,190,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3506 feature_id 23 batch_size 684 divergence str\(\'H\'\) detect_batch 3 statistic str\(\'tstat\'\) significance 0.001 subsets 44,3506,684,str\(\'H\'\),3,str\(\'tstat\'\),0.001,44,23,0.12,175,0,None,i7171,175,687.98046875,687.9447265625,-1,0.23013084112149532,3929937
1743452578,1743452827,249,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 894 feature_id 23 batch_size 1368 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.5292821718175628 subsets 1,894,1368,str\(\'H\'\),2,str\(\'stdev\'\),0.5292821718175628,1,23,0.12,232,0,None,i7175,232,688.6171875,688.5836838942307,-1,0.47730841121495327,3929892
1743452578,1743452830,252,module load GCCcore/10.3.0 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 23 batch_size 4124 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.001 subsets 100,5000,4124,str\(\'H\'\),1,str\(\'tstat\'\),0.001,100,23,0.13,234,0,None,i7176,234,688.796875,688.7600661057693,-1,0.15416822429906543,3929889
1743452597,1743452837,240,module load GCCcore/10.3.0 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 23 batch_size 4282 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 1,5000,4282,str\(\'KL\'\),2,str\(\'tstat\'\),0.999,1,23,0.12,228,0,None,i7171,228,688.8203125,688.7753125,-1,0.4553146417445483,3929908
1743452595,1743452844,249,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 5000 feature_id 23 batch_size 2526 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 1,5000,2526,str\(\'H\'\),2,str\(\'tstat\'\),0.999,1,23,0.12,237,0,None,i7177,237,689.2890625,689.2438401442307,-1,0.4787165109034268,3929897
1743452578,1743452851,273,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 3714 feature_id 23 batch_size 69 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.999 subsets 44,3714,69,str\(\'KL\'\),1,str\(\'stdev\'\),0.999,44,23,0.13,259,0,None,i7177,259,689.3515625,689.3327287946429,-1,0.20205607476635515,3929888
1743452657,1743452854,197,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 1488 feature_id 23 batch_size 182 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.32000331927694614 subsets 8,1488,182,str\(\'H\'\),1,str\(\'tstat\'\),0.32000331927694614,8,23,0.13,187,0,None,i7169,187,688.515625,688.4866071428571,-1,0.32657943925233646,3929956
1743452595,1743452864,269,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 5000 feature_id 23 batch_size 3275 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.24811758307180393 subsets 100,5000,3275,str\(\'H\'\),2,str\(\'tstat\'\),0.24811758307180393,100,23,0.12,257,0,None,i7184,257,688.1796875,688.1298828125,-1,0.2856697819314642,3929894
1743452656,1743452872,216,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 5000 feature_id 23 batch_size 4824 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.16758351518135273 subsets 63,5000,4824,str\(\'H\'\),1,str\(\'tstat\'\),0.16758351518135273,63,23,0.12,204,0,None,i7170,204,688.6953125,688.5112092391304,-1,0.12022429906542056,3929947
1743452677,1743452881,204,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 5000 feature_id 23 batch_size 3536 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.32948403446944025 subsets 100,5000,3536,str\(\'KL\'\),1,str\(\'tstat\'\),0.32948403446944025,100,23,0.12,193,0,None,i7168,193,687.8046875,687.7658025568181,-1,0.13218691588785048,3929968
1743452595,1743452899,304,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 237 feature_id 23 batch_size 681 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.3219896226153721 subsets 1,237,681,str\(\'KL\'\),1,str\(\'tstat\'\),0.3219896226153721,1,23,0.13,292,0,None,i7177,292,688.359375,688.3349609375,-1,0.9679501557632398,3929896
1743452625,1743452903,278,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 4400 feature_id 23 batch_size 389 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.44428583865637933 subsets 1,4400,389,str\(\'H\'\),1,str\(\'stdev\'\),0.44428583865637933,1,23,0.13,269,0,None,i7182,269,687.63671875,687.6194773706897,-1,0.9736448598130841,3929925
1743452594,1743452905,311,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2028 feature_id 23 batch_size 1297 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.5030631586111916 subsets 1,2028,1297,str\(\'KL\'\),1,str\(\'stdev\'\),0.5030631586111916,1,23,0.13,299,0,None,i7174,299,687.86328125,687.849609375,-1,0.9609719626168224,3929899
1743452578,1743452917,339,module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 3778 feature_id 23 batch_size 87 divergence str\(\'H\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.3481739092612534 subsets 1,3778,87,str\(\'H\'\),1,str\(\'stdev\'\),0.3481739092612534,1,23,0.13,324,0,None,i7178,324,688.56640625,688.5483258928572,-1,0.9204361370716511,3929887
1743452677,1743452927,250,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 5000 feature_id 23 batch_size 4892 divergence str\(\'KL\'\) detect_batch 2 statistic str\(\'tstat\'\) significance 0.999 subsets 1,5000,4892,str\(\'KL\'\),2,str\(\'tstat\'\),0.999,1,23,0.12,239,0,None,i7184,239,688.36328125,688.3233173076923,-1,0.46276635514018694,3929962
1743452657,1743452955,298,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 4367 feature_id 23 batch_size 1533 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'tstat\'\) significance 0.5240290657756284 subsets 1,4367,1533,str\(\'KL\'\),1,str\(\'tstat\'\),0.5240290657756284,1,23,0.13,285,0,None,i7169,285,687.58984375,687.5675403225806,-1,0.9684361370716511,3929951
1743452685,1743452972,287,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 91 feature_id 23 batch_size 1256 divergence str\(\'KL\'\) detect_batch 1 statistic str\(\'stdev\'\) significance 0.6401985812468983 subsets 84,91,1256,str\(\'KL\'\),1,str\(\'stdev\'\),0.6401985812468983,84,23,0.12,272,0,None,i7168,272,687.609375,687.5884114583333,-1,0.3599750778816199,3929977

Key	Value
config_yaml	None
config_toml	None
config_json	None
num_random_steps	20
max_eval	500
run_program	[['bW9kdWxlIGxvYWQgR0NDY29yZS8xMC4zLjAgUHl0aG9uICYmIHNvdXJjZSAvZGF0YS9ob3JzZS93cy9zNDEyMjQ4NS1jb21wUGVyZkREL2JlbmNobWFyay92ZW52L2Jpbi9hY3RpdmF0ZSAmJiBw…
experiment_name	CDBD_RialtoBridgeTimelapse_ft_23_HoeffdingTreeClassifier_ACCURACY-RUNTIME
mem_gb	32
parameter	[['recent_samples_size', 'range', '1', '5000', 'int'], ['feature_id', 'fixed', '23'], ['batch_size', 'range', '1', '5000', 'int'], ['divergence',
	'choice', "str\\(\\'KL\\'\\),str\\(\\'H\\'\\)"], ['detect_batch', 'choice', '1,2,3'], ['statistic', 'choice',
	"str\\(\\'tstat\\'\\),str\\(\\'stdev\\'\\)"], ['significance', 'range', '0.001', '0.999', 'float'], ['subsets', 'range', '1', '100', 'int']]
continue_previous_job	None
maximize	False
experiment_constraints	None
stderr_to_stdout	False
run_dir	runs
seed	None
decimalrounding	12
enforce_sequential_optimization	False
verbose_tqdm	False
model	BOTORCH_MODULAR
gridsearch	False
occ	False
show_sixel_scatter	False
show_sixel_general	False
show_sixel_trial_index_result	False
follow	False
send_anonymized_usage_stats	True
ui_url	None
root_venv_dir	/home/s4122485
exclude	None
main_process_gb	8
pareto_front_confidence	1
max_nr_of_zero_results	50
disable_search_space_exhaustion_detection	False
abbreviate_job_names	False
orchestrator_file	None
checkout_to_latest_tested_version	False
live_share	False
disable_tqdm	False
workdir	False
max_parallelism	max_eval_times_thousand_plus_thousand
occ_type	euclid
result_names	['ACCURACY=max', 'RUNTIME=min']
minkowski_p	2
signed_weighted_euclidean_weights
generation_strategy	None
generate_all_jobs_at_once	True
revert_to_random_when_seemingly_exhausted	True
num_parallel_jobs	30
worker_timeout	60
slurm_use_srun	False
time	1440
partition	romeo
reservation	None
force_local_execution	False
slurm_signal_delay_s	0
nodes_per_job	1
cpus_per_task	1
account	None
gpus	0
run_mode	local
verbose	False
verbose_break_run_search_table	False
debug	False
no_sleep	False
tests	False
show_worker_percentage_table_at_end	False
auto_exclude_defective_hosts	False
run_tests_that_fail_on_taurus	False
raise_in_eval	False
show_ram_every_n_seconds	False

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submitit INFO (2025-03-31 02:42:12,840) - Starting with JobEnvironment(job_id=3837014, hostname=i7178, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-03-31 02:42:12,866) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_23_HoeffdingTreeClassifier_ACCURACY-RUNTIME/0/single_runs/3837014/3837014_submitted.pkl

Inaktive Module:
  1) PostgreSQL/14.4     4) X11/20220504          7) freetype/2.12.1
  2) Tk/8.6.12           5) expat/2.4.8           8) util-linux/2.38
  3) Tkinter/3.10.4      6) fontconfig/2.14.0

Wegen Änderungen an MODULEPATH wurden folgende Module erneut geladen:
  1) Brotli/1.0.9     3) XZ/5.2.5        5) libpciaccess/0.16     7) xorg-macros/1.19.3
  2) GMP/6.2.1        4) bzip2/1.0.8     6) libpng/1.6.37

Die folgenden Module wurden in einer anderen Version erneut geladen:
  1) GCCcore/11.3.0 => GCCcore/10.3.0     6) libffi/3.4.2 => libffi/3.3
  2) Python/3.10.4 => Python/3.9.5        7) libreadline/8.1.2 => libreadline/8.1
  3) SQLite/3.38.3 => SQLite/3.35.4       8) ncurses/6.3 => ncurses/6.2
  4) Tcl/8.6.12 => Tcl/8.6.11             9) zlib/1.2.12 => zlib/1.2.11
  5) binutils/2.38 => binutils/2.36.1

Modules GCCcore/11.3.0, zlib/1.2.12, xorg-macros/1.19.3, binutils/2.38, bzip2/1.0.8, ncurses/6.3, libreadline/8.1.2, Tcl/8.6.12, SQLite/3.38.3, XZ/5.2.5, GMP/6.2.1, libffi/3.4.2, Python/3.10.4, expat/2.4.8, libpng/1.6.37, Brotli/1.0.9, freetype/2.12.1, util-linux/2.38, fontconfig/2.14.0, libpciaccess/0.16, X11/20220504, Tk/8.6.12, Tkinter/3.10.4, PostgreSQL/14.4 and 5 dependencies unloaded.
Modules zlib/1.2.12, binutils/2.38, bzip2/1.0.8, ncurses/6.3, GCCcore/10.3.0, zlib/1.2.12, OpenSSL/1.1, xorg-macros/1.19.3, binutils/2.38, bzip2/1.0.8, ncurses/6.3, XZ/5.2.5, GMP/6.2.1, libpng/1.6.37, Brotli/1.0.9, libpciaccess/0.16, Python/3.9.5 and 7 dependencies loaded.

Parameters: {"recent_samples_size": 2465, "batch_size": 4178, "divergence": "str\\(\\'H\\'\\)", "detect_batch": 2, "statistic": "str\\(\\'stdev\\'\\)", "significance": 0.39224478071928026, "subsets": 87, "feature_id": 23}
Debug-Infos: 
========
DEBUG INFOS START:
Program-Code: module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2465 feature_id 23 batch_size 4178 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.39224478071928026 subsets 87
pwd: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd
File: /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate
UID: 2063769
GID: 200270
SLURM_JOB_ID: 3837014
Status-Change-Time: 1724762786.0
Size: 2165 Bytes
Permissions: -rw-r--r--
Owner: s4122485
Last access: 1743381736.0
Last modification: 1724759185.0
Hostname: i7178
pwd: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd
File: main.py
UID: 2063769
GID: 200270
SLURM_JOB_ID: 3837014
Status-Change-Time: 1743181377.0
Size: 3401 Bytes
Permissions: -rw-r--r--
Owner: s4122485
Last access: 1743381739.0
Last modification: 1743181377.0
Hostname: i7178
========
DEBUG INFOS END

module load GCCcore/10.3.0 Python && source /data/horse/ws/s4122485-compPerfDD/benchmark/venv/bin/activate && python main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CDBD recent_samples_size 2465 feature_id 23 batch_size 4178 divergence str\(\'H\'\) detect_batch 2 statistic str\(\'stdev\'\) significance 0.39224478071928026 subsets 87
stdout:
 General Info: Drift Detector: CDBD
	Parameters: {'recent_samples_size': '2465', 'feature_id': '23', 'batch_size': '4178', 'divergence': "str('H')", 'detect_batch': '2', 'statistic': "str('stdev')", 'significance': '0.39224478071928026', 'subsets': '87'}
	Dataset: RialtoBridgeTimelapse
	n_training_samples: 2000
	Classifier: HoeffdingTreeClassifier
General Info: detected drift points: [8356, 25068, 41780, 54314, 79382]
General Info: True/False Predictions: [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
ACCURACY: 0.11
RUNTIME: 187
OO-Info: runtime: 187
OO-Info: peak_memory: 688.60546875
OO-Info: mean_memory: 688.5545014880952
OO-Info: lpd: -1
OO-Info: portion_req_label: 0.26031152647975075

stderr:
 
Inaktive Module:
  1) PostgreSQL/14.4     4) X11/20220504          7) freetype/2.12.1
  2) Tk/8.6.12           5) expat/2.4.8           8) util-linux/2.38
  3) Tkinter/3.10.4      6) fontconfig/2.14.0

Wegen Änderungen an MODULEPATH wurden folgende Module erneut geladen:
  1) Brotli/1.0.9     3) XZ/5.2.5        5) libpciaccess/0.16     7) xorg-macros/1.19.3
  2) GMP/6.2.1        4) bzip2/1.0.8     6) libpng/1.6.37

Die folgenden Module wurden in einer anderen Version erneut geladen:
  1) GCCcore/11.3.0 => GCCcore/10.3.0     6) libffi/3.4.2 => libffi/3.3
  2) Python/3.10.4 => Python/3.9.5        7) libreadline/8.1.2 => libreadline/8.1
  3) SQLite/3.38.3 => SQLite/3.35.4       8) ncurses/6.3 => ncurses/6.2
  4) Tcl/8.6.12 => Tcl/8.6.11             9) zlib/1.2.12 => zlib/1.2.11
  5) binutils/2.38 => binutils/2.36.1

Modules GCCcore/11.3.0, zlib/1.2.12, xorg-macros/1.19.3, binutils/2.38, bzip2/1.0.8, ncurses/6.3, libreadline/8.1.2, Tcl/8.6.12, SQLite/3.38.3, XZ/5.2.5, GMP/6.2.1, libffi/3.4.2, Python/3.10.4, expat/2.4.8, libpng/1.6.37, Brotli/1.0.9, freetype/2.12.1, util-linux/2.38, fontconfig/2.14.0, libpciaccess/0.16, X11/20220504, Tk/8.6.12, Tkinter/3.10.4, PostgreSQL/14.4 and 5 dependencies unloaded.
Modules zlib/1.2.12, binutils/2.38, bzip2/1.0.8, ncurses/6.3, GCCcore/10.3.0, zlib/1.2.12, OpenSSL/1.1, xorg-macros/1.19.3, binutils/2.38, bzip2/1.0.8, ncurses/6.3, XZ/5.2.5, GMP/6.2.1, libpng/1.6.37, Brotli/1.0.9, libpciaccess/0.16, Python/3.9.5 and 7 dependencies loaded.

Result: {'ACCURACY': 0.11, 'RUNTIME': 187.0}
EXIT_CODE: 0
submitit INFO (2025-03-31 02:45:32,162) - Job completed successfully
submitit INFO (2025-03-31 02:45:32,195) - Exiting after successful completion

Skipped tabs:

Job-Infos
Single Logs

<!DOCTYPE html>
<html lang='en'>
	<head>
		<meta charset='UTF-8'>
		<meta name='viewport' content='width=device-width, initial-scale=1.0'>
		<title>Exported &raquo;s4122485/CDBD_RialtoBridgeTimelapse_ft_23_HoeffdingTreeClassifier_ACCURACY-RUNTIME/0&laquo; from OmniOpt2-Share</title>
		<script src='https://code.jquery.com/jquery-3.7.1.js'></script>
		<script src='https://cdnjs.cloudflare.com/ajax/libs/gridjs/6.2.0/gridjs.production.min.js'></script>
		<script src='https://cdn.jsdelivr.net/npm/plotly.js-dist@3.0.1/plotly.min.js'></script>
		<link rel='stylesheet' href='https://cdnjs.cloudflare.com/ajax/libs/gridjs/6.2.0/theme/mermaid.css'>
		<style>
			#share_path {
				color: black;
			}
			
			.debug_log_pre {
				min-width: 300px;
			}
			
			body.dark-mode {
				background-color: #1e1e1e; color: #fff;
			}
			
			.plot-container {
				margin-bottom: 2rem;
			}
			
			.spinner {
				border: 4px solid #f3f3f3;
				border-top: 4px solid #3498db;
				border-radius: 50%;
				width: 40px;
				height: 40px;
				animation: spin 2s linear infinite;
				margin: auto;
			}
			
			@keyframes spin {
				0% { transform: rotate(0deg); }
				100% { transform: rotate(360deg); }
			}
			
			.tabs {
				margin-bottom: 20px;
			}
			
			.tab-content {
				display: none;
			}
			
			.tab-content.active {
				display: block;
			}
			
			pre {
				color: #00CC00 !important;
				background-color: black !important;
				font-family: monospace !important;
				line-break: anywhere;
			}
			
			menu[role="tablist"] {
				display: flex;
				flex-wrap: wrap;
				gap: 4px;
				max-width: 100%;
				max-height: 100px;
				overflow: scroll;
			}
			
			menu[role="tablist"] button {
				white-space: nowrap;
				min-width: 100px;
			}
			
			.container {
				max-width: 100% !important;
			}
			
			.gridjs-sort {
				min-width: 1px !important;
			}
			
			td.gridjs-td {
				overflow: clip;
			}
			
			.title-bar-text {
				font-size: 22px;
				display: block ruby;
			}
			
			.title-bar {
				height: fit-content;
			}
			
			.window {
				width: fit-content;
				min-width: 100%;
			}
			
			.top_link {
				display: inline-block;
				padding: 5px 5px;
				background-color: #007bff; /* Blau, kannst du anpassen */
				color: white;
				text-decoration: none;
				font-size: 16px;
				font-weight: bold;
				border-radius: 6px;
				border: 2px solid #0056b3;
				text-align: center;
				transition: all 0.3s ease-in-out;
			}
			
			.top_link:hover {
				background-color: #0056b3;
				border-color: #004494;
			}
			
			.top_link:active {
				background-color: #003366;
				border-color: #002244;
			}
			
			button {
				color: black;
			}
			
			.share_folder_buttons {
				width: fit-content;
			}
			
			button {
				background: #fcfcfe;
				border-color: #919b9c;
				border-top-color: rgb(145, 155, 156);
				border-bottom-color: rgb(145, 155, 156);
				margin-right: -1px;
				border-bottom: 1px solid transparent;
				border-top: 1px solid #e68b2c;
				box-shadow: inset 0 2px #ffc73c;
			}
			
			button {
				padding-bottom: 2px;
				margin-top: -2px;
				background-color: #ece9d8;
				position: relative;
				z-index: 8;
				margin-left: -3px;
				margin-bottom: 1px;
			}
			
			.window {
				min-width: 1100px;
			}
			
			[role="tab"] {
				padding: 10px !important;
			}
			
			[role="tabpanel"] {
				min-width: fit-content;
			}
			
			select {
				border: 1px solid #7f9db9;
				background-image: url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 -0.5 15 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e6eefc' d='M0 0h1'/%3E%3Cpath stroke='%23d1e0fd' d='M1 0h1M0 1h1m3 0h2M2 3h1M2 4h1'/%3E%3Cpath stroke='%23cad8f9' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23c4d3f7' d='M3 0h1M0 3h1M0 4h1'/%3E%3Cpath stroke='%23bfd0f8' d='M4 0h2M0 5h1'/%3E%3Cpath stroke='%23bdcef7' d='M6 0h1M0 6h1'/%3E%3Cpath stroke='%23baccf4' d='M7 0h1m6 2h1m-1 5h1m-1 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M8 0h1M0 7h1M0 8h1'/%3E%3Cpath stroke='%23b7caf5' d='M9 0h2M0 9h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 0h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 0h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 0h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 0h1'/%3E%3Cpath stroke='%23e1eafe' d='M1 1h1'/%3E%3Cpath stroke='%23dae6fe' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23d4e1fc' d='M3 1h1M1 3h1M1 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M6 1h1M1 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M7 1h1M4 2h2'/%3E%3Cpath stroke='%23cad9fd' d='M8 1h1M6 2h1M3 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M9 1h2'/%3E%3Cpath stroke='%23c5d6fc' d='M11 1h1M2 11h4'/%3E%3Cpath stroke='%23c2d3fc' d='M12 1h1m-2 1h1M1 11h1m0 1h2m-2 1h2'/%3E%3Cpath stroke='%23bccefa' d='M13 1h1m-1 1h1m-1 1h1m-1 1h1M3 15h4'/%3E%3Cpath stroke='%23b9c9f3' d='M14 1h1M3 16h4'/%3E%3Cpath stroke='%23d8e3fc' d='M2 2h1'/%3E%3Cpath stroke='%23d1defd' d='M3 2h1'/%3E%3Cpath stroke='%23c9d8fc' d='M7 2h1M4 3h3M4 4h3M3 6h1m1 0h2M1 7h1M1 8h1'/%3E%3Cpath stroke='%23c5d5fc' d='M8 2h1m-8 8h5'/%3E%3Cpath stroke='%23c5d3fc' d='M9 2h2'/%3E%3Cpath stroke='%23bed0fc' d='M12 2h1M8 3h1M8 4h1m-8 8h1m-1 1h1m0 1h1m1 0h3'/%3E%3Cpath stroke='%23cddbfc' d='M3 3h1M3 4h1M1 6h2'/%3E%3Cpath stroke='%23c8d5fb' d='M7 3h1M7 4h1'/%3E%3Cpath stroke='%23bbcefd' d='M9 3h4M9 4h4M8 5h1M7 6h1'/%3E%3Cpath stroke='%23bcccf3' d='M14 3h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23ceddfd' d='M2 5h1'/%3E%3Cpath stroke='%23c8d6fb' d='M4 5h4M1 9h3'/%3E%3Cpath stroke='%23bacdfc' d='M9 5h2m1 0h2M1 14h1'/%3E%3Cpath stroke='%23b9cdfb' d='M11 5h1M8 6h2m2 0h2m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%234d6185' d='M4 6h1m5 0h1M3 7h3m3 0h3M4 8h3m1 0h3M5 9h5m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23b7cdfc' d='M11 6h1m0 1h1m-1 1h1'/%3E%3Cpath stroke='%23cad8fd' d='M2 7h1M2 8h2'/%3E%3Cpath stroke='%23c1d3fb' d='M6 7h2M7 8h1M4 9h1'/%3E%3Cpath stroke='%23b6cefb' d='M8 7h1m2 1h1m-2 1h3m-2 1h2'/%3E%3Cpath stroke='%23b6cdfb' d='M13 9h1m-6 6h1'/%3E%3Cpath stroke='%23b9cbf3' d='M14 9h1'/%3E%3Cpath stroke='%23b4c8f6' d='M0 10h1'/%3E%3Cpath stroke='%23bdd3fb' d='M9 10h2m-4 4h1'/%3E%3Cpath stroke='%23b5cdfa' d='M13 10h1'/%3E%3Cpath stroke='%23b5c9f3' d='M14 10h1'/%3E%3Cpath stroke='%23b1c7f6' d='M0 11h1'/%3E%3Cpath stroke='%23c3d5fd' d='M6 11h1'/%3E%3Cpath stroke='%23bad4fc' d='M8 11h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b2cffb' d='M9 11h4m-2 3h1'/%3E%3Cpath stroke='%23b1cbfa' d='M13 11h1m-3 4h1'/%3E%3Cpath stroke='%23b3c8f5' d='M14 11h1m-7 5h3'/%3E%3Cpath stroke='%23adc3f6' d='M0 12h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c2d5fc' d='M4 12h4m-4 1h4'/%3E%3Cpath stroke='%23b7d3fc' d='M9 12h2m-2 1h2m-3 1h1'/%3E%3Cpath stroke='%23b3d1fc' d='M11 12h1m-1 1h1'/%3E%3Cpath stroke='%23afcdfb' d='M12 12h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afcbfa' d='M13 12h1m-1 1h1'/%3E%3Cpath stroke='%23b2c8f4' d='M14 12h1m-1 1h1m-4 3h1'/%3E%3Cpath stroke='%23c1d2fb' d='M3 14h1'/%3E%3Cpath stroke='%23b6d1fb' d='M9 14h2'/%3E%3Cpath stroke='%23adc9f9' d='M13 14h1m-2 1h1'/%3E%3Cpath stroke='%23b1c6f3' d='M14 14h1m-3 2h1'/%3E%3Cpath stroke='%23abc1f4' d='M0 15h1'/%3E%3Cpath stroke='%23b7cbf9' d='M1 15h1'/%3E%3Cpath stroke='%23b9cefb' d='M2 15h1'/%3E%3Cpath stroke='%23b9cffb' d='M7 15h1'/%3E%3Cpath stroke='%23b2cdfb' d='M9 15h2'/%3E%3Cpath stroke='%23aec8f7' d='M13 15h1'/%3E%3Cpath stroke='%23b0c5f2' d='M14 15h1m-2 1h1'/%3E%3Cpath stroke='%23dbe3f8' d='M0 16h1'/%3E%3Cpath stroke='%23b7c6f1' d='M1 16h1'/%3E%3Cpath stroke='%23b8c9f2' d='M2 16h1m4 0h1'/%3E%3Cpath stroke='%23d9e3f6' d='M14 16h1'/%3E%3C/svg%3E");
				background-size: 15px;
				font-size: 11px;
				border: none;
				background-color: #fff;
				box-sizing: border-box;
				height: 21px;
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				position: relative;
				padding: 5px 32px 32px 5px;
				background-position: top 50% right 2px;
				background-repeat: no-repeat;
				border-radius: 0;
				border: 1px solid black;
			}
			

			body {
				font-variant: oldstyle-nums;
				font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
				background-color: #fafafa;
				text-shadow: 0 0.05em 0.1em rgba(0,0,0,0.2);
				scroll-behavior: smooth;
				text-wrap: balance;
				text-rendering: optimizeLegibility;
				-webkit-font-smoothing: antialiased;
				-moz-osx-font-smoothing: grayscale;
				font-feature-settings: "ss02", "liga", "onum";
			}
			
			.marked_text {
				background-color: yellow;
			}
			
			.time_picker_container {
				font-variant: small-caps;
				width: 100%;
			}
			
			.time_picker_container > input {
				width: 50px;
			}
			
			#loader {
				display: grid;
				justify-content: center;
				align-items: center;
				height: 100%;
			}
			
			.no_linebreak {
				line-break: auto;
			}
			
			.dark_code_bg {
				background-color: #363636;
				color: white;
			}
			
			.code_bg {
				background-color: #C0C0C0;
			}
			
			#commands {
				line-break: anywhere;
			}
			
			.color_red {
				color: red;
			}
			
			.color_orange {
				color: orange;
			}
			
			table > tbody > tr:nth-child(odd) {
				background-color: #fafafa;
			}
			
			table > tbody > tr:nth-child(even) {
				background-color: #ddd;
			}
			
			table {
				border-collapse: collapse;
				margin: 0 0;
				min-width: 200px;
			}
			
			th {
				background-color: #4eae46;
				color: #ffffff;
				text-align: left;
				border: 0px;
			}
			
			.error_element {
				background-color: #e57373;
				border-radius: 10px;
				padding: 4px;
				display: none;
			}
			
			button {
				background-color: #4eae46;
				border: 1px solid #2A8387;
				border-radius: 4px;
				box-shadow: rgba(0, 0, 0, 0.12) 0 1px 1px;
				cursor: pointer;
				display: block;
				line-height: 100%;
				outline: 0;
				padding: 11px 15px 12px;
				text-align: center;
				transition: box-shadow .05s ease-in-out, opacity .05s ease-in-out;
				user-select: none;
				-webkit-user-select: none;
				touch-action: manipulation;
				font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
			}
			
			button:hover {
				box-shadow: rgba(255, 255, 255, 0.3) 0 0 2px inset, rgba(0, 0, 0, 0.4) 0 1px 2px;
				text-decoration: none;
				transition-duration: .15s, .15s;
			}
			
			button:active {
				box-shadow: rgba(0, 0, 0, 0.15) 0 2px 4px inset, rgba(0, 0, 0, 0.4) 0 1px 1px;
			}
			
			button:disabled {
				cursor: not-allowed;
				opacity: .6;
			}
			
			button:disabled:active {
				pointer-events: none;
			}
			
			button:disabled:hover {
				box-shadow: none;
			}
			
			.half_width_td {
				vertical-align: baseline;
				width: 50%;
			}
			
			#scads_bar {
				width: 100%;
				margin: 0;
				padding: 0;
				user-select: none;
				user-drag: none;
				-webkit-user-drag: none;
				user-select: none;
				-moz-user-select: none;
				-webkit-user-select: none;
				-ms-user-select: none;
				display: -webkit-box;
			}
			
			.tab {
				display: inline-block;
				padding: 0px;
				margin: 0px;
				font-size: 16px;
				font-weight: bold;
				text-align: center;
				border-radius: 25px;
				text-decoration: none !important;
				transition: background-color 0.3s, color 0.3s;
				color: unset !important;
			}
			
			.tooltipster-base {
				border: 1px solid black;
				position: absolute;
				border-radius: 8px;
				padding: 2px;
				color: white;
				background-color: #61686f;
				width: 70%;
				min-width: 200px;
				pointer-events: none;
			}
			
			td {
				padding-top: 3px;
				padding-bottom: 3px;
			}
			
			.left_side {
				text-align: right;
			}
			
			.right_side {
				text-align: left;
			}
			
			.spinner {
				border: 8px solid rgba(0, 0, 0, 0.1);
				border-left: 8px solid #3498db;
				border-radius: 50%;
				width: 50px;
				height: 50px;
				animation: spin 1s linear infinite;
			}
			
			@keyframes spin {
				0% {
					transform: rotate(0deg);
				}
			
				100% {
					transform: rotate(360deg);
				}
			}
			
			#spinner-overlay {
				-webkit-text-stroke: 1px black;
				white !important;
				position: fixed;
				top: 0;
				left: 0;
				width: 100%;
				height: 100%;
				display: flex;
				justify-content: center;
				align-items: center;
				z-index: 9999;
			}
			
			#spinner-container {
				text-align: center;
				color: #fff;
				display: contents;
			}
			
			#spinner-text {
				font-size: 3vw;
				margin-left: 10px;
			}
			
			a, a:visited, a:active, a:hover, a:link {
				color: #007bff;
				text-decoration: none;
			}
			
			.copy-container {
				display: inline-block;
				position: relative;
				cursor: pointer;
				margin-left: 10px;
				color: blue;
			}
			
			.copy-container:hover {
				text-decoration: underline;
			}
			
			.clipboard-icon {
				position: absolute;
				top: 5px;
				right: 5px;
				font-size: 1.5em;
			}
			
			#main_tab {
				overflow: scroll;
				width: max-content;
			}
			
			.ui-tabs .ui-tabs-nav li {
				user-select: none;
			}
			
			.stacktrace_table {
				background-color: black !important;
				color: white !important;
			}
			
			#breadcrumb {
				user-select: none;
			}
			
			#statusBar {
				user-select: none;
			}
			
			.error_line {
				background-color: red !important;
				color: white !important;
			}
			
			.header_table {
				border: 0px !important;
				padding: 0px !important;
				width: revert !important;
				min-width: revert !important;
			}
			
			.img_auto_width {
				max-width: revert !important;
			}
			
			#main_dir_or_plot_view {
				display: inline-grid;
			}
			
			#refresh_button {
				width: 300px;
			}
			
			._share_link {
				color: black !important;
			}
			
			#footer_element {
				height: 30px;
				background-color: #f8f9fa;
				padding: 0px;
				text-align: center;
				border-top: 1px solid #dee2e6;
				width: 100%;
				box-sizing: border-box;
				position: fixed;
				bottom: 0;
				z-index: 2;
				margin-left: -9px;
				z-index: 99;
			}
			
			.switch {
				position: relative;
				display: inline-block;
				width: 50px;
				height: 26px;
			}
			
			.switch input {
				opacity: 0;
				width: 0;
				height: 0;
			}
			
			.slider {
				position: absolute;
				cursor: pointer;
				top: 0;
				left: 0;
				right: 0;
				bottom: 0;
				background-color: #ccc;
				transition: .4s;
				border-radius: 26px;
			}
			
			.slider:before {
				position: absolute;
				content: "";
				height: 20px;
				width: 20px;
				left: 3px;
				bottom: 3px;
				background-color: white;
				transition: .4s;
				border-radius: 50%;
			}
			
			input:checked + .slider {
				background-color: #444;
			}
			
			input:checked + .slider:before {
				transform: translateX(24px);
			}
			
			.mode-text {
				position: absolute;
				top: 5px;
				left: 65px;
				font-size: 14px;
				color: black;
				transition: .4s;
				width: 65px;
				display: block;
				font-size: 0.7rem;
				text-align: center;
			}
			
			input:checked + .slider .mode-text {
				content: "Dark Mode";
				color: white;
			}
			
			#mainContent {
				height: fit-content;
				min-height: 100%;
			}
			
			li {
				text-align: left;
			}
			
			#share_path {
				margin-bottom: 20px;
				margin-top: 20px;
			}
			
			#sortForm {
				margin-bottom: 20px;
			}
			
			.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 8h1'/%3E%3Cpath stroke='%23b5c8f7' d='M11 1h1'/%3E%3Cpath stroke='%23b3c7f5' d='M12 1h1'/%3E%3Cpath stroke='%23afc5f4' d='M13 1h1'/%3E%3Cpath stroke='%23dce6f9' d='M14 1h1'/%3E%3Cpath stroke='%23dfe2e1' d='M16 1h1'/%3E%3Cpath stroke='%23e1eafe' d='M3 2h1'/%3E%3Cpath stroke='%23dae6fe' d='M4 2h1M3 3h1'/%3E%3Cpath stroke='%23d4e1fc' d='M5 2h1M3 4h1'/%3E%3Cpath stroke='%23d1e0fd' d='M6 2h1M4 4h1'/%3E%3Cpath stroke='%23d0ddfc' d='M7 2h1M3 5h1'/%3E%3Cpath stroke='%23cedbfd' d='M8 2h1M6 3h1'/%3E%3Cpath stroke='%23cad9fd' d='M9 2h1M7 3h1M5 5h1'/%3E%3Cpath stroke='%23c8d8fb' d='M10 2h1'/%3E%3Cpath stroke='%23c5d6fc' d='M11 2h1m-8 8h1m1 0h1'/%3E%3Cpath stroke='%23c2d3fc' d='M12 2h1m-2 1h1m-9 7h1m0 1h1'/%3E%3Cpath stroke='%23bccefa' d='M13 2h1m-1 2h1m-9 9h2'/%3E%3Cpath stroke='%23b9c9f3' d='M14 2h1M5 14h3'/%3E%3Cpath stroke='%23cfd7dd' d='M16 2h1'/%3E%3Cpath stroke='%23d8e3fc' d='M4 3h1'/%3E%3Cpath stroke='%23d1defd' d='M5 3h1'/%3E%3Cpath stroke='%23c9d8fc' d='M8 3h1M6 4h2M5 6h2M3 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			}
			
			: :-webkit-scrollbar-button: vertical: end{
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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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			        30,
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			    [
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			        30,
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			    ],
			    [
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			        30,
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			        30,
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			    ],
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			    ],
			    [
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			        30,
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			    ],
			    [
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			    ],
			    [
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			        30,
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			    ],
			    [
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			        100
			    ],
			    [
			        1743452628.5432186,
			        30,
			        30,
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			    ]
			];
			var tab_main_worker_cpu_ram_csv_json = [
			    [
			        1743373053,
			        595.59375,
			        4.3
			    ],
			    [
			        1743373053,
			        595.59375,
			        4.9
			    ],
			    [
			        1743373053,
			        595.88671875,
			        4.3
			    ],
			    [
			        1743373053,
			        595.88671875,
			        3.3
			    ],
			    [
			        1743373053,
			        595.88671875,
			        4.2
			    ],
			    [
			        1743373053,
			        595.88671875,
			        5.1
			    ],
			    [
			        1743373053,
			        595.88671875,
			        4.6
			    ],
			    [
			        1743373135,
			        612.13671875,
			        2.8
			    ],
			    [
			        1743373135,
			        612.17578125,
			        2.2
			    ],
			    [
			        1743373135,
			        612.17578125,
			        2.4
			    ],
			    [
			        1743373135,
			        612.17578125,
			        2.1
			    ],
			    [
			        1743385014,
			        690.44140625,
			        1.1
			    ],
			    [
			        1743385014,
			        690.44140625,
			        5
			    ],
			    [
			        1743385014,
			        690.44140625,
			        5
			    ],
			    [
			        1743385014,
			        690.44140625,
			        3
			    ],
			    [
			        1743391494,
			        694.91015625,
			        3.8
			    ],
			    [
			        1743391495,
			        694.91015625,
			        2.5
			    ],
			    [
			        1743391495,
			        694.91015625,
			        2.6
			    ],
			    [
			        1743391495,
			        694.91015625,
			        2.1
			    ],
			    [
			        1743395774,
			        700.70703125,
			        4.5
			    ],
			    [
			        1743395774,
			        700.70703125,
			        4.4
			    ],
			    [
			        1743395774,
			        700.70703125,
			        4.6
			    ],
			    [
			        1743395774,
			        700.70703125,
			        3.8
			    ],
			    [
			        1743399516,
			        713.265625,
			        4.8
			    ],
			    [
			        1743399516,
			        713.265625,
			        4.6
			    ],
			    [
			        1743399516,
			        713.265625,
			        4.6
			    ],
			    [
			        1743399516,
			        713.265625,
			        6.4
			    ],
			    [
			        1743407117,
			        727.0703125,
			        5.3
			    ],
			    [
			        1743407117,
			        727.0703125,
			        4.6
			    ],
			    [
			        1743407117,
			        727.0703125,
			        4.7
			    ],
			    [
			        1743407117,
			        727.0703125,
			        2.9
			    ],
			    [
			        1743413543,
			        730.734375,
			        4.1
			    ],
			    [
			        1743413543,
			        730.734375,
			        3.2
			    ],
			    [
			        1743413544,
			        730.734375,
			        3.5
			    ],
			    [
			        1743413544,
			        730.734375,
			        5.1
			    ],
			    [
			        1743417087,
			        772,
			        3.9
			    ],
			    [
			        1743417087,
			        772,
			        6.9
			    ],
			    [
			        1743417087,
			        772,
			        7
			    ],
			    [
			        1743417087,
			        772,
			        10.8
			    ],
			    [
			        1743419315,
			        751.46484375,
			        4.1
			    ],
			    [
			        1743419315,
			        751.46484375,
			        6.2
			    ],
			    [
			        1743419316,
			        751.46484375,
			        6.6
			    ],
			    [
			        1743419316,
			        751.46484375,
			        7
			    ],
			    [
			        1743422607,
			        790.265625,
			        6.9
			    ],
			    [
			        1743422607,
			        790.265625,
			        6.4
			    ],
			    [
			        1743422607,
			        790.265625,
			        6.4
			    ],
			    [
			        1743422607,
			        790.265625,
			        5.7
			    ],
			    [
			        1743427867,
			        787.9296875,
			        6.2
			    ],
			    [
			        1743427867,
			        787.9296875,
			        4.9
			    ],
			    [
			        1743427868,
			        787.9296875,
			        5.1
			    ],
			    [
			        1743427868,
			        787.9296875,
			        5.9
			    ],
			    [
			        1743433905,
			        804.609375,
			        6.2
			    ],
			    [
			        1743433905,
			        804.609375,
			        5.2
			    ],
			    [
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			        804.609375,
			        5.4
			    ],
			    [
			        1743433905,
			        804.609375,
			        6.4
			    ],
			    [
			        1743438572,
			        812.45703125,
			        7
			    ],
			    [
			        1743438572,
			        812.45703125,
			        7.1
			    ],
			    [
			        1743438572,
			        812.45703125,
			        7.4
			    ],
			    [
			        1743438572,
			        812.45703125,
			        7.4
			    ],
			    [
			        1743446226,
			        798.921875,
			        7.1
			    ],
			    [
			        1743446226,
			        798.921875,
			        6
			    ],
			    [
			        1743446226,
			        798.921875,
			        6.2
			    ],
			    [
			        1743446226,
			        798.921875,
			        8.3
			    ],
			    [
			        1743452633,
			        805.8125,
			        8
			    ],
			    [
			        1743452633,
			        805.8125,
			        8.9
			    ],
			    [
			        1743452633,
			        805.8125,
			        8.7
			    ],
			    [
			        1743452633,
			        805.8125,
			        8.4
			    ]
			];
			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) &&
						!col.startsWith("OO_Info") &&
						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) &&
						!col.startsWith("OO_Info") &&
						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) &&
					!col.startsWith("OO_Info") &&
					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;
					}
					if (!col.startsWith("OO_Info")) {
						return;
					}
					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) &&
					!col.startsWith("OO_Info") &&
					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) &&
					!col.startsWith("OO_Info") &&
					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();
			}
			
			function plotTimelineFromGlobals() {
				if (
					typeof tab_results_headers_json === "undefined" ||
					typeof tab_results_csv_json === "undefined" ||
					!Array.isArray(tab_results_headers_json) ||
					!Array.isArray(tab_results_csv_json)
				) {
					console.warn("Global variables 'tab_results_headers_json' or 'tab_results_csv_json' missing or invalid.");
					return null;
				}
			
				const headers = tab_results_headers_json;
				const data = tab_results_csv_json;
			
				const col = name => headers.indexOf(name);
				const ix_trial_index = col("trial_index");
				const ix_start_time = col("start_time");
				const ix_end_time = col("end_time");
				const ix_status = col("trial_status");
			
				if ([ix_trial_index, ix_start_time, ix_end_time, ix_status].some(ix => ix === -1)) {
					console.warn("One or more needed columns missing");
					return null;
				}
			
				const traces = [];
			
				// Add dummy traces for legend
				traces.push({
					type: "scatter",
					mode: "lines",
					x: [null, null],
					y: [null, null],
					line: { color: "green", width: 4 },
					name: "COMPLETED",
					showlegend: true,
					hoverinfo: "none"
				});
				traces.push({
					type: "scatter",
					mode: "lines",
					x: [null, null],
					y: [null, null],
					line: { color: "yellow", width: 4 },
					name: "RUNNING",
					showlegend: true,
					hoverinfo: "none"
				});
				traces.push({
					type: "scatter",
					mode: "lines",
					x: [null, null],
					y: [null, null],
					line: { color: "red", width: 4 },
					name: "FAILED/OTHER",
					showlegend: true,
					hoverinfo: "none"
				});
			
				for (const row of data) {
					const trial_index = row[ix_trial_index];
					const start = row[ix_start_time];
					const end = row[ix_end_time];
					const status = row[ix_status];
			
					if (
						trial_index === "" || start === "" || end === "" ||
						isNaN(start) || isNaN(end)
					) continue;
			
					let color = "red"; // default
					if (status === "COMPLETED") color = "green";
					else if (status === "RUNNING") color = "yellow";
			
					traces.push({
						type: "scatter",
						mode: "lines",
						x: [new Date(start * 1000), new Date(end * 1000)],
						y: [trial_index, trial_index],
						line: { color: color, width: 4 },
						name: `Trial ${trial_index} (${status})`,
						showlegend: false,
						hoverinfo: "x+y+name"
					});
				}
			
				if (traces.length <= 3) { // only dummy traces added
					console.warn("No valid data for plotting found.");
					return null;
				}
			
				const layout = {
					title: "Trial Timeline",
					xaxis: {
						title: "Time",
						type: "date"
					},
					yaxis: {
						title: "Trial Index",
						autorange: "reversed"
					},
					margin: { t: 50 }
				};
			
				Plotly.newPlot('plot_timeline', traces, add_default_layout_data(layout));
				return true;
			}
			
			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();

				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> Model for non-random steps</td><td>BOTORCH_MODULAR </td></tr><tr><td> Max. nr. evaluations</td><td>500             </td></tr><tr><td> Number random steps</td><td>20              </td></tr><tr><td> Nr. of workers (parameter)</td><td>30              </td></tr><tr><td> Main process memory (GB)</td><td>8               </td></tr><tr><td> Worker memory (GB)</td><td>32              </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>range</td><td>1</td><td>5000</td><td></td><td>int</td><td>No         </td></tr><tr><td> feature_id</td><td>fixed</td><td></td><td></td><td>23</td><td></td><td></td></tr><tr><td> batch_size</td><td>range</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>range</td><td>0.001</td><td>0.999</td><td></td><td>float</td><td>No         </td></tr><tr><td> subsets</td><td>range</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>24</td>
			<td>423</td>
			<td>3</td>
			<td>450</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>
			
			<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_0,0.110000000000000000555111512313,187.000000000000000000000000000000,2465,4178,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.392244780719280261571668688703,87,23
1,1_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,168.000000000000000000000000000000,3778,1234,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.928840154070407097997019718605,21,23
2,2_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,182.000000000000000000000000000000,3481,2543,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.705434621689841168112877767271,59,23
3,3_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,168.000000000000000000000000000000,263,2050,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.246336168229579932642536732601,49,23
4,4_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,160.000000000000000000000000000000,938,3412,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.287790234444662951229787495322,71,23
5,5_0,COMPLETED,Sobol,GenerationStep_0,0.130000000000000004440892098501,188.000000000000000000000000000000,2799,1337,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.764302504479885125299176706903,37,23
6,6_0,COMPLETED,Sobol,GenerationStep_0,0.130000000000000004440892098501,162.000000000000000000000000000000,4924,4872,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.599388781460002073586679216533,99,23
7,7_0,COMPLETED,Sobol,GenerationStep_0,0.130000000000000004440892098501,190.000000000000000000000000000000,1316,384,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.075592964548617608278036072988,9,23
8,8_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,184.000000000000000000000000000000,1567,3033,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.137550467878580107150554567852,65,23
9,9_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,175.000000000000000000000000000000,4677,2217,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.661209178296849109379706987966,30,23
10,10_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,169.000000000000000000000000000000,3051,3995,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.951864661086350705154757179116,94,23
11,11_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,202.000000000000000000000000000000,692,748,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.475459041001275173332629719880,2,23
12,12_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,152.000000000000000000000000000000,589,4376,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.058656583655625579853953865950,80,23
13,13_0,COMPLETED,Sobol,GenerationStep_0,0.130000000000000004440892098501,184.000000000000000000000000000000,3152,211,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.517892143609002264525997816236,16,23
14,14_0,COMPLETED,Sobol,GenerationStep_0,0.110000000000000000555111512313,161.000000000000000000000000000000,4102,3589,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.867008942037820862225316886907,52,23
15,15_0,COMPLETED,Sobol,GenerationStep_0,0.110000000000000000555111512313,162.000000000000000000000000000000,2135,1817,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.330306928960606482892359281323,44,23
16,16_0,COMPLETED,Sobol,GenerationStep_0,0.110000000000000000555111512313,149.000000000000000000000000000000,1985,3642,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.548727784469723700588872361550,24,23
17,17_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,176.000000000000000000000000000000,4275,1576,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.024925069106742740732141072613,82,23
18,18_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,173.000000000000000000000000000000,3316,4642,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.361872578803449862494545641312,46,23
19,19_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,307.000000000000000000000000000000,449,145,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.838391275124624413273011214187,60,23
20,20_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,163.000000000000000000000000000000,851,3773,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.631375389281660370777160551370,32,23
21,21_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,161.000000000000000000000000000000,2907,858,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.172285265641286960969225106055,75,23
22,22_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,163.000000000000000000000000000000,4524,2948,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.442944120720028866600159744848,10,23
23,23_0,COMPLETED,Sobol,GenerationStep_0,0.130000000000000004440892098501,200.000000000000000000000000000000,1736,2426,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.979530681861564556101029666024,96,23
24,24_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,164.000000000000000000000000000000,1480,4782,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.792949443804100151389491202281,28,23
25,25_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,250.000000000000000000000000000000,4785,587,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.256256232712417819641359528759,66,23
26,26_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,177.000000000000000000000000000000,2650,3184,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.109355647606775172042681276707,6,23
27,27_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,238.000000000000000000000000000000,1112,1441,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.568582877740263947785592790751,88,23
28,28_0,COMPLETED,Sobol,GenerationStep_0,0.119999999999999995559107901499,183.000000000000000000000000000000,109,2803,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.901203398270532529679144317925,16,23
29,29_0,COMPLETED,Sobol,GenerationStep_0,0.130000000000000004440892098501,204.000000000000000000000000000000,3649,1978,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.424791351899504676659091728652,78,23
30,30_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,188.000000000000000000000000000000,4888,4932,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.244072528604733912649393801075,100,23
31,31_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,203.000000000000000000000000000000,1,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,23
32,32_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,158.000000000000000000000000000000,1,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
33,33_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,143.000000000000000000000000000000,4691,1867,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,23
34,34_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,184.000000000000000000000000000000,2032,4512,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
35,35_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,187.000000000000000000000000000000,5000,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
36,36_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,161.000000000000000000000000000000,4481,280,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,23
37,37_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,167.000000000000000000000000000000,4013,2862,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
38,38_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,157.000000000000000000000000000000,1,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
39,39_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,215.000000000000000000000000000000,1,4634,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,23
40,40_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,247.000000000000000000000000000000,4641,1,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
41,41_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,236.000000000000000000000000000000,2789,1,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.321850470175898739633169043373,1,23
42,42_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,184.000000000000000000000000000000,1,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
43,43_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,266.000000000000000000000000000000,1614,5000,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
44,44_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,160.000000000000000000000000000000,3121,4314,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
45,45_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,207.000000000000000000000000000000,5000,4116,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
46,46_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,244.000000000000000000000000000000,1,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
47,47_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,172.000000000000000000000000000000,1,2472,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
48,48_0,COMPLETED,BoTorch,GenerationStep_1,0.140000000000000013322676295502,171.000000000000000000000000000000,5000,1259,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
49,49_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,203.000000000000000000000000000000,5000,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
50,50_0,FAILED,BoTorch,GenerationStep_1,,,2568,1,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,23
51,51_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,267.000000000000000000000000000000,1,5000,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
52,52_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,150.000000000000000000000000000000,725,2123,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
53,53_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,242.000000000000000000000000000000,1,1,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
54,54_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,252.000000000000000000000000000000,5000,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,23
55,55_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,160.000000000000000000000000000000,5000,5000,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,40,23
56,56_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,218.000000000000000000000000000000,5000,4359,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,23
57,57_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,160.000000000000000000000000000000,5000,5000,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
58,58_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,267.000000000000000000000000000000,5000,5000,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,23
59,59_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,260.000000000000000000000000000000,4945,3539,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
60,60_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,172.000000000000000000000000000000,5000,1260,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.994509569168528950733332294476,3,23
61,61_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,173.000000000000000000000000000000,3667,1582,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,73,23
62,62_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,173.000000000000000000000000000000,5000,1143,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,24,23
63,63_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,168.000000000000000000000000000000,5000,1342,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,62,23
64,64_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,171.000000000000000000000000000000,2429,3875,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,52,23
65,65_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,177.000000000000000000000000000000,469,1403,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,79,23
66,66_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,156.000000000000000000000000000000,3443,2055,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.483768517237680883980033286207,25,23
67,67_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,177.000000000000000000000000000000,2510,1229,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,41,23
68,68_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,175.000000000000000000000000000000,2130,1808,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.986345323571729992728762681509,79,23
69,69_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,149.000000000000000000000000000000,4665,4292,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.199296300052771019339914460033,4,23
70,70_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,169.000000000000000000000000000000,3859,1380,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.994110621629834323442764798529,15,23
71,71_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,162.000000000000000000000000000000,891,2297,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.785525111598185166172925164574,29,23
72,72_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,157.000000000000000000000000000000,1,2722,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,42,23
73,73_0,COMPLETED,BoTorch,GenerationStep_1,0.140000000000000013322676295502,175.000000000000000000000000000000,4975,1255,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.825802362179093085714498556626,42,23
74,74_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,155.000000000000000000000000000000,1,3810,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,52,23
75,75_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,169.000000000000000000000000000000,4409,1966,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.914473132881980399488952571119,62,23
76,76_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,204.000000000000000000000000000000,2347,1996,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.589536835838823658484386669443,1,23
77,77_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,173.000000000000000000000000000000,1550,1578,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.895251848481729783912896891707,63,23
78,78_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,172.000000000000000000000000000000,4913,1429,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,78,23
79,79_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,160.000000000000000000000000000000,4921,1986,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.801956195782898673130034694623,11,23
80,80_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,163.000000000000000000000000000000,2119,4927,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.231940173049142411842993283244,80,23
81,81_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,153.000000000000000000000000000000,1,2984,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,39,23
82,82_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,174.000000000000000000000000000000,4493,1156,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.829395133836446918351725798857,1,23
83,83_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,171.000000000000000000000000000000,4920,3754,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.037309692651394527063146711043,10,23
84,84_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,146.000000000000000000000000000000,5000,1771,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,81,23
85,85_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,176.000000000000000000000000000000,3909,1672,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,43,23
86,86_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,166.000000000000000000000000000000,4855,2300,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.588856970669109358595960657112,30,23
87,87_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,159.000000000000000000000000000000,4109,2970,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,45,23
88,88_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,155.000000000000000000000000000000,5000,3669,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,47,23
89,89_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,187.000000000000000000000000000000,16,2923,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,44,23
90,90_0,COMPLETED,BoTorch,GenerationStep_1,0.140000000000000013322676295502,162.000000000000000000000000000000,4756,1216,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,86,23
91,91_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,180.000000000000000000000000000000,2987,1251,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,10,23
92,92_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,180.000000000000000000000000000000,2750,4025,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.705931805720644955925990871037,34,23
93,93_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,170.000000000000000000000000000000,3784,2363,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.232343844023395001974208184947,19,23
94,94_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,161.000000000000000000000000000000,162,3830,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.670719367938895638658891584782,30,23
95,95_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,173.000000000000000000000000000000,2819,2345,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.863752107351948072988534477190,100,23
96,96_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,161.000000000000000000000000000000,5000,1213,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,67,23
97,97_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,166.000000000000000000000000000000,4510,3482,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.637338122541146967492409203260,27,23
98,98_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,179.000000000000000000000000000000,4460,4045,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,45,23
99,99_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,168.000000000000000000000000000000,4589,1209,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.433408122585955901673315793232,88,23
100,100_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,170.000000000000000000000000000000,3379,1422,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,87,23
101,101_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,157.000000000000000000000000000000,4144,1235,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,16,23
102,102_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,189.000000000000000000000000000000,260,3363,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.971685543725428590988713040133,35,23
103,103_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,158.000000000000000000000000000000,5000,1243,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,5,23
104,104_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,179.000000000000000000000000000000,4159,2078,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.608709035241982276964733955538,100,23
105,105_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,183.000000000000000000000000000000,4802,4091,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.545999868939977983650635451340,30,23
106,106_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,148.000000000000000000000000000000,443,1542,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,79,23
107,107_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,156.000000000000000000000000000000,540,4515,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.962361519252075692243408866489,30,23
108,108_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,157.000000000000000000000000000000,4314,2700,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,100,23
109,109_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,163.000000000000000000000000000000,4332,1210,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.447681428742986831093020327899,72,23
110,110_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,153.000000000000000000000000000000,4483,4446,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.905931258241682524356974681723,10,23
111,111_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,181.000000000000000000000000000000,4722,3867,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.273602437865556102902786506093,29,23
112,112_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,169.000000000000000000000000000000,489,1260,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.005969196969030884578066853408,99,23
113,113_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,192.000000000000000000000000000000,3160,1057,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,91,23
114,114_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,147.000000000000000000000000000000,4729,1217,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,99,23
115,115_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,165.000000000000000000000000000000,1321,3783,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,27,23
116,116_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,153.000000000000000000000000000000,4724,1232,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,21,23
117,117_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,171.000000000000000000000000000000,1758,4129,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.448208672904483296228761446400,31,23
118,118_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,177.000000000000000000000000000000,2202,3469,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,29,23
119,119_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,156.000000000000000000000000000000,5000,1195,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,93,23
120,120_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,158.000000000000000000000000000000,3219,1622,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
121,121_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,171.000000000000000000000000000000,5000,1943,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.770536776039013049377501829440,100,23
122,122_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,169.000000000000000000000000000000,4910,870,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,34,23
123,123_0,FAILED,BoTorch,GenerationStep_1,,,1,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,47,23
124,124_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,178.000000000000000000000000000000,1,367,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,39,23
125,125_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,169.000000000000000000000000000000,5000,1756,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
126,126_0,FAILED,BoTorch,GenerationStep_1,,,5000,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,53,23
127,127_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,170.000000000000000000000000000000,5000,2758,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
128,128_0,FAILED,BoTorch,GenerationStep_1,,,5000,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,23
129,129_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,169.000000000000000000000000000000,5000,2404,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
130,130_0,COMPLETED,BoTorch,GenerationStep_1,0.140000000000000013322676295502,165.000000000000000000000000000000,395,1387,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
131,131_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,180.000000000000000000000000000000,3088,867,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,40,23
132,132_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,169.000000000000000000000000000000,5000,2010,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,29,23
133,133_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,182.000000000000000000000000000000,1,1017,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,35,23
134,134_0,FAILED,BoTorch,GenerationStep_1,,,1,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,23
135,135_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,183.000000000000000000000000000000,1,4038,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
136,136_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,1374.000000000000000000000000000000,1456,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,34,23
137,137_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,177.000000000000000000000000000000,5000,1427,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,33,23
138,138_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,172.000000000000000000000000000000,2817,918,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,40,23
139,139_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,171.000000000000000000000000000000,5000,974,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,34,23
140,140_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,172.000000000000000000000000000000,5000,3691,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
141,141_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,245.000000000000000000000000000000,1,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,68,23
142,142_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,178.000000000000000000000000000000,5000,1623,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
143,143_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,180.000000000000000000000000000000,4484,5000,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,73,23
144,144_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,170.000000000000000000000000000000,1,5000,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,61,23
145,145_0,FAILED,BoTorch,GenerationStep_1,,,498,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.960238961296232540476580652467,58,23
146,146_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,238.000000000000000000000000000000,1,1,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,68,23
147,147_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,186.000000000000000000000000000000,5000,792,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,40,23
148,148_0,FAILED,BoTorch,GenerationStep_1,,,5000,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,60,23
149,149_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,147.000000000000000000000000000000,888,1200,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
150,150_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,827.000000000000000000000000000000,656,291,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,23
151,151_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,172.000000000000000000000000000000,1303,2128,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.932422205020228211935773288133,100,23
152,152_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,262.000000000000000000000000000000,795,357,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
153,153_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,175.000000000000000000000000000000,3062,910,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.865194337034176919054573318135,100,23
154,154_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,171.000000000000000000000000000000,3363,79,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
155,155_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,181.000000000000000000000000000000,1274,2182,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
156,156_0,FAILED,BoTorch,GenerationStep_1,,,676,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,23
157,157_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,267.000000000000000000000000000000,2070,938,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
158,158_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,178.000000000000000000000000000000,3191,627,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
159,159_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,165.000000000000000000000000000000,1578,1855,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.420891406627151132280317824552,100,23
160,160_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,439.000000000000000000000000000000,966,859,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,23
161,161_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,240.000000000000000000000000000000,3921,1,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
162,162_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,2072.000000000000000000000000000000,1,81,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.542388840418774664797751938750,100,23
163,163_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,1369.000000000000000000000000000000,26,120,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.421148071105559240745463966960,100,23
164,164_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,261.000000000000000000000000000000,4103,1,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
165,165_0,FAILED,BoTorch,GenerationStep_1,,,1,1,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.410976771355218484238491782889,88,23
166,166_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,419.000000000000000000000000000000,699,365,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
167,167_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,269.000000000000000000000000000000,789,973,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
168,168_0,FAILED,BoTorch,GenerationStep_1,,,4095,1,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
169,169_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,173.000000000000000000000000000000,3116,914,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.661289807646492366899337866926,100,23
170,170_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,155.000000000000000000000000000000,1893,861,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
171,171_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,173.000000000000000000000000000000,3487,63,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.886049212575266142977170602535,100,23
172,172_0,FAILED,BoTorch,GenerationStep_1,,,3604,1,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
173,173_0,FAILED,BoTorch,GenerationStep_1,,,327,1,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
174,174_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,381.000000000000000000000000000000,97,927,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.505197007524938790368196350755,100,23
175,175_0,FAILED,BoTorch,GenerationStep_1,,,2904,1,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,23
176,176_0,FAILED,BoTorch,GenerationStep_1,,,4189,1,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,100,23
177,177_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,183.000000000000000000000000000000,166,158,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.289943129144823397158603484058,10,23
178,178_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,410.000000000000000000000000000000,2129,950,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,23
179,179_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,239.000000000000000000000000000000,743,1,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
180,180_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,243.000000000000000000000000000000,4913,1010,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
181,181_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,203.000000000000000000000000000000,1,2276,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
182,182_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,265.000000000000000000000000000000,9,315,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.662365731682529879087439894647,1,23
183,183_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,176.000000000000000000000000000000,2582,960,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,33,23
184,184_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,172.000000000000000000000000000000,4393,411,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
185,185_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,273.000000000000000000000000000000,4263,686,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
186,186_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,173.000000000000000000000000000000,2174,3623,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
187,187_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,226.000000000000000000000000000000,1,1144,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
188,188_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,165.000000000000000000000000000000,4558,337,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,100,23
189,189_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,262.000000000000000000000000000000,2931,578,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
190,190_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,156.000000000000000000000000000000,4602,3522,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
191,191_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,291.000000000000000000000000000000,4996,1990,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
192,192_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,268.000000000000000000000000000000,1,2201,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
193,193_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,203.000000000000000000000000000000,5000,542,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
194,194_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,267.000000000000000000000000000000,3730,375,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.831843570629202999633378112776,1,23
195,195_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,167.000000000000000000000000000000,4896,2959,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.360724716195134686991252692678,100,23
196,196_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,200.000000000000000000000000000000,5000,3536,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
197,197_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,176.000000000000000000000000000000,4333,2759,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
198,198_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,174.000000000000000000000000000000,5000,548,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,23
199,199_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,200.000000000000000000000000000000,4771,402,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,23
200,200_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,170.000000000000000000000000000000,4724,332,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.329347099437787893538853722930,100,23
201,201_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,261.000000000000000000000000000000,530,845,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,23
202,202_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,4245,4214,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,23
203,203_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,203.000000000000000000000000000000,1775,3692,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
204,204_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,171.000000000000000000000000000000,5000,259,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,23
205,205_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,230.000000000000000000000000000000,4134,2578,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,23
206,206_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,242.000000000000000000000000000000,5000,914,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
207,207_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,194.000000000000000000000000000000,5000,3739,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.661836501830504464471971459716,100,23
208,208_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,177.000000000000000000000000000000,5000,3637,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
209,209_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,252.000000000000000000000000000000,841,1934,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
210,210_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,292.000000000000000000000000000000,65,1733,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
211,211_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,353.000000000000000000000000000000,852,655,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.780542334519168212736417444830,100,23
212,212_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,170.000000000000000000000000000000,4071,2745,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.555882885329420206588224573352,100,23
213,213_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,156.000000000000000000000000000000,4692,1300,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,7,23
214,214_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,177.000000000000000000000000000000,5000,2605,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.526967972678304863443088379427,100,23
215,215_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,191.000000000000000000000000000000,2030,1005,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.306081173146155849362770595690,9,23
216,216_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,174.000000000000000000000000000000,956,1402,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
217,217_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,255.000000000000000000000000000000,1,1264,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.996626379150792440775319391832,100,23
218,218_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,268.000000000000000000000000000000,3550,2669,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.715651705916832803211491409456,1,23
219,219_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,364.000000000000000000000000000000,191,1393,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,23
220,220_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,266.000000000000000000000000000000,4751,1815,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.683486297813867049733005387679,1,23
221,221_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,264.000000000000000000000000000000,1,1856,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.402630775720551670193714244306,1,23
222,222_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,265.000000000000000000000000000000,4229,1009,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.405191750255294025340191410578,1,23
223,223_0,COMPLETED,BoTorch,GenerationStep_1,0.140000000000000013322676295502,197.000000000000000000000000000000,5000,1089,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,100,23
224,224_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,277.000000000000000000000000000000,1306,1836,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,23
225,225_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,178.000000000000000000000000000000,1632,4782,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.361988767562275548517192191866,100,23
226,226_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,331.000000000000000000000000000000,419,1817,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
227,227_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,175.000000000000000000000000000000,4629,3768,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.463238874073731543568044344283,100,23
228,228_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,266.000000000000000000000000000000,5000,4211,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
229,229_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,315.000000000000000000000000000000,5000,1841,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,23
230,230_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,243.000000000000000000000000000000,62,1597,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.391128814505751154495527543986,88,23
231,231_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,322.000000000000000000000000000000,4761,772,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.397602925936645423821147460330,99,23
232,232_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,266.000000000000000000000000000000,2639,4948,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.573177838273639772559420180187,1,23
233,233_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,238.000000000000000000000000000000,5000,1,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
234,234_0,FAILED,BoTorch,GenerationStep_1,,,4119,1,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.281396475951317615038504982294,1,23
235,235_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,249.000000000000000000000000000000,3554,1144,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.517499814546001579707024120580,100,23
236,236_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,216.000000000000000000000000000000,3444,4646,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
237,237_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,207.000000000000000000000000000000,1104,1632,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,100,23
238,238_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,214.000000000000000000000000000000,4825,3211,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
239,239_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,273.000000000000000000000000000000,1,1165,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,23
240,240_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,273.000000000000000000000000000000,1554,1511,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,23
241,241_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,274.000000000000000000000000000000,1112,1455,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
242,242_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,265.000000000000000000000000000000,1,1508,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
243,243_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,350.000000000000000000000000000000,3508,850,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,100,23
244,244_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,259.000000000000000000000000000000,5000,1539,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,23
245,245_0,FAILED,BoTorch,GenerationStep_1,,,2636,1,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,7,23
246,246_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,273.000000000000000000000000000000,928,1149,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
247,247_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,273.000000000000000000000000000000,5000,1471,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,23
248,248_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,279.000000000000000000000000000000,3904,1146,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
249,249_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,219.000000000000000000000000000000,1,821,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,50,23
250,250_0,FAILED,BoTorch,GenerationStep_1,,,1,1,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,16,23
251,251_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,253.000000000000000000000000000000,1,1660,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,23
252,252_0,FAILED,BoTorch,GenerationStep_1,,,1782,1,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
253,253_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,274.000000000000000000000000000000,5000,1651,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,23
254,254_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,273.000000000000000000000000000000,5000,1531,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
255,255_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,274.000000000000000000000000000000,4649,507,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
256,256_0,FAILED,BoTorch,GenerationStep_1,,,5000,1,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,99,23
257,257_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,262.000000000000000000000000000000,1,1401,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,23
258,258_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,203.000000000000000000000000000000,1,543,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
259,259_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,269.000000000000000000000000000000,546,1389,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
260,260_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,238.000000000000000000000000000000,5000,849,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,58,23
261,261_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,205.000000000000000000000000000000,5000,2808,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.445293898638677299040011803299,1,23
262,262_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,269.000000000000000000000000000000,1975,3236,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
263,263_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,211.000000000000000000000000000000,1,1556,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
264,264_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,180.000000000000000000000000000000,644,2205,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,100,23
265,265_0,FAILED,BoTorch,GenerationStep_1,,,1,1,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,52,23
266,266_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,273.000000000000000000000000000000,4927,1039,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
267,267_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,221.000000000000000000000000000000,4141,3742,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
268,268_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,316.000000000000000000000000000000,489,582,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,75,23
269,269_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,272.000000000000000000000000000000,1,1357,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,1,23
270,270_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,178.000000000000000000000000000000,4535,1350,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,47,23
271,271_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,164.000000000000000000000000000000,4970,1268,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,27,23
272,272_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,170.000000000000000000000000000000,5000,1438,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,56,23
273,273_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,166.000000000000000000000000000000,636,1234,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,23,23
274,274_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,156.000000000000000000000000000000,480,1301,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,38,23
275,275_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,146.000000000000000000000000000000,2665,1451,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,42,23
276,276_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,180.000000000000000000000000000000,553,1224,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,14,23
277,277_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,186.000000000000000000000000000000,2619,1028,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,22,23
278,278_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,267.000000000000000000000000000000,3460,1410,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,23
279,279_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,185.000000000000000000000000000000,117,1005,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.392004113982450275166513620206,11,23
280,280_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,196.000000000000000000000000000000,714,1350,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.839162915304066725497023071512,53,23
281,281_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,188.000000000000000000000000000000,4794,1344,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.800256372869630028255016895855,44,23
282,282_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,158.000000000000000000000000000000,5000,1232,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,17,23
283,283_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,206.000000000000000000000000000000,2162,1358,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.745478404878984313164380637318,40,23
284,284_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,178.000000000000000000000000000000,1,985,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.753236601406001349268137801118,61,23
285,285_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,217.000000000000000000000000000000,1879,1249,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,23
286,286_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,202.000000000000000000000000000000,4474,990,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,52,23
287,287_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,168.000000000000000000000000000000,5000,1306,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,38,23
288,288_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,208.000000000000000000000000000000,4824,1596,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.280878370044299052743497213669,1,23
289,289_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,203.000000000000000000000000000000,5000,5000,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,23
290,290_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,168.000000000000000000000000000000,5000,1514,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,30,23
291,291_0,FAILED,BoTorch,GenerationStep_1,,,1,1,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,23
292,292_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,207.000000000000000000000000000000,343,1251,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.916040962265573410761021477811,31,23
293,293_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,195.000000000000000000000000000000,67,808,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,13,23
294,294_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,191.000000000000000000000000000000,4688,915,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,11,23
295,295_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,180.000000000000000000000000000000,4517,1478,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.854793372001215412403496429761,52,23
296,296_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,156.000000000000000000000000000000,5000,1483,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,24,23
297,297_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,189.000000000000000000000000000000,4617,1126,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.427320277157019534630677526366,19,23
298,298_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,156.000000000000000000000000000000,1,1500,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,29,23
299,299_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,271.000000000000000000000000000000,4955,1139,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.435476613759577135809308856551,20,23
300,300_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,165.000000000000000000000000000000,1472,1357,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,17,23
301,301_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,172.000000000000000000000000000000,694,375,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,26,23
302,302_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,251.000000000000000000000000000000,1961,364,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,56,23
303,303_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,247.000000000000000000000000000000,1608,355,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,59,23
304,304_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,164.000000000000000000000000000000,4378,1344,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,18,23
305,305_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,189.000000000000000000000000000000,1,405,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,27,23
306,306_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,159.000000000000000000000000000000,1,1514,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,42,23
307,307_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,199.000000000000000000000000000000,1,525,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,67,23
308,308_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,173.000000000000000000000000000000,146,830,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,20,23
309,309_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,259.000000000000000000000000000000,1,54,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,23
310,310_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,189.000000000000000000000000000000,1,1338,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,11,23
311,311_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,177.000000000000000000000000000000,1,1697,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,15,23
312,312_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,179.000000000000000000000000000000,2569,371,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,48,23
313,313_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,184.000000000000000000000000000000,1083,331,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,16,23
314,314_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,187.000000000000000000000000000000,1,367,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,73,23
315,315_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,194.000000000000000000000000000000,1,1684,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,55,23
316,316_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,208.000000000000000000000000000000,1432,544,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,26,23
317,317_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,181.000000000000000000000000000000,328,168,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,81,23
318,318_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,210.000000000000000000000000000000,616,426,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,38,23
319,319_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,224.000000000000000000000000000000,1,376,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,51,23
320,320_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,174.000000000000000000000000000000,3377,1686,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,23
321,321_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,182.000000000000000000000000000000,1,925,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
322,322_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,151.000000000000000000000000000000,1380,1839,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,23,23
323,323_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,184.000000000000000000000000000000,181,1312,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,13,23
324,324_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,265.000000000000000000000000000000,5000,3860,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
325,325_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,218.000000000000000000000000000000,2567,305,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,25,23
326,326_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,180.000000000000000000000000000000,675,587,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,68,23
327,327_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,243.000000000000000000000000000000,4737,2892,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,100,23
328,328_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,197.000000000000000000000000000000,308,1338,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,12,23
329,329_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,192.000000000000000000000000000000,318,797,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,26,23
330,330_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,159.000000000000000000000000000000,472,505,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,29,23
331,331_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,163.000000000000000000000000000000,3735,1770,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,26,23
332,332_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,180.000000000000000000000000000000,2615,724,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,86,23
333,333_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,171.000000000000000000000000000000,1028,1550,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,79,23
334,334_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,189.000000000000000000000000000000,1078,1777,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,22,23
335,335_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,191.000000000000000000000000000000,3973,3786,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,70,23
336,336_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,161.000000000000000000000000000000,348,867,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,61,23
337,337_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,174.000000000000000000000000000000,1913,4636,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
338,338_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,181.000000000000000000000000000000,77,496,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,47,23
339,339_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,167.000000000000000000000000000000,5000,4369,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,55,23
340,340_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,263.000000000000000000000000000000,5000,2425,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
341,341_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,156.000000000000000000000000000000,692,928,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,46,23
342,342_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,155.000000000000000000000000000000,1,1867,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,18,23
343,343_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,165.000000000000000000000000000000,272,1135,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,77,23
344,344_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,155.000000000000000000000000000000,4741,2097,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,24,23
345,345_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,165.000000000000000000000000000000,5000,2304,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,100,23
346,346_0,RUNNING,BoTorch,GenerationStep_1,,,5,1694,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,27,23
347,347_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,174.000000000000000000000000000000,5000,4782,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,46,23
348,348_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,175.000000000000000000000000000000,1,587,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,33,23
349,349_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,175.000000000000000000000000000000,1444,724,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,84,23
350,350_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,179.000000000000000000000000000000,223,750,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,50,23
351,351_0,RUNNING,BoTorch,GenerationStep_1,,,4312,2227,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,74,23
352,352_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,175.000000000000000000000000000000,2530,1450,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,67,23
353,353_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,207.000000000000000000000000000000,57,4598,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,76,23
354,354_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,154.000000000000000000000000000000,721,1865,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,22,23
355,355_0,RUNNING,BoTorch,GenerationStep_1,,,1207,423,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,18,23
356,356_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,265.000000000000000000000000000000,5000,2990,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
357,357_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,181.000000000000000000000000000000,5000,4658,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,19,23
358,358_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,158.000000000000000000000000000000,4617,2009,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,15,23
359,359_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,223.000000000000000000000000000000,638,1274,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,4,23
360,360_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,195.000000000000000000000000000000,619,623,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.975804200384414288649281843391,60,23
361,361_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,182.000000000000000000000000000000,573,1327,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,5,23
362,362_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,194.000000000000000000000000000000,225,376,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.546882485385833505553421218792,37,23
363,363_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,183.000000000000000000000000000000,4490,1754,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,31,23
364,364_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,183.000000000000000000000000000000,94,920,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.403174740095408989937197929976,18,23
365,365_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,181.000000000000000000000000000000,3974,3803,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.277276577758019115993448622248,60,23
366,366_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,267.000000000000000000000000000000,4939,2693,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,23
367,367_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,170.000000000000000000000000000000,5000,4008,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
368,368_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,253.000000000000000000000000000000,4874,2904,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
369,369_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,194.000000000000000000000000000000,243,637,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.633668274032663458861236449593,52,23
370,370_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,183.000000000000000000000000000000,4743,636,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.270865868883936988797955791597,1,23
371,371_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,154.000000000000000000000000000000,4768,4295,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.322588854678318381896673372466,52,23
372,372_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,179.000000000000000000000000000000,940,654,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.356283849771462157729473574364,100,23
373,373_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,173.000000000000000000000000000000,1554,712,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.376028017539636327803975746065,43,23
374,374_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,174.000000000000000000000000000000,4794,3760,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,41,23
375,375_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,159.000000000000000000000000000000,4014,3717,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.097377795281805679117326235428,49,23
376,376_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,172.000000000000000000000000000000,3132,2927,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.906562800207789121209600580187,100,23
377,377_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,208.000000000000000000000000000000,5000,1444,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
378,378_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,169.000000000000000000000000000000,4699,4215,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.164855889171908742696359695401,27,23
379,379_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,172.000000000000000000000000000000,2744,3862,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,35,23
380,380_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,175.000000000000000000000000000000,3609,2285,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.695594785989640862489125083812,24,23
381,381_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,204.000000000000000000000000000000,383,668,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,60,23
382,382_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,204.000000000000000000000000000000,783,1248,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,23
383,383_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,155.000000000000000000000000000000,4950,3098,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.564148549488393213913184354169,29,23
384,384_0,COMPLETED,BoTorch,GenerationStep_1,0.100000000000000005551115123126,169.000000000000000000000000000000,1654,3725,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,29,23
385,385_0,FAILED,BoTorch,GenerationStep_1,,,2754,1,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.256296058180090546851914723447,1,23
386,386_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,272.000000000000000000000000000000,5000,2999,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,23
387,387_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,175.000000000000000000000000000000,4047,1680,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.442991579959726988757751087178,1,23
388,388_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,179.000000000000000000000000000000,1222,588,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.455943357636697910173495529307,28,23
389,389_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,312.000000000000000000000000000000,5000,4119,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,1,23
390,390_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,267.000000000000000000000000000000,5000,3116,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
391,391_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,161.000000000000000000000000000000,2095,2015,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,31,23
392,392_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,186.000000000000000000000000000000,1106,2116,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,76,23
393,393_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,207.000000000000000000000000000000,5000,3103,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,100,23
394,394_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,187.000000000000000000000000000000,871,234,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,4,23
395,395_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,200.000000000000000000000000000000,5000,4342,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
396,396_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,173.000000000000000000000000000000,737,1281,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,90,23
397,397_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,150.000000000000000000000000000000,4207,2569,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,27,23
398,398_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,183.000000000000000000000000000000,2753,2494,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,30,23
399,399_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,184.000000000000000000000000000000,720,2247,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,76,23
400,400_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,172.000000000000000000000000000000,23,1904,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,82,23
401,401_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,270.000000000000000000000000000000,190,2102,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
402,402_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,269.000000000000000000000000000000,1,167,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,23
403,403_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,191.000000000000000000000000000000,5000,2609,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,83,23
404,404_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,248.000000000000000000000000000000,5000,3053,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,100,23
405,405_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,185.000000000000000000000000000000,4511,4324,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,51,23
406,406_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,187.000000000000000000000000000000,5000,2104,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,50,23
407,407_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,167.000000000000000000000000000000,5000,4478,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,18,23
408,408_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,168.000000000000000000000000000000,1059,388,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,15,23
409,409_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,191.000000000000000000000000000000,4402,1973,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,16,23
410,410_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,160.000000000000000000000000000000,5000,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,29,23
411,411_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,159.000000000000000000000000000000,4815,2970,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,27,23
412,412_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,173.000000000000000000000000000000,4016,829,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,8,23
413,413_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,162.000000000000000000000000000000,3872,2455,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,67,23
414,414_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,186.000000000000000000000000000000,5000,4452,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,28,23
415,415_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,171.000000000000000000000000000000,5000,1644,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.001000000000000000020816681712,88,23
416,416_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,163.000000000000000000000000000000,986,1951,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,1,23
417,417_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,206.000000000000000000000000000000,5000,3117,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,91,23
418,418_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,186.000000000000000000000000000000,5000,4704,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,72,23
419,419_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,159.000000000000000000000000000000,4381,4855,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,59,23
420,420_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,230.000000000000000000000000000000,831,1302,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.694100587850654204835620930680,100,23
421,421_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,173.000000000000000000000000000000,449,1325,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,25,23
422,422_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,324.000000000000000000000000000000,3778,87,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.348173909261253411440151239731,1,23
423,423_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,259.000000000000000000000000000000,3714,69,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,44,23
424,424_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,234.000000000000000000000000000000,5000,4124,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
425,425_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,224.000000000000000000000000000000,855,630,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.685096351512105017356191183353,1,23
426,426_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,232.000000000000000000000000000000,894,1368,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.529282171817562763216358234786,1,23
427,427_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,257.000000000000000000000000000000,5000,3275,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.248117583071803926575427112766,100,23
428,428_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,171.000000000000000000000000000000,5000,5000,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
429,429_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,292.000000000000000000000000000000,237,681,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.321989622615372117486742808978,1,23
430,430_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,237.000000000000000000000000000000,5000,2526,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
431,431_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,299.000000000000000000000000000000,2028,1297,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.503063158611191552083141687035,1,23
432,432_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,177.000000000000000000000000000000,3815,609,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.435305227595857158195968850123,60,23
433,433_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,207.000000000000000000000000000000,769,740,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
434,434_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,174.000000000000000000000000000000,4469,1324,str\(\&#039;KL\&#039;\),3.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.728804232733500700902595781372,100,23
435,435_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,228.000000000000000000000000000000,5000,4282,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
436,436_0,FAILED,BoTorch,GenerationStep_1,,,98,1,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.998999999999999999111821580300,41,23
437,437_0,COMPLETED,BoTorch,GenerationStep_1,0.110000000000000000555111512313,170.000000000000000000000000000000,4785,4219,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,85,23
438,438_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,188.000000000000000000000000000000,5000,4608,str\(\&#039;H\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,100,23
439,439_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,179.000000000000000000000000000000,1,1267,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.375769138196920982064597183125,100,23
440,440_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,269.000000000000000000000000000000,4400,389,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.444285838656379328259760086439,1,23
441,441_0,FAILED,BoTorch,GenerationStep_1,,,3059,1,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.178294098355883556816792179234,22,23
442,442_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,169.000000000000000000000000000000,4664,4348,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.255940870583188706532951073314,100,23
443,443_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,175.000000000000000000000000000000,3506,684,str\(\&#039;H\&#039;\),3.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.001000000000000000020816681712,44,23
444,444_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,204.000000000000000000000000000000,5000,4824,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.167583515181352726841623734799,63,23
445,445_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,285.000000000000000000000000000000,4367,1533,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.524029065775628355439152983308,1,23
446,446_0,COMPLETED,BoTorch,GenerationStep_1,0.130000000000000004440892098501,187.000000000000000000000000000000,1488,182,str\(\&#039;H\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.320003319276946140981010557880,8,23
447,447_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,239.000000000000000000000000000000,5000,4892,str\(\&#039;KL\&#039;\),2.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.998999999999999999111821580300,1,23
448,448_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,193.000000000000000000000000000000,5000,3536,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;tstat\&#039;\),0.329484034469440245640470266153,100,23
449,449_0,COMPLETED,BoTorch,GenerationStep_1,0.119999999999999995559107901499,272.000000000000000000000000000000,91,1256,str\(\&#039;KL\&#039;\),1.000000000000000000000000000000,str\(\&#039;stdev\&#039;\),0.640198581246898323904304106691,84,23
</pre>
			<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>
			<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 3863121 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_23_HoeffdingTreeClassifier_ACCURACY-RUNTIME/0/single_runs/3863121/3863121_0_result.pkl
has not produced any output (state: TIMEOUT)
Error stream produced:
----------------------------------------
slurmstepd: error: *** JOB 3863121 ON i7170 CANCELLED AT 2025-03-31T09:15:54 DUE TO TIME LIMIT ***



⚠ Job 3863130 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_23_HoeffdingTreeClassifier_ACCURACY-RUNTIME/0/single_runs/3863130/3863130_0_result.pkl
has not produced any output (state: TIMEOUT)
Error stream produced:
----------------------------------------
slurmstepd: error: *** JOB 3863130 ON i7163 CANCELLED AT 2025-03-31T09:15:54 DUE TO TIME LIMIT ***



⚠ Job 3863136 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_23_HoeffdingTreeClassifier_ACCURACY-RUNTIME/0/single_runs/3863136/3863136_0_result.pkl
has not produced any output (state: TIMEOUT)
Error stream produced:
----------------------------------------
slurmstepd: error: *** JOB 3863136 ON i7180 CANCELLED AT 2025-03-31T09:16:24 DUE TO TIME LIMIT ***



⚠ Job 3863149 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_23_HoeffdingTreeClassifier_ACCURACY-RUNTIME/0/single_runs/3863149/3863149_0_result.pkl
has not produced any output (state: TIMEOUT)
Error stream produced:
----------------------------------------
slurmstepd: error: *** JOB 3863149 ON i7179 CANCELLED AT 2025-03-31T09:16:24 DUE TO TIME LIMIT ***



⚠ Job 3863173 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_23_HoeffdingTreeClassifier_ACCURACY-RUNTIME/0/single_runs/3863173/3863173_0_result.pkl
has not produced any output (state: TIMEOUT)
Error stream produced:
----------------------------------------
slurmstepd: error: *** JOB 3863173 ON i7173 CANCELLED AT 2025-03-31T09:16:54 DUE TO TIME LIMIT ***



⚠ Job 3863182 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_23_HoeffdingTreeClassifier_ACCURACY-RUNTIME/0/single_runs/3863182/3863182_0_result.pkl
has not produced any output (state: TIMEOUT)
Error stream produced:
----------------------------------------
slurmstepd: error: *** JOB 3863182 ON i7159 CANCELLED AT 2025-03-31T09:16:54 DUE TO TIME LIMIT ***



⚠ Job 3872577 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_23_HoeffdingTreeClassifier_ACCURACY-RUNTIME/0/single_runs/3872577/3872577_0_result.pkl
has not produced any output (state: TIMEOUT)
Error stream produced:
----------------------------------------
slurmstepd: error: *** JOB 3872577 ON i7147 CANCELLED AT 2025-03-31T11:01:30 DUE TO TIME LIMIT ***



⚠ Job 3872659 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CDBD_RialtoBridgeTimelapse_ft_23_HoeffdingTreeClassifier_ACCURACY-RUNTIME/0/single_runs/3872659/3872659_0_result.pkl
has not produced any output (state: TIMEOUT)
Error stream produced:
----------------------------------------
slurmstepd: error: *** JOB 3872659 ON i7141 CANCELLED AT 2025-03-31T11:02:30 DUE TO TIME LIMIT ***


</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>500                                                                                                                                                      </td></tr><tr><td> run_program</td><td>[[&#039;bW9kdWxlIGxvYWQgR0NDY29yZS8xMC4zLjAgUHl0aG9uICYmIHNvdXJjZSAvZGF0YS9ob3JzZS93cy9zNDEyMjQ4NS1jb21wUGVyZkREL2JlbmNobWFyay92ZW52L2Jpbi9hY3RpdmF0ZSAmJiBw&hellip; </td></tr><tr><td> experiment_name</td><td>CDBD_RialtoBridgeTimelapse_ft_23_HoeffdingTreeClassifier_ACCURACY-RUNTIME                                                                                </td></tr><tr><td> mem_gb</td><td>32                                                                                                                                                       </td></tr><tr><td> parameter</td><td>[[&#039;recent_samples_size&#039;, &#039;range&#039;, &#039;1&#039;, &#039;5000&#039;, &#039;int&#039;], [&#039;feature_id&#039;, &#039;fixed&#039;, &#039;23&#039;], [&#039;batch_size&#039;, &#039;range&#039;, &#039;1&#039;, &#039;5000&#039;, &#039;int&#039;], [&#039;divergence&#039;,        </td></tr><tr><td></td><td>&#039;choice&#039;, &quot;str\\(\\&#039;KL\\&#039;\\),str\\(\\&#039;H\\&#039;\\)&quot;], [&#039;detect_batch&#039;, &#039;choice&#039;, &#039;1,2,3&#039;], [&#039;statistic&#039;, &#039;choice&#039;,                                            </td></tr><tr><td></td><td>&quot;str\\(\\&#039;tstat\\&#039;\\),str\\(\\&#039;stdev\\&#039;\\)&quot;], [&#039;significance&#039;, &#039;range&#039;, &#039;0.001&#039;, &#039;0.999&#039;, &#039;float&#039;], [&#039;subsets&#039;, &#039;range&#039;, &#039;1&#039;, &#039;100&#039;, &#039;int&#039;]]             </td></tr><tr><td> continue_previous_job</td><td>None                                                                                                                                                     </td></tr><tr><td> maximize</td><td>False                                                                                                                                                    </td></tr><tr><td> experiment_constraints</td><td>None                                                                                                                                                     </td></tr><tr><td> stderr_to_stdout</td><td>False                                                                                                                                                    </td></tr><tr><td> run_dir</td><td>runs                                                                                                                                                     </td></tr><tr><td> seed</td><td>None                                                                                                                                                     </td></tr><tr><td> decimalrounding</td><td>12                                                                                                                                                       </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>BOTORCH_MODULAR                                                                                                                                          </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>True                                                                                                                                                     </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>50                                                                                                                                                       </td></tr><tr><td> disable_search_space_exhaustion_detection</td><td>False                                                                                                                                                    </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> max_parallelism</td><td>max_eval_times_thousand_plus_thousand                                                                                                                    </td></tr><tr><td> occ_type</td><td>euclid                                                                                                                                                   </td></tr><tr><td> result_names</td><td>[&#039;ACCURACY=max&#039;, &#039;RUNTIME=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>True                                                                                                                                                     </td></tr><tr><td> revert_to_random_when_seemingly_exhausted</td><td>True                                                                                                                                                     </td></tr><tr><td> num_parallel_jobs</td><td>30                                                                                                                                                       </td></tr><tr><td> worker_timeout</td><td>60                                                                                                                                                       </td></tr><tr><td> slurm_use_srun</td><td>False                                                                                                                                                    </td></tr><tr><td> time</td><td>1440                                                                                                                                                     </td></tr><tr><td> partition</td><td>romeo                                                                                                                                                    </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>
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<pre id="pre_tab_worker_usage">1743373053.31984,30,0,0
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1743438842.2717397,30,8,27
1743438843.2456298,30,7,23
1743438843.480224,30,7,23
1743438846.468085,30,6,20
1743438846.5616071,30,6,20
1743438851.8865116,30,6,20
1743438852.0219195,30,6,20
1743438854.671991,30,5,17
1743438854.7594936,30,5,17
1743438860.2312896,30,5,17
1743438860.4421794,30,5,17
1743438861.7349222,30,4,13
1743438861.816958,30,4,13
1743438864.524075,30,3,10
1743438864.6469827,30,3,10
1743438869.926794,30,3,10
1743438870.0064816,30,3,10
1743438872.9135246,30,2,7
1743438873.0271242,30,2,7
1743438880.6746643,30,2,7
1743438887.8256855,30,2,7
1743438895.2639763,30,2,7
1743438902.338786,30,2,7
1743438909.8553329,30,2,7
1743438917.2810342,30,2,7
1743438924.4766676,30,2,7
1743438931.887029,30,2,7
1743438939.1834173,30,2,7
1743438946.8551655,30,2,7
1743438953.9894261,30,2,7
1743438959.4193792,30,2,7
1743438959.6541893,30,2,7
1743438962.683464,30,1,3
1743438962.8591204,30,1,3
1743438970.2581415,30,1,3
1743438977.3876812,30,1,3
1743438984.9014916,30,1,3
1743438992.287626,30,1,3
1743438999.2919416,30,1,3
1743439006.784557,30,1,3
1743439013.8147173,30,1,3
1743439019.282096,30,1,3
1743439019.5069568,30,1,3
1743439022.50998,30,0,0
1743439025.110877,30,0,0
1743439027.1481888,30,0,0
1743439262.332295,30,0,0
1743439458.4622848,30,0,0
1743439676.3051918,30,0,0
1743439843.5524201,30,0,0
1743440033.005772,30,0,0
1743440284.002816,30,0,0
1743440446.0481336,30,0,0
1743440647.6568286,30,0,0
1743440850.3916118,30,0,0
1743441129.1749315,30,0,0
1743441338.7156618,30,0,0
1743441629.8593817,30,0,0
1743441895.4985971,30,0,0
1743442124.9701161,30,0,0
1743442422.1017854,30,0,0
1743442663.9688025,30,0,0
1743442894.8964128,30,0,0
1743443180.8805718,30,0,0
1743443456.9670823,30,0,0
1743443723.75954,30,0,0
1743443883.9478238,30,0,0
1743444159.6709747,30,0,0
1743444325.1838813,30,0,0
1743444533.717671,30,0,0
1743444802.355019,30,0,0
1743445139.6487558,30,0,0
1743445382.2783442,30,0,0
1743445638.9415555,30,0,0
1743445948.140398,30,0,0
1743446151.6372647,30,0,0
1743446151.933564,30,0,0
1743446153.827469,30,1,3
1743446154.0147274,30,1,3
1743446154.17439,30,1,3
1743446156.0126667,30,2,7
1743446156.0693438,30,2,7
1743446156.2288322,30,2,7
1743446157.9310327,30,3,10
1743446157.971261,30,3,10
1743446158.2206247,30,3,10
1743446160.1728268,30,4,13
1743446160.223953,30,4,13
1743446160.3902545,30,4,13
1743446162.1544285,30,5,17
1743446162.20759,30,5,17
1743446162.36113,30,5,17
1743446164.4723127,30,6,20
1743446164.601263,30,6,20
1743446164.838737,30,6,20
1743446166.431673,30,7,23
1743446166.574478,30,7,23
1743446166.732804,30,7,23
1743446171.3706462,30,8,27
1743446171.7143052,30,8,27
1743446171.9018342,30,8,27
1743446173.4983087,30,9,30
1743446173.5423672,30,9,30
1743446173.7667043,30,9,30
1743446175.535386,30,10,33
1743446175.5783546,30,10,33
1743446175.732625,30,10,33
1743446177.3552725,30,11,37
1743446177.3989434,30,11,37
1743446177.5656703,30,11,37
1743446179.2914734,30,12,40
1743446179.4717474,30,12,40
1743446179.7547612,30,12,40
1743446181.41602,30,13,43
1743446181.4787664,30,13,43
1743446181.7416425,30,13,43
1743446183.6067934,30,14,47
1743446183.6529005,30,14,47
1743446183.9737892,30,14,47
1743446186.0306752,30,15,50
1743446186.3362327,30,15,50
1743446186.6306913,30,15,50
1743446188.435186,30,16,53
1743446188.4751794,30,16,53
1743446188.6119928,30,16,53
1743446190.3567114,30,17,57
1743446190.4136257,30,17,57
1743446190.563649,30,17,57
1743446192.323681,30,18,60
1743446192.6818511,30,18,60
1743446192.9136176,30,18,60
1743446195.227842,30,19,63
1743446195.2909932,30,19,63
1743446195.5611386,30,19,63
1743446197.1400645,30,20,67
1743446197.2295508,30,20,67
1743446197.430184,30,20,67
1743446199.4188082,30,21,70
1743446199.5700176,30,21,70
1743446199.9247057,30,21,70
1743446201.5817304,30,22,73
1743446201.7002163,30,22,73
1743446202.0909135,30,22,73
1743446204.0652165,30,23,77
1743446204.1286705,30,23,77
1743446204.3591576,30,23,77
1743446206.341688,30,24,80
1743446206.4001935,30,24,80
1743446206.7017066,30,24,80
1743446208.9361649,30,25,83
1743446208.9742477,30,25,83
1743446209.1185834,30,25,83
1743446211.0424178,30,26,87
1743446211.1287322,30,26,87
1743446211.3966188,30,26,87
1743446213.335665,30,27,90
1743446213.439423,30,27,90
1743446213.5945556,30,27,90
1743446215.332827,30,28,93
1743446215.3947806,30,28,93
1743446215.670926,30,28,93
1743446217.559518,30,29,97
1743446217.610711,30,29,97
1743446217.84718,30,29,97
1743446219.4866903,30,30,100
1743446222.3097832,30,30,100
1743446226.38956,30,30,100
1743446234.3842444,30,30,100
1743446242.805985,30,30,100
1743446250.2726393,30,30,100
1743446258.7387235,30,30,100
1743446266.4668846,30,30,100
1743446273.7762947,30,30,100
1743446281.925788,30,30,100
1743446289.3134873,30,30,100
1743446296.9437094,30,30,100
1743446304.1791997,30,30,100
1743446312.1724136,30,30,100
1743446319.9599195,30,30,100
1743446327.6092732,30,30,100
1743446335.3204918,30,30,100
1743446343.1879094,30,30,100
1743446350.7434878,30,30,100
1743446358.1241431,30,30,100
1743446366.1620555,30,30,100
1743446371.6478264,30,30,100
1743446371.73629,30,30,100
1743446375.193199,30,29,97
1743446375.2681828,30,29,97
1743446380.736208,30,29,97
1743446380.9617448,30,29,97
1743446383.8588035,30,28,93
1743446384.0498457,30,28,93
1743446389.4606328,30,28,93
1743446389.5508916,30,28,93
1743446392.8837817,30,27,90
1743446393.0767279,30,27,90
1743446398.3956437,30,27,90
1743446398.4794016,30,27,90
1743446401.821653,30,26,87
1743446402.0528493,30,26,87
1743446407.4553363,30,26,87
1743446407.6471322,30,26,87
1743446410.7103317,30,25,83
1743446410.7983851,30,25,83
1743446416.059852,30,25,83
1743446416.1772563,30,25,83
1743446417.493717,30,24,80
1743446417.638914,30,24,80
1743446418.9258366,30,23,77
1743446419.0969524,30,23,77
1743446421.9992404,30,22,73
1743446422.1333833,30,22,73
1743446429.8398545,30,22,73
1743446435.1985712,30,22,73
1743446435.3262858,30,22,73
1743446436.2679555,30,21,70
1743446436.3515773,30,21,70
1743446437.3440144,30,20,67
1743446437.4437742,30,20,67
1743446438.7580884,30,19,63
1743446438.8695648,30,19,63
1743446439.8123734,30,18,60
1743446439.9076917,30,18,60
1743446440.9874508,30,17,57
1743446441.1086607,30,17,57
1743446444.3222034,30,16,53
1743446444.4680915,30,16,53
1743446450.0136423,30,16,53
1743446450.1814647,30,16,53
1743446451.10313,30,15,50
1743446451.367828,30,15,50
1743446452.235161,30,14,47
1743446452.3409326,30,14,47
1743446453.3486211,30,13,43
1743446453.4844887,30,13,43
1743446456.7329116,30,12,40
1743446456.9054558,30,12,40
1743446462.3619223,30,12,40
1743446462.4400146,30,12,40
1743446463.534671,30,11,37
1743446463.8481882,30,11,37
1743446464.7383444,30,10,33
1743446464.8331358,30,10,33
1743446465.9042487,30,9,30
1743446466.0684137,30,9,30
1743446467.0336506,30,8,27
1743446467.1464608,30,8,27
1743446468.1009464,30,7,23
1743446468.2477074,30,7,23
1743446471.480747,30,6,20
1743446471.5496562,30,6,20
1743446476.9446602,30,6,20
1743446477.0387611,30,6,20
1743446480.4271636,30,5,17
1743446480.5207913,30,5,17
1743446487.9563217,30,5,17
1743446495.6597285,30,5,17
1743446500.9936194,30,5,17
1743446501.1820219,30,5,17
1743446504.5572112,30,4,13
1743446504.6316133,30,4,13
1743446511.9687495,30,4,13
1743446517.3006403,30,4,13
1743446517.5111194,30,4,13
1743446518.828951,30,3,10
1743446518.9556103,30,3,10
1743446519.8949642,30,2,7
1743446519.981999,30,2,7
1743446520.8508325,30,1,3
1743446520.9510431,30,1,3
1743446524.2200806,30,0,0
1743446526.4998474,30,0,0
1743446529.0591428,30,0,0
1743446719.334441,30,0,0
1743446895.3203435,30,0,0
1743447073.9479752,30,0,0
1743447239.437251,30,0,0
1743447404.5740166,30,0,0
1743447587.4695532,30,0,0
1743447832.47141,30,0,0
1743448027.2858152,30,0,0
1743448204.2628086,30,0,0
1743448433.852826,30,0,0
1743448668.568599,30,0,0
1743448831.5325518,30,0,0
1743449018.3715086,30,0,0
1743449231.0181103,30,0,0
1743449400.2465022,30,0,0
1743449624.7987075,30,0,0
1743449788.6531425,30,0,0
1743449988.931998,30,0,0
1743450167.7869244,30,0,0
1743450359.846411,30,0,0
1743450597.6497715,30,0,0
1743450838.0649843,30,0,0
1743451046.4798334,30,0,0
1743451225.6515813,30,0,0
1743451430.115212,30,0,0
1743451685.6335037,30,0,0
1743451873.94816,30,0,0
1743452079.8309095,30,0,0
1743452316.4602165,30,0,0
1743452559.5218718,30,0,0
1743452559.835763,30,0,0
1743452561.9088736,30,1,3
1743452561.9959319,30,1,3
1743452562.2721431,30,1,3
1743452564.4085977,30,2,7
1743452564.4471445,30,2,7
1743452564.606698,30,2,7
1743452566.4321852,30,3,10
1743452566.497981,30,3,10
1743452566.7189882,30,3,10
1743452568.3323967,30,4,13
1743452568.3824465,30,4,13
1743452568.6198175,30,4,13
1743452570.2747397,30,5,17
1743452570.3218374,30,5,17
1743452570.5093474,30,5,17
1743452572.2240467,30,6,20
1743452572.2835925,30,6,20
1743452572.4644399,30,6,20
1743452574.2453318,30,7,23
1743452574.2981775,30,7,23
1743452574.453172,30,7,23
1743452576.4140406,30,8,27
1743452576.4636338,30,8,27
1743452576.6437666,30,8,27
1743452578.180571,30,9,30
1743452578.2791674,30,9,30
1743452578.8361166,30,9,30
1743452580.6811838,30,10,33
1743452581.0516922,30,10,33
1743452581.3216922,30,10,33
1743452583.2581227,30,11,37
1743452583.3086743,30,11,37
1743452583.5191875,30,11,37
1743452585.2623258,30,12,40
1743452585.4242575,30,12,40
1743452585.6992085,30,12,40
1743452587.4871886,30,13,43
1743452587.5681999,30,13,43
1743452587.7946692,30,13,43
1743452590.544796,30,14,47
1743452590.703437,30,14,47
1743452590.9162583,30,14,47
1743452592.6508586,30,15,50
1743452592.6914365,30,15,50
1743452592.8642774,30,15,50
1743452594.514621,30,16,53
1743452594.6151593,30,16,53
1743452594.83465,30,16,53
1743452596.7484977,30,17,57
1743452596.7909806,30,17,57
1743452597.0392535,30,17,57
1743452599.0023134,30,18,60
1743452599.0411901,30,18,60
1743452599.2792926,30,18,60
1743452601.225343,30,19,63
1743452601.374505,30,19,63
1743452601.5846741,30,19,63
1743452603.2881916,30,20,67
1743452603.5003452,30,20,67
1743452603.646645,30,20,67
1743452605.4279325,30,21,70
1743452605.4836364,30,21,70
1743452605.709667,30,21,70
1743452607.5142703,30,22,73
1743452607.8471148,30,22,73
1743452608.0682712,30,22,73
1743452609.912538,30,23,77
1743452609.9663165,30,23,77
1743452610.1063044,30,23,77
1743452611.8943765,30,24,80
1743452611.9637346,30,24,80
1743452612.1232817,30,24,80
1743452613.884125,30,25,83
1743452613.9335551,30,25,83
1743452614.15515,30,25,83
1743452615.9918845,30,26,87
1743452616.0334527,30,26,87
1743452616.2438438,30,26,87
1743452618.0932162,30,27,90
1743452618.1368322,30,27,90
1743452618.4644802,30,27,90
1743452620.2835867,30,28,93
1743452620.5015297,30,28,93
1743452620.7674098,30,28,93
1743452622.4463952,30,29,97
1743452622.595789,30,29,97
1743452622.7853878,30,29,97
1743452624.5114565,30,30,100
1743452628.5432186,30,30,100
</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
1743373053,595.59375,4.3
1743373053,595.59375,4.9
1743373053,595.88671875,4.3
1743373053,595.88671875,3.3
1743373053,595.88671875,4.2
1743373053,595.88671875,5.1
1743373053,595.88671875,4.6
1743373135,612.13671875,2.8
1743373135,612.17578125,2.2
1743373135,612.17578125,2.4
1743373135,612.17578125,2.1
1743385014,690.44140625,1.1
1743385014,690.44140625,5.0
1743385014,690.44140625,5.0
1743385014,690.44140625,3.0
1743391494,694.91015625,3.8
1743391495,694.91015625,2.5
1743391495,694.91015625,2.6
1743391495,694.91015625,2.1
1743395774,700.70703125,4.5
1743395774,700.70703125,4.4
1743395774,700.70703125,4.6
1743395774,700.70703125,3.8
1743399516,713.265625,4.8
1743399516,713.265625,4.6
1743399516,713.265625,4.6
1743399516,713.265625,6.4
1743407117,727.0703125,5.3
1743407117,727.0703125,4.6
1743407117,727.0703125,4.7
1743407117,727.0703125,2.9
1743413543,730.734375,4.1
1743413543,730.734375,3.2
1743413544,730.734375,3.5
1743413544,730.734375,5.1
1743417087,772.0,3.9
1743417087,772.0,6.9
1743417087,772.0,7.0
1743417087,772.0,10.8
1743419315,751.46484375,4.1
1743419315,751.46484375,6.2
1743419316,751.46484375,6.6
1743419316,751.46484375,7.0
1743422607,790.265625,6.9
1743422607,790.265625,6.4
1743422607,790.265625,6.4
1743422607,790.265625,5.7
1743427867,787.9296875,6.2
1743427867,787.9296875,4.9
1743427868,787.9296875,5.1
1743427868,787.9296875,5.9
1743433905,804.609375,6.2
1743433905,804.609375,5.2
1743433905,804.609375,5.4
1743433905,804.609375,6.4
1743438572,812.45703125,7.0
1743438572,812.45703125,7.1
1743438572,812.45703125,7.4
1743438572,812.45703125,7.4
1743446226,798.921875,7.1
1743446226,798.921875,6.0
1743446226,798.921875,6.2
1743446226,798.921875,8.3
1743452633,805.8125,8.0
1743452633,805.8125,8.9
1743452633,805.8125,8.7
1743452633,805.8125,8.4
</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>
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